From b72c1ceec1a8fe70f7a931db6e791cae5f4e2e66 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 26 Nov 2024 15:18:54 +0800 Subject: [PATCH 001/288] Fix (#69701) --- .../fluid/framework/ir/pass_tester_helper.h | 124 +++++++++--------- 1 file changed, 62 insertions(+), 62 deletions(-) diff --git a/paddle/fluid/framework/ir/pass_tester_helper.h b/paddle/fluid/framework/ir/pass_tester_helper.h index b75e4677d48f70..d9f108dd8edb8b 100644 --- a/paddle/fluid/framework/ir/pass_tester_helper.h +++ b/paddle/fluid/framework/ir/pass_tester_helper.h @@ -39,7 +39,7 @@ struct Layers { std::vector shape = {}, bool is_persistable = false, proto::VarType::Type data_type = proto::VarType::FP32) { - return lod_tensor(name, shape, is_persistable, data_type); + return dense_tensor(name, shape, is_persistable, data_type); } VarDesc* conv2d(VarDesc* input, @@ -52,7 +52,7 @@ struct Layers { std::vector dilations = {1, 1}, std::string data_format = "NCHW", bool use_cudnn = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("conv2d"); op->SetInput("Input", {input->Name()}); @@ -80,7 +80,7 @@ struct Layers { std::string padding_algorithm = "EXPLICIT", std::vector dilations = {1, 1}, std::string data_format = "NCHW") { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("conv2d_transpose"); op->SetInput("Input", {input->Name()}); @@ -102,7 +102,7 @@ struct Layers { VarDesc* filter, VarDesc* bias, bool use_cudnn) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("depthwise_conv2d"); op->SetInput("Input", {input->Name()}); @@ -118,7 +118,7 @@ struct Layers { VarDesc* pool2d(VarDesc* x, bool use_cudnn, const AttributeMap* attrs = nullptr) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("pool2d"); op->SetInput("X", {x->Name()}); @@ -137,21 +137,21 @@ struct Layers { VarDesc* squeeze2(VarDesc* x, const std::vector axes = {-1}, bool with_xshape = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("squeeze2"); op->SetInput("X", {x->Name()}); op->SetOutput("Out", {out->Name()}); op->SetAttr("axes", axes); if (with_xshape) { - VarDesc* xshape = lod_tensor(unique_name()); + VarDesc* xshape = dense_tensor(unique_name()); op->SetOutput("XShape", {xshape->Name()}); } return out; } VarDesc* unsqueeze2(VarDesc* x, const std::vector axes = {-1}) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("unsqueeze2"); op->SetInput("X", {x->Name()}); @@ -197,7 +197,7 @@ struct Layers { VarDesc* bias, int in_num_col_dims = 1, std::string activation_type = "") { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("fc"); op->SetInput("Input", {input->Name()}); @@ -322,7 +322,7 @@ struct Layers { VarDesc* dropout(VarDesc* x, float dropout_prob, std::string dropout_implementation) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("dropout"); op->SetInput("X", {x->Name()}); @@ -336,7 +336,7 @@ struct Layers { } VarDesc* concat(std::vector inputs, int axis = -1) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("concat"); std::vector input_names(inputs.size()); @@ -354,9 +354,9 @@ struct Layers { std::vector layer_norm(VarDesc* x, VarDesc* scale = nullptr, VarDesc* bias = nullptr) { - VarDesc* y = lod_tensor(unique_name()); - VarDesc* mean = lod_tensor(unique_name()); - VarDesc* variance = lod_tensor(unique_name()); + VarDesc* y = dense_tensor(unique_name()); + VarDesc* mean = dense_tensor(unique_name()); + VarDesc* variance = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("layer_norm"); op->SetInput("X", {x->Name()}); @@ -387,7 +387,7 @@ struct Layers { } std::vector outs(out_num); for (int i = 0; i < out_num; i++) { - outs[i] = lod_tensor(unique_name()); + outs[i] = dense_tensor(unique_name()); } std::vector out_names(out_num); for (int i = 0; i < out_num; i++) { @@ -409,7 +409,7 @@ struct Layers { } VarDesc* assign(VarDesc* x) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("assign"); op->SetInput("X", {x->Name()}); @@ -424,7 +424,7 @@ struct Layers { VarDesc* alpha = nullptr, bool transpose_x = false, bool transpose_y = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("matmul"); op->SetInput("X", {x->Name()}); @@ -437,7 +437,7 @@ struct Layers { } VarDesc* clip(VarDesc* x, VarDesc* min, VarDesc* max) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("clip"); op->SetInput("X", {x->Name()}); @@ -452,7 +452,7 @@ struct Layers { VarDesc* alpha = nullptr, bool trans_x = false, bool trans_y = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("matmul_v2"); op->SetInput("X", {x->Name()}); @@ -466,14 +466,14 @@ struct Layers { VarDesc* transpose2(VarDesc* x, std::vector axis, bool with_xshape = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("transpose2"); op->SetInput("X", {x->Name()}); op->SetAttr("axis", axis); op->SetOutput("Out", {out->Name()}); if (with_xshape) { - VarDesc* xshape = lod_tensor(unique_name()); + VarDesc* xshape = dense_tensor(unique_name()); op->SetOutput("XShape", {xshape->Name()}); } return out; @@ -482,21 +482,21 @@ struct Layers { VarDesc* reshape2(VarDesc* x, std::vector shape, bool with_xshape = false) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("reshape2"); op->SetInput("X", {x->Name()}); op->SetAttr("shape", shape); op->SetOutput("Out", {out->Name()}); if (with_xshape) { - VarDesc* xshape = lod_tensor(unique_name()); + VarDesc* xshape = dense_tensor(unique_name()); op->SetOutput("XShape", {xshape->Name()}); } return out; } VarDesc* softmax(VarDesc* x, int axis) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("softmax"); op->SetInput("X", {x->Name()}); @@ -509,7 +509,7 @@ struct Layers { float scale = 1., float bias = 0., bool bias_after = true) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("scale"); op->SetInput("X", {x->Name()}); @@ -525,11 +525,11 @@ struct Layers { VarDesc* bias, VarDesc* mean, VarDesc* variance) { - VarDesc* y = lod_tensor(unique_name()); - VarDesc* mean_out = lod_tensor(unique_name()); - VarDesc* variance_out = lod_tensor(unique_name()); - VarDesc* saved_mean = lod_tensor(unique_name()); - VarDesc* saved_variance = lod_tensor(unique_name()); + VarDesc* y = dense_tensor(unique_name()); + VarDesc* mean_out = dense_tensor(unique_name()); + VarDesc* variance_out = dense_tensor(unique_name()); + VarDesc* saved_mean = dense_tensor(unique_name()); + VarDesc* saved_variance = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("batch_norm"); op->SetInput("X", {x->Name()}); @@ -551,7 +551,7 @@ struct Layers { } VarDesc* embedding(VarDesc* x, VarDesc* weights) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("lookup_table"); op->SetInput("Ids", {x->Name()}); @@ -561,9 +561,9 @@ struct Layers { } VarDesc* while_loop(std::vector xs, VarDesc* cond = nullptr) { - VarDesc* out = lod_tensor(unique_name()); - VarDesc* step_scopes = lod_tensor(unique_name()); - if (cond == nullptr) cond = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); + VarDesc* step_scopes = dense_tensor(unique_name()); + if (cond == nullptr) cond = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("while"); @@ -579,7 +579,7 @@ struct Layers { } VarDesc* shape(VarDesc* input) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("shape"); op->SetInput("Input", {input->Name()}); @@ -591,7 +591,7 @@ struct Layers { std::vector axes, std::vector starts, std::vector ends) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("slice"); op->SetInput("Input", {input->Name()}); @@ -608,7 +608,7 @@ struct Layers { int output_dim_idx, std::vector shape, float value) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("fill_constant_batch_size_like"); op->SetInput("Input", {x->Name()}); @@ -648,8 +648,8 @@ struct Layers { std::vector out_linear_in_scale = {}, std::vector ffn1_in_scale = {}, std::vector ffn2_in_scale = {}) { - VarDesc* out = lod_tensor(unique_name()); - VarDesc* cache_kv_out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); + VarDesc* cache_kv_out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); std::string op_type = qkv_out_scale ? "fused_multi_transformer_int8" : "fused_multi_transformer"; @@ -700,7 +700,7 @@ struct Layers { VarDesc* zero_point, int bit_length = 8, int quant_axis = -1) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("dequantize_linear"); op->SetInput("X", {x->Name()}); @@ -722,7 +722,7 @@ struct Layers { none_op->SetType("none"); none_op->SetInput("X", {var->Name()}); VarDesc* grad_var = - lod_tensor(GradVarName(var->Name()), var->GetShape(), false); + dense_tensor(GradVarName(var->Name()), var->GetShape(), false); none_op->SetOutput("Out", {grad_var->Name()}); } for (int i = forward_ops.size() - 1; i >= 0; --i) { @@ -743,7 +743,7 @@ struct Layers { for (auto var_name : op->Output(name)) { VarDesc* var = block->FindVar(var_name); VarDesc* grad_var = - lod_tensor(GradVarName(var_name), var->GetShape(), false); + dense_tensor(GradVarName(var_name), var->GetShape(), false); grad_var_names.push_back(grad_var->Name()); } grad_op->SetInput(GradVarName(name), grad_var_names); @@ -754,7 +754,7 @@ struct Layers { for (auto var_name : op->Input(name)) { VarDesc* var = block->FindVar(var_name); VarDesc* grad_var = - lod_tensor(GradVarName(var_name), var->GetShape(), false); + dense_tensor(GradVarName(var_name), var->GetShape(), false); grad_var_names.push_back(grad_var->Name()); } grad_op->SetOutput(GradVarName(name), grad_var_names); @@ -764,7 +764,7 @@ struct Layers { } VarDesc* cast(VarDesc* input, int in_dtype = 5, int out_dtype = 5) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("cast"); op->SetInput("X", {input->Name()}); @@ -775,7 +775,7 @@ struct Layers { } VarDesc* range(VarDesc* start, VarDesc* end, VarDesc* step) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("range"); op->SetInput("Start", {start->Name()}); @@ -786,7 +786,7 @@ struct Layers { } VarDesc* flatten_contiguous_range(VarDesc* input) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("flatten_contiguous_range"); op->SetInput("X", {input->Name()}); @@ -799,9 +799,9 @@ struct Layers { VarDesc* pre_ids, VarDesc* pre_scores, int beam_size = 1) { - VarDesc* parent_idx = lod_tensor(unique_name()); - VarDesc* selected_ids = lod_tensor(unique_name()); - VarDesc* selected_scores = lod_tensor(unique_name()); + VarDesc* parent_idx = dense_tensor(unique_name()); + VarDesc* selected_ids = dense_tensor(unique_name()); + VarDesc* selected_scores = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("beam_search"); op->SetInput("ids", {ids->Name()}); @@ -816,7 +816,7 @@ struct Layers { } VarDesc* lod_reset(VarDesc* x, VarDesc* y) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("lod_reset"); op->SetInput("X", {x->Name()}); @@ -826,7 +826,7 @@ struct Layers { } VarDesc* write_to_array(VarDesc* x, VarDesc* i) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("write_to_array"); op->SetInput("X", {x->Name()}); @@ -836,7 +836,7 @@ struct Layers { } VarDesc* read_from_array(VarDesc* x, VarDesc* i) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("read_from_array"); op->SetInput("X", {x->Name()}); @@ -846,7 +846,7 @@ struct Layers { } VarDesc* gather(VarDesc* x, VarDesc* index, int axis) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("gather"); op->SetInput("X", {x->Name()}); @@ -863,7 +863,7 @@ struct Layers { } VarDesc* not_equal(VarDesc* x, VarDesc* y, int axis = -1) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("not_equal"); op->SetInput("X", {x->Name()}); @@ -874,7 +874,7 @@ struct Layers { } VarDesc* stack(std::vector inputs, int axis = -1) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("stack"); std::vector input_names; @@ -888,7 +888,7 @@ struct Layers { } VarDesc* tile(VarDesc* x, const std::vector& repeat_times = {2}) { - VarDesc* out = lod_tensor(unique_name()); + VarDesc* out = dense_tensor(unique_name()); OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType("tile"); op->SetInput("X", {x->Name()}); @@ -898,10 +898,10 @@ struct Layers { } private: - VarDesc* lod_tensor(std::string name, - std::vector shape = {}, - bool is_persistable = false, - proto::VarType::Type data_type = proto::VarType::FP32) { + VarDesc* dense_tensor(std::string name, + std::vector shape = {}, + bool is_persistable = false, + proto::VarType::Type data_type = proto::VarType::FP32) { auto* var = program_.MutableBlock(0)->Var(name); var->SetType(proto::VarType::DENSE_TENSOR); var->SetDataType(data_type); @@ -915,7 +915,7 @@ struct Layers { VarDesc* out = nullptr, const AttributeMap* attrs = nullptr) { if (!out) { - out = lod_tensor(unique_name()); + out = dense_tensor(unique_name()); } OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType(type); @@ -937,7 +937,7 @@ struct Layers { VarDesc* out = nullptr, const AttributeMap* attrs = nullptr) { if (!out) { - out = lod_tensor(unique_name()); + out = dense_tensor(unique_name()); } OpDesc* op = program_.MutableBlock(0)->AppendOp(); op->SetType(type); From 0780ed14e33e743304b8f17400af186c87f8cad1 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 26 Nov 2024 17:04:52 +0800 Subject: [PATCH 002/288] [Lod][fluid_ops] data_transform.cc (#69702) --- paddle/fluid/framework/data_transform.cc | 12 ++++++------ paddle/fluid/framework/executor_gc_helper.cc | 10 +++++----- .../control_flow/select_input_instruction.cc | 12 ++++++------ .../control_flow/select_output_instruction.cc | 12 ++++++------ .../fluid/framework/new_executor/pir_interpreter.cc | 4 ++-- .../framework/new_executor/program_interpreter.cc | 4 ++-- 6 files changed, 27 insertions(+), 27 deletions(-) diff --git a/paddle/fluid/framework/data_transform.cc b/paddle/fluid/framework/data_transform.cc index fa4144befb734d..c8cf06fe27aec8 100644 --- a/paddle/fluid/framework/data_transform.cc +++ b/paddle/fluid/framework/data_transform.cc @@ -137,14 +137,14 @@ void SetTensorToVariable(const Variable &in_var, const phi::DenseTensor &tensor, Variable *out_var) { if (in_var.IsType()) { - auto &in_lod_tensor = in_var.Get(); - auto *tran_lod_tensor = out_var->GetMutable(); - tran_lod_tensor->set_lod(in_lod_tensor.lod()); - tran_lod_tensor->set_layout(in_lod_tensor.layout()); + auto &in_dense_tensor = in_var.Get(); + auto *tran_dense_tensor = out_var->GetMutable(); + tran_dense_tensor->set_lod(in_dense_tensor.lod()); + tran_dense_tensor->set_layout(in_dense_tensor.layout()); #ifdef PADDLE_WITH_DNNL - tran_lod_tensor->set_mem_desc(in_lod_tensor.mem_desc()); + tran_dense_tensor->set_mem_desc(in_dense_tensor.mem_desc()); #endif - tran_lod_tensor->ShareDataWith(tensor); + tran_dense_tensor->ShareDataWith(tensor); } else if (in_var.IsType()) { auto &in_selected_rows = in_var.Get(); auto *trans_selected_rows = out_var->GetMutable(); diff --git a/paddle/fluid/framework/executor_gc_helper.cc b/paddle/fluid/framework/executor_gc_helper.cc index 5c59df2410aebe..2fdc160a82bc01 100644 --- a/paddle/fluid/framework/executor_gc_helper.cc +++ b/paddle/fluid/framework/executor_gc_helper.cc @@ -194,13 +194,13 @@ void DeleteUnusedTensors(const Scope &scope, ->mutable_value() ->MoveMemoryHolder()); } else if (var->IsType()) { - auto *lod_tensor_arr = var->GetMutable(); - for (auto &t : *lod_tensor_arr) { + auto *dense_tensor_arr = var->GetMutable(); + for (auto &t : *dense_tensor_arr) { garbages.emplace_back(t.MoveMemoryHolder()); } - // NOTE(wangxi): need clear the vector, otherwise lod_tensor_arr.size() is - // wrong, if size() decrease in next step, an error maybe occur. - lod_tensor_arr->clear(); + // NOTE(wangxi): need clear the vector, otherwise dense_tensor_arr.size() + // is wrong, if size() decrease in next step, an error maybe occur. + dense_tensor_arr->clear(); } else if (var->IsType()) { } else { PADDLE_THROW(common::errors::Unimplemented( diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc index 35aad9fef3860c..10db08c920ba42 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc @@ -79,9 +79,9 @@ class AssignFunctor { public: explicit AssignFunctor(Variable *out) : out_(out) {} - void operator()(const phi::DenseTensor &lod_tensor) const { + void operator()(const phi::DenseTensor &dense_tensor) const { auto &out_tensor = *out_->GetMutable(); - copy_tensor(lod_tensor, &out_tensor); + copy_tensor(dense_tensor, &out_tensor); } void operator()(const phi::TensorArray &array) const { @@ -111,12 +111,12 @@ class AssignFunctor { } private: - void copy_tensor(const phi::DenseTensor &lod_tensor, + void copy_tensor(const phi::DenseTensor &dense_tensor, phi::DenseTensor *out) const { - if (!lod_tensor.IsInitialized()) return; + if (!dense_tensor.IsInitialized()) return; auto &out_tensor = *out; - TensorCopy(lod_tensor, lod_tensor.place(), &out_tensor); - out_tensor.set_lod(lod_tensor.lod()); + TensorCopy(dense_tensor, dense_tensor.place(), &out_tensor); + out_tensor.set_lod(dense_tensor.lod()); } Variable *out_; diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc index 3a785df4cd49d1..2f913ca4847906 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc @@ -78,9 +78,9 @@ class AssignFunctor { public: explicit AssignFunctor(Variable *out) : out_(out) {} - void operator()(const phi::DenseTensor &lod_tensor) const { + void operator()(const phi::DenseTensor &dense_tensor) const { auto &out_tensor = *out_->GetMutable(); - copy_tensor(lod_tensor, &out_tensor); + copy_tensor(dense_tensor, &out_tensor); } void operator()(const phi::TensorArray &array) const { @@ -110,12 +110,12 @@ class AssignFunctor { } private: - void copy_tensor(const phi::DenseTensor &lod_tensor, + void copy_tensor(const phi::DenseTensor &dense_tensor, phi::DenseTensor *out) const { - if (!lod_tensor.IsInitialized()) return; + if (!dense_tensor.IsInitialized()) return; auto &out_tensor = *out; - TensorCopy(lod_tensor, lod_tensor.place(), &out_tensor); - out_tensor.set_lod(lod_tensor.lod()); + TensorCopy(dense_tensor, dense_tensor.place(), &out_tensor); + out_tensor.set_lod(dense_tensor.lod()); } Variable *out_; diff --git a/paddle/fluid/framework/new_executor/pir_interpreter.cc b/paddle/fluid/framework/new_executor/pir_interpreter.cc index 5007c18a97021e..3601e7c5d0f4a1 100644 --- a/paddle/fluid/framework/new_executor/pir_interpreter.cc +++ b/paddle/fluid/framework/new_executor/pir_interpreter.cc @@ -482,8 +482,8 @@ void PirInterpreter::ClearLoDTensorArrayInLocalScope() { auto vars = local_scope_->LocalVars(); for (auto var : vars) { if (var->IsType()) { - auto* lod_tensor_arr = var->GetMutable(); - lod_tensor_arr->clear(); + auto* dense_tensor_arr = var->GetMutable(); + dense_tensor_arr->clear(); } } } diff --git a/paddle/fluid/framework/new_executor/program_interpreter.cc b/paddle/fluid/framework/new_executor/program_interpreter.cc index c854558f65d09c..f899080d2389c4 100644 --- a/paddle/fluid/framework/new_executor/program_interpreter.cc +++ b/paddle/fluid/framework/new_executor/program_interpreter.cc @@ -674,8 +674,8 @@ void ProgramInterpreter::ClearLoDTensorArrayInLocalScope() { auto vars = local_scope_->LocalVars(); for (auto var : vars) { if (var->IsType()) { - auto* lod_tensor_arr = var->GetMutable(); - lod_tensor_arr->clear(); + auto* dense_tensor_arr = var->GetMutable(); + dense_tensor_arr->clear(); } } } From 28137ec08f04a3b67dd7a6db4c70c89fef0ec1e8 Mon Sep 17 00:00:00 2001 From: Chen Zhiyang <1792266893@qq.com> Date: Tue, 26 Nov 2024 17:38:59 +0800 Subject: [PATCH 003/288] [PIR save/load]Fix save combine memory (#69683) * fix save combine memory * fix --- .../src/save_load_parameters.cc | 17 ++--- .../kernels/impl/save_combine_kernel_impl.h | 66 ++++++++++++------- 2 files changed, 49 insertions(+), 34 deletions(-) diff --git a/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc b/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc index 07af8bfb6d3c0a..183bfc034bfb16 100644 --- a/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc +++ b/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc @@ -115,7 +115,11 @@ void SaveCombineFunction(const std::vector& x, MkDirRecursively(DirName(file_path).c_str()); VLOG(6) << "save func save path: " << file_path; - std::ostringstream ss; + std::ofstream fout(file_path, std::ios::binary); + PADDLE_ENFORCE_EQ(static_cast(fout), + true, + common::errors::Unavailable( + "Cannot open %s to save variables.", file_path)); PADDLE_ENFORCE_GT(x.size(), 0UL, common::errors::InvalidArgument( @@ -134,18 +138,11 @@ void SaveCombineFunction(const std::vector& x, auto out_dtype = save_as_fp16 ? phi::DataType::FLOAT16 : in_dtype; if (in_dtype != out_dtype) { auto out = CastTensorType(dev_ctx, tensor, out_dtype); - paddle::framework::SerializeToStream(ss, out, *dev_ctx); + paddle::framework::SerializeToStream(fout, out, *dev_ctx); } else { - paddle::framework::SerializeToStream(ss, tensor, *dev_ctx); + paddle::framework::SerializeToStream(fout, tensor, *dev_ctx); } } - MkDirRecursively(DirName(file_path).c_str()); - std::ofstream fout(file_path, std::ios::binary); - PADDLE_ENFORCE_EQ(static_cast(fout), - true, - common::errors::Unavailable( - "Cannot open %s to save variables.", file_path)); - fout << ss.str(); fout.close(); VLOG(6) << "save combine done "; } diff --git a/paddle/phi/kernels/impl/save_combine_kernel_impl.h b/paddle/phi/kernels/impl/save_combine_kernel_impl.h index 32cbecf344ee9c..e83bb5895f6a15 100644 --- a/paddle/phi/kernels/impl/save_combine_kernel_impl.h +++ b/paddle/phi/kernels/impl/save_combine_kernel_impl.h @@ -58,29 +58,10 @@ inline void SaveToMemory(const std::string& file_path, } template -void SaveCombineTensorKernel(const Context& dev_ctx, - const std::vector& x, - const std::string& file_path, - bool overwrite, - bool save_as_fp16, - bool save_to_memory, - phi::ExtendedTensor* out) { - std::string* y = nullptr; - if (out != nullptr) { - auto raw_out = static_cast(out); - y = raw_out->GetMutable(); - } - - bool is_present = FileExists(file_path); - if (is_present && !overwrite) { - PADDLE_THROW(common::errors::PreconditionNotMet( - "%s exists! Cannot save_combine to it when overwrite is set to " - "false.", - file_path, - overwrite)); - } - - std::ostringstream ss; +void SerializeCombineTensor(const Context& dev_ctx, + const std::vector& x, + bool save_as_fp16, + std::ostream& ss) { PADDLE_ENFORCE_GT(x.size(), 0UL, common::errors::InvalidArgument( @@ -114,8 +95,45 @@ void SaveCombineTensorKernel(const Context& dev_ctx, SerializeToStream(ss, tensor, dev_ctx); } } +} - SaveToMemory(file_path, ss, save_to_memory, y); +template +void SaveCombineTensorKernel(const Context& dev_ctx, + const std::vector& x, + const std::string& file_path, + bool overwrite, + bool save_as_fp16, + bool save_to_memory, + phi::ExtendedTensor* out) { + std::string* y = nullptr; + if (out != nullptr) { + auto raw_out = static_cast(out); + y = raw_out->GetMutable(); + } + + bool is_present = FileExists(file_path); + if (is_present && !overwrite) { + PADDLE_THROW(common::errors::PreconditionNotMet( + "%s exists! Cannot save_combine to it when overwrite is set to " + "false.", + file_path, + overwrite)); + } + + if (save_to_memory) { + std::ostringstream ss; + SerializeCombineTensor(dev_ctx, x, save_as_fp16, ss); + SaveToMemory(file_path, ss, save_to_memory, y); + } else { + MkDirRecursively(DirName(file_path).c_str()); + std::ofstream fout(file_path, std::ios::binary); + PADDLE_ENFORCE_EQ(static_cast(fout), + true, + common::errors::Unavailable( + "Cannot open %s to save variables.", file_path)); + SerializeCombineTensor(dev_ctx, x, save_as_fp16, fout); + fout.close(); + } } template From e5c83788a8bd879b9e8149e5aa674f7e09123823 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Tue, 26 Nov 2024 20:40:45 +0800 Subject: [PATCH 004/288] [CINN] Add `cinn_op.split` infersymbolicshape (#69344) * Add cinn_op.split * revert * fix * fix compile --- .../hlir/dialect/operator/ir/manual_op.cc | 6 ++ .../cinn/hlir/dialect/operator/ir/manual_op.h | 5 +- .../infer_symbolic_shape/cinn_op_infer_sym.cc | 96 +++++++++++++++++++ .../infer_symbolic_shape/cinn_op_infer_sym.h | 1 + 4 files changed, 107 insertions(+), 1 deletion(-) diff --git a/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc b/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc index 61362d14da399c..fc02cf954aa5ca 100644 --- a/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc +++ b/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc @@ -326,6 +326,12 @@ void SplitOp::Build(pir::Builder& builder, // NOLINT "axis", pir::Int32Attribute::get(pir::IrContext::Instance(), axis)); } +bool SplitOp::InferSymbolicShape( + pir::InferSymbolicShapeContext* infer_context) { + VLOG(4) << "Infer symbolic shape for cinn_op.split"; + return SplitOpInferSymbolicShape(this->operation(), infer_context); +} + const char* GenerateShapeOp::attributes_name[attributes_num] = { "output_dim_exprs", "symbol_bindings"}; diff --git a/paddle/cinn/hlir/dialect/operator/ir/manual_op.h b/paddle/cinn/hlir/dialect/operator/ir/manual_op.h index 9d55846057493d..20f74f3b4faf19 100644 --- a/paddle/cinn/hlir/dialect/operator/ir/manual_op.h +++ b/paddle/cinn/hlir/dialect/operator/ir/manual_op.h @@ -133,7 +133,8 @@ class IR_API ConcatOp bool InferSymbolicShape(pir::InferSymbolicShapeContext *infer_context); }; -class IR_API SplitOp : public pir::Op { +class IR_API SplitOp + : public pir::Op { public: using Op::Op; @@ -150,6 +151,8 @@ class IR_API SplitOp : public pir::Op { int axis); void VerifySig() const {} + + bool InferSymbolicShape(pir::InferSymbolicShapeContext *infer_context); }; class IR_API GenerateShapeOp diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.cc index 07a36666e2d7be..7f99fa00841ecb 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.cc @@ -124,6 +124,102 @@ bool ConcatOpInferSymbolicShape(pir::Operation *op, return true; } +bool SplitOpInferSymbolicShape(pir::Operation *op, + pir::InferSymbolicShapeContext *infer_context) { + const auto &x_shape_or_data = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + PADDLE_ENFORCE_EQ(x_shape_or_data.data().has_value(), + false, + common::errors::InvalidArgument( + "InferSymbolicShape of SplitOp only support input with " + "value now.")); + const auto &x_dims_sym = x_shape_or_data.shape(); + + // axis + int64_t axis = static_cast( + op->attribute("axis").dyn_cast().data()); + size_t rank = x_dims_sym.size(); + axis = axis >= 0 ? axis : std::max(int64_t(0), int64_t(axis + rank)); + + // sections + auto sections_array = op->attribute("num_or_sections") + .dyn_cast() + .AsVector(); + std::vector sections_sym; + if (sections_array.size() > 0) { + PADDLE_ENFORCE_EQ( + sections_array[0].isa(), + true, + common::errors::PreconditionNotMet( + "Element in sections_array MUST be pir::Int64Attribute ")); + + for (size_t i = 0; i < sections_array.size(); ++i) { + sections_sym.push_back( + sections_array[i].dyn_cast().data()); + } + } + + // output + const symbol::TensorListShapeOrDataDimExprs &output_shape_data_list = [&] { + const auto &GetSum = [&](const auto &dim_exprs, const auto &Filter) { + symbol::DimExpr sum{0}; + for (const auto &dim_expr : dim_exprs) { + if (Filter(dim_expr)) { + sum = sum + dim_expr; + } + } + return sum; + }; + const auto &All = [&](const auto &dim_exprs, const auto &Cond) { + for (const auto &dim_expr : dim_exprs) { + if (!Cond(dim_expr)) { + return false; + } + } + return true; + }; + const auto &IsNotMinusOne = [&](const symbol::DimExpr &dim_expr) { + if (dim_expr.isa()) { + return dim_expr.dyn_cast() != static_cast(-1); + } + return true; + }; + const auto &sum_exclude_minus_one = GetSum(sections_sym, IsNotMinusOne); + + const bool &all_sections_sym_not_minus_one = + All(sections_sym, IsNotMinusOne); + if (all_sections_sym_not_minus_one) { + infer_context->AddEqualCstr(x_dims_sym.at(axis), sum_exclude_minus_one); + } + + symbol::TensorListShapeOrDataDimExprs shape_data_list; + std::vector output_dims_sym = x_dims_sym; + if (!all_sections_sym_not_minus_one && sections_sym.size() == 1) { + VLOG(3) << "[SplitOp]-1 is the only split section. The output shape is " + "identical to the input shape."; + shape_data_list.push_back( + symbol::TensorShapeOrDataDimExprs(output_dims_sym)); + return shape_data_list; + } + for (uint32_t idx = 0; idx < sections_sym.size(); idx++) { + const auto §ion_sym = sections_sym.at(idx); + output_dims_sym.at(axis) = + IsNotMinusOne(section_sym) + ? section_sym + : x_dims_sym.at(axis) - sum_exclude_minus_one; + + shape_data_list.push_back( + symbol::TensorShapeOrDataDimExprs(output_dims_sym)); + } + return shape_data_list; + }(); + + infer_context->SetShapeOrDataForValue( + op->result(0), symbol::ShapeOrDataDimExprs{output_shape_data_list}); + + return true; +} + bool Pool2dOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { const auto &kernel_size_shape_or_data = diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.h index 6b6904105d40a0..d244f86db6aed7 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/cinn_op_infer_sym.h @@ -25,6 +25,7 @@ OP_DECLARE_INFER_SYMBOLIC_SHAPE(ReduceProd) OP_DECLARE_INFER_SYMBOLIC_SHAPE(ReduceSum) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Reshape) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Slice) +OP_DECLARE_INFER_SYMBOLIC_SHAPE(Split) OP_DECLARE_INFER_SYMBOLIC_SHAPE(UniformRandom) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Gather) } // namespace cinn::dialect From 68609240cd000c264a17e5cb145cd1164f9ab143 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Tue, 26 Nov 2024 21:36:31 +0800 Subject: [PATCH 005/288] [SOT][3.13] Adapt for `f_lasti` usage in `test_analysis_inputs` (#69707) --- test/sot/skip_files_py313 | 1 - test/sot/test_analysis_inputs.py | 8 +++++++- 2 files changed, 7 insertions(+), 2 deletions(-) diff --git a/test/sot/skip_files_py313 b/test/sot/skip_files_py313 index 9d6104646c3a0e..308a8457b5303a 100644 --- a/test/sot/skip_files_py313 +++ b/test/sot/skip_files_py313 @@ -1,7 +1,6 @@ test/sot/test_11_jumps.py test/sot/test_12_for_loop.py test/sot/test_19_closure.py -test/sot/test_analysis_inputs.py test/sot/test_break_graph.py test/sot/test_min_graph_size.py test/sot/test_numpy.py diff --git a/test/sot/test_analysis_inputs.py b/test/sot/test_analysis_inputs.py index b400d5f781150d..8b37813028262a 100644 --- a/test/sot/test_analysis_inputs.py +++ b/test/sot/test_analysis_inputs.py @@ -24,6 +24,9 @@ calc_offset_from_bytecode_offset, get_instructions, ) +from paddle.jit.sot.opcode_translator.instruction_utils.opcode_info import ( + PYOPCODE_CACHE_SIZE, +) def assert_inputs_equals(instruction_offset: int, expected_inputs: set[str]): @@ -33,8 +36,11 @@ def assert_inputs_equals(instruction_offset: int, expected_inputs: set[str]): assert test_frame is not None instructions = get_instructions(test_frame.f_code) + current_offset = test_frame.f_lasti + if sys.version_info >= (3, 13): + current_offset += PYOPCODE_CACHE_SIZE.get("CALL") * 2 current_instr_idx = calc_offset_from_bytecode_offset( - test_frame.f_lasti + 2, instructions + current_offset + 2, instructions ) reads, writes = analysis_used_names( instructions, current_instr_idx + instruction_offset From a07119de629b2086b2598f1298fd42a018e64b03 Mon Sep 17 00:00:00 2001 From: moonlighti <45419662+fxy1699@users.noreply.github.com> Date: Wed, 27 Nov 2024 01:39:33 +0800 Subject: [PATCH 006/288] [CodeStyle][Typos][A-[1-10]] Fix typos (`Acceses`, `accessable`, `accesss`, `accoding`, `accurary`, `Accuarcy`, `actived`, `acitve`, `actualy`, `Actural`, `actural`) (#69627) --- _typos.toml | 11 ----------- .../operator/transforms/accuracy_check_pass.cc | 8 ++++---- .../dialect/operator/transforms/accuracy_check_pass.h | 2 +- .../hlir/dialect/operator/transforms/add_cinn_pass.cc | 2 +- paddle/fluid/inference/goapi/config.go | 4 ++-- .../infer_symbolic_shape/multiary_infer_sym.cc | 2 +- paddle/fluid/pybind/pir.cc | 2 +- .../phi/core/platform/device/gpu/gpu_launch_config.h | 4 ++-- .../predicated_tile_iterator_residual_last.h | 2 +- .../paddle/incubate/distributed/fleet/fleet_util.py | 2 +- test/custom_runtime/test_custom_cpu_to_static.py | 2 +- test/legacy_test/op_test.py | 2 +- test/legacy_test/test_eigvals_op.py | 4 ++-- tools/print_signatures.py | 2 +- 14 files changed, 19 insertions(+), 30 deletions(-) diff --git a/_typos.toml b/_typos.toml index 23e1b8c3636443..5a96ffed456064 100644 --- a/_typos.toml +++ b/_typos.toml @@ -24,17 +24,6 @@ UE = "UE" unpacket = "unpacket" # These words need to be fixed -Acceses = 'Acceses' -accessable = 'accessable' -accesss = 'accesss' -accoding = 'accoding' -accurary = 'accurary' -Accuarcy = 'Accuarcy' -actived = 'actived' -acitve = 'acitve' -actualy = 'actualy' -actural = 'actural' -Actural = 'Actural' assigend = 'assigend' assined = 'assined' assgin = 'assgin' diff --git a/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.cc index 5b8aaec5faa67e..a642fb43fd6f70 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.cc @@ -183,9 +183,9 @@ class AddAccuracyCheckPattern } }; -class AccuarcyCheckPass : public pir::Pass { +class AccuracyCheckPass : public pir::Pass { public: - AccuarcyCheckPass() : pir::Pass("accuracy_check_pass", /*opt_level=*/1) {} + AccuracyCheckPass() : pir::Pass("accuracy_check_pass", /*opt_level=*/1) {} bool Initialize(pir::IrContext* context) override { pir::RewritePatternSet ps(context); @@ -218,8 +218,8 @@ class AccuarcyCheckPass : public pir::Pass { pir::FrozenRewritePatternSet patterns_; }; -std::unique_ptr CreateAccuarcyCheckPass() { - return std::make_unique(); +std::unique_ptr CreateAccuracyCheckPass() { + return std::make_unique(); } } // namespace cinn::dialect::ir diff --git a/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.h b/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.h index 68e1e7a1c71674..cb3b2ecd82f89a 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.h +++ b/paddle/cinn/hlir/dialect/operator/transforms/accuracy_check_pass.h @@ -19,6 +19,6 @@ namespace cinn::dialect::ir { -std::unique_ptr CreateAccuarcyCheckPass(); +std::unique_ptr CreateAccuracyCheckPass(); } // namespace cinn::dialect::ir diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc index e57e8b756e0030..7ee13878a29f3b 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc @@ -218,7 +218,7 @@ void ApplyCinnLowerPass( if (FLAGS_enable_cinn_accuracy_check) { VLOG(0) << "Enable CINN Accuracy Check Pass"; - pass_manager->AddPass(cinn::dialect::ir::CreateAccuarcyCheckPass()); + pass_manager->AddPass(cinn::dialect::ir::CreateAccuracyCheckPass()); } if (FLAGS_enable_fusion_fallback) { VLOG(0) << "Enable Fusion Fallback Pass"; diff --git a/paddle/fluid/inference/goapi/config.go b/paddle/fluid/inference/goapi/config.go index 652ba51dca93e0..a3c95364c59346 100644 --- a/paddle/fluid/inference/goapi/config.go +++ b/paddle/fluid/inference/goapi/config.go @@ -270,7 +270,7 @@ func (config *Config) FractionOfGpuMemoryForPool() float32 { /// \brief Control whether to perform IR graph optimization. /// If turned off, the AnalysisConfig will act just like a NativeConfig. /// -/// \param x Whether the ir graph optimization is actived. +/// \param x Whether the ir graph optimization is active. /// func (config *Config) SwitchIrOptim(x bool) { C.PD_ConfigSwitchIrOptim(config.c, cvtGoBoolToPD(x)) @@ -278,7 +278,7 @@ func (config *Config) SwitchIrOptim(x bool) { /// /// \brief A boolean state telling whether the ir graph optimization is -/// actived. +/// active. /// /// \return bool Whether to use ir graph optimization. /// diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc index b3388cbff7b749..19cf11a1a3e88b 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/multiary_infer_sym.cc @@ -650,7 +650,7 @@ bool AssignPosOpInferSymbolicShape( const auto &eff_num_len_shape_or_data = infer_context->GetShapeOrDataForValue(op->operand_source(2)); if (eff_num_len_shape_or_data.data() - .has_value()) { // accoding to the kernel code + .has_value()) { // according to the kernel code infer_context->SetShapeOrDataForValue( op->result(0), symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs( diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index f18013706b4079..73849ee5f7a1f3 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -1372,7 +1372,7 @@ void BindValue(py::module *m) { } } PADDLE_THROW(common::errors::InvalidArgument( - "only support accesss index from op_result or positional " + "only support accessing index from op_result or positional " "block arg.")); }) .def("is_dense_tensor_type", diff --git a/paddle/phi/core/platform/device/gpu/gpu_launch_config.h b/paddle/phi/core/platform/device/gpu/gpu_launch_config.h index 68eb88c87fae8a..d817bf6a4e06e8 100644 --- a/paddle/phi/core/platform/device/gpu/gpu_launch_config.h +++ b/paddle/phi/core/platform/device/gpu/gpu_launch_config.h @@ -117,11 +117,11 @@ inline GpuLaunchConfig GetGpuLaunchConfig1D(const phi::GPUContext& context, int64_t active_threads_num = numel / vec_size; if (active_threads_num / (sm_count << 1) < limit_threads) { // Round up threads number into an exponential multiple of 2, while number - // of acitve blocks is about twice of SM, to acquire better performance. + // of active blocks is about twice of SM, to acquire better performance. threads = RoundToPowerOfTwo(active_threads_num / (sm_count << 1)); } else if (active_threads_num / (sm_count << 2) < limit_threads) { // Round up threads number into an exponential multiple of 2, while number - // of acitve blocks is about 4 times of SM, to acquire better performance. + // of active blocks is about 4 times of SM, to acquire better performance. threads = RoundToPowerOfTwo(active_threads_num / (sm_count << 2)); } // Number of threads per block shall be larger than 64. diff --git a/paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/iterators/predicated_tile_iterator_residual_last.h b/paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/iterators/predicated_tile_iterator_residual_last.h index 97536199e8fd1e..6e51ede94d11e9 100644 --- a/paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/iterators/predicated_tile_iterator_residual_last.h +++ b/paddle/phi/kernels/fusion/cutlass/memory_efficient_attention/iterators/predicated_tile_iterator_residual_last.h @@ -106,7 +106,7 @@ namespace threadblock { /// To be efficient, this assumes the iterator will be dereferenced and advanced /// at least once outside any looping structure to minimize integer arithmetic. /// -/// Acceses out of bounds are safe so long as `clear_mask()` is called prior to +/// Accesses out of bounds are safe so long as `clear_mask()` is called prior to /// dereferencing the iterator. /// /// diff --git a/python/paddle/incubate/distributed/fleet/fleet_util.py b/python/paddle/incubate/distributed/fleet/fleet_util.py index 5962ff6a84d7dd..b290bb0d6e8bcf 100644 --- a/python/paddle/incubate/distributed/fleet/fleet_util.py +++ b/python/paddle/incubate/distributed/fleet/fleet_util.py @@ -1699,7 +1699,7 @@ def print_global_metrics( ) self.rank0_print( f"{print_prefix} global AUC={auc:.6f} BUCKET_ERROR={bucket_error:.6f} MAE={mae:.6f} " - f"RMSE={rmse:.6f} Actural_CTR={actual_ctr:.6f} Predicted_CTR={predicted_ctr:.6f} " + f"RMSE={rmse:.6f} Actual_CTR={actual_ctr:.6f} Predicted_CTR={predicted_ctr:.6f} " f"COPC={copc:.6f} MEAN Q_VALUE={mean_predict_qvalue:.6f} Ins number={total_ins_num}" ) diff --git a/test/custom_runtime/test_custom_cpu_to_static.py b/test/custom_runtime/test_custom_cpu_to_static.py index 630e5b79783b37..06532ca136b8f1 100644 --- a/test/custom_runtime/test_custom_cpu_to_static.py +++ b/test/custom_runtime/test_custom_cpu_to_static.py @@ -92,7 +92,7 @@ def test_func(epoch_id, test_loader, model, cost): avg_acc[1].append(acc_top5.numpy()) model.train() print( - f"Epoch ID: {epoch_id + 1}, Top1 accurary: {np.array(avg_acc[0]).mean()}, Top5 accurary: {np.array(avg_acc[1]).mean()}" + f"Epoch ID: {epoch_id + 1}, Top1 accuracy: {np.array(avg_acc[0]).mean()}, Top5 accuracy: {np.array(avg_acc[1]).mean()}" ) diff --git a/test/legacy_test/op_test.py b/test/legacy_test/op_test.py index a94526f1e57cae..922050ede5608e 100644 --- a/test/legacy_test/op_test.py +++ b/test/legacy_test/op_test.py @@ -2612,7 +2612,7 @@ def init(self): self.checker_name = "symbol infer checker" def infer_and_compare_symbol(self): - """infer symbol and compare it with actualy shape and data""" + """infer symbol and compare it with actual shape and data""" self.is_python_api_test = True self.op_test._infer_and_compare_symbol(place) diff --git a/test/legacy_test/test_eigvals_op.py b/test/legacy_test/test_eigvals_op.py index c54a4070be3a44..9a1b8e64b38ef4 100644 --- a/test/legacy_test/test_eigvals_op.py +++ b/test/legacy_test/test_eigvals_op.py @@ -205,8 +205,8 @@ def set_input_data(self): + np.random.random(self.input_dims) * 1j ).astype(self.dtype) - def verify_output(self, actural_outs, expect_outs): - actual_outs = np.array(actural_outs) + def verify_output(self, actual_outs, expect_outs): + actual_outs = np.array(actual_outs) expect_outs = np.array(expect_outs) self.assertTrue( actual_outs.shape == expect_outs.shape, diff --git a/tools/print_signatures.py b/tools/print_signatures.py index 94d27613fbb357..b03c8358da6171 100644 --- a/tools/print_signatures.py +++ b/tools/print_signatures.py @@ -312,7 +312,7 @@ def api_filter(api_name: str) -> bool: all_api_names_to_k = {} for k, api_info in api_info_dict.items(): # 1. the shortest suggested_name may be renamed; - # 2. some api's fullname is not accessable, the module name of it is overrided by the function with the same name; + # 2. some api's fullname is not accessible, the module name of it is overrided by the function with the same name; api_name = sorted(api_info['all_names'])[0] all_api_names_to_k[api_name] = k all_api_names_sorted = sorted(all_api_names_to_k.keys()) From c6b195e08f887084c650e580b5f73de1cbaad1a8 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BD=A0=E7=8C=9C?= Date: Wed, 27 Nov 2024 03:28:03 +0800 Subject: [PATCH 007/288] [SOT][3.13] Copy opcode `TO_BOOL` when necessary (#69700) --- .../executor/opcode_executor.py | 28 +++++++++++++------ .../instruction_utils/opcode_info.py | 1 + test/sot/skip_files_py313 | 3 -- test/sot/test_break_graph.py | 10 +++++++ 4 files changed, 31 insertions(+), 11 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py index ec91fd85dc2781..fd476b4b3b8a92 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py +++ b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py @@ -49,7 +49,12 @@ calc_stack_effect, get_instructions, ) -from ..instruction_utils.opcode_info import RETURN, JumpDirection, PopJumpCond +from ..instruction_utils.opcode_info import ( + NEED_TO_BOOL, + RETURN, + JumpDirection, + PopJumpCond, +) from .dispatch_functions import ( operator_BAD, operator_exception_match, @@ -1344,11 +1349,9 @@ def COMPARE_OP(self, instr: Instruction): def TO_BOOL(self, instr: Instruction): # we don't do anything in TO_BOOL, we simply check if the bytecode is legal next_instr = self._instructions[self._lasti] - assert next_instr.opname in [ - 'POP_JUMP_IF_TRUE', - 'POP_JUMP_IF_FALSE', - 'UNARY_NOT', - ], f"The bytecode is illegal! The opcode following TO_BOOL must be in ['POP_JUMP_IF_TRUE', 'POP_JUMP_IF_FALSE', 'UNARY_NOT'], the next instuction now is {next_instr.opname}" + assert ( + next_instr.opname in NEED_TO_BOOL + ), f"The bytecode is illegal! The opcode following TO_BOOL must be in ['POP_JUMP_IF_TRUE', 'POP_JUMP_IF_FALSE', 'UNARY_NOT'], the next instuction now is {next_instr.opname}" @call_break_graph_decorator(push_n=1) def IS_OP(self, instr: Instruction): @@ -2038,6 +2041,7 @@ def _break_graph_when_if(self, result: TensorVariable, instr: Instruction): # 1. analyse info cur_index = self.indexof(instr) + prefix_opname = self._instructions[cur_index - 1].opname true_fn_start_index = cur_index + 1 false_fn_start_index = self.indexof(instr.jump_to) stack_size_after_if = len(self.stack) - 1 @@ -2112,6 +2116,11 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): # 5. create if sturcture and call true_fn and false_fn var_loader.load(result) + + # in 3.13, we have to copy the original 'TO_BOOL' to make the generated bytecode valid. + if sys.version_info >= (3, 13) and prefix_opname == "TO_BOOL": + self._graph.pycode_gen.add_instr('TO_BOOL') + if_code = self._graph.pycode_gen.add_instr(instr.opname) assert true_fn is not None @@ -2174,6 +2183,7 @@ def _break_graph_when_call( push_n = push_n(instr.arg) if callable(push_n) else push_n is_precall = instr.opname == "PRECALL" cur_index = self.indexof(instr) + prefix_opname = self._instructions[cur_index - 1].opname # Use CALL instead of PRECALL to calculate the real stack effect call_instr = self._instructions[cur_index + int(is_precall)] # skip CALL if current instr is PRECALL @@ -2223,6 +2233,10 @@ def create_resume_fn(): # 5. run the break CALL with origin python # NOTE(SigureMo): In Python 3.11 and 3.12，we need generate KW_NAMES if the call shape is not None. self._graph.pycode_gen.gen_kw_names(self._call_shape) + # in 3.13, We have to copy the original 'TO_BOOL' to make the generated bytecode valid. + if sys.version_info >= (3, 13) and prefix_opname == 'TO_BOOL': + self._graph.pycode_gen.add_instr('TO_BOOL') + self._graph.pycode_gen.extend_instrs( self._instructions[cur_index:next_index] ) @@ -2243,10 +2257,8 @@ def create_resume_fn(): self._graph.pycode_gen.gen_call_function( argc=resume_fn.__code__.co_argcount, ) - # gen RETURN_VALUE self._graph.pycode_gen.gen_return() - self.new_code = self._graph.pycode_gen.gen_pycode() self.guard_fn = self._graph.guard_fn diff --git a/python/paddle/jit/sot/opcode_translator/instruction_utils/opcode_info.py b/python/paddle/jit/sot/opcode_translator/instruction_utils/opcode_info.py index f0e18fce6c95c3..29ff82963fddbf 100644 --- a/python/paddle/jit/sot/opcode_translator/instruction_utils/opcode_info.py +++ b/python/paddle/jit/sot/opcode_translator/instruction_utils/opcode_info.py @@ -23,6 +23,7 @@ ABS_JUMP = {opcode.opname[x] for x in opcode.hasjabs} HAS_LOCAL = {opcode.opname[x] for x in opcode.haslocal} HAS_FREE = {opcode.opname[x] for x in opcode.hasfree} +NEED_TO_BOOL = {"UNARY_NOT", "POP_JUMP_IF_FALSE", "POP_JUMP_IF_TRUE"} ALL_JUMP = REL_JUMP | ABS_JUMP UNCONDITIONAL_JUMP = {"JUMP_ABSOLUTE", "JUMP_FORWARD"} if sys.version_info >= (3, 11): diff --git a/test/sot/skip_files_py313 b/test/sot/skip_files_py313 index 308a8457b5303a..64f4f710bf849e 100644 --- a/test/sot/skip_files_py313 +++ b/test/sot/skip_files_py313 @@ -1,8 +1,5 @@ -test/sot/test_11_jumps.py -test/sot/test_12_for_loop.py test/sot/test_19_closure.py test/sot/test_break_graph.py test/sot/test_min_graph_size.py test/sot/test_numpy.py -test/sot/test_resume_cache.py test/sot/test_simulate_initialize.py diff --git a/test/sot/test_break_graph.py b/test/sot/test_break_graph.py index 4a2ef40c36c595..e5be054393f6a5 100644 --- a/test/sot/test_break_graph.py +++ b/test/sot/test_break_graph.py @@ -200,5 +200,15 @@ def test_break_graph_when_call_generator_function(self): self.assert_results(break_graph_call_generator_function, [x, y]) +def unary_not_break_graph(x): + return not x + + +class TestUnaryNot(TestCaseBase): + def test_unary_not_break_graph(self): + x = paddle.to_tensor(0) + self.assert_results(unary_not_break_graph, x) + + if __name__ == "__main__": unittest.main() From e59944507cc4344eb8bb599dad7198f4eba70796 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Wed, 27 Nov 2024 10:08:28 +0800 Subject: [PATCH 008/288] [SOT][3.13] Don't pass `NULL` to resume fn (#69735) --- .../executor/function_graph.py | 4 +- .../executor/opcode_executor.py | 46 +++++++++++++++---- .../executor/pycode_generator.py | 23 ++++++++-- 3 files changed, 58 insertions(+), 15 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py index 032cda7c928668..73e568cd9843d5 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py +++ b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py @@ -363,7 +363,7 @@ def _restore_origin_opcode(self, stack_vars, store_var_info, instr_idx): self.pycode_gen.gen_enable_eval_frame() - name_gen = NameGenerator("___compile_fn_saved_orig_") + name_gen = NameGenerator("___graph_fn_saved_orig_") # here is not update changed values, it just give names to stack vars # and want keep same interface as _build_compile_fn_with_name_store @@ -394,7 +394,7 @@ def _build_compile_fn_with_name_store( filter(lambda x: not isinstance(x, NullVariable), to_store_vars) ) self.compile_function(compile_graph_result, to_store_vars) - name_gen = NameGenerator("___compile_fn_saved_") + name_gen = NameGenerator("___graph_fn_saved_") for var in to_store_vars[::-1]: if not store_var_info[var.id]: diff --git a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py index fd476b4b3b8a92..e9ffe9a90dff5b 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py +++ b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py @@ -2045,9 +2045,12 @@ def _break_graph_when_if(self, result: TensorVariable, instr: Instruction): true_fn_start_index = cur_index + 1 false_fn_start_index = self.indexof(instr.jump_to) stack_size_after_if = len(self.stack) - 1 + null_indices = self._calc_null_indices(1) # 2. create true_fn and false_fn - def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): + def create_if_branch_fn( + start_idx, input_var_names, is_pop_jump_branch, null_indices + ): # JUMP_IF_* maybe jump to the RETURN_VALUE, we should skip this case # We shouldn't skip POP_JUMP_* case, because it will cause the stack size to be incorrect if ( @@ -2064,7 +2067,9 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): pycode_gen = resume_fn_creator.codegen origin_instrs = get_instructions(pycode_gen._origin_code) resume_fn_creator.set_inputs( - input_var_names, stack_size=stack_size_after_if + input_var_names, + stack_size=stack_size_after_if, + null_indices=null_indices, ) pycode_gen.extend_instrs(origin_instrs[start_idx:]) # the resume_fn contains return code, so we don't need set output here @@ -2083,6 +2088,7 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): start_idx=true_fn_start_index, input_var_names=true_fn_input_var_names, is_pop_jump_branch=False, + null_indices=null_indices, ) false_fn_read_names, _ = analysis_used_names( @@ -2096,6 +2102,7 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): start_idx=false_fn_start_index, input_var_names=false_fn_input_var_names, is_pop_jump_branch=instr.opname.startswith("POP_JUMP"), + null_indices=null_indices, ) # 4. setup vars which is created in loop as Undefind @@ -2128,7 +2135,9 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): self._graph.pycode_gen.gen_load_object( true_fn, true_fn.__code__.co_name ) - for stack_arg in list(self.stack)[:-1]: + for i, stack_arg in enumerate(list(self.stack)[:-1]): + if i in null_indices: + continue var_loader.load(stack_arg) for name in true_fn_input_var_names: @@ -2143,7 +2152,10 @@ def create_if_branch_fn(start_idx, input_var_names, is_pop_jump_branch): false_start_code = self._graph.pycode_gen.gen_load_object( false_fn, false_fn.__code__.co_name ) - for stack_arg in list(self.stack)[:-1]: + null_indices = [] + for i, stack_arg in enumerate(list(self.stack)[:-1]): + if i in null_indices: + continue var_loader.load(stack_arg) for name in false_fn_input_var_names: var_loader.load(self.get_var(name, allow_undefined=True)) @@ -2191,6 +2203,7 @@ def _break_graph_when_call( stack_effect = calc_stack_effect(call_instr) pop_n = push_n - stack_effect stack_size_after_call = len(self.stack) - pop_n + push_n + null_indices = self._calc_null_indices(pop_n) # 2. create resume function read_names, _ = analysis_used_names(self._instructions, next_index) @@ -2199,7 +2212,7 @@ def _break_graph_when_call( read_names, (Space.locals, Space.cells) ) - def create_resume_fn(): + def create_resume_fn(null_indices): if self._instructions[next_index].opname == "RETURN_VALUE": return None cache_key = (ResumeFunctionType.CALL_RESUME, self._code, next_index) @@ -2211,7 +2224,9 @@ def create_resume_fn(): pycode_gen = resume_fn_creator.codegen origin_instrs = get_instructions(pycode_gen._origin_code) resume_fn_creator.set_inputs( - input_var_names, stack_size=stack_size_after_call + input_var_names, + stack_size=stack_size_after_call, + null_indices=null_indices, ) pycode_gen.extend_instrs(origin_instrs[next_index:]) # the resume_fn contains return code, so we don't need set output here @@ -2219,7 +2234,7 @@ def create_resume_fn(): resume_fn = resume_fn_creator.generate(cache_key=cache_key) return resume_fn - resume_fn = create_resume_fn() + resume_fn = create_resume_fn(null_indices=null_indices) # 3. compile sub graph before call var_loader = self.get_compute_fn_and_update_changed_vars( @@ -2227,7 +2242,9 @@ def create_resume_fn(): ) # 4. recover stack - for stack_arg in self.stack: + for i, stack_arg in enumerate(self.stack): + if i in null_indices: + continue var_loader.load(stack_arg) # 5. run the break CALL with origin python @@ -2250,7 +2267,7 @@ def create_resume_fn(): # In Python 3.11+, NULL + resume_fn should be shifted together. shift_n = 2 if sys.version_info >= (3, 11) else 1 self._graph.pycode_gen.gen_shift_n( - shift_n, stack_size_after_call + shift_n + shift_n, stack_size_after_call - len(null_indices) + shift_n ) for name in input_var_names: var_loader.load(self.get_var(name, allow_undefined=True)) @@ -2617,3 +2634,14 @@ def create_inline_call_fn(): for name, var in zip(output_var_names[:-1], ret[slice_variable]): self.set_var(name, var) + + def _calc_null_indices(self, pop_n): + return [ + i + for i, stack_arg in enumerate(self.stack) + if ( + i < len(self.stack) - pop_n + and isinstance(stack_arg, NullVariable) + and CALL_METHOD_LAYOUT_NULL_AFTER_VALUE + ) + ] diff --git a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py index 3f170b02e730b7..8550514ef6e7f8 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py +++ b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py @@ -1009,12 +1009,23 @@ def __init__( self.codegen = PyCodeGen(frame, disable_eval_frame) self.name = ResumeFnNameFactory().next() - def set_inputs(self, inputs: list[str], stack_size: int): + def set_inputs( + self, inputs: list[str], stack_size: int, null_indices: list[int] = [] + ): stack_arg_str = self.name + '_stack_{}' + assert all( + idx < stack_size for idx in null_indices + ), "null index out of range" - self.codegen._code_options['co_argcount'] = len(inputs) + stack_size + self.codegen._code_options['co_argcount'] = ( + len(inputs) + stack_size - len(null_indices) + ) self.codegen._code_options['co_varnames'] = list( - [stack_arg_str.format(i) for i in range(stack_size)] + [ + stack_arg_str.format(i) + for i in range(stack_size) + if i not in null_indices + ] + inputs + [ var_name @@ -1025,7 +1036,11 @@ def set_inputs(self, inputs: list[str], stack_size: int): self.codegen._instructions.extend( [ - gen_instr('LOAD_FAST', argval=stack_arg_str.format(i)) + ( + gen_instr("PUSH_NULL") + if i in null_indices + else gen_instr('LOAD_FAST', argval=stack_arg_str.format(i)) + ) for i in range(stack_size) ] ) From 4d99ac377bb7476adada5fc9e27a0d475c87402f Mon Sep 17 00:00:00 2001 From: gouzil <66515297+gouzil@users.noreply.github.com> Date: Wed, 27 Nov 2024 10:09:45 +0800 Subject: [PATCH 009/288] [SOT][3.13] fix get `frame.f_locals` segmentation fault (#69738) --- paddle/fluid/pybind/sot/cpython_internals.c | 140 +++++--------------- paddle/fluid/pybind/sot/cpython_internals.h | 2 +- paddle/fluid/pybind/sot/eval_frame.c | 2 +- 3 files changed, 38 insertions(+), 106 deletions(-) diff --git a/paddle/fluid/pybind/sot/cpython_internals.c b/paddle/fluid/pybind/sot/cpython_internals.c index 1c12c51066e885..6c997399e4066a 100644 --- a/paddle/fluid/pybind/sot/cpython_internals.c +++ b/paddle/fluid/pybind/sot/cpython_internals.c @@ -167,127 +167,59 @@ void Internal_PyEvalFrameClearAndPop(PyThreadState *tstate, } #if PY_3_13_PLUS -// Returns borrowed reference or NULL -static PyObject *framelocalsproxy_getval(_PyInterpreterFrame *frame, - PyCodeObject *co, - int i) { - PyObject **fast = _PyFrame_GetLocalsArray(frame); - _PyLocals_Kind kind = _PyLocals_GetKind(co->co_localspluskinds, i); - - PyObject *value = fast[i]; - PyObject *cell = NULL; - - if (value == NULL) { - return NULL; +// This function is used to get the locals mapping of the frame. +void update_framelocals_mapping(PyObject *mapping, + PyCodeObject *code, + int i, + PyObject *value) { + _PyLocals_Kind kind = _PyLocals_GetKind(code->co_localspluskinds, i); + + if (kind & CO_FAST_FREE && !(code->co_flags & CO_OPTIMIZED)) { + return; } - if (kind == CO_FAST_FREE || kind & CO_FAST_CELL) { - // The cell was set when the frame was created from - // the function's closure. - assert(PyCell_Check(value)); - cell = value; + if (kind & CO_FAST_HIDDEN) { + return; } - if (cell != NULL) { - value = PyCell_GET(cell); + if (kind & CO_FAST_FREE) { + assert(value != NULL && PyCell_Check(value)); + value = PyCell_GET(value); } - if (value == NULL) { - return NULL; + if (value != NULL) { + PyDict_SetItem( + mapping, PyTuple_GET_ITEM(code->co_localsplusnames, i), value); } - - return value; } -bool Internal_PyFrame_HasHiddenLocals(_PyInterpreterFrame *frame) { - /* - * This function returns if there are hidden locals introduced by PEP 709, - * which are the isolated fast locals for inline comprehensions - */ - PyCodeObject *co = _PyFrame_GetCode(frame); - - for (int i = 0; i < co->co_nlocalsplus; i++) { - _PyLocals_Kind kind = _PyLocals_GetKind(co->co_localspluskinds, i); - - if (kind & CO_FAST_HIDDEN) { - PyObject *value = framelocalsproxy_getval(frame, co, i); +// simplified version `frame_get_var`, `frame_init_get_vars` and +// `PyFrame_GetLocals` +PyObject *get_framelocals_mapping(_PyInterpreterFrame *frame) { + PyObject *mapping = PyDict_New(); - if (value != NULL) { - return true; - } - } + // If the frame is not yet executed, return an empty mapping, see + // `frame_get_var` function + if (!frame->stacktop) { + return mapping; } - return false; -} -static PyObject *Internal_framelocalsproxy_new(PyTypeObject *type, - PyObject *args, - PyObject *kwds) { - if (PyTuple_GET_SIZE(args) != 1) { - PyErr_Format(PyExc_TypeError, - "FrameLocalsProxy expected 1 argument, got %zd", - PyTuple_GET_SIZE(args)); - return NULL; - } - PyObject *item = PyTuple_GET_ITEM(args, 0); + PyCodeObject *co = PyFrame_GET_CODE(frame); - if (!PyFrame_Check(item)) { - PyErr_Format(PyExc_TypeError, "expect frame, not %T", item); - return NULL; + // Get local variables, see `frame_get_var` function + int offset = co->co_nlocalsplus - co->co_nfreevars; + for (int i = 0; i < offset; i++) { + update_framelocals_mapping(mapping, co, i, frame->localsplus[i]); } - PyFrameObject *frame = (PyFrameObject *)item; - if (kwds != NULL && PyDict_Size(kwds) != 0) { - PyErr_SetString(PyExc_TypeError, - "FrameLocalsProxy takes no keyword arguments"); - return 0; - } - - PyFrameLocalsProxyObject *self = - (PyFrameLocalsProxyObject *)type->tp_alloc(type, 0); - if (self == NULL) { - return NULL; - } - - ((PyFrameLocalsProxyObject *)self)->frame = (PyFrameObject *)Py_NewRef(frame); - - return (PyObject *)self; -} - -PyObject *Internal_PyFrameLocalsProxy_New(PyFrameObject *frame) { - PyObject *args = PyTuple_Pack(1, frame); - if (args == NULL) { - return NULL; - } - - PyObject *proxy = (PyObject *)Internal_framelocalsproxy_new( - &PyFrameLocalsProxy_Type, args, NULL); - Py_DECREF(args); - return proxy; -} - -PyObject *Internal_PyFrame_GetLocals(_PyInterpreterFrame *frame) { - // We should try to avoid creating the FrameObject if possible. - // So we check if the frame is a module or class level scope - PyCodeObject *co = _PyFrame_GetCode(frame); - - if (!(co->co_flags & CO_OPTIMIZED) && - !Internal_PyFrame_HasHiddenLocals(frame)) { - if (frame->f_locals == NULL) { - // We found cases when f_locals is NULL for non-optimized code. - // We fill the f_locals with an empty dict to avoid crash until - // we find the root cause. - frame->f_locals = PyDict_New(); - if (frame->f_locals == NULL) { - return NULL; - } - } - return Py_NewRef(frame->f_locals); + // Get closure variables, see `frame_init_get_vars` function + PyObject *closure = ((PyFunctionObject *)frame->f_funcobj)->func_closure; + for (int i = 0; i < co->co_nfreevars; ++i) { + update_framelocals_mapping( + mapping, co, offset + i, PyTuple_GET_ITEM(closure, i)); } - PyFrameObject *f = Internal_PyFrame_GetFrameObject(frame); - - return Internal_PyFrameLocalsProxy_New(f); + return mapping; } #else diff --git a/paddle/fluid/pybind/sot/cpython_internals.h b/paddle/fluid/pybind/sot/cpython_internals.h index d0ccb0e27bbf9b..3fb6b323b1f32e 100644 --- a/paddle/fluid/pybind/sot/cpython_internals.h +++ b/paddle/fluid/pybind/sot/cpython_internals.h @@ -40,7 +40,7 @@ static int Internal_PyFrame_OpAlreadyRan(_PyInterpreterFrame *frame, int opcode, int oparg); #if PY_3_13_PLUS -PyObject *Internal_PyFrame_GetLocals(_PyInterpreterFrame *frame); +PyObject *get_framelocals_mapping(_PyInterpreterFrame *frame); #else int Internal_PyFrame_FastToLocalsWithError(_PyInterpreterFrame *frame); #endif diff --git a/paddle/fluid/pybind/sot/eval_frame.c b/paddle/fluid/pybind/sot/eval_frame.c index d098d71c7d021c..e156fff0de64a8 100644 --- a/paddle/fluid/pybind/sot/eval_frame.c +++ b/paddle/fluid/pybind/sot/eval_frame.c @@ -354,7 +354,7 @@ static PyObject *_custom_eval_frame(PyThreadState *tstate, // _PyFrame_FastToLocalsWithError directly. But this is an internal API, so we // copy many code from CPython project into our project. #if PY_3_13_PLUS - PyObject *f_locals = Internal_PyFrame_GetLocals(frame); + PyObject *f_locals = get_framelocals_mapping(frame); if (f_locals == NULL) { #else if (Internal_PyFrame_FastToLocalsWithError(frame) < 0) { From 7d94171a814d6b471ca80ef9ca22ce537f81b4ef Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Wed, 27 Nov 2024 10:17:32 +0800 Subject: [PATCH 010/288] [CINN] Make slice op not enter CINN when there is no data in the parameters (#69503) * Make slice op not enter CINN when there is no data data in the parameters * delete const * fix bug * delete log and refine code * refine code * delete enforece and refine code * fix bug * refine code --- .../lower_cinn_fusion_op_pass.cc | 2 - paddle/cinn/hlir/framework/pir/utils.cc | 22 ++++++- .../element_wise_binary.cc | 12 +++- .../same_operands_result.cc | 19 ++++-- .../infer_symbolic_shape/unary_infer_sym.cc | 65 +++++++++++++++---- 5 files changed, 100 insertions(+), 20 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc index 32640cd1ab899d..87223efd62aa3b 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc @@ -39,8 +39,6 @@ class FusionOpPattern : public pir::OpRewritePattern { ::pir::IrContext* ctx = ::pir::IrContext::Instance(); auto* program = fusion_op->GetParentProgram(); auto& shape_analysis = pir::ShapeAnalysisManager::Instance().Get(program); - VLOG(4) << "Program before lowering: \n" - << pir::CustomPrintHelper(*program, shape_analysis.PrintHook()); // TODO(zhangyuqin1998): Replace pir::Group with a new structure OpLoweringGroupPtr group = GetGroup(fusion_op); diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 82d6e1ffad86fc..256d3052c61161 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -349,11 +349,29 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { if (FLAGS_disable_dyshape_in_train) { return false; } + + auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( + const_cast<::pir::Operation&>(op).GetParentProgram()); + + const auto& isProcessableSlice = [&]() -> bool { + const ::pir::Value& starts_value = op.operand_source(1); + const ::pir::Value& ends_value = op.operand_source(2); + const symbol::ShapeOrDataDimExprs& starts_shape_data = + shape_analysis.GetShapeOrDataForValue(starts_value); + const symbol::ShapeOrDataDimExprs& ends_shape_data = + shape_analysis.GetShapeOrDataForValue(ends_value); + return starts_shape_data.data().has_value() && + ends_shape_data.data().has_value(); + }; + + if (op.isa() && !isProcessableSlice()) { + return true; + } + if (!HaveUnkDim(op)) { return false; } - auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( - const_cast<::pir::Operation&>(op).GetParentProgram()); + std::unordered_set input_exprs = [&]() { std::unordered_set res; for (const auto& input_value : op.operands_source()) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc index 3c8b88af98c7cb..36585f74596533 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc @@ -147,6 +147,17 @@ bool FloorDivideOpInferSymbolicShape( }); } +bool MinimumOpInferSymbolicShape( + pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { + return InferSymbolicShapeElementWiseBinary( + op, + infer_context, + [](const symbol::DimExpr &x, const symbol::DimExpr &y) { + symbol::DimExprBuilder builder; + return builder.Min(x, y); + }); +} + OP_ELEMENT_WISE_BINARY(Add_) OP_ELEMENT_WISE_BINARY(BitwiseAnd) OP_ELEMENT_WISE_BINARY(BitwiseAnd_) @@ -186,7 +197,6 @@ OP_ELEMENT_WISE_BINARY(LogicalOr_) OP_ELEMENT_WISE_BINARY(LogicalXor) OP_ELEMENT_WISE_BINARY(LogicalXor_) OP_ELEMENT_WISE_BINARY(Maximum) -OP_ELEMENT_WISE_BINARY(Minimum) OP_ELEMENT_WISE_BINARY(MultiplySr) OP_ELEMENT_WISE_BINARY(MultiplySr_) OP_ELEMENT_WISE_BINARY(Multiply_) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc index 39e788f520c647..07f566d52b4e81 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc @@ -49,8 +49,6 @@ OP_SAME_OPERANDS_AND_RESULT(Hardtanh_) OP_SAME_OPERANDS_AND_RESULT(Bernoulli) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot_) -OP_SAME_OPERANDS_AND_RESULT(Ceil) -OP_SAME_OPERANDS_AND_RESULT(Ceil_) OP_SAME_OPERANDS_AND_RESULT(Celu) OP_SAME_OPERANDS_AND_RESULT(Clip) OP_SAME_OPERANDS_AND_RESULT(Clip_) @@ -255,13 +253,13 @@ bool ScaleOpInferSymbolicShape(pir::Operation *op, return GetOptionalAttributeData("scale"); }; - if (operand_shape_or_data.data()) { + if (operand_shape_or_data.data().has_value()) { const std::optional &opt_scale = GetOptionalScaleData(); const std::optional &opt_bias = GetOptionalAttributeData("bias"); if (opt_scale && opt_bias) { std::vector data; - for (auto &val : *(operand_shape_or_data.data())) { + for (auto &val : operand_shape_or_data.data().value()) { data.push_back(val * (opt_scale.value()) + (opt_bias.value())); } SetOutputWithShapeAndData(data); @@ -284,6 +282,19 @@ bool ArgsortOpInferSymbolicShape( return true; } +bool CeilOpInferSymbolicShape(pir::Operation *op, + pir::InferSymbolicShapeContext *infer_context) { + const symbol::ShapeOrDataDimExprs &operand_shape_or_data = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + infer_context->SetShapeOrDataForValue(op->result(0), operand_shape_or_data); + return true; +} + +bool Ceil_OpInferSymbolicShape(pir::Operation *op, + pir::InferSymbolicShapeContext *infer_context) { + return CeilOpInferSymbolicShape(op, infer_context); +} + } // namespace paddle::dialect namespace cinn::dialect {} // namespace cinn::dialect diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 411d9042e19bcf..0214c9d61e137a 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3210,24 +3210,67 @@ bool ShuffleChannelOpInferSymbolicShape( bool SliceOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { pir::Value operand_source = op->operand_source(0); - pir::Value operand_starts = op->operand_source(1); - pir::Value operand_ends = op->operand_source(2); pir::Value res = op->result(0); - const symbol::ShapeOrDataDimExprs &starts_shape_data = - infer_context->GetShapeOrDataForValue(operand_starts); - const symbol::ShapeOrDataDimExprs &ends_shape_data = - infer_context->GetShapeOrDataForValue(operand_ends); - std::vector axes_vec = details::GetVectorAttr(op, "axes"); - - ExprVec starts = slice_utils::GetExprVecFromData(starts_shape_data); - ExprVec ends = slice_utils::GetExprVecFromData(ends_shape_data); - std::vector infer_flags = details::GetVectorAttr(op, "infer_flags"); const std::vector decrease_axis = details::GetVectorAttr(op, "decrease_axis"); + auto GetExprVec = [&](std::vector *expr_vec, + const int &operand_idx, + const std::string &attr_name) -> bool { + if (op->operand_source(operand_idx)) { + const symbol::ShapeOrDataDimExprs &se_shape_data = + infer_context->GetShapeOrDataForValue( + op->operand_source(operand_idx)); + if (se_shape_data.data().has_value()) { + *expr_vec = se_shape_data.data().value(); + return true; + } + PADDLE_ENFORCE_EQ( + se_shape_data.shape().at(0).isa() && + (static_cast(axes_vec.size()) == + se_shape_data.shape().at(0).dyn_cast()), + true, + common::errors::InvalidArgument( + "The size of axes must equal size of starts and ends.")); + return false; + } else { + if (op->attributes().find(attr_name) != op->attributes().end()) { + const std::vector se_raw = + paddle::dialect::details::GetVectorAttr(op, attr_name); + for (const int64_t &se : se_raw) { + expr_vec->push_back(symbol::DimExpr{se}); + } + return true; + } + return false; + } + }; + + std::vector starts; + std::vector ends; + if (!GetExprVec(&starts, 1, "starts") || !GetExprVec(&ends, 2, "ends")) { + const auto &in_shapeordata = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + // NOTE(gongshaotian): When there is no data value in the starts and ends + // parameters, only the shape value is processed regardless of whether the + // input has a data value, and the data value is no longer processed. + std::vector out_shape = in_shapeordata.shape(); + for (size_t i = 0; i < axes_vec.size(); i++) { + int64_t axis = axes_vec[i]; + out_shape[axis] = infer_context->GetNextSymName(); + } + ExprVec out_dims = paddle::dialect::slice_utils::GetDecreasedDims( + out_shape, decrease_axis); + infer_context->SetShapeOrDataForValue( + res, + symbol::ShapeOrDataDimExprs{ + symbol::TensorShapeOrDataDimExprs(out_dims)}); + return true; + } + infer_context->SetShapeOrDataForValue( res, slice_utils::SliceRawInferSymbolicShape(operand_source, From a4e00804e1624c1e2fa7d7b0fbbdd64c72016bf9 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Wed, 27 Nov 2024 10:24:36 +0800 Subject: [PATCH 011/288] add cross to all (#69715) --- python/paddle/linalg.py | 2 ++ 1 file changed, 2 insertions(+) diff --git a/python/paddle/linalg.py b/python/paddle/linalg.py index 55a1acba16c07d..f9aaa5f5b3a9a8 100644 --- a/python/paddle/linalg.py +++ b/python/paddle/linalg.py @@ -20,6 +20,7 @@ cond, corrcoef, cov, + cross, det, eig, eigh, @@ -58,6 +59,7 @@ 'cond', 'cov', 'corrcoef', + 'cross', 'inv', 'eig', 'eigvals', From b481575e082ef87899317fb07981dc01c01d2014 Mon Sep 17 00:00:00 2001 From: co63oc Date: Wed, 27 Nov 2024 10:29:21 +0800 Subject: [PATCH 012/288] [Lod][fluid_ops] tdm_sampler (#69704) --- paddle/phi/infermeta/ternary.cc | 2 +- paddle/phi/infermeta/ternary.h | 2 +- paddle/phi/kernels/cpu/tdm_sampler_kernel.cc | 41 ++++++++++---------- paddle/phi/ops/yaml/ops.yaml | 2 +- python/paddle/incubate/layers/nn.py | 8 ++-- test/legacy_test/test_tdm_sampler_op.py | 6 +-- 6 files changed, 30 insertions(+), 31 deletions(-) diff --git a/paddle/phi/infermeta/ternary.cc b/paddle/phi/infermeta/ternary.cc index 6715721ab85eb2..e2566301a45b23 100644 --- a/paddle/phi/infermeta/ternary.cc +++ b/paddle/phi/infermeta/ternary.cc @@ -2295,7 +2295,7 @@ void TdmSamplerInferMeta(const MetaTensor& x, const MetaTensor& layer, bool output_positive, const std::vector& neg_samples_num_list, - const std::vector& layer_offset_lod, + const std::vector& layer_offset, int seed, int dtype, MetaTensor* out, diff --git a/paddle/phi/infermeta/ternary.h b/paddle/phi/infermeta/ternary.h index ccb4cf8b16f50f..b05e64b4262123 100644 --- a/paddle/phi/infermeta/ternary.h +++ b/paddle/phi/infermeta/ternary.h @@ -387,7 +387,7 @@ void TdmSamplerInferMeta(const MetaTensor& x, const MetaTensor& layer, bool output_positive, const std::vector& neg_samples_num_list, - const std::vector& layer_offset_lod, + const std::vector& layer_offset, int seed, int dtype, MetaTensor* out, diff --git a/paddle/phi/kernels/cpu/tdm_sampler_kernel.cc b/paddle/phi/kernels/cpu/tdm_sampler_kernel.cc index 029a9fae092d0b..f64e90decbe0a3 100644 --- a/paddle/phi/kernels/cpu/tdm_sampler_kernel.cc +++ b/paddle/phi/kernels/cpu/tdm_sampler_kernel.cc @@ -33,11 +33,11 @@ template void TDMSamplerInner(const Context &dev_ctx, const phi::DenseTensor &input_tensor, - const phi::DenseTensor &travel_lod_tensor, - const phi::DenseTensor &layer_lod_tensor, + const phi::DenseTensor &travel_dense_tensor, + const phi::DenseTensor &layer_dense_tensor, bool output_positive, std::vector neg_samples_num_list, - std::vector layer_offset_lod, + std::vector layer_offset, int seed, phi::DenseTensor *out, phi::DenseTensor *label, @@ -55,13 +55,13 @@ void TDMSamplerInner(const Context &dev_ctx, } VLOG(3) << "TDM: sample res length: " << sample_res_length; - auto travel_dim = common::vectorize(travel_lod_tensor.dims()); + auto travel_dim = common::vectorize(travel_dense_tensor.dims()); auto total_sample_nums = input_ids_num * sample_res_length; // get all data auto *input_data = input_tensor.data(); - auto *travel_data = travel_lod_tensor.data(); - auto *layer_data = layer_lod_tensor.data(); + auto *travel_data = travel_dense_tensor.data(); + auto *layer_data = layer_dense_tensor.data(); OutT zero = 0; OutT one = 1; @@ -75,7 +75,7 @@ void TDMSamplerInner(const Context &dev_ctx, std::vector sampler_vec{}; for (size_t layer_index = 0; layer_index < layer_nums; layer_index++) { int layer_node_nums = - layer_offset_lod[layer_index + 1] - layer_offset_lod[layer_index]; + layer_offset[layer_index + 1] - layer_offset[layer_index]; Sampler *sampler = new math::UniformSampler(layer_node_nums - 1, seed); sampler_vec.push_back(sampler); } @@ -112,8 +112,7 @@ void TDMSamplerInner(const Context &dev_ctx, int sample_num = neg_samples_num_list[layer_idx]; VLOG(3) << "TDM: Sample num: " << sample_num; - int node_nums = - layer_offset_lod[layer_idx + 1] - layer_offset_lod[layer_idx]; + int node_nums = layer_offset[layer_idx + 1] - layer_offset[layer_idx]; VLOG(3) << "TDM: layer - " << layer_idx + 1 << " - has node_nums: " << node_nums; @@ -128,8 +127,8 @@ void TDMSamplerInner(const Context &dev_ctx, node_nums, sample_num)); - int node_id_min = layer_offset_lod[layer_idx]; - int node_id_max = layer_offset_lod[layer_idx + 1]; + int node_id_min = layer_offset[layer_idx]; + int node_id_max = layer_offset[layer_idx + 1]; OutT positive_node_id = static_cast(travel_data[start_offset + layer_idx]); @@ -197,14 +196,14 @@ void TDMSamplerInner(const Context &dev_ctx, do { sample_res = sampler_vec[layer_idx]->Sample(); } while (positive_node_id == - layer_data[layer_offset_lod[layer_idx] + sample_res] || + layer_data[layer_offset[layer_idx] + sample_res] || find(sample_res_vec.begin(), sample_res_vec.end(), sample_res) != sample_res_vec.end()); sample_res_vec.push_back(sample_res); - output_vec[i * sample_res_length + offset] = static_cast( - layer_data[layer_offset_lod[layer_idx] + sample_res]); + output_vec[i * sample_res_length + offset] = + static_cast(layer_data[layer_offset[layer_idx] + sample_res]); label_vec[i * sample_res_length + offset] = 0; mask_vec[i * sample_res_length + offset] = 1; VLOG(3) << "TDM: node id: " << travel_data[start_offset + layer_idx] @@ -216,7 +215,7 @@ void TDMSamplerInner(const Context &dev_ctx, << mask_vec[i * sample_res_length + offset]; PADDLE_ENFORCE_LE( - layer_data[layer_offset_lod[layer_idx] + sample_res], + layer_data[layer_offset[layer_idx] + sample_res], node_id_max, common::errors::InvalidArgument( "Negative node id of OP(fluid.layers.tdm_sampler) at layer %ld" @@ -225,7 +224,7 @@ void TDMSamplerInner(const Context &dev_ctx, layer_idx, node_id_min, node_id_max, - layer_data[layer_offset_lod[layer_idx] + sample_res])); + layer_data[layer_offset[layer_idx] + sample_res])); offset += 1; } // end layer nce @@ -252,7 +251,7 @@ void TDMSamplerKernel(const Context &dev_ctx, const DenseTensor &layer, bool output_positive, const std::vector &neg_samples_num_list, - const std::vector &layer_offset_lod, + const std::vector &layer_offset, int seed, int dtype, DenseTensor *out, @@ -311,7 +310,7 @@ void TDMSamplerKernel(const Context &dev_ctx, layer, output_positive, neg_samples_num_list, - layer_offset_lod, + layer_offset, seed, out, labels, @@ -324,7 +323,7 @@ void TDMSamplerKernel(const Context &dev_ctx, layer, output_positive, neg_samples_num_list, - layer_offset_lod, + layer_offset, seed, out, labels, @@ -337,7 +336,7 @@ void TDMSamplerKernel(const Context &dev_ctx, layer, output_positive, neg_samples_num_list, - layer_offset_lod, + layer_offset, seed, out, labels, @@ -350,7 +349,7 @@ void TDMSamplerKernel(const Context &dev_ctx, layer, output_positive, neg_samples_num_list, - layer_offset_lod, + layer_offset, seed, out, labels, diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index 6240a32c65494f..e35b46ecd7d121 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -4929,7 +4929,7 @@ traits : paddle::dialect::ForwardOnlyTrait - op : tdm_sampler - args: (Tensor x, Tensor travel, Tensor layer, bool output_positive=true, int[] neg_samples_num_list={}, int[] layer_offset_lod={}, int seed = 0, int dtype=2) + args: (Tensor x, Tensor travel, Tensor layer, bool output_positive=true, int[] neg_samples_num_list={}, int[] layer_offset={}, int seed = 0, int dtype=2) output: Tensor(out), Tensor(labels), Tensor(mask) infer_meta: func : TdmSamplerInferMeta diff --git a/python/paddle/incubate/layers/nn.py b/python/paddle/incubate/layers/nn.py index 8d30d9b879fa34..0f49208ec2cd9b 100644 --- a/python/paddle/incubate/layers/nn.py +++ b/python/paddle/incubate/layers/nn.py @@ -747,11 +747,11 @@ def tdm_sampler( layer_nums = 0 node_nums = 0 - tree_layer_offset_lod = [0] + tree_layer_offset = [0] for layer_idx, layer_node_num in enumerate(layer_node_num_list): layer_nums += 1 node_nums += layer_node_num - tree_layer_offset_lod.append(node_nums) + tree_layer_offset.append(node_nums) if neg_samples_num_list[layer_idx] >= layer_node_num_list[layer_idx]: raise ValueError( "The number of negative samples must be less than the number of nodes " @@ -785,7 +785,7 @@ def tdm_sampler( layer, output_positive, neg_samples_num_list, - tree_layer_offset_lod, + tree_layer_offset, seed, c_dtype, ) @@ -806,7 +806,7 @@ def tdm_sampler( attrs={ 'neg_samples_num_list': neg_samples_num_list, 'output_positive': output_positive, - 'layer_offset_lod': tree_layer_offset_lod, + 'layer_offset': tree_layer_offset, 'seed': seed, 'dtype': c_dtype, }, diff --git a/test/legacy_test/test_tdm_sampler_op.py b/test/legacy_test/test_tdm_sampler_op.py index d50fb2e12da6b6..5e341bb8268b0b 100644 --- a/test/legacy_test/test_tdm_sampler_op.py +++ b/test/legacy_test/test_tdm_sampler_op.py @@ -74,13 +74,13 @@ def setUp(self): self.layer_sample_nums = [1 + i for i in self.neg_samples_num_list] layer_node_num_list = [len(i) for i in self.tree_layer] - tree_layer_offset_lod = [0] + tree_layer_offset = [0] tree_layer_flat = [] node_nums = 0 for layer_idx, layer_node in enumerate(layer_node_num_list): tree_layer_flat += self.tree_layer[layer_idx] node_nums += layer_node - tree_layer_offset_lod.append(node_nums) + tree_layer_offset.append(node_nums) travel_np = np.array(self.tree_travel).astype(self.tree_dtype) layer_np = np.array(tree_layer_flat).astype(self.tree_dtype) @@ -97,7 +97,7 @@ def setUp(self): self.attrs = { 'neg_samples_num_list': self.neg_samples_num_list, 'output_positive': True, - 'layer_offset_lod': tree_layer_offset_lod, + 'layer_offset': tree_layer_offset, 'seed': 0, 'dtype': type_dict[self.out_dtype], } From 77bd1e9171f493ca307e14749ee7d87d874c190f Mon Sep 17 00:00:00 2001 From: xiaoguoguo626807 <100397923+xiaoguoguo626807@users.noreply.github.com> Date: Wed, 27 Nov 2024 10:36:23 +0800 Subject: [PATCH 013/288] modify logic op stopgradient (#69716) --- .../pir/dialect/op_generator/op_build_gen.py | 16 ++++++++++++++-- paddle/pir/include/core/builtin_op.h | 1 + paddle/pir/src/core/builtin_op.cc | 11 +++++++++++ 3 files changed, 26 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/pir/dialect/op_generator/op_build_gen.py b/paddle/fluid/pir/dialect/op_generator/op_build_gen.py index cccc7b79267d36..34f23bb0c4e661 100644 --- a/paddle/fluid/pir/dialect/op_generator/op_build_gen.py +++ b/paddle/fluid/pir/dialect/op_generator/op_build_gen.py @@ -136,6 +136,15 @@ _PREPARE_DATA_WITH_VECTOR_INT64_MTTABLE_ATTRIBUTE = {'FrobeniusNormOp'} +LOGIC_OP_LIST = { + 'LogicalAndOp', + 'LogicalAnd_Op', + 'LogicalOrOp', + 'LogicalOr_Op', + 'LogicalNotOp', + 'LogicalNot_Op', + 'LogicalXorOp', +} OP_BUILD_TEMPLATE = """ void {op_name}::Build({build_args}) {{ {build_info} @@ -841,8 +850,11 @@ def gen_build_func_str( build_outputs_str = f""" std::vector argument_outputs = {op_info.class_name}::InferMeta(argument_inputs, &argument_attributes); argument.AddAttributes(argument_attributes); - argument.AddOutputs(argument_outputs.begin(), argument_outputs.end()); - ::pir::PassStopGradientsDefaultly(argument);""" + argument.AddOutputs(argument_outputs.begin(), argument_outputs.end());\n""" + if op_info.class_name in LOGIC_OP_LIST: + build_outputs_str += "::pir::TrueStopGradientsDefaultly(argument);\n" + else: + build_outputs_str += "::pir::PassStopGradientsDefaultly(argument);" "" GET_ATTRIBUTES_FROM_MAP_TEMPLATE = """ PADDLE_ENFORCE_NE( diff --git a/paddle/pir/include/core/builtin_op.h b/paddle/pir/include/core/builtin_op.h index bcdc560d6cb4be..7968dc5553dad0 100644 --- a/paddle/pir/include/core/builtin_op.h +++ b/paddle/pir/include/core/builtin_op.h @@ -258,6 +258,7 @@ class IR_API ConstantTensorOp : public ConstantOp { }; void PassStopGradientsDefaultly(OperationArgument &argument); // NOLINT +void TrueStopGradientsDefaultly(OperationArgument &argument); // NOLINT void RefreshStopGradientsDefaultly(Operation *Op); } // namespace pir diff --git a/paddle/pir/src/core/builtin_op.cc b/paddle/pir/src/core/builtin_op.cc index b0d9bd838e3dfc..1666d7da479be9 100644 --- a/paddle/pir/src/core/builtin_op.cc +++ b/paddle/pir/src/core/builtin_op.cc @@ -63,6 +63,17 @@ void PassStopGradientsDefaultly(OperationArgument &argument) { // NOLINT pir::ArrayAttribute::get(pir::IrContext::Instance(), outs_stop_gradient)); } +void TrueStopGradientsDefaultly(OperationArgument &argument) { // NOLINT + VLOG(10) << "Builder construction stop gradient as True for OpResults."; + bool stop_gradient = true; + std::vector outs_stop_gradient( + argument.output_types.size(), + pir::BoolAttribute::get(pir::IrContext::Instance(), stop_gradient)); + argument.AddAttribute( + kStopGradientAttrName, + pir::ArrayAttribute::get(pir::IrContext::Instance(), outs_stop_gradient)); +} + void RefreshStopGradientsDefaultly(Operation *op) { bool stop_gradient = true; for (auto value : op->operands_source()) { From 8cdf2c108a257d136fb026dcc74338ebb3e79e22 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Wed, 27 Nov 2024 10:42:31 +0800 Subject: [PATCH 014/288] [CINN]Add InferSymbolicShape for tensor_to_array (#69069) * test * Refine logic * Refine logic * revert useless code * apply review * fix codestyle * revert select_output * fix codestyle --- paddle/fluid/pir/dialect/operator/ir/manual_op.cc | 11 +++++++++++ paddle/fluid/pir/dialect/operator/ir/manual_op.h | 7 +++++-- 2 files changed, 16 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc index d72c7e0123945c..2e66b02c39ddbf 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc @@ -2613,6 +2613,17 @@ std::vector TensorToArrayOp::InferMeta( return argument_outputs; } +bool TensorToArrayOp::InferSymbolicShape( + pir::InferSymbolicShapeContext *infer_context) { + const auto &x_shape_or_data = + infer_context->GetShapeOrDataForValue(x()) + .dyn_cast(); + infer_context->SetShapeOrDataForValue( + x_grad(), symbol::ShapeOrDataDimExprs{x_shape_or_data}); + + return true; +} + OpInfoTuple SliceArrayOp::GetOpInfo() { std::vector inputs = { paddle::dialect::OpInputInfo("input", diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.h b/paddle/fluid/pir/dialect/operator/ir/manual_op.h index a6c518911c0fce..41fac710e28e21 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op.h +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.h @@ -407,8 +407,10 @@ class TEST_API ArrayToTensorOp : public pir::Op> &stop_gradients); }; -class TEST_API TensorToArrayOp - : public pir::Op { +class TEST_API TensorToArrayOp : public pir::Op { public: using Op::Op; static const char *name() { return "pd_op.tensor_to_array"; } @@ -429,6 +431,7 @@ class TEST_API TensorToArrayOp static std::vector InferMeta( const std::vector &input_values, pir::AttributeMap *p_attributes); + bool InferSymbolicShape(pir::InferSymbolicShapeContext *infer_context); }; class TEST_API SliceArrayOp From 4456a569c4a63336e368170ec6c9ee07d78fca9f Mon Sep 17 00:00:00 2001 From: blacksheep-Aristotle Date: Wed, 27 Nov 2024 10:54:02 +0800 Subject: [PATCH 015/288] [AutoParallel]:fix vpp error when use acc (#69578) --- .../distributed/auto_parallel/static/engine.py | 2 +- .../auto_parallel/static/pir_pass.py | 18 +++++++++++++++--- 2 files changed, 16 insertions(+), 4 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/static/engine.py b/python/paddle/distributed/auto_parallel/static/engine.py index 4c1f22983b5e1c..9b1ca75b63a438 100644 --- a/python/paddle/distributed/auto_parallel/static/engine.py +++ b/python/paddle/distributed/auto_parallel/static/engine.py @@ -838,7 +838,7 @@ def _parallel_pir(self, mode): # TODO(hitywt) Step 3.2: Reshard Pass # resolute the reshard op into special collective operation. # collect the communicator created during resolution. - ReshardPasses.apply_reshard_pass(dist_program) + ReshardPasses.apply_reshard_pass(dist_program, global_params_grads) # Note(luchang): When using VPP pipeline pass, we need to split the whole graph into # multiple chunks and adjust the process mesh accordingly. Here, we need to store the diff --git a/python/paddle/distributed/auto_parallel/static/pir_pass.py b/python/paddle/distributed/auto_parallel/static/pir_pass.py index 28373305aa69d3..167c1fd2c604fb 100644 --- a/python/paddle/distributed/auto_parallel/static/pir_pass.py +++ b/python/paddle/distributed/auto_parallel/static/pir_pass.py @@ -294,7 +294,7 @@ def fold_reshard_pass(dist_program): op.erase() @staticmethod - def reshard_op_pass(dist_program, block=None): + def reshard_op_pass(dist_program, global_params_grads=None, block=None): if block is None: block = dist_program.global_block() for op in block.ops: @@ -313,6 +313,10 @@ def reshard_op_pass(dist_program, block=None): if src_dist_attr == dst_dist_attr: op.result(0).replace_all_uses_with(var) + if global_params_grads is not None: + for idx, (p, g) in enumerate(global_params_grads): + if g is not None and g.is_same(op.result(0)): + global_params_grads[idx] = (p, var) op.erase() continue @@ -336,13 +340,21 @@ def reshard_op_pass(dist_program, block=None): op.result(0).replace_all_uses_with(out_value) if op.result(0).use_empty(): + if global_params_grads is not None: + for idx, (p, g) in enumerate(global_params_grads): + if g is not None and g.is_same(op.result(0)): + global_params_grads[idx] = ( + (p, out_value) + if out_value is not None + else (p, var) + ) op.erase() @staticmethod - def apply_reshard_pass(dist_program): + def apply_reshard_pass(dist_program, global_params_grads=None): ReshardPasses.decompose_reshard_pass(dist_program) ReshardPasses.fold_reshard_pass(dist_program) - ReshardPasses.reshard_op_pass(dist_program) + ReshardPasses.reshard_op_pass(dist_program, global_params_grads) # Replace the specific MoE-related dist op with the From 7f5a82f896ffe7f0d001ee99714ba856d27ca699 Mon Sep 17 00:00:00 2001 From: zhangbo9674 <82555433+zhangbo9674@users.noreply.github.com> Date: Wed, 27 Nov 2024 11:26:51 +0800 Subject: [PATCH 016/288] refine (#69733) --- .../operator/transforms/pd_to_cinn_pass.cc | 22 +++++++++++++++++-- 1 file changed, 20 insertions(+), 2 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc index d11f401b6f8004..b0740bfd93a0f1 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc @@ -238,8 +238,26 @@ class ScaleOpPattern : public pir::OpRewritePattern { mul_in = add_op.result(0); } - auto mul_op = rewriter.Build( - mul_in, op->operand_source(1)); + pir::Value rhs_value = [&] { + const auto &lhs_dtype = + mul_in.type().dyn_cast().dtype(); + const auto &rhs_dtype = + op->operand_source(1) + .type() + .dyn_cast() + .dtype(); + if (lhs_dtype != rhs_dtype) { + return rewriter + .Build( + op->operand_source(1), + paddle::dialect::TransToPhiDataType(lhs_dtype)) + .out(); + } + return op->operand_source(1); + }(); + + auto mul_op = + rewriter.Build(mul_in, rhs_value); rewriter.ReplaceAllUsesWith(op.result(0), mul_op.result(0)); rewriter.EraseOp(op); From afaf00dd80b63cce07a4dac22510973f724668f2 Mon Sep 17 00:00:00 2001 From: liuruyan <44316842+liuruyan@users.noreply.github.com> Date: Wed, 27 Nov 2024 11:37:22 +0800 Subject: [PATCH 017/288] =?UTF-8?q?=E3=80=90CINN=E3=80=91Bug=20fix=20in=20?= =?UTF-8?q?IterExpr=20(#69719)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * delete continue in rf * fix --- paddle/cinn/common/iter_simplify.cc | 16 ++++++++++++---- paddle/cinn/ir/schedule/factorize_reduction.h | 8 +++++++- paddle/cinn/optim/ir_simplify.cc | 1 + 3 files changed, 20 insertions(+), 5 deletions(-) diff --git a/paddle/cinn/common/iter_simplify.cc b/paddle/cinn/common/iter_simplify.cc index e4970420235286..70af1fff19bfc9 100644 --- a/paddle/cinn/common/iter_simplify.cc +++ b/paddle/cinn/common/iter_simplify.cc @@ -52,8 +52,7 @@ ir::IndexExpr IterMapToExprNormalizer::ConvertIterSplit(ir::IterSplit* expr) { ir::IndexExpr source; ir::IterMark* mark = expr->source.As(); if (auto opt = mark->source.As()) { - // For unit loop, we can't simplify loop_var to `0`. - if (IsOne(mark->extent)) return opt; + if (IsOne(mark->extent)) return ir::IndexExpr(0); source = opt; } else if (auto opt = mark->source.As()) { source = ConvertIterSum(opt); @@ -505,8 +504,14 @@ std::optional IterMapRewriter::TryFuse( ir::IndexExpr(), -1, first_possible_unit_extent_pos); - // If not found iter with expected scale, return nullopt. - if (matched_pos == -1) return std::nullopt; + // If not found iter with expected scale, search above case: + // D(i)=2, D(j)=8, Split loop from (j, 0, 8) to (-1, 32) + // (i * 8 + j) % 16 ==> (i * 8 + j0 * 32 + j1) + if (matched_pos == -1) { + matched_pos = FindBaseSplit(*iter_sum, visited, ir::IndexExpr(), -1); + // // If not found iter with expected scale again, return nullopt. + if (matched_pos == -1) return std::nullopt; + } matched_scale = expected_scale; visited[matched_pos] = true; @@ -680,8 +685,11 @@ void IterMapSimplify(std::vector& indices, // NOLINT IterMapRewriter rewriter(input_iters, analyzer); IterMapToExprNormalizer converter(analyzer); for (auto& value : indices) { + VLOG(5) << "before rewrite: " << value; rewriter.Rewrite(&value); + VLOG(5) << "after rewrite: " << value; converter.Convert(&value); + VLOG(5) << "after convert: " << value; } } diff --git a/paddle/cinn/ir/schedule/factorize_reduction.h b/paddle/cinn/ir/schedule/factorize_reduction.h index 0c67c4b1fa22fe..7c68370d34b818 100644 --- a/paddle/cinn/ir/schedule/factorize_reduction.h +++ b/paddle/cinn/ir/schedule/factorize_reduction.h @@ -132,14 +132,20 @@ class ReduceBlockCreater { std::vector new_loops(num_loops); Expr body = new_update_block_realize_; bool has_add_init_block = false; + // `is_inside_rf_loop` is used to skip loop inside rf_loop. + bool is_inside_rf_loop = true; for (int i = num_loops - 1; i >= 0; --i) { bool is_spatial_loop = new_spatial_loop_var_names_.count( original_loops_[i].As()->loop_var->name) > 0; bool is_rf_loop = rf_loop_.As()->loop_var->name == original_loops_[i].As()->loop_var->name; + // Outter loop should not skip. + if (is_rf_loop) { + is_inside_rf_loop = false; + } // Skip non rf reduction loops of write back block. - if (!is_rf_block_ && !is_spatial_loop && !is_rf_loop) { + if (!is_rf_block_ && is_inside_rf_loop && !is_spatial_loop) { continue; } // Add reduce init block. diff --git a/paddle/cinn/optim/ir_simplify.cc b/paddle/cinn/optim/ir_simplify.cc index f4e0b2c482d092..4a8cbd43c1ef6d 100644 --- a/paddle/cinn/optim/ir_simplify.cc +++ b/paddle/cinn/optim/ir_simplify.cc @@ -474,6 +474,7 @@ void Simplify(Expr* expr) { mutator(expr); ReplaceFracWithDivMutator()(expr); + VLOG(3) << "End Simplify " << *expr; } void SimplifyCast(Expr* expr) { SimplifyCastMutator()(expr); } From 06d424603b336b0100cfc494b747f28586e10c5f Mon Sep 17 00:00:00 2001 From: zhanghonggeng <43205915+zhanghonggeng@users.noreply.github.com> Date: Wed, 27 Nov 2024 12:54:11 +0800 Subject: [PATCH 018/288] Only ResultDataType of type flaot is processed in AMP. (#69720) * AMP skip clip operator output type modification * Only ResultDataType of type flaot is processed in AMP. --- .../general/auto_mixed_precision_pass.cc | 38 +++++++++++-------- 1 file changed, 22 insertions(+), 16 deletions(-) diff --git a/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc b/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc index cb159c7d9dac18..ccdf341a4d4134 100644 --- a/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc @@ -408,21 +408,7 @@ class AutoMixedPrecisionPass : public pir::Pass { auto type = result.type(); if (type.isa()) { auto dense_type = type.dyn_cast(); - auto new_type = paddle::dialect::DenseTensorType::get( - context, - paddle::dialect::TransToIrDataType(precision, context), - dense_type.dims(), - dense_type.data_layout(), - dense_type.lod(), - dense_type.offset()); - result.set_type(new_type); - } else if (type.isa()) { - auto vec_type = type.dyn_cast(); - auto output_num = vec_type.size(); - std::vector results_type(output_num); - for (size_t idx = 0; idx < output_num; ++idx) { - auto dense_type = - vec_type[idx].dyn_cast(); + if (IsDenseTensorTypeFloat(dense_type)) { auto new_type = paddle::dialect::DenseTensorType::get( context, paddle::dialect::TransToIrDataType(precision, context), @@ -430,7 +416,27 @@ class AutoMixedPrecisionPass : public pir::Pass { dense_type.data_layout(), dense_type.lod(), dense_type.offset()); - results_type[idx] = new_type; + result.set_type(new_type); + } + } else if (type.isa()) { + auto vec_type = type.dyn_cast(); + auto output_num = vec_type.size(); + std::vector results_type(output_num); + for (size_t idx = 0; idx < output_num; ++idx) { + auto dense_type = + vec_type[idx].dyn_cast(); + if (IsDenseTensorTypeFloat(dense_type)) { + auto new_type = paddle::dialect::DenseTensorType::get( + context, + paddle::dialect::TransToIrDataType(precision, context), + dense_type.dims(), + dense_type.data_layout(), + dense_type.lod(), + dense_type.offset()); + results_type[idx] = new_type; + } else { + results_type[idx] = dense_type; + } } auto new_vec_type = pir::VectorType::get(context, results_type); result.set_type(new_vec_type); From df0bbf66bb69d4eef4ae24fed2bab6f36e357e51 Mon Sep 17 00:00:00 2001 From: doggy-tao <3160391266@qq.com> Date: Wed, 27 Nov 2024 13:02:38 +0800 Subject: [PATCH 019/288] [Prim][PIR] Support dynamic shape for tile_grad (#69684) * support dynamic shape for tile_grad * modified details.h --- .../decomp_rule/decomp_vjp/details.h | 130 ++++++++++++------ python/paddle/autograd/backward_utils.py | 1 + ..._sub_graph_pqrst_backward_dynamic_shape.py | 68 +++++++++ 3 files changed, 160 insertions(+), 39 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index bfe9fb828a4508..b844d6a5b70b6d 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1705,54 +1705,106 @@ void tile_grad(const Tensor& x, Tensor* x_grad) { if (x_grad) { std::vector repeat_times_data = repeat_times.GetData(); - std::vector out_grad_shape(out_grad.shape()); Tensor out_grad_tmp = out_grad; + Tensor x_grad_tmp; - if (repeat_times_data.size() != 0) { - while (true) { - std::vector expand_shape(out_grad_tmp.shape()); - - int num_reduce = 0; - // By definition, out_grad_shape.size() is guaranteed to be greater than - // or equal to repeat_times.size(). Paddle only supports up to 9 - // dimensions. - while (repeat_times_data.size() != 0 && expand_shape.size() <= 8) { - // We construct the reduction from the backward direction, as the - // repeats are aligned with the output from right to left. - int64_t repeat = repeat_times_data.back(); - int64_t orig_size = out_grad_shape.back() / repeat; - size_t out_grad_last_index = out_grad_shape.size() - 1; - - // Reshape the corresponding dimension to be `repeat` multiplied by - // `orig_size`. - expand_shape[out_grad_last_index] = repeat; - expand_shape.insert( - expand_shape.begin() + out_grad_shape.size(), 1, orig_size); - - repeat_times_data.pop_back(); - out_grad_shape.pop_back(); - ++num_reduce; - } + if (has_dynamic_shape(x.shape()) || has_dynamic_shape(out_grad.shape())) { + std::vector out_grad_shape_vec; + for (int64_t i = 0; i < out_grad.dims().size(); ++i) { + auto out_grad_shape_slice = get_slice(shape(out_grad_tmp), i); + out_grad_shape_vec.push_back(out_grad_shape_slice); + } + if (repeat_times_data.size() != 0) { + while (true) { + std::vector expand_shape_vec; + for (int64_t i = 0; i < out_grad_tmp.dims().size(); ++i) { + auto expand_shape = get_slice(shape(out_grad_tmp), i); + expand_shape_vec.push_back(expand_shape); + } + int num_reduce = 0; + while (repeat_times_data.size() != 0 && + expand_shape_vec.size() <= 8) { + auto repeat = repeat_times_data.back(); + auto orig_size = + cast(out_grad_shape_vec.back() / repeat, DataType::INT32); + size_t out_grad_last_index = out_grad_shape_vec.size() - 1; + expand_shape_vec[out_grad_last_index] = + full({1}, repeat, DataType::INT32); + expand_shape_vec.insert( + expand_shape_vec.begin() + out_grad_shape_vec.size(), + orig_size); + + repeat_times_data.pop_back(); + out_grad_shape_vec.pop_back(); + ++num_reduce; + } + int axis = static_cast(out_grad_shape_vec.size()); + std::vector reduce_axes_vec; + for (int i = 0; i < num_reduce; ++i) { + reduce_axes_vec.push_back(full({1}, axis, DataType::INT32)); + axis += 2; + } + out_grad_tmp = + backend::reshape(out_grad_tmp, concat(expand_shape_vec)); + out_grad_tmp = + backend::sum(out_grad_tmp, concat(reduce_axes_vec)); - // Find the reduce_axes, which are determined from the forward - // direction. Since there can be some axes that haven't been reduced, we - // simply skip them this round. - int64_t axis = static_cast(out_grad_shape.size()); - std::vector reduce_axes; - for (int i = 0; i < num_reduce; ++i) { - reduce_axes.push_back(axis); - axis += 2; + if (repeat_times_data.size() == 0) { + break; + } } - out_grad_tmp = reshape(out_grad_tmp, expand_shape); - out_grad_tmp = sum(out_grad_tmp, reduce_axes); + } + x_grad_tmp = backend::reshape(out_grad_tmp, shape(x)); + } else { + std::vector out_grad_shape(out_grad.shape()); + + if (repeat_times_data.size() != 0) { + while (true) { + std::vector expand_shape(out_grad_tmp.shape()); + + int num_reduce = 0; + // By definition, out_grad_shape.size() is guaranteed to be greater + // than or equal to repeat_times.size(). Paddle only supports up to 9 + // dimensions. + while (repeat_times_data.size() != 0 && expand_shape.size() <= 8) { + // We construct the reduction from the backward direction, as the + // repeats are aligned with the output from right to left. + int64_t repeat = repeat_times_data.back(); + int64_t orig_size = out_grad_shape.back() / repeat; + size_t out_grad_last_index = out_grad_shape.size() - 1; + + // Reshape the corresponding dimension to be `repeat` multiplied by + // `orig_size`. + expand_shape[out_grad_last_index] = repeat; + expand_shape.insert( + expand_shape.begin() + out_grad_shape.size(), 1, orig_size); - if (repeat_times_data.size() == 0) { - break; + repeat_times_data.pop_back(); + out_grad_shape.pop_back(); + ++num_reduce; + } + + // Find the reduce_axes, which are determined from the forward + // direction. Since there can be some axes that haven't been reduced, + // we simply skip them this round. + int64_t axis = static_cast(out_grad_shape.size()); + std::vector reduce_axes; + for (int i = 0; i < num_reduce; ++i) { + reduce_axes.push_back(axis); + axis += 2; + } + out_grad_tmp = reshape(out_grad_tmp, expand_shape); + out_grad_tmp = sum(out_grad_tmp, reduce_axes); + + if (repeat_times_data.size() == 0) { + break; + } } } + x_grad_tmp = reshape(out_grad_tmp, x.shape()); } - set_output(reshape(out_grad_tmp, x.shape()), x_grad); + set_output(x_grad_tmp, x_grad); } } diff --git a/python/paddle/autograd/backward_utils.py b/python/paddle/autograd/backward_utils.py index 5291c078a1b4a6..9dd999baa31e86 100644 --- a/python/paddle/autograd/backward_utils.py +++ b/python/paddle/autograd/backward_utils.py @@ -94,6 +94,7 @@ "pd_op.swish", "pd_op.take_along_axis", "pd_op.tanh", + "pd_op.tile", "pd_op.topk", "pd_op.transpose", "pd_op.trunc", diff --git a/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py b/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py index 30e8cde7b295ab..6f21e217cfa60a 100644 --- a/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py +++ b/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py @@ -182,6 +182,22 @@ def tanh_net(x): return paddle.tanh(x) +def tile_net1(x): + return paddle.tile(x, [2, 1, 3, 5, 4]) + + +def tile_net2(x): + return paddle.tile(x, [2, 2]) + + +def tile_net3(x): + return paddle.tile(x, [2, 3, 4]) + + +def tile_net4(x): + return paddle.tile(x, [5]) + + def topk_net(x): return paddle.topk(x, k=3, axis=-1)[0] @@ -1180,6 +1196,58 @@ def setUp(self): self.tol = 1e-6 +class TestPrimTileWithGrad1(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.tile_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 5] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = tile_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimTileWithGrad2(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.tile_grad" + self.dtype = "float32" + self.x_shape = [5, 5, 4, 3, 5, 6] + self.init_x_shape = [None, None, None, None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = tile_net2 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimTileWithGrad3(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.tile_grad" + self.dtype = "float32" + self.x_shape = [5, 5, 4, 3, 2] + self.init_x_shape = [None, None, None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = tile_net3 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimTileWithGrad4(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.tile_grad" + self.dtype = "float32" + self.x_shape = [5, 5, 4, 3] + self.init_x_shape = [None, None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = tile_net4 + self.enable_cinn = False + self.tol = 1e-6 + + class TestPrimTopkWithGrad1(TestPrimBaseWithGrad): def setUp(self): np.random.seed(2024) From c112fc8608807904e2bd25cf92c365ef42b45ad5 Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Wed, 27 Nov 2024 13:40:59 +0800 Subject: [PATCH 020/288] no model for parallelize_optimizer (#69677) --- .../intermediate/parallel_base.py | 48 +++++++++++-------- .../auto_parallel/intermediate/parallelize.py | 43 ++++++++++++++--- .../intermediate/sharded_data_parallel.py | 26 +++++----- .../hybrid_strategy/parallel_api.py | 23 +++++---- 4 files changed, 90 insertions(+), 50 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/intermediate/parallel_base.py b/python/paddle/distributed/auto_parallel/intermediate/parallel_base.py index b877ed2b6c8e46..3cee3cbd16c5c5 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/parallel_base.py +++ b/python/paddle/distributed/auto_parallel/intermediate/parallel_base.py @@ -37,14 +37,21 @@ def is_tensor(tensor): class ParallelOptimizer: - def __init__(self, optimizer, level=None): + def __init__( + self, + optimizer, + level=None, + sharding_mesh_dim=None, + ): self.level = None + self.sharding_mesh_dim = None self.optimizer = None if isinstance(optimizer, ParallelOptimizer): self.optimizer = optimizer.optimizer if level is None: self.level = optimizer.level + self.sharding_mesh_dim = optimizer.sharding_mesh_dim else: if isinstance(level, int): level = str(level) @@ -54,6 +61,7 @@ def __init__(self, optimizer, level=None): level == optimizer.level ), f"The level passed in is not identical with previous level. Current level is {level}, previous level is {optimizer.level}" self.level = level + self.sharding_mesh_dim = sharding_mesh_dim else: assert isinstance(optimizer, Optimizer) self.optimizer = optimizer @@ -62,43 +70,46 @@ def __init__(self, optimizer, level=None): assert level in ("0", "1", "2", "3", None) # level=0 and level=None are all mean pure dp self.level = level + self.sharding_mesh_dim = sharding_mesh_dim self.is_initialized = False - def parallelize(self, parallelized_parameters): + def parallelize(self): assert self.optimizer is not None if self.is_initialized: return self.optimizer - # 1.replace optimizer parameters - self.optimizer._parameter_list = parallelized_parameters - if isinstance(parallelized_parameters[0], dict): - self.optimizer._param_groups = [] - for param_group in self.parallelized_parameters: - self.optimizer._add_param_group(param_group.copy()) - else: - self.optimizer._param_groups = self.optimizer._parameter_list - - # 2.wrap with shard_optimizer mesh = fleet.auto.get_mesh() if self.level == "1": self.optimizer = dist.shard_optimizer( - self.optimizer, dist.ShardingStage1("dp", mesh) + self.optimizer, + dist.ShardingStage1(self.sharding_mesh_dim, mesh), ) elif self.level == "2": self.optimizer = dist.shard_optimizer( - self.optimizer, dist.ShardingStage2("dp", mesh) + self.optimizer, + dist.ShardingStage2(self.sharding_mesh_dim, mesh), ) elif self.level == "3": self.optimizer = dist.shard_optimizer( - self.optimizer, dist.ShardingStage3("dp", mesh) + self.optimizer, + dist.ShardingStage3(self.sharding_mesh_dim, mesh), ) else: - self.optimizer = dist.shard_optimizer(self.optimizer) + self.optimizer = dist.shard_optimizer(self.optimizer, None) self.is_initialized = True return self.optimizer + def update_param_list(self, parallelized_parameters): + self.optimizer._parameter_list = parallelized_parameters + if isinstance(parallelized_parameters[0], dict): + self.optimizer._param_groups = [] + for param_group in self.parallelized_parameters: + self.optimizer._add_param_group(param_group.copy()) + else: + self.optimizer._param_groups = self.optimizer._parameter_list + class ParallelModel: def __init__(self, model): @@ -192,8 +203,7 @@ def parallelize_model_and_optimizer(model, optimizer=None): parallelized_optimizer = None if optimizer is not None: assert isinstance(optimizer, ParallelOptimizer) - parallelized_optimizer = optimizer.parallelize( - parallelized_model.parameters() - ) + optimizer.update_param_list(parallelized_model.parameters()) + parallelized_optimizer = optimizer.parallelize() return parallelized_model, parallelized_optimizer diff --git a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py index ae580447492495..246fc8ea33078c 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py +++ b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py @@ -11,6 +11,7 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +import warnings from .parallel_base import ParallelOptimizer, parallelize_model_and_optimizer from .pipeline_parallel import pipeline_parallel @@ -34,30 +35,60 @@ def parallelize( ) if dp_config is not None: assert isinstance(dp_config, dict) + if 'sharding_level' not in dp_config.keys(): + warnings.warn( + "The dp_config doesn't contain sharding_level, will run under dp." + ) model, optimizer = sharded_data_parallel( model, optimizer, - level=dp_config.get('sharding_level'), - offload=bool(dp_config.get('offload')), - exclude_layer=dp_config.get('exclude_layer'), + config=dp_config, ) model, optimizer = parallelize_model_and_optimizer(model, optimizer) return model, optimizer +has_parallelized_model = False + + def parallelize_model( model, mesh=None, dp_config=None, mp_config=None, pp_config=None ): + global has_parallelized_model + has_parallelized_model = True model, _ = parallelize(model, None, mesh, dp_config, mp_config, pp_config) return model def parallelize_optimizer( - model, optimizer, mesh=None, dp_config=None, mp_config=None, pp_config=None + optimizer, mesh=None, dp_config=None, mp_config=None, pp_config=None ): + global has_parallelized_model + assert ( + has_parallelized_model + ), "Please parallelize the model before parallelize optimizer." + param_list = optimizer._parameter_list + if isinstance(param_list[0], dict): + for param_group in param_list: + for param in param_group['params']: + assert ( + param.is_dist() + ), "Please use model after parallelize to create optimizer." + else: + for param in param_list: + assert ( + param.is_dist() + ), "Please use model after parallelize to create optimizer." + level = None + sharding_mesh_dim = None if dp_config is not None: + if 'sharding_level' not in dp_config.keys(): + warnings.warn( + "The dp_config doesn't contain sharding_level, will run under dp." + ) level = dp_config.get('sharding_level') - optimizer = ParallelOptimizer(optimizer, level) - optimizer = optimizer.parallelize(model.parameters()) + sharding_mesh_dim = dp_config.get('sharding_mesh_dim', "dp") + optimizer = ParallelOptimizer(optimizer, level, sharding_mesh_dim) + optimizer = optimizer.parallelize() return optimizer diff --git a/python/paddle/distributed/auto_parallel/intermediate/sharded_data_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/sharded_data_parallel.py index eeab5ee081596a..d088974f4a0647 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/sharded_data_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/sharded_data_parallel.py @@ -51,31 +51,31 @@ def sharding_parallelizer_func(self, model): return model -def sharded_data_parallel( - model, optimizer=None, level=None, offload=False, exclude_layer=None -): +def sharded_data_parallel(model, optimizer=None, config=None): """ sharded_data_parallel converts model and optimizer to distributed and supports set zero stage1/2/3 Args: model (paddle.nn.Layer): A single card model to be distributed optimizer (paddle.optimizer.Optimizer): an optimizer to be distributed - level (str): Zero stage, can be the following values: - 0: no sharding (pure dp) - 1: Zero Stage1 - 2: Zero Stage2 - 3: Zero Stage3 - Default: None, which means optimizer is replicated among all process. - offload (bool): whether enable cpu offload strategy, not implemented currently. - exclude_layer (list): Specify which layers do not use the zero stage strategy, not implemented currently. + config (dict): { + "sharding_level": 0, + "offload": False, + "exclude_layer": None, + "sharding_mesh_dim": "dp", + } Returns: ShardedDataParallel: a distributed model ParallelOptimizer: a distributed optimizer """ - sdp_model = ShardedDataParallel(model, offload, exclude_layer) + sdp_model = ShardedDataParallel( + model, bool(config.get('offload')), config.get('exclude_layer') + ) if optimizer is not None: - optimizer = ParallelOptimizer(optimizer, level) + level = config.get('sharding_level') + sharding_mesh_dim = config.get('sharding_mesh_dim', "dp") + optimizer = ParallelOptimizer(optimizer, level, sharding_mesh_dim) # check global_mesh mesh = fleet.auto.get_mesh() diff --git a/test/auto_parallel/hybrid_strategy/parallel_api.py b/test/auto_parallel/hybrid_strategy/parallel_api.py index eee1d333f1837b..c1dec86e2197d9 100644 --- a/test/auto_parallel/hybrid_strategy/parallel_api.py +++ b/test/auto_parallel/hybrid_strategy/parallel_api.py @@ -200,7 +200,7 @@ def check_mp(self, layer): dist.Shard(0), ] - def parallel_model(self, layer, optimizer=None): + def parallel_model(self, layer): dp_config = None mp_config = None pp_config = None @@ -275,17 +275,8 @@ def parallel_model(self, layer, optimizer=None): mp_config=mp_config, pp_config=pp_config, ) - optimizer = parallelize_optimizer( - layer, - optimizer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, - ) self.check_mp(layer) - if optimizer is None: - return layer - return layer, optimizer + return layer, dp_config, mp_config, pp_config def run_llama( self, share_embedding=False, position_embedding=False, to_static=0 @@ -300,11 +291,19 @@ def run_llama( self.config, share_embedding, position_embedding ) + model, dp_config, mp_config, pp_config = self.parallel_model(model) + lr_scheduler = paddle.optimizer.lr.LinearWarmup( learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001 ) optimizer = create_optimizer(model, lr_scheduler) - model, optimizer = self.parallel_model(model, optimizer) + + optimizer = parallelize_optimizer( + optimizer, + dp_config=dp_config, + mp_config=mp_config, + pp_config=pp_config, + ) criterion = LlamaPretrainingCriterion(self.config) From 6082a2b5a538191ba5364fc88fb3687c6b7a94d5 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Wed, 27 Nov 2024 13:55:47 +0800 Subject: [PATCH 021/288] [Paddle TensorRT No.17] Add pd_op.celu converter (#69359) * pir tensorrt celu * Update paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc * Refine logic * apply review * fix * fix typos --- .../transforms/tensorrt/trt_op_marker_pass.cc | 65 ++++++++++--------- python/paddle/tensorrt/converter_utils.py | 8 +++ python/paddle/tensorrt/impls/activation.py | 34 ++++++++++ test/tensorrt/test_converter_activation.py | 19 +++++- 4 files changed, 92 insertions(+), 34 deletions(-) diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index 9b5245d9753d45..cc182a17191706 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -1,5 +1,3 @@ - - // Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); @@ -85,6 +83,7 @@ DEFINE_GENERAL_PATTERN(Swish, paddle::dialect::SwishOp) DEFINE_GENERAL_PATTERN(Log, paddle::dialect::LogOp) DEFINE_GENERAL_PATTERN(Floor, paddle::dialect::FloorOp) DEFINE_GENERAL_PATTERN(Roll, paddle::dialect::RollOp) +DEFINE_GENERAL_PATTERN(Softplus, paddle::dialect::SoftplusOp) DEFINE_GENERAL_PATTERN(ThresholdedRelu, paddle::dialect::ThresholdedReluOp) #undef DEFINE_GENERAL_PATTERN @@ -230,6 +229,35 @@ using FloorDivideOpPattern = using RemainderOpPattern = ElementwiseCommonOpPattern; +template +class ActOpPattern : public pir::OpRewritePattern { + public: + using pir::OpRewritePattern::OpRewritePattern; + bool MatchAndRewrite(OpType op, + pir::PatternRewriter &rewriter) const override { + if (op->HasAttribute(kCanRunTrtAttr) && + op->template attribute(kCanRunTrtAttr).data()) { + return false; + } +#if IS_TRT_VERSION_LT(8600) + pir::Value x = op.operand_source(0); + auto x_type = x.type().dyn_cast(); + auto x_shape = x_type.dims(); + int dims = x_shape.size(); + if (dims < 1) { + VLOG(3) << op->name() + << " op does not support 0 dim input when TensorRT < 8.6."; + return false; + } +#endif + + op->set_attribute(kCanRunTrtAttr, rewriter.bool_attr(true)); + return true; + } +}; +using TanhOpPattern = ActOpPattern; +using CeluOpPattern = ActOpPattern; + class Pool2dOpPattern : public pir::OpRewritePattern { public: @@ -1596,34 +1624,6 @@ class StackOpPattern : public pir::OpRewritePattern { } }; -template -class ActOpPattern : public pir::OpRewritePattern { - public: - using pir::OpRewritePattern::OpRewritePattern; - bool MatchAndRewrite(OpType op, - pir::PatternRewriter &rewriter) const override { - if (op->HasAttribute(kCanRunTrtAttr) && - op->template attribute(kCanRunTrtAttr).data()) { - return false; - } -#if IS_TRT_VERSION_LT(8600) - pir::Value x = op.operand_source(0); - auto x_type = x.type().dyn_cast(); - auto x_shape = x_type.dims(); - int dims = x_shape.size(); - if (dims < 1) { - VLOG(3) << "Tanh op does not support 0 dim input when TensorRT < 8.6."; - return false; - } -#endif - - op->set_attribute(kCanRunTrtAttr, rewriter.bool_attr(true)); - return true; - } -}; -using TanhOpPattern = ActOpPattern; -using SoftplusOpPatten = ActOpPattern; - class WherePattern : public pir::OpRewritePattern { public: using pir::OpRewritePattern::OpRewritePattern; @@ -2136,6 +2136,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ADD_PATTERN(Log) ADD_PATTERN(Floor) ADD_PATTERN(Roll) + ADD_PATTERN(Softplus) ADD_PATTERN(ThresholdedRelu) #if IS_TRT_VERSION_GE(8600) ADD_PATTERN(Layer_norm) @@ -2188,7 +2189,6 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); - ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); @@ -2198,9 +2198,10 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); - ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); + ps.Add(std::make_unique(context)); + ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index 2a658431025eb0..29928e55e46a8e 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -170,6 +170,14 @@ def add_1D_constant_layer(network, data, dtype=np.int32, is_scalar=False): return constant_layer.get_output(0) +# Create and add ND constant layer +def add_constant_layer(network, data, shape, dtype=np.int32): + constant_data = np.array(data, dtype=dtype) + constant_data = np.resize(constant_data, shape) + constant_layer = network.add_constant(shape, constant_data) + return constant_layer.get_output(0) + + # Create an constant layer with shape_tensor and value def fill_constant_layer(network, shape_tensor, tensor_rank, data, trt_dtype): fill_layer = network.add_fill( diff --git a/python/paddle/tensorrt/impls/activation.py b/python/paddle/tensorrt/impls/activation.py index 14831f6565a2ff..cb278a1bfdc633 100644 --- a/python/paddle/tensorrt/impls/activation.py +++ b/python/paddle/tensorrt/impls/activation.py @@ -16,8 +16,13 @@ import tensorrt as trt from paddle.tensorrt.converter_utils import ( + add_constant_layer, get_trt_plugin, + trt_div, + trt_min, trt_prod, + trt_sub, + trt_sum, ) from paddle.tensorrt.register import converter_registry @@ -130,6 +135,35 @@ def swish_silu_converter(network, paddle_op, inputs): return trt_prod(network, inputs[0], layer_output) +@converter_registry.register("pd_op.celu", trt_version="8.x") +def celu_converter(network, paddle_op, inputs): + input_tensor = inputs[0] + alpha = paddle_op.attrs()["alpha"] + input_rank = len(input_tensor.shape) + constant_shape = trt.Dims([1] * input_rank) + alpha_data = add_constant_layer( + network, [alpha], constant_shape, dtype="float32" + ) + constant_zero_data = add_constant_layer( + network, [0.0], constant_shape, dtype="float32" + ) + constant_one_data = add_constant_layer( + network, [1.0], constant_shape, dtype="float32" + ) + input_div_with_alpha = trt_div(network, input_tensor, alpha_data) + input_exp_layer = network.add_unary( + input_div_with_alpha, trt.UnaryOperation.EXP + ) + input_sub_with_one = trt_sub( + network, input_exp_layer.get_output(0), constant_one_data + ) + input_prod_with_alpha = trt_prod(network, input_sub_with_one, alpha_data) + min_input = trt_min(network, input_prod_with_alpha, constant_zero_data) + relu_layer = network.add_activation(input_tensor, trt.ActivationType.RELU) + output_tensor = trt_sum(network, relu_layer.get_output(0), min_input) + return output_tensor + + @converter_registry.register("pd_op.thresholded_relu", trt_version="8.x") def thresholded_relu_converter(network, paddle_op, inputs): x = inputs[0] diff --git a/test/tensorrt/test_converter_activation.py b/test/tensorrt/test_converter_activation.py index f2853d49a22d39..fa14d69e8721b4 100644 --- a/test/tensorrt/test_converter_activation.py +++ b/test/tensorrt/test_converter_activation.py @@ -24,7 +24,7 @@ class TestHardSigmoidTRTPattern(TensorRTBaseTest): def setUp(self): self.python_api = paddle.nn.functional.hardsigmoid self.api_args = { - "x": np.random.randn(2, 3).astype(np.float32), + "x": np.random.randn(2, 3).astype("float32"), } self.program_config = {"feed_list": ["x"]} self.min_shape = {"x": [1, 3], "y": [1, 3]} @@ -38,7 +38,7 @@ class TestHardSwishTRTPattern(TensorRTBaseTest): def setUp(self): self.python_api = paddle.nn.functional.hardswish self.api_args = { - "x": np.random.randn(2, 3).astype(np.float32), + "x": np.random.randn(2, 3).astype("float32"), } self.program_config = {"feed_list": ["x"]} self.min_shape = {"x": [1, 3], "y": [1, 3]} @@ -128,6 +128,21 @@ def test_trt_result(self): self.check_trt_result() +class TestCeluTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.functional.celu + self.api_args = { + "x": np.random.randn(2, 3).astype("float32"), + "alpha": 1.0, + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3]} + self.max_shape = {"x": [5, 3]} + + def test_trt_result(self): + self.check_trt_result() + + class TestThresholdedReluTRTPattern(TensorRTBaseTest): def setUp(self): self.python_api = paddle.nn.functional.thresholded_relu From eae4c8334de24dc4f5e406f89a4a9d3d37665e39 Mon Sep 17 00:00:00 2001 From: ZHOU05030 <112939094+ZHOU05030@users.noreply.github.com> Date: Wed, 27 Nov 2024 14:58:59 +0800 Subject: [PATCH 022/288] =?UTF-8?q?[Paddle=20Tensor=20No.12][BUPT]=20?= =?UTF-8?q?=E6=96=B0=E5=A2=9E=20Tensor.=5F=5Fdlpack=5Fdevice=5F=5F=20(#696?= =?UTF-8?q?32)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 新增__dlpack_device__ * 修改为DLDeviceType * 修改cpu的device_id * 添加单侧文件 * 添加Place判断逻辑 * 修改单侧文件 * 修改device_id --- .../base/dygraph/tensor_patch_methods.py | 37 +++++++++ .../test_tensor_attr_consistency.py | 1 + test/legacy_test/test_dlpack.py | 80 ++++++++++++++++++ test/legacy_test/test_eager_tensor.py | 83 +++++++++++++++++++ 4 files changed, 201 insertions(+) diff --git a/python/paddle/base/dygraph/tensor_patch_methods.py b/python/paddle/base/dygraph/tensor_patch_methods.py index a612a7106d5f2b..ebb123df3cc593 100644 --- a/python/paddle/base/dygraph/tensor_patch_methods.py +++ b/python/paddle/base/dygraph/tensor_patch_methods.py @@ -29,8 +29,10 @@ _PADDLE_DTYPE_2_NUMPY_DTYPE, convert_uint16_to_float, ) +from paddle.base.libpaddle import Place from paddle.profiler.utils import in_profiler_mode from paddle.utils import deprecated +from paddle.utils.dlpack import DLDeviceType from .. import core, framework, unique_name from ..framework import ( @@ -127,6 +129,7 @@ def _to_static_var(self, to_parameter=False, **kwargs): 'strides', 'offset', '__cuda_array_interface__', + '__dlpack_device__', ] param_keys = ['stop_gradient', 'trainable'] if isinstance(self, EagerParamBase): @@ -1257,6 +1260,39 @@ def coalesce(self: Tensor, name: str | None = None) -> Tensor: """ return _C_ops.sparse_coalesce(self) + @framework.dygraph_only + def __dlpack_device__(self): + """ + Extract the DLPack device type and device ID for the current tensor. + + Returns: + tuple: A tuple containing the DLPack device type and device ID. + - device_type (DLDeviceType): The type of device (e.g., kDLCPU, kDLCUDA, etc.). + - device_id (int): The device ID. + """ + place = self.place + if isinstance(place, Place): + if place.is_gpu_place(): + return DLDeviceType.kDLCUDA, place.gpu_device_id() + elif place.is_cpu_place(): + return DLDeviceType.kDLCPU, None + elif place.is_cuda_pinned_place(): + return DLDeviceType.kDLCUDAHost, None + elif place.is_xpu_place(): + return DLDeviceType.kDLOneAPI, place.xpu_device_id() + else: + raise RuntimeError(f"Unsupported Paddle device type {place}") + elif place.is_cpu_place(): + return DLDeviceType.kDLCPU, None + elif place.is_cuda_pinned_place(): + return DLDeviceType.kDLCUDAHost, None + elif place.is_gpu_place(): + return DLDeviceType.kDLCUDA, place.get_device_id() + elif place.is_xpu_place(): + return DLDeviceType.kDLOneAPI, place.get_device_id() + else: + raise ValueError(f"Unsupported tensor place: {place}") + @property def __cuda_array_interface__(self): """Array view description for cuda tensors. @@ -1374,6 +1410,7 @@ def __cuda_array_interface__(self): ("_use_gpudnn", _use_gpudnn), ("_md5sum", _md5sum), ("__cuda_array_interface__", __cuda_array_interface__), + ("__dlpack_device__", __dlpack_device__), ): setattr(core.eager.Tensor, method_name, method) diff --git a/test/dygraph_to_static/test_tensor_attr_consistency.py b/test/dygraph_to_static/test_tensor_attr_consistency.py index d39494a84a559b..48518f9927cc08 100644 --- a/test/dygraph_to_static/test_tensor_attr_consistency.py +++ b/test/dygraph_to_static/test_tensor_attr_consistency.py @@ -78,6 +78,7 @@ 'value', 'zero_', "__cuda_array_interface__", + "__dlpack_device__", ] ) STATIC_ONLY_TENSOR_ATTRS_ALLOW_LIST = OrderedSet( diff --git a/test/legacy_test/test_dlpack.py b/test/legacy_test/test_dlpack.py index 064318f19e886b..d0fce4e313798d 100644 --- a/test/legacy_test/test_dlpack.py +++ b/test/legacy_test/test_dlpack.py @@ -326,6 +326,86 @@ def test_to_dlpack_from_zero_size(self): np.testing.assert_array_equal(x.numpy(), y2.numpy()) +from paddle.utils.dlpack import DLDeviceType + + +class TestDLPackDevice(unittest.TestCase): + def test_dlpack_device(self): + with dygraph_guard(): + + tensor_cpu = paddle.to_tensor([1, 2, 3], place=base.CPUPlace()) + device_type, device_id = tensor_cpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor( + [1, 2, 3], place=base.CUDAPlace(0) + ) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + if paddle.is_compiled_with_cuda(): + tensor_pinned = paddle.to_tensor( + [1, 2, 3], place=base.CUDAPinnedPlace() + ) + device_type, device_id = tensor_pinned.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDAHost) + self.assertEqual(device_id, None) + + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor([1, 2, 3], place=base.XPUPlace(0)) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + + def test_dlpack_device_zero_dim(self): + with dygraph_guard(): + + tensor = paddle.to_tensor(5.0, place=base.CPUPlace()) + device_type, device_id = tensor.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor(5.0, place=base.CUDAPlace(0)) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor(5.0, place=base.XPUPlace(0)) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + + def test_dlpack_device_zero_size(self): + with dygraph_guard(): + tensor = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.CPUPlace() + ) + device_type, device_id = tensor.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.CUDAPlace(0) + ) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.XPUPlace(0) + ) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + + class TestRaiseError(unittest.TestCase): def test_to_dlpack_raise_type_error(self): self.assertRaises(TypeError, paddle.utils.dlpack.to_dlpack, np.zeros(5)) diff --git a/test/legacy_test/test_eager_tensor.py b/test/legacy_test/test_eager_tensor.py index e644b3fea6b06c..7384034a87370c 100644 --- a/test/legacy_test/test_eager_tensor.py +++ b/test/legacy_test/test_eager_tensor.py @@ -23,6 +23,7 @@ import paddle.nn.functional as F from paddle import base from paddle.base import core +from paddle.utils.dlpack import DLDeviceType class TestEagerTensor(unittest.TestCase): @@ -1292,6 +1293,88 @@ def test___cuda_array_interface__(self): self.assertIn("version", interface) self.assertEqual(interface["version"], 2) + def test_dlpack_device(self): + """test Tensor.__dlpack_device__""" + with dygraph_guard(): + # test CPU + tensor_cpu = paddle.to_tensor([1, 2, 3], place=base.CPUPlace()) + device_type, device_id = tensor_cpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + # test CUDA + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor( + [1, 2, 3], place=base.CUDAPlace(0) + ) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + # test CUDA Pinned + if paddle.is_compiled_with_cuda(): + tensor_pinned = paddle.to_tensor( + [1, 2, 3], place=base.CUDAPinnedPlace() + ) + device_type, device_id = tensor_pinned.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDAHost) + self.assertEqual(device_id, None) + + # test XPU + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor([1, 2, 3], place=base.XPUPlace(0)) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + + # zero_dim + # test CPU + tensor = paddle.to_tensor(5.0, place=base.CPUPlace()) + device_type, device_id = tensor.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + # test CUDA + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor(5.0, place=base.CUDAPlace(0)) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + # test XPU + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor(5.0, place=base.XPUPlace(0)) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + + # zero_size + # test CPU + tensor = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.CPUPlace() + ) + device_type, device_id = tensor.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCPU) + self.assertEqual(device_id, None) + + # test CUDA + if paddle.is_compiled_with_cuda(): + tensor_cuda = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.CUDAPlace(0) + ) + device_type, device_id = tensor_cuda.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLCUDA) + self.assertEqual(device_id, 0) + + # test XPU + if paddle.is_compiled_with_xpu(): + tensor_xpu = paddle.to_tensor( + paddle.zeros([0, 10]), place=base.XPUPlace(0) + ) + device_type, device_id = tensor_xpu.__dlpack_device__() + self.assertEqual(device_type, DLDeviceType.kDLOneAPI) + self.assertEqual(device_id, 0) + def test_tensor__format__(self): # test for floating point scalar for width in range(0, 5): From 297ca00b40797ff7d42d4580b353e28fff8f7b21 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Wed, 27 Nov 2024 15:20:53 +0800 Subject: [PATCH 023/288] [PIR] add shape64 op (#69589) * shape op return int64 --- .../transforms/check_infer_symbolic_pass.cc | 2 +- .../transforms/insert_broadcast_pass.cc | 4 +- .../operator/transforms/pd_to_cinn_pass.cc | 2 +- ...plit_generate_shape_into_shape_ops_pass.cc | 2 +- .../instruction/instruction_util.cc | 3 +- .../ir_adaptor/translator/op_translator.cc | 4 +- .../infer_symbolic_shape/unary_infer_sym.cc | 17 +++ .../infer_symbolic_shape/unary_infer_sym.h | 2 + .../pir/dialect/operator/ir/manual_op.cc | 10 +- .../fluid/pir/dialect/operator/utils/utils.cc | 3 +- .../transforms/tensorrt/trt_op_marker_pass.cc | 2 + paddle/fluid/primitive/base/primitive_ops.h | 1 + .../decomp_rule/decomp_rule/composite.h | 63 +++++------ .../decomp_rule/decomp_vjp/details.h | 105 +++++++++--------- .../primitive/decomp_utils/decomp_utils.h | 8 +- .../fluid/primitive/primitive/primitive.yaml | 2 +- paddle/phi/backends/xpu/xpu1_op_list.cc | 6 + paddle/phi/backends/xpu/xpu2_op_list.cc | 6 + paddle/phi/backends/xpu/xpu3_op_list.cc | 6 + paddle/phi/infermeta/unary.cc | 12 ++ paddle/phi/infermeta/unary.h | 4 + .../phi/kernels/selected_rows/shape_kernel.cc | 83 ++++++++++++++ .../phi/kernels/selected_rows/shape_kernel.h | 5 + paddle/phi/kernels/shape_kernel.cc | 92 +++++++++++++++ paddle/phi/kernels/shape_kernel.h | 5 + paddle/phi/ops/yaml/ops.yaml | 13 +++ python/paddle/nn/layer/rnn.py | 1 + python/paddle/tensor/attribute.py | 2 +- python/paddle/tensor/manipulation.py | 2 +- python/paddle/tensorrt/impls/attribute.py | 7 ++ test/dygraph_to_static/test_tensor_shape.py | 38 ++++--- .../cinn/symbolic/test_llama_slice_concat.py | 6 +- test/ir/pir/cinn/test_trivial_fusion.py | 4 +- test/ir/pir/test_standalone_pir.py | 2 +- test/tensorrt/test_trt_marker_shape.py | 2 +- 35 files changed, 403 insertions(+), 123 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc index 1eb441df328aee..133928e0cfc421 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc @@ -216,7 +216,7 @@ class BlockDimExprsAsserter { } pir::Value BuildShapeTensorFromInferMeta(pir::Value output) { - return builder_.Build(output).out(); + return builder_.Build(output).out(); } void TryAssertDimExprsForOutputData(const pir::Operation* op, diff --git a/paddle/cinn/hlir/dialect/operator/transforms/insert_broadcast_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/insert_broadcast_pass.cc index b3ed7dda3e81e6..2011d625be7cd0 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/insert_broadcast_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/insert_broadcast_pass.cc @@ -37,8 +37,8 @@ namespace { pir::Value GetOutputDimTensor(pir::PatternRewriter* rewriter, pir::Value x, pir::Value y) { - pir::Operation* x_shape_op = rewriter->Build(x); - pir::Operation* y_shape_op = rewriter->Build(y); + pir::Operation* x_shape_op = rewriter->Build(x); + pir::Operation* y_shape_op = rewriter->Build(y); pir::Operation* shape_broadcast_op = rewriter->Build(x_shape_op->result(0), y_shape_op->result(0)); diff --git a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc index b0740bfd93a0f1..3ff94995a26c33 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc @@ -1110,7 +1110,7 @@ class FlattenOpPattern .dims() .size(); auto x_shape = - rewriter.Build(op->operand_source(0)) + rewriter.Build(op->operand_source(0)) .result(0); for (size_t i = 0; i < x_rank;) { if (i == static_cast(start_axis)) { diff --git a/paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.cc index 3452ef3cd01ea6..afcf5764dbbf08 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.cc @@ -75,7 +75,7 @@ struct CachedDimExprToValueConverter { auto iter = tensor2shape_.find(input_tensor); if (iter == tensor2shape_.end()) { pir::Value shape = - rewriter->Build(input_tensor).out(); + rewriter->Build(input_tensor).out(); pir::Value cast_shape = rewriter->Build(shape, phi::DataType::INT64) .out(); diff --git a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc index 874a6e292b6f0d..40c82e4e1d7a1a 100644 --- a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc +++ b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc @@ -235,7 +235,8 @@ OpFuncType AnalyseOpFuncType(pir::Operation* op, const phi::Place& place) { return OpFuncType::kGpuSync; } - if (op_name.compare(paddle::dialect::ShapeOp::name()) == 0) { + if (op_name.compare(paddle::dialect::ShapeOp::name()) == 0 || + op_name.compare(paddle::dialect::Shape64Op::name()) == 0) { return OpFuncType::kGpuSync; } } diff --git a/paddle/fluid/ir_adaptor/translator/op_translator.cc b/paddle/fluid/ir_adaptor/translator/op_translator.cc index 0b589cf8c033ec..76e4e532767268 100644 --- a/paddle/fluid/ir_adaptor/translator/op_translator.cc +++ b/paddle/fluid/ir_adaptor/translator/op_translator.cc @@ -2897,10 +2897,10 @@ struct ElementwiseTranscriber : public OpTranscriber { << y_tensor_type.dims() << " to " << common::make_ddim(y_new_shape); } else { - auto shape_op = builder.Build(y_value); + auto shape_op = builder.Build(y_value); auto append_shape_op = builder.Build( std::vector(append_size, 1), - phi::DataType::INT32, + phi::DataType::INT64, phi::CPUPlace()); auto y_true_shape_op = builder.Build( std::vector{shape_op.out(), append_shape_op.out()}); diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 0214c9d61e137a..0a0045e5512dab 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3088,6 +3088,19 @@ bool ShapeOpInferSymbolicShape(pir::Operation *op, return true; } +bool Shape64OpInferSymbolicShape( + pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { + const symbol::ShapeOrDataDimExprs &operand_shape_or_data = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + const auto &out_data = operand_shape_or_data.shape(); + const std::vector shape{std::int64_t(out_data.size())}; + symbol::ShapeOrDataDimExprs shape_or_data{ + symbol::TensorShapeOrDataDimExprs(shape, out_data)}; + + infer_context->SetShapeOrDataForValue(op->result(0), shape_or_data); + return true; +} + bool ShardIndexOpInferSymbolicShape( pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { const auto &in_shape_or_data = @@ -3182,6 +3195,10 @@ bool ShapeSrOpInferSymbolicShape( return ShapeOpInferSymbolicShape(op, infer_context); } +bool Shape64SrOpInferSymbolicShape( + pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { + return Shape64OpInferSymbolicShape(op, infer_context); +} // bool ShardIndexOpInferSymbolicShape(pir::Operation *op, // pir::InferSymbolicShapeContext // *infer_context) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h index 8c8002389b0eb2..4004f4afd48b0d 100755 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h @@ -126,8 +126,10 @@ OP_DECLARE_INFER_SYMBOLIC_SHAPE(Reshape_) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Rrelu) OP_DECLARE_INFER_SYMBOLIC_SHAPE(SequencePool) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Shape) +OP_DECLARE_INFER_SYMBOLIC_SHAPE(Shape64) OP_DECLARE_INFER_SYMBOLIC_SHAPE(ShardIndex) OP_DECLARE_INFER_SYMBOLIC_SHAPE(ShapeSr) +OP_DECLARE_INFER_SYMBOLIC_SHAPE(Shape64Sr) // OP_DECLARE_INFER_SYMBOLIC_SHAPE(ShardIndex) OP_DECLARE_INFER_SYMBOLIC_SHAPE(ShuffleChannel) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Slice) diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc index 2e66b02c39ddbf..6b8fbaaf105a15 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc @@ -3563,7 +3563,8 @@ std::vector ExpandOp::InferMeta( vec_shape.insert(vec_shape.end(), tmp.begin(), tmp.end()); } } else if (shape.isa() && - shape.defining_op()->isa()) { + (shape.defining_op()->isa() || + shape.defining_op()->isa())) { // tensor_shape may come from shape op // x0.shape = [-1,3] // tensor_shape = shape(x0) @@ -3598,7 +3599,8 @@ std::vector ExpandOp::InferMeta( if (shape_dim.size() == 1 && shape_dim[0] == static_cast(inputs.size())) { for (auto item : inputs) { - if (item.defining_op()->isa()) { + if (item.defining_op()->isa() || + item.defining_op()->isa()) { pir::Value shape_input = item.defining_op()->operand_source(0); int64_t value = shape_input.type() .dyn_cast() @@ -4448,11 +4450,11 @@ bool ShapeBroadcastOp::InferSymbolicShape( PADDLE_ENFORCE_EQ(x_data_shape.data().has_value(), true, common::errors::InvalidArgument( - "Value x comes from ShapeOp, it must have data")); + "Value x comes from Shape64Op, it must have data")); PADDLE_ENFORCE_EQ(y_data_shape.data().has_value(), true, common::errors::InvalidArgument( - "Value y comes from ShapeOp, it must have data")); + "Value y comes from Shape64Op, it must have data")); const auto &x_data = x_data_shape.data().value(); const auto &y_data = y_data_shape.data().value(); diff --git a/paddle/fluid/pir/dialect/operator/utils/utils.cc b/paddle/fluid/pir/dialect/operator/utils/utils.cc index 1b7bef5f1731ff..91f7bf7c261e0b 100644 --- a/paddle/fluid/pir/dialect/operator/utils/utils.cc +++ b/paddle/fluid/pir/dialect/operator/utils/utils.cc @@ -446,7 +446,8 @@ std::vector ParseValueShape(const pir::Value& shape, vec_shape.insert(vec_shape.end(), tmp.begin(), tmp.end()); } } else if (shape.isa() && - shape.defining_op()->isa() && + (shape.defining_op()->isa() || + shape.defining_op()->isa()) && shape.type().isa()) { // tensor_shape may come from shape op // x0.shape = [-1,3] diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index cc182a17191706..e6887de9618de5 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -69,6 +69,7 @@ DEFINE_GENERAL_PATTERN(Conv2d, paddle::dialect::Conv2dOp) DEFINE_GENERAL_PATTERN(FusedConv2dAddAct, paddle::dialect::FusedConv2dAddActOp) DEFINE_GENERAL_PATTERN(DepthwiseConv2d, paddle::dialect::DepthwiseConv2dOp) DEFINE_GENERAL_PATTERN(Shape, paddle::dialect::ShapeOp) +DEFINE_GENERAL_PATTERN(Shape64, paddle::dialect::Shape64Op) DEFINE_GENERAL_PATTERN(Expand, paddle::dialect::ExpandOp) DEFINE_GENERAL_PATTERN(ExpandAs, paddle::dialect::ExpandAsOp) DEFINE_GENERAL_PATTERN(Sigmoid, paddle::dialect::SigmoidOp) @@ -2122,6 +2123,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ADD_PATTERN(Nonzero) ADD_PATTERN(Gelu) ADD_PATTERN(Shape) + ADD_PATTERN(Shape64) ADD_PATTERN(Expand) ADD_PATTERN(ExpandAs) ADD_PATTERN(Sigmoid) diff --git a/paddle/fluid/primitive/base/primitive_ops.h b/paddle/fluid/primitive/base/primitive_ops.h index 7fb2fa4fcf96af..dc897ca3267e15 100644 --- a/paddle/fluid/primitive/base/primitive_ops.h +++ b/paddle/fluid/primitive/base/primitive_ops.h @@ -85,6 +85,7 @@ const std::set& GetPrimitiveOpNames() { "pd_op.slice", "pd_op.uniform", "pd_op.shape", + "pd_op.shape64", "pd_op.full", "pd_op.full_int_array", "pd_op.full_with_tensor", diff --git a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h index 26f47ddcc8fd23..9553c69420a2be 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h @@ -64,7 +64,7 @@ Tensor mean_decomp(const Tensor& x, const IntArray& axis, bool keepdim) { } } if (switch_dynamic) { - auto x_shape = shape(x); + auto x_shape = shape64(x); value = slice(x_shape, {0}, {axis_[0]}, {axis_[0] + 1}, {1}, {0}); for (size_t i = 1; i < axis_.size(); ++i) { value = @@ -166,7 +166,7 @@ std::tuple huber_loss_decomp(const Tensor& input, Tensor delta_full; if (has_dynamic_shape(input.shape())) { delta_full = - backend::full_with_tensor(shape(input), delta, input.dtype()); + backend::full_with_tensor(shape64(input), delta, input.dtype()); } else { delta_full = full(input.shape(), delta, input.dtype(), input.place()); } @@ -377,7 +377,7 @@ Tensor stack_decomp(const std::vector& x, const int& axis) { if (is_dynamic && has_dynamic_shape(combined_shape)) { std::vector shapes; - Tensor temp_shape = shape(x[0]); + Tensor temp_shape = shape64(x[0]); for (size_t j = 0; j < rank; j++) { if (combined_shape[j] == -1) { shapes.push_back(get_slice(temp_shape, j)); @@ -479,9 +479,9 @@ std::vector meshgrid_decomp(const std::vector& x) { for (int64_t i = 0; i < rank; i++) { if (tar_shape[i] == 1) { tmp_shape.push_back( - full({1}, tar_shape[i], DataType::INT32, x[0].place())); + full({1}, tar_shape[i], DataType::INT64, x[0].place())); } else { - tmp_shape.push_back(shape(x[i])); + tmp_shape.push_back(shape64(x[i])); } } auto tar_tensor_shape = concat(tmp_shape); @@ -588,7 +588,7 @@ Tensor full_like_decomp(const Tensor& x, const Place& place) { std::vector x_shape = x.shape(); if (has_dynamic_shape(x_shape)) { - return backend::full_with_tensor(shape(x), value, dtype); + return backend::full_with_tensor(shape64(x), value, dtype); } else { return full(x_shape, value, dtype, place); } @@ -620,7 +620,7 @@ std::tuple dropout_decomp( } Tensor uniform_tensor; if (has_dynamic_shape(x.shape())) { - auto shape_tensor = shape(x); + auto shape_tensor = shape64(x); auto zero = full_scalar(0.0, dtype_tmp, x.place()); auto one = full_scalar(1.0, dtype_tmp, x.place()); uniform_tensor = backend::uniform( @@ -717,10 +717,10 @@ template Tensor heaviside_decomp(const Tensor& x, const Tensor& y) { Tensor zero, one; if (has_dynamic_shape(x.shape())) { - Tensor zero_x = backend::full_with_tensor(shape(x), 0.0, x.dtype()); - Tensor zero_y = backend::full_with_tensor(shape(y), 0.0, x.dtype()); + Tensor zero_x = backend::full_with_tensor(shape64(x), 0.0, x.dtype()); + Tensor zero_y = backend::full_with_tensor(shape64(y), 0.0, x.dtype()); zero = zero_x + zero_y; - one = backend::full_with_tensor(shape(zero), 1.0, x.dtype()); + one = backend::full_with_tensor(shape64(zero), 1.0, x.dtype()); } else { auto out_dims = phi::funcs::BroadcastTwoDims(x.dims(), y.dims()); zero = full(phi::vectorize(out_dims), 0.0, x.dtype(), x.place()); @@ -769,14 +769,14 @@ std::tuple instance_norm_decomp( auto var_tmp1 = difference * difference; auto variance = reduce_axes_empty ? var_tmp1 : mean_decomp(var_tmp1, axis, true); - auto var_shape = shape(variance); + auto var_shape = shape64(variance); auto var_tmp3 = variance + full_scalar(epsilon, variance.dtype(), variance.place()); auto rsqrt_var = rsqrt(var_tmp3); auto out = difference * rsqrt_var; int dim_size = x_dims.size(); - auto x_shape_tensor = shape(x); + auto x_shape_tensor = shape64(x); std::vector slice_shape_concat; slice_shape_concat.push_back(full({1}, 1, x_shape_tensor.dtype())); @@ -869,7 +869,7 @@ Tensor flatten_decomp(const Tensor& x, int start_axis, int end_axis) { } if (has_dynamic_shape(x.shape())) { - auto x_shape = shape(x); + auto x_shape = shape64(x); if (end_axis == start_axis) { return backend::reshape(x, x_shape); } @@ -931,8 +931,8 @@ Tensor clip_decomp(const Tensor& x, const Tensor& min, const Tensor& max) { } if (has_dynamic_shape(x.shape())) { - min_reshape = backend::expand(min_reshape, shape(x)); - max_reshape = backend::expand(max_reshape, shape(x)); + min_reshape = backend::expand(min_reshape, shape64(x)); + max_reshape = backend::expand(max_reshape, shape64(x)); } else { min_reshape = expand(min_reshape, x.shape()); max_reshape = expand(max_reshape, x.shape()); @@ -990,7 +990,7 @@ std::tuple group_norm_decomp( Tensor x_dim_t; Tensor out, mean_, var_; if (has_dynamic_shape(x_cast.shape())) { - x_dim_t = shape(x_cast); + x_dim_t = shape64(x_cast); Tensor tar_shape; if (data_format == "NCHW") { tar_shape = get_slice(x_dim_t, 0) * groups; @@ -1008,9 +1008,9 @@ std::tuple group_norm_decomp( mean_ = mean_decomp(x_cast, c_axis, true); Tensor var_tmp_ = mean_decomp(x_cast * x_cast, c_axis, true) - mean_ * mean_; - var_ = maximum( - var_tmp_, - backend::full_with_tensor(shape(var_tmp_), 0, var_tmp_.dtype())); + var_ = maximum(var_tmp_, + backend::full_with_tensor( + shape64(var_tmp_), 0, var_tmp_.dtype())); Tensor var_inv = rsqrt(var_ + full_scalar(epsilon, var_.dtype(), var_.place())); Tensor res = (x_cast - mean_) * var_inv; @@ -1136,7 +1136,7 @@ Tensor mean_all_decomp(const Tensor& x) { Tensor ans; if (has_dynamic_shape(x_shape)) { - Tensor x_shape_tensor = shape(x_cast); + Tensor x_shape_tensor = shape64(x_cast); Tensor value = get_slice(x_shape_tensor, 0); for (size_t i = 1; i < x_shape.size(); i++) { value = value * get_slice(x_shape_tensor, i); @@ -1165,7 +1165,7 @@ Tensor embedding_decomp(const Tensor& x, Tensor res; if (has_dynamic_shape(x.shape())) { if (padding_idx != NoPadding) { - Tensor put_shape = shape(sum(weight, {0}, weight.dtype(), true)); + Tensor put_shape = shape64(sum(weight, {0}, weight.dtype(), true)); Tensor padding_idx_tensor = backend::full_with_tensor(put_shape, padding_idx, DataType::INT64); Tensor zeros = @@ -1182,8 +1182,8 @@ Tensor embedding_decomp(const Tensor& x, std::vector tar_shape{-1}; auto x_reshape = reshape(x, tar_shape); auto out = gather(weight_tmp, x_reshape); - auto x_t_shape = shape(x); - auto token_dim = get_slice(shape(out), 1); + auto x_t_shape = shape64(x); + auto token_dim = get_slice(shape64(out), 1); auto res_t_shape = concat({x_t_shape, token_dim}, 0); res = backend::reshape(out, res_t_shape); } @@ -1220,7 +1220,7 @@ Tensor embedding_decomp(const Tensor& x, template Tensor index_sample_decomp(const Tensor& x, const Tensor& index) { std::vector tmp_shape{-1, 1}; - auto index_dim = get_slice(shape(index), 0); + auto index_dim = get_slice(shape64(index), 0); auto start = full({1}, 0, index_dim.dtype()); auto step = full({1}, 1, index_dim.dtype()); auto arange_tmp = @@ -1229,10 +1229,11 @@ Tensor index_sample_decomp(const Tensor& x, const Tensor& index) { tmp_shape); auto index_res = - reshape(backend::expand(arange_tmp, shape(index)), tmp_shape); + reshape(backend::expand(arange_tmp, shape64(index)), tmp_shape); auto index_ = reshape(index, tmp_shape); auto concat_res = concat({index_res, index_}, 1); - auto res = backend::reshape(gather_nd(x, concat_res), shape(index)); + auto res = + backend::reshape(gather_nd(x, concat_res), shape64(index)); if (res.dtype() != x.dtype()) { return cast(res, x.dtype()); @@ -1249,7 +1250,7 @@ Tensor elu_decomp(const Tensor& x, const float alpha) { Tensor tmp_res; if (has_dynamic_shape(x_cast.shape())) { - zero = backend::full_with_tensor(shape(x_cast), 0, x_cast.dtype()); + zero = backend::full_with_tensor(shape64(x_cast), 0, x_cast.dtype()); tmp_res = full_scalar(alpha, x_cast.dtype(), x_cast.place()) * (exp(x_cast) - full_scalar(1, x_cast.dtype(), x_cast.place())); @@ -1295,7 +1296,7 @@ Tensor kldiv_loss_decomp(const Tensor& x, Tensor zero = full_scalar(0.0, label.dtype(), label.place()); Tensor zeros; if (dynamic_shape) { - zeros = backend::full_with_tensor(shape(x), 0, x.dtype()); + zeros = backend::full_with_tensor(shape64(x), 0, x.dtype()); } else { zeros = full(x.shape(), 0, x.dtype(), x.place()); } @@ -1305,7 +1306,7 @@ Tensor kldiv_loss_decomp(const Tensor& x, if (reduction == "batchmean") { if (x.shape().size() > 0) { if (dynamic_shape) { - return sum(loss) / get_slice(shape(x), 0); + return sum(loss) / get_slice(shape64(x), 0); } else { return sum(loss) / x.shape()[0]; } @@ -1353,7 +1354,7 @@ std::vector unstack_decomp(const Tensor& x, int axis, const int num) { std::vector sections(num, 1); std::vector res = backend::split(x, sections, axis); if (has_dynamic_shape(x_shape)) { - const Tensor x_shape_tensor = shape(x); + const Tensor x_shape_tensor = shape64(x); // find new shape of each tensor. std::vector new_shape_vec; @@ -1385,7 +1386,7 @@ template Tensor numel_decomp(const Tensor& x) { auto x_shape = x.shape(); if (has_dynamic_shape(x_shape)) { - const Tensor x_shape_tensor = shape(x); + const Tensor x_shape_tensor = shape64(x); Tensor value = full({1}, 1, x_shape_tensor.dtype()); for (size_t i = 0; i < x_shape.size(); ++i) { value = value * get_slice(x_shape_tensor, i); diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index b844d6a5b70b6d..5bd3a32380ebe5 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -72,7 +72,7 @@ void cumsum_grad(const Tensor& x, if (x_grad) { auto grad = cumsum(out_grad, axis, flatten, exclusive, !reverse); if (has_dynamic_shape(x.shape())) { - grad = backend::reshape(grad, shape(x)); + grad = backend::reshape(grad, shape64(x)); } else { grad = reshape(grad, x.shape()); } @@ -92,10 +92,10 @@ void cumprod_grad(const Tensor& x, // dx = cumsum(out * out_grad, dim, false, exclusive, !reverse) / x Tensor zero_tensor, ones_tensor; if (has_dynamic_shape(x.shape())) { - zero_tensor = - backend::full_with_tensor(shape(x), 0.0, x.dtype(), x.place()); - ones_tensor = - backend::full_with_tensor(shape(x), 1.0, x.dtype(), x.place()); + zero_tensor = backend::full_with_tensor( + shape64(x), 0.0, x.dtype(), x.place()); + ones_tensor = backend::full_with_tensor( + shape64(x), 1.0, x.dtype(), x.place()); } else { zero_tensor = full(x.shape(), 0.0, x.dtype(), x.place()); ones_tensor = full(x.shape(), 1.0, x.dtype(), x.place()); @@ -165,7 +165,7 @@ void floor_grad(const Tensor& out_grad, Tensor* x_grad) { Tensor zero_tensor; if (has_dynamic_shape(out_grad.shape())) { zero_tensor = backend::full_with_tensor( - shape(out_grad), 0.0, out_grad.dtype(), out_grad.place()); + shape64(out_grad), 0.0, out_grad.dtype(), out_grad.place()); } else { zero_tensor = full(common::vectorize(out_grad.dims()), 0.0, @@ -197,7 +197,7 @@ void sum_grad(const Tensor& x, reduce_all = false; } if (has_dynamic_shape(x.shape())) { - Tensor x_shape = shape(x); + Tensor x_shape = shape64(x); if (x_dim_size == 1) { x_grad_tmp = backend::expand(out_grad, x_shape); } else { @@ -215,7 +215,7 @@ void sum_grad(const Tensor& x, } } } - Tensor out_grad_shape = shape(out_grad); + Tensor out_grad_shape = shape64(out_grad); size_t total_shape_size = out_grad.shape().size() + axis_.size(); std::vector result_shape; size_t j = 0, k = 0; @@ -288,7 +288,7 @@ void mean_grad(const Tensor& x, } } if (has_dynamic_shape(x_dim, axis_data)) { - auto x_shape = shape(x); + auto x_shape = shape64(x); factor_tensor = slice(x_shape, {0}, {axis_data[0]}, {axis_data[0] + 1}, {1}, {0}); for (size_t i = 1; i < axis_data.size(); ++i) { @@ -371,7 +371,7 @@ void reduce_as_grad(const Tensor& x, return; } if (has_dynamic_shape(x.shape()) || has_dynamic_shape(out_grad.shape())) { - auto x_grad_tmp = backend::expand(out_grad, shape(x)); + auto x_grad_tmp = backend::expand(out_grad, shape64(x)); set_output(x_grad_tmp, x_grad); } else { std::vector x_dim = common::vectorize(x.dims()); @@ -412,7 +412,7 @@ void reshape_grad(const Tensor& x, const Tensor& grad_out, Tensor* grad_x) { if (grad_x) { Tensor grad_x_tmp; if (has_dynamic_shape(x.shape())) { - grad_x_tmp = backend::reshape(grad_out, shape(x)); + grad_x_tmp = backend::reshape(grad_out, shape64(x)); } else { const auto& x_dims = x.dims(); grad_x_tmp = reshape(grad_out, common::vectorize(x_dims)); @@ -468,7 +468,7 @@ void scatter_grad(const Tensor& index, Tensor zero_tensor; if (has_dynamic_shape(updates.shape())) { zero_tensor = backend::full_with_tensor( - shape(updates), 0.0, updates.dtype(), updates.place()); + shape64(updates), 0.0, updates.dtype(), updates.place()); } else { zero_tensor = full(common::vectorize(updates.dims()), 0.0, @@ -560,7 +560,7 @@ void concat_grad(const std::vector& x, if (has_dynamic) { std::vector sections; for (int i = 0; i < x_num; i++) { - sections.push_back(get_slice(shape(x[i]), int64_t(axis_value))); + sections.push_back(get_slice(shape64(x[i]), int64_t(axis_value))); } Tensor sections_tensor = concat(sections); x_grad_tmp = backend::split( @@ -781,7 +781,7 @@ void stack_grad(const std::vector& x, split(out_grad, phi::IntArray(sections), axis); auto out_dim = out_grad.dims().size(); if (has_dynamic_shape(out_grad.shape())) { - Tensor out_grad_shape = shape(out_grad); + Tensor out_grad_shape = shape64(out_grad); std::vector grad_shape; for (int i = 0; i < out_dim; i++) { if (i != axis) { @@ -1305,7 +1305,7 @@ void gather_grad(const Tensor& x, Tensor zero_tensor; if (has_dynamic_shape(x.shape())) { zero_tensor = - backend::full_with_tensor(shape(x), 0.0, x.dtype(), x.place()); + backend::full_with_tensor(shape64(x), 0.0, x.dtype(), x.place()); } else { zero_tensor = full(common::vectorize(x.dims()), 0.0, x.dtype(), x.place()); @@ -1362,8 +1362,8 @@ void gather_nd_grad(const Tensor& x, if (x_grad) { Tensor zero_tensor; if (has_dynamic_shape(x.shape())) { - zero_tensor = - backend::full_with_tensor(shape(x), 0.0, x.dtype(), x.place()); + zero_tensor = backend::full_with_tensor( + shape64(x), 0.0, x.dtype(), x.place()); } else { zero_tensor = full(common::vectorize(x.dims()), 0.0, x.dtype(), x.place()); @@ -1458,7 +1458,7 @@ void pad_grad(const Tensor& input, std::vector infer_flags(rank, 1); std::vector decrease_axis({}); if (has_dynamic_shape(out_grad.shape())) { - auto out_shape = shape(out_grad); + auto out_shape = shape64(out_grad); std::vector starts, ends; for (size_t i = 0; i < rank; ++i) { starts.push_back( @@ -1507,7 +1507,7 @@ void max_grad(const Tensor& x, Tensor x_grad_tmp; if (has_dynamic_shape(x.shape())) { - const Tensor x_shape = shape(x); + const Tensor x_shape = shape64(x); const Tensor zero_tensor = backend::full_with_tensor(x_shape, 0.0, x.dtype(), x.place()); const int64_t axis_size = axis.size(); @@ -1526,7 +1526,7 @@ void max_grad(const Tensor& x, auto mask = equal(x, out_tmp); x_grad_tmp = where(mask, out_grad_tmp, zero_tensor); } else { - const Tensor out_grad_shape = shape(out_grad); + const Tensor out_grad_shape = shape64(out_grad); auto axis_ = std::vector(); if (reduce_all) { @@ -1814,8 +1814,8 @@ void hardswish_grad(const Tensor& x, const Tensor& out_grad, Tensor* x_grad) { const Tensor offset = full_scalar(3.0, x.dtype()); Tensor zero; if (has_dynamic_shape(x.shape())) { - zero = - backend::full_with_tensor(shape(x), 0.0, x.dtype(), x.place()); + zero = backend::full_with_tensor( + shape64(x), 0.0, x.dtype(), x.place()); } else { zero = full(common::vectorize(x.dims()), 0.0, x.dtype(), x.place()); } @@ -1874,7 +1874,7 @@ void topk_grad(const Tensor& x, Tensor zero_tensor; if (has_dynamic_shape(x.shape())) { zero_tensor = - backend::full_with_tensor(shape(x), 0, x.dtype(), x.place()); + backend::full_with_tensor(shape64(x), 0, x.dtype(), x.place()); } else { zero_tensor = full(common::vectorize(x.dims()), 0, x.dtype(), x.place()); @@ -1991,7 +1991,7 @@ void prod_grad(const Tensor& x, auto out_grad_tmp = Tensor(); auto x_reshape = Tensor(); if (has_dynamic_shape(x.shape())) { - Tensor x_dim = shape(x); + Tensor x_dim = shape64(x); std::vector unchange_axis, change_axis; std::vector transpose_dim, origin_position; std::vector transpose_shape, cumprod_shape; @@ -2013,7 +2013,7 @@ void prod_grad(const Tensor& x, } } Tensor out_grad_shape = - get_unsqueeze_dims(shape(out_grad), axis_); + get_unsqueeze_dims(shape64(out_grad), axis_); Tensor out_grad_ = backend::reshape(out_grad, out_grad_shape); out_grad_tmp = backend::expand(out_grad_, x_dim); } else { @@ -2428,7 +2428,7 @@ void where_grad(const Tensor& condition, Tensor zero; if (has_dynamic_shape(out_grad.shape())) { zero = backend::full_with_tensor( - shape(out_grad), 0.0, out_grad.dtype(), out_grad.place()); + shape64(out_grad), 0.0, out_grad.dtype(), out_grad.place()); } else { zero = full(common::vectorize(out_grad.dims()), 0.0, @@ -2716,9 +2716,9 @@ void dot_grad(const Tensor& x, if (has_dynamic_shape(x.shape()) || has_dynamic_shape(y.shape())) { auto out_grad_shape = - get_unsqueeze_dims(shape(out_grad_), {out_grad_dim_size}); + get_unsqueeze_dims(shape64(out_grad_), {out_grad_dim_size}); out_grad_ = backend::reshape(out_grad_, out_grad_shape); - out_grad_ = backend::expand(out_grad_, shape(x)); + out_grad_ = backend::expand(out_grad_, shape64(x)); } else { std::vector x_dim = common::vectorize(x.dims()); auto out_grad_shape = get_unsqueeze_dims(out_grad, {out_grad_dim_size}); @@ -2758,8 +2758,8 @@ void logcumsumexp_grad(const Tensor& x, if (has_dynamic_shape(x_cast.shape()) || has_dynamic_shape(out_grad_cast.shape())) { - const Tensor x_shape = shape(x_cast); - const Tensor out_grad_shape = shape(out_grad_cast); + const Tensor x_shape = shape64(x_cast); + const Tensor out_grad_shape = shape64(out_grad_cast); const Tensor reshape_x = backend::reshape(x_cast, out_grad_shape); if (out_grad_dtype == DataType::FLOAT32) { @@ -2852,7 +2852,7 @@ void logsumexp_grad(const Tensor& x, auto x_grad_tmp = Tensor(); if (has_dynamic_shape(x.shape())) { - Tensor x_shape = shape(x); + Tensor x_shape = shape64(x); if (x_dim_size == 1) { x_grad_tmp = backend::expand(out_grad, x_shape) * exp(x - out); } else { @@ -2871,7 +2871,8 @@ void logsumexp_grad(const Tensor& x, } } - auto result_shape = get_unsqueeze_dims(shape(out_grad), axis_); + auto result_shape = + get_unsqueeze_dims(shape64(out_grad), axis_); auto out_ = backend::reshape(out, result_shape); auto softmax = exp(x - backend::expand(out_, x_shape)); @@ -2922,7 +2923,7 @@ void trunc_grad(const Tensor& out_grad, Tensor* x_grad) { if (x_grad) { if (has_dynamic_shape(out_grad.shape())) { zero = backend::full_with_tensor( - shape(out_grad), 0.0, out_grad.dtype(), out_grad.place()); + shape64(out_grad), 0.0, out_grad.dtype(), out_grad.place()); } else { zero = full(out_grad.shape(), 0.0, out_grad.dtype(), out_grad.place()); } @@ -2956,7 +2957,7 @@ void kthvalue_grad(const Tensor& x, Tensor x_grad_tmp; if (has_dynamic_shape(x_cast.shape())) { zero_tensor = backend::full_with_tensor( - shape(x_cast), 0, x_cast.dtype(), x_cast.place()); + shape64(x_cast), 0, x_cast.dtype(), x_cast.place()); if (keepdim) { x_grad_tmp = backend::put_along_axis( @@ -2964,9 +2965,9 @@ void kthvalue_grad(const Tensor& x, } else { auto axis_ = std::vector(1, axis); auto out_grad_shape = - get_unsqueeze_dims(shape(out_grad_cast), axis_); + get_unsqueeze_dims(shape64(out_grad_cast), axis_); auto out_grad_ = backend::reshape(out_grad_cast, out_grad_shape); - auto indices_shape = get_unsqueeze_dims(shape(indices), axis_); + auto indices_shape = get_unsqueeze_dims(shape64(indices), axis_); auto indices_ = backend::reshape(indices, indices_shape); x_grad_tmp = backend::put_along_axis(zero_tensor, indices_, out_grad_, axis); @@ -3010,7 +3011,7 @@ void argsort_grad(const Tensor& indices, auto x_grad_tmp = Tensor(); if (has_dynamic_shape(x_cast.shape())) { zero_tensor = backend::full_with_tensor( - shape(x_cast), 0, x_cast.dtype(), x_cast.place()); + shape64(x_cast), 0, x_cast.dtype(), x_cast.place()); } else { zero_tensor = full( common::vectorize(x_cast.dims()), 0, x_cast.dtype(), x_cast.place()); @@ -3047,31 +3048,31 @@ void kron_grad(const Tensor& x, // tile std::vector x_shape_vec; for (int64_t i = 0; i < x_.dims().size(); ++i) { - auto x_shape_slice = get_slice(shape(x_), i); + auto x_shape_slice = get_slice(shape64(x_), i); x_shape_vec.push_back(x_shape_slice); } - auto y_tile = backend::tile(y_, shape(x_)); + auto y_tile = backend::tile(y_, shape64(x_)); auto out_grad_tmp = y_tile * out_grad; std::vector out_grad_shape_vec; for (int64_t i = 0; i < out_grad.dims().size(); ++i) { - auto out_grad_shape_slice = get_slice(shape(out_grad), i); + auto out_grad_shape_slice = get_slice(shape64(out_grad), i); out_grad_shape_vec.push_back(out_grad_shape_slice); } if (x_shape_vec.size() != 0) { while (true) { std::vector expand_shape_vec; for (int64_t i = 0; i < out_grad_tmp.dims().size(); ++i) { - auto expand_shape = get_slice(shape(out_grad_tmp), i); + auto expand_shape = get_slice(shape64(out_grad_tmp), i); expand_shape_vec.push_back(expand_shape); } int num_reduce = 0; while (x_shape_vec.size() != 0 && expand_shape_vec.size() <= 8) { Tensor repeat = x_shape_vec.back(); auto orig_size = - cast(out_grad_shape_vec.back() / repeat, DataType::INT32); + cast(out_grad_shape_vec.back() / repeat, DataType::INT64); size_t out_grad_last_index = out_grad_shape_vec.size() - 1; expand_shape_vec[out_grad_last_index] = repeat; expand_shape_vec.insert( @@ -3100,7 +3101,7 @@ void kron_grad(const Tensor& x, } } } - x_grad_tmp = backend::reshape(out_grad_tmp, shape(x)); + x_grad_tmp = backend::reshape(out_grad_tmp, shape64(x)); } else { auto x_shape = x.shape(); auto y_shape = y.shape(); @@ -3186,13 +3187,13 @@ void kron_grad(const Tensor& x, std::vector x_shape_vec; for (int64_t i = 0; i < x_.dims().size(); ++i) { - auto x_shape_slice = get_slice(shape(x_), i); + auto x_shape_slice = get_slice(shape64(x_), i); x_shape_vec.push_back(x_shape_slice); } for (int64_t i = 0; i < x_.dims().size(); ++i) { - auto y_shape_slice = get_slice(shape(y_), i); - auto x_shape_slice = get_slice(shape(x_), i); + auto y_shape_slice = get_slice(shape64(y_), i); + auto x_shape_slice = get_slice(shape64(x_), i); auto y_shape_tile = backend::tile(y_shape_slice, x_shape_slice); x_ = backend::repeat_interleave_with_tensor_index( x_, y_shape_tile, i); @@ -3201,7 +3202,7 @@ void kron_grad(const Tensor& x, std::vector out_grad_shape_vec; for (int64_t i = 0; i < out_grad.dims().size(); ++i) { - auto out_grad_shape_slice = get_slice(shape(out_grad_cast), i); + auto out_grad_shape_slice = get_slice(shape64(out_grad_cast), i); out_grad_shape_vec.push_back(out_grad_shape_slice); } @@ -3209,14 +3210,14 @@ void kron_grad(const Tensor& x, while (true) { std::vector expand_shape_vec; for (int64_t i = 0; i < out_grad_tmp.dims().size(); ++i) { - auto expand_shape = get_slice(shape(out_grad_tmp), i); + auto expand_shape = get_slice(shape64(out_grad_tmp), i); expand_shape_vec.push_back(expand_shape); } int num_reduce = 0; while (x_shape_vec.size() != 0 && expand_shape_vec.size() <= 8) { auto repeat = x_shape_vec.back(); auto orig_size = - cast(out_grad_shape_vec.back() / repeat, DataType::INT32); + cast(out_grad_shape_vec.back() / repeat, DataType::INT64); size_t out_grad_last_index = out_grad_shape_vec.size() - 1; expand_shape_vec[out_grad_last_index] = repeat; expand_shape_vec.insert( @@ -3245,7 +3246,7 @@ void kron_grad(const Tensor& x, } } y_grad_tmp = backend::reshape( - ConverToOrig(out_grad_tmp, out_grad.dtype()), shape(y)); + ConverToOrig(out_grad_tmp, out_grad.dtype()), shape64(y)); } else { auto x_shape = x_cast.shape(); auto y_shape = y.shape(); @@ -3300,7 +3301,7 @@ void take_along_axis_grad(const Tensor& arr, Tensor zero_tensor; if (has_dynamic_shape(arr_cast.shape())) { zero_tensor = backend::full_with_tensor( - shape(arr_cast), 0, arr_cast.dtype(), arr_cast.place()); + shape64(arr_cast), 0, arr_cast.dtype(), arr_cast.place()); } else { zero_tensor = full(common::vectorize(arr_cast.dims()), 0, @@ -3319,7 +3320,7 @@ void ceil_grad(const Tensor& out_grad, Tensor* x_grad) { Tensor zero_tensor; if (has_dynamic_shape(out_grad.shape())) { zero_tensor = backend::full_with_tensor( - shape(out_grad), 0.0, out_grad.dtype()); + shape64(out_grad), 0.0, out_grad.dtype()); } else { zero_tensor = full(common::vectorize(out_grad.dims()), 0.0, out_grad.dtype()); diff --git a/paddle/fluid/primitive/decomp_utils/decomp_utils.h b/paddle/fluid/primitive/decomp_utils/decomp_utils.h index 88dbbbb3fdab13..0509b2699f40cc 100644 --- a/paddle/fluid/primitive/decomp_utils/decomp_utils.h +++ b/paddle/fluid/primitive/decomp_utils/decomp_utils.h @@ -352,7 +352,7 @@ class LayerNormDecompHelper { return reshape(s, normlized_shape_); } else { return backend::reshape( - s, get_slice_vec(shape(x), begin_norm_axis_, x_rank_)); + s, get_slice_vec(shape64(x), begin_norm_axis_, x_rank_)); } } @@ -361,7 +361,7 @@ class LayerNormDecompHelper { if (normlized_numel_ != -1) { return full_scalar(normlized_numel_, x.dtype()); } else { - auto x_shape = shape(x); + auto x_shape = shape64(x); auto numel = get_slice(x_shape, begin_norm_axis_); for (int64_t i = begin_norm_axis_ + 1; i < x_rank_; ++i) { numel = numel * get_slice(x_shape, i); @@ -445,7 +445,7 @@ class BatchNormDecompHelper { if (static_nhw) { return full_scalar(nhw_numel, x.dtype()); } else { - auto x_shape = shape(x); + auto x_shape = shape64(x); auto nhw = get_slice(x_shape, 0); for (int64_t i = 1; i < x_rank_; ++i) { if (i == channel_axis_) { @@ -484,7 +484,7 @@ class InstanceNormDecompHelper { auto dims = phi::vectorize(x.dims()); int64_t rank = dims.size(); if (has_dynamic_shape(x.shape())) { - Tensor x_shape = shape(x); + Tensor x_shape = shape64(x); auto hw = full_scalar(1.0, x.dtype()); for (int64_t i = 2; i < rank; ++i) { hw = hw * get_slice(x_shape, i); diff --git a/paddle/fluid/primitive/primitive/primitive.yaml b/paddle/fluid/primitive/primitive/primitive.yaml index 777a10f7114fd2..fd5d09b531de46 100644 --- a/paddle/fluid/primitive/primitive/primitive.yaml +++ b/paddle/fluid/primitive/primitive/primitive.yaml @@ -88,7 +88,7 @@ - repeat_interleave - unbind - expand -- shape +- shape64 - reshape - squeeze - unsqueeze diff --git a/paddle/phi/backends/xpu/xpu1_op_list.cc b/paddle/phi/backends/xpu/xpu1_op_list.cc index 703eeeb3cf0bd6..af303570375414 100644 --- a/paddle/phi/backends/xpu/xpu1_op_list.cc +++ b/paddle/phi/backends/xpu/xpu1_op_list.cc @@ -270,6 +270,12 @@ XPUOpMap& get_kl1_ops() { phi::DataType::INT32, phi::DataType::BOOL, phi::DataType::FLOAT32})}, + {"shape64", + XPUKernelSet({phi::DataType::FLOAT64, + phi::DataType::INT64, + phi::DataType::INT32, + phi::DataType::BOOL, + phi::DataType::FLOAT32})}, {"sigmoid_grad", XPUKernelSet({phi::DataType::FLOAT32})}, {"sigmoid", XPUKernelSet({phi::DataType::FLOAT32})}, {"sign", XPUKernelSet({phi::DataType::FLOAT32})}, diff --git a/paddle/phi/backends/xpu/xpu2_op_list.cc b/paddle/phi/backends/xpu/xpu2_op_list.cc index 5664d598a3d08d..9d0e2e65871fd2 100644 --- a/paddle/phi/backends/xpu/xpu2_op_list.cc +++ b/paddle/phi/backends/xpu/xpu2_op_list.cc @@ -935,6 +935,12 @@ XPUOpMap& get_kl2_ops() { phi::DataType::INT32, phi::DataType::FLOAT16, phi::DataType::BFLOAT16})}, + {"shape64", + XPUKernelSet({phi::DataType::FLOAT32, + phi::DataType::INT64, + phi::DataType::INT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16})}, {"sigmoid", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"sigmoid_grad", diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index 3fcd266b68c601..395669034d5244 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -1084,6 +1084,12 @@ XPUOpMap& get_kl3_ops() { phi::DataType::INT32, phi::DataType::FLOAT16, phi::DataType::BFLOAT16})}, + {"shape64", + XPUKernelSet({phi::DataType::FLOAT32, + phi::DataType::INT64, + phi::DataType::INT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16})}, {"sigmoid", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"sigmoid_grad", XPUKernelSet({phi::DataType::FLOAT32})}, diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc index 86978ee9e8a3a0..7c023017fb0e59 100644 --- a/paddle/phi/infermeta/unary.cc +++ b/paddle/phi/infermeta/unary.cc @@ -4060,6 +4060,18 @@ void ShapeInferMeta(const MetaTensor& input, MetaTensor* out) { out->set_dtype(DataType::INT32); } +void Shape64InferMeta(const MetaTensor& input, + MetaTensor* out, + MetaConfig config) { + auto in_dim = input.dims(); + out->set_dims(common::make_ddim({in_dim.size()})); + if (config.is_run_mkldnn_kernel) { + out->set_dtype(DataType::INT32); + } else { + out->set_dtype(DataType::INT64); + } +} + void ShareDataInferMeta(const MetaTensor& x, MetaTensor* out) { out->set_dims(x.dims()); out->set_dtype(x.dtype()); diff --git a/paddle/phi/infermeta/unary.h b/paddle/phi/infermeta/unary.h index 916ef55ce5a061..593e102e329b16 100644 --- a/paddle/phi/infermeta/unary.h +++ b/paddle/phi/infermeta/unary.h @@ -679,6 +679,10 @@ void ShareDataInferMeta(const MetaTensor& x, MetaTensor* out); void ShapeInferMeta(const MetaTensor& input, MetaTensor* out); +void Shape64InferMeta(const MetaTensor& input, + MetaTensor* out, + MetaConfig config = MetaConfig()); + void ShardIndexInferMeta(const MetaTensor& in, int index_num, int nshards, diff --git a/paddle/phi/kernels/selected_rows/shape_kernel.cc b/paddle/phi/kernels/selected_rows/shape_kernel.cc index ee7c0d64670d46..d9a4888630cbe3 100644 --- a/paddle/phi/kernels/selected_rows/shape_kernel.cc +++ b/paddle/phi/kernels/selected_rows/shape_kernel.cc @@ -30,6 +30,13 @@ void ShapeKernel(const Context& ctx, phi::ShapeKernel(ctx, input.value(), out); } +template +void Shape64Kernel(const Context& ctx, + const SelectedRows& input, + DenseTensor* out) { + phi::Shape64Kernel(ctx, input.value(), out); +} + } // namespace phi::sr PD_REGISTER_KERNEL(shape_sr, @@ -107,3 +114,79 @@ PD_REGISTER_KERNEL(shape_sr, kernel->OutputAt(0).SetDataType(phi::DataType::INT32); } #endif + +PD_REGISTER_KERNEL(shape64_sr, + CPU, + ALL_LAYOUT, + phi::sr::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} + +#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) +PD_REGISTER_KERNEL(shape64_sr, + GPU, + ALL_LAYOUT, + phi::sr::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif + +#if defined(PADDLE_WITH_XPU) +PD_REGISTER_KERNEL(shape64_sr, + XPU, + ALL_LAYOUT, + phi::sr::Shape64Kernel, + bool, + int, + int64_t, + float, + double, + phi::dtype::float16, + phi::dtype::bfloat16) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +PD_REGISTER_KERNEL(shape64_sr, + Custom, + ALL_LAYOUT, + phi::sr::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif diff --git a/paddle/phi/kernels/selected_rows/shape_kernel.h b/paddle/phi/kernels/selected_rows/shape_kernel.h index 86ba52982b5967..1a0648595bf404 100644 --- a/paddle/phi/kernels/selected_rows/shape_kernel.h +++ b/paddle/phi/kernels/selected_rows/shape_kernel.h @@ -24,5 +24,10 @@ void ShapeKernel(const Context& ctx, const SelectedRows& input, DenseTensor* out); +template +void Shape64Kernel(const Context& ctx, + const SelectedRows& input, + DenseTensor* out); + } // namespace sr } // namespace phi diff --git a/paddle/phi/kernels/shape_kernel.cc b/paddle/phi/kernels/shape_kernel.cc index 939515edd725e0..7947246bf7a2e2 100644 --- a/paddle/phi/kernels/shape_kernel.cc +++ b/paddle/phi/kernels/shape_kernel.cc @@ -31,6 +31,18 @@ void ShapeKernel(const Context& ctx, } } +template +void Shape64Kernel(const Context& ctx, + const DenseTensor& input, + DenseTensor* out) { + auto& in_dims = input.dims(); + out->Resize({in_dims.size()}); + auto out_data = ctx.template HostAlloc(out); + for (int i = 0; i < in_dims.size(); ++i) { + out_data[i] = in_dims[i]; + } +} + } // namespace phi PD_REGISTER_KERNEL(shape, @@ -112,3 +124,83 @@ PD_REGISTER_KERNEL(shape, kernel->OutputAt(0).SetDataType(phi::DataType::INT32); } #endif + +PD_REGISTER_KERNEL(shape64, + CPU, + ALL_LAYOUT, + phi::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} + +#if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) +PD_REGISTER_KERNEL(shape64, + GPU, + ALL_LAYOUT, + phi::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex, + phi::dtype::float16, + phi::dtype::bfloat16) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif + +#if defined(PADDLE_WITH_XPU) +PD_REGISTER_KERNEL(shape64, + XPU, + ALL_LAYOUT, + phi::Shape64Kernel, + bool, + int, + int64_t, + float, + double, + phi::dtype::float16, + phi::dtype::bfloat16) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +PD_REGISTER_KERNEL(shape64, + Custom, + ALL_LAYOUT, + phi::Shape64Kernel, + bool, + int, + int8_t, + uint8_t, + int64_t, + float, + double, + phi::dtype::complex, + phi::dtype::complex, + phi::dtype::float16, + phi::dtype::bfloat16) { + kernel->InputAt(0).SetBackend(phi::Backend::ALL_BACKEND); + kernel->OutputAt(0).SetBackend(phi::Backend::CPU); + kernel->OutputAt(0).SetDataType(phi::DataType::INT64); +} +#endif diff --git a/paddle/phi/kernels/shape_kernel.h b/paddle/phi/kernels/shape_kernel.h index 444c481812e88d..c0ec8e9f91351e 100644 --- a/paddle/phi/kernels/shape_kernel.h +++ b/paddle/phi/kernels/shape_kernel.h @@ -23,4 +23,9 @@ void ShapeKernel(const Context& ctx, const DenseTensor& input, DenseTensor* out); +template +void Shape64Kernel(const Context& ctx, + const DenseTensor& input, + DenseTensor* out); + } // namespace phi diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index e35b46ecd7d121..7363e354d9f206 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -4476,6 +4476,19 @@ interfaces : paddle::dialect::InferSymbolicShapeInterface traits : paddle::dialect::ForwardOnlyTrait +- op : shape64 + args : (Tensor input) + output : Tensor(out) + infer_meta : + func : Shape64InferMeta + kernel : + func : shape64 {dense -> dense}, + shape64_sr {selected_rows -> dense} + data_transform : + skip_transform : input + interfaces : paddle::dialect::InferSymbolicShapeInterface + traits : paddle::dialect::ForwardOnlyTrait + - op : shard_index args : (Tensor input, int index_num, int nshards, int shard_id, int ignore_value=-1) output : Tensor(out) diff --git a/python/paddle/nn/layer/rnn.py b/python/paddle/nn/layer/rnn.py index 7e89a23f86ad7e..b5eee1e5e96001 100644 --- a/python/paddle/nn/layer/rnn.py +++ b/python/paddle/nn/layer/rnn.py @@ -286,6 +286,7 @@ def _switch_grad(x, stop=False): inputs = paddle.utils.map_structure(_transpose_batch_time, inputs) max_seq_len = paddle.shape(paddle.utils.flatten(inputs)[0])[0] + max_seq_len = paddle.cast(max_seq_len, paddle.int32) if sequence_length is not None: mask = paddle.static.nn.sequence_lod.sequence_mask( sequence_length, diff --git a/python/paddle/tensor/attribute.py b/python/paddle/tensor/attribute.py index d4d35bcb1e05a6..5acc0f7bacca59 100644 --- a/python/paddle/tensor/attribute.py +++ b/python/paddle/tensor/attribute.py @@ -109,7 +109,7 @@ def shape(input: Tensor) -> Tensor: [array([ 3, 100, 100], dtype=int32)] """ if in_dynamic_or_pir_mode(): - out = _C_ops.shape(input) # type: ignore + out = _C_ops.shape64(input) # type: ignore out.stop_gradient = True return out else: diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 3c452ada7b8872..8105f70eef29e3 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -6978,7 +6978,7 @@ def unflatten( new_shape = paddle.concat( [ paddle.shape(x)[:axis], - paddle.cast(shape, 'int32'), + paddle.cast(shape, 'int64'), paddle.shape(x)[axis + 1 :], ] ) diff --git a/python/paddle/tensorrt/impls/attribute.py b/python/paddle/tensorrt/impls/attribute.py index 0e09b9799ffe07..6eeb75a9f6d9b2 100644 --- a/python/paddle/tensorrt/impls/attribute.py +++ b/python/paddle/tensorrt/impls/attribute.py @@ -20,3 +20,10 @@ def shape_converter(network, paddle_op, inputs): input_tensor = inputs[0] shape_layer = network.add_shape(input_tensor) return shape_layer.get_output(0) + + +@converter_registry.register("pd_op.shape64", trt_version="8.x") +def shape64_converter(network, paddle_op, inputs): + input_tensor = inputs[0] + shape_layer = network.add_shape(input_tensor) + return shape_layer.get_output(0) diff --git a/test/dygraph_to_static/test_tensor_shape.py b/test/dygraph_to_static/test_tensor_shape.py index 3c7562a0aaaa21..6db65a8700c909 100644 --- a/test/dygraph_to_static/test_tensor_shape.py +++ b/test/dygraph_to_static/test_tensor_shape.py @@ -311,13 +311,20 @@ def _compute_op_num(self, program): slice_op_num = 0 for block in program.blocks: - shape_op_num += len([op for op in block.ops if op.type == "shape"]) + shape_op_num += len( + [ + op + for op in block.ops + if (op.type == "shape" or op.type == "shape64") + ] + ) slice_op_num += len([op for op in block.ops if op.type == "slice"]) return op_num, shape_op_num, slice_op_num def _compute_pir_op_num(self, program): op_num = program.global_block().num_ops() shape_op_num = get_op_num_in_program(program, "pd_op.shape") + shape_op_num += get_op_num_in_program(program, "pd_op.shape64") slice_op_num = get_op_num_in_program(program, "pd_op.slice") return op_num, shape_op_num, slice_op_num @@ -421,7 +428,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 2 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 13 + self.pir_expected_op_num = 11 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 2 @@ -440,7 +447,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 1 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 10 + self.pir_expected_op_num = 9 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 1 @@ -457,7 +464,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 2 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 13 + self.pir_expected_op_num = 11 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 2 @@ -474,7 +481,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 2 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 14 + self.pir_expected_op_num = 12 self.pir_expected_shape_op_num = 2 self.pir_expected_slice_op_num = 2 @@ -639,7 +646,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 1 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 10 + self.pir_expected_op_num = 9 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 1 @@ -650,7 +657,11 @@ def _compute_op_num(self, program): for block in program.blocks: self.shape_op_num += len( - [op for op in block.ops if op.type == "shape"] + [ + op + for op in block.ops + if (op.type == "shape" or op.type == "shape64") + ] ) self.slice_op_num += len( [op for op in block.ops if op.type == "slice"] @@ -659,6 +670,7 @@ def _compute_op_num(self, program): def _compute_pir_op_num(self, program): op_num = program.global_block().num_ops() shape_op_num = get_op_num_in_program(program, "pd_op.shape") + shape_op_num += get_op_num_in_program(program, "pd_op.shape64") slice_op_num = get_op_num_in_program(program, "pd_op.slice") return op_num, shape_op_num, slice_op_num @@ -694,7 +706,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 2 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 15 + self.pir_expected_op_num = 14 self.pir_expected_shape_op_num = 2 self.pir_expected_slice_op_num = 2 @@ -709,7 +721,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 1 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 10 + self.pir_expected_op_num = 9 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 1 @@ -724,7 +736,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 4 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 41 + self.pir_expected_op_num = 39 self.pir_expected_shape_op_num = 4 self.pir_expected_slice_op_num = 4 @@ -739,7 +751,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 3 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 35 + self.pir_expected_op_num = 32 self.pir_expected_shape_op_num = 2 self.pir_expected_slice_op_num = 3 @@ -754,7 +766,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 3 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 27 + self.pir_expected_op_num = 25 self.pir_expected_shape_op_num = 3 self.pir_expected_slice_op_num = 3 @@ -773,7 +785,7 @@ def _set_expected_op_num(self): self.expected_slice_op_num = 1 def _set_pir_expected_op_num(self): - self.pir_expected_op_num = 12 + self.pir_expected_op_num = 11 self.pir_expected_shape_op_num = 1 self.pir_expected_slice_op_num = 1 diff --git a/test/ir/pir/cinn/symbolic/test_llama_slice_concat.py b/test/ir/pir/cinn/symbolic/test_llama_slice_concat.py index 595a406304bd31..678327a93450b9 100644 --- a/test/ir/pir/cinn/symbolic/test_llama_slice_concat.py +++ b/test/ir/pir/cinn/symbolic/test_llama_slice_concat.py @@ -33,10 +33,10 @@ def __init__(self): def forward(self, x): x0 = paddle.shape(x)[0].reshape([1]) - x1 = paddle.full([1], 1, dtype="int32") + x1 = paddle.full([1], 1, dtype="int64") out0 = paddle.concat([x0, x1]) - y = paddle.full([1], 1, dtype="int32") + y = paddle.full([1], 1, dtype="int64") out1 = paddle.concat([x0, y]) return out0, out1 @@ -83,7 +83,7 @@ def __init__(self): def forward(self, x): x0 = paddle.shape(x)[0].reshape([1]) - x1 = paddle.full([1], 1, dtype="int32") + x1 = paddle.full([1], 1, dtype="int64") out = paddle.concat([x0, x1]) return out diff --git a/test/ir/pir/cinn/test_trivial_fusion.py b/test/ir/pir/cinn/test_trivial_fusion.py index a33af91268aef9..fe4f41e22374ba 100644 --- a/test/ir/pir/cinn/test_trivial_fusion.py +++ b/test/ir/pir/cinn/test_trivial_fusion.py @@ -65,8 +65,8 @@ def compare_result(self, dy_compute, input_spec, data_init): def test_generate_shape_concat(self): def func(x, y, z): - x = paddle.cast(x, 'int32') - y = paddle.cast(y, 'int32') + x = paddle.cast(x, 'int64') + y = paddle.cast(y, 'int64') a = paddle.shape(z)[0:1] b = paddle.concat([a, x, y], axis=0) return b diff --git a/test/ir/pir/test_standalone_pir.py b/test/ir/pir/test_standalone_pir.py index 05e675b79b814a..9585a15b357f26 100644 --- a/test/ir/pir/test_standalone_pir.py +++ b/test/ir/pir/test_standalone_pir.py @@ -294,7 +294,7 @@ def func(x, y): return paddle.concat([paddle.shape(x), y], -1) x = paddle.ones([2, 2], dtype='float32') - y = paddle.ones([2], dtype='int32') * 2 + y = paddle.ones([2], dtype='int64') * 2 z = func(x, y) diff --git a/test/tensorrt/test_trt_marker_shape.py b/test/tensorrt/test_trt_marker_shape.py index eba3206c8a9c40..2f314feeb2c7a7 100644 --- a/test/tensorrt/test_trt_marker_shape.py +++ b/test/tensorrt/test_trt_marker_shape.py @@ -46,7 +46,7 @@ def sample_program(self): def setUp(self): if core.is_compiled_with_cuda(): self.places.append(paddle.CUDAPlace(0)) - self.trt_expected_ops = {"pd_op.shape"} + self.trt_expected_ops = {"pd_op.shape64"} def test_check_output(self): self.check_pass_correct() From 71ddb7aaca0efd74fd1c504a87f90e3534f87152 Mon Sep 17 00:00:00 2001 From: mikemikimike <13286568797@163.com> Date: Wed, 27 Nov 2024 15:30:13 +0800 Subject: [PATCH 024/288] =?UTF-8?q?[CodeStyle][Typos][A-36]=20Fix=20typo(a?= =?UTF-8?q?ssigend=E3=80=81assined)=20(#69741)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- _typos.toml | 2 -- paddle/phi/infermeta/spmd_rules/utils.h | 2 +- tools/gen_ut_cmakelists.py | 2 +- 3 files changed, 2 insertions(+), 4 deletions(-) diff --git a/_typos.toml b/_typos.toml index 5a96ffed456064..3550a1fefb7d55 100644 --- a/_typos.toml +++ b/_typos.toml @@ -24,8 +24,6 @@ UE = "UE" unpacket = "unpacket" # These words need to be fixed -assigend = 'assigend' -assined = 'assined' assgin = 'assgin' axises = 'axises' Axises = 'Axises' diff --git a/paddle/phi/infermeta/spmd_rules/utils.h b/paddle/phi/infermeta/spmd_rules/utils.h index a2fdecfc708b49..5fb987965bbfab 100644 --- a/paddle/phi/infermeta/spmd_rules/utils.h +++ b/paddle/phi/infermeta/spmd_rules/utils.h @@ -195,7 +195,7 @@ struct ReplicateInferSpmdDynamicHelper // the annotated axes after inferring forward or backward. The parameter axis // stores the axes of the tensor. "1" is a special axis, for the axis "1", set // its dims mapping to -1. -// if unsharded_miss_axis, "-1" is assigend to axes that has no key in +// if unsharded_miss_axis, "-1" is assigned to axes that has no key in // axis_to_dim_map. std::vector GetDimsMappingForAxes( const std::string& axes, diff --git a/tools/gen_ut_cmakelists.py b/tools/gen_ut_cmakelists.py index a8eb6a0f6d76ed..865cbf18c558af 100644 --- a/tools/gen_ut_cmakelists.py +++ b/tools/gen_ut_cmakelists.py @@ -318,7 +318,7 @@ def parse_assigned_dist_ut_ports(self, current_work_dir, depth=0): if depth == 0: # After all directories are scanned and processed # 1. Get the num_port of last added test and set DIST_UT_PORT+=num_port - # to guarantee the DIST_UT_PORT is not assined + # to guarantee the DIST_UT_PORT is not assigned # 2. Summary all the directories which include csv but no cmake and show an error # if such a drectory exists From 700347f0834521fddd4bb2eb4a00c7b18961e8d8 Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Wed, 27 Nov 2024 15:45:53 +0800 Subject: [PATCH 025/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20No.25=E3=80=91=E6=96=B0=E5=A2=9E`paddle.vecdot`=20,=20?= =?UTF-8?q?`paddle.linalg.vecdot`=20(#69477)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add_vecdot * update vecdot * fix codestyle * fix codestyle * Update test/legacy_test/test_linalg_vecdot.py Co-authored-by: HydrogenSulfate <490868991@qq.com> * Update python/paddle/tensor/linalg.py Co-authored-by: HydrogenSulfate <490868991@qq.com> * update_vecdot * fix_codestyle * fix codestyle * fix codestyle * update * skip_xpu * Update test_linalg_vecdot.py * fix codestyle * fix codestyle again * fix * fix * fix * fix again * change_example * delete --------- Co-authored-by: HydrogenSulfate <490868991@qq.com> --- python/paddle/__init__.py | 2 + python/paddle/linalg.py | 2 + python/paddle/tensor/__init__.py | 1 + python/paddle/tensor/linalg.py | 36 ++++ test/legacy_test/test_linalg_vecdot.py | 278 +++++++++++++++++++++++++ 5 files changed, 319 insertions(+) create mode 100644 test/legacy_test/test_linalg_vecdot.py diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py index cf70643d717ef4..b9a6ac235abd24 100644 --- a/python/paddle/__init__.py +++ b/python/paddle/__init__.py @@ -241,6 +241,7 @@ t_, transpose, transpose_, + vecdot, ) from .tensor.logic import ( allclose, @@ -1215,6 +1216,7 @@ 'to_dlpack', 'inf', 'newaxis', + 'vecdot', 'nan', 'pi', 'e', diff --git a/python/paddle/linalg.py b/python/paddle/linalg.py index f9aaa5f5b3a9a8..8705a78df52a82 100644 --- a/python/paddle/linalg.py +++ b/python/paddle/linalg.py @@ -47,6 +47,7 @@ svd, svd_lowrank, triangular_solve, + vecdot, vector_norm, ) @@ -55,6 +56,7 @@ 'cholesky_inverse', 'norm', 'matrix_norm', + 'vecdot', 'vector_norm', 'cond', 'cov', diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index b967000589d248..7688a68897f3e6 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -102,6 +102,7 @@ t_, transpose, transpose_, + vecdot, ) from .logic import ( # noqa: F401 __rand__, diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index 16268e5d67cab7..e55ffa4eb48b1b 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -1868,6 +1868,42 @@ def dot(x: Tensor, y: Tensor, name: str | None = None) -> Tensor: return out +def vecdot( + x: Tensor, + y: Tensor, + axis: int = -1, + name: str | None = None, +) -> Tensor: + """ + Computes the dot product of two tensors along a specified axis. + + This function multiplies two tensors element-wise and sums them along a specified axis to compute their dot product. It supports tensors of any dimensionality, including 0-D tensors, as long as the shapes of `x` and `y` are broadcastable along the specified axis. + + Args: + x (Tensor): The first input tensor. It should be a tensor with dtype of float32, float64, int32, int64, complex64, or complex128. + y (Tensor): The second input tensor. Its shape must be broadcastable with `x` along the specified `axis`, and it must have the same dtype as `x`. + axis (int, optional): The axis along which to compute the dot product. Default is -1, which indicates the last axis. + name (str|None, optional): Name of the output. Default is None. It's used to print debug info for developers. Details: :ref:`api_guide_Name` + + Returns: + Tensor: A tensor containing the dot product of `x` and `y` along the specified axis. + + Examples: + + .. code-block:: python + + >>> import paddle + >>> x = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32') + >>> y = paddle.to_tensor([[1, 2, 3], [4, 5, 6]], dtype='float32') + >>> result = paddle.linalg.vecdot(x, y, axis=1) + >>> print(result) + Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, + [14.0, 77.0]) + """ + out = (x.conj() * y).sum(axis=axis) + return out + + def cov( x: Tensor, rowvar: bool = True, diff --git a/test/legacy_test/test_linalg_vecdot.py b/test/legacy_test/test_linalg_vecdot.py new file mode 100644 index 00000000000000..e42755b8d8a8d1 --- /dev/null +++ b/test/legacy_test/test_linalg_vecdot.py @@ -0,0 +1,278 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import sys +import unittest + +import numpy as np + +import paddle +from paddle.base import core + +if sys.platform == 'win32': + RTOL = {'float32': 1e-02, 'float64': 1e-04} + ATOL = {'float32': 1e-02, 'float64': 1e-04} +else: + RTOL = {'float32': 1e-06, 'float64': 1e-15} + ATOL = {'float32': 1e-06, 'float64': 1e-15} + + +class VecDotTestCase(unittest.TestCase): + def setUp(self): + self.init_config() + self.generate_input() + self.generate_expected_output() + self.places = [paddle.CPUPlace()] + if paddle.is_compiled_with_cuda(): + self.places.append(paddle.CUDAPlace(0)) + + def generate_input(self): + np.random.seed(123) + self.x = np.random.random(self.input_shape).astype(self.dtype) + self.y = np.random.random(self.input_shape).astype(self.dtype) + + def generate_expected_output(self): + self.expected_output = np.sum(self.x * self.y, axis=self.axis) + + def init_config(self): + self.dtype = 'float64' + self.input_shape = (3, 4) + self.axis = -1 + + def test_dygraph(self): + for place in self.places: + paddle.disable_static(place) + x_tensor = paddle.to_tensor(self.x, dtype=self.dtype, place=place) + y_tensor = paddle.to_tensor(self.y, dtype=self.dtype, place=place) + result = paddle.vecdot(x_tensor, y_tensor, axis=self.axis) + + np.testing.assert_allclose( + result.numpy(), + self.expected_output, + rtol=RTOL[self.dtype], + atol=ATOL[self.dtype], + ) + + def test_static(self): + paddle.enable_static() + for place in self.places: + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + x = paddle.static.data( + name="x", shape=self.input_shape, dtype=self.dtype + ) + y = paddle.static.data( + name="y", shape=self.input_shape, dtype=self.dtype + ) + + result = paddle.vecdot(x, y, axis=self.axis) + exe = paddle.static.Executor(place) + output = exe.run( + feed={"x": self.x, "y": self.y}, + fetch_list=[result], + )[0] + + np.testing.assert_allclose( + output, + self.expected_output, + rtol=RTOL[self.dtype], + atol=ATOL[self.dtype], + ) + + +class VecDotTestCaseFloat32(VecDotTestCase): + def init_config(self): + self.dtype = 'float32' + self.input_shape = (3, 4) + self.axis = -1 + + +class VecDotTestCaseHigherDim(VecDotTestCase): + def init_config(self): + self.dtype = 'float64' + self.input_shape = (2, 3, 4) + self.axis = -1 + + +class VecDotTestCaseAxis(VecDotTestCase): + def init_config(self): + self.dtype = 'float64' + self.input_shape = (3, 4, 5) + self.axis = 1 + + +class VecDotTestCaseError(unittest.TestCase): + def test_axis_mismatch(self): + with self.assertRaises(ValueError): + x = paddle.rand([3, 4], dtype="float32") + y = paddle.rand([3, 5], dtype="float32") + paddle.vecdot(x, y, axis=-1) + + @unittest.skipIf( + core.is_compiled_with_xpu(), + "Skip XPU for not support uniform(dtype=int)", + ) + def test_dtype_mismatch(self): + with self.assertRaises(TypeError): + x = paddle.rand([3, 4], dtype="float32") + y = paddle.rand([3, 4], dtype="int32") + paddle.vecdot(x, y, axis=-1) + + +@unittest.skipIf( + core.is_compiled_with_xpu(), + "Skip XPU for not support uniform(dtype=int)", +) +class VecDotTestCaseComplex(unittest.TestCase): + def run_test_dynamic(self): + paddle.disable_static() + x = paddle.to_tensor( + [[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]], dtype="complex64" + ) + y = paddle.to_tensor( + [[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]], dtype="complex64" + ) + result = paddle.vecdot(x, y, axis=-1) + expected = np.sum((x.numpy().conj() * y.numpy()), axis=-1) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-5, atol=1e-5 + ) + + def run_test_static(self): + paddle.enable_static() + place = paddle.CPUPlace() + with paddle.static.program_guard(paddle.static.Program()): + x = paddle.static.data(name="x", shape=[2, 2], dtype="complex64") + y = paddle.static.data(name="y", shape=[2, 2], dtype="complex64") + result = paddle.vecdot(x, y, axis=-1) + exe = paddle.static.Executor(place) + output = exe.run( + feed={ + "x": np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]]).astype( + "complex64" + ), + "y": np.array([[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]]).astype( + "complex64" + ), + }, + fetch_list=[result], + )[0] + expected = np.sum( + np.conj(np.array([[1 + 2j, 3 + 4j], [5 + 6j, 7 + 8j]])).astype( + "complex64" + ) + * np.array([[9 + 1j, 8 + 2j], [7 + 3j, 6 + 4j]]).astype( + "complex64" + ), + axis=-1, + ) + np.testing.assert_allclose(output, expected, rtol=1e-5, atol=1e-5) + + def test_complex_conjugate(self): + self.run_test_dynamic() + self.run_test_static() + + +@unittest.skipIf( + core.is_compiled_with_xpu(), + "Skip XPU for not support uniform(dtype=int)", +) +class VecDotTestCaseTypePromotion1(unittest.TestCase): + def test_float32_float64_promotion(self): + paddle.disable_static() + x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float32") + y = paddle.to_tensor([[5.0, 6.0], [7.0, 8.0]], dtype="float64") + result = paddle.vecdot(x, y, axis=-1) + + expected = np.sum(x.numpy().astype("float64") * y.numpy(), axis=-1) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-6, atol=1e-6 + ) + + +@unittest.skipIf( + core.is_compiled_with_xpu(), + "Skip XPU for not support uniform(dtype=int)", +) +class VecDotTestCaseTypePromotion2(unittest.TestCase): + def test_float64_complex64_promotion(self): + paddle.disable_static() + x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float64") + y = paddle.to_tensor( + [[5 + 6j, 7 + 8j], [9 + 1j, 2 + 3j]], dtype="complex64" + ) + result = paddle.vecdot(x, y, axis=-1) + + expected = np.sum(x.numpy().astype("complex64") * y.numpy(), axis=-1) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-5, atol=1e-5 + ) + + +class VecDotTestCaseBroadcast0DTensor(unittest.TestCase): + def test_0d_tensor_broadcast(self): + paddle.disable_static() + x = paddle.to_tensor(2.0, dtype="float32") + y = paddle.to_tensor(3.0, dtype="float32") + result = paddle.vecdot(x, y) + + expected = x.numpy() * y.numpy() + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-6, atol=1e-6 + ) + + +class VecDotTestCaseBroadcast1DTensor(unittest.TestCase): + def test_1d_tensor_broadcast(self): + paddle.disable_static() + x = paddle.to_tensor([1.0, 2.0, 3.0], dtype="float32") + y = paddle.to_tensor([4.0, 5.0, 6.0], dtype="float32") + result = paddle.vecdot(x, y) + + expected = np.dot(x.numpy(), y.numpy()) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-6, atol=1e-6 + ) + + +class VecDotTestCaseBroadcast1DNDTensor(unittest.TestCase): + def test_1d_nd_tensor_broadcast(self): + paddle.disable_static() + x = paddle.to_tensor([1.0, 2.0], dtype="float32") + y = paddle.to_tensor([[3.0, 4.0], [5.0, 6.0]], dtype="float32") + result = paddle.vecdot(x, y, axis=-1) + + expected = np.sum(x.numpy() * y.numpy(), axis=-1) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-6, atol=1e-6 + ) + + +class VecDotTestCaseBroadcastNDTensor(unittest.TestCase): + def test_nd_nd_tensor_broadcast(self): + paddle.disable_static() + x = paddle.to_tensor([[1.0, 2.0], [3.0, 4.0]], dtype="float32") + y = paddle.to_tensor([5.0, 6.0], dtype="float32") + result = paddle.vecdot(x, y, axis=-1) + + expected = np.sum(x.numpy() * y.numpy(), axis=-1) + np.testing.assert_allclose( + result.numpy(), expected, rtol=1e-6, atol=1e-6 + ) + + +if __name__ == '__main__': + unittest.main() From 6e52d233ef00840cf08cc26a772704b47816ff09 Mon Sep 17 00:00:00 2001 From: Shuhao Liang <50269654+lshpku@users.noreply.github.com> Date: Wed, 27 Nov 2024 16:10:12 +0800 Subject: [PATCH 026/288] [CINN] Set smaller size limit for local buffer (#69740) --- paddle/cinn/optim/eliminate_common_global_memory_read.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/cinn/optim/eliminate_common_global_memory_read.cc b/paddle/cinn/optim/eliminate_common_global_memory_read.cc index 8b7259f759b5f6..4af31da3b2ecaa 100644 --- a/paddle/cinn/optim/eliminate_common_global_memory_read.cc +++ b/paddle/cinn/optim/eliminate_common_global_memory_read.cc @@ -191,7 +191,7 @@ struct GlobalTensorInfoCollector : public ir::IRMutator { VLOG(6) << "Total buffer size: " << size; common::cas_intervals_t var_intervals; common::SymbolicExprAnalyzer analyzer(var_intervals); - std::optional prove_gt = analyzer.ProveGT(size, ir::Expr(128)); + std::optional prove_gt = analyzer.ProveGT(size, ir::Expr(8)); return prove_gt.value_or(false); }; From b78f6970473d06f234caac0079069e5f077ddf4e Mon Sep 17 00:00:00 2001 From: Lei Ding <69283446+Dmovic@users.noreply.github.com> Date: Wed, 27 Nov 2024 16:23:15 +0800 Subject: [PATCH 027/288] [CINN] Compute at without block (#69668) --- paddle/cinn/ir/schedule/impl/compute_location.cc | 13 +++++++++++-- 1 file changed, 11 insertions(+), 2 deletions(-) diff --git a/paddle/cinn/ir/schedule/impl/compute_location.cc b/paddle/cinn/ir/schedule/impl/compute_location.cc index 1c4a543a6dac55..7365bddc0f3e77 100644 --- a/paddle/cinn/ir/schedule/impl/compute_location.cc +++ b/paddle/cinn/ir/schedule/impl/compute_location.cc @@ -229,8 +229,17 @@ void DyScheduleImpl::SimpleComputeAt(const Expr& block, const Expr& loop) { new_loop.As()->body.As()->stmts.insert(pos, result); } } else { - new_loop.As()->body = - ir::Block::Make({result, new_loop.As()->body}); + if (new_loop.As()->body.As()) { + std::vector new_body{result}; + for (const auto& stmt : + new_loop.As()->body.As()->stmts) { + new_body.push_back(stmt); + } + new_loop.As()->body = ir::Block::Make(new_body); + } else { + new_loop.As()->body = + ir::Block::Make({result, new_loop.As()->body}); + } } Expr source_expr{nullptr}; From bcb9a712402a645f320817762adddc63e29a6081 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Wed, 27 Nov 2024 16:26:49 +0800 Subject: [PATCH 028/288] refine (#69765) --- paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index 5bd3a32380ebe5..c4ad2a474f9e60 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1711,14 +1711,14 @@ void tile_grad(const Tensor& x, if (has_dynamic_shape(x.shape()) || has_dynamic_shape(out_grad.shape())) { std::vector out_grad_shape_vec; for (int64_t i = 0; i < out_grad.dims().size(); ++i) { - auto out_grad_shape_slice = get_slice(shape(out_grad_tmp), i); + auto out_grad_shape_slice = get_slice(shape64(out_grad_tmp), i); out_grad_shape_vec.push_back(out_grad_shape_slice); } if (repeat_times_data.size() != 0) { while (true) { std::vector expand_shape_vec; for (int64_t i = 0; i < out_grad_tmp.dims().size(); ++i) { - auto expand_shape = get_slice(shape(out_grad_tmp), i); + auto expand_shape = get_slice(shape64(out_grad_tmp), i); expand_shape_vec.push_back(expand_shape); } int num_reduce = 0; @@ -1754,7 +1754,7 @@ void tile_grad(const Tensor& x, } } } - x_grad_tmp = backend::reshape(out_grad_tmp, shape(x)); + x_grad_tmp = backend::reshape(out_grad_tmp, shape64(x)); } else { std::vector out_grad_shape(out_grad.shape()); From 96229dc20f4dd1163ff3946e7f1598a2eb2f1acc Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Wed, 27 Nov 2024 16:44:52 +0800 Subject: [PATCH 029/288] [SOT][3.13] Generate `TO_BOOL` when has implicit to bool in `COMPARE_OP` (#69744) --- .../executor/opcode_executor.py | 23 +++++++++++++++---- test/sot/skip_files_py313 | 4 ---- 2 files changed, 19 insertions(+), 8 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py index e9ffe9a90dff5b..7f4544897f1b92 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py +++ b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py @@ -2041,7 +2041,6 @@ def _break_graph_when_if(self, result: TensorVariable, instr: Instruction): # 1. analyse info cur_index = self.indexof(instr) - prefix_opname = self._instructions[cur_index - 1].opname true_fn_start_index = cur_index + 1 false_fn_start_index = self.indexof(instr.jump_to) stack_size_after_if = len(self.stack) - 1 @@ -2125,7 +2124,7 @@ def create_if_branch_fn( var_loader.load(result) # in 3.13, we have to copy the original 'TO_BOOL' to make the generated bytecode valid. - if sys.version_info >= (3, 13) and prefix_opname == "TO_BOOL": + if self._need_insert_to_bool(cur_index): self._graph.pycode_gen.add_instr('TO_BOOL') if_code = self._graph.pycode_gen.add_instr(instr.opname) @@ -2195,7 +2194,6 @@ def _break_graph_when_call( push_n = push_n(instr.arg) if callable(push_n) else push_n is_precall = instr.opname == "PRECALL" cur_index = self.indexof(instr) - prefix_opname = self._instructions[cur_index - 1].opname # Use CALL instead of PRECALL to calculate the real stack effect call_instr = self._instructions[cur_index + int(is_precall)] # skip CALL if current instr is PRECALL @@ -2251,7 +2249,7 @@ def create_resume_fn(null_indices): # NOTE(SigureMo): In Python 3.11 and 3.12，we need generate KW_NAMES if the call shape is not None. self._graph.pycode_gen.gen_kw_names(self._call_shape) # in 3.13, We have to copy the original 'TO_BOOL' to make the generated bytecode valid. - if sys.version_info >= (3, 13) and prefix_opname == 'TO_BOOL': + if self._need_insert_to_bool(cur_index): self._graph.pycode_gen.add_instr('TO_BOOL') self._graph.pycode_gen.extend_instrs( @@ -2645,3 +2643,20 @@ def _calc_null_indices(self, pop_n): and CALL_METHOD_LAYOUT_NULL_AFTER_VALUE ) ] + + def _has_to_bool_prefix(self, cur_index): + if sys.version_info < (3, 13): + return False + prefix_instr = self._instructions[cur_index - 1] + if prefix_instr.opname == "TO_BOOL": + return True + if prefix_instr.opname == "COMPARE_OP" and prefix_instr.arg & 0b10000: + return True + return False + + def _need_insert_to_bool(self, cur_index): + current_instr = self._instructions[cur_index] + return ( + current_instr.opname in NEED_TO_BOOL + and self._has_to_bool_prefix(cur_index) + ) diff --git a/test/sot/skip_files_py313 b/test/sot/skip_files_py313 index 64f4f710bf849e..3147448e8ecf0a 100644 --- a/test/sot/skip_files_py313 +++ b/test/sot/skip_files_py313 @@ -1,5 +1 @@ test/sot/test_19_closure.py -test/sot/test_break_graph.py -test/sot/test_min_graph_size.py -test/sot/test_numpy.py -test/sot/test_simulate_initialize.py From f55c6bd0d605b8b6956658302744cac042216763 Mon Sep 17 00:00:00 2001 From: XnneHang Date: Wed, 27 Nov 2024 17:19:21 +0800 Subject: [PATCH 030/288] fix: remove Duplicate op. (#69747) --- paddle/fluid/primitive/base/primitive_ops.h | 5 +---- 1 file changed, 1 insertion(+), 4 deletions(-) diff --git a/paddle/fluid/primitive/base/primitive_ops.h b/paddle/fluid/primitive/base/primitive_ops.h index dc897ca3267e15..85e220a5a4af6e 100644 --- a/paddle/fluid/primitive/base/primitive_ops.h +++ b/paddle/fluid/primitive/base/primitive_ops.h @@ -26,8 +26,8 @@ const std::set& GetPrimitiveOpNames() { "pd_op.subtract", "pd_op.multiply", "pd_op.divide", - "pd_op.less_equal", "pd_op.less_than", + "pd_op.less_equal", "pd_op.equal", "pd_op.not_equal", "pd_op.greater_equal", @@ -69,9 +69,6 @@ const std::set& GetPrimitiveOpNames() { "pd_op.pad", "pd_op.cumsum", "pd_op.put_along_axis", - "pd_op.equal", - "pd_op.greater_than", - "pd_op.less_equal", "pd_op.sin", "pd_op.cos", "pd_op.where", From 42e83d5ceb2a03f74dbea6e4b56d9f7669626a93 Mon Sep 17 00:00:00 2001 From: Han YANG Date: Wed, 27 Nov 2024 17:41:32 +0800 Subject: [PATCH 031/288] [Docathon][Add API Legend No.1] Add the picture of broadcast_to (#69729) --- python/paddle/tensor/manipulation.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 8105f70eef29e3..6b48610fe28465 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -4701,6 +4701,12 @@ def broadcast_to( Both the number of dimensions of ``x`` and the number of elements in ``shape`` should be less than or equal to 6. The dimension to broadcast to must have a value 0. + The following figure shows the process of broadcasting a one-dimensional tensor of shape [3] to a two-dimensional tensor of shape [2,3] based on the shape specified by 'shape'. + + .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/broadcast_to.png + :width: 500 + :alt: broadcast_to API + :align: center Args: x (Tensor): The input tensor, its data type is bool, float16, float32, float64, int32, int64, uint8 or uint16. From 28153dcaa6a186a75ec564fced875ef71420f93f Mon Sep 17 00:00:00 2001 From: Lucas Date: Wed, 27 Nov 2024 17:47:05 +0800 Subject: [PATCH 032/288] [XPU] Support bf16 clip_grad and redirect xpu kernel to clamp_grad (#69723) --- cmake/external/xpu.cmake | 2 +- paddle/phi/backends/xpu/xpu3_op_list.cc | 1 + .../fusion/xpu/cross_attention_xpu_kernel.cc | 2 +- .../fusion/xpu/fused_layernorm_kernel.cc | 4 +- .../phi/kernels/fusion/xpu/fused_rope_utils.h | 178 ++++++++++++------ .../fusion/xpu/multi_encoder_xpu_kernel.cc | 10 +- .../fusion/xpu/qkv_attention_xpu_kernel.cc | 20 +- .../xpu/weight_only_linear_kernel_xpu.cc | 7 +- paddle/phi/kernels/xpu/clip_grad_kernel.cc | 15 +- test/legacy_test/test_clip_op.py | 2 +- test/xpu/test_clip_op_xpu.py | 29 ++- 11 files changed, 183 insertions(+), 87 deletions(-) diff --git a/cmake/external/xpu.cmake b/cmake/external/xpu.cmake index 8f0b9646628f85..dd01a51b546d17 100644 --- a/cmake/external/xpu.cmake +++ b/cmake/external/xpu.cmake @@ -29,7 +29,7 @@ set(XPU_XFA_LIB_NAME "libxpu_flash_attention.so") set(XPU_XPUDNN_LIB_NAME "libxpu_dnn.so") if(NOT DEFINED XPU_XHPC_BASE_DATE) - set(XPU_XHPC_BASE_DATE "dev/20241118") + set(XPU_XHPC_BASE_DATE "dev/20241127") endif() set(XPU_XCCL_BASE_VERSION "3.0.0.5") # For XRE5 if(NOT DEFINED XPU_XFT_BASE_VERSION) diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index 395669034d5244..72342291be4b89 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -229,6 +229,7 @@ XPUOpMap& get_kl3_ops() { {"clip_grad", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, + phi::DataType::BFLOAT16, phi::DataType::INT64, phi::DataType::INT32})}, {"coalesce_tensor", diff --git a/paddle/phi/kernels/fusion/xpu/cross_attention_xpu_kernel.cc b/paddle/phi/kernels/fusion/xpu/cross_attention_xpu_kernel.cc index 6525ea0591919d..901fe1b7eb0cac 100644 --- a/paddle/phi/kernels/fusion/xpu/cross_attention_xpu_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/cross_attention_xpu_kernel.cc @@ -159,7 +159,7 @@ void CrossAttentionXPUKernelImpl( PADDLE_ENFORCE_XDNN_SUCCESS(r, "qkv_attention_xpu"); if (input_q.dtype() == DataType::FLOAT32) { - int r_cast_out = xpu::cast_v2( + int r_cast_out = xpu::cast( ctx.x_context(), qkv_temp_data, qkv_data, qkv->numel()); PADDLE_ENFORCE_XDNN_SUCCESS( r_cast_out, "cross_attention_xpu(cast out from fp16 to fp32)"); diff --git a/paddle/phi/kernels/fusion/xpu/fused_layernorm_kernel.cc b/paddle/phi/kernels/fusion/xpu/fused_layernorm_kernel.cc index cac0182feaa2ba..2c506c7f17b5c3 100644 --- a/paddle/phi/kernels/fusion/xpu/fused_layernorm_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/fused_layernorm_kernel.cc @@ -75,12 +75,12 @@ void FusedLayerNormKernel(const Context& dev_ctx, residual_alpha); PADDLE_ENFORCE_XDNN_SUCCESS(r, "constant"); - r = baidu::xpu::api::cast_v2( + r = baidu::xpu::api::cast( xpu_ctx->x_context(), residual_alpha_tmp.data(), reinterpret_cast(residual_alpha_ptr.data()), 1); - PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast_v2"); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast"); if (residual) { dev_ctx.template Alloc(residual_out); diff --git a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h index 16e4d496407a0a..79da31d3d7d252 100644 --- a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h +++ b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h @@ -274,69 +274,135 @@ void XPUFusedRotaryEveryTwo(const Context& dev_ctx, DenseTensor* out_q, DenseTensor* out_k, DenseTensor* out_v) { - auto single_func = &xpu::rotary_embedding_v3_single; - auto fusion_func = &xpu::rotary_embedding_v3; + auto single_func_fwd = &xpu::rotary_embedding_v3_single; + auto fusion_func_fwd = &xpu::rotary_embedding_v3; + auto single_func_bwd = + &xpu::rotary_embedding_v3_single_grad; + auto fusion_func_bwd = &xpu::rotary_embedding_v3_grad; const char* single_func_name = "rotary_embedding_v3_single"; const char* fusion_func_name = "rotary_embedding_v3"; if (is_bwd) { - single_func = &xpu::rotary_embedding_v3_single_grad; - fusion_func = &xpu::rotary_embedding_v3_grad; single_func_name = "rotary_embedding_v3_single_grad"; fusion_func_name = "rotary_embedding_v3_grad"; } - if (!in_k) { - int ret = single_func( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + if (is_bwd) { + if (!in_k) { + int ret = single_func_bwd( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + } else { + int64_t num_heads_k = in_k->dims()[2]; + int ret = fusion_func_bwd( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + reinterpret_cast(in_k->data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + reinterpret_cast(out_k->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + {seq_len * num_heads_k * head_dim, + num_heads_k * head_dim, + head_dim, + 1}, + num_heads_k, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); + } + if (in_v) { + int64_t num_heads_v = in_v->dims()[2]; + int ret = single_func_bwd(dev_ctx.x_context(), + reinterpret_cast(in_v->data()), + cos_data, + sin_data, + reinterpret_cast(out_v->data()), + batch_size, + seq_len, + num_heads_v, + head_dim, + {seq_len * num_heads_v * head_dim, + num_heads_v * head_dim, + head_dim, + 1}, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + } } else { - int64_t num_heads_k = in_k->dims()[2]; - int ret = fusion_func( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - reinterpret_cast(in_k->data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - reinterpret_cast(out_k->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - {seq_len * num_heads_k * head_dim, num_heads_k * head_dim, head_dim, 1}, - num_heads_k, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); - } - - if (in_v) { - int64_t num_heads_v = in_v->dims()[2]; - int ret = single_func( - dev_ctx.x_context(), - reinterpret_cast(in_v->data()), - cos_data, - sin_data, - reinterpret_cast(out_v->data()), - batch_size, - seq_len, - num_heads_v, - head_dim, - {seq_len * num_heads_v * head_dim, num_heads_v * head_dim, head_dim, 1}, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + if (!in_k) { + int ret = single_func_fwd( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + "BLHD", + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + } else { + int64_t num_heads_k = in_k->dims()[2]; + int ret = fusion_func_fwd( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + reinterpret_cast(in_k->data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + reinterpret_cast(out_k->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + {seq_len * num_heads_k * head_dim, + num_heads_k * head_dim, + head_dim, + 1}, + num_heads_k, + "BLHD", + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); + } + if (in_v) { + int64_t num_heads_v = in_v->dims()[2]; + int ret = single_func_fwd(dev_ctx.x_context(), + reinterpret_cast(in_v->data()), + cos_data, + sin_data, + reinterpret_cast(out_v->data()), + batch_size, + seq_len, + num_heads_v, + head_dim, + {seq_len * num_heads_v * head_dim, + num_heads_v * head_dim, + head_dim, + 1}, + "BLHD", + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); + } } } diff --git a/paddle/phi/kernels/fusion/xpu/multi_encoder_xpu_kernel.cc b/paddle/phi/kernels/fusion/xpu/multi_encoder_xpu_kernel.cc index d7fa9bea060fd7..fffd0fd1bb94e9 100644 --- a/paddle/phi/kernels/fusion/xpu/multi_encoder_xpu_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/multi_encoder_xpu_kernel.cc @@ -104,7 +104,7 @@ void MultiEncoderXPUKernel( if (x_dtype == phi::DataType::FLOAT32) { auto* x_fp16_data_t = reinterpret_cast( ctx.template Alloc(x_fp16)); - int r_cast_x = xpu::cast_v2( + int r_cast_x = xpu::cast( ctx.x_context(), x.data(), x_fp16_data_t, x.numel()); PADDLE_ENFORCE_XDNN_SUCCESS(r_cast_x, "multi_encoder_xpu(cast x from fp32 to fp16)"); @@ -331,10 +331,10 @@ void MultiEncoderXPUKernel( if (x_dtype == phi::DataType::FLOAT32) { int r_cast_out = - xpu::cast_v2(ctx.x_context(), - out_fp16_data, - ctx.template Alloc(out), - out->numel()); + xpu::cast(ctx.x_context(), + out_fp16_data, + ctx.template Alloc(out), + out->numel()); PADDLE_ENFORCE_XDNN_SUCCESS( r_cast_out, "multi_encoder_xpu(cast out from fp16 to fp32)"); } diff --git a/paddle/phi/kernels/fusion/xpu/qkv_attention_xpu_kernel.cc b/paddle/phi/kernels/fusion/xpu/qkv_attention_xpu_kernel.cc index b8a9d3202cc28b..43cba5400aeaa1 100644 --- a/paddle/phi/kernels/fusion/xpu/qkv_attention_xpu_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/qkv_attention_xpu_kernel.cc @@ -107,23 +107,27 @@ void QKVAttentionXPUKernelImpl(const Context& ctx, XPUTypeFP16* k_data_fp16 = nullptr; XPUTypeFP16* v_data_fp16 = nullptr; if (qkv_fc_fusion) { - r_cast_x = xpu::cast_v2( + r_cast_x = xpu::cast( ctx.x_context(), q.data(), x_fp16_data_t, q.numel()); + PADDLE_ENFORCE_XDNN_SUCCESS(r_cast_x, "cast"); q_data_fp16 = x_fp16_data_t; k_data_fp16 = x_fp16_data_t + head_num * head_dim; v_data_fp16 = x_fp16_data_t + 2 * head_num * head_dim; } else { - r_cast_x = xpu::cast_v2( + r_cast_x = xpu::cast( ctx.x_context(), q.data(), x_fp16_data_t, q.numel()); - r_cast_x = xpu::cast_v2(ctx.x_context(), - k.data(), - x_fp16_data_t + q.numel(), - k.numel()); - r_cast_x = xpu::cast_v2( + PADDLE_ENFORCE_XDNN_SUCCESS(r_cast_x, "cast"); + r_cast_x = xpu::cast(ctx.x_context(), + k.data(), + x_fp16_data_t + q.numel(), + k.numel()); + PADDLE_ENFORCE_XDNN_SUCCESS(r_cast_x, "cast"); + r_cast_x = xpu::cast( ctx.x_context(), v.data(), x_fp16_data_t + q.numel() + k.numel(), v.numel()); + PADDLE_ENFORCE_XDNN_SUCCESS(r_cast_x, "cast"); q_data_fp16 = x_fp16_data_t; k_data_fp16 = x_fp16_data_t + q.numel(); v_data_fp16 = x_fp16_data_t + q.numel() + k.numel(); @@ -153,7 +157,7 @@ void QKVAttentionXPUKernelImpl(const Context& ctx, tmp_mask, qk_max_data); PADDLE_ENFORCE_XDNN_SUCCESS(r, "qkv_attention_xpu"); - int r_cast_out = xpu::cast_v2( + int r_cast_out = xpu::cast( ctx.x_context(), out_fp16_data, qkv->data(), qkv->numel()); PADDLE_ENFORCE_XDNN_SUCCESS( r_cast_out, "multi_encoder_xpu(cast out from fp16 to fp32)"); diff --git a/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc b/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc index 744828ae37cbe5..bc6900826b0674 100644 --- a/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc +++ b/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc @@ -63,7 +63,7 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, 0, common::errors::Fatal( "scale failed, scale related variable `r` is %d", r)); - r = baidu::xpu::api::cast_v2( + r = baidu::xpu::api::cast( xpu_ctx->x_context(), reinterpret_cast( max_value_fp16.data()), @@ -72,7 +72,7 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, PADDLE_ENFORCE_EQ(r, 0, common::errors::Fatal( - "cast_v2 failed, related variable `r` is %d", r)); + "cast failed, related variable `r` is %d", r)); } else if (weight_scale.dtype() == phi::DataType::FLOAT32) { r = baidu::xpu::api::scale(xpu_ctx->x_context(), weight_scale.data(), @@ -95,12 +95,13 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, bias.get().dtype() == phi::DataType::FLOAT16) { bias_fp32.Resize(bias.get().dims()); dev_ctx.template Alloc(&bias_fp32); - r = baidu::xpu::api::cast_v2( + r = baidu::xpu::api::cast( xpu_ctx->x_context(), reinterpret_cast( bias.get().data()), bias_fp32.data(), n); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast"); } if (weight_dtype == "int8") { r = baidu::xpu::api::gpt_fc_fusion( diff --git a/paddle/phi/kernels/xpu/clip_grad_kernel.cc b/paddle/phi/kernels/xpu/clip_grad_kernel.cc index 5e1e7812e74895..710732e52ee8d2 100644 --- a/paddle/phi/kernels/xpu/clip_grad_kernel.cc +++ b/paddle/phi/kernels/xpu/clip_grad_kernel.cc @@ -29,13 +29,13 @@ void ClipGradKernel(const Context& ctx, ctx.template Alloc(x_grad); using XPUDataType = typename XPUTypeTrait::Type; int r = - xpu::clip_grad(ctx.x_context(), - reinterpret_cast(x.data()), - reinterpret_cast(out_grad.data()), - reinterpret_cast(x_grad->data()), - x.numel(), - static_cast(min.to()), - static_cast(max.to())); + xpu::clamp_grad(ctx.x_context(), + reinterpret_cast(x.data()), + reinterpret_cast(out_grad.data()), + reinterpret_cast(x_grad->data()), + x.numel(), + static_cast(min.to()), + static_cast(max.to())); PADDLE_ENFORCE_XDNN_SUCCESS(r, "clip_grad"); } } // namespace phi @@ -46,5 +46,6 @@ PD_REGISTER_KERNEL(clip_grad, phi::ClipGradKernel, float, phi::dtype::float16, + phi::dtype::bfloat16, int64_t, int) {} diff --git a/test/legacy_test/test_clip_op.py b/test/legacy_test/test_clip_op.py index adec147df2c04c..00d5e40f3bf00a 100644 --- a/test/legacy_test/test_clip_op.py +++ b/test/legacy_test/test_clip_op.py @@ -166,7 +166,7 @@ def initTestCase(self): @unittest.skipIf( not core.is_compiled_with_cuda() or not core.is_bfloat16_supported(core.CUDAPlace(0)), - "core is not compiled with CUDA and not support the bfloat16", + "core is not compiled with CUDA or not support the bfloat16", ) class TestClipBF16Op(OpTest): def setUp(self): diff --git a/test/xpu/test_clip_op_xpu.py b/test/xpu/test_clip_op_xpu.py index a29c9747df6711..2c9229f2afbec4 100644 --- a/test/xpu/test_clip_op_xpu.py +++ b/test/xpu/test_clip_op_xpu.py @@ -20,6 +20,7 @@ create_test_class, get_xpu_op_support_types, ) +from op_test import convert_float_to_uint16, convert_uint16_to_float from op_test_xpu import XPUOpTest import paddle @@ -43,9 +44,24 @@ def setUp(self): self.init_data() self.set_attrs() self.set_inputs() - self.outputs = { - 'Out': np.clip(self.inputs['X'], self.min_v, self.max_v) - } + if self.dtype == np.uint16: + self.outputs = { + 'Out': convert_float_to_uint16( + np.clip( + convert_uint16_to_float(self.inputs['X']), + np.array([self.min_v]).astype(np.float32).item(), + np.array([self.min_v]).astype(np.float32).item(), + ) + ) + } + else: + self.outputs = { + 'Out': np.clip( + self.inputs['X'], + np.array([self.min_v]).astype(self.dtype).item(), + np.array([self.max_v]).astype(self.dtype).item(), + ) + } def set_xpu(self): self.__class__.use_xpu = True @@ -74,6 +90,10 @@ def set_inputs(self): input = np.random.random(self.shape).astype("float32") input[np.abs(input - min_v) < self.max_relative_error] = 0.5 input[np.abs(input - max_v) < self.max_relative_error] = 0.5 + if self.dtype == np.uint16: + input = convert_float_to_uint16(input) + else: + input = input.astype(self.dtype) self.inputs['X'] = input def set_attrs(self): @@ -246,6 +266,9 @@ def _executed_api(self, x, min=None, max=None): support_types = get_xpu_op_support_types('clip') for stype in support_types: + # TODO(lilujia): disable int32 and int64 test temporarily, as xdnn not support corresponding resuce_mean + if stype in ["int32", "int64"]: + continue create_test_class(globals(), XPUTestClipOp, stype) if __name__ == '__main__': From d47c3f13e4b5fec5aba278d51009fdf7bba85618 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Wed, 27 Nov 2024 18:07:11 +0800 Subject: [PATCH 033/288] [CINN]clear module optimize pass (#69726) --- paddle/cinn/ir/module.cc | 2 -- paddle/cinn/optim/lower_intrin.cc | 18 ++++++++++-------- paddle/cinn/optim/lower_intrin.h | 2 +- paddle/cinn/optim/optimize.cc | 15 +++------------ paddle/cinn/optim/remove_schedule_block.cc | 4 ++-- paddle/cinn/optim/remove_schedule_block.h | 2 +- 6 files changed, 17 insertions(+), 26 deletions(-) diff --git a/paddle/cinn/ir/module.cc b/paddle/cinn/ir/module.cc index 0e30e4132ee21e..9af57787a37d0b 100644 --- a/paddle/cinn/ir/module.cc +++ b/paddle/cinn/ir/module.cc @@ -100,8 +100,6 @@ Module Module::Builder::Build() { } auto res = ir::Module(module_.get()); - - res = optim::Optimize(res, module_->target); return res; } diff --git a/paddle/cinn/optim/lower_intrin.cc b/paddle/cinn/optim/lower_intrin.cc index 71603871095ef0..fd6cdbd769a207 100644 --- a/paddle/cinn/optim/lower_intrin.cc +++ b/paddle/cinn/optim/lower_intrin.cc @@ -26,11 +26,11 @@ namespace cinn { namespace optim { template -void LowerIntrinImpl(const T &, const Target &target, ir::Module m) { +void LowerIntrinImpl(const T &, const Target &target, ir::Expr *expr) { // Do nothing. } -void LowerIntrinImpl(common::X86Arch, const Target &target, ir::Module module) { +void LowerIntrinImpl(common::X86Arch, const Target &target, ir::Expr *expr) { codegen::RegisterCpuIntrinRule(); struct Mutator : ir::IRMutator { @@ -38,7 +38,9 @@ void LowerIntrinImpl(common::X86Arch, const Target &target, ir::Module module) { explicit Mutator(Target target) : target(target) {} - void operator()(ir::Module m) { IRMutator::Visit(m.As()); } + void operator()(ir::Expr *expr) { + ir::IRMutator::Visit(expr, expr); + } void Visit(const ir::Add *op, Expr *expr) override { auto *node = expr->As(); @@ -105,17 +107,17 @@ void LowerIntrinImpl(common::X86Arch, const Target &target, ir::Module module) { }; Mutator m(target); - m(module); + m(expr); } -void LowerIntrinByArch(ir::Module m, const Target &target) { +void LowerIntrinByArch(ir::Expr *expr, const Target &target) { return std::visit( - [&](const auto &impl) { return LowerIntrinImpl(impl, target, m); }, + [&](const auto &impl) { return LowerIntrinImpl(impl, target, expr); }, target.arch.variant()); } -void LowerIntrin(ir::Module m, Target target) { - return LowerIntrinByArch(m, target); +void LowerIntrin(ir::Expr *expr, Target target) { + return LowerIntrinByArch(expr, target); } } // namespace optim diff --git a/paddle/cinn/optim/lower_intrin.h b/paddle/cinn/optim/lower_intrin.h index 0701bde1db1fe2..f8c099fd8a75c3 100644 --- a/paddle/cinn/optim/lower_intrin.h +++ b/paddle/cinn/optim/lower_intrin.h @@ -37,7 +37,7 @@ static const std::set kIntrinsicCalls{ * * Notes: only support cpu currently. */ -void LowerIntrin(ir::Module m, Target target); +void LowerIntrin(ir::Expr *expr, Target target); } // namespace optim } // namespace cinn diff --git a/paddle/cinn/optim/optimize.cc b/paddle/cinn/optim/optimize.cc index f848d30515e21e..86afc0482ed067 100644 --- a/paddle/cinn/optim/optimize.cc +++ b/paddle/cinn/optim/optimize.cc @@ -112,19 +112,10 @@ ir::LoweredFunc Optimize(ir::LoweredFunc fn, Simplify(&copied->body); VLOG(10) << "After Optimize Simplify" << copied; - return copied; -} - -ir::Module Optimize(const ir::Module& module, const Target& target) { - auto copied = ir::ir_utils::IRCopy(module); - - RemoveScheduleBlock(copied); + RemoveScheduleBlock(&copied->body); VLOG(10) << "After RemoveScheduleBlock:" << copied; - LowerFunctionCallBindVars(copied); - VLOG(10) << "After LowerFunctionCallBindVars:" << copied; - CallArgListToPodValue(copied); - VLOG(10) << "After CallArgListToPodValue:" << copied; - LowerIntrin(copied, target); + + LowerIntrin(&copied->body, target); VLOG(10) << "After LowerIntrin:" << copied; return copied; diff --git a/paddle/cinn/optim/remove_schedule_block.cc b/paddle/cinn/optim/remove_schedule_block.cc index ed95b25ce4249b..397b8f9399b379 100644 --- a/paddle/cinn/optim/remove_schedule_block.cc +++ b/paddle/cinn/optim/remove_schedule_block.cc @@ -23,7 +23,7 @@ namespace cinn { namespace optim { struct ScheduleBlockRemover : public ir::IRMutator { - void operator()(ir::Module m) { IRMutator::Visit(m.As()); } + void operator()(Expr* expr) { ir::IRMutator::Visit(expr, expr); } private: void Visit(const ir::ScheduleBlockRealize* op, Expr* expr) override { @@ -57,7 +57,7 @@ struct ScheduleBlockRemover : public ir::IRMutator { } }; -void RemoveScheduleBlock(ir::Module m) { ScheduleBlockRemover()(m); } +void RemoveScheduleBlock(ir::Expr* expr) { ScheduleBlockRemover()(expr); } } // namespace optim } // namespace cinn diff --git a/paddle/cinn/optim/remove_schedule_block.h b/paddle/cinn/optim/remove_schedule_block.h index 25711b4ddacafd..dec56b33eaa480 100644 --- a/paddle/cinn/optim/remove_schedule_block.h +++ b/paddle/cinn/optim/remove_schedule_block.h @@ -27,7 +27,7 @@ namespace optim { /** * Remove schedule block. */ -void RemoveScheduleBlock(ir::Module m); +void RemoveScheduleBlock(ir::Expr *expr); } // namespace optim } // namespace cinn From 6fc62be0602ba708a90d634d7a1a325d2c0d7383 Mon Sep 17 00:00:00 2001 From: winter-wang <78149749+winter-wang@users.noreply.github.com> Date: Wed, 27 Nov 2024 21:33:41 +0800 Subject: [PATCH 034/288] =?UTF-8?q?add=20cf.stack=5Fcreate=E3=80=81cf.tupl?= =?UTF-8?q?e=5Fpush=20op=20to=20always=5Fforward=5Fops=20(#69760)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- python/paddle/base/framework.py | 7 ++++++- 1 file changed, 6 insertions(+), 1 deletion(-) diff --git a/python/paddle/base/framework.py b/python/paddle/base/framework.py index 1774e240d7d082..4c6080a97ac763 100644 --- a/python/paddle/base/framework.py +++ b/python/paddle/base/framework.py @@ -8468,7 +8468,12 @@ def set_op_roles(block, op_role, always_forward_ops): yield finally: if paddle.framework.in_pir_mode() and is_dist_block(block): - always_forward_ops = ["pd_op.data", "builtin.parameter"] + always_forward_ops = [ + "pd_op.data", + "builtin.parameter", + "cf.stack_create", + "cf.tuple_push", + ] set_op_roles(block, op_role, always_forward_ops) From 85279af249f133befbaf0971fc55cd59fbc5eac5 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Wed, 27 Nov 2024 22:37:14 +0800 Subject: [PATCH 035/288] [SOT] Add dispatch for `min/max(*args)` and add `paddle.geometric` to paddle API list (#69746) --- .../executor/variable_dispatch.py | 26 +++++++++++++++++++ .../executor/variables/container.py | 2 +- .../paddle/jit/sot/utils/paddle_api_config.py | 1 + test/sot/test_builtin_dispatch.py | 16 ++++++++++++ 4 files changed, 44 insertions(+), 1 deletion(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/variable_dispatch.py b/python/paddle/jit/sot/opcode_translator/executor/variable_dispatch.py index a273b40c39827d..8c9655d352254d 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variable_dispatch.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variable_dispatch.py @@ -1242,6 +1242,32 @@ def dispatch_sum( ) +@Dispatcher.register_decorator(max) +def dispatch_max_star_args(*args: VariableBase): + if not args: + raise TypeError("max expected at least 1 arguments, got 0") + res = args[0] + graph = res.graph + for arg in args: + gt = BuiltinVariable(operator.gt, graph, DanglingTracker())(arg, res) + if gt.get_py_value() is True: + res = arg + return res + + +@Dispatcher.register_decorator(min) +def dispatch_min_star_args(*args: VariableBase): + if not args: + raise TypeError("min expected at least 1 arguments, got 0") + res = args[0] + graph = res.graph + for arg in args: + lt = BuiltinVariable(operator.lt, graph, DanglingTracker())(arg, res) + if lt.get_py_value() is True: + res = arg + return res + + # math functions, e.g. math.log, math.sqrt, math.sin, etc. def get_math_unary_functions(): unary_fns = [] diff --git a/python/paddle/jit/sot/opcode_translator/executor/variables/container.py b/python/paddle/jit/sot/opcode_translator/executor/variables/container.py index 390e4852d6c33c..cc51ab70d0ef1f 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variables/container.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variables/container.py @@ -410,7 +410,7 @@ def max(self): def min(self): if len(self) == 0: - raise ValueError("max() arg is an empty sequence") + raise ValueError("min() arg is an empty sequence") res = self[0] getitem = BuiltinVariable( operator.getitem, self.graph, DanglingTracker() diff --git a/python/paddle/jit/sot/utils/paddle_api_config.py b/python/paddle/jit/sot/utils/paddle_api_config.py index e4cd75b7fee111..cc0302a9bb8444 100644 --- a/python/paddle/jit/sot/utils/paddle_api_config.py +++ b/python/paddle/jit/sot/utils/paddle_api_config.py @@ -48,6 +48,7 @@ def get_paddle_api(): paddle.fft, paddle.vision.ops, paddle.metric, + paddle.geometric, ] special_paddle_apis = [paddle.tensor.fill_constant] non_operator_related_apis = [ diff --git a/test/sot/test_builtin_dispatch.py b/test/sot/test_builtin_dispatch.py index 2d0e8e5bd8853f..71e61b688cfea5 100644 --- a/test/sot/test_builtin_dispatch.py +++ b/test/sot/test_builtin_dispatch.py @@ -114,6 +114,16 @@ def test_ord(x: str): return ord(x) +@check_no_breakgraph +def test_min(): + return min(9, 8, 2, 4, 1, 7, 3, 5, 6) + + +@check_no_breakgraph +def test_max(): + return max(9, 8, 2, 4, 1, 7, 3, 5, 6) + + @check_no_breakgraph def test_sqrt(x: int): return math.sqrt(x) @@ -259,6 +269,12 @@ def test_dispatch_sqrt(self): def test_dispatch_log(self): self.assert_results(test_log, math.e) + def test_dispatch_min(self): + self.assert_results(test_min) + + def test_dispatch_max(self): + self.assert_results(test_max) + def run_getattr(x: paddle.Tensor): attr = 'dtype' From 1af272d6b6be47aa395de61c572f9c29e9102d58 Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Thu, 28 Nov 2024 03:59:53 +0800 Subject: [PATCH 036/288] [CodeStyle][Typos][C-[22-24]] Fix typos (`Chunck`,`clen`,`Clas`,`clas`) (#69737) --------- Co-authored-by: SigureMo --- _typos.toml | 6 ++---- paddle/fluid/pir/dialect/operator/ir/api_builder.h | 4 ++-- paddle/fluid/pybind/pir.cc | 4 ++-- paddle/phi/api/profiler/event_tracing.h | 4 ++-- 4 files changed, 8 insertions(+), 10 deletions(-) diff --git a/_typos.toml b/_typos.toml index 3550a1fefb7d55..7f160a0d6e9b9e 100644 --- a/_typos.toml +++ b/_typos.toml @@ -12,6 +12,8 @@ extend-exclude = [ anc = 'anc' arange = "arange" astroid = 'astroid' +Clas = 'Clas' +clen = 'clen' dout = "dout" eles = 'eles' grad = "grad" @@ -60,10 +62,6 @@ cann = 'cann' vart = 'vart' checkings = 'checkings' childs = 'childs' -Chunck = 'Chunck' -clen = 'clen' -Clas = 'Clas' -clas = 'clas' compability = 'compability' compatiblity = 'compatiblity' Compitable = 'Compitable' diff --git a/paddle/fluid/pir/dialect/operator/ir/api_builder.h b/paddle/fluid/pir/dialect/operator/ir/api_builder.h index 6d6fb89912f9fa..251905a1eb831e 100644 --- a/paddle/fluid/pir/dialect/operator/ir/api_builder.h +++ b/paddle/fluid/pir/dialect/operator/ir/api_builder.h @@ -78,8 +78,8 @@ class ApiBuilder { void SetOpRole(int op_role) { builder_->set_op_role(op_role); } int GetOpRole() const { return builder_->op_role(); } - void SetChunckId(int chunk_id) { builder_->set_chunk_id(chunk_id); } - int GetChunckId() const { return builder_->chunk_id(); } + void SetChunkId(int chunk_id) { builder_->set_chunk_id(chunk_id); } + int GetChunkId() const { return builder_->chunk_id(); } private: ApiBuilder(); diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 73849ee5f7a1f3..e73ea069b72358 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -2191,8 +2191,8 @@ void BindUtils(pybind11::module *m) { m->def("reset_insertion_point_to_end", []() { ApiBuilder::Instance().ResetInsertionPointToEnd(); }); m->def("set_chunk_id", - [](int chunk_id) { ApiBuilder::Instance().SetChunckId(chunk_id); }); - m->def("get_chunk_id", []() { return ApiBuilder::Instance().GetChunckId(); }); + [](int chunk_id) { ApiBuilder::Instance().SetChunkId(chunk_id); }); + m->def("get_chunk_id", []() { return ApiBuilder::Instance().GetChunkId(); }); m->def("set_op_role", [](int op_role) { ApiBuilder::Instance().SetOpRole(op_role); }); m->def("get_op_role", []() { return ApiBuilder::Instance().GetOpRole(); }); diff --git a/paddle/phi/api/profiler/event_tracing.h b/paddle/phi/api/profiler/event_tracing.h index cb1717458f380f..d44192b45206fe 100644 --- a/paddle/phi/api/profiler/event_tracing.h +++ b/paddle/phi/api/profiler/event_tracing.h @@ -26,8 +26,8 @@ namespace phi { // It is Recommended to set the level explicitly. static constexpr uint32_t kDefaultTraceLevel = 4; -// Host event tracing. A trace starts when an object of this clas is created and -// stops when the object is destroyed. +// Host event tracing. A trace starts when an object of this class is created +// and stops when the object is destroyed. // Chrome Trace Viewer Format: Duration Event/Complete Event class TEST_API RecordEvent { public: From 796db76c2f9e6a555197a26569b3d65d49894805 Mon Sep 17 00:00:00 2001 From: guixxiic <3010764962@qq.com> Date: Thu, 28 Nov 2024 09:47:18 +0800 Subject: [PATCH 037/288] [CodeStyle][Typos][O-7,O-21,O-23] Fix typos (`olny`, `outputing`, `ouside`) (#69756) * [CodeStyle][Typos][0-7,0-21,0-23] Fix typos (olny, outputing, ouside) * Update python/paddle/static/nn/control_flow.py --------- Co-authored-by: Nyakku Shigure --- _typos.toml | 3 --- python/paddle/profiler/profiler.py | 4 ++-- python/paddle/static/nn/control_flow.py | 2 +- test/ir/inference/test_trt_convert_sum.py | 2 +- 4 files changed, 4 insertions(+), 7 deletions(-) diff --git a/_typos.toml b/_typos.toml index 7f160a0d6e9b9e..07da2cd966f084 100644 --- a/_typos.toml +++ b/_typos.toml @@ -420,7 +420,6 @@ fo = 'fo' offets = 'offets' offseted = 'offseted' OLT = 'OLT' -olny = 'olny' pn = 'pn' Operants = 'Operants' operants = 'operants' @@ -439,13 +438,11 @@ orginal = 'orginal' onces = 'onces' outter = 'outter' outpus = 'outpus' -outputing = 'outputing' outout = 'outout' ouput = 'ouput' outpout = 'outpout' ouptut = 'ouptut' Ouput = 'Ouput' -ouside = 'ouside' overriden = 'overriden' Overide = 'Overide' overide = 'overide' diff --git a/python/paddle/profiler/profiler.py b/python/paddle/profiler/profiler.py index 7a7a15f4279423..615742ec28bc45 100644 --- a/python/paddle/profiler/profiler.py +++ b/python/paddle/profiler/profiler.py @@ -228,7 +228,7 @@ def export_chrome_tracing( dir_name: str, worker_name: str | None = None ) -> Callable[[Profiler], None]: r""" - Return a callable, used for outputing tracing data to chrome tracing format file. + Return a callable, used for outputting tracing data to chrome tracing format file. The output file will be saved in directory ``dir_name``, and file name will be set as `worker_name`. if `worker_name` is not set, the default name is `[hostname]_[pid]`. @@ -281,7 +281,7 @@ def export_protobuf( dir_name: str, worker_name: str | None = None ) -> Callable[[Profiler], None]: r""" - Return a callable, used for outputing tracing data to protobuf file. + Return a callable, used for outputting tracing data to protobuf file. The output file will be saved in directory ``dir_name``, and file name will be set as ``worker_name``. if ``worker_name`` is not set, the default name is `[hostname]_[pid]`. diff --git a/python/paddle/static/nn/control_flow.py b/python/paddle/static/nn/control_flow.py index d5d0ea09ac3f1f..b70658b07b9287 100644 --- a/python/paddle/static/nn/control_flow.py +++ b/python/paddle/static/nn/control_flow.py @@ -551,7 +551,7 @@ class While: >>> loop_len = paddle.full(shape=[1], dtype='int64', fill_value=10) >>> one = paddle.full(shape=[1], dtype='float32', fill_value=1) >>> data = paddle.static.data(name='data', shape=[1], dtype='float32') - >>> sums = paddle.full(shape=[1], dtype='float32', fill_value=0) # Define the variable to be obtained >>> ouside of While, which name should be different from the variable inside the While to be obtained + >>> sums = paddle.full(shape=[1], dtype='float32', fill_value=0) # Define the variable to be obtained outside of While, which name should be different from the variable inside the While to be obtained >>> cond = paddle.less_than(x=i, y=loop_len) >>> while_op = paddle.static.nn.control_flow.While(cond=cond) diff --git a/test/ir/inference/test_trt_convert_sum.py b/test/ir/inference/test_trt_convert_sum.py index 9571a1317af3ae..9d1d1c6581695d 100644 --- a/test/ir/inference/test_trt_convert_sum.py +++ b/test/ir/inference/test_trt_convert_sum.py @@ -217,7 +217,7 @@ def test(self): self.run_test() -# special case when sum having olny one input +# special case when sum having only one input class TrtConvertSumTest1(TrtLayerAutoScanTest): def is_program_valid(self, program_config: ProgramConfig) -> bool: return True From bf9019fdbf8b2d38d0e34b4d667c3f053673c653 Mon Sep 17 00:00:00 2001 From: winffke <92244347+winffke@users.noreply.github.com> Date: Thu, 28 Nov 2024 09:52:03 +0800 Subject: [PATCH 038/288] fix docs bugs (#69766) --- _typos.toml | 7 ------- paddle/common/flags.cc | 4 ++-- paddle/fluid/eager/autograd_meta.h | 2 +- paddle/fluid/framework/ir/lock_free_optimize_pass.h | 2 +- paddle/phi/infermeta/spmd_rules/reshape.cc | 2 +- python/paddle/distributed/passes/auto_parallel_sharding.py | 2 +- .../parameter_server/distribute_transpiler/__init__.py | 2 +- test/cpp/inference/api/trt_dynamic_shape_test.cc | 2 +- 8 files changed, 8 insertions(+), 15 deletions(-) diff --git a/_typos.toml b/_typos.toml index 07da2cd966f084..3ed9daf72b8568 100644 --- a/_typos.toml +++ b/_typos.toml @@ -134,13 +134,6 @@ defind = 'defind' defeine = 'defeine' defition = 'defition' defination = 'defination' -delet = 'delet' -dependecies = 'dependecies' -dependecy = 'dependecy' -decprecated = 'decprecated' -derivated = 'derivated' -descripor = 'descripor' -deserailize = 'deserailize' Destory = 'Destory' DEIVCE = 'DEIVCE' dictionnary = 'dictionnary' diff --git a/paddle/common/flags.cc b/paddle/common/flags.cc index 6ea2442d3070ab..1efa3c64f7ab17 100644 --- a/paddle/common/flags.cc +++ b/paddle/common/flags.cc @@ -1521,8 +1521,8 @@ PHI_DEFINE_EXPORTED_bool(use_shm_cache, * Since Version: 2.6.2 * Value Range: bool, default=false * Example: - * Note: . If True, mmap_allocator will use file descripor to open shared memory - * operation. + * Note: . If True, mmap_allocator will use file descriptor to open shared + * memory operation. */ PHI_DEFINE_EXPORTED_bool(dataloader_use_file_descriptor, false, diff --git a/paddle/fluid/eager/autograd_meta.h b/paddle/fluid/eager/autograd_meta.h index 11476d011b8b90..0b98f796d8af4c 100644 --- a/paddle/fluid/eager/autograd_meta.h +++ b/paddle/fluid/eager/autograd_meta.h @@ -56,7 +56,7 @@ using AbstractAutogradMeta = paddle::AbstractAutogradMeta; * * **/ -// No other AutogradMeta class should be derivated from AbstractAutogradMeta. +// No other AutogradMeta class should be derived from AbstractAutogradMeta. // It's only used by class AutogradMeta : public AbstractAutogradMeta { public: diff --git a/paddle/fluid/framework/ir/lock_free_optimize_pass.h b/paddle/fluid/framework/ir/lock_free_optimize_pass.h index cece1d1a015f7d..aed92a30195e8d 100644 --- a/paddle/fluid/framework/ir/lock_free_optimize_pass.h +++ b/paddle/fluid/framework/ir/lock_free_optimize_pass.h @@ -30,7 +30,7 @@ class Graph; /* * Remove the sum op of all gradients of the backward op. - * And remove the dependecies of the optimizer related to the + * And remove the dependencies of the optimizer related to the * same backward op. * * Before this pass: diff --git a/paddle/phi/infermeta/spmd_rules/reshape.cc b/paddle/phi/infermeta/spmd_rules/reshape.cc index f881812ac3b510..b7367509514b2b 100644 --- a/paddle/phi/infermeta/spmd_rules/reshape.cc +++ b/paddle/phi/infermeta/spmd_rules/reshape.cc @@ -313,7 +313,7 @@ SpmdInfo ReshapeInferSpmdReverse(const DistMetaTensor& x, return {{x_dist_attr}, {out_dist_attr_dst}}; } -// FIXME(dev): XShape will be decprecated in the future, so we +// FIXME(dev): XShape will be deprecated in the future, so we // need unify inferSpmd into ReshapeInferSpmd function. SpmdInfo ReshapeInferSpmdDynamic(const DistMetaTensor& x, const std::vector& shape) { diff --git a/python/paddle/distributed/passes/auto_parallel_sharding.py b/python/paddle/distributed/passes/auto_parallel_sharding.py index d870f2014afcb4..4b7814af7f53ea 100644 --- a/python/paddle/distributed/passes/auto_parallel_sharding.py +++ b/python/paddle/distributed/passes/auto_parallel_sharding.py @@ -1303,7 +1303,7 @@ def _overlap_grad_comm( ) idx += 1 - # NOTE(Ruibiao): Why add dependecy here? + # NOTE(Ruibiao): Why add dependency here? # It is hack to delay GC for coalesce_var, which significantly reduce memory usage. # With the pattern of reduce_sum + scale, the coalesce_var is used by the reduce_sum # op on the comm-stream, and then released by the scale op on the comp-stream. Since diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py b/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py index 1e8de775acd53d..f2e336b1355195 100644 --- a/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py +++ b/python/paddle/incubate/distributed/fleet/parameter_server/distribute_transpiler/__init__.py @@ -869,7 +869,7 @@ def _build_trainer_programs(self, compiled_config): # for startup program _startup = worker.fake_init_ops_pass(_startup, compiled_config) _startup = worker.init_from_server_pass(_startup, compiled_config) - _startup = worker.delet_extra_optimizes_pass( + _startup = worker.delete_extra_optimizes_pass( _startup, compiled_config ) else: diff --git a/test/cpp/inference/api/trt_dynamic_shape_test.cc b/test/cpp/inference/api/trt_dynamic_shape_test.cc index 517765d2930f5f..71cd80bcc2559b 100644 --- a/test/cpp/inference/api/trt_dynamic_shape_test.cc +++ b/test/cpp/inference/api/trt_dynamic_shape_test.cc @@ -295,7 +295,7 @@ TEST(AnalysisPredictor, trt_dynamic) { TestDynamic(true); } TEST(AnalysisPredictor, trt_memory_serialize) { // serailize TestDynamic(true, true, true); - // deserailize + // deserialize TestDynamic(true, false, true); } TEST(AnalysisPredictor, trt_dynamic2) { TestDynamic2(); } From 596c99fb5381f1baebc05dcde50e758a4aa3ae7f Mon Sep 17 00:00:00 2001 From: co63oc Date: Thu, 28 Nov 2024 09:59:13 +0800 Subject: [PATCH 039/288] Fix (#69743) --- paddle/fluid/framework/framework.proto | 10 +++--- paddle/fluid/framework/op_registry.h | 4 +-- paddle/fluid/framework/shape_inference.h | 4 +-- paddle/fluid/framework/var_desc.cc | 40 +++++++++++------------ paddle/phi/core/framework/framework.proto | 10 +++--- 5 files changed, 34 insertions(+), 34 deletions(-) diff --git a/paddle/fluid/framework/framework.proto b/paddle/fluid/framework/framework.proto index b3535c28edf042..f9f0210bd34ee6 100644 --- a/paddle/fluid/framework/framework.proto +++ b/paddle/fluid/framework/framework.proto @@ -195,19 +195,19 @@ message VarType { } optional TensorDesc selected_rows = 2; - message LoDTensorDesc { + message DenseTensorDesc { required TensorDesc tensor = 1; optional int32 lod_level = 2 [ default = 0 ]; } - optional LoDTensorDesc lod_tensor = 3; + optional DenseTensorDesc dense_tensor = 3; - message LoDTensorArrayDesc { + message DenseTensorArrayDesc { required TensorDesc tensor = 1; optional int32 lod_level = 2 [ default = 0 ]; } - optional LoDTensorArrayDesc tensor_array = 4; + optional DenseTensorArrayDesc tensor_array = 4; - message ReaderDesc { repeated LoDTensorDesc lod_tensor = 1; } + message ReaderDesc { repeated DenseTensorDesc dense_tensor = 1; } optional ReaderDesc reader = 5; message Tuple { repeated Type element_type = 1; } diff --git a/paddle/fluid/framework/op_registry.h b/paddle/fluid/framework/op_registry.h index 8fbdbacc71e41e..bd57e2ee3b5ba6 100644 --- a/paddle/fluid/framework/op_registry.h +++ b/paddle/fluid/framework/op_registry.h @@ -61,8 +61,8 @@ class OpVersionMap_OpVersionPair; class ProgramDesc; class VarDesc; class VarType; -class VarType_LoDTensorArrayDesc; -class VarType_LoDTensorDesc; +class VarType_DenseTensorArrayDesc; +class VarType_DenseTensorDesc; class VarType_ReaderDesc; class VarType_TensorDesc; class VarType_Tuple; diff --git a/paddle/fluid/framework/shape_inference.h b/paddle/fluid/framework/shape_inference.h index 427d4be4558e9e..09c9b7665fa2c5 100644 --- a/paddle/fluid/framework/shape_inference.h +++ b/paddle/fluid/framework/shape_inference.h @@ -41,8 +41,8 @@ class OpVersionMap_OpVersionPair; class ProgramDesc; class VarDesc; class VarType; -class VarType_LoDTensorArrayDesc; -class VarType_LoDTensorDesc; +class VarType_DenseTensorArrayDesc; +class VarType_DenseTensorDesc; class VarType_ReaderDesc; class VarType_TensorDesc; class VarType_Tuple; diff --git a/paddle/fluid/framework/var_desc.cc b/paddle/fluid/framework/var_desc.cc index 89ec0ca797d617..497b1c636088ec 100644 --- a/paddle/fluid/framework/var_desc.cc +++ b/paddle/fluid/framework/var_desc.cc @@ -58,11 +58,11 @@ void VarDesc::SetShape(const std::vector &dims) { void VarDesc::SetTensorDescNum(size_t num) { switch (desc_.type().type()) { case proto::VarType::READER: { - auto *lod_tensors_ptr = - desc_.mutable_type()->mutable_reader()->mutable_lod_tensor(); - lod_tensors_ptr->Clear(); + auto *dense_tensors_ptr = + desc_.mutable_type()->mutable_reader()->mutable_dense_tensor(); + dense_tensors_ptr->Clear(); for (size_t i = 0; i < num; ++i) { - lod_tensors_ptr->Add(); + dense_tensors_ptr->Add(); } return; } break; @@ -78,7 +78,7 @@ void VarDesc::SetTensorDescNum(size_t num) { size_t VarDesc::GetTensorDescNum() const { switch (desc_.type().type()) { case proto::VarType::READER: - return desc_.type().reader().lod_tensor_size(); + return desc_.type().reader().dense_tensor_size(); break; default: PADDLE_THROW( @@ -162,7 +162,7 @@ std::vector VarDesc::GetDataTypes() const { void VarDesc::SetLoDLevel(int32_t lod_level) { switch (desc_.type().type()) { case proto::VarType::DENSE_TENSOR: - desc_.mutable_type()->mutable_lod_tensor()->set_lod_level(lod_level); + desc_.mutable_type()->mutable_dense_tensor()->set_lod_level(lod_level); break; case proto::VarType::DENSE_TENSOR_ARRAY: desc_.mutable_type()->mutable_tensor_array()->set_lod_level(lod_level); @@ -187,9 +187,9 @@ void VarDesc::SetLoDLevels(const std::vector &multiple_lod_level) { switch (desc_.type().type()) { case proto::VarType::READER: { size_t i = 0; - for (auto &lod_tensor : - *desc_.mutable_type()->mutable_reader()->mutable_lod_tensor()) { - lod_tensor.set_lod_level(multiple_lod_level[i++]); + for (auto &dense_tensor : + *desc_.mutable_type()->mutable_reader()->mutable_dense_tensor()) { + dense_tensor.set_lod_level(multiple_lod_level[i++]); } } break; default: @@ -203,7 +203,7 @@ void VarDesc::SetLoDLevels(const std::vector &multiple_lod_level) { int32_t VarDesc::GetLoDLevel() const { switch (desc_.type().type()) { case proto::VarType::DENSE_TENSOR: - return desc_.type().lod_tensor().lod_level(); + return desc_.type().dense_tensor().lod_level(); case proto::VarType::DENSE_TENSOR_ARRAY: return desc_.type().tensor_array().lod_level(); default: @@ -217,9 +217,9 @@ std::vector VarDesc::GetLoDLevels() const { std::vector res; switch (desc_.type().type()) { case proto::VarType::READER: - res.reserve(desc_.type().reader().lod_tensor_size()); - for (auto &lod_tensor : desc_.type().reader().lod_tensor()) { - res.push_back(lod_tensor.lod_level()); + res.reserve(desc_.type().reader().dense_tensor_size()); + for (auto &dense_tensor : desc_.type().reader().dense_tensor()) { + res.push_back(dense_tensor.lod_level()); } return res; break; @@ -243,7 +243,7 @@ const proto::VarType::TensorDesc &VarDesc::tensor_desc() const { case proto::VarType::SELECTED_ROWS: return desc_.type().selected_rows(); case proto::VarType::DENSE_TENSOR: - return desc_.type().lod_tensor().tensor(); + return desc_.type().dense_tensor().tensor(); case proto::VarType::DENSE_TENSOR_ARRAY: return desc_.type().tensor_array().tensor(); case proto::VarType::STRINGS: @@ -268,8 +268,8 @@ std::vector VarDesc::tensor_descs() const { res.reserve(GetTensorDescNum()); switch (desc_.type().type()) { case proto::VarType::READER: - for (const auto &lod_tensor : desc_.type().reader().lod_tensor()) { - res.push_back(lod_tensor.tensor()); + for (const auto &dense_tensor : desc_.type().reader().dense_tensor()) { + res.push_back(dense_tensor.tensor()); } return res; default: @@ -292,7 +292,7 @@ proto::VarType::TensorDesc *VarDesc::mutable_tensor_desc() { case proto::VarType::SELECTED_ROWS: return desc_.mutable_type()->mutable_selected_rows(); case proto::VarType::DENSE_TENSOR: - return desc_.mutable_type()->mutable_lod_tensor()->mutable_tensor(); + return desc_.mutable_type()->mutable_dense_tensor()->mutable_tensor(); case proto::VarType::DENSE_TENSOR_ARRAY: return desc_.mutable_type()->mutable_tensor_array()->mutable_tensor(); case proto::VarType::STRINGS: @@ -323,9 +323,9 @@ std::vector VarDesc::mutable_tensor_descs() { res.reserve(GetTensorDescNum()); switch (desc_.type().type()) { case proto::VarType::READER: - for (auto &lod_tensor : - *desc_.mutable_type()->mutable_reader()->mutable_lod_tensor()) { - res.push_back(lod_tensor.mutable_tensor()); + for (auto &dense_tensor : + *desc_.mutable_type()->mutable_reader()->mutable_dense_tensor()) { + res.push_back(dense_tensor.mutable_tensor()); } return res; default: diff --git a/paddle/phi/core/framework/framework.proto b/paddle/phi/core/framework/framework.proto index 73df6bf6589487..4a27346ceb9f54 100644 --- a/paddle/phi/core/framework/framework.proto +++ b/paddle/phi/core/framework/framework.proto @@ -195,19 +195,19 @@ message VarType { } optional TensorDesc selected_rows = 2; - message LoDTensorDesc { + message DenseTensorDesc { required TensorDesc tensor = 1; optional int32 lod_level = 2 [ default = 0 ]; } - optional LoDTensorDesc lod_tensor = 3; + optional DenseTensorDesc dense_tensor = 3; - message LoDTensorArrayDesc { + message DenseTensorArrayDesc { required TensorDesc tensor = 1; optional int32 lod_level = 2 [ default = 0 ]; } - optional LoDTensorArrayDesc tensor_array = 4; + optional DenseTensorArrayDesc tensor_array = 4; - message ReaderDesc { repeated LoDTensorDesc lod_tensor = 1; } + message ReaderDesc { repeated DenseTensorDesc dense_tensor = 1; } optional ReaderDesc reader = 5; message Tuple { repeated Type element_type = 1; } From 5c1124b8aa2a89b1fabbe2081a87be8c129d4c2c Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Thu, 28 Nov 2024 10:36:51 +0800 Subject: [PATCH 040/288] [PIR] speed up set device id (#69676) * speed up set device id --- paddle/phi/backends/gpu/cuda/cuda_info.cc | 40 +++++++++++++++++------ paddle/phi/backends/gpu/rocm/rocm_info.cc | 39 +++++++++++++++++----- 2 files changed, 60 insertions(+), 19 deletions(-) diff --git a/paddle/phi/backends/gpu/cuda/cuda_info.cc b/paddle/phi/backends/gpu/cuda/cuda_info.cc index 962a63a808cada..af8c38be531a10 100644 --- a/paddle/phi/backends/gpu/cuda/cuda_info.cc +++ b/paddle/phi/backends/gpu/cuda/cuda_info.cc @@ -245,16 +245,36 @@ const gpuDeviceProp &GetDeviceProperties(int id) { } void SetDeviceId(int id) { - // TODO(qijun): find a better way to cache the cuda device count - PADDLE_ENFORCE_LT(id, - GetGPUDeviceCount(), - common::errors::InvalidArgument( - "Device id must be less than GPU count, " - "but received id is: %d. GPU count is: %d.", - id, - GetGPUDeviceCount())); - PADDLE_RETRY_CUDA_SUCCESS(cudaSetDevice(id)); - VLOG(4) << "SetDeviceId " << id; + static thread_local bool first_call = true; + if (first_call) { + PADDLE_ENFORCE_LT(id, + GetGPUDeviceCount(), + common::errors::InvalidArgument( + "Device id must be less than GPU count, " + "but received id is: %d. GPU count is: %d.", + id, + GetGPUDeviceCount())); + + PADDLE_RETRY_CUDA_SUCCESS(cudaSetDevice(id)); + VLOG(4) << "SetDeviceId " << id; + first_call = false; + return; + } + + int prev_id; + PADDLE_ENFORCE_GPU_SUCCESS(cudaGetDevice(&prev_id)); + if (prev_id != id) { + PADDLE_ENFORCE_LT(id, + GetGPUDeviceCount(), + common::errors::InvalidArgument( + "Device id must be less than GPU count, " + "but received id is: %d. GPU count is: %d.", + id, + GetGPUDeviceCount())); + + PADDLE_RETRY_CUDA_SUCCESS(cudaSetDevice(id)); + VLOG(4) << "SetDeviceId " << id; + } } void GpuMemcpyAsync(void *dst, diff --git a/paddle/phi/backends/gpu/rocm/rocm_info.cc b/paddle/phi/backends/gpu/rocm/rocm_info.cc index f40a6a46c4e9a7..45f6ecf556f466 100644 --- a/paddle/phi/backends/gpu/rocm/rocm_info.cc +++ b/paddle/phi/backends/gpu/rocm/rocm_info.cc @@ -241,15 +241,36 @@ const gpuDeviceProp &GetDeviceProperties(int id) { } void SetDeviceId(int id) { - // TODO(qijun): find a better way to cache the cuda device count - PADDLE_ENFORCE_LT(id, - GetGPUDeviceCount(), - common::errors::InvalidArgument( - "Device id must be less than GPU count, " - "but received id is: %d. GPU count is: %d.", - id, - GetGPUDeviceCount())); - PADDLE_RETRY_CUDA_SUCCESS(hipSetDevice(id)); + static thread_local bool first_call = true; + if (first_call) { + PADDLE_ENFORCE_LT(id, + GetGPUDeviceCount(), + common::errors::InvalidArgument( + "Device id must be less than GPU count, " + "but received id is: %d. GPU count is: %d.", + id, + GetGPUDeviceCount())); + + PADDLE_RETRY_CUDA_SUCCESS(hipSetDevice(id)); + VLOG(4) << "SetDeviceId " << id; + first_call = false; + return; + } + + int prev_id; + PADDLE_ENFORCE_GPU_SUCCESS(hipGetDevice(&prev_id)); + if (prev_id != id) { + PADDLE_ENFORCE_LT(id, + GetGPUDeviceCount(), + common::errors::InvalidArgument( + "Device id must be less than GPU count, " + "but received id is: %d. GPU count is: %d.", + id, + GetGPUDeviceCount())); + + PADDLE_RETRY_CUDA_SUCCESS(hipSetDevice(id)); + VLOG(4) << "SetDeviceId " << id; + } } void GpuMemcpyAsync(void *dst, From 5b091ce37001ef75968a2ef7af30a31e38215d39 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Thu, 28 Nov 2024 10:45:49 +0800 Subject: [PATCH 041/288] [PIR] shape64 bug fix2 (#69768) * fix bug * refine * refine --- paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index c4ad2a474f9e60..833be0e82335d6 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1726,10 +1726,10 @@ void tile_grad(const Tensor& x, expand_shape_vec.size() <= 8) { auto repeat = repeat_times_data.back(); auto orig_size = - cast(out_grad_shape_vec.back() / repeat, DataType::INT32); + cast(out_grad_shape_vec.back() / repeat, DataType::INT64); size_t out_grad_last_index = out_grad_shape_vec.size() - 1; expand_shape_vec[out_grad_last_index] = - full({1}, repeat, DataType::INT32); + full({1}, repeat, DataType::INT64); expand_shape_vec.insert( expand_shape_vec.begin() + out_grad_shape_vec.size(), orig_size); From 9e405f7d04565f7e8ff2dfbcd840716f0f1566bf Mon Sep 17 00:00:00 2001 From: zhengzhonghui Date: Thu, 28 Nov 2024 10:58:58 +0800 Subject: [PATCH 042/288] [Auto Parallel] support global mesh output with pipeline (#69628) * [Auto Parallel] add global mesh plan * [Auto Parallel] support global mesh output with pipeline * [Auto Parallel] support global mesh output with pipeline * [Auto Parallel] support global mesh output with pipeline --- .../auto_parallel/intermediate/parallelize.py | 8 +- .../intermediate/pipeline_parallel.py | 143 ++++++++++++++---- .../intermediate/tensor_parallel.py | 18 ++- .../hybrid_strategy/parallel_api.py | 5 +- .../hybrid_strategy/single_llama_model.py | 23 ++- 5 files changed, 151 insertions(+), 46 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py index 246fc8ea33078c..5d1510e01a946e 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py +++ b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py @@ -26,13 +26,13 @@ def parallelize( if pp_config is not None: assert isinstance(pp_config, dict) model, optimizer = pipeline_parallel( - model, optimizer, pp_config.get('split_spec') + model, + optimizer, + pp_config, ) if mp_config is not None: assert isinstance(mp_config, dict) - model, optimizer = tensor_parallel( - model, optimizer, mp_config.get('parallelize_plan') - ) + model, optimizer = tensor_parallel(model, optimizer, mp_config) if dp_config is not None: assert isinstance(dp_config, dict) if 'sharding_level' not in dp_config.keys(): diff --git a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py index 6f0a104a1a2981..efd28a96318819 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py @@ -13,6 +13,7 @@ # limitations under the License. import itertools +import logging import re from collections import OrderedDict from enum import Enum @@ -32,10 +33,21 @@ class SplitPoint(Enum): class PipelineParallel(ParallelModel): - def __init__(self, model, split_spec): + def __init__(self, model, split_spec, global_spec, pipeline_layers): super().__init__(model) self.split_spec = split_spec + self.global_spec = global_spec + self.pipeline_layers = pipeline_layers self.pp_parallelizer = self.pipeline_parallel_fn + self.name_to_layer = {} + for layer_name, layer in model.named_sublayers(): + self.name_to_layer[layer_name] = layer + + def get_layer_by_name(self, name): + assert ( + name in self.name_to_layer + ), f"layer name:{name} not in the model, please check the split_spec" + return self.name_to_layer[name] def pipeline_parallel_fn(self, model): mesh = fleet.auto.get_mesh() @@ -46,12 +58,6 @@ def pipeline_parallel_fn(self, model): for layer_name, layer in model.named_sublayers(): name_to_layer[layer_name] = layer - def get_layer_by_name(name): - assert ( - name in name_to_layer - ), f"layer name:{name} not in the model, please check the split_spec" - return name_to_layer[name] - def forward_post_hook(layer, input, output): pipeline_stage_index = layer.pipeline_stage_index split_point = layer.split_point @@ -86,6 +92,7 @@ def forward_post_hook(layer, input, output): return output def forward_pre_hook(layer, input): + split_point = layer.split_point assert split_point == SplitPoint.BEGINNING # TODO(deepllz): support in the future return input @@ -120,7 +127,7 @@ def forward_pre_hook(layer, input): # step2: insert reshard for name in split_layer_names: - layer = get_layer_by_name(name) + layer = self.get_layer_by_name(name) split_point = self.split_spec[name] layer.split_point = split_point if split_point == SplitPoint.END: @@ -130,45 +137,104 @@ def forward_pre_hook(layer, input): "SplitPoint.BEGINNING is not supported currently" ) layer.register_forward_pre_hook(forward_pre_hook) - + if self.global_spec: + self.process_global_mesh_layers() return model + def process_global_mesh_layers(self): + g_mesh = fleet.auto.get_mesh() + g_mesh = g_mesh.get_mesh_with_dim("pp") + + def forward_post_hook(layer, input, output): + if isinstance(output, (list, tuple)): + global_output = list(output) + for ind in range(len(global_output)): + if is_tensor(global_output[ind]): + global_output[ind] = dist.shard_tensor( + global_output[ind], + g_mesh, + [ + dist.Replicate() + for _ in range(len(g_mesh._shape)) + ], + ) + if isinstance(output, tuple): + global_output = tuple(global_output) + return global_output + elif is_tensor(output): + return dist.shard_tensor( + output, + g_mesh, + [dist.Replicate() for _ in range(len(g_mesh._shape))], + ) + else: + raise TypeError( + "layer output can only be tensor or list/tuple of tensor" + ) + + def forward_pre_hook(layer, input): + pp_idx = getattr(layer, "pipeline_stage_index", 0) + new_input = [] + for t in input: + if is_tensor(t) and t.is_dist() and t.process_mesh == g_mesh: + new_input.append( + dist.reshard( + t, + self.get_mesh(pp_idx), + [dist.Replicate(), dist.Replicate()], + ) + ) + else: + new_input.append(t) + return tuple(new_input) + + for layer_name in self.global_spec: + layer = self.get_layer_by_name(layer_name) + layer.register_forward_post_hook(forward_post_hook) + + for layer_name in self.pipeline_layers: + layer = self.get_layer_by_name(layer_name) + layer.register_forward_pre_hook(forward_pre_hook) -def pipeline_parallel(model, optimizer, split_spec, mesh=None, dimension=None): + +def pipeline_parallel(model, optimizer=None, config=None): """ pipeline_parallel converts model and optimizer to pipelined distributed model Args: model (paddle.nn.Layer): A single card model to be distributed optimizer (paddle.optimizer.Optimizer): An optimizer to be distributed - split_spec (OrderedDict|dict|str|list(str)): The pipeline parallel split point. - if split_spec is a string or list, such as "llama.layer" or ["llama.layerA", "llama.layerB"], Then the layer with same prefix a will be divided equally according to the size of pipeline degree. - if split_spec is a OrderedDict|dict, key is the layer name, and the value is the split position that can be SplitPoint.BEGINNING or SplitPoint.END, the order of the keys is the order of the pipeline stage. - NOTE: dict is also ordered after python3.7, so use dict at this time. - the order of the keys is the order of the pipeline stage - mesh (ProcessMesh): A ProcessMesh Object. - dimension (int|str): The mesh dimension to pipeline the model. + config (dict): { + "split_spec": OrderedDict|dict|str|list(str), The pipeline parallel split point. + if split_spec is a string or list, such as "llama.layer" or ["llama.layerA", "llama.layerB"], Then the layer with same prefix a will be divided equally according to the size of pipeline degree. + if split_spec is a OrderedDict|dict, key is the layer name, and the value is the split position that can be SplitPoint.BEGINNING or SplitPoint.END, the order of the keys is the order of the pipeline stage. + NOTE: dict is also ordered after python3.7, so use dict at this time. + "global_spec": str|list(str), make the output tensor of specific layers on global mesh. + } Returns: PipelineParallel: a distributed model ParallelOptimizer: a distributed optimizer """ - if mesh is None: - mesh = fleet.auto.get_mesh() - assert ( - mesh is not None - ), "global mesh must not be None, please call fleet.auto.set_mesh(global_mesh) firstly" - assert ( - "pp" in mesh.dim_names - ), "pp must in the mesh dim_names when use pipeline_parallel" - else: - assert NotImplementedError( - "Specifying a custom mesh is not supported currently" - ) + split_spec = config.get("split_spec") + if split_spec is None: + logging.warning("No split_spec, pipeline parallel won't do anything.") + return model, optimizer + + mesh = fleet.auto.get_mesh() + assert ( + mesh is not None + ), "global mesh must not be None, please call fleet.auto.set_mesh(global_mesh) firstly" + assert ( + "pp" in mesh.dim_names + ), "pp must in the mesh dim_names when use pipeline_parallel" + + global_spec = config.get("global_spec") if isinstance(split_spec, str): split_spec = [split_spec] + matched_layer_name = None if isinstance(split_spec, (list, tuple)): # match layer_name with split_spec following by a dot and numbers and no other characters # such as split_spec = ["llama.layer"], then llama.layer.0 is matched, llama.layer.0.mlp is not matched @@ -204,10 +270,25 @@ def is_match(layer_name): ) else: split_spec_dict = split_spec + if global_spec: + raise NotImplementedError( + "global_spec should be None if split_spec is a dict" + ) + + if isinstance(global_spec, str): + global_spec = [global_spec] + else: + assert isinstance( + global_spec, (list, tuple) + ), f"global_spec can only be list or list(str), but got:{type(global_spec)}" - logger.info(f"split_spec_dict: {split_spec_dict}") + logger.info( + f"split_spec_dict: {split_spec_dict}, global_spec: {global_spec}" + ) - model = PipelineParallel(model, split_spec_dict) + model = PipelineParallel( + model, split_spec_dict, global_spec, matched_layer_name + ) if optimizer is not None: optimizer = ParallelOptimizer(optimizer) diff --git a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py index 8793d2be5f9b03..b728147c0a488e 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py @@ -395,15 +395,18 @@ def tensor_parallelizer_fn(self, model): return model -def tensor_parallel(model, optimizer=None, parallelize_plan=None): +def tensor_parallel(model, optimizer=None, config=None): """ Tensor parallel. - :param model: paddle.nn.Layer, the model to be shard into tensor parallel. - :param parallelize_plan: Dict, the plan to shard the layer. - :param optimizer: paddle.optimizer.Optimizer, the optimizer. - :return: - model: model after sharding - optimizer: optimizer after sharding + Args: + model (paddle.nn.Layer): the model to be shard into tensor parallel. + optimizer (paddle.optimizer.Optimizer): the optimizer. + config (dict): { + "parallelize_plan": dict, the plan to shard the layer. + } + Returns: + model: model after tp + optimizer: optimizer after tp NOTE: the plan should be a dict maps layer name or parameter name to a split_plan, which will be used to split the layer or the parameter. The name can be written in regular format. @@ -423,6 +426,7 @@ def tensor_parallel(model, optimizer=None, parallelize_plan=None): } ``` """ + parallelize_plan = config.get("parallelize_plan") if parallelize_plan is None: # Do nothing if no plan. logging.warning( diff --git a/test/auto_parallel/hybrid_strategy/parallel_api.py b/test/auto_parallel/hybrid_strategy/parallel_api.py index c1dec86e2197d9..dc6d00936aa7c5 100644 --- a/test/auto_parallel/hybrid_strategy/parallel_api.py +++ b/test/auto_parallel/hybrid_strategy/parallel_api.py @@ -210,7 +210,10 @@ def parallel_model(self, layer): # f"llama.layers.{i * decoders_per_rank - 1}": SplitPoint.END # for i in range(1, self.pp) # } - pp_config = {'split_spec': "llama.layers"} + pp_config = { + 'split_spec': "llama.layers", + "global_spec": "llama.global_layer", + } if self.dp > 1: dp_config = {'sharding_level': self.level} if self.mp > 1: diff --git a/test/auto_parallel/hybrid_strategy/single_llama_model.py b/test/auto_parallel/hybrid_strategy/single_llama_model.py index 34e6911b56494e..9715b313cc401d 100644 --- a/test/auto_parallel/hybrid_strategy/single_llama_model.py +++ b/test/auto_parallel/hybrid_strategy/single_llama_model.py @@ -136,8 +136,8 @@ def __init__(self, config): self.input_layernorm = LlamaRMSNorm(self.config) self.post_attention_layernorm = LlamaRMSNorm(self.config) - def forward(self, hidden_states): - residual = hidden_states + def forward(self, hidden_states, global_tensor): + residual = hidden_states + global_tensor hidden_states = self.input_layernorm(hidden_states) hidden_states = self.self_attn(hidden_states) hidden_states = residual + hidden_states @@ -150,6 +150,19 @@ def forward(self, hidden_states): return hidden_states +class GlobalOutputNet(nn.Layer): + def __init__(self, config) -> None: + super().__init__() + self.config = config + + def forward(self, input): + return ( + input + if input is not None + else paddle.rand([self.config.hidden_size], dtype="float32") + ) + + class LlamaModel(nn.Layer): def __init__(self, config, position_embedding=False): super().__init__() @@ -171,6 +184,8 @@ def __init__(self, config, position_embedding=False): else None ) + self.global_layer = GlobalOutputNet(self.config) + decoder_layers = [] for i in range(self.config.num_hidden_layers): decoder_layers.append(LlamaDecoderLayer(self.config)) @@ -187,8 +202,10 @@ def forward(self, input_ids): position_embeddings = self.position_embedding(position_ids) hidden_states = hidden_states + position_embeddings + global_tensor = self.global_layer(None) + for idx, (decoder_layer) in enumerate(self.layers): - hidden_states = decoder_layer(hidden_states) + hidden_states = decoder_layer(hidden_states, global_tensor) hidden_states = self.norm(hidden_states) From d684a95ee727a0f150f1ed32cd819d98c0126aa4 Mon Sep 17 00:00:00 2001 From: co63oc Date: Thu, 28 Nov 2024 14:04:27 +0800 Subject: [PATCH 043/288] [Lod][fluid_ops] fusion_seqexpand_concat_fc (#69680) * Fix * Fix * Fix --- .../cpu/fusion_seqexpand_concat_fc_kernel.cc | 170 ------------------ paddle/phi/ops/yaml/fused_ops.yaml | 11 -- paddle/phi/ops/yaml/op_compat.yaml | 9 - test/deprecated/legacy_test/CMakeLists.txt | 5 - test/ir/inference/CMakeLists.txt | 1 - .../inference/test_seq_concat_fc_fuse_pass.py | 149 --------------- test/legacy_test/CMakeLists.txt | 5 - .../test_fusion_seqexpand_concat_fc_op.py | 142 --------------- 8 files changed, 492 deletions(-) delete mode 100644 paddle/phi/kernels/fusion/cpu/fusion_seqexpand_concat_fc_kernel.cc delete mode 100644 test/ir/inference/test_seq_concat_fc_fuse_pass.py delete mode 100644 test/legacy_test/test_fusion_seqexpand_concat_fc_op.py diff --git a/paddle/phi/kernels/fusion/cpu/fusion_seqexpand_concat_fc_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_seqexpand_concat_fc_kernel.cc deleted file mode 100644 index f6e6271d93fdb2..00000000000000 --- a/paddle/phi/kernels/fusion/cpu/fusion_seqexpand_concat_fc_kernel.cc +++ /dev/null @@ -1,170 +0,0 @@ -// Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include - -#include "paddle/common/errors.h" -#include "paddle/phi/backends/cpu/cpu_info.h" -#include "paddle/phi/common/float16.h" -#include "paddle/phi/core/enforce.h" -#include "paddle/phi/core/kernel_registry.h" -#include "paddle/phi/core/tensor_utils.h" -#include "paddle/phi/kernels/funcs/blas/blas.h" -#include "paddle/phi/kernels/funcs/cpu_vec.h" -#include "paddle/phi/kernels/funcs/fc_functor.h" - -namespace phi { -namespace fusion { -template -void FusionSeqExpandConcatFCKernel(const Context& dev_ctx, - const std::vector& x, - const DenseTensor& fc_weight, - const paddle::optional& fc_bias, - const std::string& fc_activation, - DenseTensor* out, - DenseTensor* fc_out) { - auto* ref_in = x[0]; - auto ref_lod = ref_in->lod(); - auto in1_lod = x[1]->lod(); - auto ref_dims = ref_in->dims(); // T x M0 - auto in1_dims = x[1]->dims(); // N x M1 - auto w_dims = fc_weight.dims(); - const int N = static_cast(ref_lod[0].size() - 1); - const int total_T = static_cast(ref_dims[0]); - const int M0 = static_cast(ref_dims[1]); - const int M1 = static_cast(in1_dims[1]); - const int D = static_cast(w_dims[1]); - - // some check and fcout should be reshape here - // since infershape can not get lod info - PADDLE_ENFORCE_EQ( - ref_lod.size(), - 1UL, - common::errors::InvalidArgument( - "Only support input lod size is 1, but received value is: %d.", - ref_lod.size())); - PADDLE_ENFORCE_EQ( - in1_lod.size(), - 1UL, - common::errors::InvalidArgument( - "Only support input lod size is 1, but received value is: %d.", - in1_lod.size())); - PADDLE_ENFORCE_EQ(static_cast(in1_lod[0].size() - 1), - N, - common::errors::InvalidArgument( - "Batch size of all inputs should be equal to %d, but " - "received value is: %d.", - N, - static_cast(in1_lod[0].size() - 1))); - PADDLE_ENFORCE_EQ( - static_cast(in1_lod[0][N]), - N, - common::errors::InvalidArgument("Seq_length of other inputs should " - "be %d, but received value is: %d.", - N, - static_cast(in1_lod[0][N]))); - PADDLE_ENFORCE_EQ( - in1_dims[0], - N, - common::errors::InvalidArgument( - "input height should be batch size: %d, but received value is %d.", - N, - in1_dims[0])); - for (size_t i = 2; i < x.size(); ++i) { - PADDLE_ENFORCE_EQ(x[i]->dims()[0], - N, - common::errors::InvalidArgument( - "All other inputs height should be equal to %d, " - "but received value is: %d.", - N, - x[i]->dims()[0])); - PADDLE_ENFORCE_EQ(x[i]->lod(), - in1_lod, - common::errors::InvalidArgument( - "All other inputs should have same lod: %d, but " - "received value is: %d.", - in1_lod, - x[i]->lod())); - } - fc_out->Resize({N, D}); - - std::function fc_act; - if (phi::backends::cpu::MayIUse(phi::backends::cpu::avx)) { - phi::funcs::VecActivations act_functor; - fc_act = act_functor(fc_activation); - } else { - phi::funcs::VecActivations act_functor; - fc_act = act_functor(fc_activation); - } - - const T* ref_in_data = ref_in->data(); - const T* in1_data = x[1]->data(); - const T* w_data = fc_weight.data(); - T* out_data = dev_ctx.template Alloc(out); - T* fc_out_data = dev_ctx.template Alloc(fc_out); - - auto blas = phi::funcs::GetBlas(dev_ctx); - - phi::funcs::FCFunctor fc; - fc(dev_ctx, - total_T, - D, - M0, - ref_in_data, - w_data, - out_data, - fc_bias ? fc_bias->data() : nullptr); - w_data = w_data + M0 * D; - // first write on - blas.MatMul(N, D, M1, in1_data, w_data, fc_out_data); - w_data = w_data + M1 * D; - for (size_t i = 2; i < x.size(); ++i) { - // add on - const T* in_data = x[i]->data(); - const int K = static_cast(x[i]->dims()[1]); - blas.GEMM(CblasNoTrans, - CblasNoTrans, - N, - D, - K, - static_cast(1), - in_data, - K, - w_data, - D, - static_cast(1), - fc_out_data, - D); - w_data = w_data + K * D; - } - T* cur_out_data = out_data; - for (int i = 0; i < N; ++i) { - int seq_len = static_cast(ref_lod[0][i + 1] - ref_lod[0][i]); - T* src = fc_out_data + i * D; - for (int step = 0; step < seq_len; ++step) { - blas.VADD(D, cur_out_data, src, cur_out_data); - cur_out_data = cur_out_data + D; - } - } - fc_act(total_T * D, out_data, out_data); -} -} // namespace fusion -} // namespace phi - -PD_REGISTER_KERNEL(fusion_seqexpand_concat_fc, - CPU, - ALL_LAYOUT, - phi::fusion::FusionSeqExpandConcatFCKernel, - float, - double) {} diff --git a/paddle/phi/ops/yaml/fused_ops.yaml b/paddle/phi/ops/yaml/fused_ops.yaml index 4dad5a03287d1a..845d32b2b67c5d 100644 --- a/paddle/phi/ops/yaml/fused_ops.yaml +++ b/paddle/phi/ops/yaml/fused_ops.yaml @@ -526,17 +526,6 @@ data_type : x intermediate : col_mat -- op : fusion_seqexpand_concat_fc - args : (Tensor[] x, Tensor fc_weight, Tensor fc_bias, str fc_activation="identity") - output : Tensor(out), Tensor(fc_out) - infer_meta : - func : FusionSeqExpandConcatFCInferMeta - kernel : - func : fusion_seqexpand_concat_fc - data_type : x - optional : fc_bias - intermediate : fc_out - - op : fusion_seqpool_concat args: (Tensor[] x, str pooltype = "SUM", int axis = 1) output: Tensor (out) diff --git a/paddle/phi/ops/yaml/op_compat.yaml b/paddle/phi/ops/yaml/op_compat.yaml index 5a036758064461..0506bd4f7c51de 100755 --- a/paddle/phi/ops/yaml/op_compat.yaml +++ b/paddle/phi/ops/yaml/op_compat.yaml @@ -1765,15 +1765,6 @@ context_start : contextStart context_stride : contextStride -- op : fusion_seqexpand_concat_fc - inputs : - x : X - fc_weight : FCWeight - fc_bias : FCBias - outputs : - out : Out - fc_out : FCOut - - op : fusion_seqpool_concat inputs: x : X diff --git a/test/deprecated/legacy_test/CMakeLists.txt b/test/deprecated/legacy_test/CMakeLists.txt index 0d047a8539c116..a4a52074d942f3 100644 --- a/test/deprecated/legacy_test/CMakeLists.txt +++ b/test/deprecated/legacy_test/CMakeLists.txt @@ -204,13 +204,8 @@ if(APPLE) "These tests has been disabled in OSX before being fixed:\n test_fuse_elewise_add_act_pass_deprecated \n test_dist_se_resnext_*" ) # this op is not support on mac - list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op) list(REMOVE_ITEM TEST_OPS test_fuse_elewise_add_act_pass_deprecated) endif() -if(NOT WITH_MKLML) - # this op is not support on openblas - list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op) -endif() if(NOT WITH_MKL OR NOT WITH_AVX) list(REMOVE_ITEM TEST_OPS test_match_matrix_tensor_op) diff --git a/test/ir/inference/CMakeLists.txt b/test/ir/inference/CMakeLists.txt index b589f123db4f0f..cbb6af67baf7db 100755 --- a/test/ir/inference/CMakeLists.txt +++ b/test/ir/inference/CMakeLists.txt @@ -211,7 +211,6 @@ if(WITH_GPU AND TENSORRT_FOUND) PROPERTIES TIMEOUT 250) set_tests_properties(test_conv_eltwiseadd_bn_fuse_pass PROPERTIES TIMEOUT 300) - set_tests_properties(test_seq_concat_fc_fuse_pass PROPERTIES TIMEOUT 200) if(WIN32) set_tests_properties(test_matmul_scale_fuse_pass PROPERTIES TIMEOUT 300) set_tests_properties(test_matmul_v2_scale_fuse_pass PROPERTIES TIMEOUT diff --git a/test/ir/inference/test_seq_concat_fc_fuse_pass.py b/test/ir/inference/test_seq_concat_fc_fuse_pass.py deleted file mode 100644 index 68e446c5a64691..00000000000000 --- a/test/ir/inference/test_seq_concat_fc_fuse_pass.py +++ /dev/null @@ -1,149 +0,0 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest -from functools import partial - -import hypothesis.strategies as st -import numpy as np -from auto_scan_test import IgnoreReasons, PassAutoScanTest -from program_config import OpConfig, ProgramConfig, TensorConfig - - -class TestSeqConcatFcFusePass(PassAutoScanTest): - def is_program_valid(self, program_config: ProgramConfig) -> bool: - return True - - def sample_program_config(self, draw): - ref_level = draw(st.sampled_from([0])) - axis1 = draw(st.sampled_from([1])) - x_col = draw(st.sampled_from([1])) - y_col = draw(st.sampled_from([1])) - axis2 = draw(st.sampled_from([1])) - use_cudnn = False - use_mkldnn = False - act_type = draw(st.sampled_from(["tanh", "sigmoid", "relu"])) - batch_size = draw(st.integers(min_value=1, max_value=1)) - dim = draw(st.integers(min_value=1, max_value=1000)) - - def generate_input(shape): - return np.random.random(shape).astype(np.float32) - - def generate_weight(shape): - return np.random.random(shape).astype(np.float32) - - sequence_expand_op1 = OpConfig( - type="sequence_expand", - inputs={"X": ["input_data1"], "Y": ["input_data2"]}, - outputs={"Out": ["seq_exp1_out"]}, - attrs={"ref_level": ref_level}, - ) - - sequence_expand_op2 = OpConfig( - type="sequence_expand", - inputs={"X": ["input_data1"], "Y": ["input_data3"]}, - outputs={"Out": ["seq_exp2_out"]}, - attrs={"ref_level": ref_level}, - ) - - concat_op = OpConfig( - type="concat", - inputs={"X": ["input_data1", "seq_exp1_out", "seq_exp2_out"]}, - outputs={"Out": ["concat_output"]}, - attrs={'axis': axis1}, - ) - - mul_op = OpConfig( - type="mul", - inputs={"X": ["concat_output"], "Y": ["mul_weight"]}, - outputs={"Out": ["mul_out"]}, - attrs={"x_num_col_dims": x_col, "y_num_col_dims": y_col}, - ) - - elt_op = OpConfig( - type="elementwise_add", - inputs={"X": ["mul_out"], "Y": ["elt_weight"]}, - outputs={"Out": ["elt_out"]}, - attrs={"axis": axis2}, - ) - - act_op = OpConfig( - type=act_type, - inputs={"X": ["elt_out"]}, - outputs={"Out": ["act_out"]}, - attrs={"use_cudnn": use_cudnn, "use_mkldnn": use_mkldnn}, - ) - - model_net = [ - sequence_expand_op1, - sequence_expand_op2, - concat_op, - mul_op, - elt_op, - act_op, - ] - - program_config = ProgramConfig( - ops=model_net, - weights={ - "mul_weight": TensorConfig( - data_gen=partial(generate_weight, [384, dim]) - ), - "elt_weight": TensorConfig( - data_gen=partial(generate_weight, [dim]) - ), - }, - inputs={ - "input_data1": TensorConfig( - data_gen=partial(generate_input, [batch_size, 128]), - lod=[[0, 1]], - ), - "input_data2": TensorConfig( - data_gen=partial(generate_input, [batch_size, 128]), - lod=[[0, 1]], - ), - "input_data3": TensorConfig( - data_gen=partial(generate_input, [batch_size, 128]), - lod=[[0, 1]], - ), - }, - outputs=["act_out"], - ) - - return program_config - - def sample_predictor_configs(self, program_config): - config = self.create_inference_config() - yield config, ["fusion_seqexpand_concat_fc"], (1e-5, 1e-5) - - def add_ignore_pass_case(self): - def teller1(program_config, predictor_config): - if program_config.ops[-1].type == "relu": - return True - return False - - self.add_ignore_check_case( - teller1, - IgnoreReasons.PASS_ACCURACY_ERROR, - "The pass output has diff in a specific case. We need to fix it as soon as possible.", - ) - - def test(self): - self.run_and_statis( - quant=False, passes=["seq_concat_fc_fuse_pass"], max_duration=1000 - ) - - -if __name__ == "__main__": - unittest.main() diff --git a/test/legacy_test/CMakeLists.txt b/test/legacy_test/CMakeLists.txt index d63362ad2d26eb..bc7bb48b1b8aa8 100644 --- a/test/legacy_test/CMakeLists.txt +++ b/test/legacy_test/CMakeLists.txt @@ -236,13 +236,8 @@ if(APPLE) "These tests has been disabled in OSX before being fixed: \n test_detection_map_op \n test_dist_se_resnext_*" ) # this op is not support on mac - list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op) list(REMOVE_ITEM TEST_OPS test_detection_map_op) endif() -if(NOT WITH_MKLML) - # this op is not support on openblas - list(REMOVE_ITEM TEST_OPS test_fusion_seqexpand_concat_fc_op) -endif() if(NOT WITH_MKL OR NOT WITH_AVX) list(REMOVE_ITEM TEST_OPS test_match_matrix_tensor_op) diff --git a/test/legacy_test/test_fusion_seqexpand_concat_fc_op.py b/test/legacy_test/test_fusion_seqexpand_concat_fc_op.py deleted file mode 100644 index 77bbb1e2387678..00000000000000 --- a/test/legacy_test/test_fusion_seqexpand_concat_fc_op.py +++ /dev/null @@ -1,142 +0,0 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import unittest - -import numpy as np -from op_test import OpTest -from test_fusion_lstm_op import ACTIVATION, fc - - -def fusion_seqexpand_concat_fc(xs, lod, w, b, fc_act): - T = sum(lod[0]) - N = len(lod[0]) - num_inputs = len(xs) - D = w.shape[1] - - expanded_inputs = [xs[0]] - for i in range(num_inputs - 1): - x = xs[i + 1] - assert x.shape[0] == N - expanded = np.repeat(x, lod[0], axis=0) - assert expanded.shape[0] == T - assert expanded.shape[1] == x.shape[1] - expanded_inputs.append(expanded) - - fc_input = np.concatenate(expanded_inputs, axis=1) - assert fc_input.shape[0] == T - assert fc_input.shape[1] == w.shape[0] - fc_out = fc(fc_input, w, b) - fc_out = fc_act(fc_out) - assert fc_out.shape[0] == T - assert fc_out.shape[1] == D - return fc_out - - -class TestFusionSeqExpandConcatFCOp(OpTest): - def set_conf(self): - pass - - def setUp(self): - self.op_type = 'fusion_seqexpand_concat_fc' - self.lod = [[3, 5, 8, 2]] - self.inputs_M = [15, 10, 10] - self.D = 20 - self.with_bias = True - self.fc_act = 'relu' - self.set_conf() - - T = sum(self.lod[0]) - bs = len(self.lod[0]) - num_inputs = len(self.inputs_M) - - x0 = np.random.normal(size=(T, self.inputs_M[0])).astype('float32') - xs = [x0] - for i in range(num_inputs - 1): - xi = np.random.normal(size=(bs, self.inputs_M[i + 1])).astype( - 'float32' - ) - xs.append(xi) - - # fc weight and bias - w = np.random.normal(size=(sum(self.inputs_M), self.D)).astype( - 'float32' - ) - b = ( - np.random.normal(size=(1, self.D)).astype('float32') - if self.with_bias - else np.zeros((1, self.D)).astype('float32') - ) - - out = fusion_seqexpand_concat_fc( - xs, self.lod, w, b, ACTIVATION[self.fc_act] - ) - - self.inputs = {'X': [('x0', (x0, self.lod))], 'FCWeight': w} - normal_lod = [[1] * bs] - for i in range(num_inputs - 1): - self.inputs['X'].append(('x%d' % (i + 1), (xs[i + 1], normal_lod))) - - if self.with_bias: - self.inputs['FCBias'] = b - - self.outputs = {'Out': (out, self.lod)} - self.attrs = {'fc_activation': self.fc_act} - - def test_check_output(self): - self.check_output(check_dygraph=False) - - -class TestFusionSECFCOpNonBias(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.with_bias = False - - -class TestFusionSECFCOpNonAct(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.fc_act = 'identity' - - -class TestFusionSECFCOpMD1(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.inputs_M = [3, 4, 2, 1, 5] - self.D = 8 - - -class TestFusionSECFCOpMD2(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.lod = [[5, 6]] - self.inputs_M = [1, 1] - - -class TestFusionSECFCOpBS1_1(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.lod = [[1]] - self.inputs_M = [3, 4, 2] - - -class TestFusionSECFCOpBS1_2(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.lod = [[1]] - self.inputs_M = [3, 4] - - -class TestFusionSECFCOpBS1_3(TestFusionSeqExpandConcatFCOp): - def set_conf(self): - self.lod = [[5]] - self.inputs_M = [6, 3] - - -if __name__ == '__main__': - unittest.main() From dc1f0eb365bcaff363f15e0b217cea7adea349be Mon Sep 17 00:00:00 2001 From: Jianbang Yang Date: Thu, 28 Nov 2024 15:17:49 +0800 Subject: [PATCH 044/288] [XPU] bump XCCL to 3.0.1.1 (#69752) * [XPU] bump XCCL to 3.0.1.1 * fix * fix * fix --- cmake/external/xpu.cmake | 22 +++++++++++++--------- python/env_dict.py.in | 2 ++ python/setup.py.in | 4 ++++ setup.py | 6 ++++++ tools/xpu/pack_paddle_dependence.sh | 3 +++ 5 files changed, 28 insertions(+), 9 deletions(-) diff --git a/cmake/external/xpu.cmake b/cmake/external/xpu.cmake index dd01a51b546d17..99d984b2ba189e 100644 --- a/cmake/external/xpu.cmake +++ b/cmake/external/xpu.cmake @@ -22,6 +22,7 @@ set(XPU_API_LIB_NAME "libxpuapi.so") set(XPU_RT_LIB_NAME "libxpurt.so") set(XPU_CUDA_LIB_NAME "libxpucuda.so") set(XPU_CUDA_RT_LIB_NAME "libcudart.so") +set(XPU_ML_LIB_NAME "libxpuml.so") set(XPU_XFT_LIB_NAME "libxft.so") set(XPU_XPTI_LIB_NAME "libxpti.so") set(XPU_XBLAS_LIB_NAME "libxpu_blas.so") @@ -31,7 +32,7 @@ set(XPU_XPUDNN_LIB_NAME "libxpu_dnn.so") if(NOT DEFINED XPU_XHPC_BASE_DATE) set(XPU_XHPC_BASE_DATE "dev/20241127") endif() -set(XPU_XCCL_BASE_VERSION "3.0.0.5") # For XRE5 +set(XPU_XCCL_BASE_VERSION "3.0.1.1") # For XRE5 if(NOT DEFINED XPU_XFT_BASE_VERSION) set(XPU_XFT_BASE_VERSION "20230602") endif() @@ -146,6 +147,7 @@ set(XPU_XBLAS_LIB "${XPU_LIB_DIR}/${XPU_XBLAS_LIB_NAME}") set(XPU_RT_LIB "${XPU_LIB_DIR}/${XPU_RT_LIB_NAME}") set(XPU_CUDA_LIB "${XPU_LIB_DIR}/${XPU_CUDA_LIB_NAME}") set(XPU_CUDA_RT_LIB "${XPU_LIB_DIR}/${XPU_CUDA_RT_LIB_NAME}") +set(XPU_ML_LIB "${XPU_LIB_DIR}/${XPU_ML_LIB_NAME}") set(XPU_XFA_LIB "${XPU_LIB_DIR}/${XPU_XFA_LIB_NAME}") set(XPU_XPUDNN_LIB "${XPU_LIB_DIR}/${XPU_XPUDNN_LIB_NAME}") @@ -190,6 +192,7 @@ if(WITH_XPU_XRE5) BUILD_BYPRODUCTS ${XPU_XFA_LIB} BUILD_BYPRODUCTS ${XPU_RT_LIB} BUILD_BYPRODUCTS ${XPU_CUDA_RT_LIB} + BUILD_BYPRODUCTS ${XPU_ML_LIB} BUILD_BYPRODUCTS ${XPU_BKCL_LIB}) else() ExternalProject_Add( @@ -221,12 +224,6 @@ set_property(TARGET shared_xpuapi PROPERTY IMPORTED_LOCATION "${XPU_API_LIB}") # for cc_library(xxx SRCS xxx.c DEPS xpulib) generate_dummy_static_lib(LIB_NAME "xpulib" GENERATOR "xpu.cmake") -if(WITH_XPU_XRE5) - target_link_libraries(xpulib ${XPU_API_LIB} ${XPU_RT_LIB} ${XPU_CUDA_RT_LIB}) -else() - target_link_libraries(xpulib ${XPU_API_LIB} ${XPU_RT_LIB}) -endif() - if(WITH_XPU_XFT) message(STATUS "Compile with XPU XFT!") add_definitions(-DPADDLE_WITH_XPU_XFT) @@ -272,13 +269,20 @@ if(WITH_XPU_XRE5) xpulib ${XPU_RT_LIB} ${XPU_CUDA_RT_LIB} - ${XPU_BKCL_LIB} ${XPU_XBLAS_LIB} ${XPU_API_LIB} ${XPU_XFA_LIB} ${XPU_XPUDNN_LIB}) else() - target_link_libraries(xpulib ${XPU_RT_LIB} ${XPU_BKCL_LIB} ${XPU_API_LIB}) + target_link_libraries(xpulib ${XPU_RT_LIB} ${XPU_API_LIB}) +endif() + +if(WITH_XPU_BKCL) + if(WITH_XPU_XRE5) + target_link_libraries(xpulib ${XPU_ML_LIB} ${XPU_BKCL_LIB}) + else() + target_link_libraries(xpulib ${XPU_BKCL_LIB}) + endif() endif() add_dependencies(xpulib ${XPU_PROJECT}) diff --git a/python/env_dict.py.in b/python/env_dict.py.in index bce8b0900fab66..ff35b5691c4fa8 100644 --- a/python/env_dict.py.in +++ b/python/env_dict.py.in @@ -65,6 +65,8 @@ env_dict={ 'WITH_XPU_XRE5':'@WITH_XPU_XRE5@', 'XPU_CUDA_RT_LIB':'@XPU_CUDA_RT_LIB@', 'XPU_CUDA_RT_LIB_NAME':'@XPU_CUDA_RT_LIB_NAME@', + 'XPU_ML_LIB':'@XPU_ML_LIB@', + 'XPU_ML_LIB_NAME':'@XPU_ML_LIB_NAME@', 'WITH_XPU_BKCL':'@WITH_XPU_BKCL@', 'XPU_BKCL_LIB':'@XPU_BKCL_LIB@', 'XPU_BKCL_LIB_NAME':'@XPU_BKCL_LIB_NAME@', diff --git a/python/setup.py.in b/python/setup.py.in index c4d8df719e5fb1..7ecea3146b0233 100644 --- a/python/setup.py.in +++ b/python/setup.py.in @@ -1027,6 +1027,10 @@ if '${WITH_XPU}' == 'ON': for xpu_cuda_rt_lib_file in xpu_cuda_rt_lib_list: shutil.copy(xpu_cuda_rt_lib_file, libs_path) package_data['paddle.libs'] += [os.path.basename(xpu_cuda_rt_lib_file)] + xpu_ml_lib_list = glob.glob('${XPU_ML_LIB}*') + for xpu_ml_lib_file in xpu_ml_lib_list: + shutil.copy(xpu_ml_lib_file, libs_path) + package_data['paddle.libs'] += [os.path.basename(xpu_ml_lib_file)] shutil.copy('${XPU_XBLAS_LIB}', libs_path) package_data['paddle.libs'] += ['${XPU_XBLAS_LIB_NAME}'] shutil.copy('${XPU_XFA_LIB}', libs_path) diff --git a/setup.py b/setup.py index 95cd638ab49e7d..c5e5e4b134b605 100644 --- a/setup.py +++ b/setup.py @@ -1482,6 +1482,12 @@ def get_package_data_and_package_dir(): package_data['paddle.libs'] += [ os.path.basename(xpu_cuda_rt_lib_file) ] + xpu_ml_lib_list = glob.glob(env_dict.get("XPU_ML_LIB") + '*') + for xpu_ml_lib_file in xpu_ml_lib_list: + shutil.copy(xpu_ml_lib_file, libs_path) + package_data['paddle.libs'] += [ + os.path.basename(xpu_ml_lib_file) + ] shutil.copy(env_dict.get("XPU_XBLAS_LIB"), libs_path) package_data['paddle.libs'] += [env_dict.get("XPU_XBLAS_LIB_NAME")] shutil.copy(env_dict.get("XPU_XFA_LIB"), libs_path) diff --git a/tools/xpu/pack_paddle_dependence.sh b/tools/xpu/pack_paddle_dependence.sh index 9d8ed66db6a24a..0d2165a5f64c80 100644 --- a/tools/xpu/pack_paddle_dependence.sh +++ b/tools/xpu/pack_paddle_dependence.sh @@ -64,6 +64,7 @@ function xre_prepare() { check_files ${XRE_DIR_NAME}/include/xpu/runtime.h ${XRE_DIR_NAME}/so/libxpurt.so if [ "$WITH_XPU_XRE5" -eq 1 ]; then check_files ${XRE_DIR_NAME}/so/libcudart.so + check_files ${XRE_DIR_NAME}/so/libxpuml.so fi cp -r ${XRE_DIR_NAME}/include/xpu/* xpu/include/xpu/ cp -r ${XRE_DIR_NAME}/so/* xpu/lib/ @@ -94,6 +95,8 @@ function xccl_prepare() { check_files ${XCCL_DIR_NAME}/include/bkcl.h ${XCCL_DIR_NAME}/so/libbkcl.so cp -r ${XCCL_DIR_NAME}/include/* xpu/include/xpu/ cp -r ${XCCL_DIR_NAME}/so/* xpu/lib/ + # FIXME(yangjianbang): 待bkcl增加RPATH后, 删除以下代码 + patchelf --set-rpath '$ORIGIN/' xpu/lib/libbkcl.so } function local_prepare() { From 510b3ed160507d6d5d5c974d2f2838b31ce64313 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 28 Nov 2024 15:39:40 +0800 Subject: [PATCH 045/288] [API] Optimize `paddle.where` and `paddle.where_` in eager mode (#69556) * optimize where * fix code * split code into dynamic and pir mode * fix where for pir/old ir bug --- python/paddle/tensor/search.py | 140 +++++++++++++++++------------- test/legacy_test/test_inplace.py | 16 ++-- test/legacy_test/test_where_op.py | 94 +++++++++++++++++++- test/xpu/test_where_op_xpu.py | 12 ++- 4 files changed, 190 insertions(+), 72 deletions(-) diff --git a/python/paddle/tensor/search.py b/python/paddle/tensor/search.py index 58a20c37661cf9..0d75bb92a38130 100755 --- a/python/paddle/tensor/search.py +++ b/python/paddle/tensor/search.py @@ -770,58 +770,83 @@ def where( if x is None or y is None: raise ValueError("either both or neither of x and y should be given") + # NOTE: We might need to adapt the broadcast_shape and broadcast_to for dynamic shape + # so dynamic and pir branch can be merged into one code block condition_shape = list(condition.shape) x_shape = list(x.shape) y_shape = list(y.shape) - if x_shape == y_shape and condition_shape == x_shape: - broadcast_condition = condition + if in_dynamic_mode(): + broadcast_shape = paddle.broadcast_shape(x_shape, y_shape) + broadcast_shape = paddle.broadcast_shape( + broadcast_shape, condition_shape + ) + broadcast_x = x broadcast_y = y - else: - zeros_like_x = paddle.zeros_like(x) - zeros_like_y = paddle.zeros_like(y) - zeros_like_condition = paddle.zeros_like(condition) - zeros_like_condition = paddle.cast(zeros_like_condition, x.dtype) - cast_cond = paddle.cast(condition, x.dtype) - - broadcast_zeros = paddle.add(zeros_like_x, zeros_like_y) - broadcast_zeros = paddle.add(broadcast_zeros, zeros_like_condition) - broadcast_x = paddle.add(x, broadcast_zeros) - broadcast_y = paddle.add(y, broadcast_zeros) - broadcast_condition = paddle.add(cast_cond, broadcast_zeros) - broadcast_condition = paddle.cast(broadcast_condition, 'bool') + broadcast_condition = condition + + if condition_shape != broadcast_shape: + broadcast_condition = paddle.broadcast_to( + broadcast_condition, broadcast_shape + ) + if x_shape != broadcast_shape: + broadcast_x = paddle.broadcast_to(broadcast_x, broadcast_shape) + if y_shape != broadcast_shape: + broadcast_y = paddle.broadcast_to(broadcast_y, broadcast_shape) - if in_dynamic_or_pir_mode(): return _C_ops.where(broadcast_condition, broadcast_x, broadcast_y) + else: - check_variable_and_dtype(condition, 'condition', ['bool'], 'where') - check_variable_and_dtype( - x, - 'x', - ['uint16', 'float16', 'float32', 'float64', 'int32', 'int64'], - 'where', - ) - check_variable_and_dtype( - y, - 'y', - ['uint16', 'float16', 'float32', 'float64', 'int32', 'int64'], - 'where', - ) - helper = LayerHelper("where", **locals()) - out = helper.create_variable_for_type_inference(dtype=x.dtype) + # for PIR and old IR + if x_shape == y_shape and condition_shape == x_shape: + broadcast_condition = condition + broadcast_x = x + broadcast_y = y + else: + zeros_like_x = paddle.zeros_like(x) + zeros_like_y = paddle.zeros_like(y) + zeros_like_condition = paddle.zeros_like(condition) + zeros_like_condition = paddle.cast(zeros_like_condition, x.dtype) + cast_cond = paddle.cast(condition, x.dtype) + + broadcast_zeros = paddle.add(zeros_like_x, zeros_like_y) + broadcast_zeros = paddle.add(broadcast_zeros, zeros_like_condition) + broadcast_x = paddle.add(x, broadcast_zeros) + broadcast_y = paddle.add(y, broadcast_zeros) + broadcast_condition = paddle.add(cast_cond, broadcast_zeros) + broadcast_condition = paddle.cast(broadcast_condition, 'bool') - helper.append_op( - type='where', - inputs={ - 'Condition': broadcast_condition, - 'X': broadcast_x, - 'Y': broadcast_y, - }, - outputs={'Out': [out]}, - ) + if in_pir_mode(): + return _C_ops.where(broadcast_condition, broadcast_x, broadcast_y) + else: + check_variable_and_dtype(condition, 'condition', ['bool'], 'where') + check_variable_and_dtype( + x, + 'x', + ['uint16', 'float16', 'float32', 'float64', 'int32', 'int64'], + 'where', + ) + check_variable_and_dtype( + y, + 'y', + ['uint16', 'float16', 'float32', 'float64', 'int32', 'int64'], + 'where', + ) + helper = LayerHelper("where", **locals()) + out = helper.create_variable_for_type_inference(dtype=x.dtype) + + helper.append_op( + type='where', + inputs={ + 'Condition': broadcast_condition, + 'X': broadcast_x, + 'Y': broadcast_y, + }, + outputs={'Out': [out]}, + ) - return out + return out @inplace_apis_in_dygraph_only @@ -844,23 +869,22 @@ def where_( condition_shape = list(condition.shape) x_shape = list(x.shape) y_shape = list(y.shape) - if x_shape == y_shape and condition_shape == x_shape: - broadcast_condition = condition - broadcast_x = x - broadcast_y = y - else: - zeros_like_x = paddle.zeros_like(x) - zeros_like_y = paddle.zeros_like(y) - zeros_like_condition = paddle.zeros_like(condition) - zeros_like_condition = paddle.cast(zeros_like_condition, x.dtype) - cast_cond = paddle.cast(condition, x.dtype) - - broadcast_zeros = paddle.add(zeros_like_x, zeros_like_y) - broadcast_zeros = paddle.add(broadcast_zeros, zeros_like_condition) - broadcast_x = x.add_(broadcast_zeros) - broadcast_y = paddle.add(y, broadcast_zeros) - broadcast_condition = paddle.add(cast_cond, broadcast_zeros) - broadcast_condition = paddle.cast(broadcast_condition, 'bool') + + broadcast_shape = paddle.broadcast_shape(x_shape, y_shape) + broadcast_shape = paddle.broadcast_shape(broadcast_shape, condition_shape) + + broadcast_x = x + broadcast_y = y + broadcast_condition = condition + + if condition_shape != broadcast_shape: + broadcast_condition = paddle.broadcast_to( + broadcast_condition, broadcast_shape + ) + if x_shape != broadcast_shape: + broadcast_x = paddle.broadcast_to(broadcast_x, broadcast_shape) + if y_shape != broadcast_shape: + broadcast_y = paddle.broadcast_to(broadcast_y, broadcast_shape) if in_dynamic_mode(): return _C_ops.where_(broadcast_condition, broadcast_x, broadcast_y) diff --git a/test/legacy_test/test_inplace.py b/test/legacy_test/test_inplace.py index bb704f600857ae..8095110678f9a9 100755 --- a/test/legacy_test/test_inplace.py +++ b/test/legacy_test/test_inplace.py @@ -271,13 +271,13 @@ def test_forward_version(self): self.assertEqual(var.inplace_version, 0) inplace_var = self.inplace_api_processing(var) - self.assertEqual(var.inplace_version, 2) + self.assertEqual(var.inplace_version, 1) inplace_var[0] = 2 - self.assertEqual(var.inplace_version, 3) + self.assertEqual(var.inplace_version, 2) inplace_var = self.inplace_api_processing(inplace_var) - self.assertEqual(var.inplace_version, 5) + self.assertEqual(var.inplace_version, 3) def test_backward_error(self): # It raises an error because the inplace operator will result @@ -295,7 +295,7 @@ def test_backward_error(self): loss = paddle.nn.functional.relu(var_c) with self.assertRaisesRegex( RuntimeError, - f"received tensor_version:{2} != wrapper_version_snapshot:{0}", + f"received tensor_version:{1} != wrapper_version_snapshot:{0}", ): loss.backward() @@ -1298,13 +1298,13 @@ def test_forward_version(self): self.assertEqual(var.inplace_version, 0) inplace_var = self.inplace_api_processing(var) - self.assertEqual(var.inplace_version, 2) + self.assertEqual(var.inplace_version, 1) inplace_var[0] = 2 - self.assertEqual(var.inplace_version, 3) + self.assertEqual(var.inplace_version, 2) inplace_var = self.inplace_api_processing(inplace_var) - self.assertEqual(var.inplace_version, 5) + self.assertEqual(var.inplace_version, 3) def test_backward_error(self): # It raises an error because the inplace operator will result @@ -1322,7 +1322,7 @@ def test_backward_error(self): loss = paddle.nn.functional.relu(var_c) with self.assertRaisesRegex( RuntimeError, - "received tensor_version:2 != wrapper_version_snapshot:0", + "received tensor_version:1 != wrapper_version_snapshot:0", ): loss.backward() diff --git a/test/legacy_test/test_where_op.py b/test/legacy_test/test_where_op.py index 8b5d967e32ba13..626d98aabf4f1c 100644 --- a/test/legacy_test/test_where_op.py +++ b/test/legacy_test/test_where_op.py @@ -276,9 +276,15 @@ def test_api_broadcast(self, use_cuda=False): with paddle.static.program_guard(main_program): x = paddle.static.data(name='x', shape=[-1, 4, 1], dtype='float32') y = paddle.static.data(name='y', shape=[-1, 4, 2], dtype='float32') - x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype('float32') - y_i = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]).astype( - 'float32' + x_i = ( + np.array([[0.9383, 0.1983, 3.2, 1.2]]) + .astype('float32') + .reshape([1, 4, 1]) + ) + y_i = ( + np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]) + .astype('float32') + .reshape([1, 4, 2]) ) result = paddle.where((x > 1), x=x, y=y) for use_cuda in [False, True]: @@ -805,6 +811,88 @@ def test_where_condition(self): np.testing.assert_allclose(expect_out, np.array(res), rtol=1e-05) +class TestWhereDygraphAPIBroadcast(unittest.TestCase): + def test_broadcast_scalar(self): + with base.dygraph.guard(): + x_i = np.random.randn(4, 5, 6).astype('float64') + y_i = -1.0 + cond_i = np.random.randn(1, 1, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + out = paddle.where(cond, x, y) + np.testing.assert_array_equal( + out.numpy(), np.where(cond_i, x_i, y_i) + ) + + def test_broadcast_to_x(self): + with base.dygraph.guard(): + x_i = np.random.randn(4, 5, 6).astype('float64') + y_i = np.random.randn(1, 5, 6).astype('float64') + cond_i = np.random.randn(1, 1, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + out = paddle.where(cond, x, y) + np.testing.assert_array_equal( + out.numpy(), np.where(cond_i, x_i, y_i) + ) + + def test_broadcast_to_y(self): + with base.dygraph.guard(): + x_i = np.random.randn(1, 5, 6).astype('float64') + y_i = np.random.randn(4, 5, 6).astype('float64') + cond_i = np.random.randn(1, 1, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + out = paddle.where(cond, x, y) + np.testing.assert_array_equal( + out.numpy(), np.where(cond_i, x_i, y_i) + ) + + def test_broadcast_to_cond(self): + with base.dygraph.guard(): + x_i = np.random.randn(1, 1, 6).astype('float64') + y_i = np.random.randn(1, 5, 1).astype('float64') + cond_i = np.random.randn(4, 5, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + out = paddle.where(cond, x, y) + np.testing.assert_array_equal( + out.numpy(), np.where(cond_i, x_i, y_i) + ) + + def test_can_not_broadcast(self): + with base.dygraph.guard(): + x_i = np.random.randn(1, 1, 6).astype('float64') + y_i = np.random.randn(1, 5, 3).astype('float64') + cond_i = np.random.randn(4, 5, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + + with self.assertRaises(ValueError): + _ = paddle.where(cond, x, y) + + +class TestWhereDygraphAPIDtypePromotion(unittest.TestCase): + def test_dtype_auto_promotion_float(self): + with base.dygraph.guard(): + x_i = np.random.randn(4, 5, 6).astype('float32') + y_i = np.random.randn(4, 5, 6).astype('float64') + cond_i = np.random.randn(4, 5, 6).astype('bool') + x = paddle.to_tensor(x_i) + y = paddle.to_tensor(y_i) + cond = paddle.to_tensor(cond_i) + out = paddle.where(cond, x, y) + self.assertEqual(out.dtype, y.dtype) + np.testing.assert_array_equal( + out.numpy(), np.where(cond_i, x_i, y_i) + ) + + class TestWhereOpError(unittest.TestCase): def test_errors(self): with paddle.static.program_guard( diff --git a/test/xpu/test_where_op_xpu.py b/test/xpu/test_where_op_xpu.py index 10dd2fa13a9566..71e6c8996fcfd5 100644 --- a/test/xpu/test_where_op_xpu.py +++ b/test/xpu/test_where_op_xpu.py @@ -172,9 +172,15 @@ def test_api_broadcast(self, use_cuda=False): with base.program_guard(train_prog, startup): x = paddle.static.data(name='x', shape=[-1, 4, 1], dtype='float32') y = paddle.static.data(name='y', shape=[-1, 4, 2], dtype='float32') - x_i = np.array([[0.9383, 0.1983, 3.2, 1.2]]).astype("float32") - y_i = np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]).astype( - "float32" + x_i = ( + np.array([[0.9383, 0.1983, 3.2, 1.2]]) + .astype("float32") + .reshape([1, 4, 1]) + ) + y_i = ( + np.array([[1.0, 1.0, 1.0, 1.0], [1.0, 1.0, 1.0, 1.0]]) + .astype("float32") + .reshape([1, 4, 2]) ) result = paddle.where(x > 1, x=x, y=y) From cd3d98898d41080edf10ed5efde319ac4946dee4 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Thu, 28 Nov 2024 16:31:17 +0800 Subject: [PATCH 046/288] [CINN]fix FullWithTensor pd_to_cinn (#69769) --- .../dialect/operator/transforms/pd_to_cinn_pass.cc | 14 ++++++++++++-- 1 file changed, 12 insertions(+), 2 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc index 3ff94995a26c33..84938f27878578 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/pd_to_cinn_pass.cc @@ -969,8 +969,18 @@ class FullWithTensorOpPattern .result(0); } - auto out = - rewriter.Build(value, shape).result(0); + const auto &out = [&]() -> pir::Value { + const auto &out_type = + op->result(0).type().dyn_cast(); + if (out_type.dims().size() == 0) { + const auto &dtype = + op->attribute("dtype").data(); + return rewriter + .Build(std::vector{}, 0.0, dtype) + .result(0); + } + return rewriter.Build(value, shape).result(0); + }(); rewriter.ReplaceAllUsesWith(op.result(0), out); From 121745f1f724b8de1276a483a19ab9716aed9416 Mon Sep 17 00:00:00 2001 From: Nana <49900969+NKNaN@users.noreply.github.com> Date: Thu, 28 Nov 2024 17:22:26 +0800 Subject: [PATCH 047/288] =?UTF-8?q?=E3=80=90Hackathon=207th=20No.25?= =?UTF-8?q?=E3=80=91=E4=B8=BA=20Paddle=20=E6=96=B0=E5=A2=9E=20sparse=5Fdim?= =?UTF-8?q?/dense=5Fdim=20-part=20(#69132)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add sparse dim dense dim * fix example code * fix test * resolve conflict * update pir --- paddle/fluid/pybind/eager_method.cc | 123 +++++++++ paddle/fluid/pybind/pir.cc | 39 ++- paddle/phi/core/sparse_csr_tensor.cc | 12 + paddle/phi/core/sparse_csr_tensor.h | 8 + python/paddle/tensor/tensor.prototype.pyi | 2 + test/legacy_test/test_dense_dim.py | 319 ++++++++++++++++++++++ test/legacy_test/test_sparse_dim.py | 297 ++++++++++++++++++++ 7 files changed, 797 insertions(+), 3 deletions(-) create mode 100644 test/legacy_test/test_dense_dim.py create mode 100644 test/legacy_test/test_sparse_dim.py diff --git a/paddle/fluid/pybind/eager_method.cc b/paddle/fluid/pybind/eager_method.cc index 295e01f84b984f..8b704bfb1f1098 100644 --- a/paddle/fluid/pybind/eager_method.cc +++ b/paddle/fluid/pybind/eager_method.cc @@ -3316,6 +3316,121 @@ static PyObject* tensor_is_contiguous(TensorObject* self, EAGER_CATCH_AND_THROW_RETURN_NULL } +PyDoc_STRVAR(tensor_method_sparse_dim__doc__, + R"DOC(sparse_dim($self, /) +-- + +Returns the number of sparse dimensions of sparse Tensor. + +Note: + **If self is not sparse Tensor, return 0.** + +Returns: + int, sparse dim of self Tensor + +Examples: + + .. code-block:: python + + >>> import paddle + + >>> indices = [[0, 1, 2], [1, 2, 0]] + >>> values = [1.0, 2.0, 3.0] + >>> dense_shape = [3, 3] + >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) + >>> coo.sparse_dim() + 2 + + >>> crows = [0, 2, 3, 5] + >>> cols = [1, 3, 2, 0, 1] + >>> values = [1, 2, 3, 4, 5] + >>> dense_shape = [3, 4] + >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) + >>> csr.sparse_dim() + 2 + + >>> dense = paddle.to_tensor([1, 2, 3]) + >>> dense.sparse_dim() + 0 + +)DOC"); // NOLINT + +static PyObject* tensor_method_sparse_dim(TensorObject* self, + PyObject* args, + PyObject* kwargs) { + EAGER_TRY + if (self->tensor.is_sparse_coo_tensor()) { + auto sparse_coo_tensor = + std::dynamic_pointer_cast(self->tensor.impl()); + return ToPyObject(sparse_coo_tensor->sparse_dim()); + } else if (self->tensor.is_sparse_csr_tensor()) { + auto sparse_csr_tensor = + std::dynamic_pointer_cast(self->tensor.impl()); + return ToPyObject(sparse_csr_tensor->sparse_dim()); + } else { + return ToPyObject(0); + } + EAGER_CATCH_AND_THROW_RETURN_NULL +} + +PyDoc_STRVAR(tensor_method_dense_dim__doc__, + R"DOC(dense_dim($self, /) +-- + +Returns the number of dense dimensions of sparse Tensor. + +Note: + **If self is not sparse Tensor, return len(self.shape).** + +Returns: + int, dense dim of self Tensor + +Examples: + + .. code-block:: python + + >>> import paddle + >>> import numpy as np + + >>> indices = [[0, 1, 1], [2, 0, 2]] + >>> values = np.array([[3, 4], [5, 6], [7, 8]]) + >>> dense_shape = [2, 3, 2] + >>> coo = paddle.sparse.sparse_coo_tensor(indices, values, dense_shape) + >>> coo.dense_dim() + 1 + + >>> crows = [0, 2, 3, 5] + >>> cols = [1, 3, 2, 0, 1] + >>> values = [1, 2, 3, 4, 5] + >>> dense_shape = [3, 4] + >>> csr = paddle.sparse.sparse_csr_tensor(crows, cols, values, dense_shape) + >>> csr.dense_dim() + 0 + + >>> dense = paddle.to_tensor([[1, 2, 3]]) + >>> dense.dense_dim() + >>> 2 + +)DOC"); // NOLINT + +static PyObject* tensor_method_dense_dim(TensorObject* self, + PyObject* args, + PyObject* kwargs) { + EAGER_TRY + if (self->tensor.is_sparse_coo_tensor()) { + auto sparse_coo_tensor = + std::dynamic_pointer_cast(self->tensor.impl()); + return ToPyObject(sparse_coo_tensor->dense_dim()); + } else if (self->tensor.is_sparse_csr_tensor()) { + auto sparse_csr_tensor = + std::dynamic_pointer_cast(self->tensor.impl()); + return ToPyObject(sparse_csr_tensor->dense_dim()); + } else { + return ToPyObject(self->tensor.shape().size()); + } + EAGER_CATCH_AND_THROW_RETURN_NULL +} + static PyObject* tensor_method__set_impl(TensorObject* self, PyObject* args, PyObject* kwargs) { @@ -3581,6 +3696,14 @@ PyMethodDef variable_methods[] = { // NOLINT (PyCFunction)(void (*)())tensor_method_is_coalesced, METH_VARARGS | METH_KEYWORDS, tensor_is_coalesced__doc__}, + {"sparse_dim", + (PyCFunction)(void (*)())tensor_method_sparse_dim, + METH_VARARGS | METH_KEYWORDS, + tensor_method_sparse_dim__doc__}, + {"dense_dim", + (PyCFunction)(void (*)())tensor_method_dense_dim, + METH_VARARGS | METH_KEYWORDS, + tensor_method_dense_dim__doc__}, /***the method of sparse tensor****/ {"element_size", (PyCFunction)(void (*)())tensor_method_element_size, diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index e73ea069b72358..becb0b1f81f397 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -1461,9 +1461,42 @@ void BindValue(py::module *m) { return py::cast(Py_None); } }) - .def("_clone", [](Value self) { - // Return a new value owned by python side - return self; + .def("_clone", + [](Value self) { + // Return a new value owned by python side + return self; + }) + .def("sparse_dim", + [](Value self) -> int32_t { + auto op_result = self.dyn_cast(); + pir::Operation *operation = op_result.owner(); + if (self.type().isa() && + operation->name() == "pd_op.sparse_coo_tensor_sp") { + std::vector sources = operation->operands_source(); + Value non_zero_indices = sources[1]; + return phi::vectorize(GetValueDims(non_zero_indices))[0]; + } else if (self.type().isa()) { + PADDLE_THROW(common::errors::InvalidType( + "SparseCsrTensor is unsupported in pir mode.")); + } else { + return 0; + } + }) + .def("dense_dim", [](Value self) -> int32_t { + auto op_result = self.dyn_cast(); + pir::Operation *operation = op_result.owner(); + if (self.type().isa() && + operation->name() == "pd_op.sparse_coo_tensor_sp") { + std::vector sources = operation->operands_source(); + Value non_zero_indices = sources[1]; + int32_t dims = phi::vectorize(GetValueDims(self)).size(); + return dims - phi::vectorize(GetValueDims(non_zero_indices))[0]; + } else if (self.type().isa()) { + PADDLE_THROW(common::errors::InvalidType( + "SparseCsrTensor is unsupported in pir mode.")); + } else { + return phi::vectorize(GetValueDims(self)).size(); + } }); } diff --git a/paddle/phi/core/sparse_csr_tensor.cc b/paddle/phi/core/sparse_csr_tensor.cc index bf7a0e3331a14d..68bc6c11d9fd5e 100644 --- a/paddle/phi/core/sparse_csr_tensor.cc +++ b/paddle/phi/core/sparse_csr_tensor.cc @@ -160,4 +160,16 @@ void SparseCsrTensor::set_meta(const SparseTensorMeta& meta) { meta_.dtype = meta.dtype; meta_.layout = meta.layout; } + +int32_t SparseCsrTensor::sparse_dim() const { return 2; } + +int32_t SparseCsrTensor::dense_dim() const { + int32_t nze_dim = this->non_zero_elements_.dims().size(); + int32_t batch_dim = this->non_zero_crows_.dims().size() - 1; + // layout of SparseCsrTensor has not been implemented yet + // int32_t block_dim = = (layout_ == kSparseBsr || layout_ == kSparseBsc ? 2 + // : 0); + int32_t block_dim = 0; + return nze_dim - batch_dim - block_dim - 1; +} } // namespace phi diff --git a/paddle/phi/core/sparse_csr_tensor.h b/paddle/phi/core/sparse_csr_tensor.h index ca9feca201374a..c3eb15461e8b0a 100644 --- a/paddle/phi/core/sparse_csr_tensor.h +++ b/paddle/phi/core/sparse_csr_tensor.h @@ -110,6 +110,14 @@ class SparseCsrTensor : public TensorBase, /// \return The data type of the tensor. DataType dtype() const noexcept override { return meta_.dtype; } + /// \brief get the sparse dim + /// \return The sparse dim of the tensor. + int32_t sparse_dim() const; + + /// \brief get the dense dim + /// \return The dense dim of the tensor. + int32_t dense_dim() const; + #ifndef PADDLE_WITH_CUSTOM_KERNEL void set_type(const DataType dtype); #endif diff --git a/python/paddle/tensor/tensor.prototype.pyi b/python/paddle/tensor/tensor.prototype.pyi index 5157448b9cdcc7..d739991f955889 100644 --- a/python/paddle/tensor/tensor.prototype.pyi +++ b/python/paddle/tensor/tensor.prototype.pyi @@ -225,6 +225,7 @@ class AbstractTensor: @data.setter def data(self, value: Tensor) -> None: ... def data_ptr(self) -> int: ... + def dense_dim(self) -> int: ... def detach(self) -> Tensor: ... def detach_(self) -> Tensor: ... @property @@ -286,6 +287,7 @@ class AbstractTensor: def shape(self) -> list[int]: ... @property def size(self) -> int: ... + def sparse_dim(self) -> int: ... @property def stop_gradient(self) -> bool: ... @stop_gradient.setter diff --git a/test/legacy_test/test_dense_dim.py b/test/legacy_test/test_dense_dim.py new file mode 100644 index 00000000000000..a4d065cb353c14 --- /dev/null +++ b/test/legacy_test/test_dense_dim.py @@ -0,0 +1,319 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle +from paddle.base import core + + +def coo_dense_dim_ref(coo, indices): + return len(coo.shape) - indices.shape[0] + + +def csr_dense_dim_ref(crows, values): + nze_dim = len(values.shape) + batch_dim = len(crows.shape) - 1 + return nze_dim - batch_dim - 1 + + +def dense_dense_dim_ref(dense): + return len(dense.shape) + + +class TestDenseDimAPI(unittest.TestCase): + def setUp(self): + self.dtype = "float32" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]) + coo_values = np.array([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 3, 5]) + csr_cols = np.array([1, 3, 2, 0, 1]) + self.csr_values = np.array([1, 2, 3, 4, 5.0]) + csr_shape = [3, 4] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + def test_dense_dim(self): + expected_result = [ + coo_dense_dim_ref(self.tensors[0], self.coo_indices), + csr_dense_dim_ref(self.csr_crows, self.csr_values), + dense_dense_dim_ref(self.tensors[2]), + ] + places = [core.CPUPlace()] + if core.is_compiled_with_cuda(): + places.append(core.CUDAPlace(0)) + + for place in places: + paddle.disable_static(place) + for i, t in enumerate(self.tensors): + self.assertEqual(t.dense_dim(), expected_result[i]) + + +class TestDenseDimAPI1(TestDenseDimAPI): + def setUp(self): + self.dtype = "float64" + self.coo_indices = np.array([[0, 0, 1, 2], [0, 1, 1, 2], [0, 1, 1, 2]]) + coo_values = np.array([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 3, 5]) + csr_cols = np.array([1, 3, 2, 0, 1]) + self.csr_values = np.array([1, 2, 3, 4, 5.0]) + csr_shape = [3, 4] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestDenseDimAPI2(TestDenseDimAPI): + def setUp(self): + self.dtype = "int16" + self.coo_indices = np.array([[0, 0, 1, 2], [0, 1, 1, 2], [0, 1, 1, 2]]) + coo_values = np.array([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 4, 0, 2, 2, 0, 1, 2]) + csr_cols = np.array([0, 1, 0, 1, 0, 1, 1, 1]) + self.csr_values = np.array([1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]) + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor([[1, 2, 3, 4]], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestDenseDimAPI3(TestDenseDimAPI): + def setUp(self): + self.dtype = "int32" + self.coo_indices = np.array([[0, 0, 1, 2], [0, 1, 1, 2]]) + coo_values = np.array([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 4, 0, 2, 2, 0, 1, 2]) + csr_cols = np.array([0, 1, 0, 1, 0, 1, 1, 1]) + self.csr_values = np.array([1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]) + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor( + [[[1], [2], [3], [4]]], dtype=self.dtype + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestDenseDimAPI4(TestDenseDimAPI): + def setUp(self): + self.dtype = "int64" + self.coo_indices = np.array( + [ + [0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 2, 2], + [0, 0, 1, 1, 0, 0, 0, 1], + [0, 1, 0, 1, 0, 1, 1, 1], + ] + ) + coo_values = np.array([1, 2, 3, 4, 1, 2, 2, 4]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 4, 0, 2, 2, 0, 1, 2]) + csr_cols = np.array([0, 1, 0, 1, 0, 1, 1, 1]) + self.csr_values = np.array([1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]) + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor( + [[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]], + dtype=self.dtype, + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestDenseDimAPI5(TestDenseDimAPI): + def setUp(self): + self.dtype = "uint8" + self.coo_indices = np.array( + [[0, 0, 0, 0, 0], [0, 0, 1, 2, 2], [0, 1, 0, 0, 1]] + ) + coo_values = np.array([[1, 2], [3, 4], [1, 2], [0, 2], [0, 4]]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + self.csr_crows = np.array([0, 2, 4, 0, 2, 2, 0, 1, 2]) + csr_cols = np.array([0, 1, 0, 1, 0, 1, 1, 1]) + self.csr_values = np.array([1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0]) + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + self.csr_crows, + csr_cols, + self.csr_values, + csr_shape, + dtype=self.dtype, + ) + other_tensor = paddle.to_tensor( + [[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]], + dtype=self.dtype, + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestDenseDimAPIStatic(unittest.TestCase): + def setUp(self): + self.dtype = "float32" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([[[1, 2, 3, 4]]]).astype(self.dtype) + + def test_is_coalesced(self): + paddle.enable_static() + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + coo_indices = paddle.static.data( + name='coo_indices', + shape=self.coo_indices.shape, + dtype='int64', + ) + coo_values = paddle.static.data( + name='coo_values', + shape=self.coo_indices.shape, + dtype=self.dtype, + ) + coo = paddle.sparse.sparse_coo_tensor( + coo_indices, + coo_values, + shape=self.coo_shape, + dtype=self.dtype, + ) + other = paddle.static.data( + name='other', + shape=self.other_tensor_arr.shape, + dtype=self.dtype, + ) + + exe = paddle.static.Executor() + exe.run( + feed={ + 'coo_indices': self.coo_indices, + 'coo_values': self.coo_values, + 'other': self.other_tensor_arr, + } + ) + expected_result = [ + coo_dense_dim_ref(coo, self.coo_indices), + dense_dense_dim_ref(self.other_tensor_arr), + ] + self.assertEqual(coo.dense_dim(), expected_result[0]) + self.assertEqual(other.dense_dim(), expected_result[1]) + paddle.disable_static() + + +class TestDenseDimAPIStatic1(TestDenseDimAPIStatic): + def setUp(self): + self.dtype = "float64" + self.coo_indices = np.array( + [[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]] + ).astype('int64') + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3, 3] + self.other_tensor_arr = np.array([[[[1, 2, 3, 4]]]]).astype(self.dtype) + + +class TestDenseDimAPIStatic2(TestDenseDimAPIStatic): + def setUp(self): + self.dtype = "int16" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([[[1, 2, 3, 4]]]).astype(self.dtype) + + +class TestDenseDimAPIStatic3(TestDenseDimAPIStatic): + def setUp(self): + self.dtype = "int32" + self.coo_indices = np.array( + [[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]] + ).astype('int64') + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3, 3] + self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype) + + +class TestDenseDimAPIStatic4(TestDenseDimAPIStatic): + def setUp(self): + self.dtype = "int64" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 2, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype) + + +class TestDenseDimAPIStatic5(TestDenseDimAPIStatic): + def setUp(self): + self.dtype = "uint8" + self.coo_indices = np.array([[0, 0, 1, 2, 2], [0, 1, 0, 0, 1]]).astype( + 'int64' + ) + self.coo_values = np.array( + [[1.0, 2.0], [3.0, 4.0], [1.0, 2.0], [0.0, 4.0], [2.0, 4.0]] + ).astype(self.dtype) + self.coo_shape = [3, 2, 2] + self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype) + + +if __name__ == "__main__": + unittest.main() diff --git a/test/legacy_test/test_sparse_dim.py b/test/legacy_test/test_sparse_dim.py new file mode 100644 index 00000000000000..a5f7ddec69fa9e --- /dev/null +++ b/test/legacy_test/test_sparse_dim.py @@ -0,0 +1,297 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle +from paddle.base import core + + +def coo_sparse_dim_ref(indices): + return len(indices) + + +def csr_sparse_dim_ref(): + return 2 + + +def dense_sparse_dim_ref(): + return 0 + + +class TestSparseDimAPI(unittest.TestCase): + def setUp(self): + self.dtype = "float32" + self.coo_indices = [[0, 0, 0, 1], [0, 0, 1, 2]] + coo_values = [1.0, 2.0, 3.0, 4.0] + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 3, 5] + csr_cols = [1, 3, 2, 0, 1] + csr_values = [1, 2, 3, 4, 5] + csr_shape = [3, 4] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + def test_sparse_dim(self): + expected_result = [ + coo_sparse_dim_ref(self.coo_indices), + csr_sparse_dim_ref(), + dense_sparse_dim_ref(), + ] + places = [core.CPUPlace()] + if core.is_compiled_with_cuda(): + places.append(core.CUDAPlace(0)) + + for place in places: + paddle.disable_static(place) + for i, t in enumerate(self.tensors): + self.assertEqual(t.sparse_dim(), expected_result[i]) + + +class TestSparseDimAPI1(TestSparseDimAPI): + def setUp(self): + self.dtype = "float64" + self.coo_indices = [[0, 0, 1, 2], [0, 1, 1, 2], [0, 1, 1, 2]] + coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 3, 5] + csr_cols = [1, 3, 2, 0, 1] + csr_values = [1, 2, 3, 4, 5] + csr_shape = [3, 4] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestSparseDimAPI2(TestSparseDimAPI): + def setUp(self): + self.dtype = "int16" + self.coo_indices = [ + [0, 0, 1, 2], + [0, 2, 0, 2], + [0, 1, 1, 0], + [0, 1, 1, 0], + ] + coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 3, 5] + csr_cols = [1, 3, 2, 0, 1] + csr_values = [1, 2, 3, 4, 5] + csr_shape = [3, 4] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor([1, 2, 3, 4], dtype=self.dtype) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestSparseDimAPI3(TestSparseDimAPI): + def setUp(self): + self.dtype = "int32" + self.coo_indices = [[0, 0, 0], [0, 1, 2]] + coo_values = paddle.to_tensor( + [[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]] + ) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2] + csr_cols = [0, 1, 0, 1, 0, 1, 1, 1] + csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0] + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor( + [[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]], + dtype=self.dtype, + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestSparseDimAPI4(TestSparseDimAPI): + def setUp(self): + self.dtype = "int64" + self.coo_indices = [[0, 0, 1, 2], [0, 1, 1, 2]] + coo_values = paddle.to_tensor([1.0, 2.0, 3.0, 4.0]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2] + csr_cols = [0, 1, 0, 1, 0, 1, 1, 1] + csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0] + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor( + [[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]], + dtype=self.dtype, + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestSparseDimAPI5(TestSparseDimAPI): + def setUp(self): + self.dtype = "uint8" + self.coo_indices = [ + [0, 0, 0, 0, 0, 0, 0, 0], + [0, 0, 0, 0, 1, 1, 2, 2], + [0, 0, 1, 1, 0, 0, 0, 1], + [0, 1, 0, 1, 0, 1, 1, 1], + ] + coo_values = paddle.to_tensor([1, 2, 3, 4, 1, 2, 2, 4]) + coo_tensor = paddle.sparse.sparse_coo_tensor( + self.coo_indices, coo_values, dtype=self.dtype + ) + csr_crows = [0, 2, 4, 0, 2, 2, 0, 1, 2] + csr_cols = [0, 1, 0, 1, 0, 1, 1, 1] + csr_values = [1.0, 2.0, 3.0, 4.0, 1.0, 2.0, 2.0, 4.0] + csr_shape = [3, 2, 2] + csr_tensor = paddle.sparse.sparse_csr_tensor( + csr_crows, csr_cols, csr_values, csr_shape, dtype=self.dtype + ) + other_tensor = paddle.to_tensor( + [[[[1, 2], [3, 4]], [[1, 2], [0, 0]], [[0, 2], [0, 4]]]], + dtype=self.dtype, + ) + self.tensors = [coo_tensor, csr_tensor, other_tensor] + + +class TestSparseDimAPIStatic(unittest.TestCase): + def setUp(self): + self.dtype = "float32" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype) + + def test_sparse_dim(self): + expected_result = [ + coo_sparse_dim_ref(self.coo_indices), + dense_sparse_dim_ref(), + ] + paddle.enable_static() + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + coo_indices = paddle.static.data( + name='coo_indices', + shape=self.coo_indices.shape, + dtype='int64', + ) + coo_values = paddle.static.data( + name='coo_values', + shape=self.coo_indices.shape, + dtype=self.dtype, + ) + coo = paddle.sparse.sparse_coo_tensor( + coo_indices, + coo_values, + shape=self.coo_shape, + dtype=self.dtype, + ) + other = paddle.static.data( + name='other', + shape=self.other_tensor_arr.shape, + dtype=self.dtype, + ) + + exe = paddle.static.Executor() + exe.run( + feed={ + 'coo_indices': self.coo_indices, + 'coo_values': self.coo_values, + 'other': self.other_tensor_arr, + } + ) + self.assertEqual(coo.sparse_dim(), expected_result[0]) + self.assertEqual(other.sparse_dim(), expected_result[1]) + paddle.disable_static() + + +class TestSparseDimAPIStatic1(TestSparseDimAPIStatic): + def setUp(self): + self.dtype = "float64" + self.coo_indices = np.array( + [[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]] + ).astype('int64') + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3, 3] + self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype) + + +class TestSparseDimAPIStatic2(TestSparseDimAPIStatic): + def setUp(self): + self.dtype = "int16" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 0, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([[[1, 2, 3, 4]]]).astype(self.dtype) + + +class TestSparseDimAPIStatic3(TestSparseDimAPIStatic): + def setUp(self): + self.dtype = "int32" + self.coo_indices = np.array( + [[0, 1, 0, 1], [0, 0, 1, 2], [0, 0, 1, 2]] + ).astype('int64') + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3, 3] + self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype) + + +class TestSparseDimAPIStatic4(TestSparseDimAPIStatic): + def setUp(self): + self.dtype = "int64" + self.coo_indices = np.array([[0, 0, 0, 1], [0, 2, 1, 2]]).astype( + 'int64' + ) + self.coo_values = np.array([1.0, 2.0, 3.0, 4.0]).astype(self.dtype) + self.coo_shape = [2, 3] + self.other_tensor_arr = np.array([[1, 2, 3, 4]]).astype(self.dtype) + + +class TestSparseDimAPIStatic5(TestSparseDimAPIStatic): + def setUp(self): + self.dtype = "uint8" + self.coo_indices = np.array([[0, 0, 1, 2, 2], [0, 1, 0, 0, 1]]).astype( + 'int64' + ) + self.coo_values = np.array( + [[1.0, 2.0], [3.0, 4.0], [1.0, 2.0], [0.0, 4.0], [2.0, 4.0]] + ).astype(self.dtype) + self.coo_shape = [3, 2, 2] + self.other_tensor_arr = np.array([1, 2, 3, 4]).astype(self.dtype) + + +if __name__ == "__main__": + unittest.main() From 1c912a87cf8932b232aa8be2a5d6a30b343b1459 Mon Sep 17 00:00:00 2001 From: XnneHang Date: Thu, 28 Nov 2024 17:41:25 +0800 Subject: [PATCH 048/288] chore: remove duplicate op in `recompute.py` (#69784) --- python/paddle/decomposition/recompute.py | 14 ++++---------- 1 file changed, 4 insertions(+), 10 deletions(-) diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index 0d8ae6ee936c9c..84e42e938de2c6 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -61,29 +61,23 @@ "pd_op.expand", "pd_op.scale", "pd_op.exp", - "pd_op.equal", - "pd_op.where", "pd_op.sin", "pd_op.cos", "pd_op.add_n", "pd_op.any", - "pd_op.bitwise_and", "pd_op.cast", "pd_op.concat", "pd_op.full_with_tensor", "pd_op.gather_nd", - "pd_op.greater_than", - "pd_op.less_than", "pd_op.logical_and", "pd_op.logical_not", - "pd_op.not_equal", + "pd_op.where", "pd_op.pow", "pd_op.shape", "pd_op.slice", "pd_op.squeeze", "pd_op.unsqueeze", "pd_op.transpose", - "pd_op.where", "pd_op.prod", "pd_op.log", "pd_op.log1p", @@ -112,7 +106,6 @@ "pd_op.frac", "pd_op.round", "pd_op.trunc", - "pd_op.equal", "pd_op.angle", "pd_op.as_complex", "pd_op.as_real", @@ -120,15 +113,16 @@ "pd_op.real", "pd_op.imag", "pd_op.conj", - "pd_op.not_equal", "pd_op.greater_equal", "pd_op.greater_than", + "pd_op.not_equal", + "pd_op.equal", "pd_op.less_equal", "pd_op.less_than", "pd_op.bitwise_and", - "pd_op.bitwise_not", "pd_op.bitwise_or", "pd_op.bitwise_xor", + "pd_op.bitwise_not", "pd_op.isinf", "pd_op.isnan", # "pd_op.gather", From 717aea166aadac134a5c0bf0c51276010a73bf83 Mon Sep 17 00:00:00 2001 From: zhangbo9674 <82555433+zhangbo9674@users.noreply.github.com> Date: Thu, 28 Nov 2024 18:49:12 +0800 Subject: [PATCH 049/288] [CINN] Add traits for nop (#69759) * refine * refine --- paddle/phi/ops/yaml/fused_ops.yaml | 1 + paddle/phi/ops/yaml/inconsistent/static_ops.yaml | 1 + 2 files changed, 2 insertions(+) diff --git a/paddle/phi/ops/yaml/fused_ops.yaml b/paddle/phi/ops/yaml/fused_ops.yaml index 845d32b2b67c5d..67bd2059c70529 100644 --- a/paddle/phi/ops/yaml/fused_ops.yaml +++ b/paddle/phi/ops/yaml/fused_ops.yaml @@ -385,6 +385,7 @@ func : fused_linear_param_grad_add data_type : dout support_dygraph_mode : true + traits : pir::SideEffectTrait - op : fused_multi_transformer_ args : (Tensor x, Tensor[] ln_scales, Tensor[] ln_biases, Tensor[] qkv_weights, Tensor[] qkv_biases, Tensor[] cache_kvs, Tensor[] pre_caches, Tensor rotary_tensor, Tensor beam_offset, Tensor time_step, Tensor seq_lengths, Tensor src_mask, Tensor[] out_linear_weights, Tensor[] out_linear_biases, Tensor[] ffn_ln_scales, Tensor[] ffn_ln_biases, Tensor[] ffn1_weights, Tensor[] ffn1_biases, Tensor[] ffn2_weights, Tensor[] ffn2_biases, bool pre_layer_norm = true, float epsilon = 1e-5, float residual_alpha = 1.0f, float dropout_rate = .5f, int rotary_emb_dims = 0, bool is_test = false, str dropout_implementation = "downgrade_in_infer", str act_method = "gelu", bool trans_qkvw = true, int ring_id = -1, str norm_type = "layernorm", bool use_neox_rotary_style=true, int gqa_group_size=-1) diff --git a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml index a08d392f2b774b..bb6000f1d39353 100644 --- a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml +++ b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml @@ -641,6 +641,7 @@ func : nop inplace: (x -> out) interfaces : paddle::dialect::ParseKernelKeyInterface + traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : not_equal args : (Tensor x, Tensor y) From 6c1c486e150214689084652a316603bec4b49d44 Mon Sep 17 00:00:00 2001 From: gouzil <66515297+gouzil@users.noreply.github.com> Date: Fri, 29 Nov 2024 01:05:29 +0800 Subject: [PATCH 050/288] [SOT][3.11] Fix `gen_dup_top` generated `COPY` instruction with wrong oparg setting (#69774) --- .../jit/sot/opcode_translator/executor/pycode_generator.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py index 8550514ef6e7f8..4ab8333e962a35 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py +++ b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py @@ -899,7 +899,7 @@ def gen_shift_n(self, s: int, n: int): def gen_dup_top(self): if sys.version_info >= (3, 11): - return self.add_instr("COPY", arg=0) + return self.add_instr("COPY", arg=1) return self.add_instr("DUP_TOP") def gen_swap(self, n): From 9d49a11609325cfab251c10104d8473bd052cf95 Mon Sep 17 00:00:00 2001 From: gouzil <66515297+gouzil@users.noreply.github.com> Date: Fri, 29 Nov 2024 01:08:17 +0800 Subject: [PATCH 051/288] [SOT][3.13] Support closure (#69753) --- .../executor/opcode_executor.py | 48 +++++++++++++++++-- .../executor/variables/__init__.py | 1 + .../executor/variables/callable.py | 24 +++++++++- test/sot/skip_files_py313 | 1 - test/sot/test_13_make_function.py | 3 +- 5 files changed, 68 insertions(+), 9 deletions(-) delete mode 100644 test/sot/skip_files_py313 diff --git a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py index 7f4544897f1b92..4ac178e923b2e6 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py +++ b/python/paddle/jit/sot/opcode_translator/executor/opcode_executor.py @@ -101,6 +101,7 @@ SymbolicVariable, TensorVariable, TupleVariable, + UserCodeVariable, UserDefinedFunctionVariable, UserDefinedGeneratorFunctionVariable, VariableBase, @@ -1384,14 +1385,28 @@ def MAKE_FUNCTION(self, instr: Instruction): # the function has no default values in 3.13 if sys.version_info >= (3, 13): - flag = 0 - else: - flag = instr.arg + if len(codeobj.get_py_value().co_freevars) > 0: + self.stack.push( + UserCodeVariable( + codeobj, self._graph, DummyTracker([codeobj]) + ) + ) + else: + self.push_new_fn_on_stack( + codeobj.get_py_value(), + global_dict, + fn_name.get_py_value(), + (), + (), + related_list, + (), + ) + return + flag = instr.arg closure, related_list, kw_defaults, default_args = ( self.attach_new_attribute(flag, related_list) ) - self.push_new_fn_on_stack( codeobj.get_py_value(), global_dict, @@ -1404,13 +1419,36 @@ def MAKE_FUNCTION(self, instr: Instruction): def SET_FUNCTION_ATTRIBUTE(self, instr: Instruction): origin_func = self.stack.pop() + flag = instr.arg + + if isinstance(origin_func, UserCodeVariable): + origin_codeobj = origin_func.codeobj + fn_name = ConstantVariable( + origin_codeobj.value.co_qualname, + self._graph, + DummyTracker([origin_codeobj]), + ) + related_list = [fn_name, origin_codeobj] + closure, related_list, kw_defaults, default_args = ( + self.attach_new_attribute(flag, related_list) + ) + self.push_new_fn_on_stack( + origin_codeobj.get_py_value(), + self._globals.get_value(), + fn_name.get_py_value(), + default_args, + closure, + related_list, + kw_defaults, + ) + return + # The object we manipulate must be a functionVariable assert isinstance( origin_func, (UserDefinedGeneratorFunctionVariable, UserDefinedFunctionVariable), ), f"The object we manipulate must be a function object. But now got {type(origin_func)}" origin_func_val = origin_func.get_py_value() - flag = instr.arg related_list = [origin_func] closure, related_list, kw_defaults, default_args = ( self.attach_new_attribute(flag, related_list) diff --git a/python/paddle/jit/sot/opcode_translator/executor/variables/__init__.py b/python/paddle/jit/sot/opcode_translator/executor/variables/__init__.py index bc4c60fd71d07a..9fe156958acbba 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variables/__init__.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variables/__init__.py @@ -44,6 +44,7 @@ MethodVariable, PaddleApiVariable, PaddleLayerVariable, + UserCodeVariable, UserDefinedFunctionVariable, UserDefinedGeneratorFunctionVariable, UserDefinedLayerVariable, diff --git a/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py b/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py index 73d779a1bdd0f2..1f0a31fb62d983 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py @@ -44,6 +44,7 @@ from ....utils.exceptions import ( BreakGraphError, FallbackError, + InnerError, SotErrorBase, ) from ..dispatcher import Dispatcher @@ -65,7 +66,12 @@ Tracker, ) from .base import VariableBase, VariableFactory -from .basic import ConstantVariable, PrintStmtVariable, SliceVariable +from .basic import ( + ConstantVariable, + ObjectVariable, + PrintStmtVariable, + SliceVariable, +) if TYPE_CHECKING: from ..function_graph import FunctionGraph @@ -230,6 +236,22 @@ def main_info(self) -> dict[str, Any]: } +class UserCodeVariable(FunctionVariable): + """ + UserCodeVariable is a subclass of Function + Variable used to wrap a make function variable. + """ + + def __init__( + self, codeobj: ObjectVariable, graph: FunctionGraph, tracker: Tracker + ): + super().__init__(codeobj, graph, tracker) + self.codeobj = codeobj + + def call_function(self, /, *args, **kwargs): + raise InnerError("UserCodeVariable call_function is not implemented.") + + class PaddleApiVariable(FunctionVariable): """ PaddleApiVariable is a subclass of FunctionVariable used to wrap a paddlepaddle API function. diff --git a/test/sot/skip_files_py313 b/test/sot/skip_files_py313 deleted file mode 100644 index 3147448e8ecf0a..00000000000000 --- a/test/sot/skip_files_py313 +++ /dev/null @@ -1 +0,0 @@ -test/sot/test_19_closure.py diff --git a/test/sot/test_13_make_function.py b/test/sot/test_13_make_function.py index 0f4ca93a669b0b..9fc710e360a925 100644 --- a/test/sot/test_13_make_function.py +++ b/test/sot/test_13_make_function.py @@ -73,8 +73,7 @@ def test_simple(self): self.assert_results(make_fn_default, paddle.to_tensor(1)) self.assert_results(make_fn_annotation, paddle.to_tensor(1)) self.assert_results(make_fn_kwdefault, paddle.to_tensor(1)) - # self.assert_results(make_fn_closure, paddle.to_tensor(1)) - # we haven't pass this test yet + self.assert_results(make_fn_closure, paddle.to_tensor(1)) self.assert_results(make_fn_mix, paddle.to_tensor(1)) From c76d2dbaf1963251cae0983bc76ebd6842b1c6b2 Mon Sep 17 00:00:00 2001 From: Zhan Rongrui <46243324+zrr1999@users.noreply.github.com> Date: Fri, 29 Nov 2024 01:08:34 +0800 Subject: [PATCH 052/288] [SOT][Faster Guard] adapt to faster guard for more variables (#69672) --- paddle/fluid/pybind/jit.cc | 3 +++ paddle/fluid/pybind/sot/guards.cc | 2 ++ paddle/fluid/pybind/sot/guards.h | 13 +++++++++++++ test/sot/test_faster_guard.py | 8 ++++++++ 4 files changed, 26 insertions(+) diff --git a/paddle/fluid/pybind/jit.cc b/paddle/fluid/pybind/jit.cc index 6bfceaadd395eb..f78068e11feb1c 100644 --- a/paddle/fluid/pybind/jit.cc +++ b/paddle/fluid/pybind/jit.cc @@ -76,6 +76,9 @@ void BindGuard(pybind11::module *m) { py::class_>( *m, "TypeMatchGuard", R"DOC(TypeMatchGuard Class.)DOC") .def(py::init(), py::arg("py_type")); + py::class_>( + *m, "IdMatchGuard", R"DOC(IdMatchGuard Class.)DOC") + .def(py::init(), py::arg("py_obj")); py::class_>( *m, "LengthMatchGuard", R"DOC(LengthMatchGuard Class.)DOC") .def(py::init(), py::arg("length")); diff --git a/paddle/fluid/pybind/sot/guards.cc b/paddle/fluid/pybind/sot/guards.cc index efdd88a108815f..6abef8ce9172e2 100644 --- a/paddle/fluid/pybind/sot/guards.cc +++ b/paddle/fluid/pybind/sot/guards.cc @@ -74,6 +74,8 @@ bool TypeMatchGuard::check(PyObject* value) { return Py_TYPE(value) == expected_; } +bool IdMatchGuard::check(PyObject* value) { return value == expected_; } + bool ValueMatchGuard::check(PyObject* value) { return PyObject_Equal(value, expected_value_); } diff --git a/paddle/fluid/pybind/sot/guards.h b/paddle/fluid/pybind/sot/guards.h index 0d3cd2b2dc0fa9..df36e75446f239 100644 --- a/paddle/fluid/pybind/sot/guards.h +++ b/paddle/fluid/pybind/sot/guards.h @@ -84,6 +84,19 @@ class TypeMatchGuard : public GuardBase { PyTypeObject* expected_; }; +class IdMatchGuard : public GuardBase { + public: + explicit IdMatchGuard(PyObject* obj_ptr) + : expected_(reinterpret_cast(obj_ptr)) {} + explicit IdMatchGuard(const py::object& py_obj) + : expected_(reinterpret_cast(py_obj.ptr())) {} + + bool check(PyObject* value); + + private: + PyObject* expected_; +}; + class ValueMatchGuard : public GuardBase { public: explicit ValueMatchGuard(PyObject* value_ptr) diff --git a/test/sot/test_faster_guard.py b/test/sot/test_faster_guard.py index 52b284ef560709..7ca7d7fd0fd9b4 100644 --- a/test/sot/test_faster_guard.py +++ b/test/sot/test_faster_guard.py @@ -85,6 +85,14 @@ def test_layer_match_guard(self): layer.train() self.assertTrue(guard_layer.check(layer)) + def test_id_match_guard(self): + layer = paddle.nn.Linear(10, 10) + guard_id = paddle.framework.core.IdMatchGuard(layer) + self.assertTrue(guard_id.check(layer)) + layer.eval() + self.assertTrue(guard_id.check(layer)) + self.assertFalse(guard_id.check(paddle.nn.Linear(10, 10))) + class TestFasterGuardGroup(unittest.TestCase): def test_guard_group(self): From 109965ced94f90b6d02fee56af896f3e112e4c17 Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Fri, 29 Nov 2024 07:07:13 +0800 Subject: [PATCH 053/288] [Auto Parallel] Add two flags to mp plan. (#69776) --- .../intermediate/tensor_parallel.py | 140 ++++++++++-------- .../hybrid_strategy/parallel_api.py | 85 +++++++---- .../test_parallel_api_with_llama_2d.py | 3 + .../test_parallel_api_with_llama_3d.py | 1 + 4 files changed, 139 insertions(+), 90 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py index b728147c0a488e..67b83a5de63617 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py @@ -20,11 +20,61 @@ from .parallel_base import ParallelModel, ParallelOptimizer, is_tensor +def c_split(x, process_mesh, need_transpose): + index = process_mesh.dim_names.index('mp') # get the axis for the split + if isinstance(x, tuple): + target_x = x[0] + else: + target_x = x + assert is_tensor(target_x) + assert len(target_x.shape) == 3 + if need_transpose: + target_x = paddle.transpose(target_x, perm=[1, 0, 2]) + placements = target_x.placements + if placements is None: + placements = [dist.Replicate() for _ in range(len(process_mesh.shape))] + placements[index] = dist.Shard(0) + target_x = dist.reshard(target_x, process_mesh, placements) + if isinstance(x, tuple): + x = list(x) + x[0] = target_x + x = tuple(x) + else: + x = target_x + + return x + + +def c_concat(x, process_mesh, need_transpose): + index = process_mesh.dim_names.index('mp') # get the axis for the split + if isinstance(x, tuple): + target_x = x[0] + else: + target_x = x + assert is_tensor(target_x) + assert len(target_x.shape) == 3 + placements = target_x.placements + if placements is None: + placements = [dist.Replicate() for _ in range(len(process_mesh.shape))] + placements[index] = dist.Replicate() + target_x = dist.reshard(target_x, process_mesh, placements) + if need_transpose: + target_x = paddle.transpose(target_x, perm=[1, 0, 2]) + if isinstance(x, tuple): + x = list(x) + x[0] = target_x + x = tuple(x) + else: + x = target_x + + return x + + class PlanBase: def __init__(self): pass - def apply(self, param, process_mesh, shard_weight, shard_bias): + def apply(self, layer, process_mesh, shard_weight, shard_bias): raise NotImplementedError("Don't call the PlanBase directly.") @@ -40,8 +90,16 @@ class ColWiseParallel(PlanBase): Note: `layer.bias` should have one dim. """ - def __init__(self): + def __init__(self, gather_output=False): super().__init__() + self.gather_output = gather_output + + def gather_output_hook(self, process_mesh): + def gather_hook(layer, input, output): + assert output is not None + return c_concat(output, process_mesh, False) + + return gather_hook def apply(self, layer, process_mesh, shard_weight=True, shard_bias=True): """ @@ -79,6 +137,11 @@ def apply(self, layer, process_mesh, shard_weight=True, shard_bias=True): assert len(layer.bias.shape) == 1 layer.bias = dist.shard_tensor(layer.bias, process_mesh, placement) + if self.gather_output: + layer.register_forward_post_hook( + self.gather_output_hook(process_mesh) + ) + class RowWiseParallel(PlanBase): """ @@ -90,8 +153,15 @@ class RowWiseParallel(PlanBase): Note: `layer.weight` should have two dims. """ - def __init__(self): + def __init__(self, is_input_parallel=True): super().__init__() + self.is_input_parallel = is_input_parallel + + def split_input_hook(self, process_mesh): + def split_hook(layer, input, output): + return c_split(input, process_mesh, False) + + return split_hook def apply(self, layer, process_mesh, shard_weight=True, shard_bias=False): """ @@ -124,6 +194,8 @@ def apply(self, layer, process_mesh, shard_weight=True, shard_bias=False): process_mesh, placement, ) + if not self.is_input_parallel: + layer.register_forward_pre_hook(self.split_input_hook(process_mesh)) class PrepareLayerInput(PlanBase): @@ -156,56 +228,6 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): layer.register_forward_post_hook(self.fn(process_mesh=process_mesh)) -def sp_split(x, process_mesh, need_transpose): - index = process_mesh.dim_names.index('mp') # get the axis for the split - if isinstance(x, tuple): - target_x = x[0] - else: - target_x = x - assert is_tensor(target_x) - assert len(target_x.shape) == 3 - if need_transpose: - target_x = paddle.transpose(target_x, perm=[1, 0, 2]) - placements = target_x.placements - if placements is None: - placements = [dist.Replicate() for _ in range(len(process_mesh.shape))] - placements[index] = dist.Shard(0) - target_x = dist.reshard(target_x, process_mesh, placements) - if isinstance(x, tuple): - x = list(x) - x[0] = target_x - x = tuple(x) - else: - x = target_x - - return x - - -def sp_reduce_scatter(x, process_mesh, need_transpose): - index = process_mesh.dim_names.index('mp') # get the axis for the split - if isinstance(x, tuple): - target_x = x[0] - else: - target_x = x - assert is_tensor(target_x) - assert len(target_x.shape) == 3 - placements = target_x.placements - if placements is None: - placements = [dist.Replicate() for _ in range(len(process_mesh.shape))] - placements[index] = dist.Replicate() - target_x = dist.reshard(target_x, process_mesh, placements) - if need_transpose: - target_x = paddle.transpose(target_x, perm=[1, 0, 2]) - if isinstance(x, tuple): - x = list(x) - x[0] = target_x - x = tuple(x) - else: - x = target_x - - return x - - class SequenceParallelBegin(PlanBase): """ With need_transpose=True, this plan will transpose and reshard the output from [b, s, h] to [s/mp, b, h]. @@ -222,7 +244,7 @@ def __init__(self, need_transpose=True): def sequence_parallel_begin(self, process_mesh): def begin(layer, input, output): assert output is not None - return sp_split(output, process_mesh, self.need_transpose) + return c_split(output, process_mesh, self.need_transpose) return begin @@ -248,7 +270,7 @@ def __init__(self, need_transpose=True): def sequence_parallel_end(self, process_mesh): def end(layer, input, output=None): assert input is not None - return sp_reduce_scatter(input, process_mesh, self.need_transpose) + return c_concat(input, process_mesh, self.need_transpose) return end @@ -269,14 +291,14 @@ def __init__(self): def sequence_parallel_begin(self, process_mesh): def begin(layer, input, output=None): assert input is not None - return sp_split(input, process_mesh, True) + return c_split(input, process_mesh, True) return begin def sequence_parallel_end(self, process_mesh): def end(layer, input, output): assert output is not None - return sp_reduce_scatter(output, process_mesh, True) + return c_concat(output, process_mesh, True) return end @@ -310,13 +332,13 @@ def __init__(self, need_transpose=True): def sequence_parallel_begin(self, process_mesh): def begin(layer, input, output=None): - return sp_split(output, process_mesh, self.need_transpose) + return c_split(output, process_mesh, self.need_transpose) return begin def sequence_parallel_end(self, process_mesh): def end(layer, input, output=None): - return sp_reduce_scatter(input, process_mesh, self.need_transpose) + return c_concat(input, process_mesh, self.need_transpose) return end diff --git a/test/auto_parallel/hybrid_strategy/parallel_api.py b/test/auto_parallel/hybrid_strategy/parallel_api.py index dc6d00936aa7c5..cdacf26bcd9387 100644 --- a/test/auto_parallel/hybrid_strategy/parallel_api.py +++ b/test/auto_parallel/hybrid_strategy/parallel_api.py @@ -23,6 +23,7 @@ import paddle.distributed as dist from paddle import LazyGuard from paddle.distributed.auto_parallel.intermediate.parallelize import ( + parallelize, parallelize_model, parallelize_optimizer, ) @@ -55,12 +56,12 @@ def get_mesh(pp_idx=None): class Config: - vocab_size = 32000 - hidden_size = 4096 - intermediate_size = 11008 - seq_length = 2048 + vocab_size = 8192 + hidden_size = 512 + intermediate_size = 2048 + seq_length = 512 num_hidden_layers = 2 - num_attention_heads = 32 + num_attention_heads = 8 rms_norm_eps = 1e-6 use_lazy_init = False @@ -146,6 +147,10 @@ def __init__(self): if num_hidden_layers: self.config.num_hidden_layers = int(num_hidden_layers) + self.one_api = False + if os.getenv("one_api") == "true": + self.one_api = True + seed = int(os.getenv("seed", 2024)) np.random.seed(seed) random.seed(seed) @@ -219,12 +224,20 @@ def parallel_model(self, layer): if self.mp > 1: if not self.sequence_parallel: plan = { - "llama.embed_tokens": ColWiseParallel(), + "llama.embed_tokens": ColWiseParallel(gather_output=True), "llama.position_embedding": ColWiseParallel(), - "llama.layers.*.self_attn.q_proj": ColWiseParallel(), - "llama.layers.*.self_attn.k_proj": ColWiseParallel(), - "llama.layers.*.self_attn.v_proj": ColWiseParallel(), - "llama.layers.*.self_attn.o_proj": RowWiseParallel(), + "llama.layers.*.self_attn.q_proj": ColWiseParallel( + gather_output=True + ), + "llama.layers.*.self_attn.k_proj": ColWiseParallel( + gather_output=True + ), + "llama.layers.*.self_attn.v_proj": ColWiseParallel( + gather_output=True + ), + "llama.layers.*.self_attn.o_proj": RowWiseParallel( + is_input_parallel=False + ), "llama.layers.*.mlp.gate_proj": ColWiseParallel(), "llama.layers.*.mlp.up_proj": ColWiseParallel(), "llama.layers.*.mlp.down_proj": RowWiseParallel(), @@ -272,14 +285,36 @@ def parallel_model(self, layer): "lm_head": SequenceParallelEnd(), } mp_config = {'parallelize_plan': plan} - layer = parallelize_model( - layer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, + + lr_scheduler = paddle.optimizer.lr.LinearWarmup( + learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001 ) + + if self.one_api: + optimizer = create_optimizer(layer, lr_scheduler) + model, optimizer = parallelize( + layer, + optimizer, + dp_config=dp_config, + mp_config=mp_config, + pp_config=pp_config, + ) + else: + layer = parallelize_model( + layer, + dp_config=dp_config, + mp_config=mp_config, + pp_config=pp_config, + ) + optimizer = create_optimizer(layer, lr_scheduler) + optimizer = parallelize_optimizer( + optimizer, + dp_config=dp_config, + mp_config=mp_config, + pp_config=pp_config, + ) self.check_mp(layer) - return layer, dp_config, mp_config, pp_config + return layer, optimizer, lr_scheduler def run_llama( self, share_embedding=False, position_embedding=False, to_static=0 @@ -294,19 +329,7 @@ def run_llama( self.config, share_embedding, position_embedding ) - model, dp_config, mp_config, pp_config = self.parallel_model(model) - - lr_scheduler = paddle.optimizer.lr.LinearWarmup( - learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001 - ) - optimizer = create_optimizer(model, lr_scheduler) - - optimizer = parallelize_optimizer( - optimizer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, - ) + model, optimizer, lr_scheduler = self.parallel_model(model) criterion = LlamaPretrainingCriterion(self.config) @@ -403,7 +426,7 @@ def run_llama( tr_loss = 0 global_step += 1 - if global_step // self.gradient_accumulation_steps >= 10: + if global_step // self.gradient_accumulation_steps >= 3: break else: strategy = dist.Strategy() @@ -433,7 +456,7 @@ def run_llama( input_ids, labels = inputs loss = dist_model(input_ids, labels) logging.info(f"step: {step} loss: {loss}") - if step >= 10: + if step >= 3: break def run_test_cases(self, share_embedding=False, position_embedding=False): diff --git a/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_2d.py b/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_2d.py index bee82ba770cfe1..f0f4f3986620cc 100644 --- a/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_2d.py +++ b/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_2d.py @@ -44,6 +44,7 @@ def setUp(self): "test_position_embedding": [ "1", ], + "one_api": ["true", "false"], } def test_simple_net_mp2_pp2(self): @@ -86,6 +87,7 @@ def setUp(self): "test_position_embedding": [ "1", ], + "one_api": ["true", "false"], } def test_simple_net_dp2_pp2(self): @@ -129,6 +131,7 @@ def setUp(self): "test_position_embedding": [ "1", ], + "one_api": ["true", "false"], } def test_simple_net_mp2_pp2(self): diff --git a/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_3d.py b/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_3d.py index 356080505fc4dd..4e843f34a1ac78 100644 --- a/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_3d.py +++ b/test/auto_parallel/hybrid_strategy/test_parallel_api_with_llama_3d.py @@ -45,6 +45,7 @@ def setUp(self): "test_position_embedding": [ "1", ], + "one_api": ["true", "false"], } def test_simple_net_dp2_mp2_pp2(self): From e5df6eb4bd42795f71f1edf9d27a362c57ef2f8a Mon Sep 17 00:00:00 2001 From: PuQing Date: Fri, 29 Nov 2024 10:28:07 +0800 Subject: [PATCH 054/288] =?UTF-8?q?=E3=80=90Infer=20Symbolic=20Shape?= =?UTF-8?q?=E3=80=91Fix=20infer=5Fsymbol=5Fshape=20for=20repeat=5Finterlea?= =?UTF-8?q?ve=5Fwith=5Ftensor=5Findex=20(#69787)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix * fix * fix --- .../infer_symbolic_shape/binary_infer_sym.cc | 22 ++++++++++--------- 1 file changed, 12 insertions(+), 10 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc index ee9b02f407ef64..969d0a609450e1 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc @@ -1833,16 +1833,18 @@ bool RepeatInterleaveWithTensorIndexOpInferSymbolicShape( "shape is %d-D.", repeats_shape_or_data.shape().size())); - ExprVec repeat_times_shape = - paddle::dialect::details::GetOrCreateExprVecFromData( - repeats_shape_or_data, infer_context); - - const auto &GetSum = [&](const auto &dim_exprs) { - symbol::DimExpr sum{0}; - for (const auto &dim_expr : dim_exprs) { - sum = sum + dim_expr; - } - return sum; + ExprVec repeat_times_shape; + if (repeats_shape_or_data.data().has_value()) { + repeat_times_shape.assign(repeats_shape_or_data.data()->begin(), + repeats_shape_or_data.data()->end()); + } else { + symbol::DimExpr out_unknown = infer_context->GetNextSymName(); + repeat_times_shape.push_back(out_unknown); + } + + const auto &GetSum = [](const auto &dim_exprs) { + return std::accumulate( + dim_exprs.begin(), dim_exprs.end(), symbol::DimExpr{0}, std::plus<>()); }; int x_rank = x_shape.size(); From 780d08cfa823d2002abe882018842f35254f7595 Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Fri, 29 Nov 2024 10:44:46 +0800 Subject: [PATCH 055/288] Optimize Compile Time (#69804) --- python/paddle/decomposition/recompute.py | 44 +++++++++++------------- 1 file changed, 20 insertions(+), 24 deletions(-) diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index 84e42e938de2c6..ca4be198d9e860 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -161,7 +161,9 @@ def __init__(self, program, unrecomputable_ops): self.operand_value_set = set() self.result_value_set = set() self.unrecomputable_ops = unrecomputable_ops - self.has_unfusible_on_path_map = self._set_has_unfusible_on_path_map() + self.downstream_unrecomputable_ops_map = {op: set() for op in self.ops} + self.upstream_unrecomputable_ops_map = {op: set() for op in self.ops} + self._set_has_unfusible_on_path_map() def _set_has_unfusible_on_path_map(self): def _get_used_external_value(op): @@ -221,56 +223,51 @@ def _get_consumer_ops(op): def _get_producer_ops_recursivly(root): visited = set() queue = deque() - result = set() queue.append(root) visited.add(root) while queue: cur = queue.popleft() - result.add(cur) + self.downstream_unrecomputable_ops_map[cur].add(root) for new_op in _get_producer_ops(cur): if new_op in visited: continue visited.add(new_op) queue.append(new_op) - return result def _get_consumer_ops_recursivly(root): visited = set() queue = deque() - result = set() queue.append(root) visited.add(root) while queue: cur = queue.popleft() - result.add(cur) + self.upstream_unrecomputable_ops_map[cur].add(root) for new_op in _get_consumer_ops(cur): if new_op in visited: continue visited.add(new_op) queue.append(new_op) - return result - has_unfusible_on_path_map = { - op1: {op2: False for op2 in self.ops} for op1 in self.ops - } for op in self.ops: if op.name() in self.unrecomputable_ops: - upstream_set = _get_producer_ops_recursivly(op) - downstream_set = _get_consumer_ops_recursivly(op) - - for upstream_op in upstream_set: - for downstream_op in downstream_set: - has_unfusible_on_path_map[upstream_op][ - downstream_op - ] = True - has_unfusible_on_path_map[downstream_op][ - upstream_op - ] = True - return has_unfusible_on_path_map + _get_producer_ops_recursivly(op) + _get_consumer_ops_recursivly(op) def _has_unfusible_op_on_any_path(self, op1, op2): + no_unfusible_op_on_path = ( + len( + self.downstream_unrecomputable_ops_map[op1] + & self.upstream_unrecomputable_ops_map[op2] + ) + == 0 + and len( + self.downstream_unrecomputable_ops_map[op2] + & self.upstream_unrecomputable_ops_map[op1] + ) + == 0 + ) return ( - self.has_unfusible_on_path_map[op1][op2] + not no_unfusible_op_on_path if op1 is not None and op2 is not None else False ) @@ -826,7 +823,6 @@ def getIdx(program, op): if cloned_op in parent_ops and cloned_op not in reseted_ops: cloned_op.move_before(op) reseted_ops.add(cloned_op) - DebugPrint("recompute program", program) return program, fwd_op_end_idx From 64c7181e725fe80ba5c89614b475e5d232d051fc Mon Sep 17 00:00:00 2001 From: JYChen Date: Fri, 29 Nov 2024 11:41:21 +0800 Subject: [PATCH 056/288] [Inference] Remove fleetexe in Predictor (#69710) * remove fleetexe in Predictor * remove dist_config dist and relative API --- paddle/fluid/inference/api/analysis_config.cc | 3 - .../fluid/inference/api/analysis_predictor.cc | 349 +----------------- .../fluid/inference/api/analysis_predictor.h | 59 --- .../inference/api/paddle_analysis_config.h | 57 --- .../inference/api/paddle_inference_api.h | 1 - paddle/fluid/inference/paddle_inference.map | 1 - paddle/fluid/pybind/inference_api.cc | 19 +- .../api/analyzer_dist_model_xpu_tester.cc | 5 - .../test_trt_c_allreduce_infer_script.py | 8 - 9 files changed, 2 insertions(+), 500 deletions(-) diff --git a/paddle/fluid/inference/api/analysis_config.cc b/paddle/fluid/inference/api/analysis_config.cc index c7cc03e5523335..45936e477baf4d 100644 --- a/paddle/fluid/inference/api/analysis_config.cc +++ b/paddle/fluid/inference/api/analysis_config.cc @@ -585,9 +585,6 @@ AnalysisConfig::AnalysisConfig(const AnalysisConfig &other) { CP_MEMBER(ipu_custom_ops_info_); CP_MEMBER(ipu_custom_patterns_); - // fleet exe related - CP_MEMBER(dist_config_); - // custom device related. CP_MEMBER(use_custom_device_); CP_MEMBER(custom_device_type_); diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 685c3d0f43209b..1640f8092c5670 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -71,12 +71,6 @@ #include "paddle/phi/kernels/funcs/data_type_transform.h" #include "paddle/utils/string/split.h" -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) -#include "paddle/fluid/distributed/fleet_executor/fleet_executor.h" -#include "paddle/fluid/distributed/fleet_executor/fleet_executor_desc.pb.h" -#include "paddle/fluid/distributed/fleet_executor/task_node.h" -#endif - #ifdef PADDLE_WITH_MKLML #include "paddle/phi/backends/dynload/mklml.h" #endif @@ -1326,17 +1320,6 @@ bool AnalysisPredictor::PrepareExecutor() { common::errors::PreconditionNotMet( "The sub_scope should not be nullptr.")); - if (config_.dist_config().use_dist_model()) { -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - VLOG(3) << "use_dist_model is enabled, will init FleetExecutor."; - return PrepareFleetExecutor(); -#else - PADDLE_THROW(common::errors::PermissionDenied( - "Paddle can't use FleetExecutor since it's not compiled with PSCORE," - "Please recompile or reinstall Paddle with PSCORE support.")); -#endif - } - if (config_.new_ir_enabled()) { executor_->Prepare(sub_scope_); } else { @@ -1378,293 +1361,6 @@ bool AnalysisPredictor::PrepareExecutor() { return true; } -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) -bool AnalysisPredictor::PrepareFleetExecutor() { - VLOG(3) << "AnalysisPredictor::PrepareFleetExecutor()"; - if (config_.dist_config().nranks() > 1 && !CommInit()) { - return false; - } - task_node_ = std::make_unique( - inference_program_.get(), config_.dist_config().rank()); - // With auto cut, there is no concept of pp, no need to add dependency. - task_node_->SetType("Compute"); - task_node_->Init(config_.use_feed_fetch_ops_enabled()); - executor_desc_ = distributed::FleetExecutorDesc(); - executor_desc_.set_cur_rank(config_.dist_config().rank()); - std::unordered_map id_to_rank; - for (int i = 0; i < config_.dist_config().nranks(); ++i) { - distributed::RankInfo *rank_info = executor_desc_.add_cluster_info(); - rank_info->set_rank(i); - rank_info->set_ip_port(config_.dist_config().trainer_endpoints()[i]); - id_to_rank.insert({i, i}); - } - fleet_exe_ = std::make_unique(executor_desc_); - // NOTE: Vars of feed fetch ops are not persistable, - // which will result in that those vars will be created in - // the subscope (microscope) in fleet executor. This will - // cause that the GetInputTensor/GetOutputTensor funct - // in analysis predictor cannot find those vars in the scope - // returned by the DistModel, since DistModel only return the - // root scope. So, those vars must to be created in the root - // scope instead of in the microscope - std::vector feed_fetch_vars; - for (auto pair : idx2feeds_) { - feed_fetch_vars.emplace_back(pair.second); - } - for (auto pair : idx2fetches_) { - feed_fetch_vars.emplace_back(pair.second); - } - fleet_exe_->Init(config_.dist_config().carrier_id(), - *(inference_program_.get()), - scope_.get(), - place_, - 1, - {task_node_.get()}, - id_to_rank, - feed_fetch_vars); - return true; -} - -bool AnalysisPredictor::CommInit() { - std::map> ring_id_to_ranks{}; - std::map> rank_to_ring_ids{}; - if (!LoadConverterConfig(&ring_id_to_ranks, &rank_to_ring_ids)) { - VLOG(3) << "Load converter config failed, DistModel init failed."; - return false; - } - std::unique_ptr comm_init_program( - new framework::ProgramDesc()); - framework::BlockDesc *comm_init_block = comm_init_program->MutableBlock(0); - std::vector &ring_ids = - rank_to_ring_ids[config_.dist_config().rank()]; - int64_t order = 0; - std::string var_name_base = "comm_init_"; - for (int64_t ring_id : ring_ids) { - VLOG(3) << "Init comm for ring id: " << ring_id; - int64_t ranks_in_group = ring_id_to_ranks[ring_id].size(); - int64_t rank_in_group = 0; - std::vector &ranks = ring_id_to_ranks[ring_id]; - for (int64_t rank : ranks) { - if (config_.dist_config().rank() == rank) { - break; - } - rank_in_group += 1; - } - std::vector peer_endpoints; - for (int64_t rank : ranks) { - if (config_.dist_config().rank() == rank) { - continue; - } - peer_endpoints.emplace_back( - config_.dist_config().trainer_endpoints()[rank]); - } - InsertCommOp(var_name_base + std::to_string(order), - ranks_in_group, - rank_in_group, - peer_endpoints, - comm_init_block, - ring_id); - order += 1; - } - framework::NaiveExecutor e(place_); - e.CreateVariables(*comm_init_program, 0, true, scope_.get()); - e.Prepare(scope_.get(), *comm_init_program, 0); - e.Run(); - VLOG(3) << "Comm init successful."; - return true; -} - -void AnalysisPredictor::InsertCommOp( - std::string tmp_var_name, - int nranks, - int rank, - const std::vector &peer_endpoints, - framework::BlockDesc *block, - int ring_id) { - /* - * tmp_var_name: the var name for var comm_id - * nranks: number of total ranks - * rank: the rank of local rank in the comm group - * peer_endpoints: peer's endpoints - * block: the block where to insert the comm ops - * ring_id: the ring_id to be inited - */ - const std::string &endpoint = config_.dist_config().current_endpoint(); - std::stringstream ss; - ss << "Init comm with tmp var: " << tmp_var_name - << ". The ring id is: " << ring_id << ". The group has: " << nranks - << " ranks. Current rank in the group is: " << rank - << ". The endpoint is: " << endpoint << ". Peer endpoints are: "; - for (auto ep : peer_endpoints) { - ss << ep << ", "; - } - VLOG(3) << ss.str(); - std::string endpoints_str = config_.dist_config().current_endpoint(); - for (const auto &peer : peer_endpoints) { - endpoints_str += "," + peer; - } - if (config_.use_gpu()) { - framework::VarDesc *new_var = block->Var(tmp_var_name); - new_var->SetType(framework::proto::VarType::RAW); - new_var->SetPersistable(true); - framework::OpDesc *gen_nccl_id_op = block->AppendOp(); - gen_nccl_id_op->SetType("c_gen_nccl_id"); - gen_nccl_id_op->SetOutput("Out", {tmp_var_name}); - gen_nccl_id_op->SetAttr("rank", rank); - gen_nccl_id_op->SetAttr("endpoint", - config_.dist_config().current_endpoint()); - gen_nccl_id_op->SetAttr("other_endpoints", peer_endpoints); - gen_nccl_id_op->SetAttr("ring_id", ring_id); - gen_nccl_id_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - gen_nccl_id_op->CheckAttrs(); - framework::OpDesc *comm_init_op = block->AppendOp(); - comm_init_op->SetType("c_comm_init"); - comm_init_op->SetInput("X", {tmp_var_name}); - comm_init_op->SetAttr("rank", rank); - comm_init_op->SetAttr("nranks", nranks); - comm_init_op->SetAttr("ring_id", ring_id); - comm_init_op->SetAttr("endpoints", endpoints_str); - comm_init_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - comm_init_op->CheckAttrs(); - } else if (config_.use_xpu()) { - framework::VarDesc *new_var = block->Var(tmp_var_name); - new_var->SetType(framework::proto::VarType::RAW); - new_var->SetPersistable(true); - framework::OpDesc *gen_bkcl_id_op = block->AppendOp(); - gen_bkcl_id_op->SetType("c_gen_bkcl_id"); - gen_bkcl_id_op->SetOutput("Out", {tmp_var_name}); - gen_bkcl_id_op->SetAttr("rank", rank); - gen_bkcl_id_op->SetAttr("endpoint", - config_.dist_config().current_endpoint()); - gen_bkcl_id_op->SetAttr("other_endpoints", peer_endpoints); - gen_bkcl_id_op->SetAttr("ring_id", ring_id); - gen_bkcl_id_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - gen_bkcl_id_op->CheckAttrs(); - framework::OpDesc *comm_init_op = block->AppendOp(); - comm_init_op->SetType("c_comm_init"); - comm_init_op->SetInput("X", {tmp_var_name}); - comm_init_op->SetAttr("rank", rank); - comm_init_op->SetAttr("nranks", nranks); - comm_init_op->SetAttr("ring_id", ring_id); - comm_init_op->SetAttr("endpoints", endpoints_str); - comm_init_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - comm_init_op->CheckAttrs(); - } else if (config_.use_custom_device()) { - framework::VarDesc *new_var = block->Var(tmp_var_name); - new_var->SetType(framework::proto::VarType::RAW); - new_var->SetPersistable(true); - framework::OpDesc *gen_bkcl_id_op = block->AppendOp(); - gen_bkcl_id_op->SetType("c_gen_xccl_id"); - gen_bkcl_id_op->SetOutput("Out", {tmp_var_name}); - gen_bkcl_id_op->SetAttr("rank", rank); - gen_bkcl_id_op->SetAttr("endpoint", - config_.dist_config().current_endpoint()); - gen_bkcl_id_op->SetAttr("other_endpoints", peer_endpoints); - gen_bkcl_id_op->SetAttr("ring_id", ring_id); - gen_bkcl_id_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - gen_bkcl_id_op->CheckAttrs(); - framework::OpDesc *comm_init_op = block->AppendOp(); - comm_init_op->SetType("c_comm_init"); - comm_init_op->SetInput("X", {tmp_var_name}); - comm_init_op->SetAttr("rank", rank); - comm_init_op->SetAttr("nranks", nranks); - comm_init_op->SetAttr("ring_id", ring_id); - comm_init_op->SetAttr("endpoints", endpoints_str); - comm_init_op->SetAttr("op_role", - static_cast(framework::OpRole::kForward)); - comm_init_op->CheckAttrs(); - } else { - LOG(WARNING) << "DistModelInf doesn't init comm."; - // TODO(fleet exe dev): comm init for more devices - } -} - -bool AnalysisPredictor::LoadConverterConfig( - std::map> *ring_id_to_ranks, - std::map> *rank_to_ring_ids) { - VLOG(3) << "Going to load converter config from: " - << config_.dist_config().comm_init_config() << "\n"; - std::ifstream fin(config_.dist_config().comm_init_config(), std::ios::in); - PADDLE_ENFORCE_EQ( - static_cast(fin.is_open()), - true, - common::errors::NotFound( - "Cannot open file %s, please confirm whether the file is normal.", - config_.dist_config().comm_init_config())); - std::string line; - bool ring_to_rank{true}; - // Reading config from file, the config file should like these format - // [ring_id -> ranks] - // 0,0,1,2,3 - // 1,0,1 - // 2,2,3 - // 21,0,1 - // 22,1,2 - // 23,2,3 - // [rank -> ring_ids] - // 0,0,1,21 - // 1,0,1,21,22 - // 2,0,2,22,23 - // 3,0,2,23 - while (std::getline(fin, line)) { - std::vector one_line = paddle::string::Split(line, ','); - if (one_line.size() == 1) { - // start a new section of the config - if (line == "[ring_id -> ranks]") { - ring_to_rank = true; - } else if (line == "[rank -> ring_ids]") { - ring_to_rank = false; - } - } else { - // parse key - values pairs in one section - int64_t key = std::stoll(one_line[0]); - for (size_t i = 1; i < one_line.size(); ++i) { - int64_t val = std::stoll(one_line[i]); - if (ring_to_rank) { // NOLINT - if (ring_id_to_ranks->find(key) == ring_id_to_ranks->end()) { - ring_id_to_ranks->insert({key, std::vector()}); - } - ring_id_to_ranks->at(key).emplace_back(val); - } else { - if (rank_to_ring_ids->find(key) == rank_to_ring_ids->end()) { - rank_to_ring_ids->insert({key, std::vector()}); - } - rank_to_ring_ids->at(key).emplace_back(val); - } - // NOTE: add more configuration sections here - } - } - } - std::stringstream ss; - ss << "Loaded the following converter config:\n"; - ss << "ring_id_to_ranks:\n"; - for (auto pair : *ring_id_to_ranks) { - int64_t key = pair.first; - ss << "\t" << key << "\t->\t"; - for (auto value : pair.second) { - ss << value << "\t"; - } - ss << "\n"; - } - ss << "rank_to_ring_ids:\n"; - for (auto pair : *rank_to_ring_ids) { - int64_t key = pair.first; - ss << "\t" << key << "\t->\t"; - for (auto value : pair.second) { - ss << value << "\t"; - } - ss << "\n"; - } - VLOG(3) << ss.str(); - return true; -} -#endif - void AnalysisPredictor::MkldnnPreSet(const std::vector &inputs) { #ifdef PADDLE_WITH_DNNL std::vector> inputs_shape; @@ -1824,15 +1520,8 @@ bool AnalysisPredictor::Run(const std::vector &inputs, VLOG(3) << "predict start"; // set feed variable framework::Scope *scope{nullptr}; -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - if (config_.dist_config().use_dist_model()) { // NOLINT - scope = scope_.get(); - } else { - scope = executor_->GetScope(); - } -#else + scope = executor_->GetScope(); -#endif PADDLE_ENFORCE_NOT_NULL( scope, common::errors::PreconditionNotMet("The scope should not be nullptr.")); @@ -1880,18 +1569,6 @@ bool AnalysisPredictor::Run(const std::vector &inputs, } #endif -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - if (config_.dist_config().use_dist_model()) { // NOLINT - VLOG(3) << "ZeroCopyRun will use the fleet executor."; - fleet_exe_->Run(config_.dist_config().carrier_id()); - } else if (config_.new_executor_enabled()) { // NOLINT - executor_->RunInterpreterCore(); - } else { - // Run the inference program - // if share variables, we need not create variables - executor_->Run(); - } -#else if (config_.new_executor_enabled()) { // NOLINT executor_->RunInterpreterCore(); } else { @@ -1899,7 +1576,6 @@ bool AnalysisPredictor::Run(const std::vector &inputs, // if share variables, we need not create variables executor_->Run(); } -#endif inference::DisplayMemoryInfo(place_, "after run"); #ifdef PADDLE_WITH_XPU @@ -2689,15 +2365,7 @@ AnalysisPredictor::GetOutputTypes() { std::unique_ptr AnalysisPredictor::GetInputTensor( const std::string &name) { framework::Scope *scope = nullptr; -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - if (config_.dist_config().use_dist_model()) { // NOLINT - scope = scope_.get(); - } else { - scope = executor_->GetScope(); - } -#else scope = executor_->GetScope(); -#endif PADDLE_ENFORCE_NOT_NULL( scope->FindVar(name), common::errors::PreconditionNotMet( @@ -2731,15 +2399,7 @@ std::unique_ptr AnalysisPredictor::GetInputTensor( std::unique_ptr AnalysisPredictor::GetOutputTensor( const std::string &name) { framework::Scope *scope; // NOLINT -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - if (config_.dist_config().use_dist_model()) { // NOLINT - scope = scope_.get(); - } else { - scope = executor_->GetScope(); - } -#else scope = executor_->GetScope(); -#endif PADDLE_ENFORCE_NOT_NULL( scope->FindVar(name), common::errors::PreconditionNotMet( @@ -2772,13 +2432,6 @@ std::unique_ptr AnalysisPredictor::GetOutputTensor( bool AnalysisPredictor::ZeroCopyRun(bool switch_stream) { inference::DisplayMemoryInfo(place_, "before run"); -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - if (config_.dist_config().use_dist_model()) { // NOLINT - VLOG(3) << "ZeroCopyRun will use the fleet executor."; - fleet_exe_->Run(config_.dist_config().carrier_id()); - return true; - } -#endif if (private_context_) { phi::DeviceContextPool::SetDeviceContexts(&device_contexts_); auto &pool = paddle::experimental::DeviceContextPool::Instance(); diff --git a/paddle/fluid/inference/api/analysis_predictor.h b/paddle/fluid/inference/api/analysis_predictor.h index 8a6f06170e8367..445fcdd0301588 100644 --- a/paddle/fluid/inference/api/analysis_predictor.h +++ b/paddle/fluid/inference/api/analysis_predictor.h @@ -33,10 +33,6 @@ #include "paddle/phi/core/platform/device/gpu/gpu_types.h" #include "paddle/utils/string/printf.h" -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) -#include "paddle/fluid/distributed/fleet_executor/fleet_executor.h" -#endif - #ifdef PADDLE_WITH_TESTING #include #include @@ -513,55 +509,6 @@ class AnalysisPredictor : public PaddlePredictor { std::string GetOptimizedModelPath(); void ClearExtraParams(); -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - // fleet exe related - - /// - /// \brief prepare for fleet executor to run - /// - /// Used in AnalysisPredictor::Init(), - /// - bool PrepareFleetExecutor(); - - /// - /// \brief init NCCL env for multi gpus inference - /// - /// Used in AnalysisPredictor::PrepareFleetExecutor() - /// - bool CommInit(); - - /// - /// \brief read the config to init NCCL env - /// - /// Used in AnalysisPredictor::CommInit() - /// - /// \param[in] ring_id_to_ranks: a ptr to ring_id_to_ranks - /// \param[in] rank_to_ring_ids: a ptr to rank_to_ring_ids - /// - bool LoadConverterConfig( - std::map> *ring_id_to_ranks, - std::map> *rank_to_ring_ids); - - /// - /// \brief add ops and run them with NaiveExecutor to init NCCL env - /// - /// Used in AnalysisPredictor::CommInit() - /// - /// \param[in] tmp_var_name: var name to hold NCCL unique id - /// \param[in] nranks: number of ranks in one comm group - /// \param[in] rank: relative rank of current rank in the comm group - /// \param[in] peer_endpoints: group's peers' endpoints - /// \param[in] block: the block to insert comm ops - /// \param[in] ring_id: the ring id to be used to init NCCL env - /// - void InsertCommOp(std::string tmp_var_name, - int nranks, - int rank, - const std::vector &peer_endpoints, - framework::BlockDesc *block, - int ring_id); -#endif - private: AnalysisConfig config_; std::unique_ptr argument_ = nullptr; @@ -613,12 +560,6 @@ class AnalysisPredictor : public PaddlePredictor { std::map>> device_contexts_; -#if defined(PADDLE_WITH_DISTRIBUTE) && defined(PADDLE_WITH_PSCORE) - // fleet executor related - distributed::FleetExecutorDesc executor_desc_; - std::shared_ptr fleet_exe_; - std::shared_ptr task_node_; -#endif friend class paddle_infer::experimental::InternalUtils; }; diff --git a/paddle/fluid/inference/api/paddle_analysis_config.h b/paddle/fluid/inference/api/paddle_analysis_config.h index dab3a66dcab329..00dbd651938ea5 100644 --- a/paddle/fluid/inference/api/paddle_analysis_config.h +++ b/paddle/fluid/inference/api/paddle_analysis_config.h @@ -120,54 +120,6 @@ struct PD_INFER_DECL XpuConfig { std::map quant_post_dynamic_weight_methods; }; -struct DistConfig { - bool use_dist_model() const { return use_dist_model_; } - void EnableDistModel(bool use_dist_model) { - use_dist_model_ = use_dist_model; - } - - std::vector trainer_endpoints() const { - return trainer_endpoints_; - } - - std::string current_endpoint() const { return current_endpoint_; } - - void SetEndpoints(const std::vector& trainer_endpoints, - const std::string& current_endpoint) { - trainer_endpoints_ = trainer_endpoints; - current_endpoint_ = current_endpoint; - } - - int64_t nranks() const { return nranks_; } - - int64_t rank() const { return rank_; } - - void SetRanks(int64_t nranks, int64_t rank) { - nranks_ = nranks; - rank_ = rank; - } - - std::string comm_init_config() const { return comm_init_config_; } - - void SetCommInitConfig(const std::string& comm_init_config) { - comm_init_config_ = comm_init_config; - } - - void SetCarrierId(const std::string& carrier_id) { carrier_id_ = carrier_id; } - - std::string carrier_id() const { return carrier_id_; } - - protected: - // DistModel Inference related - bool use_dist_model_{false}; // whether use DistModel or not - std::vector trainer_endpoints_{}; // all trainers' endpoints - std::string current_endpoint_{}; // current trainer's endpoint - int64_t nranks_{1}; // total ranks (number of trainers) - int64_t rank_{0}; // rank - std::string comm_init_config_{}; // converter config path - std::string carrier_id_{"inference"}; -}; - /// /// \brief configuration manager for AnalysisPredictor. /// \since 1.7.0 @@ -1106,12 +1058,6 @@ struct PD_INFER_DECL AnalysisConfig { /// std::string Summary(); - void SetDistConfig(const DistConfig& dist_config) { - dist_config_ = dist_config; - } - - const DistConfig& dist_config() const { return dist_config_; } - /// /// \brief Set a list of operators that do not support mixed precision. This /// interface is in the experimental stage and may change in the future. Note @@ -1365,9 +1311,6 @@ struct PD_INFER_DECL AnalysisConfig { std::string opt_cache_dir_; friend class paddle_infer::experimental::InternalUtils; - // fleet exe related - DistConfig dist_config_{}; - // jit engine related // NOTE(Aureliue84): In case of Predictor in JITLayer, program is from outer // which means Predictor should apply optimization by calling diff --git a/paddle/fluid/inference/api/paddle_inference_api.h b/paddle/fluid/inference/api/paddle_inference_api.h index ac117abe23c447..a23de7cf5518b9 100644 --- a/paddle/fluid/inference/api/paddle_inference_api.h +++ b/paddle/fluid/inference/api/paddle_inference_api.h @@ -46,7 +46,6 @@ namespace paddle_infer { using PrecisionType = paddle::AnalysisConfig::Precision; using Config = paddle::AnalysisConfig; -using DistConfig = paddle::DistConfig; using XpuConfig = paddle::XpuConfig; /// diff --git a/paddle/fluid/inference/paddle_inference.map b/paddle/fluid/inference/paddle_inference.map index 180d4e643ba23d..2e5af030961bb8 100644 --- a/paddle/fluid/inference/paddle_inference.map +++ b/paddle/fluid/inference/paddle_inference.map @@ -34,7 +34,6 @@ *paddle::PaddleTensor*; *paddle::UpdateDllFlag*; *paddle::MakeCipher*; - *paddle::DistConfig*; *paddle::DefaultGPUPlace*; *paddle::ResourceManager*; *paddle::GPUContextResource*; diff --git a/paddle/fluid/pybind/inference_api.cc b/paddle/fluid/pybind/inference_api.cc index 997e705d8be5ae..17255063979e27 100644 --- a/paddle/fluid/pybind/inference_api.cc +++ b/paddle/fluid/pybind/inference_api.cc @@ -1018,24 +1018,7 @@ void BindAnalysisConfig(py::module *m) { py::arg("custom_pass_only") = false) .def("set_optimization_level", &AnalysisConfig::SetOptimizationLevel, - py::arg("opt_level") = 2) - .def("set_dist_config", &AnalysisConfig::SetDistConfig) - .def("dist_config", &AnalysisConfig::dist_config); - - py::class_(*m, "DistConfig") - .def(py::init<>()) - .def("set_carrier_id", &DistConfig::SetCarrierId) - .def("set_comm_init_config", &DistConfig::SetCommInitConfig) - .def("set_endpoints", &DistConfig::SetEndpoints) - .def("set_ranks", &DistConfig::SetRanks) - .def("enable_dist_model", &DistConfig::EnableDistModel) - .def("carrier_id", &DistConfig::carrier_id) - .def("current_endpoint", &DistConfig::current_endpoint) - .def("trainer_endpoints", &DistConfig::trainer_endpoints) - .def("nranks", &DistConfig::nranks) - .def("rank", &DistConfig::rank) - .def("comm_init_config", &DistConfig::comm_init_config) - .def("use_dist_model", &DistConfig::use_dist_model); + py::arg("opt_level") = 2); } void BindXpuConfig(py::module *m) { diff --git a/test/cpp/inference/api/analyzer_dist_model_xpu_tester.cc b/test/cpp/inference/api/analyzer_dist_model_xpu_tester.cc index 570c23b3eb7691..8ab239de5c362c 100644 --- a/test/cpp/inference/api/analyzer_dist_model_xpu_tester.cc +++ b/test/cpp/inference/api/analyzer_dist_model_xpu_tester.cc @@ -30,11 +30,6 @@ TEST(test_dist_model_xpu, dist_model_xpu) { FLAGS_infer_model + "/__params__"); config.EnableXpu(); config.SetXpuDeviceId(0); - DistConfig dist_config; - dist_config.SetRanks(1, 0); - dist_config.EnableDistModel(true); - dist_config.SetEndpoints({""}, ""); - config.SetDistConfig(dist_config); auto predictor = paddle_infer::CreatePredictor(config); int batch_size = 1; diff --git a/test/ir/inference/test_trt_c_allreduce_infer_script.py b/test/ir/inference/test_trt_c_allreduce_infer_script.py index 52302b97389380..ae8eb38e96f16a 100644 --- a/test/ir/inference/test_trt_c_allreduce_infer_script.py +++ b/test/ir/inference/test_trt_c_allreduce_infer_script.py @@ -19,7 +19,6 @@ import numpy as np import paddle -from paddle.base import core from paddle.distributed import fleet from paddle.inference import Config, PrecisionType, create_predictor @@ -66,12 +65,6 @@ def run(op_type, precision): current_endpoint = "127.0.0.1:600" + str(fleet.worker_index()) trainer_endpoints = ["127.0.0.1:6000", "127.0.0.1:6001"] - dist_config = core.DistConfig() - dist_config.set_carrier_id("inference") - dist_config.set_endpoints(trainer_endpoints, current_endpoint) - dist_config.set_ranks(nranks, fleet.worker_index()) - dist_config.enable_dist_model(True) - with tempfile.TemporaryDirectory(prefix="allreduce_") as tmpdir: paddle.static.save_inference_model( os.path.join(tmpdir, "model"), @@ -86,7 +79,6 @@ def run(op_type, precision): ) config.enable_memory_optim() config.enable_use_gpu(1000, fleet.worker_index()) - config.set_dist_config(dist_config) config.enable_tensorrt_engine( workspace_size=1 << 30, max_batch_size=1, From 9ab72a8139b2254925dd0e6fd32d521ed99250b4 Mon Sep 17 00:00:00 2001 From: co63oc Date: Fri, 29 Nov 2024 12:11:21 +0800 Subject: [PATCH 057/288] [Lod][fluid_ops] tensor.lod (#69780) * Fix * Fix * Fix * Fix --- paddle/fluid/framework/infershape_utils.cc | 2 +- paddle/fluid/operators/controlflow/feed_op.cc | 2 +- paddle/phi/core/dense_tensor.cc | 6 ++++-- paddle/phi/core/dense_tensor.h | 6 +++--- paddle/phi/core/dense_tensor_impl.cc | 17 +++++++++++------ paddle/phi/core/meta_tensor.cc | 16 ++++++++-------- paddle/phi/core/meta_tensor.h | 2 +- paddle/phi/core/tensor_meta.cc | 14 +++++++++----- paddle/phi/core/tensor_meta.h | 6 +++--- .../kernels/cpu/match_matrix_tensor_kernel.cc | 4 ++-- paddle/phi/kernels/funcs/common_shape.h | 2 +- test/cpp/phi/core/test_dense_tensor.cc | 12 ++++++------ 12 files changed, 50 insertions(+), 39 deletions(-) diff --git a/paddle/fluid/framework/infershape_utils.cc b/paddle/fluid/framework/infershape_utils.cc index 03dee0721eb534..f3450a14abb27f 100644 --- a/paddle/fluid/framework/infershape_utils.cc +++ b/paddle/fluid/framework/infershape_utils.cc @@ -397,7 +397,7 @@ void CompatMetaTensor::share_lod(const MetaTensor& meta_tensor) { if (var == nullptr) return; if (var->IsType() && meta_tensor.is_dense()) { auto* tensor = var->GetMutable(); - phi::DenseTensorUtils::GetMutableMeta(tensor)->lod = + phi::DenseTensorUtils::GetMutableMeta(tensor)->legacy_lod = static_cast(meta_tensor).GetRuntimeLoD(); } else { // NOTE(chenweihang): do nothing diff --git a/paddle/fluid/operators/controlflow/feed_op.cc b/paddle/fluid/operators/controlflow/feed_op.cc index aeecb576e14256..780b20f6e3e640 100644 --- a/paddle/fluid/operators/controlflow/feed_op.cc +++ b/paddle/fluid/operators/controlflow/feed_op.cc @@ -75,7 +75,7 @@ class FeedOp : public framework::OperatorWithKernel { meta.dims = feed_tensor.dims(); meta.dtype = feed_tensor.dtype(); meta.layout = feed_tensor.layout(); - meta.lod = feed_tensor.lod(); + meta.legacy_lod = feed_tensor.lod(); meta.strides = feed_tensor.strides(); if (meta.strides.size() == -1) { meta.strides = meta.calc_strides(meta.dims); diff --git a/paddle/phi/core/dense_tensor.cc b/paddle/phi/core/dense_tensor.cc index d82ce449a4cf5b..8fafa6fa62eca1 100644 --- a/paddle/phi/core/dense_tensor.cc +++ b/paddle/phi/core/dense_tensor.cc @@ -214,7 +214,7 @@ void DenseTensor::set_meta(const DenseTensorMeta& meta) { meta_.dtype = meta.dtype; meta_.is_scalar = meta.is_scalar; meta_.layout = meta.layout; - meta_.lod = meta.lod; + meta_.legacy_lod = meta.legacy_lod; meta_.offset = meta.offset; meta_.use_gpudnn = meta.use_gpudnn; if (meta.strides.size() == -1) { @@ -255,7 +255,9 @@ void DenseTensor::ResizeAndAllocate(const DDim& dims) { } } -void DenseTensor::ResetLoD(const LoD& lod) { meta_.lod = lod; } +void DenseTensor::ResetLoD(const LoD& legacy_lod) { + meta_.legacy_lod = legacy_lod; +} #define DATA_MEMBER_FUNC_INSTANTIATION(dtype) \ template TEST_API const dtype* DenseTensor::data() const; \ diff --git a/paddle/phi/core/dense_tensor.h b/paddle/phi/core/dense_tensor.h index 46d61efbc1d9aa..44bf8a24eff72c 100644 --- a/paddle/phi/core/dense_tensor.h +++ b/paddle/phi/core/dense_tensor.h @@ -95,7 +95,7 @@ class TEST_API DenseTensor : public TensorBase, /// \brief Returns the lod of the tensor. /// \return The lod of the tensor. - const LoD& lod() const noexcept { return meta_.lod; } + const LoD& lod() const noexcept { return meta_.legacy_lod; } /// \brief Returns the data type of the tensor. /// \return The data type of the tensor. @@ -153,8 +153,8 @@ class TEST_API DenseTensor : public TensorBase, DenseTensor& Resize(const DDim& dims); /// \brief Change the lod information in the metadata. - /// \param lod The new lod of the dense tensor. - void ResetLoD(const LoD& lod); + /// \param legacy_lod The new lod of the dense tensor. + void ResetLoD(const LoD& legacy_lod); /// \brief Returns the actual allocation size occupied by tensor, may be /// larger diff --git a/paddle/phi/core/dense_tensor_impl.cc b/paddle/phi/core/dense_tensor_impl.cc index 7a886f90b03760..79084a014275fe 100644 --- a/paddle/phi/core/dense_tensor_impl.cc +++ b/paddle/phi/core/dense_tensor_impl.cc @@ -227,11 +227,15 @@ LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::phi::dtype::complex) /* From phi::DenseTensor */ /* ------------------------------ */ -DenseTensor::DenseTensor(const LoD& lod) : DenseTensor() { meta_.lod = lod; } +DenseTensor::DenseTensor(const LoD& legacy_lod) : DenseTensor() { + meta_.legacy_lod = legacy_lod; +} -void DenseTensor::set_lod(const LoD& lod) { meta_.lod = lod; } +void DenseTensor::set_lod(const LoD& legacy_lod) { + meta_.legacy_lod = legacy_lod; +} -LoD* DenseTensor::mutable_lod() { return &meta_.lod; } +LoD* DenseTensor::mutable_lod() { return &meta_.legacy_lod; } std::pair DenseTensor::lod_element(size_t level, size_t elem) const { @@ -254,10 +258,11 @@ std::pair DenseTensor::lod_element(size_t level, elem, NumElements(level))); - return std::make_pair((meta_.lod)[level][elem], (meta_.lod)[level][elem + 1]); + return std::make_pair((meta_.legacy_lod)[level][elem], + (meta_.legacy_lod)[level][elem + 1]); } -size_t DenseTensor::NumLevels() const { return meta_.lod.size(); } +size_t DenseTensor::NumLevels() const { return meta_.legacy_lod.size(); } size_t DenseTensor::NumElements(size_t level) const { PADDLE_ENFORCE_LT( @@ -270,7 +275,7 @@ size_t DenseTensor::NumElements(size_t level) const { NumLevels())); // the last offset is the end of last element - return (meta_.lod)[level].size() - 1; + return (meta_.legacy_lod)[level].size() - 1; } DenseTensor& DenseTensor::Resize(const DDim& dims) { diff --git a/paddle/phi/core/meta_tensor.cc b/paddle/phi/core/meta_tensor.cc index 07cf0709e38591..fbc5bcf79f3caf 100644 --- a/paddle/phi/core/meta_tensor.cc +++ b/paddle/phi/core/meta_tensor.cc @@ -207,12 +207,12 @@ void MetaTensor::share_lod(const MetaTensor& meta_tensor) { return; } if (phi::DenseTensor::classof(tensor_)) { - DenseTensorUtils::GetMutableMeta(static_cast(tensor_))->lod = - meta_tensor.lod(); + DenseTensorUtils::GetMutableMeta(static_cast(tensor_)) + ->legacy_lod = meta_tensor.lod(); } else if (phi::SelectedRows::classof(tensor_)) { DenseTensorUtils::GetMutableMeta( static_cast(tensor_)->mutable_value()) - ->lod = meta_tensor.lod(); + ->legacy_lod = meta_tensor.lod(); } else { PADDLE_THROW(common::errors::Unimplemented( "Unsupported sharing lod inplace for `%s`.", @@ -220,24 +220,24 @@ void MetaTensor::share_lod(const MetaTensor& meta_tensor) { } } -void MetaTensor::share_lod(const LoD& lod) { +void MetaTensor::share_lod(const LoD& legacy_lod) { ValidCheck(*this); if (phi::SparseCooTensor::classof(tensor_) || phi::SparseCsrTensor::classof(tensor_) || phi::distributed::DistTensor::classof(tensor_)) { return; } - if (lod.empty()) { + if (legacy_lod.empty()) { // no need share return; } if (phi::DenseTensor::classof(tensor_)) { - DenseTensorUtils::GetMutableMeta(static_cast(tensor_))->lod = - lod; + DenseTensorUtils::GetMutableMeta(static_cast(tensor_)) + ->legacy_lod = legacy_lod; } else if (phi::SelectedRows::classof(tensor_)) { DenseTensorUtils::GetMutableMeta( static_cast(tensor_)->mutable_value()) - ->lod = lod; + ->legacy_lod = legacy_lod; } else { PADDLE_THROW(common::errors::Unimplemented( "Unsupported sharing lod inplace for `%s`.", diff --git a/paddle/phi/core/meta_tensor.h b/paddle/phi/core/meta_tensor.h index 004dd2f7a53328..c31274966c7972 100644 --- a/paddle/phi/core/meta_tensor.h +++ b/paddle/phi/core/meta_tensor.h @@ -75,7 +75,7 @@ class TEST_API MetaTensor { virtual void set_strides(const DDim& strides); virtual void share_lod(const MetaTensor& meta_tensor); - void share_lod(const LoD& lod); + void share_lod(const LoD& legacy_lod); void share_lod(const MetaTensor& meta_tensor, int64_t index); virtual void share_meta(const MetaTensor& meta_tensor); virtual void share_dims(const MetaTensor& meta_tensor); diff --git a/paddle/phi/core/tensor_meta.cc b/paddle/phi/core/tensor_meta.cc index 82e375a9c582b2..8fa10eb4d3cae8 100644 --- a/paddle/phi/core/tensor_meta.cc +++ b/paddle/phi/core/tensor_meta.cc @@ -148,9 +148,13 @@ DenseTensorMeta::DenseTensorMeta(DataType dtype, DenseTensorMeta::DenseTensorMeta(DataType dtype, const DDim& dims, DataLayout layout, - const LoD& lod, + const LoD& legacy_lod, size_t offset) - : dims(dims), dtype(dtype), layout(layout), lod(lod), offset(offset) { + : dims(dims), + dtype(dtype), + layout(layout), + legacy_lod(legacy_lod), + offset(offset) { strides = calc_strides(dims); use_gpudnn = true; } @@ -161,7 +165,7 @@ DenseTensorMeta::DenseTensorMeta(const DenseTensorMeta& other) { dims = other.dims; dtype = other.dtype; layout = other.layout; - lod = other.lod; + legacy_lod = other.legacy_lod; offset = other.offset; if (other.strides.size() == -1) { strides = calc_strides(dims); @@ -176,7 +180,7 @@ DenseTensorMeta& DenseTensorMeta::operator=(const DenseTensorMeta& other) { dims = other.dims; dtype = other.dtype; layout = other.layout; - lod = other.lod; + legacy_lod = other.legacy_lod; offset = other.offset; if (other.strides.size() == -1) { strides = calc_strides(dims); @@ -193,7 +197,7 @@ DenseTensorMeta& DenseTensorMeta::operator=( // NOLINT dims = other.dims; dtype = other.dtype; layout = other.layout; - lod = std::move(other.lod); + legacy_lod = std::move(other.legacy_lod); offset = other.offset; if (other.strides.size() == -1) { strides = calc_strides(dims); diff --git a/paddle/phi/core/tensor_meta.h b/paddle/phi/core/tensor_meta.h index 7c2994e95f483f..5135f2efdcb672 100644 --- a/paddle/phi/core/tensor_meta.h +++ b/paddle/phi/core/tensor_meta.h @@ -57,7 +57,7 @@ struct TEST_API DenseTensorMeta { DenseTensorMeta(DataType dtype, const DDim& dims, DataLayout layout, - const LoD& lod, + const LoD& legacy_lod, size_t offset = 0); DenseTensorMeta(const DenseTensorMeta& other); @@ -80,7 +80,7 @@ struct TEST_API DenseTensorMeta { DDim dims; DataType dtype{DataType::UNDEFINED}; DataLayout layout{DataLayout::NCHW}; - LoD lod; + LoD legacy_lod; size_t offset{0}; DDim strides; }; @@ -88,7 +88,7 @@ struct TEST_API DenseTensorMeta { inline bool operator==(const DenseTensorMeta& lhs, const DenseTensorMeta& rhs) { return (lhs.is_scalar == rhs.is_scalar) && lhs.use_gpudnn == rhs.use_gpudnn && (lhs.dims == rhs.dims) && (lhs.dtype == rhs.dtype) && - (lhs.layout == rhs.layout) && (lhs.lod == rhs.lod) && + (lhs.layout == rhs.layout) && (lhs.legacy_lod == rhs.legacy_lod) && (lhs.offset == rhs.offset) && (lhs.strides == rhs.strides); } diff --git a/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc b/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc index 9b4c4a3fc6400b..2d6e3cda65bb65 100644 --- a/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc +++ b/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc @@ -112,14 +112,14 @@ void CPUMatchMatrixTensorOPKernel(const Context& dev_ctx, phi::DenseTensorMeta new_out_meta(out_meta.dtype, common::make_ddim(out_dims_vec), out_meta.layout, - out_meta.lod); + out_meta.legacy_lod); out->set_meta(new_out_meta); auto& tmp_meta = tmp->meta(); phi::DenseTensorMeta new_tmp_meta(tmp_meta.dtype, common::make_ddim(tmp_dims_vec), tmp_meta.layout, - tmp_meta.lod); + tmp_meta.legacy_lod); tmp->set_meta(new_tmp_meta); int64_t dim_in = x->dims()[1]; diff --git a/paddle/phi/kernels/funcs/common_shape.h b/paddle/phi/kernels/funcs/common_shape.h index d2bead941b5e63..396de46e325f9f 100644 --- a/paddle/phi/kernels/funcs/common_shape.h +++ b/paddle/phi/kernels/funcs/common_shape.h @@ -29,7 +29,7 @@ inline void SetXShape(const DenseTensor &x, DenseTensor *xshape) { xshape_dims[i + 1] = in_dims[i]; } xshape->ResizeAndAllocate(common::make_ddim(xshape_dims)); - xshape->ResetLoD(x.meta().lod); + xshape->ResetLoD(x.meta().legacy_lod); } inline void GetBroadcastDimsArrays(const DDim &x_dims, diff --git a/test/cpp/phi/core/test_dense_tensor.cc b/test/cpp/phi/core/test_dense_tensor.cc index 695354e2e48417..e4364c42a8f499 100644 --- a/test/cpp/phi/core/test_dense_tensor.cc +++ b/test/cpp/phi/core/test_dense_tensor.cc @@ -108,13 +108,13 @@ TEST(dense_tensor, meta) { "lod. Expected layout: %s, but got: %s", layout, meta_3.layout)); - PADDLE_ENFORCE_EQ(meta_3.lod, + PADDLE_ENFORCE_EQ(meta_3.legacy_lod, lod, common::errors::InvalidArgument( "Fail in DenseTensorMeta with dtype, dims, layout and " "lod. Expected lod: %s, but got: %s", lod, - meta_3.lod)); + meta_3.legacy_lod)); PADDLE_ENFORCE_EQ(meta_3.valid(), true, common::errors::InvalidArgument( @@ -145,12 +145,12 @@ TEST(dense_tensor, meta) { layout, meta_4.layout)); PADDLE_ENFORCE_EQ( - meta_4.lod, + meta_4.legacy_lod, lod, common::errors::InvalidArgument( "Fail in copy DenseTensorMeta. Expected lod: %s, but got: %s", lod, - meta_4.lod)); + meta_4.legacy_lod)); PADDLE_ENFORCE_EQ( meta_4.valid(), true, @@ -181,12 +181,12 @@ TEST(dense_tensor, meta) { layout, meta_5.layout)); PADDLE_ENFORCE_EQ( - meta_5.lod, + meta_5.legacy_lod, lod, common::errors::InvalidArgument( "Fail in copy DenseTensorMeta. Expected lod: %s, but got: %s", lod, - meta_5.lod)); + meta_5.legacy_lod)); PADDLE_ENFORCE_EQ( meta_5.valid(), true, From 54ec0350c98902fb479ffbc767711eba0a599e4d Mon Sep 17 00:00:00 2001 From: Ayakouji <148307532+aquagull@users.noreply.github.com> Date: Fri, 29 Nov 2024 12:43:28 +0800 Subject: [PATCH 058/288] =?UTF-8?q?=E3=80=90Paddle=20Tensor=20No.26?= =?UTF-8?q?=E3=80=91Svdvals=20new=20branch=20(#69796)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add svdvals_kernel * fix bug * fix bug * fix bug * fix bug * fix some bug * fix bug * fix bug * fix bug * add include * fix bug in svdvals_kernel * fix bug * fix bug * fix bug in func SvdvalsInferMeta * add test * fix codestyle * fix lwork and int * fix * use guard to control enable/disable * add test_check_grad * fix test_svdvals_op * fix bug * fix bug in svdvals_kernel * fix bug * fix bug * fix bug * fix bug in svdvals_grad_kernel * fix * fix * add debug * dix * fix * fix * fix * fix bug in svdvals_grad_kernel * fix * fix * fix * fix * delete VLOG * delete head * fix * fix op_gen --- .../hlir/dialect/operator/ir/op_dialect.cc | 8 + .../fluid/pir/dialect/op_generator/op_gen.py | 18 +- .../pir/dialect/operator/ir/op_dialect.cc | 11 ++ .../dialect/operator/ir/op_onednn_dialect.cc | 9 + paddle/phi/infermeta/unary.cc | 25 +++ paddle/phi/infermeta/unary.h | 2 + paddle/phi/kernels/cpu/svdvals_grad_kernel.cc | 20 +++ paddle/phi/kernels/cpu/svdvals_kernel.cc | 130 ++++++++++++++ .../kernels/impl/svdvals_grad_kernel_impl.h | 62 +++++++ paddle/phi/kernels/svdvals_grad_kernel.h | 26 +++ paddle/phi/kernels/svdvals_kernel.h | 27 +++ paddle/phi/ops/yaml/backward.yaml | 10 ++ paddle/phi/ops/yaml/ops.yaml | 9 + python/paddle/linalg.py | 2 + python/paddle/tensor/__init__.py | 1 + python/paddle/tensor/linalg.py | 49 ++++++ test/legacy_test/test_svdvals_op.py | 158 ++++++++++++++++++ 17 files changed, 565 insertions(+), 2 deletions(-) create mode 100644 paddle/phi/kernels/cpu/svdvals_grad_kernel.cc create mode 100644 paddle/phi/kernels/cpu/svdvals_kernel.cc create mode 100644 paddle/phi/kernels/impl/svdvals_grad_kernel_impl.h create mode 100644 paddle/phi/kernels/svdvals_grad_kernel.h create mode 100644 paddle/phi/kernels/svdvals_kernel.h create mode 100644 test/legacy_test/test_svdvals_op.py diff --git a/paddle/cinn/hlir/dialect/operator/ir/op_dialect.cc b/paddle/cinn/hlir/dialect/operator/ir/op_dialect.cc index a6eb7805b212de..e89bd215d57dd7 100644 --- a/paddle/cinn/hlir/dialect/operator/ir/op_dialect.cc +++ b/paddle/cinn/hlir/dialect/operator/ir/op_dialect.cc @@ -44,6 +44,14 @@ void OperatorDialect::initialize() { >(); RegisterOps< #define GET_OP_LIST2 +#include "paddle/cinn/hlir/dialect/operator/ir/cinn_op_info.cc" // NOLINT + >(); + RegisterOps< +#define GET_OP_LIST3 +#include "paddle/cinn/hlir/dialect/operator/ir/cinn_op_info.cc" // NOLINT + >(); + RegisterOps< +#define GET_OP_LIST4 #include "paddle/cinn/hlir/dialect/operator/ir/cinn_op_info.cc" // NOLINT >(); #else diff --git a/paddle/fluid/pir/dialect/op_generator/op_gen.py b/paddle/fluid/pir/dialect/op_generator/op_gen.py index 93dcc59415b7be..c9f99eafebc210 100644 --- a/paddle/fluid/pir/dialect/op_generator/op_gen.py +++ b/paddle/fluid/pir/dialect/op_generator/op_gen.py @@ -245,6 +245,12 @@ class {TEST_API} {op_name} : public pir::Op<{op_name}{interfaces}{traits}> {{ #elif defined(GET_OP_LIST2) #undef GET_OP_LIST2 {op_declare_second_part} +#elif defined(GET_OP_LIST3) +#undef GET_OP_LIST3 +{op_declare_third_part} +#elif defined(GET_OP_LIST4) +#undef GET_OP_LIST4 +{op_declare_fourth_part} """ CC_OP_INFO_FILE_TEMPLATE_PART2 = """ @@ -2390,9 +2396,11 @@ def OpGenerator( if op_info_file is not None: if sys.platform == "win32": - n = len(op_list_strs) // 2 + n = len(op_list_strs) // 4 first_part_op_info = op_list_strs[:n] - second_part_op_info = op_list_strs[n:] + second_part_op_info = op_list_strs[n : 2 * n] + third_part_op_info = op_list_strs[2 * n : 3 * n] + fourth_part_op_info = op_list_strs[3 * n :] CC_OP_INFO_FILE_TEMPLATE = ( CC_OP_INFO_FILE_TEMPLATE_WIN_PART1 + CC_OP_INFO_FILE_TEMPLATE_PART2 @@ -2404,6 +2412,12 @@ def OpGenerator( op_declare_second_part=",".join(second_part_op_info).replace( "\n", "" ), + op_declare_third_part=",".join(third_part_op_info).replace( + "\n", "" + ), + op_declare_fourth_part=",".join(fourth_part_op_info).replace( + "\n", "" + ), other_info=other_info_str, h_file=op_def_h_file[:-4], ) diff --git a/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc b/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc index 0cd4f7d9ba9804..68fa94c8f1b516 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc +++ b/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc @@ -369,6 +369,17 @@ void OperatorDialect::initialize() { #define GET_OP_LIST2 #include "paddle/fluid/pir/dialect/operator/ir/pd_op_info.cc" // NOLINT >(); + + RegisterOps< +#define GET_OP_LIST3 +#include "paddle/fluid/pir/dialect/operator/ir/pd_op_info.cc" // NOLINT + >(); + + RegisterOps< +#define GET_OP_LIST4 +#include "paddle/fluid/pir/dialect/operator/ir/pd_op_info.cc" // NOLINT + >(); + #else RegisterOps< #define GET_OP_LIST diff --git a/paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.cc b/paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.cc index bc1a3701be6141..8f59860ce1fc00 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.cc +++ b/paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.cc @@ -57,6 +57,15 @@ void OneDNNOperatorDialect::initialize() { #define GET_OP_LIST2 #include "paddle/fluid/pir/dialect/operator/ir/onednn_op_info.cc" // NOLINT >(); + RegisterOps< +#define GET_OP_LIST3 +#include "paddle/fluid/pir/dialect/operator/ir/onednn_op_info.cc" // NOLINT + >(); + RegisterOps< +#define GET_OP_LIST4 +#include "paddle/fluid/pir/dialect/operator/ir/onednn_op_info.cc" // NOLINT + >(); + #else RegisterOps< #define GET_OP_LIST diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc index 7c023017fb0e59..704afc8a769537 100644 --- a/paddle/phi/infermeta/unary.cc +++ b/paddle/phi/infermeta/unary.cc @@ -4914,6 +4914,31 @@ void PartialConcatInferMeta(const std::vector& xs, out->set_dtype(xs[0]->dtype()); } +void SvdvalsInferMeta(const MetaTensor& x, MetaTensor* s) { + auto SDDim = [](const DDim& x_dim, int k) { + auto x_vec = common::vectorize(x_dim); + x_vec.erase(x_vec.end() - 2, x_vec.end()); + x_vec.push_back(k); + return common::make_ddim(x_vec); + }; + + auto in_dims = x.dims(); + int64_t x_rank = in_dims.size(); + + PADDLE_ENFORCE_GE( + x_rank, + 2, + common::errors::InvalidArgument("The rank of input tensor must be >= 2")); + + int64_t m = in_dims[x_rank - 2]; + int64_t n = in_dims[x_rank - 1]; + + int64_t k = std::min(m, n); + s->set_dims(SDDim(in_dims, k)); + s->share_lod(x); + s->set_dtype(x.dtype()); +} + void SvdInferMeta(const MetaTensor& x, bool full_matrices, MetaTensor* u, diff --git a/paddle/phi/infermeta/unary.h b/paddle/phi/infermeta/unary.h index 593e102e329b16..6251c81702d0eb 100644 --- a/paddle/phi/infermeta/unary.h +++ b/paddle/phi/infermeta/unary.h @@ -814,6 +814,8 @@ void PartialSumInferMeta(const std::vector& xs, MetaTensor* out, MetaConfig config = MetaConfig()); +void SvdvalsInferMeta(const MetaTensor& x, MetaTensor* s); + void SvdInferMeta(const MetaTensor& x, bool full_matrices, MetaTensor* u, diff --git a/paddle/phi/kernels/cpu/svdvals_grad_kernel.cc b/paddle/phi/kernels/cpu/svdvals_grad_kernel.cc new file mode 100644 index 00000000000000..edcc207847559e --- /dev/null +++ b/paddle/phi/kernels/cpu/svdvals_grad_kernel.cc @@ -0,0 +1,20 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/kernels/svdvals_grad_kernel.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/kernels/impl/svdvals_grad_kernel_impl.h" + +PD_REGISTER_KERNEL( + svdvals_grad, CPU, ALL_LAYOUT, phi::SvdvalsGradKernel, float, double) {} diff --git a/paddle/phi/kernels/cpu/svdvals_kernel.cc b/paddle/phi/kernels/cpu/svdvals_kernel.cc new file mode 100644 index 00000000000000..b8c59ae1e615aa --- /dev/null +++ b/paddle/phi/kernels/cpu/svdvals_kernel.cc @@ -0,0 +1,130 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/kernels/svdvals_kernel.h" +#include "paddle/phi/backends/cpu/cpu_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/kernels/funcs/complex_functors.h" +#include "paddle/phi/kernels/funcs/lapack/lapack_function.h" +#include "paddle/phi/kernels/transpose_kernel.h" + +namespace phi { + +template +void LapackSvdvals(const T* X, T* S, int rows, int cols) { + // Using N to neglect computing U、VH + char jobz = 'N'; + T* a = const_cast(X); + int lda = rows; + int lwork = -1; + std::vector work(1); + int info = 0; + // Get the best lwork + phi::funcs::lapackSvd(jobz, + rows, + cols, + a, + lda, + S, + nullptr, // U is not needed + 1, // dummy dimension for U + nullptr, // VH is not needed + 1, // dummy dimension for VH + work.data(), + lwork, + nullptr, // iwork is not needed + &info); + if (info != 0) { + PADDLE_THROW(phi::errors::InvalidArgument( + "Error during LAPACK lwork query. Invalid matrix or arguments.")); + } + lwork = static_cast(work[0]); + work.resize(lwork); + phi::funcs::lapackSvd(jobz, + rows, + cols, + a, + lda, + S, + nullptr, // U is not needed + 1, // dummy dimension for U + nullptr, // VH is not needed + 1, // dummy dimension for VH + work.data(), + lwork, + nullptr, // iwork is not needed + &info); + if (info < 0) { + PADDLE_THROW(phi::errors::InvalidArgument( + "This %s-th argument has an illegal value.", info)); + } + if (info > 0) { + PADDLE_THROW(phi::errors::InvalidArgument( + "SVD computation did not converge. Input matrix may be invalid.")); + } +} + +template +void BatchSvdvals(const T* X, T* S, int rows, int cols, int batches) { + int stride = rows * cols; + int stride_s = std::min(rows, cols); + for (int i = 0; i < batches; i++) { + LapackSvdvals(X + i * stride, S + i * stride_s, rows, cols); + } +} + +template +void SvdvalsKernel(const Context& dev_ctx, + const DenseTensor& X, + DenseTensor* S) { + auto x_dims = X.dims(); + int rows = static_cast(x_dims[x_dims.size() - 2]); + int cols = static_cast(x_dims[x_dims.size() - 1]); + // Validate dimensions + PADDLE_ENFORCE_GT( + rows, + 0, + phi::errors::InvalidArgument("The row of Input(X) must be > 0.")); + PADDLE_ENFORCE_GT( + cols, + 0, + phi::errors::InvalidArgument("The column of Input(X) must be > 0.")); + int k = std::min(rows, cols); + int batches = static_cast(X.numel() / (rows * cols)); + PADDLE_ENFORCE_GT( + batches, + 0, + phi::errors::InvalidArgument("The batch size of Input(X) must be > 0.")); + DDim s_dims; + if (batches == 1) { + s_dims = {k}; + } else { + s_dims = {batches, k}; + } + S->Resize(s_dims); + // Allocate memory for output + auto* S_out = dev_ctx.template Alloc>(S); + + // Transpose the last two dimensions for LAPACK compatibility + DenseTensor trans_x = ::phi::TransposeLast2Dim(dev_ctx, X); + auto* x_data = trans_x.data(); + // Perform batch SVD computation for singular values + BatchSvdvals(x_data, S_out, rows, cols, batches); +} + +} // namespace phi + +// Register the kernel for CPU +PD_REGISTER_KERNEL( + svdvals, CPU, ALL_LAYOUT, phi::SvdvalsKernel, float, double) {} diff --git a/paddle/phi/kernels/impl/svdvals_grad_kernel_impl.h b/paddle/phi/kernels/impl/svdvals_grad_kernel_impl.h new file mode 100644 index 00000000000000..c88fe76b1aa95b --- /dev/null +++ b/paddle/phi/kernels/impl/svdvals_grad_kernel_impl.h @@ -0,0 +1,62 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/phi/core/dense_tensor.h" +#include "paddle/phi/infermeta/unary.h" +#include "paddle/phi/kernels/activation_kernel.h" +#include "paddle/phi/kernels/diag_kernel.h" +#include "paddle/phi/kernels/elementwise_multiply_kernel.h" +#include "paddle/phi/kernels/funcs/lapack/lapack_function.h" +#include "paddle/phi/kernels/funcs/math_function.h" +#include "paddle/phi/kernels/impl/diag_embed_impl.h" +#include "paddle/phi/kernels/matmul_kernel.h" +#include "paddle/phi/kernels/slice_kernel.h" +#include "paddle/phi/kernels/svd_kernel.h" +#include "paddle/phi/kernels/transpose_kernel.h" + +namespace phi { + +template +void SvdvalsGradKernel(const Context& dev_ctx, + const DenseTensor& x, + const DenseTensor& s_grad, + DenseTensor* x_grad) { + auto x_dims = x.dims(); + int rows = static_cast(x_dims[x_dims.size() - 2]); + int cols = static_cast(x_dims[x_dims.size() - 1]); + int batches = static_cast(x.numel() / (rows * cols)); + DenseTensor dX_term; + if (batches == 1) { + dX_term = Diag(dev_ctx, s_grad, 0, 0); + } else { + MetaTensor meta_dX(&dX_term); + DiagEmbedInferMeta(s_grad, 0, -1, -2, &meta_dX); + phi::DiagEmbedKernel(dev_ctx, s_grad, 0, -1, -2, &dX_term); + } + + DenseTensor U, VH, S_recomputed; + MetaTensor meta_u(&U), meta_s(&S_recomputed), meta_vh(&VH); + SvdInferMeta(x, false, &meta_u, &meta_s, &meta_vh); + phi::SvdKernel(dev_ctx, + x, + false, + &U, + &S_recomputed, + &VH); // Crucial: recomputing SVD + *x_grad = + Matmul(dev_ctx, Matmul(dev_ctx, U, dX_term), VH); +} +} // namespace phi diff --git a/paddle/phi/kernels/svdvals_grad_kernel.h b/paddle/phi/kernels/svdvals_grad_kernel.h new file mode 100644 index 00000000000000..f01b9d328b5638 --- /dev/null +++ b/paddle/phi/kernels/svdvals_grad_kernel.h @@ -0,0 +1,26 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/phi/core/dense_tensor.h" + +namespace phi { + +template +void SvdvalsGradKernel(const Context& dev_ctx, + const DenseTensor& x, + const DenseTensor& s_grad, + DenseTensor* x_grad); +} // namespace phi diff --git a/paddle/phi/kernels/svdvals_kernel.h b/paddle/phi/kernels/svdvals_kernel.h new file mode 100644 index 00000000000000..676da0679c872a --- /dev/null +++ b/paddle/phi/kernels/svdvals_kernel.h @@ -0,0 +1,27 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/phi/core/dense_tensor.h" +#include "paddle/phi/core/kernel_registry.h" + +namespace phi { + +template +void SvdvalsKernel(const Context& dev_ctx, + const DenseTensor& X, + DenseTensor* S); + +} // namespace phi diff --git a/paddle/phi/ops/yaml/backward.yaml b/paddle/phi/ops/yaml/backward.yaml index 782244f345a6d3..665c70bec889ef 100644 --- a/paddle/phi/ops/yaml/backward.yaml +++ b/paddle/phi/ops/yaml/backward.yaml @@ -3242,6 +3242,16 @@ func : svd_grad optional: u_grad, vh_grad, s_grad +- backward_op : svdvals_grad + forward : svdvals (Tensor x) -> Tensor(s) + args : (Tensor x, Tensor s_grad) + output : Tensor(x_grad) + infer_meta : + func : UnchangedInferMeta + param : [x] + kernel : + func : svdvals_grad + - backward_op : swiglu_grad forward : swiglu (Tensor x, Tensor y) -> Tensor(out) args: (Tensor x, Tensor y, Tensor out_grad) diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index 7363e354d9f206..c8a140ec277ab8 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -4839,6 +4839,15 @@ backward : svd_grad interfaces : paddle::dialect::InferSymbolicShapeInterface +- op : svdvals + args : (Tensor x) + output : Tensor(s) + infer_meta : + func : SvdvalsInferMeta + kernel : + func : svdvals + backward : svdvals_grad + - op : swiglu args : (Tensor x, Tensor y) output : Tensor(out) diff --git a/python/paddle/linalg.py b/python/paddle/linalg.py index 8705a78df52a82..2becdf5ab62753 100644 --- a/python/paddle/linalg.py +++ b/python/paddle/linalg.py @@ -46,6 +46,7 @@ solve, svd, svd_lowrank, + svdvals, triangular_solve, vecdot, vector_norm, @@ -69,6 +70,7 @@ 'matrix_rank', 'matrix_transpose', 'svd', + 'svdvals', 'qr', 'householder_product', 'pca_lowrank', diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 7688a68897f3e6..926d4bcfb26cbe 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -98,6 +98,7 @@ solve, svd, svd_lowrank, + svdvals, t, t_, transpose, diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index e55ffa4eb48b1b..7b22af1285138b 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -2982,6 +2982,55 @@ def svd( return u, s, vh +def svdvals(x: Tensor, name: str | None = None) -> Tensor: + r""" + Computes the singular values of one matrix or a batch of matrices. + + Let :math:`X` be the input matrix or a batch of input matrices, + the output singular values :math:`S` are the diagonal elements of the matrix + produced by singular value decomposition: + + .. math:: + X = U * diag(S) * VH + + Args: + x (Tensor): The input tensor. Its shape should be `[..., M, N]`, where + `...` is zero or more batch dimensions. The data type of x should + be float32 or float64. + name (str|None, optional): Name for the operation. For more + information, please refer to :ref:`api_guide_Name`. + Default: None. + + Returns: + Tensor: Singular values of x. The shape is `[..., K]`, where `K = min(M, N)`. + + Examples: + .. code-block:: python + + >>> import paddle + + >>> x = paddle.to_tensor([[1.0, 2.0], [1.0, 3.0], [4.0, 6.0]]) + >>> s = paddle.linalg.svdvals(x) + >>> print(s) + Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, + [8.14753819, 0.78589684]) + """ + if in_dynamic_or_pir_mode(): + return _C_ops.svdvals(x) + else: + check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svdvals') + helper = LayerHelper('svdvals', **locals()) + s = helper.create_variable_for_type_inference(dtype=x.dtype) + attrs = {} + helper.append_op( + type='svdvals', + inputs={'X': [x]}, + outputs={'S': s}, + attrs=attrs, + ) + return s + + def _conjugate(x): if x.is_complex(): return x.conj() diff --git a/test/legacy_test/test_svdvals_op.py b/test/legacy_test/test_svdvals_op.py new file mode 100644 index 00000000000000..3e8c264b6dc955 --- /dev/null +++ b/test/legacy_test/test_svdvals_op.py @@ -0,0 +1,158 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from op_test import OpTest, skip_check_grad_ci +from utils import dygraph_guard, static_guard + +import paddle + + +class TestSvdvalsOp(OpTest): + def setUp(self): + self.op_type = "svdvals" + self.python_api = paddle.linalg.svdvals + self.init_data() + + def init_data(self): + """Generate input data and expected output.""" + self._input_shape = (100, 1) + self._input_data = np.random.random(self._input_shape).astype("float64") + self._output_data = np.linalg.svd( + self._input_data, compute_uv=False, hermitian=False + ) + self.inputs = {'x': self._input_data} + self.outputs = {'s': self._output_data} + + def test_check_output(self): + self.check_output(check_pir=True) + + def test_svdvals_forward(self): + """Check singular values calculation.""" + with dygraph_guard(): + dy_x = paddle.to_tensor(self._input_data) + dy_s = paddle.linalg.svdvals(dy_x) + np.testing.assert_allclose( + dy_s.numpy(), self._output_data, rtol=1e-6, atol=1e-8 + ) + + def test_check_grad(self): + self.check_grad(['x'], ['s'], numeric_grad_delta=0.001, check_pir=True) + + +class TestSvdvalsBatched(TestSvdvalsOp): + """Test svdvals operation with batched input.""" + + def init_data(self): + """Generate batched input matrix.""" + self._input_shape = (10, 3, 6) + self._input_data = np.random.random(self._input_shape).astype("float64") + + self._output_data = np.linalg.svd( + self._input_data, compute_uv=False, hermitian=False + ) + + self.inputs = {'x': self._input_data} + self.outputs = {"s": self._output_data} + + +@skip_check_grad_ci( + reason="'check_grad' on singular values is not required for svdvals." +) +class TestSvdvalsBigMatrix(TestSvdvalsOp): + def init_data(self): + """Generate large input matrix.""" + self._input_shape = (200, 300) + self._input_data = np.random.random(self._input_shape).astype("float64") + self._output_data = np.linalg.svd( + self._input_data, compute_uv=False, hermitian=False + ) + self.inputs = {'x': self._input_data} + self.outputs = {'s': self._output_data} + + def test_check_grad(self): + pass + + +class TestSvdvalsAPI(unittest.TestCase): + def setUp(self): + np.random.seed(1024) + self.x_np = np.random.uniform(-3, 3, [10, 12]).astype('float32') + self.place = ( + paddle.CUDAPlace(0) + if paddle.is_compiled_with_cuda() + else paddle.CPUPlace() + ) + + def test_dygraph_api(self): + with dygraph_guard(): + x = paddle.to_tensor(self.x_np) + # Test dynamic graph for svdvals + s = paddle.linalg.svdvals(x) + np_s = np.linalg.svd(self.x_np, compute_uv=False, hermitian=False) + self.assertTrue(np.allclose(np_s, s.numpy(), rtol=1e-6)) + + # Test with reshaped input + x_reshaped = x.reshape([-1, 12, 10]) + s_reshaped = paddle.linalg.svdvals(x_reshaped) + np_s_reshaped = np.array( + [ + np.linalg.svd(matrix, compute_uv=False, hermitian=False) + for matrix in self.x_np.reshape([-1, 12, 10]) + ] + ) + self.assertTrue( + np.allclose(np_s_reshaped, s_reshaped.numpy(), rtol=1e-6) + ) + + def test_static_api(self): + with static_guard(): + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + x = paddle.static.data('x', [10, 12], dtype='float32') + s = paddle.linalg.svdvals(x) + exe = paddle.static.Executor(self.place) + res = exe.run(feed={'x': self.x_np}, fetch_list=[s]) + + np_s = np.linalg.svd(self.x_np, compute_uv=False, hermitian=False) + for r in res: + self.assertTrue(np.allclose(np_s, r, rtol=1e-6)) + + def test_error(self): + """Test invalid inputs for svdvals""" + with paddle.base.dygraph.guard(): + + def test_invalid_shape(): + """Test invalid shape input""" + x_np_invalid_shape = np.random.uniform(-3, 3, [10]).astype( + 'float32' + ) + x_invalid_shape = paddle.to_tensor(x_np_invalid_shape) + paddle.linalg.svdvals(x_invalid_shape) + + def test_empty_tensor(): + """Test empty tensor""" + x_np_empty = np.empty([0, 10], dtype='float32') + x_empty = paddle.to_tensor(x_np_empty) + paddle.linalg.svdvals(x_empty) + + self.assertRaises(ValueError, test_invalid_shape) + self.assertRaises(ValueError, test_empty_tensor) + + +if __name__ == "__main__": + unittest.main() From 838eed87a28f7401ca8859382d499bd88ee600b4 Mon Sep 17 00:00:00 2001 From: doggy-tao <3160391266@qq.com> Date: Fri, 29 Nov 2024 12:44:07 +0800 Subject: [PATCH 059/288] Add back_decomp and support dynamic shape for amax_grad and amin_grad (#68818) * add back_decomp and support dynamic shape for amax_grad, amin_grad * delete OpTest * add amax amin prim test * fixed bugs in test/prim/pir_prim/test_prim_amax_amin_op.py * change shape to shape64 * add timeout limit * fixed bugs * reduce test case size --------- Co-authored-by: cubehan3 --- .../fluid/primitive/codegen/decomp_vjp_gen.py | 2 + .../decomp_rule/decomp_vjp/details.h | 118 +++++++ python/paddle/autograd/backward_utils.py | 2 + test/prim/pir_prim/CMakeLists.txt | 3 +- test/prim/pir_prim/test_prim_amax_amin_op.py | 290 ++++++++++++++++++ ..._sub_graph_abcde_backward_dynamic_shape.py | 198 ++++++++++++ 6 files changed, 612 insertions(+), 1 deletion(-) create mode 100644 test/prim/pir_prim/test_prim_amax_amin_op.py diff --git a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py index 274156fb972eb0..0dcd82f8a19a6e 100644 --- a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py +++ b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py @@ -139,6 +139,8 @@ PRIM_VJP = UNARY_PRIM_VJP_OPS + BINARY_PRIM_VJP_OPS + OTHER_PRIM_VJP_OPS CUSTOM_VJP = [ + 'amax_grad', + 'amin_grad', 'bce_loss_grad', 'batch_norm_grad', 'dropout_grad', diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index 833be0e82335d6..c79137ed004d30 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -3329,6 +3329,124 @@ void ceil_grad(const Tensor& out_grad, Tensor* x_grad) { } } +template +void amax_grad(const Tensor& x, + const Tensor& out, + const Tensor& out_grad, + const IntArray& axis, + bool keepdim, + bool reduce_all, + Tensor* x_grad) { + if (x_grad) { + Tensor x_grad_tmp; + if (has_dynamic_shape(x.shape())) { + const Tensor x_shape = shape64(x); + const Tensor zero_tensor = + backend::full_with_tensor(x_shape, 0.0, x.dtype()); + const int64_t axis_size = axis.size(); + const int64_t x_dim_size = x.dims().size(); + + reduce_all = false; + if (reduce_all || axis_size == 0 || axis_size == x_dim_size) { + reduce_all = true; + } + + if (x_dim_size == 0 || x_dim_size == 1 || keepdim) { + auto out_grad_tmp = backend::expand(out_grad, x_shape); + auto out_tmp = backend::expand(out, x_shape); + auto mask = equal(x, out_tmp); + auto mask_sum = backend::sum(mask, axis, x.dtype(), keepdim = true); + auto grad_tmp = out_grad_tmp / mask_sum; + x_grad_tmp = where(mask, grad_tmp, zero_tensor); + } else { + const Tensor out_grad_shape = shape64(out_grad); + auto axis_ = std::vector(); + + if (reduce_all) { + for (int64_t i = 0; i < x_dim_size; i++) { + axis_.push_back(i); + } + } else { + axis_ = axis.GetData(); + for (int64_t i = 0; i < axis_size; i++) { + if (axis[i] < 0) { + axis_[i] = axis[i] + x_dim_size; + } + } + } + const Tensor out_grad_shape_extend = + get_unsqueeze_dims(out_grad_shape, axis_); + auto out_grad_ = backend::reshape(out_grad, out_grad_shape_extend); + auto out_ = backend::reshape(out, out_grad_shape_extend); + auto out_grad_tmp = backend::expand(out_grad_, x_shape); + auto out_tmp = backend::expand(out_, x_shape); + auto mask = equal(x, out_tmp); + auto mask_sum = backend::sum(mask, axis_, x.dtype(), keepdim = true); + auto grad_tmp = out_grad_tmp / mask_sum; + x_grad_tmp = where(mask, grad_tmp, zero_tensor); + } + } else { + auto zero_tensor = full(common::vectorize(x.dims()), 0.0, x.dtype()); + std::vector x_dim = common::vectorize(x.dims()); + int64_t axis_size = axis.size(); + int64_t x_dim_size = x_dim.size(); + reduce_all = false; + if (reduce_all || axis_size == 0 || axis_size == x_dim_size) { + reduce_all = true; + } + + if (x_dim_size == 0 || x_dim_size == 1 || keepdim) { + auto out_grad_tmp = out_grad.expand(IntArray(x_dim)); + auto out_tmp = out.expand(IntArray(x_dim)); + auto mask = equal(x, out_tmp); + auto mask_sum = sum(mask, axis, x.dtype(), keepdim = true); + auto grad_tmp = out_grad_tmp / mask_sum; + x_grad_tmp = where(mask, grad_tmp, zero_tensor); + } else { + auto axis_ = std::vector(); + if (reduce_all) { + for (int64_t i = 0; i < x_dim_size; i++) { + axis_.push_back(i); + } + } else { + axis_ = axis.GetData(); + for (int64_t i = 0; i < axis_size; i++) { + if (axis[i] < 0) { + axis_[i] = axis[i] + x_dim_size; + } + } + } + auto out_grad_shape = get_unsqueeze_dims(out_grad, axis_); + auto out_grad_ = reshape(out_grad, out_grad_shape); + auto out_ = reshape(out, out_grad_shape); + auto out_grad_tmp = out_grad_.expand(IntArray(x_dim)); + auto out_tmp = out_.expand(IntArray(x_dim)); + auto mask = equal(x, out_tmp); + auto mask_sum = sum(mask, axis_, x.dtype(), keepdim = true); + auto grad_tmp = out_grad_tmp / mask_sum; + x_grad_tmp = where(mask, grad_tmp, zero_tensor); + } + } + set_output(x_grad_tmp, x_grad); + } +} + +template +void amin_grad(const Tensor& x, + const Tensor& out, + const Tensor& out_grad, + const IntArray& axis, + bool keepdim, + bool reduce_all, + Tensor* x_grad) { + if (x_grad) { + Tensor x_grad_tmp; + amax_grad(x, out, out_grad, axis, keepdim, reduce_all, &x_grad_tmp); + + set_output(x_grad_tmp, x_grad); + } +} + } // namespace details } // namespace primitive } // namespace paddle diff --git a/python/paddle/autograd/backward_utils.py b/python/paddle/autograd/backward_utils.py index 9dd999baa31e86..6f33200fc040ae 100644 --- a/python/paddle/autograd/backward_utils.py +++ b/python/paddle/autograd/backward_utils.py @@ -31,6 +31,8 @@ ALLOW_DYNAMIC_SHAPE_VJP_OPS = [ "pd_op.abs", "pd_op.add", + "pd_op.amax", + "pd_op.amin", "pd_op.argsort", "pd_op.assign", "pd_op.batch_norm_", diff --git a/test/prim/pir_prim/CMakeLists.txt b/test/prim/pir_prim/CMakeLists.txt index 58f28efacefea7..cb440c201938a8 100644 --- a/test/prim/pir_prim/CMakeLists.txt +++ b/test/prim/pir_prim/CMakeLists.txt @@ -22,7 +22,8 @@ set(TEST_PRIM_PURE_PIR_CASES test_decomp_whole_program test_dynamic_combine1 test_dynamic_combine2 - test_decomp_fallback) + test_decomp_fallback + test_prim_amax_amin_op) foreach(target ${TEST_PRIM_PURE_PIR_CASES}) py_test_modules( diff --git a/test/prim/pir_prim/test_prim_amax_amin_op.py b/test/prim/pir_prim/test_prim_amax_amin_op.py new file mode 100644 index 00000000000000..127a9ffd95259c --- /dev/null +++ b/test/prim/pir_prim/test_prim_amax_amin_op.py @@ -0,0 +1,290 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle +from paddle.framework import core +from paddle.static import InputSpec + + +def apply_to_static(net, use_cinn, input_spec=None): + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = use_cinn + return paddle.jit.to_static( + net, + input_spec=input_spec, + build_strategy=build_strategy, + full_graph=True, + ) + + +class TestPrimBaseWithGrad(unittest.TestCase): + def setUp(self): + np.random.seed(2023) + self.op_name = None + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = None + self.enable_cinn = False + self.tol = 1e-6 + + def base_net(self, flag=None): + if flag == "prim": + core._set_prim_all_enabled(True) + x = paddle.to_tensor(self.x, stop_gradient=False) + if flag == "prim": + fn = apply_to_static( + self.net, + use_cinn=self.enable_cinn, + input_spec=[ + InputSpec(shape=self.init_x_shape, dtype='float32'), + ], + ) + fn.train() + else: + fn = self.net + res = fn(x) + res.backward() + x_grad = x.gradient() + if flag == "prim": + ops = [ + op.name() + for op in fn.get_concrete_program(x)[-1] + .program.backward_program.global_block() + .ops + ] + assert self.op_name not in ops + core._set_prim_all_enabled(False) + return res, x_grad + + def test_prim_all(self): + if self.net is None: + return + res_ref, grad_ref = self.base_net() + res, grad = self.base_net("prim") + + for ref, actual in zip(res_ref, res): + np.testing.assert_allclose( + ref, actual, rtol=self.tol, atol=self.tol + ) + + for dr, d in zip(grad_ref, grad): + np.testing.assert_allclose(dr, d, rtol=self.tol, atol=self.tol) + + +def amax_net1(x): + return paddle.amax(x, keepdim=True) + + +def amax_net2(x): + return paddle.amax(x, keepdim=False) + + +def amax_net3(x): + return paddle.amax(x, axis=[0, 1], keepdim=False) + + +def amax_net4(x): + return paddle.amax(x, axis=[-1, -2], keepdim=False) + + +def amax_net5(x): + return paddle.amax(x, axis=[-1, 0], keepdim=False) + + +def amin_net1(x): + return paddle.amin(x, keepdim=True) + + +def amin_net2(x): + return paddle.amin(x, keepdim=False) + + +def amin_net3(x): + return paddle.amin(x, axis=[0, 1], keepdim=False) + + +def amin_net4(x): + return paddle.amin(x, axis=[-1, -2], keepdim=False) + + +def amin_net5(x): + return paddle.amin(x, axis=[-1, 0], keepdim=False) + + +class TestPrimAmaxWithGrad1(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.ones(self.x_shape).astype(self.dtype) + self.net = amax_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad2(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [30] + self.init_x_shape = [30] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad3(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net2 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad4(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net3 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad5(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.x[2] = self.x[4] + self.net = amax_net4 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad6(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net5 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad1(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.ones(self.x_shape).astype(self.dtype) + self.net = amin_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad2(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [30] + self.init_x_shape = [30] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad3(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net2 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad4(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net3 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad5(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net4 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad6(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [10, 10, 10] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.x[4] = self.x[7] + self.net = amin_net5 + self.enable_cinn = False + self.tol = 1e-6 + + +if __name__ == "__main__": + unittest.main() diff --git a/test/prim/pir_prim/test_prim_sub_graph_abcde_backward_dynamic_shape.py b/test/prim/pir_prim/test_prim_sub_graph_abcde_backward_dynamic_shape.py index 43db1b3ff56e07..d186930fcde01c 100644 --- a/test/prim/pir_prim/test_prim_sub_graph_abcde_backward_dynamic_shape.py +++ b/test/prim/pir_prim/test_prim_sub_graph_abcde_backward_dynamic_shape.py @@ -31,6 +31,46 @@ def add_net(x, y): return x + y +def amax_net1(x): + return paddle.amax(x, keepdim=True) + + +def amax_net2(x): + return paddle.amax(x, keepdim=False) + + +def amax_net3(x): + return paddle.amax(x, axis=[0, 1], keepdim=False) + + +def amax_net4(x): + return paddle.amax(x, axis=[-1, -2], keepdim=False) + + +def amax_net5(x): + return paddle.amax(x, axis=[-1, 0], keepdim=False) + + +def amin_net1(x): + return paddle.amin(x, keepdim=True) + + +def amin_net2(x): + return paddle.amin(x, keepdim=False) + + +def amin_net3(x): + return paddle.amin(x, axis=[0, 1], keepdim=False) + + +def amin_net4(x): + return paddle.amin(x, axis=[-1, -2], keepdim=False) + + +def amin_net5(x): + return paddle.amin(x, axis=[-1, 0], keepdim=False) + + def argsort_net1(x): return paddle.argsort(x, axis=-1) @@ -297,6 +337,164 @@ def setUp(self): self.tol = 1e-6 +class TestPrimAmaxWithGrad1(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad2(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [30] + self.init_x_shape = [None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad3(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net2 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad4(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net3 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad5(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amax_net4 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAmaxWithGrad6(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amax_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.x[3] = self.x[7] + self.net = amax_net5 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad1(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad2(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [30] + self.init_x_shape = [None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net1 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad3(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.x[4] = self.x[7] + self.net = amin_net2 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad4(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net3 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad5(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net4 + self.enable_cinn = False + self.tol = 1e-6 + + +class TestPrimAminWithGrad6(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.amin_grad" + self.dtype = "float32" + self.x_shape = [10, 10, 10] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = amin_net5 + self.enable_cinn = False + self.tol = 1e-6 + + class TestPrimArgsortWithGrad1(TestPrimBaseWithGrad): def setUp(self): np.random.seed(2024) From 315a0cbc1a46f63ea10a46910f8e28b6ca650205 Mon Sep 17 00:00:00 2001 From: haosicheng <47998305+HarperCy@users.noreply.github.com> Date: Fri, 29 Nov 2024 13:57:47 +0800 Subject: [PATCH 060/288] [xpu] use new rotary_half interface && update xhpc date (#69800) --- cmake/external/xpu.cmake | 2 +- .../phi/kernels/fusion/xpu/fused_rope_utils.h | 271 ++++++------------ 2 files changed, 84 insertions(+), 189 deletions(-) diff --git a/cmake/external/xpu.cmake b/cmake/external/xpu.cmake index 99d984b2ba189e..78157615a5a68e 100644 --- a/cmake/external/xpu.cmake +++ b/cmake/external/xpu.cmake @@ -30,7 +30,7 @@ set(XPU_XFA_LIB_NAME "libxpu_flash_attention.so") set(XPU_XPUDNN_LIB_NAME "libxpu_dnn.so") if(NOT DEFINED XPU_XHPC_BASE_DATE) - set(XPU_XHPC_BASE_DATE "dev/20241127") + set(XPU_XHPC_BASE_DATE "dev/20241128") endif() set(XPU_XCCL_BASE_VERSION "3.0.1.1") # For XRE5 if(NOT DEFINED XPU_XFT_BASE_VERSION) diff --git a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h index 79da31d3d7d252..b68701651aca97 100644 --- a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h +++ b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h @@ -274,135 +274,68 @@ void XPUFusedRotaryEveryTwo(const Context& dev_ctx, DenseTensor* out_q, DenseTensor* out_k, DenseTensor* out_v) { - auto single_func_fwd = &xpu::rotary_embedding_v3_single; - auto fusion_func_fwd = &xpu::rotary_embedding_v3; - auto single_func_bwd = - &xpu::rotary_embedding_v3_single_grad; - auto fusion_func_bwd = &xpu::rotary_embedding_v3_grad; + auto single_func = &xpu::rotary_embedding_v3_single; + auto fusion_func = &xpu::rotary_embedding_v3; const char* single_func_name = "rotary_embedding_v3_single"; const char* fusion_func_name = "rotary_embedding_v3"; if (is_bwd) { + single_func = &xpu::rotary_embedding_v3_single_grad; + fusion_func = &xpu::rotary_embedding_v3_grad; single_func_name = "rotary_embedding_v3_single_grad"; fusion_func_name = "rotary_embedding_v3_grad"; } - if (is_bwd) { - if (!in_k) { - int ret = single_func_bwd( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - std::string("BLHD").c_str(), - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - } else { - int64_t num_heads_k = in_k->dims()[2]; - int ret = fusion_func_bwd( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - reinterpret_cast(in_k->data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - reinterpret_cast(out_k->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - {seq_len * num_heads_k * head_dim, - num_heads_k * head_dim, - head_dim, - 1}, - num_heads_k, - std::string("BLHD").c_str(), - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); - } - if (in_v) { - int64_t num_heads_v = in_v->dims()[2]; - int ret = single_func_bwd(dev_ctx.x_context(), - reinterpret_cast(in_v->data()), - cos_data, - sin_data, - reinterpret_cast(out_v->data()), - batch_size, - seq_len, - num_heads_v, - head_dim, - {seq_len * num_heads_v * head_dim, - num_heads_v * head_dim, - head_dim, - 1}, - std::string("BLHD").c_str(), - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - } + if (!in_k) { + int ret = single_func( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); } else { - if (!in_k) { - int ret = single_func_fwd( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - } else { - int64_t num_heads_k = in_k->dims()[2]; - int ret = fusion_func_fwd( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - reinterpret_cast(in_k->data()), - cos_data, - sin_data, - reinterpret_cast(out_q->data()), - reinterpret_cast(out_k->data()), - batch_size, - seq_len, - num_heads, - head_dim, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - {seq_len * num_heads_k * head_dim, - num_heads_k * head_dim, - head_dim, - 1}, - num_heads_k, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); - } - if (in_v) { - int64_t num_heads_v = in_v->dims()[2]; - int ret = single_func_fwd(dev_ctx.x_context(), - reinterpret_cast(in_v->data()), - cos_data, - sin_data, - reinterpret_cast(out_v->data()), - batch_size, - seq_len, - num_heads_v, - head_dim, - {seq_len * num_heads_v * head_dim, - num_heads_v * head_dim, - head_dim, - 1}, - "BLHD", - true); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - } + int64_t num_heads_k = in_k->dims()[2]; + int ret = fusion_func( + dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + reinterpret_cast(in_k->data()), + cos_data, + sin_data, + reinterpret_cast(out_q->data()), + reinterpret_cast(out_k->data()), + batch_size, + seq_len, + num_heads, + head_dim, + {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, + {seq_len * num_heads_k * head_dim, num_heads_k * head_dim, head_dim, 1}, + num_heads_k, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); + } + if (in_v) { + int64_t num_heads_v = in_v->dims()[2]; + int ret = single_func( + dev_ctx.x_context(), + reinterpret_cast(in_v->data()), + cos_data, + sin_data, + reinterpret_cast(out_v->data()), + batch_size, + seq_len, + num_heads_v, + head_dim, + {seq_len * num_heads_v * head_dim, num_heads_v * head_dim, head_dim, 1}, + std::string("BLHD").c_str(), + true); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); } } @@ -422,79 +355,41 @@ void XPUFusedRotaryHalf(const Context& dev_ctx, DenseTensor* out_q, DenseTensor* out_k, DenseTensor* out_v) { - PADDLE_ENFORCE_EQ( - (std::is_same::value), - true, - common::errors::Unimplemented("The xpu rotary half do not support " - "sin/cos with different dtype as input.")); - auto single_func = &xpu::rotary_no_freqs_embedding_v2; - auto fusion_func = &xpu::rotary_no_freqs_qk_embedding_v2; - const char* single_func_name = "rotary_no_freqs_embedding_v2"; - const char* fusion_func_name = "xpu::rotary_no_freqs_qk_embedding_v2"; + auto single_func = + &xpu::rotary_embedding_half_unary_freqs; + auto fusion_func = + &xpu::rotary_embedding_half_binary_freqs; + const char* single_func_name = "rotary_embedding_half_unary_freqs"; + const char* fusion_func_name = "xpu::rotary_embedding_half_binary_freqs"; if (is_bwd) { - single_func = &xpu::rotary_no_freqs_embedding_v2_grad; + single_func = + &xpu::rotary_embedding_half_unary_freqs_grad; fusion_func = - &xpu::rotary_no_freqs_qk_embedding_v2_grad; + &xpu::rotary_embedding_half_binary_freqs_grad; + single_func_name = "rotary_embedding_half_unary_freqs_grad"; + fusion_func_name = "xpu::rotary_embedding_half_binary_freqs_grad"; } - if (head_dim * sizeof(XPUType) <= 1024 && head_dim % 64 == 0 && in_k) { - int64_t num_heads_k = in_k->dims()[2]; - int ret = fusion_func( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - reinterpret_cast(in_k->data()), - reinterpret_cast(sin_data), - reinterpret_cast(cos_data), - reinterpret_cast(out_q->data()), - reinterpret_cast(out_k->data()), - {batch_size, seq_len, num_heads, head_dim}, - {batch_size, seq_len, 1, head_dim}, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - {seq_len * head_dim, head_dim, head_dim, 1}, - num_heads_k); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); - } else { - int ret = single_func( - dev_ctx.x_context(), - reinterpret_cast(in_q.data()), - reinterpret_cast(sin_data), - reinterpret_cast(cos_data), - reinterpret_cast(out_q->data()), - {batch_size, seq_len, num_heads, head_dim}, - {batch_size, seq_len, 1, head_dim}, - {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - {seq_len * head_dim, head_dim, head_dim, 1}); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - if (in_k) { - int64_t num_heads_k = in_k->dims()[2]; - int ret = single_func(dev_ctx.x_context(), - reinterpret_cast(in_k->data()), - reinterpret_cast(sin_data), - reinterpret_cast(cos_data), - reinterpret_cast(out_k->data()), - {batch_size, seq_len, num_heads_k, head_dim}, - {batch_size, seq_len, 1, head_dim}, - {seq_len * num_heads_k * head_dim, - num_heads_k * head_dim, - head_dim, - 1}, - {seq_len * head_dim, head_dim, head_dim, 1}); - PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); - } - } + int64_t num_heads_k = in_k->dims()[2]; + int ret = fusion_func(dev_ctx.x_context(), + reinterpret_cast(in_q.data()), + reinterpret_cast(in_k->data()), + reinterpret_cast(sin_data), + reinterpret_cast(cos_data), + reinterpret_cast(out_q->data()), + reinterpret_cast(out_k->data()), + {batch_size, seq_len, num_heads, head_dim}, + num_heads_k); + PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); if (in_v) { int64_t num_heads_v = in_v->dims()[2]; - int ret = single_func( - dev_ctx.x_context(), - reinterpret_cast(in_v->data()), - reinterpret_cast(sin_data), - reinterpret_cast(cos_data), - reinterpret_cast(out_v->data()), - {batch_size, seq_len, num_heads_v, head_dim}, - {batch_size, seq_len, 1, head_dim}, - {seq_len * num_heads_v * head_dim, num_heads_v * head_dim, head_dim, 1}, - {seq_len * head_dim, head_dim, head_dim, 1}); + int ret = single_func(dev_ctx.x_context(), + reinterpret_cast(in_v->data()), + reinterpret_cast(sin_data), + reinterpret_cast(cos_data), + reinterpret_cast(out_v->data()), + {batch_size, seq_len, num_heads_v, head_dim}); PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); } } From 97bacd154e3a37753b354c6b6c938e5cb55e41d8 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Fri, 29 Nov 2024 14:07:33 +0800 Subject: [PATCH 061/288] [SOT] Add inline call codeobj to global guard (#69803) --- .../executor/opcode_inline_executor.py | 4 ++- .../executor/variables/callable.py | 7 +++-- test/sot/test_06_call_function.py | 30 ++++++++++++++++++- 3 files changed, 37 insertions(+), 4 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/opcode_inline_executor.py b/python/paddle/jit/sot/opcode_translator/executor/opcode_inline_executor.py index 4ffa26360dec05..f8de4cc3ef95de 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/opcode_inline_executor.py +++ b/python/paddle/jit/sot/opcode_translator/executor/opcode_inline_executor.py @@ -163,7 +163,8 @@ def __init__( self._fn_var = fn_variable self.return_value: VariableBase | None = None self._fn_value = fn_variable.value - super().__init__(fn_variable.get_code(), fn_variable.graph) + self._code_var = fn_variable.get_code() + super().__init__(self._code_var.value, fn_variable.graph) self._name = "Inline" self._prepare_locals(*args, **kwargs) self._prepare_closure() @@ -273,6 +274,7 @@ def inline_call(self) -> VariableBase: """ Execute the inline call of the function. """ + self._graph.add_global_guarded_variable(self._code_var) self.run() assert self.return_value is not None return self.return_value diff --git a/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py b/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py index 1f0a31fb62d983..6a971cf074d0d2 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variables/callable.py @@ -125,8 +125,11 @@ def __init__( def get_py_value(self, allow_tensor=False): return self.value - def get_code(self) -> types.CodeType: - return self.value.__code__ + def get_code(self) -> VariableBase: + code_obj_var = VariableFactory.from_value( + self.value.__code__, self.graph, GetAttrTracker(self, "__code__") + ) + return code_obj_var def bind(self, instance: VariableBase, name: str): method_var = MethodVariable( diff --git a/test/sot/test_06_call_function.py b/test/sot/test_06_call_function.py index 696fd1451cf564..978eff9133a3d1 100644 --- a/test/sot/test_06_call_function.py +++ b/test/sot/test_06_call_function.py @@ -14,7 +14,10 @@ import unittest -from test_case_base import TestCaseBase +from test_case_base import ( + TestCaseBase, + test_instruction_translator_cache_context, +) import paddle @@ -150,5 +153,30 @@ def test_call8(self): self.assert_results(foo_8, paddle.to_tensor(9)) +def apply_fn(fn, x): + return fn(x) + + +def fn1(x): + return x + 1 + + +def fn2(x): + return x - 1 + + +class TestApplyDifferentFunctions(TestCaseBase): + def test_apply_fn(self): + x = 1 + with test_instruction_translator_cache_context() as ctx: + self.assertEqual(ctx.translate_count, 0) + self.assert_results(apply_fn, fn1, x) + self.assertEqual(ctx.translate_count, 1) + self.assert_results(apply_fn, fn2, x) + self.assertEqual(ctx.translate_count, 2) + self.assert_results(apply_fn, fn1, x) + self.assertEqual(ctx.translate_count, 2) + + if __name__ == "__main__": unittest.main() From 1a58803758fad27a9ef602b55e3850e7e3a9fa6b Mon Sep 17 00:00:00 2001 From: yangrongxinuser <109195068+yangrongxinuser@users.noreply.github.com> Date: Fri, 29 Nov 2024 14:18:10 +0800 Subject: [PATCH 062/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20No.7=E3=80=91=E6=96=B0=E5=A2=9E=20Tensor.=5F=5Frxor=5F?= =?UTF-8?q?=5F=20=E5=A4=8D=E7=94=A8=E5=B7=B2=E6=9C=89=E6=8E=A5=E5=8F=A3Ten?= =?UTF-8?q?sor.=5F=5Fxor=5F=5F=20(#69779)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * rxor实现 * 提交信息 --- python/paddle/tensor/__init__.py | 2 + python/paddle/tensor/logic.py | 15 ++++++ python/paddle/tensor/tensor.prototype.pyi | 1 + test/legacy_test/test_math_op_patch.py | 36 ++++++++++++++ test/legacy_test/test_math_op_patch_pir.py | 57 ++++++++++++++++++++++ 5 files changed, 111 insertions(+) diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 926d4bcfb26cbe..55daaae0873ede 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -108,6 +108,7 @@ from .logic import ( # noqa: F401 __rand__, __ror__, + __rxor__, allclose, bitwise_and, bitwise_and_, @@ -872,6 +873,7 @@ ('__or__', 'bitwise_or'), ('__ror__', '__ror__'), ('__xor__', 'bitwise_xor'), + ('__rxor__', '__rxor__'), ('__invert__', 'bitwise_not'), ('__pos__', 'positive'), ('__lshift__', '__lshift__'), diff --git a/python/paddle/tensor/logic.py b/python/paddle/tensor/logic.py index d5714c4136b791..cb00e7b7002a8b 100755 --- a/python/paddle/tensor/logic.py +++ b/python/paddle/tensor/logic.py @@ -1416,6 +1416,21 @@ def bitwise_xor( ) +def __rxor__( + x: Tensor, + y: int | bool, + out: Tensor | None = None, + name: str | None = None, +) -> Tensor: + if isinstance(y, (int, bool)): + y = paddle.to_tensor(y, dtype=x.dtype) + return bitwise_xor(y, x, out=out, name=name) + else: + raise TypeError( + f"unsupported operand type(s) for |: '{type(y).__name__}' and 'Tensor'" + ) + + @inplace_apis_in_dygraph_only def bitwise_xor_(x: Tensor, y: Tensor, name: str | None = None) -> Tensor: r""" diff --git a/python/paddle/tensor/tensor.prototype.pyi b/python/paddle/tensor/tensor.prototype.pyi index d739991f955889..ccbc46306a7909 100644 --- a/python/paddle/tensor/tensor.prototype.pyi +++ b/python/paddle/tensor/tensor.prototype.pyi @@ -172,6 +172,7 @@ class AbstractTensor: def __pow__(self, y: _typing.TensorLike) -> Tensor: ... def __and__(self, y: _typing.TensorLike) -> Tensor: ... def __ror__(self, y: _typing.TensorLike) -> Tensor: ... + def __rxor__(self, y: _typing.TensorLike) -> Tensor: ... def __div__(self, y: _typing.TensorLike) -> Tensor: ... def __radd__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore def __rsub__(self, y: _typing.TensorLike) -> Tensor: ... # type: ignore diff --git a/test/legacy_test/test_math_op_patch.py b/test/legacy_test/test_math_op_patch.py index d61db98cb4460a..c27dae10188ee4 100644 --- a/test/legacy_test/test_math_op_patch.py +++ b/test/legacy_test/test_math_op_patch.py @@ -389,6 +389,42 @@ def test_bitwise_xor(self): ) np.testing.assert_array_equal(out[0], out_np) + @prog_scope() + def test_rxor(self): + place = ( + paddle.CUDAPlace(0) + if paddle.is_compiled_with_cuda() + else paddle.CPUPlace() + ) + x_int = 5 + y_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") + y = paddle.static.data("y", y_np.shape, dtype=y_np.dtype) + z = x_int ^ y + exe = paddle.static.Executor(place) + out = exe.run( + feed={'y': y_np}, + fetch_list=[z], + ) + out_ref = x_int ^ y_np + np.testing.assert_array_equal(out[0], out_ref) + x_bool = True + res_rxor_bool = x_bool ^ y + out_bool = exe.run( + feed={'y': y_np}, + fetch_list=[res_rxor_bool], + ) + res_py_bool = x_bool ^ y_np + np.testing.assert_array_equal(out_bool[0], res_py_bool) + + for x_invalid in ( + np.float32(5.0), + np.float64(5.0), + np.complex64(5), + np.complex128(5.0 + 2j), + ): + with self.assertRaises(TypeError): + x_invalid ^ y + @prog_scope() def test_bitwise_not(self): x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") diff --git a/test/legacy_test/test_math_op_patch_pir.py b/test/legacy_test/test_math_op_patch_pir.py index 48eb26e19bf576..3ca932e2ccf57b 100644 --- a/test/legacy_test/test_math_op_patch_pir.py +++ b/test/legacy_test/test_math_op_patch_pir.py @@ -225,6 +225,63 @@ def test_bitwise_xor(self): np.testing.assert_array_equal(res_np_c, c_np) np.testing.assert_array_equal(res_np_d, d_np) + def test_rxor(self): + with dygraph_guard(): + x_int32 = 5 + x_bool = True + y_np = np.random.randint(0, 2, [2, 3, 5]).astype("int32") + y_tensor = paddle.to_tensor(y_np) + res_ror_int32 = x_int32 ^ y_tensor + res_py_int32 = x_int32 ^ y_tensor.numpy() + np.testing.assert_array_equal(res_py_int32, res_ror_int32.numpy()) + res_ror_bool = x_bool ^ y_tensor + res_py_bool = x_bool ^ y_tensor.numpy() + np.testing.assert_array_equal(res_py_bool, res_ror_bool.numpy()) + for x_np in ( + np.float32(5.0), + np.float64(5.0), + np.complex64(5), + np.complex128(5.0 + 2j), + ): + with self.assertRaises(TypeError): + x_np ^ y_tensor + + with static_guard(): + with paddle.pir_utils.IrGuard(): + main_program, exe, program_guard = new_program() + with program_guard: + x_int = 5 + y_np = np.random.randint(-100, 100, [2, 3, 5]).astype( + "int32" + ) + y = paddle.static.data("y", y_np.shape, dtype=y_np.dtype) + z = x_int ^ y + out = exe.run( + main_program, + feed={'y': y_np}, + fetch_list=[z], + ) + out_ref = x_int ^ y_np + np.testing.assert_array_equal(out[0], out_ref) + x_bool = True + res_rxor_bool = x_bool ^ y + out_bool = exe.run( + main_program, + feed={'y': y_np}, + fetch_list=[res_rxor_bool], + ) + res_py_bool = x_bool ^ y_np + np.testing.assert_array_equal(out_bool[0], res_py_bool) + + for x_invalid in ( + np.float32(5.0), + np.float64(5.0), + np.complex64(5), + np.complex128(5.0 + 2j), + ): + with self.assertRaises(TypeError): + x_invalid ^ y + def test_bitwise_or(self): paddle.disable_static() x_np = np.random.randint(-100, 100, [2, 3, 5]).astype("int32") From 62d2e63f08e8d1ea5d20249a825550e91a328351 Mon Sep 17 00:00:00 2001 From: zty-king <129518799+zty-king@users.noreply.github.com> Date: Fri, 29 Nov 2024 14:24:59 +0800 Subject: [PATCH 063/288] =?UTF-8?q?=E4=BC=98=E5=8C=96vpp=E7=BC=96=E6=8E=92?= =?UTF-8?q?=EF=BC=8C=E5=B9=B6=E5=85=B6=E6=94=AF=E6=8C=81=E9=9D=9E=E5=9D=87?= =?UTF-8?q?=E8=A1=A1=E5=88=87=E5=88=86vpp=20(#69728)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../pipeline_scheduler_pass/pipeline_vpp.py | 19 +++++++++++-------- .../pir/vpp_pass_unittest_pir.py | 12 +++++++++++- 2 files changed, 22 insertions(+), 9 deletions(-) diff --git a/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py b/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py index 45455cf24ed433..711943f54bcb2b 100644 --- a/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py +++ b/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py @@ -64,26 +64,29 @@ def _create_job_list(self): num_stages = self.get_attr("pp_degree") num_model_chunks = self.get_attr("vpp_degree") split_backward = self.get_attr("split_backward", False) + remainder = accumulate_steps % num_stages for i in range(num_model_chunks): self._forward_micro_step_counter[i] = 0 self._backward_micro_step_counter[i] = 0 - assert accumulate_steps % num_stages == 0 + assert accumulate_steps >= num_stages def _get_virtual_pp_rank(micro_step, forward): virtual_pp_stage = micro_step % (num_stages * num_model_chunks) - virtual_pp_stage = virtual_pp_stage // num_stages + if micro_step <= (accumulate_steps // num_stages) * ( + num_stages * num_model_chunks + ): + virtual_pp_stage = virtual_pp_stage // num_stages + else: + virtual_pp_stage = virtual_pp_stage // remainder if not forward: virtual_pp_stage = num_model_chunks - virtual_pp_stage - 1 return virtual_pp_stage total_num_steps = accumulate_steps * num_model_chunks - if accumulate_steps == num_stages: - warmup_steps = total_num_steps - else: - warmup_steps = (num_stages - stage_id - 1) * 2 - warmup_steps += (num_model_chunks - 1) * num_stages - warmup_steps = min(warmup_steps, total_num_steps) + warmup_steps = (num_stages - stage_id - 1) * 2 + warmup_steps += (num_model_chunks - 1) * num_stages + warmup_steps = min(warmup_steps, total_num_steps) steady_steps = total_num_steps - warmup_steps real_split_backward = ( diff --git a/test/auto_parallel/pir/vpp_pass_unittest_pir.py b/test/auto_parallel/pir/vpp_pass_unittest_pir.py index eacd32e772793b..6323f0541ecdbd 100644 --- a/test/auto_parallel/pir/vpp_pass_unittest_pir.py +++ b/test/auto_parallel/pir/vpp_pass_unittest_pir.py @@ -216,6 +216,7 @@ def run_pipeline( acc_step, manual=True, enable_send_recv_overlap=False, + batch_size=BATCH_SIZE, ): self.init() @@ -226,7 +227,7 @@ def run_pipeline( ) loss_fn = nn.MSELoss() - loader = self.create_data_loader() + loader = self.create_data_loader(batch_size) dist_loader = dist.shard_dataloader( loader, meshes=[PP_MESH_0, PP_MESH_1] ) @@ -257,6 +258,15 @@ def test_pp_pass(self): schedule_mode="FThenB", acc_step=4, manual=False ) self.check_result(loss_fthenb, loss_vpp) + # Non-uniform-vpp + Non_uniform_loss_vpp = self.run_pipeline( + schedule_mode="VPP", acc_step=3, manual=False, batch_size=3 + ) + Non_uniform_loss_vpp_manual = self.run_pipeline( + schedule_mode="VPP", acc_step=3, manual=True, batch_size=3 + ) + self.check_result(Non_uniform_loss_vpp, Non_uniform_loss_vpp_manual) + self.check_result(loss_fthenb, Non_uniform_loss_vpp) def check_result(self, loss1, loss2): return np.array_equal(loss1, loss2) From 8773ea2f2070053fad21f30aa33b2098d885d443 Mon Sep 17 00:00:00 2001 From: co63oc Date: Fri, 29 Nov 2024 16:02:06 +0800 Subject: [PATCH 064/288] Revert "[Lod][fluid_ops] revert recursive_sequence_lengths (#69551)" (#69811) This reverts commit f07a351a54b387907e0bcbea7cad0c605c7794a8. --- paddle/fluid/pybind/tensor.cc | 26 -------------------------- 1 file changed, 26 deletions(-) diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index 43c3c1a90e0519..8336dbbc725bf5 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -633,32 +633,6 @@ void BindTensor(pybind11::module &m) { // NOLINT >>> print(t.lod()) [[0, 2, 5]] )DOC") - // Set above comments of set_lod. - .def( - "recursive_sequence_lengths", - [](phi::DenseTensor &self) -> std::vector> { - // output the length-based lod info - LoD lod = phi::ConvertToLengthBasedLoD(self.lod()); - std::vector> new_lod; - new_lod.reserve(lod.size()); - std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); - return new_lod; - }, - R"DOC( - Return the recursive sequence lengths corresponding to of the LodD - of the Tensor. - Returns: - list[list[int]]: The recursive sequence lengths. - Examples: - .. code-block:: python - >>> import paddle - >>> import numpy as np - >>> t = paddle.framework.core.Tensor() - >>> t.set(np.ndarray([5, 30]), paddle.CPUPlace()) - >>> t.set_recursive_sequence_lengths([[2, 3]]) - >>> print(t.recursive_sequence_lengths()) - [[2, 3]] - )DOC") .def("_as_type", [](const phi::DenseTensor &self, paddle::framework::proto::VarType::Type type) { From 418887cbc68c763424377f9c68ed7fa5d627ab73 Mon Sep 17 00:00:00 2001 From: vivienfanghuagood <89012307+vivienfanghuagood@users.noreply.github.com> Date: Fri, 29 Nov 2024 17:15:26 +0800 Subject: [PATCH 065/288] add_cpu_perf (#69819) --- paddle/fluid/inference/api/analysis_predictor.cc | 14 ++++++++++---- 1 file changed, 10 insertions(+), 4 deletions(-) diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 1640f8092c5670..1eeabed005e232 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -418,13 +418,15 @@ bool AnalysisPredictor::Init( const std::shared_ptr &parent_scope, const std::shared_ptr &program) { VLOG(3) << "Predictor::init()"; -#ifdef PADDLE_WITH_NVTX + if (config_.with_profile_) { LOG(WARNING) << "Profiler is activated, which might affect the performance"; +#ifdef PADDLE_WITH_NVTX platform::CudaProfilerStart(); platform::NvprofEnableRecordEvent(); - } #endif + platform::EnableProfiler(platform::ProfilerState::kAll); + } if (!status_is_cloned_) { root_predictor_id_ = predictor_id_; @@ -2965,12 +2967,16 @@ AnalysisPredictor::~AnalysisPredictor() { // NOLINT SaveTrtCalibToDisk(); } #endif -#ifdef PADDLE_WITH_NVTX + if (config_.with_profile_) { +#ifdef PADDLE_WITH_NVTX platform::NvprofDisableRecordEvent(); platform::CudaProfilerStop(); - } #endif + platform::DisableProfiler(platform::EventSortingKey::kTotal, + "./profile.log"); + } + if (sub_scope_) { if (framework::global_transfer_scope_key().find(sub_scope_) != framework::global_transfer_scope_key().end()) { From d5b4b1fceb4a92999c11698544ef5a9fd049404f Mon Sep 17 00:00:00 2001 From: 0x3878f <37301539+0x3878f@users.noreply.github.com> Date: Fri, 29 Nov 2024 17:19:13 +0800 Subject: [PATCH 066/288] fix compilation issue with gcc13 on cpu (#69785) --- cmake/external/brpc.cmake | 10 ++++++++++ patches/brpc/http2.h.patch | 12 ++++++++++++ 2 files changed, 22 insertions(+) create mode 100644 patches/brpc/http2.h.patch diff --git a/cmake/external/brpc.cmake b/cmake/external/brpc.cmake index ad414418caefee..7f6d1b65ece8bf 100755 --- a/cmake/external/brpc.cmake +++ b/cmake/external/brpc.cmake @@ -48,6 +48,13 @@ set(prefix_path "${THIRD_PARTY_PATH}/install/gflags|${THIRD_PARTY_PATH}/install/leveldb|${THIRD_PARTY_PATH}/install/snappy|${THIRD_PARTY_PATH}/install/gtest|${THIRD_PARTY_PATH}/install/protobuf|${THIRD_PARTY_PATH}/install/zlib|${THIRD_PARTY_PATH}/install/glog" ) +if(CMAKE_CXX_COMPILER_ID STREQUAL "GNU" AND ${CMAKE_CXX_COMPILER_VERSION} + VERSION_GREATER_EQUAL 13.0) + file(TO_NATIVE_PATH ${PADDLE_SOURCE_DIR}/patches/brpc/http2.h.patch + http2_h_patch) + set(BRPC_PATCH_COMMAND_GCC13 git apply ${http2_h_patch}) +endif() + # If minimal .a is need, you can set WITH_DEBUG_SYMBOLS=OFF ExternalProject_Add( extern_brpc @@ -55,6 +62,9 @@ ExternalProject_Add( SOURCE_DIR ${BRPC_SOURCE_DIR} PREFIX ${BRPC_PREFIX_DIR} UPDATE_COMMAND "" + PATCH_COMMAND + COMMAND git checkout -- . && git checkout ${BRPC_TAG} + COMMAND ${BRPC_PATCH_COMMAND_GCC13} CMAKE_ARGS -DCMAKE_CXX_COMPILER=${CMAKE_CXX_COMPILER} -DCMAKE_C_COMPILER=${CMAKE_C_COMPILER} -DCMAKE_CXX_FLAGS=${CMAKE_CXX_FLAGS} diff --git a/patches/brpc/http2.h.patch b/patches/brpc/http2.h.patch new file mode 100644 index 00000000000000..ea92913a928eea --- /dev/null +++ b/patches/brpc/http2.h.patch @@ -0,0 +1,12 @@ +diff --git a/src/brpc/http2.h b/src/brpc/http2.h +index 9a40d40d..5da47e60 100644 +--- a/src/brpc/http2.h ++++ b/src/brpc/http2.h +@@ -19,6 +19,7 @@ + #define BAIDU_RPC_HTTP2_H + + #include "brpc/http_status_code.h" ++#include + + // To baidu-rpc developers: This is a header included by user, don't depend + // on internal structures, use opaque pointers instead. From e3a8e5a111f996ebd1ed7efd699476515bb7ffdb Mon Sep 17 00:00:00 2001 From: winter-wang <78149749+winter-wang@users.noreply.github.com> Date: Fri, 29 Nov 2024 17:59:13 +0800 Subject: [PATCH 067/288] fix the bug because of PR69357 (#69790) --- python/paddle/nn/clip.py | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/python/paddle/nn/clip.py b/python/paddle/nn/clip.py index df8aae7ec45cd1..f0875f632f1f25 100644 --- a/python/paddle/nn/clip.py +++ b/python/paddle/nn/clip.py @@ -965,7 +965,7 @@ def async_add_n(var_list): global_norm_dist = [] global_norm_not_dist = [] if len(no_fusion_sum_square_fp16) > 0: - global_norm_var_fp16 = async_add_n(sum_square_dist_fp16) + global_norm_var_fp16 = async_add_n(no_fusion_sum_square_fp16) no_fusion_global_norm.append(global_norm_var_fp16.astype(sum_dtype)) if len(sum_square_dist_fp16) > 0: global_norm_var_fp16 = async_add_n(sum_square_dist_fp16) From 682bd54efbeeab59f18ab7a398791cc2fd31c351 Mon Sep 17 00:00:00 2001 From: zhengzhonghui Date: Fri, 29 Nov 2024 18:13:08 +0800 Subject: [PATCH 068/288] [Auto Parallel] fix pipeline parallel api bug (#69795) --- .../intermediate/pipeline_parallel.py | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py index efd28a96318819..5604866044e317 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py @@ -274,13 +274,13 @@ def is_match(layer_name): raise NotImplementedError( "global_spec should be None if split_spec is a dict" ) - - if isinstance(global_spec, str): - global_spec = [global_spec] - else: - assert isinstance( - global_spec, (list, tuple) - ), f"global_spec can only be list or list(str), but got:{type(global_spec)}" + if global_spec: + if isinstance(global_spec, str): + global_spec = [global_spec] + else: + assert isinstance( + global_spec, (list, tuple) + ), f"global_spec can only be list or list(str), but got:{type(global_spec)}" logger.info( f"split_spec_dict: {split_spec_dict}, global_spec: {global_spec}" From 5adcf8b62b9b5df12ecaab9021005c9d43adda21 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?Victor=C2=B7Bayim?= <145201987+Victor-Bayim@users.noreply.github.com> Date: Sat, 30 Nov 2024 01:36:05 +0800 Subject: [PATCH 069/288] [CodeStyle][Typos][C-42] Fix typo (`conect`) (#69807) --- _typos.toml | 1 - python/paddle/static/pir_io.py | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/_typos.toml b/_typos.toml index 3ed9daf72b8568..e74be5d3a2bd5b 100644 --- a/_typos.toml +++ b/_typos.toml @@ -76,7 +76,6 @@ configurated = 'configurated' configed = 'configed' confict = 'confict' conjuction = 'conjuction' -conect = 'conect' consequtive = 'consequtive' consistant = 'consistant' contraints = 'contraints' diff --git a/python/paddle/static/pir_io.py b/python/paddle/static/pir_io.py index ba01cdcb8777e0..d5c6cacd582ea7 100644 --- a/python/paddle/static/pir_io.py +++ b/python/paddle/static/pir_io.py @@ -310,7 +310,7 @@ def normalize_pir_program(program, feed_vars, fetch_vars, **kwargs): global_block.remove_op(op) skip_prune_program = kwargs.get('skip_prune_program', False) - # if feed var is not conect with target_vars, it will be delete. + # if feed var is not connect with target_vars, it will be delete. if not skip_prune_program: pir_prune_with_input(copy_program, clone_feed_vars, clone_fetch_vars) _inference_optimize(copy_program, prune_read_op=True) From 350f03dfb5b5bebe1a36748cb18d57260dcf2d3e Mon Sep 17 00:00:00 2001 From: houj04 <35131887+houj04@users.noreply.github.com> Date: Mon, 2 Dec 2024 10:04:23 +0800 Subject: [PATCH 070/288] [XPU] fp16 for multinomial op (#69767) --- paddle/phi/backends/xpu/xpu2_op_list.cc | 2 + paddle/phi/backends/xpu/xpu3_op_list.cc | 3 +- .../kernels/funcs/multinomial_kernel_helper.h | 62 +++++++++++++++++++ paddle/phi/kernels/gpu/multinomial_kernel.cu | 31 +--------- paddle/phi/kernels/xpu/multinomial_kernel.cc | 50 +++++++++++---- 5 files changed, 107 insertions(+), 41 deletions(-) create mode 100644 paddle/phi/kernels/funcs/multinomial_kernel_helper.h diff --git a/paddle/phi/backends/xpu/xpu2_op_list.cc b/paddle/phi/backends/xpu/xpu2_op_list.cc index 9d0e2e65871fd2..b8e6d04efbeeb9 100644 --- a/paddle/phi/backends/xpu/xpu2_op_list.cc +++ b/paddle/phi/backends/xpu/xpu2_op_list.cc @@ -712,6 +712,8 @@ XPUOpMap& get_kl2_ops() { {"multi_encoder_xpu", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"multiclass_nms3", XPUKernelSet({phi::DataType::FLOAT32})}, + {"multinomial", + XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"nearest_interp_v2", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index 72342291be4b89..ce6c464ebb21a8 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -812,7 +812,8 @@ XPUOpMap& get_kl3_ops() { {"multi_encoder_xpu", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"multiclass_nms3", XPUKernelSet({phi::DataType::FLOAT32})}, - {"multinomial", XPUKernelSet({phi::DataType::FLOAT32})}, + {"multinomial", + XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"nearest_interp_v2", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, diff --git a/paddle/phi/kernels/funcs/multinomial_kernel_helper.h b/paddle/phi/kernels/funcs/multinomial_kernel_helper.h new file mode 100644 index 00000000000000..b6cb819a812530 --- /dev/null +++ b/paddle/phi/kernels/funcs/multinomial_kernel_helper.h @@ -0,0 +1,62 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/phi/common/amp_type_traits.h" +#include "paddle/phi/common/scalar.h" +#include "paddle/phi/core/dense_tensor.h" +#include "paddle/phi/core/tensor_utils.h" + +namespace phi { + +template +void MultinomialInputChecker(const Context& dev_ctx, + const DenseTensor& x, + const Scalar& num_samples) { + using MT = typename phi::dtype::MPTypeTrait::Type; + auto in_dims = x.dims(); + int64_t dim_size = in_dims.size(); + const int64_t num_categories = in_dims[dim_size - 1]; + const int64_t num_distributions = dim_size > 1 ? in_dims[dim_size - 2] : 1; + auto int_num_samples = num_samples.to(); + + phi::DenseTensor cpu_tensor; + phi::Copy(dev_ctx, x, phi::CPUPlace(), false, &cpu_tensor); + T* cpu_in_data = cpu_tensor.data(); + for (int64_t i = 0; i < num_distributions; ++i) { + int zero_num = 0; + for (int64_t j = 0; j < num_categories; ++j) { + T weight = cpu_in_data[i * num_categories + j]; + PADDLE_ENFORCE_GE( + static_cast(weight), + 0, + errors::InvalidArgument( + "Each element of multinomial'input must >= 0, but got %f.", + static_cast(weight))); + if (weight == static_cast(0)) { + zero_num++; + } + } + int valid_samples = num_categories - zero_num; + PADDLE_ENFORCE_LE( + int_num_samples, + valid_samples, + errors::InvalidArgument("When replacement=False, 'num_samples' " + "must less than or equal to the number of " + "positive item of input")); + } +} + +} // namespace phi diff --git a/paddle/phi/kernels/gpu/multinomial_kernel.cu b/paddle/phi/kernels/gpu/multinomial_kernel.cu index 2b8b600e56ddb4..342e2c52b3a513 100644 --- a/paddle/phi/kernels/gpu/multinomial_kernel.cu +++ b/paddle/phi/kernels/gpu/multinomial_kernel.cu @@ -13,6 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/phi/kernels/multinomial_kernel.h" +#include "paddle/phi/kernels/funcs/multinomial_kernel_helper.h" #ifdef __NVCC__ #include "cub/cub.cuh" @@ -148,35 +149,7 @@ void MultinomialKernel(const Context& dev_ctx, // If replacement is False, it's not a replaceable sample. Every category // can be used only once. if (!replacement) { - int64_t in_data_numel = x.numel(); - int64_t out_data_numel = out->numel(); - - phi::DenseTensor cpu_tensor; - phi::Copy(dev_ctx, x, phi::CPUPlace(), false, &cpu_tensor); - T* cpu_in_data = cpu_tensor.data(); - for (size_t i = 0; i < num_distributions; ++i) { - int zero_num = 0; - for (size_t j = 0; j < num_categories; ++j) { - T weight = cpu_in_data[i * num_categories + j]; - PADDLE_ENFORCE_GE( - static_cast(weight), - 0, - errors::InvalidArgument( - "Each element of multinomial'input must >= 0, but got %f.", - static_cast(weight))); - if (weight == static_cast(0)) { - zero_num++; - } - } - int valid_samples = num_categories - zero_num; - PADDLE_ENFORCE_LE( - int_num_samples, - valid_samples, - errors::InvalidArgument("When replacement=False, 'num_samples' " - "must less than or equal to the number of " - "positive item of input")); - } - + MultinomialInputChecker(dev_ctx, x, num_samples); // Refer to [gumbel softmax algorithm] DenseTensor rand = EmptyLike(dev_ctx, x); T* rand_data = rand.data(); diff --git a/paddle/phi/kernels/xpu/multinomial_kernel.cc b/paddle/phi/kernels/xpu/multinomial_kernel.cc index 76d18e401ae908..3874c44b9b6e8a 100644 --- a/paddle/phi/kernels/xpu/multinomial_kernel.cc +++ b/paddle/phi/kernels/xpu/multinomial_kernel.cc @@ -13,6 +13,7 @@ // limitations under the License. #include "paddle/phi/kernels/multinomial_kernel.h" +#include "paddle/phi/kernels/funcs/multinomial_kernel_helper.h" #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" @@ -26,7 +27,6 @@ void MultinomialKernel(const Context& dev_ctx, bool replacement, DenseTensor* out) { auto int_num_samples = num_samples.to(); - auto* in_data = x.data(); int64_t* out_data = dev_ctx.template Alloc(out); auto in_dims = x.dims(); int64_t dim_size = in_dims.size(); @@ -34,23 +34,51 @@ void MultinomialKernel(const Context& dev_ctx, const int64_t num_distributions = dim_size > 1 ? in_dims[dim_size - 2] : 1; int64_t seed = dev_ctx.GetGenerator()->Random64(); + // If replacement is False, it's not a replaceable sample. Every category + // can be used only once. + if (!replacement) { + MultinomialInputChecker(dev_ctx, x, num_samples); + } + + xpu::ctx_guard RAII_GUARD(dev_ctx.x_context()); + const float* in_data = nullptr; + if (!std::is_same::value) { + // multinomial only accept float as input + using XPUType = typename XPUTypeTrait::Type; + auto numel = x.numel(); + float* cast_buffer = RAII_GUARD.alloc_l3_or_gm(numel); + int r = + xpu::cast(dev_ctx.x_context(), + reinterpret_cast(x.data()), + cast_buffer, + numel); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast"); + in_data = cast_buffer; + } else { + in_data = reinterpret_cast(x.data()); + } + // int multinomial(Context* ctx, const T* x, TID* y, int64_t num_samples, // int64_t num_categories, int64_t num_distributions, bool replacement, // int64_t seed); - int r = xpu::multinomial(dev_ctx.x_context(), - in_data, - out_data, - int_num_samples, - num_categories, - num_distributions, - replacement, - seed); + int r = xpu::multinomial(dev_ctx.x_context(), + in_data, + out_data, + int_num_samples, + num_categories, + num_distributions, + replacement, + seed); PADDLE_ENFORCE_XDNN_SUCCESS(r, "multinomial"); } } // namespace phi -PD_REGISTER_KERNEL( - multinomial, XPU, ALL_LAYOUT, phi::MultinomialKernel, float) { +PD_REGISTER_KERNEL(multinomial, + XPU, + ALL_LAYOUT, + phi::MultinomialKernel, + float, + phi::dtype::float16) { kernel->OutputAt(0).SetDataType(phi::DataType::INT64); } From 744128c5e1b7e4541dc1aeda99c8861b35700beb Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Mon, 2 Dec 2024 10:36:02 +0800 Subject: [PATCH 071/288] =?UTF-8?q?Revert=20"[CINN]=20Make=20slice=20op=20?= =?UTF-8?q?not=20enter=20CINN=20when=20there=20is=20no=20data=20in=20the?= =?UTF-8?q?=20para=E2=80=A6"=20(#69818)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit 7d94171a814d6b471ca80ef9ca22ce537f81b4ef. --- .../lower_cinn_fusion_op_pass.cc | 2 + paddle/cinn/hlir/framework/pir/utils.cc | 22 +------ .../element_wise_binary.cc | 12 +--- .../same_operands_result.cc | 19 ++---- .../infer_symbolic_shape/unary_infer_sym.cc | 65 ++++--------------- 5 files changed, 20 insertions(+), 100 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc index 87223efd62aa3b..32640cd1ab899d 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc @@ -39,6 +39,8 @@ class FusionOpPattern : public pir::OpRewritePattern { ::pir::IrContext* ctx = ::pir::IrContext::Instance(); auto* program = fusion_op->GetParentProgram(); auto& shape_analysis = pir::ShapeAnalysisManager::Instance().Get(program); + VLOG(4) << "Program before lowering: \n" + << pir::CustomPrintHelper(*program, shape_analysis.PrintHook()); // TODO(zhangyuqin1998): Replace pir::Group with a new structure OpLoweringGroupPtr group = GetGroup(fusion_op); diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 256d3052c61161..82d6e1ffad86fc 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -349,29 +349,11 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { if (FLAGS_disable_dyshape_in_train) { return false; } - - auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( - const_cast<::pir::Operation&>(op).GetParentProgram()); - - const auto& isProcessableSlice = [&]() -> bool { - const ::pir::Value& starts_value = op.operand_source(1); - const ::pir::Value& ends_value = op.operand_source(2); - const symbol::ShapeOrDataDimExprs& starts_shape_data = - shape_analysis.GetShapeOrDataForValue(starts_value); - const symbol::ShapeOrDataDimExprs& ends_shape_data = - shape_analysis.GetShapeOrDataForValue(ends_value); - return starts_shape_data.data().has_value() && - ends_shape_data.data().has_value(); - }; - - if (op.isa() && !isProcessableSlice()) { - return true; - } - if (!HaveUnkDim(op)) { return false; } - + auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( + const_cast<::pir::Operation&>(op).GetParentProgram()); std::unordered_set input_exprs = [&]() { std::unordered_set res; for (const auto& input_value : op.operands_source()) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc index 36585f74596533..3c8b88af98c7cb 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc @@ -147,17 +147,6 @@ bool FloorDivideOpInferSymbolicShape( }); } -bool MinimumOpInferSymbolicShape( - pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { - return InferSymbolicShapeElementWiseBinary( - op, - infer_context, - [](const symbol::DimExpr &x, const symbol::DimExpr &y) { - symbol::DimExprBuilder builder; - return builder.Min(x, y); - }); -} - OP_ELEMENT_WISE_BINARY(Add_) OP_ELEMENT_WISE_BINARY(BitwiseAnd) OP_ELEMENT_WISE_BINARY(BitwiseAnd_) @@ -197,6 +186,7 @@ OP_ELEMENT_WISE_BINARY(LogicalOr_) OP_ELEMENT_WISE_BINARY(LogicalXor) OP_ELEMENT_WISE_BINARY(LogicalXor_) OP_ELEMENT_WISE_BINARY(Maximum) +OP_ELEMENT_WISE_BINARY(Minimum) OP_ELEMENT_WISE_BINARY(MultiplySr) OP_ELEMENT_WISE_BINARY(MultiplySr_) OP_ELEMENT_WISE_BINARY(Multiply_) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc index 07f566d52b4e81..39e788f520c647 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc @@ -49,6 +49,8 @@ OP_SAME_OPERANDS_AND_RESULT(Hardtanh_) OP_SAME_OPERANDS_AND_RESULT(Bernoulli) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot_) +OP_SAME_OPERANDS_AND_RESULT(Ceil) +OP_SAME_OPERANDS_AND_RESULT(Ceil_) OP_SAME_OPERANDS_AND_RESULT(Celu) OP_SAME_OPERANDS_AND_RESULT(Clip) OP_SAME_OPERANDS_AND_RESULT(Clip_) @@ -253,13 +255,13 @@ bool ScaleOpInferSymbolicShape(pir::Operation *op, return GetOptionalAttributeData("scale"); }; - if (operand_shape_or_data.data().has_value()) { + if (operand_shape_or_data.data()) { const std::optional &opt_scale = GetOptionalScaleData(); const std::optional &opt_bias = GetOptionalAttributeData("bias"); if (opt_scale && opt_bias) { std::vector data; - for (auto &val : operand_shape_or_data.data().value()) { + for (auto &val : *(operand_shape_or_data.data())) { data.push_back(val * (opt_scale.value()) + (opt_bias.value())); } SetOutputWithShapeAndData(data); @@ -282,19 +284,6 @@ bool ArgsortOpInferSymbolicShape( return true; } -bool CeilOpInferSymbolicShape(pir::Operation *op, - pir::InferSymbolicShapeContext *infer_context) { - const symbol::ShapeOrDataDimExprs &operand_shape_or_data = - infer_context->GetShapeOrDataForValue(op->operand_source(0)); - infer_context->SetShapeOrDataForValue(op->result(0), operand_shape_or_data); - return true; -} - -bool Ceil_OpInferSymbolicShape(pir::Operation *op, - pir::InferSymbolicShapeContext *infer_context) { - return CeilOpInferSymbolicShape(op, infer_context); -} - } // namespace paddle::dialect namespace cinn::dialect {} // namespace cinn::dialect diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 0a0045e5512dab..2bb863f9a46f3e 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3227,67 +3227,24 @@ bool ShuffleChannelOpInferSymbolicShape( bool SliceOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { pir::Value operand_source = op->operand_source(0); + pir::Value operand_starts = op->operand_source(1); + pir::Value operand_ends = op->operand_source(2); pir::Value res = op->result(0); + const symbol::ShapeOrDataDimExprs &starts_shape_data = + infer_context->GetShapeOrDataForValue(operand_starts); + const symbol::ShapeOrDataDimExprs &ends_shape_data = + infer_context->GetShapeOrDataForValue(operand_ends); + std::vector axes_vec = details::GetVectorAttr(op, "axes"); + + ExprVec starts = slice_utils::GetExprVecFromData(starts_shape_data); + ExprVec ends = slice_utils::GetExprVecFromData(ends_shape_data); + std::vector infer_flags = details::GetVectorAttr(op, "infer_flags"); const std::vector decrease_axis = details::GetVectorAttr(op, "decrease_axis"); - auto GetExprVec = [&](std::vector *expr_vec, - const int &operand_idx, - const std::string &attr_name) -> bool { - if (op->operand_source(operand_idx)) { - const symbol::ShapeOrDataDimExprs &se_shape_data = - infer_context->GetShapeOrDataForValue( - op->operand_source(operand_idx)); - if (se_shape_data.data().has_value()) { - *expr_vec = se_shape_data.data().value(); - return true; - } - PADDLE_ENFORCE_EQ( - se_shape_data.shape().at(0).isa() && - (static_cast(axes_vec.size()) == - se_shape_data.shape().at(0).dyn_cast()), - true, - common::errors::InvalidArgument( - "The size of axes must equal size of starts and ends.")); - return false; - } else { - if (op->attributes().find(attr_name) != op->attributes().end()) { - const std::vector se_raw = - paddle::dialect::details::GetVectorAttr(op, attr_name); - for (const int64_t &se : se_raw) { - expr_vec->push_back(symbol::DimExpr{se}); - } - return true; - } - return false; - } - }; - - std::vector starts; - std::vector ends; - if (!GetExprVec(&starts, 1, "starts") || !GetExprVec(&ends, 2, "ends")) { - const auto &in_shapeordata = - infer_context->GetShapeOrDataForValue(op->operand_source(0)); - // NOTE(gongshaotian): When there is no data value in the starts and ends - // parameters, only the shape value is processed regardless of whether the - // input has a data value, and the data value is no longer processed. - std::vector out_shape = in_shapeordata.shape(); - for (size_t i = 0; i < axes_vec.size(); i++) { - int64_t axis = axes_vec[i]; - out_shape[axis] = infer_context->GetNextSymName(); - } - ExprVec out_dims = paddle::dialect::slice_utils::GetDecreasedDims( - out_shape, decrease_axis); - infer_context->SetShapeOrDataForValue( - res, - symbol::ShapeOrDataDimExprs{ - symbol::TensorShapeOrDataDimExprs(out_dims)}); - return true; - } - infer_context->SetShapeOrDataForValue( res, slice_utils::SliceRawInferSymbolicShape(operand_source, From 341fce9e3ad2365f813fcf0428964cd5551c7802 Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 2 Dec 2024 11:18:07 +0800 Subject: [PATCH 072/288] [fluid_ops] lod_tensor.h (#69775) --- .../fleet_executor/compute_interceptor.cc | 4 +- .../distributed/ps/service/brpc_ps_client.cc | 2 +- .../framework/details/nan_inf_utils_detail.h | 3 +- paddle/fluid/framework/io/save_load_tensor.cc | 4 +- paddle/fluid/framework/lod_tensor.cc | 111 ------------------ paddle/fluid/framework/lod_tensor.h | 22 +--- paddle/fluid/framework/selected_rows_utils.cc | 82 +------------ paddle/fluid/framework/selected_rows_utils.h | 21 +--- paddle/fluid/jit/serializer.cc | 2 +- paddle/fluid/operators/load_combine_op.h | 2 +- paddle/fluid/operators/save_combine_op.h | 4 +- .../src/save_load_parameters.cc | 14 +-- paddle/fluid/pybind/io.cc | 16 +-- .../phi/core/framework/data_type_transform.cc | 6 +- paddle/phi/core/framework/var_type_helper.cc | 6 +- paddle/phi/core/framework/var_type_helper.h | 10 +- .../framework/selected_rows_utils_test.cc | 4 +- test/cpp/fluid/framework/tensor_util_test.cc | 13 +- 18 files changed, 50 insertions(+), 276 deletions(-) diff --git a/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc b/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc index 2eebe84b5d7b13..c9be8264ee7329 100644 --- a/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc +++ b/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc @@ -64,7 +64,7 @@ void ComputeInterceptor::DecodeMsgVars(const InterceptorMessage& msg) { std::istringstream ss(var_iter.stensor()); auto* var = scope->Var(name); auto* tensor = var->GetMutable(); - framework::DeserializeFromStream(ss, tensor, dev_ctx); + phi::DeserializeFromStream(ss, tensor, dev_ctx); VLOG(3) << "Set vars " << name << " with value in scope " << scope_id << " with dims " << tensor->dims() << " with dtype " @@ -98,7 +98,7 @@ InterceptorMessage ComputeInterceptor::PrepareVarsMsg() { common::errors::NotFound( "Variable %s not exists in scope %ld", var_name, cur_scope_id_)); const auto& tensor = var->Get(); - framework::SerializeToStream(ss, tensor, dev_ctx); + phi::SerializeToStream(ss, tensor, dev_ctx); vars->set_stensor(ss.str()); VLOG(3) << "Prepare vars msg " << var_name << " with dimension " << tensor.dims() << " dtype " << tensor.dtype(); diff --git a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc index ddeb15305e91af..232dbc944c7aa1 100644 --- a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc +++ b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc @@ -1520,7 +1520,7 @@ int32_t BrpcPsClient::RecvAndSaveTable(const uint64_t table_id, common::errors::Unavailable( "Cannot open %s to save variables.", file_name)); - framework::SerializeToStream(fout, *var_tensor, dev_ctx); + phi::SerializeToStream(fout, *var_tensor, dev_ctx); fout.close(); return 0; diff --git a/paddle/fluid/framework/details/nan_inf_utils_detail.h b/paddle/fluid/framework/details/nan_inf_utils_detail.h index f8b1353b05d8db..cf7b3da6d8fd52 100644 --- a/paddle/fluid/framework/details/nan_inf_utils_detail.h +++ b/paddle/fluid/framework/details/nan_inf_utils_detail.h @@ -86,7 +86,8 @@ void tensor_check(const std::string& op_type, const phi::DenseTensor& tensor, const phi::Place& place) { TensorCheckerVisitor vistor(op_type, var_name, tensor, place); - VisitDataType(framework::TransToProtoVarType(tensor.dtype()), vistor); + framework::VisitDataType(framework::TransToProtoVarType(tensor.dtype()), + vistor); } void InitWhiteListFormEnv(); diff --git a/paddle/fluid/framework/io/save_load_tensor.cc b/paddle/fluid/framework/io/save_load_tensor.cc index 5f0d4a16b01223..5c2158c664595a 100644 --- a/paddle/fluid/framework/io/save_load_tensor.cc +++ b/paddle/fluid/framework/io/save_load_tensor.cc @@ -34,7 +34,7 @@ void SaveTensor(const phi::DenseTensor& x, true, common::errors::Unavailable( "Cannot open %s to save variables.", new_path)); - framework::SerializeToStream(fout, x); + phi::SerializeToStream(fout, x); fout.close(); } @@ -51,6 +51,6 @@ void LoadTensor(const std::string& file_path, phi::DenseTensor* out) { common::errors::InvalidArgument( "The variable to be loaded cannot be found.")); - framework::DeserializeFromStream(fin, out); + phi::DeserializeFromStream(fin, out); } } // namespace paddle::framework diff --git a/paddle/fluid/framework/lod_tensor.cc b/paddle/fluid/framework/lod_tensor.cc index 8e886deef1e55e..05dfbed223b2f9 100644 --- a/paddle/fluid/framework/lod_tensor.cc +++ b/paddle/fluid/framework/lod_tensor.cc @@ -75,117 +75,6 @@ bool CheckLoD(const LoD &in, int tensor_height) { return true; } -void SerializeToStream(std::ostream &os, - const phi::DenseTensor &tensor, - const phi::DeviceContext &dev_ctx) { - { // the 1st field, uint32_t version for DenseTensor - os.write( - reinterpret_cast(&paddle::framework::kCurTensorVersion), - sizeof(paddle::framework::kCurTensorVersion)); - } - { - // the 2st field, LoD information - // uint64_t lod_level - // uint64_t lod_level_1 size in byte. - // int* lod_level_1 data - // ... - auto lod = tensor.lod(); - uint64_t size = lod.size(); - os.write(reinterpret_cast(&size), sizeof(size)); - - for (auto &each : lod) { - size = each.size() * sizeof(phi::LoD::value_type::value_type); - os.write(reinterpret_cast(&size), sizeof(size)); - os.write(reinterpret_cast(each.data()), - static_cast(size)); - } - } - // the 3st field, Tensor - paddle::framework::TensorToStream( - os, static_cast(tensor), dev_ctx); -} - -void SerializeToStream(std::ostream &os, const phi::DenseTensor &tensor) { - phi::DeviceContextPool &pool = phi::DeviceContextPool::Instance(); - const phi::DeviceContext *dev_ctx = nullptr; - auto place = tensor.place(); - dev_ctx = pool.Get(place); - SerializeToStream(os, tensor, *dev_ctx); -} - -void DeserializeFromStream(std::istream &os, phi::DenseTensor *tensor) { - phi::DeviceContextPool &pool = phi::DeviceContextPool::Instance(); - const phi::DeviceContext *dev_ctx = nullptr; - dev_ctx = pool.Get(phi::CPUPlace()); - DeserializeFromStream(os, tensor, *dev_ctx); -} - -void DeserializeFromStream(std::istream &is, - phi::DenseTensor *tensor, - const phi::DeviceContext &dev_ctx, - const size_t &seek, - const std::vector &shape) { - { - // the 1st field, unit32_t version for DenseTensor - uint32_t version = 0; - is.read(reinterpret_cast(&version), sizeof(version)); - - PADDLE_ENFORCE_EQ( - version, - 0U, - common::errors::InvalidArgument( - "Deserialize to tensor failed, maybe the loaded file is " - "not a paddle model(expected file format: 0, but %u found).", - version)); - } - { - // the 2st field, LoD information - uint64_t lod_level = 0; - is.read(reinterpret_cast(&lod_level), sizeof(lod_level)); - auto &lod = *tensor->mutable_lod(); - lod.resize(lod_level); - } - // the 3st filed, Tensor - paddle::framework::TensorFromStream( - is, static_cast(tensor), dev_ctx, seek, shape); -} - -void DeserializeFromStream(std::istream &is, - phi::DenseTensor *tensor, - const phi::DeviceContext &dev_ctx) { - { - // the 1st field, unit32_t version for DenseTensor - uint32_t version = 0; - is.read(reinterpret_cast(&version), sizeof(version)); - - PADDLE_ENFORCE_EQ( - version, - 0U, - common::errors::InvalidArgument( - "Deserialize to tensor failed, maybe the loaded file is " - "not a paddle model(expected file format: 0, but %u found).", - version)); - } - { - // the 2st field, LoD information - uint64_t lod_level = 0; - is.read(reinterpret_cast(&lod_level), sizeof(lod_level)); - auto &lod = *tensor->mutable_lod(); - lod.resize(lod_level); - for (uint64_t i = 0; i < lod_level; ++i) { - uint64_t size = 0; - is.read(reinterpret_cast(&size), sizeof(size)); - std::vector tmp(size / sizeof(size_t)); - is.read(reinterpret_cast(tmp.data()), - static_cast(size)); - lod[i] = tmp; - } - } - // the 3st filed, Tensor - paddle::framework::TensorFromStream( - is, static_cast(tensor), dev_ctx); -} - LoD ConvertToOffsetBasedLoD(const LoD &length_lod) { LoD offset_lod; offset_lod.reserve(length_lod.size()); diff --git a/paddle/fluid/framework/lod_tensor.h b/paddle/fluid/framework/lod_tensor.h index 5f07e33130ba5f..a41a68911bbecf 100644 --- a/paddle/fluid/framework/lod_tensor.h +++ b/paddle/fluid/framework/lod_tensor.h @@ -26,6 +26,7 @@ limitations under the License. */ #include "paddle/fluid/platform/enforce.h" #include "paddle/phi/common/place.h" #include "paddle/phi/core/dense_tensor.h" +#include "paddle/phi/core/framework/dense_tensor_serialize.h" #include "paddle/phi/core/mixed_vector.h" #include "paddle/utils/test_macros.h" @@ -73,26 +74,5 @@ TEST_API bool CheckLoD(const LoD& in, int tensor_height = -1); TEST_API LoD ConvertToOffsetBasedLoD(const LoD& length_lod); -/* - * Serialize/Deserialize phi::DenseTensor to std::ostream - * You can pass ofstream or ostringstream to serialize to file - * or to a in memory string. GPU tensor will be copied to CPU. - */ -void SerializeToStream(std::ostream& os, - const phi::DenseTensor& tensor, - const phi::DeviceContext& dev_ctx); -void DeserializeFromStream(std::istream& is, - phi::DenseTensor* tensor, - const phi::DeviceContext& dev_ctx); -void DeserializeFromStream(std::istream& is, - phi::DenseTensor* tensor, - const phi::DeviceContext& dev_ctx, - const size_t& seek, - const std::vector& shape); - -void SerializeToStream(std::ostream& os, const phi::DenseTensor& tensor); - -void DeserializeFromStream(std::istream& os, phi::DenseTensor* tensor); - } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/selected_rows_utils.cc b/paddle/fluid/framework/selected_rows_utils.cc index 8df8b6d01dc0d9..91184120c90795 100644 --- a/paddle/fluid/framework/selected_rows_utils.cc +++ b/paddle/fluid/framework/selected_rows_utils.cc @@ -14,84 +14,4 @@ limitations under the License. */ #include "paddle/fluid/framework/selected_rows_utils.h" -namespace paddle::framework { - -void SerializeToStream(std::ostream& os, - const phi::SelectedRows& selected_rows, - const phi::DeviceContext& dev_ctx) { - { // the 1st field, uint32_t version - constexpr uint32_t version = 0; - os.write(reinterpret_cast(&version), sizeof(version)); - } - { - // the 2st field, rows information - auto& rows = selected_rows.rows(); - uint64_t size = rows.size(); - os.write(reinterpret_cast(&size), sizeof(size)); - for (uint64_t i = 0; i < size; ++i) { - os.write(reinterpret_cast(&rows[i]), sizeof(rows[i])); - } - } - { - // the 3st field, the height of SelectedRows - int64_t height = selected_rows.height(); - os.write(reinterpret_cast(&height), sizeof(height)); - } - // the 4st field, Tensor data - paddle::framework::TensorToStream(os, selected_rows.value(), dev_ctx); -} - -void SerializeToStream(std::ostream& os, - const phi::SelectedRows& selected_rows) { - phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance(); - const phi::DeviceContext* dev_ctx = nullptr; - auto place = selected_rows.place(); - dev_ctx = pool.Get(place); - SerializeToStream(os, selected_rows, *dev_ctx); -} - -void DeserializeFromStream(std::istream& is, phi::SelectedRows* selected_rows) { - phi::DeviceContextPool& pool = phi::DeviceContextPool::Instance(); - const phi::DeviceContext* dev_ctx = nullptr; - dev_ctx = pool.Get(phi::CPUPlace()); - DeserializeFromStream(is, selected_rows, *dev_ctx); -} - -void DeserializeFromStream(std::istream& is, - phi::SelectedRows* selected_rows, - const phi::DeviceContext& dev_ctx) { - { - // the 1st field, unit32_t version for SelectedRows - uint32_t version = 0; - is.read(reinterpret_cast(&version), sizeof(version)); - PADDLE_ENFORCE_EQ(version, - 0U, - common::errors::InvalidArgument( - "Only version 0 SelectedRows is supported.")); - } - { - // the 2st field, rows information - uint64_t size = 0; - is.read(reinterpret_cast(&size), sizeof(size)); - PADDLE_ENFORCE_EQ( - is.good(), - true, - common::errors::Unavailable("Cannot read the number of rows.")); - auto& rows = *selected_rows->mutable_rows(); - rows.resize(size); - for (uint64_t i = 0; i < size; ++i) { - is.read(reinterpret_cast(&rows[i]), sizeof(int64_t)); - } - } - { - // the 3st field, the height of the SelectedRows - int64_t height = 0; - is.read(reinterpret_cast(&height), sizeof(int64_t)); - selected_rows->set_height(height); - } - // the 4st field, tensor which contains the data - paddle::framework::TensorFromStream( - is, selected_rows->mutable_value(), dev_ctx); -} - -} // namespace paddle::framework +namespace paddle::framework {} // namespace paddle::framework diff --git a/paddle/fluid/framework/selected_rows_utils.h b/paddle/fluid/framework/selected_rows_utils.h index e376a6fd925d48..efa61bdc4889f9 100644 --- a/paddle/fluid/framework/selected_rows_utils.h +++ b/paddle/fluid/framework/selected_rows_utils.h @@ -22,27 +22,10 @@ limitations under the License. */ #include #include "paddle/fluid/framework/tensor_util.h" +#include "paddle/phi/core/framework/selected_rows_serialize.h" #include "paddle/phi/core/platform/device_context.h" #include "paddle/phi/core/selected_rows.h" namespace paddle { -namespace framework { -/* - * Serialize/Deserialize SelectedRows to std::ostream - * You can pass ofstream or ostringstream to serialize to file - * or to a in memory string. GPU tensor will be copied to CPU. - */ -void SerializeToStream(std::ostream& os, - const phi::SelectedRows& selected_rows, - const phi::DeviceContext& dev_ctx); -void DeserializeFromStream(std::istream& is, - phi::SelectedRows* selected_rows, - const phi::DeviceContext& dev_ctx); - -void SerializeToStream(std::ostream& os, - const phi::SelectedRows& selected_rows); - -void DeserializeFromStream(std::istream& is, phi::SelectedRows* selected_rows); - -} // namespace framework +namespace framework {} // namespace framework } // namespace paddle diff --git a/paddle/fluid/jit/serializer.cc b/paddle/fluid/jit/serializer.cc index 65c765fde4204a..a23277869a41f9 100644 --- a/paddle/fluid/jit/serializer.cc +++ b/paddle/fluid/jit/serializer.cc @@ -136,7 +136,7 @@ void Deserializer::ReadTensorData( Variable v; // TODO(dev): Support framework::Vocab DenseTensor* dense_tensor = v.GetMutable(); - framework::DeserializeFromStream(fin, dense_tensor, dev_ctx); + phi::DeserializeFromStream(fin, dense_tensor, dev_ctx); (*params_dict)[item] = std::make_shared(v); } } diff --git a/paddle/fluid/operators/load_combine_op.h b/paddle/fluid/operators/load_combine_op.h index 4b09268dec4e1e..28fd2db174e445 100644 --- a/paddle/fluid/operators/load_combine_op.h +++ b/paddle/fluid/operators/load_combine_op.h @@ -114,7 +114,7 @@ class LoadCombineOpKernel : public framework::OpKernel { auto *tensor = out_vars[i]->GetMutable(); // Get data from fin to tensor - paddle::framework::DeserializeFromStream(*buffer, tensor, dev_ctx); + phi::DeserializeFromStream(*buffer, tensor, dev_ctx); auto in_dtype = tensor->dtype(); auto out_dtype = load_as_fp16 ? phi::DataType::FLOAT16 : in_dtype; diff --git a/paddle/fluid/operators/save_combine_op.h b/paddle/fluid/operators/save_combine_op.h index e0bd7de94c1724..48d0589ab77612 100644 --- a/paddle/fluid/operators/save_combine_op.h +++ b/paddle/fluid/operators/save_combine_op.h @@ -110,9 +110,9 @@ void SaveCombineTensorKernel(const Context& dev_ctx, framework::TransDataType(in_kernel_type, out_kernel_type, tensor, &out); // copy LoD info to the new tensor out.set_lod(tensor.lod()); - framework::SerializeToStream(ss, out, dev_ctx); + phi::SerializeToStream(ss, out, dev_ctx); } else { - framework::SerializeToStream(ss, tensor, dev_ctx); + phi::SerializeToStream(ss, tensor, dev_ctx); } } diff --git a/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc b/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc index 183bfc034bfb16..1d563c326a943a 100644 --- a/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc +++ b/paddle/fluid/pir/serialize_deserialize/src/save_load_parameters.cc @@ -91,9 +91,9 @@ void SaveFunction(const phi::DenseTensor& x, const phi::DeviceContext* dev_ctx = GetDeviceContext(x); if (in_dtype != out_dtype) { auto out = CastTensorType(dev_ctx, x, out_dtype); - paddle::framework::SerializeToStream(fout, out, *dev_ctx); + phi::SerializeToStream(fout, out, *dev_ctx); } else { - paddle::framework::SerializeToStream(fout, x, *dev_ctx); + phi::SerializeToStream(fout, x, *dev_ctx); } fout.close(); VLOG(6) << "save func done "; @@ -138,9 +138,9 @@ void SaveCombineFunction(const std::vector& x, auto out_dtype = save_as_fp16 ? phi::DataType::FLOAT16 : in_dtype; if (in_dtype != out_dtype) { auto out = CastTensorType(dev_ctx, tensor, out_dtype); - paddle::framework::SerializeToStream(fout, out, *dev_ctx); + phi::SerializeToStream(fout, out, *dev_ctx); } else { - paddle::framework::SerializeToStream(fout, tensor, *dev_ctx); + phi::SerializeToStream(fout, tensor, *dev_ctx); } } fout.close(); @@ -170,9 +170,9 @@ void LoadFunction(const std::string& file_path, 0, common::errors::InvalidArgument( "seek with tensor must great than or equal to 0")); - paddle::framework::DeserializeFromStream(fin, out, *dev_ctx, seek, shape); + phi::DeserializeFromStream(fin, out, *dev_ctx, seek, shape); } else { - paddle::framework::DeserializeFromStream(fin, out, *dev_ctx); + phi::DeserializeFromStream(fin, out, *dev_ctx); } auto in_dtype = out->dtype(); @@ -205,7 +205,7 @@ void LoadCombineFunction(const std::string& file_path, const phi::DeviceContext* dev_ctx = GetDeviceContext(*(out->at(0)), place); for (size_t i = 0; i < names.size(); i++) { auto tensor = out->at(i); - paddle::framework::DeserializeFromStream(fin, tensor, *dev_ctx); + phi::DeserializeFromStream(fin, tensor, *dev_ctx); auto in_dtype = tensor->dtype(); auto out_dtype = load_as_fp16 ? phi::DataType::FLOAT16 : in_dtype; diff --git a/paddle/fluid/pybind/io.cc b/paddle/fluid/pybind/io.cc index b69efc7c1a02f5..834386d1fdf459 100644 --- a/paddle/fluid/pybind/io.cc +++ b/paddle/fluid/pybind/io.cc @@ -53,7 +53,7 @@ void BindIO(pybind11::module *m) { true, common::errors::Unavailable("Cannot open %s to save variables.", str_file_name)); - paddle::framework::SerializeToStream(fout, tensor); + phi::SerializeToStream(fout, tensor); int64_t tellp = fout.tellp(); fout.close(); @@ -69,7 +69,7 @@ void BindIO(pybind11::module *m) { common::errors::Unavailable("Cannot open %s to load variables.", str_file_name)); - paddle::framework::DeserializeFromStream(fin, &tensor); + phi::DeserializeFromStream(fin, &tensor); int64_t tellg = fin.tellg(); fin.close(); return tellg; @@ -85,7 +85,7 @@ void BindIO(pybind11::module *m) { common::errors::Unavailable( "Cannot open %s to save SelectedRows.", str_file_name)); - paddle::framework::SerializeToStream(fout, selected_rows); + phi::SerializeToStream(fout, selected_rows); int64_t tellp = fout.tellp(); fout.close(); return tellp; @@ -101,7 +101,7 @@ void BindIO(pybind11::module *m) { common::errors::Unavailable("Cannot open %s to load SelectedRows.", str_file_name)); - paddle::framework::DeserializeFromStream(fin, &selected_rows); + phi::DeserializeFromStream(fin, &selected_rows); int64_t tellg = fin.tellg(); fin.close(); return tellg; @@ -110,7 +110,7 @@ void BindIO(pybind11::module *m) { m->def("save_dense_tensor_to_memory", [](const phi::DenseTensor &tensor) -> py::bytes { std::ostringstream ss; - paddle::framework::SerializeToStream(ss, tensor); + phi::SerializeToStream(ss, tensor); return ss.str(); }); @@ -118,13 +118,13 @@ void BindIO(pybind11::module *m) { [](phi::DenseTensor &tensor, const std::string &tensor_bytes) { std::istringstream fin(tensor_bytes, std::ios::in | std::ios::binary); - paddle::framework::DeserializeFromStream(fin, &tensor); + phi::DeserializeFromStream(fin, &tensor); }); m->def("save_selected_rows_to_memory", [](const phi::SelectedRows &selected_rows) -> py::bytes { std::ostringstream ss; - paddle::framework::SerializeToStream(ss, selected_rows); + phi::SerializeToStream(ss, selected_rows); return ss.str(); }); @@ -133,7 +133,7 @@ void BindIO(pybind11::module *m) { const std::string &selected_rows_bytes) { std::istringstream fin(selected_rows_bytes, std::ios::in | std::ios::binary); - paddle::framework::DeserializeFromStream(fin, &selected_rows); + phi::DeserializeFromStream(fin, &selected_rows); }); m->def("load_dense_tensor", [](const std::string path) { diff --git a/paddle/phi/core/framework/data_type_transform.cc b/paddle/phi/core/framework/data_type_transform.cc index 6662dba8c1d01c..c20da1023b3310 100644 --- a/paddle/phi/core/framework/data_type_transform.cc +++ b/paddle/phi/core/framework/data_type_transform.cc @@ -73,7 +73,7 @@ static void XPUTransDataType( } else { PADDLE_THROW(common::errors::Unimplemented( "Data type (%s) is not supported in XPU when casting data type.", - DataTypeToString(dst_type))); + VarDataTypeToString(dst_type))); } } @@ -180,7 +180,7 @@ void TransDataType(const phi::DenseTensor& in, default: PADDLE_THROW(common::errors::Unimplemented( "Data type (%s) is not supported in XPU when casting data type.", - DataTypeToString(src_type))); + VarDataTypeToString(src_type))); } #else @@ -226,7 +226,7 @@ void TransDataType(const phi::DenseTensor& in, default: PADDLE_THROW(common::errors::Unimplemented( "Data type (%s) is not supported when casting data type.", - DataTypeToString(src_type))); + VarDataTypeToString(src_type))); } #endif } diff --git a/paddle/phi/core/framework/var_type_helper.cc b/paddle/phi/core/framework/var_type_helper.cc index 03d2708f8bb8c0..ba5b185c4ea6e6 100644 --- a/paddle/phi/core/framework/var_type_helper.cc +++ b/paddle/phi/core/framework/var_type_helper.cc @@ -86,7 +86,7 @@ std::type_index ToTypeIndex(proto::VarType::Type type) { static_cast(type))); } -std::string DataTypeToString(const proto::VarType::Type type) { +std::string VarDataTypeToString(const proto::VarType::Type type) { auto it = gDataTypeMap().proto_to_str_.find(static_cast(type)); if (it != gDataTypeMap().proto_to_str_.end()) { return it->second; @@ -106,7 +106,7 @@ size_t SizeOfType(proto::VarType::Type type) { return it->second; } PADDLE_THROW(common::errors::Unimplemented("Not support %s as tensor type.", - DataTypeToString(type))); + VarDataTypeToString(type))); } // Now only supports promotion of complex type @@ -126,7 +126,7 @@ int DataTypeNumAlign(const proto::VarType::Type t) { PADDLE_THROW(common::errors::Unavailable( "Only supports to align data type include float32, float64, complex64 " "and complex128, but received data type is `s`.", - DataTypeToString(t))); + VarDataTypeToString(t))); } return cast_type_num; } diff --git a/paddle/phi/core/framework/var_type_helper.h b/paddle/phi/core/framework/var_type_helper.h index 0be8e88de4f45a..81636930019331 100644 --- a/paddle/phi/core/framework/var_type_helper.h +++ b/paddle/phi/core/framework/var_type_helper.h @@ -31,7 +31,7 @@ namespace proto = paddle::framework::proto; namespace phi { -TEST_API std::string DataTypeToString(const proto::VarType::Type type); +TEST_API std::string VarDataTypeToString(const proto::VarType::Type type); TEST_API extern size_t SizeOfType(proto::VarType::Type type); template @@ -185,7 +185,7 @@ inline void VisitIntDataType(proto::VarType::Type type, Visitor visitor) { _ForEachIntDataType_(VisitIntDataTypeCallback); PADDLE_THROW(common::errors::Unimplemented( - "Expected integral data type, but got %s", DataTypeToString(type))); + "Expected integral data type, but got %s", VarDataTypeToString(type))); #undef VisitIntDataTypeCallback } @@ -220,7 +220,7 @@ inline void VisitDataTypeForHIP(proto::VarType::Type type, Visitor visitor) { inline std::ostream& operator<<(std::ostream& out, const proto::VarType::Type& type) { - out << DataTypeToString(type); + out << VarDataTypeToString(type); return out; } @@ -242,7 +242,7 @@ extern inline proto::VarType::Type ToComplexType(proto::VarType::Type t) { PADDLE_THROW(common::errors::Unimplemented( "Unknown real value data type (%s), now only support float32 and " "float64.", - DataTypeToString(t))); + VarDataTypeToString(t))); } } @@ -257,7 +257,7 @@ extern inline proto::VarType::Type ToRealType(proto::VarType::Type t) { "Unknown complex value data type (%s), now only support complex64 " "and " "complex128.", - DataTypeToString(t))); + VarDataTypeToString(t))); } } diff --git a/test/cpp/fluid/framework/selected_rows_utils_test.cc b/test/cpp/fluid/framework/selected_rows_utils_test.cc index 4d80f846a2bf36..1220c6670aacf0 100644 --- a/test/cpp/fluid/framework/selected_rows_utils_test.cc +++ b/test/cpp/fluid/framework/selected_rows_utils_test.cc @@ -57,10 +57,10 @@ TEST_F(SelectedRowsTester, SerializeAndDeserialize) { phi::CPUContext cpu_ctx(place_); std::ostringstream oss; - SerializeToStream(oss, *selected_rows_, cpu_ctx); + phi::SerializeToStream(oss, *selected_rows_, cpu_ctx); std::istringstream iss(oss.str()); - DeserializeFromStream(iss, &dst_tensor, cpu_ctx); + phi::DeserializeFromStream(iss, &dst_tensor, cpu_ctx); ASSERT_EQ(selected_rows_->rows(), dst_tensor.rows()); ASSERT_EQ(selected_rows_->height(), dst_tensor.height()); diff --git a/test/cpp/fluid/framework/tensor_util_test.cc b/test/cpp/fluid/framework/tensor_util_test.cc index 146d5e6f507c70..17139682cabf08 100644 --- a/test/cpp/fluid/framework/tensor_util_test.cc +++ b/test/cpp/fluid/framework/tensor_util_test.cc @@ -475,10 +475,10 @@ TEST(Tensor, FromAndToStream) { auto place = new phi::CPUPlace(); phi::CPUContext cpu_ctx(*place); std::ostringstream oss; - TensorToStream(oss, src_tensor, cpu_ctx); + phi::TensorToStream(oss, src_tensor, cpu_ctx); std::istringstream iss(oss.str()); - TensorFromStream(iss, &dst_tensor, cpu_ctx); + phi::TensorFromStream(iss, &dst_tensor, cpu_ctx); int* dst_ptr = dst_tensor.mutable_data(phi::CPUPlace()); for (int i = 0; i < 5; ++i) { EXPECT_EQ(dst_ptr[i], array[i]); @@ -502,12 +502,13 @@ TEST(Tensor, FromAndToStream) { TensorCopy(src_tensor, *gpu_place, gpu_ctx, &gpu_tensor); std::ostringstream oss; - TensorToStream(oss, gpu_tensor, gpu_ctx); + phi::TensorToStream(oss, gpu_tensor, gpu_ctx); std::istringstream iss(oss.str()); - TensorFromStream(iss, - &dst_tensor, - *phi::DeviceContextPool::Instance().Get(phi::CPUPlace())); + phi::TensorFromStream( + iss, + &dst_tensor, + *phi::DeviceContextPool::Instance().Get(phi::CPUPlace())); int* dst_ptr = dst_tensor.mutable_data(phi::CPUPlace()); for (int i = 0; i < 6; ++i) { From ba21de32a000eb08676301ac8a8d9cda4fbc4466 Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 2 Dec 2024 11:24:40 +0800 Subject: [PATCH 073/288] [Lod][fluid_ops]LoDTensorBlocking (#69814) --- paddle/fluid/framework/compiled_program.cc | 4 +- .../event_garbage_collector.cc | 2 +- .../fast_garbage_collector.cc | 2 +- .../no_event_garbage_collector.cc | 2 +- .../framework/new_executor/pir_interpreter.cc | 2 +- .../new_executor/program_interpreter.cc | 2 +- paddle/fluid/framework/var_type_traits.cc | 2 +- paddle/fluid/framework/var_type_traits.h | 8 +- .../operators/reader/create_ctr_reader_op.cc | 8 +- .../operators/reader/create_py_reader_op.cc | 20 +++-- paddle/fluid/pybind/reader_py.cc | 90 ++++++++++--------- ..._queue.h => dense_tensor_blocking_queue.h} | 54 +++++------ paddle/phi/core/operators/reader/py_reader.cc | 4 +- paddle/phi/core/operators/reader/py_reader.h | 8 +- python/paddle/base/reader.py | 2 +- .../paddle/io/dataloader/dataloader_iter.py | 2 +- .../fluid/framework/var_type_traits_test.cc | 2 +- 17 files changed, 111 insertions(+), 103 deletions(-) rename paddle/phi/core/operators/reader/{lod_tensor_blocking_queue.h => dense_tensor_blocking_queue.h} (76%) diff --git a/paddle/fluid/framework/compiled_program.cc b/paddle/fluid/framework/compiled_program.cc index 092d32bc8f94d7..48e67f73495f5d 100755 --- a/paddle/fluid/framework/compiled_program.cc +++ b/paddle/fluid/framework/compiled_program.cc @@ -28,7 +28,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/graph_helper.h" #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h" #include "paddle/fluid/framework/ir/memory_optimize_pass/reference_count_pass_helper.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) #include "paddle/phi/core/platform/cuda_device_guard.h" @@ -733,7 +733,7 @@ void CompiledProgram::InitReaderQueueDeviceCount(ir::Graph *graph, const Scope &scope, size_t dev_cnt) { using QueueHolder = - operators::reader::OrderedMultiDeviceLoDTensorBlockingQueueHolder; + operators::reader::OrderedMultiDeviceDenseTensorBlockingQueueHolder; auto reader_ops = ReaderOpSet(); for (auto &node : graph->Nodes()) { diff --git a/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc index ac57282136e9af..34b6c7cf37132b 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc @@ -92,7 +92,7 @@ void InterpreterCoreEventGarbageCollector::Add(Variable* var, } else if ( var->IsType< operators::reader:: - OrderedMultiDeviceLoDTensorBlockingQueueHolder>()) { // NOLINT + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? } else if (var->IsType()) { diff --git a/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc index 013844a3d59ccf..0133d8d0313344 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc @@ -37,7 +37,7 @@ void InterpreterCoreFastGarbageCollector::Add(Variable* var) { } else if ( var->IsType< operators::reader:: - OrderedMultiDeviceLoDTensorBlockingQueueHolder>()) { // NOLINT + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? } else if (var->IsType()) { diff --git a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc index 0ca58eec6defb4..6b4ac89038475d 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc @@ -52,7 +52,7 @@ void InterpreterCoreNoEventGarbageCollector::Add( } else if ( var->IsType< operators::reader:: - OrderedMultiDeviceLoDTensorBlockingQueueHolder>()) { // NOLINT + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? } else if (var->IsType()) { diff --git a/paddle/fluid/framework/new_executor/pir_interpreter.cc b/paddle/fluid/framework/new_executor/pir_interpreter.cc index 3601e7c5d0f4a1..d49e41d274ef03 100644 --- a/paddle/fluid/framework/new_executor/pir_interpreter.cc +++ b/paddle/fluid/framework/new_executor/pir_interpreter.cc @@ -1261,7 +1261,7 @@ void PirInterpreter::RecordStreamForGC(InstructionBase* instr) { } else if ( var->IsType< operators::reader:: - OrderedMultiDeviceLoDTensorBlockingQueueHolder>()) { // NOLINT + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // do nothing } else if (var->IsType()) { TensorRecordStream( diff --git a/paddle/fluid/framework/new_executor/program_interpreter.cc b/paddle/fluid/framework/new_executor/program_interpreter.cc index f899080d2389c4..45bdeed567eabf 100644 --- a/paddle/fluid/framework/new_executor/program_interpreter.cc +++ b/paddle/fluid/framework/new_executor/program_interpreter.cc @@ -1472,7 +1472,7 @@ void ProgramInterpreter::RecordStreamForGC(const Instruction& instr) { } else if ( var->IsType< operators::reader:: - OrderedMultiDeviceLoDTensorBlockingQueueHolder>()) { // NOLINT + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // do nothing } else if (var->IsType()) { TensorRecordStream( diff --git a/paddle/fluid/framework/var_type_traits.cc b/paddle/fluid/framework/var_type_traits.cc index 3680a26ece0c3b..696b1df185231f 100644 --- a/paddle/fluid/framework/var_type_traits.cc +++ b/paddle/fluid/framework/var_type_traits.cc @@ -18,7 +18,7 @@ #include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/scope.h" #include "paddle/phi/core/framework/reader.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" #ifdef PADDLE_WITH_CUDA #if defined(PADDLE_WITH_NCCL) #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" diff --git a/paddle/fluid/framework/var_type_traits.h b/paddle/fluid/framework/var_type_traits.h index 5ef6a5f1902756..b2931ada75c4fc 100644 --- a/paddle/fluid/framework/var_type_traits.h +++ b/paddle/fluid/framework/var_type_traits.h @@ -87,8 +87,8 @@ namespace operators { class CUDAGraphWithInOuts; namespace reader { -class LoDTensorBlockingQueueHolder; -class OrderedMultiDeviceLoDTensorBlockingQueueHolder; +class DenseTensorBlockingQueueHolder; +class OrderedMultiDeviceDenseTensorBlockingQueueHolder; } // namespace reader } // namespace operators @@ -187,10 +187,10 @@ using VarTypeRegistry = detail::VarTypeRegistryImpl< ReaderHolder, String, Scope *, - operators::reader::LoDTensorBlockingQueueHolder, + operators::reader::DenseTensorBlockingQueueHolder, FetchList, FeedList, - operators::reader::OrderedMultiDeviceLoDTensorBlockingQueueHolder, + operators::reader::OrderedMultiDeviceDenseTensorBlockingQueueHolder, #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) ncclUniqueId, diff --git a/paddle/fluid/operators/reader/create_ctr_reader_op.cc b/paddle/fluid/operators/reader/create_ctr_reader_op.cc index 3500ee4515da01..85ddaccac291b6 100644 --- a/paddle/fluid/operators/reader/create_ctr_reader_op.cc +++ b/paddle/fluid/operators/reader/create_ctr_reader_op.cc @@ -14,7 +14,7 @@ #include "paddle/fluid/operators/reader/ctr_reader.h" #include "paddle/fluid/operators/reader/reader_op_registry.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" namespace paddle { namespace operators { @@ -36,10 +36,10 @@ class CreateCTRReaderOp : public framework::OperatorBase { PADDLE_ENFORCE_NOT_NULL( queue_holder_var, common::errors::PreconditionNotMet( - "No LoDTensorBlockingQueueHolder variable with name %s found", + "No DenseTensorBlockingQueueHolder variable with name %s found", queue_name)); auto* queue_holder = - queue_holder_var->template GetMutable(); + queue_holder_var->template GetMutable(); auto thread_num = Attr("thread_num"); auto sparse_slots = Attr>("sparse_slots"); @@ -66,7 +66,7 @@ class CreateCTRReaderOpMaker : public FileReaderMakerBase { protected: void Apply() override { AddInput("blocking_queue", - "Name of the `LoDTensorBlockingQueueHolder` variable"); + "Name of the `DenseTensorBlockingQueueHolder` variable"); AddAttr("thread_num", "the thread num to read data"); AddAttr("batch_size", "the batch size of read data"); AddAttr("file_type", "plain or gzip").SetDefault("plain"); diff --git a/paddle/fluid/operators/reader/create_py_reader_op.cc b/paddle/fluid/operators/reader/create_py_reader_op.cc index 996716cf9706d4..472de92a88630d 100644 --- a/paddle/fluid/operators/reader/create_py_reader_op.cc +++ b/paddle/fluid/operators/reader/create_py_reader_op.cc @@ -36,20 +36,22 @@ class CreatePyReaderOp : public framework::OperatorBase { PADDLE_ENFORCE_NOT_NULL( queue_holder_var, common::errors::NotFound( - "No LoDTensorBlockingQueueHolder variable with name %s found. This " + "No DenseTensorBlockingQueueHolder variable with name %s found. " + "This " "may be because the DataLoader is defined in another Scope, " "which is different from the Scope when calling Executor.run.", queue_name)); - std::shared_ptr queue; - std::shared_ptr ordered_queue; + std::shared_ptr queue; + std::shared_ptr ordered_queue; int dev_idx = -1; - if (queue_holder_var->IsType()) { - queue = queue_holder_var->Get().GetQueue(); - } else if (queue_holder_var - ->IsType()) { + if (queue_holder_var->IsType()) { + queue = + queue_holder_var->Get().GetQueue(); + } else if (queue_holder_var->IsType< + OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { auto* queue_holder = queue_holder_var - ->GetMutable(); + ->GetMutable(); dev_idx = Attr("device_index"); ordered_queue = queue_holder->GetQueue(); ordered_queue->SetDeviceCount(Attr("device_count")); @@ -99,7 +101,7 @@ class CreatePyReaderOpMaker : public FileReaderMakerBase { protected: void Apply() override { AddInput("blocking_queue", - "Name of the `LoDTensorBlockingQueueHolder` variable"); + "Name of the `DenseTensorBlockingQueueHolder` variable"); AddAttr("device_index", "The device index this reader offers data") .SetDefault(0); diff --git a/paddle/fluid/pybind/reader_py.cc b/paddle/fluid/pybind/reader_py.cc index 668114145b10d8..2e0bec5da6b420 100644 --- a/paddle/fluid/pybind/reader_py.cc +++ b/paddle/fluid/pybind/reader_py.cc @@ -30,7 +30,7 @@ #include "paddle/phi/common/place.h" #include "paddle/phi/core/framework/reader.h" #include "paddle/phi/core/operators/reader/buffered_reader.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" #include "paddle/phi/core/operators/reader/py_reader.h" #include "pybind11/stl.h" @@ -111,13 +111,14 @@ static paddle::optional> DiffTensorShapeWithVarDesc( return DiffTensorShape(tensor, desc_shape, num_places); } -static const std::shared_ptr &GetQueue( - const std::shared_ptr &queue, size_t idx) { +static const std::shared_ptr &GetQueue( + const std::shared_ptr &queue, + size_t idx) { return queue; } -static const std::shared_ptr &GetQueue( - const std::shared_ptr +static const std::shared_ptr &GetQueue( + const std::shared_ptr &queue, size_t idx) { return queue->GetQueue(idx); @@ -132,7 +133,7 @@ class MultiDeviceFeedReader { static constexpr bool kKeepOrder = std::is_same::value; + reader::OrderedMultiDeviceDenseTensorBlockingQueue>::value; MultiDeviceFeedReader( const std::shared_ptr &queue, @@ -163,7 +164,7 @@ class MultiDeviceFeedReader { auto create_or_get_reader = [&](size_t idx) { if (idx == 0 || - std::is_same::value) { + std::is_same::value) { return first_reader; } else { return std::make_shared( @@ -432,11 +433,12 @@ void BindReader(py::module *module) { VLOG(1) << "init_lod_tensor_blocking_queue"; if (is_ordered) { auto *holder = var.GetMutable< - reader::OrderedMultiDeviceLoDTensorBlockingQueueHolder>(); + reader::OrderedMultiDeviceDenseTensorBlockingQueueHolder>(); holder->InitOnce(capacity, FLAGS_reader_queue_speed_test_mode); return py::cast(holder->GetQueue()); } else { - auto *holder = var.GetMutable(); + auto *holder = + var.GetMutable(); holder->InitOnce(capacity, FLAGS_reader_queue_speed_test_mode); return py::cast(holder->GetQueue()); } @@ -447,51 +449,52 @@ void BindReader(py::module *module) { .def("start", &framework::ReaderHolder::Start) .def("reset", &framework::ReaderHolder::ResetAll); - py::class_>( - m, "LoDTensorBlockingQueue", "") + py::class_>( + m, "DenseTensorBlockingQueue", "") .def( "push", - [](reader::LoDTensorBlockingQueue &self, + [](reader::DenseTensorBlockingQueue &self, const phi::TensorArray &dense_tensor_vec) { return self.Push(dense_tensor_vec); }, py::call_guard()) - .def("size", &reader::LoDTensorBlockingQueue::Size) - .def("capacity", &reader::LoDTensorBlockingQueue::Cap) - .def("close", &reader::LoDTensorBlockingQueue::Close) - .def("kill", &reader::LoDTensorBlockingQueue::Kill) + .def("size", &reader::DenseTensorBlockingQueue::Size) + .def("capacity", &reader::DenseTensorBlockingQueue::Cap) + .def("close", &reader::DenseTensorBlockingQueue::Close) + .def("kill", &reader::DenseTensorBlockingQueue::Kill) .def("wait_for_inited", - &reader::LoDTensorBlockingQueue::WaitForInited, + &reader::DenseTensorBlockingQueue::WaitForInited, py::call_guard()); - py::class_>( - m, "OrderedMultiDeviceLoDTensorBlockingQueue", "") + py::class_< + reader::OrderedMultiDeviceDenseTensorBlockingQueue, + std::shared_ptr>( + m, "OrderedMultiDeviceDenseTensorBlockingQueue", "") .def( "push", - [](reader::OrderedMultiDeviceLoDTensorBlockingQueue &self, + [](reader::OrderedMultiDeviceDenseTensorBlockingQueue &self, const phi::TensorArray &dense_tensor_vec) { return self.Push(dense_tensor_vec); }, py::call_guard()) - .def("size", &reader::OrderedMultiDeviceLoDTensorBlockingQueue::Size) - .def("capacity", &reader::OrderedMultiDeviceLoDTensorBlockingQueue::Cap) - .def("close", &reader::OrderedMultiDeviceLoDTensorBlockingQueue::Close) - .def("kill", &reader::OrderedMultiDeviceLoDTensorBlockingQueue::Kill) + .def("size", &reader::OrderedMultiDeviceDenseTensorBlockingQueue::Size) + .def("capacity", &reader::OrderedMultiDeviceDenseTensorBlockingQueue::Cap) + .def("close", &reader::OrderedMultiDeviceDenseTensorBlockingQueue::Close) + .def("kill", &reader::OrderedMultiDeviceDenseTensorBlockingQueue::Kill) .def("wait_for_inited", - &reader::OrderedMultiDeviceLoDTensorBlockingQueue::WaitForInited, + &reader::OrderedMultiDeviceDenseTensorBlockingQueue::WaitForInited, py::call_guard()) - .def("reset", &reader::OrderedMultiDeviceLoDTensorBlockingQueue::Reset); + .def("reset", &reader::OrderedMultiDeviceDenseTensorBlockingQueue::Reset); - BindMultiDeviceReader( + BindMultiDeviceReader( module, "MultiDeviceFeedReader"); - BindMultiDeviceReader( + BindMultiDeviceReader( module, "OrderedMultiDeviceFeedReader"); m.def( "create_py_reader", - [](const std::shared_ptr &queue, + [](const std::shared_ptr &queue, const std::vector &names, const std::vector> &shapes, const std::vector &dtypes, @@ -500,7 +503,7 @@ void BindReader(py::module *module) { bool use_double_buffer, bool drop_last, bool pin_memory) { - return new MultiDeviceFeedReader( + return new MultiDeviceFeedReader( queue, names, shapes, @@ -515,8 +518,8 @@ void BindReader(py::module *module) { m.def( "create_py_reader", - [](const std::shared_ptr - &queue, + [](const std::shared_ptr< + reader::OrderedMultiDeviceDenseTensorBlockingQueue> &queue, const std::vector &names, const std::vector> &shapes, const std::vector &dtypes, @@ -527,15 +530,16 @@ void BindReader(py::module *module) { bool pin_memory) { queue->SetDeviceCount(dst_places.size()); return new MultiDeviceFeedReader< - reader::OrderedMultiDeviceLoDTensorBlockingQueue>(queue, - names, - shapes, - dtypes, - need_check_feed, - dst_places, - use_double_buffer, - drop_last, - pin_memory); + reader::OrderedMultiDeviceDenseTensorBlockingQueue>( + queue, + names, + shapes, + dtypes, + need_check_feed, + dst_places, + use_double_buffer, + drop_last, + pin_memory); }, py::return_value_policy::take_ownership); } diff --git a/paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h b/paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h similarity index 76% rename from paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h rename to paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h index 0d833357b09db9..89dd6540e89177 100644 --- a/paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h +++ b/paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h @@ -27,13 +27,14 @@ namespace paddle { namespace operators { namespace reader { -class LoDTensorBlockingQueue { +class DenseTensorBlockingQueue { public: - explicit LoDTensorBlockingQueue(size_t capacity, bool speed_test_mode = false) + explicit DenseTensorBlockingQueue(size_t capacity, + bool speed_test_mode = false) : queue_(capacity, speed_test_mode) {} - ~LoDTensorBlockingQueue() { - // VLOG(10) << "Destruct LoDTensorBlockingQueue"; + ~DenseTensorBlockingQueue() { + // VLOG(10) << "Destruct DenseTensorBlockingQueue"; } bool Push(const phi::TensorArray& lod_tensor_vec) { @@ -58,7 +59,7 @@ class LoDTensorBlockingQueue { inline void ReOpen() { queue_.ReOpen(); } inline void Close() { - // VLOG(1) << "LoDTensorBlockingQueue close"; + // VLOG(1) << "DenseTensorBlockingQueue close"; queue_.Close(); } @@ -72,14 +73,14 @@ class LoDTensorBlockingQueue { BlockingQueue queue_; }; -class OrderedMultiDeviceLoDTensorBlockingQueue { +class OrderedMultiDeviceDenseTensorBlockingQueue { public: - OrderedMultiDeviceLoDTensorBlockingQueue(size_t capacity, - bool speed_test_mode = false) + OrderedMultiDeviceDenseTensorBlockingQueue(size_t capacity, + bool speed_test_mode = false) : capacity_(capacity), speed_test_mode_(speed_test_mode) {} - ~OrderedMultiDeviceLoDTensorBlockingQueue() { - // VLOG(10) << "Destruct OrderedMultiDeviceLoDTensorBlockingQueue"; + ~OrderedMultiDeviceDenseTensorBlockingQueue() { + // VLOG(10) << "Destruct OrderedMultiDeviceDenseTensorBlockingQueue"; } bool WaitForInited(size_t milliseconds) { @@ -96,7 +97,7 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { 1, common::errors::InvalidArgument( "Device count to init " - "OrderedMultiDeviceLoDTensorBlockingQueue" + "OrderedMultiDeviceDenseTensorBlockingQueue" " must be larger than 1")); if (!queues_.empty()) { PADDLE_ENFORCE_EQ(queues_.size(), @@ -110,13 +111,14 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { queues_.resize(dev_cnt); for (auto& item : queues_) { auto cap = (capacity_ + dev_cnt - 1) / dev_cnt; - item = std::make_unique(cap, speed_test_mode_); + item = + std::make_unique(cap, speed_test_mode_); } } cv_.notify_all(); } - const std::shared_ptr& GetQueue(size_t idx) const { + const std::shared_ptr& GetQueue(size_t idx) const { EnforceIsInited(); PADDLE_ENFORCE_LT( idx, @@ -160,7 +162,7 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { auto dev_cnt = queues_.size(); for (auto& item : queues_) { auto cap = (capacity_ + dev_cnt - 1) / dev_cnt; - item = std::make_unique(cap, speed_test_mode_); + item = std::make_unique(cap, speed_test_mode_); } data_index_ = 0; } @@ -178,7 +180,7 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { inline size_t Cap() const { return capacity_; } private: - const std::shared_ptr& CurQueue() { + const std::shared_ptr& CurQueue() { return queues_[(data_index_++) % queues_.size()]; } @@ -190,7 +192,7 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { } private: - std::vector> queues_; + std::vector> queues_; mutable uint64_t data_index_{0}; size_t dev_cnt_{0}; @@ -205,45 +207,45 @@ class OrderedMultiDeviceLoDTensorBlockingQueue { mutable std::condition_variable cv_; }; -class LoDTensorBlockingQueueHolder { +class DenseTensorBlockingQueueHolder { public: void InitOnce(size_t capacity, bool speed_test_mode = false) { PADDLE_ENFORCE_EQ( queue_, nullptr, - common::errors::AlreadyExists("LoDTensorBlockingQueueHolder::" + common::errors::AlreadyExists("DenseTensorBlockingQueueHolder::" "InitOnce() can only be called once")); queue_ = - std::make_unique(capacity, speed_test_mode); + std::make_unique(capacity, speed_test_mode); } - inline const std::shared_ptr& GetQueue() const { + inline const std::shared_ptr& GetQueue() const { return queue_; } private: - std::shared_ptr queue_; + std::shared_ptr queue_; }; -class OrderedMultiDeviceLoDTensorBlockingQueueHolder { +class OrderedMultiDeviceDenseTensorBlockingQueueHolder { public: void InitOnce(size_t capacity, bool speed_test_mode = false) { PADDLE_ENFORCE_EQ(queue_, nullptr, common::errors::AlreadyExists( - "OrderedMultiDeviceLoDTensorBlockingQueueHolder::" + "OrderedMultiDeviceDenseTensorBlockingQueueHolder::" "InitOnce() can only be called once")); - queue_ = std::make_unique( + queue_ = std::make_unique( capacity, speed_test_mode); } - inline const std::shared_ptr& + inline const std::shared_ptr& GetQueue() const { return queue_; } private: - std::shared_ptr queue_; + std::shared_ptr queue_; }; } // namespace reader diff --git a/paddle/phi/core/operators/reader/py_reader.cc b/paddle/phi/core/operators/reader/py_reader.cc index 4e41e00c7d3231..f470b2a01e310d 100644 --- a/paddle/phi/core/operators/reader/py_reader.cc +++ b/paddle/phi/core/operators/reader/py_reader.cc @@ -17,14 +17,14 @@ namespace paddle::operators::reader { PyReader::PyReader( - const std::shared_ptr& queue, + const std::shared_ptr& queue, const std::vector& dims, const std::vector& var_types, const std::vector& need_check_feed) : framework::FileReader(dims, var_types, need_check_feed) { PADDLE_ENFORCE_NOT_NULL(queue, common::errors::PreconditionNotMet( - "LoDTensorBlockingQueue must not be null.")); + "DenseTensorBlockingQueue must not be null.")); queue_ = queue; } diff --git a/paddle/phi/core/operators/reader/py_reader.h b/paddle/phi/core/operators/reader/py_reader.h index 6ed4a72a84154a..74706f7e951ebb 100644 --- a/paddle/phi/core/operators/reader/py_reader.h +++ b/paddle/phi/core/operators/reader/py_reader.h @@ -19,18 +19,18 @@ #include #include "paddle/phi/core/framework/reader.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" namespace paddle { namespace operators { namespace reader { -class LoDTensorBlockingQueue; +class DenseTensorBlockingQueue; class PyReader : public framework::FileReader { public: explicit PyReader( - const std::shared_ptr& queue, + const std::shared_ptr& queue, const std::vector& dims, const std::vector& var_types, const std::vector& need_check_feed); @@ -44,7 +44,7 @@ class PyReader : public framework::FileReader { void Start() override; private: - std::shared_ptr queue_; + std::shared_ptr queue_; }; } // namespace reader diff --git a/python/paddle/base/reader.py b/python/paddle/base/reader.py index 859f524dbae999..f439d00ad950f4 100644 --- a/python/paddle/base/reader.py +++ b/python/paddle/base/reader.py @@ -540,7 +540,7 @@ def __init__( # NOTE: this process is used to load data asynchronously from self._batch_reader self._process = None - # NOTE: the C++ LoDTensorBlockingQueue instance + # NOTE: the C++ DenseTensorBlockingQueue instance self._blocking_queue = None # NOTE: 1. In multiprocess mode, this thread is used to get next batch data from # self._data_queue, then push it into self._blocking_queue; 2. In single process diff --git a/python/paddle/io/dataloader/dataloader_iter.py b/python/paddle/io/dataloader/dataloader_iter.py index 9d9feea4ab6f5d..a90de3391a3f93 100644 --- a/python/paddle/io/dataloader/dataloader_iter.py +++ b/python/paddle/io/dataloader/dataloader_iter.py @@ -112,7 +112,7 @@ def __init__(self, loader): else: self._collate_fn = loader.collate_fn or default_convert_fn - # LoDTensorBlockingQueue instance for create_py_reader and a thread + # DenseTensorBlockingQueue instance for create_py_reader and a thread # to put mini-batch data to self._blocking_queue, mini-batch data # will be get from: # 1. multi-process mode: get data from workers' result queue diff --git a/test/cpp/fluid/framework/var_type_traits_test.cc b/test/cpp/fluid/framework/var_type_traits_test.cc index 8bed5cb19d910c..d90d7f0c1ee1d8 100644 --- a/test/cpp/fluid/framework/var_type_traits_test.cc +++ b/test/cpp/fluid/framework/var_type_traits_test.cc @@ -20,7 +20,7 @@ #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows_utils.h" #include "paddle/phi/core/framework/reader.h" -#include "paddle/phi/core/operators/reader/lod_tensor_blocking_queue.h" +#include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" #ifdef PADDLE_WITH_CUDA #if defined(PADDLE_WITH_NCCL) #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" From 8303dddb324e16da269200ef15aef9f7dfd5f9f0 Mon Sep 17 00:00:00 2001 From: ethan-sem <1430684912@qq.com> Date: Mon, 2 Dec 2024 11:32:22 +0800 Subject: [PATCH 074/288] [CodeStyle][Typos][A-37] Fix typo (assgin) (#69849) --- _typos.toml | 1 - paddle/fluid/operators/controlflow/pylayer_op.cc | 2 +- paddle/fluid/pybind/dist_api.cc | 2 +- python/paddle/distributed/auto_parallel/static/completion.py | 2 +- 4 files changed, 3 insertions(+), 4 deletions(-) diff --git a/_typos.toml b/_typos.toml index e74be5d3a2bd5b..6f5b412e726dee 100644 --- a/_typos.toml +++ b/_typos.toml @@ -26,7 +26,6 @@ UE = "UE" unpacket = "unpacket" # These words need to be fixed -assgin = 'assgin' axises = 'axises' Axises = 'Axises' aixs = 'aixs' diff --git a/paddle/fluid/operators/controlflow/pylayer_op.cc b/paddle/fluid/operators/controlflow/pylayer_op.cc index 764d81d6a67dbd..ab8b0dc6ca42d6 100644 --- a/paddle/fluid/operators/controlflow/pylayer_op.cc +++ b/paddle/fluid/operators/controlflow/pylayer_op.cc @@ -220,7 +220,7 @@ class PyLayerBackwardOp : public PyLayerOp { core_->Run({}, false); // NOTE: It's neccessary. The reason of associating `inside_grads` and - // `outside_grads` at runtime `RunImpl` instead of `assgin` op at block is + // `outside_grads` at runtime `RunImpl` instead of `assign` op at block is // that the Var name of grad_op's outputs may be changed in the // `append_backward` function (e.g. `_addup_repetitive_outputs_`). AssignLocalGradientToParentScope( diff --git a/paddle/fluid/pybind/dist_api.cc b/paddle/fluid/pybind/dist_api.cc index db317ed4999ecb..d5e7e565db40ac 100644 --- a/paddle/fluid/pybind/dist_api.cc +++ b/paddle/fluid/pybind/dist_api.cc @@ -152,7 +152,7 @@ std::vector> AssignValueGroupBySize( PADDLE_ENFORCE_NOT_NULL( x, common::errors::Fatal( - "Only support assgin group for dense tensor value!")); + "Only support assign group for dense tensor value!")); auto ir_tensor = std::make_shared( dialect::TransToPhiDataType(x.dtype()), x.dims(), diff --git a/python/paddle/distributed/auto_parallel/static/completion.py b/python/paddle/distributed/auto_parallel/static/completion.py index 0fb06082d3afa8..2cf1017bc4c7d7 100644 --- a/python/paddle/distributed/auto_parallel/static/completion.py +++ b/python/paddle/distributed/auto_parallel/static/completion.py @@ -1229,7 +1229,7 @@ def set_process_mesh(block, op, process_mesh, var_to_process_mesh): struct_name = ops[i].struct_name m = regex.search(struct_name) if not m: - # only assgin op created by reshard is allowed + # only assign op created by reshard is allowed if ( ops[i].type == "assign" and "reshard_api" in ops[i].output_arg_names[0] From 1cc1d8b9daf4e4e25c14e2d5274263d2bec3afd1 Mon Sep 17 00:00:00 2001 From: Mingxuan Cui <149215866+Neo-WY@users.noreply.github.com> Date: Mon, 2 Dec 2024 11:35:56 +0800 Subject: [PATCH 075/288] [CodeStyle][Typos][C-52] Fix typo (`context`) (#69839) * context * context --- _typos.toml | 1 - .../fluid/operators/controlflow/tensor_array_read_write_op.cc | 2 +- 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/_typos.toml b/_typos.toml index 6f5b412e726dee..c577359cdaec18 100644 --- a/_typos.toml +++ b/_typos.toml @@ -84,7 +84,6 @@ comsume = 'comsume' Continer = 'Continer' contenst = 'contenst' conter = 'conter' -ontext = 'ontext' Continous = 'Continous' contibute = 'contibute' controled = 'controled' diff --git a/paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc b/paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc index 4ac6ce1dc2ace9..bc59c61aff50d4 100644 --- a/paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc +++ b/paddle/fluid/operators/controlflow/tensor_array_read_write_op.cc @@ -95,7 +95,7 @@ class WriteToArrayInferShape : public framework::InferShapeBase { common::errors::NotFound("Input(I) of WriteToArrayOp is not found.")); // TODO(wangchaochaohu) control flow Op do not support runtime infer shape - // Later we add [ontext->GetInputDim("I")) == 1] check when it's supported + // Later we add [context->GetInputDim("I")) == 1] check when it's supported if (!context->HasInput("X")) { return; From 4409040cdb6ca4fbb08e8aaca0d4d6d272ff3c99 Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Mon, 2 Dec 2024 14:00:49 +0800 Subject: [PATCH 076/288] [CodeStyle][Typos][C-[39-41],C-[43-47]] Fix typos (`configed`,`configurated`,`conficte`,`conjuction`,`consequtive`,`consistant`,`contraints`,`contruction`,`consructor`) (#69855) --- _typos.toml | 9 --------- paddle/cinn/ir/group_schedule/search/config_searcher.cc | 6 +++--- paddle/cinn/ir/group_schedule/search/config_searcher.h | 4 ++-- paddle/fluid/distributed/ps/thirdparty/round_robin.h | 2 +- paddle/fluid/framework/ir/fusion_group/subgraph.h | 2 +- paddle/phi/infermeta/spmd_rules/utils.cc | 2 +- paddle/phi/kernels/funcs/dims_simplifier.h | 2 +- paddle/phi/kernels/gpu/rms_norm_kernel.cu | 2 +- paddle/utils/tribool.h | 2 +- test/cpp/phi/kernels/sequence_pooling_test.cc | 2 +- .../book/test_recommender_system_deprecated.py | 2 +- 11 files changed, 13 insertions(+), 22 deletions(-) diff --git a/_typos.toml b/_typos.toml index c577359cdaec18..7d549381267022 100644 --- a/_typos.toml +++ b/_typos.toml @@ -71,15 +71,6 @@ comple = 'comple' complition = 'complition' complext = 'complext' compsite = 'compsite' -configurated = 'configurated' -configed = 'configed' -confict = 'confict' -conjuction = 'conjuction' -consequtive = 'consequtive' -consistant = 'consistant' -contraints = 'contraints' -contruction = 'contruction' -consructor = 'consructor' comsume = 'comsume' Continer = 'Continer' contenst = 'contenst' diff --git a/paddle/cinn/ir/group_schedule/search/config_searcher.cc b/paddle/cinn/ir/group_schedule/search/config_searcher.cc index 2383d5583673c1..bd3ed6be17a636 100644 --- a/paddle/cinn/ir/group_schedule/search/config_searcher.cc +++ b/paddle/cinn/ir/group_schedule/search/config_searcher.cc @@ -202,15 +202,15 @@ bool CandidateGenerator::IsValid(const CandidateType& candidate) const { ScheduleConfigSearcher::ScheduleConfigSearcher( std::vector> objective_funcs, const std::vector>& candidate_range, - const std::vector& contraints) + const std::vector& constraints) : objective_funcs_(std::move(objective_funcs)), candidate_range_(candidate_range), - contraints_(contraints) {} + constraints_(constraints) {} std::pair ScheduleConfigSearcher::Search( bool is_search_minimun) { VLOG(6) << "Start Search..."; - CandidateGenerator candidate_generator(candidate_range_, contraints_); + CandidateGenerator candidate_generator(candidate_range_, constraints_); std::vector candidates = candidate_generator.Candidates(); VLOG(6) << "Candidate num = " << candidates.size(); for (const auto& candidate : candidates) { diff --git a/paddle/cinn/ir/group_schedule/search/config_searcher.h b/paddle/cinn/ir/group_schedule/search/config_searcher.h index 79d73bb3108f66..a176676aa9b281 100644 --- a/paddle/cinn/ir/group_schedule/search/config_searcher.h +++ b/paddle/cinn/ir/group_schedule/search/config_searcher.h @@ -85,13 +85,13 @@ class ScheduleConfigSearcher { ScheduleConfigSearcher( std::vector> objective_funcs, const std::vector>& candidate_range, - const std::vector& contraints = {}); + const std::vector& constraints = {}); std::pair Search(bool is_search_minimun = true); private: std::vector> objective_funcs_; - std::vector contraints_; + std::vector constraints_; std::vector> candidate_range_; std::map records_; diff --git a/paddle/fluid/distributed/ps/thirdparty/round_robin.h b/paddle/fluid/distributed/ps/thirdparty/round_robin.h index 313d715d47c878..0daa17bd5b52e4 100644 --- a/paddle/fluid/distributed/ps/thirdparty/round_robin.h +++ b/paddle/fluid/distributed/ps/thirdparty/round_robin.h @@ -202,7 +202,7 @@ static Counts &counts() { #define ROBIN_HOOD_PRIVATE_DEFINITION_HAS_NATIVE_WCHART() 1 #endif -// detect if MSVC supports the pair(std::piecewise_construct_t,...) consructor +// detect if MSVC supports the pair(std::piecewise_construct_t,...) constructor // being constexpr #ifdef _MSC_VER #if _MSC_VER <= 1900 diff --git a/paddle/fluid/framework/ir/fusion_group/subgraph.h b/paddle/fluid/framework/ir/fusion_group/subgraph.h index 97caa432490028..8522275c143370 100644 --- a/paddle/fluid/framework/ir/fusion_group/subgraph.h +++ b/paddle/fluid/framework/ir/fusion_group/subgraph.h @@ -123,7 +123,7 @@ class SubGraph { } std::vector GetOutputVarNodes(bool with_intermediate_out) { - // The order of output nodes should be consistant anywhere.. + // The order of output nodes should be consistent anywhere.. std::vector output_vars; for (auto* n : SortedNodes()) { if (IsOutputOfInternalOp(n)) { diff --git a/paddle/phi/infermeta/spmd_rules/utils.cc b/paddle/phi/infermeta/spmd_rules/utils.cc index be8eb09151bdca..790c03238c9511 100644 --- a/paddle/phi/infermeta/spmd_rules/utils.cc +++ b/paddle/phi/infermeta/spmd_rules/utils.cc @@ -106,7 +106,7 @@ std::unordered_map ShardingMergeForTensors( } } - // Resolute "mesh_dim shard by more than one axis" confict. + // Resolute "mesh_dim shard by more than one axis" conflict. // Now we just naive pick the first axis naively. // (TODO) use local cost model to pick the axis with lowest cost(in concern of // memory or communication or computation). diff --git a/paddle/phi/kernels/funcs/dims_simplifier.h b/paddle/phi/kernels/funcs/dims_simplifier.h index 0be813c8759d45..f092294bc081f8 100644 --- a/paddle/phi/kernels/funcs/dims_simplifier.h +++ b/paddle/phi/kernels/funcs/dims_simplifier.h @@ -52,7 +52,7 @@ struct BroadcastDimsSimplifier { } ExtendInputDimensions(axis); - // To Merge the dimensions of input_tensors while the consequtive + // To Merge the dimensions of input_tensors while the consecutive // equal-dimensions appears. Example below : // in_1.shape = [2, 3, 4, 5] in_1.shape = [2, 12, 5] // in_2.shape = [1, 3, 4, 5] -> in_2.shape = [1, 12, 5] diff --git a/paddle/phi/kernels/gpu/rms_norm_kernel.cu b/paddle/phi/kernels/gpu/rms_norm_kernel.cu index 4c51ad3263879d..9c62b8f9cfc1a6 100644 --- a/paddle/phi/kernels/gpu/rms_norm_kernel.cu +++ b/paddle/phi/kernels/gpu/rms_norm_kernel.cu @@ -568,7 +568,7 @@ inline GPU(Error_t) } } - static const bool max_smem_configed = [=]() { + static const bool max_smem_configured = [=]() { int max_smem_size = 0; GPU(Error_t) err = GPU(DeviceGetAttribute)( diff --git a/paddle/utils/tribool.h b/paddle/utils/tribool.h index f08cc5805f1fc2..7f5de993752928 100644 --- a/paddle/utils/tribool.h +++ b/paddle/utils/tribool.h @@ -168,7 +168,7 @@ inline tribool operator!(tribool x) { } /** - * \brief Computes the logical conjuction of two tribools + * \brief Computes the logical conjunction of two tribools * * \returns the result of logically ANDing the two tribool values, * according to the following table: diff --git a/test/cpp/phi/kernels/sequence_pooling_test.cc b/test/cpp/phi/kernels/sequence_pooling_test.cc index f8ccbfb610e2ed..c0b8937f7dc5c7 100644 --- a/test/cpp/phi/kernels/sequence_pooling_test.cc +++ b/test/cpp/phi/kernels/sequence_pooling_test.cc @@ -52,7 +52,7 @@ void TestSequencePoolingSum(const DeviceContext &context, common::make_ddim({static_cast(lod[0].back()), second_dim}); in_grad.mutable_data(in_dims, place); - // check tensor contruction result + // check tensor construction result PADDLE_ENFORCE_EQ( in_grad.dims().size(), out_grad.dims().size(), diff --git a/test/deprecated/book/test_recommender_system_deprecated.py b/test/deprecated/book/test_recommender_system_deprecated.py index f203cb9586127c..8a11d6f35a6dc0 100644 --- a/test/deprecated/book/test_recommender_system_deprecated.py +++ b/test/deprecated/book/test_recommender_system_deprecated.py @@ -38,7 +38,7 @@ def get_usr_combined_features(): # FIXME(dzh) : old API integer_value(10) may has range check. - # currently we don't have user configurated check. + # currently we don't have user configured check. USR_DICT_SIZE = paddle.dataset.movielens.max_user_id() + 1 From dda47a084fb801373d7ab334400ac0db125a8bbe Mon Sep 17 00:00:00 2001 From: doggy-tao <3160391266@qq.com> Date: Mon, 2 Dec 2024 14:06:38 +0800 Subject: [PATCH 077/288] [Prim][Pir] Decomp addmm op (#68835) * Decomp addmm op * add dynamic shape test file * modified code * record temp code * modified addmm_decomp() * modified test case --- .../decomp_interface_gen_op_list.py | 2 + .../decomp_rule/decomp_rule/composite.h | 11 +++ test/legacy_test/test_addmm_op.py | 80 +++++++++++++++---- .../test_prim_sub_graph_dynamic_shape.py | 26 ++++++ 4 files changed, 104 insertions(+), 15 deletions(-) diff --git a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py index 2a4a56dabb9f2e..42148cdd16b3b7 100644 --- a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py +++ b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py @@ -20,6 +20,7 @@ # manual decomp interface declare are located in manual_op.h decomp_interface_declare_gen_op_list = [ "add_n", + "addmm", "any", "batch_norm", "batch_norm_", @@ -76,6 +77,7 @@ decomp_interface_implementation_gen_op_list = [ "any", "add_n", + "addmm", "bce_loss", "bmm", "dropout", diff --git a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h index 9553c69420a2be..8f4f5aa5a51c46 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h @@ -1402,6 +1402,17 @@ Tensor swish_decomp(const Tensor& x) { return x * sigmoid(x); } +template +Tensor addmm_decomp(const Tensor& input, + const Tensor& x, + const Tensor& y, + const float beta, + const float alpha) { + Tensor x_y_mat = matmul(x, y); + return full_scalar(alpha, x_y_mat.dtype()) * x_y_mat + + full_scalar(beta, input.dtype()) * input; +} + } // namespace details } // namespace primitive diff --git a/test/legacy_test/test_addmm_op.py b/test/legacy_test/test_addmm_op.py index b41532cfe2facd..9a59b0ef702d10 100644 --- a/test/legacy_test/test_addmm_op.py +++ b/test/legacy_test/test_addmm_op.py @@ -26,7 +26,9 @@ class TestAddMMOp(OpTest): # test basic def setUp(self): self.op_type = "addmm" + self.prim_op_type = "comp" self.python_api = paddle.addmm + self.public_python_api = paddle.addmm self.init_dtype_type() self.inputs = { 'Input': np.random.random((100, 1)).astype(self.dtype), @@ -42,19 +44,42 @@ def init_dtype_type(self): self.dtype = np.float64 def test_check_output(self): - self.check_output() + self.check_output(check_pir=True, check_prim_pir=True) def test_check_grad_normal(self): - self.check_grad(['Input', 'X', 'Y'], 'Out') + self.check_grad( + ['Input', 'X', 'Y'], + 'Out', + check_pir=True, + check_prim_pir=True, + ) def test_check_grad_x(self): - self.check_grad(['X'], 'Out', no_grad_set=None) + self.check_grad( + ['X'], + 'Out', + no_grad_set=None, + check_pir=True, + check_prim_pir=True, + ) def test_check_grad_y(self): - self.check_grad(['Y'], 'Out', no_grad_set=None) + self.check_grad( + ['Y'], + 'Out', + no_grad_set=None, + check_pir=True, + check_prim_pir=True, + ) def test_check_grad_input(self): - self.check_grad(['Input'], 'Out', no_grad_set=None) + self.check_grad( + ['Input'], + 'Out', + no_grad_set=None, + check_pir=True, + check_prim_pir=True, + ) class TestAddMMFP16Op(TestAddMMOp): @@ -219,7 +244,9 @@ class TestAddMMOp2(TestAddMMOp): # test alpha and beta def setUp(self): self.op_type = "addmm" + self.prim_op_type = "comp" self.python_api = paddle.addmm + self.public_python_api = paddle.addmm self.dtype = np.float64 self.init_dtype_type() self.inputs = { @@ -241,7 +268,9 @@ class TestAddMMOp3(OpTest): # test broadcast def setUp(self): self.op_type = "addmm" + self.prim_op_type = "comp" self.python_api = paddle.addmm + self.public_python_api = paddle.addmm self.dtype = np.float64 self.init_dtype_type() self.inputs = { @@ -262,26 +291,40 @@ def init_dtype_type(self): pass def test_check_output(self): - self.check_output() + self.check_output(check_pir=True, check_prim_pir=True) def test_check_grad_normal(self): - self.check_grad(['Input', 'X', 'Y'], 'Out') + self.check_grad( + ['Input', 'X', 'Y'], 'Out', check_pir=True, check_prim_pir=True + ) def test_check_grad_x(self): - self.check_grad(['X'], 'Out', no_grad_set=None) + self.check_grad( + ['X'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True + ) def test_check_grad_y(self): - self.check_grad(['Y'], 'Out', no_grad_set=None) + self.check_grad( + ['Y'], 'Out', no_grad_set=None, check_pir=True, check_prim_pir=True + ) def test_check_grad_input(self): - self.check_grad(['Input'], 'Out', no_grad_set=None) + self.check_grad( + ['Input'], + 'Out', + no_grad_set=None, + check_pir=True, + check_prim_pir=True, + ) class TestAddMMOp4(OpTest): # test broadcast def setUp(self): self.op_type = "addmm" + self.prim_op_type = "comp" self.python_api = paddle.addmm + self.public_python_api = paddle.addmm self.dtype = np.float64 self.init_dtype_type() self.inputs = { @@ -302,19 +345,26 @@ def init_dtype_type(self): pass def test_check_output(self): - self.check_output() + self.check_output(check_pir=True, check_prim_pir=True) def test_check_grad_normal(self): - self.check_grad(['Input', 'X', 'Y'], 'Out') + self.check_grad( + ['Input', 'X', 'Y'], 'Out', check_pir=True, check_prim_pir=True + ) def test_check_grad_x(self): - self.check_grad(['X'], 'Out', no_grad_set=None) + self.check_grad(['X'], 'Out', no_grad_set=None, check_pir=True) def test_check_grad_y(self): - self.check_grad(['Y'], 'Out', no_grad_set=None) + self.check_grad(['Y'], 'Out', no_grad_set=None, check_pir=True) def test_check_grad_input(self): - self.check_grad(['Input'], 'Out', no_grad_set=None) + self.check_grad( + ['Input'], + 'Out', + no_grad_set=None, + check_pir=True, + ) class TestAddMMOp5(unittest.TestCase): diff --git a/test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py b/test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py index 687baf78b0b25a..3349ca5232c777 100644 --- a/test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py +++ b/test/prim/pir_prim/test_prim_sub_graph_dynamic_shape.py @@ -22,6 +22,10 @@ from paddle.static import InputSpec +def addmm_net(input, x, y): + return paddle.addmm(input, x, y, alpha=1.0, beta=2.0) + + def apply_to_static(net, use_cinn, input_spec=None): build_strategy = paddle.static.BuildStrategy() build_strategy.build_cinn_pass = use_cinn @@ -912,6 +916,28 @@ def test_prim_all_dynamic(self): np.testing.assert_allclose(ref, actual, rtol=self.tol) +class TestPrimAddmm(TestPrimThree): + def setUp(self): + np.random.seed(2024) + paddle.seed(2024) + self.shape_x = [30, 50] + self.shape_y = [30, 80] + self.shape_z = [80, 50] + self.dtype_x = "float32" + self.dtype_y = "float32" + self.dtype_z = "float32" + self.init_x_shape = [None, None] + self.init_y_shape = [None, None] + self.init_z_shape = [None, None] + self.x = np.random.random(self.shape_x).astype(self.dtype_x) + self.y = np.random.random(self.shape_y).astype(self.dtype_y) + self.z = np.random.random(self.shape_z).astype(self.dtype_z) + self.net = addmm_net + self.necessary_ops = "pd_op.addmm" + self.enable_cinn = False + self.tol = 1e-6 + + class TestPrimLerp1(TestPrimThree): def setUp(self): np.random.seed(2023) From 156b85ba4ea1af481e16a68110f343fb661d36f0 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Mon, 2 Dec 2024 14:17:43 +0800 Subject: [PATCH 078/288] [SOT][Faster Guard] test faster guard in separate cache (#69852) --- .../opcode_translator/executor/executor_cache.py | 12 ++++++++++++ test/sot/test_case_base.py | 16 +++++++++++++++- 2 files changed, 27 insertions(+), 1 deletion(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/executor_cache.py b/python/paddle/jit/sot/opcode_translator/executor/executor_cache.py index e9e3e977ebcd78..ccb487ba314a65 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/executor_cache.py +++ b/python/paddle/jit/sot/opcode_translator/executor/executor_cache.py @@ -81,6 +81,18 @@ def clear(self): self.translate_count = 0 self.code_symbolic_inputs.clear() + def dump_state(self): + return { + "cache": self.cache, + "translate_count": self.translate_count, + "code_symbolic_inputs": self.code_symbolic_inputs, + } + + def load_state(self, state): + self.cache = state["cache"] + self.translate_count = state["translate_count"] + self.code_symbolic_inputs = state["code_symbolic_inputs"] + def __call__(self, frame: types.FrameType, **kwargs) -> CustomCode: code: types.CodeType = frame.f_code if code not in self.cache: diff --git a/test/sot/test_case_base.py b/test/sot/test_case_base.py index e6756cf600e674..c5bb185b7baf63 100644 --- a/test/sot/test_case_base.py +++ b/test/sot/test_case_base.py @@ -38,13 +38,27 @@ def test_instruction_translator_cache_context(): cache.clear() +FASTER_GUARD_CACHE_STATE = { + "cache": {}, + "translate_count": 0, + "code_symbolic_inputs": {}, +} + + def test_with_faster_guard(func): @wraps(func) def impl(*args, **kwargs): with faster_guard_guard(False): func(*args, **kwargs) with faster_guard_guard(True): - func(*args, **kwargs) + cache = OpcodeExecutorCache() + original_cache_state = cache.dump_state() + cache.load_state(FASTER_GUARD_CACHE_STATE) + try: + func(*args, **kwargs) + finally: + FASTER_GUARD_CACHE_STATE.update(cache.dump_state()) + cache.load_state(original_cache_state) return impl From e7f09be6f884661d7dd2b3706f2cdd7901403302 Mon Sep 17 00:00:00 2001 From: Lucas Date: Mon, 2 Dec 2024 14:22:08 +0800 Subject: [PATCH 079/288] [XPU] Fix some typos on XPU (#69826) --- .../phi/kernels/fusion/xpu/fused_rope_utils.h | 6 ++--- .../xpu/weight_only_linear_kernel_xpu.cc | 23 ++++--------------- paddle/phi/kernels/xpu/clip_grad_kernel.cc | 2 +- 3 files changed, 8 insertions(+), 23 deletions(-) diff --git a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h index b68701651aca97..9690d4e5607150 100644 --- a/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h +++ b/paddle/phi/kernels/fusion/xpu/fused_rope_utils.h @@ -296,7 +296,7 @@ void XPUFusedRotaryEveryTwo(const Context& dev_ctx, num_heads, head_dim, {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, - std::string("BLHD").c_str(), + "BLHD", true); PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); } else { @@ -316,7 +316,7 @@ void XPUFusedRotaryEveryTwo(const Context& dev_ctx, {seq_len * num_heads * head_dim, num_heads * head_dim, head_dim, 1}, {seq_len * num_heads_k * head_dim, num_heads_k * head_dim, head_dim, 1}, num_heads_k, - std::string("BLHD").c_str(), + "BLHD", true); PADDLE_ENFORCE_XDNN_SUCCESS(ret, fusion_func_name); } @@ -333,7 +333,7 @@ void XPUFusedRotaryEveryTwo(const Context& dev_ctx, num_heads_v, head_dim, {seq_len * num_heads_v * head_dim, num_heads_v * head_dim, head_dim, 1}, - std::string("BLHD").c_str(), + "BLHD", true); PADDLE_ENFORCE_XDNN_SUCCESS(ret, single_func_name); } diff --git a/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc b/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc index bc6900826b0674..ddc396b5ae36e8 100644 --- a/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc +++ b/paddle/phi/kernels/fusion/xpu/weight_only_linear_kernel_xpu.cc @@ -58,21 +58,14 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, false, weight_dtype == "int8" ? 127.f : 7.f, 0.f); - PADDLE_ENFORCE_EQ( - r, - 0, - common::errors::Fatal( - "scale failed, scale related variable `r` is %d", r)); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale"); r = baidu::xpu::api::cast( xpu_ctx->x_context(), reinterpret_cast( max_value_fp16.data()), max_value.data(), max_value.numel()); - PADDLE_ENFORCE_EQ(r, - 0, - common::errors::Fatal( - "cast failed, related variable `r` is %d", r)); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "cast"); } else if (weight_scale.dtype() == phi::DataType::FLOAT32) { r = baidu::xpu::api::scale(xpu_ctx->x_context(), weight_scale.data(), @@ -81,10 +74,7 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, false, weight_dtype == "int8" ? 127.f : 7.f, 0.f); - PADDLE_ENFORCE_EQ(r, - 0, - common::errors::Fatal( - "scale failed, related variable `r` is %d", r)); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "scale"); } else { PADDLE_THROW(common::errors::Unimplemented( "Only support that weight scale as type float32 ot float16.")); @@ -129,12 +119,7 @@ void WeightOnlyLinearKernel(const Context& dev_ctx, : nullptr, baidu::xpu::api::Activation_t::LINEAR, max_value.data()); - PADDLE_ENFORCE_EQ(r, - 0, - common::errors::Fatal( - "baidu::xpu::api::gpt_fc_fusion failed, related " - "variable `r` is %d", - r)); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "gpt_fc_fusion"); } else if (weight_dtype == "int4") { PD_THROW("only support int8 weight only now"); } diff --git a/paddle/phi/kernels/xpu/clip_grad_kernel.cc b/paddle/phi/kernels/xpu/clip_grad_kernel.cc index 710732e52ee8d2..fd3d44acf32ab1 100644 --- a/paddle/phi/kernels/xpu/clip_grad_kernel.cc +++ b/paddle/phi/kernels/xpu/clip_grad_kernel.cc @@ -36,7 +36,7 @@ void ClipGradKernel(const Context& ctx, x.numel(), static_cast(min.to()), static_cast(max.to())); - PADDLE_ENFORCE_XDNN_SUCCESS(r, "clip_grad"); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "clamp_grad"); } } // namespace phi From dd59f8b9222b65be391ae8f76f14c37ed5ca98f4 Mon Sep 17 00:00:00 2001 From: Nana <49900969+NKNaN@users.noreply.github.com> Date: Mon, 2 Dec 2024 14:26:19 +0800 Subject: [PATCH 080/288] update histogram histogram_bin_edge (#69750) --- .../infer_symbolic_shape/binary_infer_sym.cc | 6 ++-- paddle/phi/infermeta/binary.cc | 6 ++-- paddle/phi/infermeta/binary.h | 4 +-- paddle/phi/kernels/cpu/histogram_kernel.cc | 4 +-- paddle/phi/kernels/gpu/histogram_kernel.cu | 4 +-- paddle/phi/kernels/histogram_kernel.h | 4 +-- paddle/phi/ops/yaml/ops.yaml | 2 +- python/paddle/tensor/linalg.py | 30 ++++++++++++------- .../test_histogram_bin_edges_op.py | 10 +++++++ test/legacy_test/test_histogram_op.py | 12 +++++++- 10 files changed, 56 insertions(+), 26 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc index 969d0a609450e1..ce1826d04095ba 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/binary_infer_sym.cc @@ -1088,8 +1088,8 @@ bool HistogramOpInferSymbolicShape( const symbol::ShapeOrDataDimExprs &input_shape_or_data = infer_context->GetShapeOrDataForValue(op->operand_source(0)); int64_t bins = op->attribute("bins").data(); - int min = op->attribute("min").data(); - int max = op->attribute("max").data(); + float min = op->attribute("min").data(); + float max = op->attribute("max").data(); PADDLE_ENFORCE_GE(bins, 1, common::errors::InvalidArgument( @@ -1100,7 +1100,7 @@ bool HistogramOpInferSymbolicShape( max, min, common::errors::InvalidArgument("max must be larger or equal to min." - "But received max is %d, min is %d", + "But received max is %f, min is %f", max, min)); if (op->operand_source(1)) { diff --git a/paddle/phi/infermeta/binary.cc b/paddle/phi/infermeta/binary.cc index 0f3b44fb6139a6..3b9f94ce44a6a8 100644 --- a/paddle/phi/infermeta/binary.cc +++ b/paddle/phi/infermeta/binary.cc @@ -2257,8 +2257,8 @@ void HingeLossInferMeta(const MetaTensor& logits, void HistogramInferMeta(const MetaTensor& input, const MetaTensor& weight, int64_t bins, - int min, - int max, + float min, + float max, bool density, MetaTensor* out) { PADDLE_ENFORCE_GE(bins, @@ -2271,7 +2271,7 @@ void HistogramInferMeta(const MetaTensor& input, max, min, common::errors::InvalidArgument("max must be larger or equal to min." - "But received max is %d, min is %d", + "But received max is %f, min is %f", max, min)); if (weight) { diff --git a/paddle/phi/infermeta/binary.h b/paddle/phi/infermeta/binary.h index d949bba1af46d8..910daa87eb036f 100644 --- a/paddle/phi/infermeta/binary.h +++ b/paddle/phi/infermeta/binary.h @@ -418,8 +418,8 @@ void HingeLossInferMeta(const MetaTensor& logits, void HistogramInferMeta(const MetaTensor& input, const MetaTensor& weight, int64_t bins, - int min, - int max, + float min, + float max, bool density, MetaTensor* out); diff --git a/paddle/phi/kernels/cpu/histogram_kernel.cc b/paddle/phi/kernels/cpu/histogram_kernel.cc index d7bf1903625243..bad45a728ea599 100644 --- a/paddle/phi/kernels/cpu/histogram_kernel.cc +++ b/paddle/phi/kernels/cpu/histogram_kernel.cc @@ -26,8 +26,8 @@ void HistogramKernel(const Context& dev_ctx, const DenseTensor& input, const paddle::optional& weight, int64_t bins, - int min, - int max, + float min, + float max, bool density, DenseTensor* output) { auto& nbins = bins; diff --git a/paddle/phi/kernels/gpu/histogram_kernel.cu b/paddle/phi/kernels/gpu/histogram_kernel.cu index 27da3e681d31ec..8111fad4057e36 100644 --- a/paddle/phi/kernels/gpu/histogram_kernel.cu +++ b/paddle/phi/kernels/gpu/histogram_kernel.cu @@ -140,8 +140,8 @@ void HistogramKernel(const Context& dev_ctx, const DenseTensor& input, const paddle::optional& weight, int64_t bins, - int min, - int max, + float min, + float max, bool density, DenseTensor* output) { auto& nbins = bins; diff --git a/paddle/phi/kernels/histogram_kernel.h b/paddle/phi/kernels/histogram_kernel.h index 0fe0fd38892c2a..70c348ed8f6d64 100644 --- a/paddle/phi/kernels/histogram_kernel.h +++ b/paddle/phi/kernels/histogram_kernel.h @@ -22,8 +22,8 @@ void HistogramKernel(const Context& dev_ctx, const DenseTensor& input, const paddle::optional& weight, int64_t bins, - int min, - int max, + float min, + float max, bool density, DenseTensor* output); diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index c8a140ec277ab8..a651956df126a1 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -2526,7 +2526,7 @@ backward: hinge_loss_grad - op : histogram - args : (Tensor input, Tensor weight, int64_t bins = 100, int min = 0, int max = 0, bool density = false) + args : (Tensor input, Tensor weight, int64_t bins = 100, float min = 0.0, float max = 0.0, bool density = false) output : Tensor(out) infer_meta : func : HistogramInferMeta diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index 7b22af1285138b..6a5e4070d0d0e4 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -2519,8 +2519,8 @@ def bmm(x: Tensor, y: Tensor, name: str | None = None) -> Tensor: def histogram( input: Tensor, bins: int = 100, - min: int = 0, - max: int = 0, + min: float = 0.0, + max: float = 0.0, weight: Tensor | None = None, density: bool = False, name: str | None = None, @@ -2533,8 +2533,8 @@ def histogram( input (Tensor): A Tensor with shape :math:`[N_1, N_2,..., N_k]` . The data type of the input Tensor should be float32, float64, int32, int64. bins (int, optional): number of histogram bins. Default: 100. - min (int, optional): lower end of the range (inclusive). Default: 0. - max (int, optional): upper end of the range (inclusive). Default: 0. + min (float, optional): lower end of the range (inclusive). Default: 0.0. + max (float, optional): upper end of the range (inclusive). Default: 0.0. weight (Tensor, optional): If provided, it must have the same shape as input. Each value in input contributes its associated weight towards the bin count (instead of 1). Default: None. density (bool, optional): If False, the result will contain the count (or total weight) in each bin. If True, the result is the @@ -2555,6 +2555,11 @@ def histogram( Tensor(shape=[4], dtype=int64, place=Place(cpu), stop_gradient=True, [0, 2, 1, 0]) """ + if isinstance(min, int): + min = float(min) + if isinstance(max, int): + max = float(max) + if in_dynamic_or_pir_mode(): return _C_ops.histogram(input, weight, bins, min, max, density) else: @@ -2596,8 +2601,8 @@ def histogram( def histogram_bin_edges( input: Tensor, bins: int = 100, - min: int = 0, - max: int = 0, + min: float = 0.0, + max: float = 0.0, name: str | None = None, ) -> Tensor: """ @@ -2607,8 +2612,8 @@ def histogram_bin_edges( Args: input (Tensor): The data type of the input Tensor should be float32, float64, int32, int64. bins (int, optional): number of histogram bins. - min (int, optional): lower end of the range (inclusive). Default: 0. - max (int, optional): upper end of the range (inclusive). Default: 0. + min (float, optional): lower end of the range (inclusive). Default: 0.0. + max (float, optional): upper end of the range (inclusive). Default: 0.0. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. Returns: @@ -2625,6 +2630,11 @@ def histogram_bin_edges( Tensor(shape=[5], dtype=float32, place=Place(cpu), stop_gradient=True, [0. , 0.75000000, 1.50000000, 2.25000000, 3. ]) """ + if isinstance(min, int): + min = float(min) + if isinstance(max, int): + max = float(max) + check_type(input, 'input', (Variable), 'histogram_bin_edges') check_dtype( input.dtype, @@ -2633,13 +2643,13 @@ def histogram_bin_edges( 'histogram_bin_edges', ) check_type(bins, 'bins', int, 'histogram_bin_edges') - if max == 0 and min == 0: + if max == 0.0 and min == 0.0: min = paddle.min(input) max = paddle.max(input) else: if max < min: raise ValueError("max must be larger than min in range parameter") - if (min - max) == 0: + if (min - max) == 0.0: max = max + 0.5 min = min - 0.5 return paddle.linspace(min, max, bins + 1, name=name) diff --git a/test/legacy_test/test_histogram_bin_edges_op.py b/test/legacy_test/test_histogram_bin_edges_op.py index e968bc87946454..32c7aceabf5991 100644 --- a/test/legacy_test/test_histogram_bin_edges_op.py +++ b/test/legacy_test/test_histogram_bin_edges_op.py @@ -62,5 +62,15 @@ def setUp(self): ) +class TestHistogramBinEdgesOpTest2(TestHistogramBinEdgesOp): + def setUp(self): + self.x = np.random.randn(5, 20).astype('float32') + self.bin = 10 + self.range = (0.2, 0.9) + self.out = np.histogram_bin_edges( + self.x, bins=self.bin, range=self.range + ) + + if __name__ == "__main__": unittest.main() diff --git a/test/legacy_test/test_histogram_op.py b/test/legacy_test/test_histogram_op.py index 6191f25592ade0..53e96e6c913676 100644 --- a/test/legacy_test/test_histogram_op.py +++ b/test/legacy_test/test_histogram_op.py @@ -108,7 +108,7 @@ def net_func(): ) paddle.histogram(input=input_value, bins=1, min=-np.inf, max=5) - with self.assertRaises(TypeError): + with self.assertRaises(ValueError): self.run_network(net_func) def test_input_range_error(self): @@ -302,6 +302,16 @@ def init_test_case(self): self.is_weight = False +class TestHistogramOpAPIWithFloatminMax(TestHistogram): + def init_test_case(self): + self.in_shape = (10, 12) + self.bins = 4 + self.min = 2.2 + self.max = 4.5 + self.density = False + self.is_weight = False + + if __name__ == "__main__": paddle.enable_static() unittest.main() From 9fa078b317dc58eb1582c61e32acea8e8a386588 Mon Sep 17 00:00:00 2001 From: YuanRisheng Date: Mon, 2 Dec 2024 14:45:13 +0800 Subject: [PATCH 081/288] [Inference]Fix PaddleX model bugs when convert to pir-trt (#69598) * fix SwinTransformer bugs * fix bugs * revert strided_slice * fix bugs * fix slice * fix reshape * fix pool2d * fix trt bugs * fix bugs * fix bugs * fix coverage bugs * revert pooling * fix input shape * fix combine bugs --- .../new_executor/collect_shape_manager.cc | 1 + .../new_executor/collect_shape_manager.h | 1 + python/paddle/tensorrt/converter.py | 54 ++++++- python/paddle/tensorrt/converter_utils.py | 21 ++- python/paddle/tensorrt/impls/common.py | 2 - python/paddle/tensorrt/impls/creation.py | 9 +- python/paddle/tensorrt/impls/logic.py | 5 +- python/paddle/tensorrt/impls/manipulation.py | 149 +++++++++++------- python/paddle/tensorrt/impls/math.py | 6 +- python/paddle/tensorrt/impls/search.py | 2 +- python/paddle/tensorrt/util.py | 41 ++++- 11 files changed, 213 insertions(+), 78 deletions(-) diff --git a/paddle/fluid/framework/new_executor/collect_shape_manager.cc b/paddle/fluid/framework/new_executor/collect_shape_manager.cc index 00534f909375c7..02c4aaae5dfe5c 100644 --- a/paddle/fluid/framework/new_executor/collect_shape_manager.cc +++ b/paddle/fluid/framework/new_executor/collect_shape_manager.cc @@ -27,6 +27,7 @@ void CollectShapeManager::CollectShapeInfo( framework::InstructionBase *instr, framework::ValueExecutionInfo *value_exe_info, framework::Scope *scope) { + std::lock_guard lock(info_mutex_); is_shape_range_info_ready_ = false; for (auto &input : instr->Inputs()) { auto var_name = value_exe_info->GetVarName(input.first); diff --git a/paddle/fluid/framework/new_executor/collect_shape_manager.h b/paddle/fluid/framework/new_executor/collect_shape_manager.h index 26678c69ef9f04..e6bd8a11fabe2a 100644 --- a/paddle/fluid/framework/new_executor/collect_shape_manager.h +++ b/paddle/fluid/framework/new_executor/collect_shape_manager.h @@ -80,6 +80,7 @@ class CollectShapeManager { std::map> max_values_; std::map> opt_values_; bool is_shape_range_info_ready_ = false; + std::mutex info_mutex_; }; } // namespace framework diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index 5e03f5d74e58c2..907b8365cf7464 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -47,7 +47,12 @@ from .impls.stat import * # noqa: F403 from .impls.vision import * # noqa: F403 from .register import converter_registry -from .util import get_trt_version_list, map_dtype +from .util import ( + get_trt_version_list, + map_dtype, + weight_to_tensor, + zero_dims_to_one_dims, +) version_list = get_trt_version_list() @@ -80,6 +85,24 @@ def remove_duplicate_value(value_list): return ret_list +# We use a special rule to judge whether a paddle value is a shape tensor. +# The rule is consistent with the rule in C++ source code(collect_shape_manager.cc). +# We use the rule for getting min/max/opt value shape from collect_shape_manager. +# We don't use trt_tensor.is_shape_tensor, because sometimes, the trt_tensor that corresponding to paddle value is not a shape tensor +# when it is a output in this trt graph, but it is a shape tensor when it is a input in next trt graph. +def is_shape_tensor(value): + dims = value.shape + total_elements = 1 + if ( + dims.count(-1) > 1 + ): # we can only deal with the situation that is has one dynamic dims + return False + for dim in dims: + total_elements *= abs(dim) # add abs for dynamic shape -1 + is_int_dtype = value.dtype == paddle.int32 or value.dtype == paddle.int64 + return total_elements <= 8 and total_elements >= 1 and is_int_dtype + + class PaddleToTensorRTConverter: def __init__(self, paddle_program, scope): self.scope = scope @@ -180,6 +203,9 @@ def convert_subgraph_to_trt(self, program, group_op): shape = value.shape dtype = map_dtype(value.dtype.name) input_name = f"input_{value.id}" + # 0-dims -> 1-dims + if len(shape) == 0: + shape = [1] input_tensor = network.add_input( name=input_name, dtype=dtype, shape=shape ) @@ -188,7 +214,7 @@ def convert_subgraph_to_trt(self, program, group_op): for op in operations: # Adding marker labels to builtin ops facilitates convert processing, but they ultimately do not enter the TensorRT subgraph. - if op.name() == "builtin.split": + if op.name() == "builtin.split" or op.name() == "builtin.combine": continue operands = [] for operand in op.operands(): @@ -203,9 +229,16 @@ def convert_subgraph_to_trt(self, program, group_op): combined_source = combined_operand.source() combined_source_id = combined_source.id if combined_source_id in value_to_trt_tensor: - operand_list.append( - value_to_trt_tensor[combined_source_id] + trt_input_tensor = weight_to_tensor( + network, + combined_source, + value_to_trt_tensor[combined_source_id], + op.name(), ) + trt_input_tensor = zero_dims_to_one_dims( + network, trt_input_tensor + ) + operand_list.append(trt_input_tensor) else: raise RuntimeError( f'{combined_source_id} not found in value_to_trt_tensor' @@ -214,7 +247,16 @@ def convert_subgraph_to_trt(self, program, group_op): else: source_id = source.id if source_id in value_to_trt_tensor: - operands.append(value_to_trt_tensor[source_id]) + trt_input_tensor = weight_to_tensor( + network, + source, + value_to_trt_tensor[source_id], + op.name(), + ) + trt_input_tensor = zero_dims_to_one_dims( + network, trt_input_tensor + ) + operands.append(trt_input_tensor) else: raise RuntimeError( f'{source_id} not found in value_to_trt_tensor' @@ -350,7 +392,7 @@ def convert_subgraph_to_trt(self, program, group_op): min_value = [] opt_value = [] max_value = [] - if output_tensor.is_shape_tensor: + if is_shape_tensor(result_value): min_value = get_value_shape_range_info( result_value, True, paddle.base.core.ShapeMode.kMIN ) diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index 29928e55e46a8e..8a9de7fc7bc721 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -308,6 +308,12 @@ def trt_equal(network, a, b): return layer.get_output(0) +def trt_gather(network, input, indices, axis=0): + indices_tensor = add_1D_constant_layer(network, indices) + result = network.add_gather(input, indices_tensor, axis).get_output(0) + return result + + def trt_prod(network, a, b): layer = network.add_elementwise(a, b, trt.ElementWiseOperation.PROD) return layer.get_output(0) @@ -386,7 +392,7 @@ def map_trt_dtype(trt_dtype): trt.DataType.HALF: np.float16, trt.DataType.INT32: np.int32, trt.DataType.INT8: np.int8, - trt.DataType.BOOL: np.bool, + trt.DataType.BOOL: bool, } if trt_dtype in dtype_map: return dtype_map[trt_dtype] @@ -635,3 +641,16 @@ def squeeze_trt(network, input_tensor, axes): reshape_layer = network.add_shuffle(input_tensor) reshape_layer.set_input(1, new_shape_tensor) return reshape_layer.get_output(0) + + +# resize shape tensor's shape to 1dim +def resize_to_1d(network, shape_tensor): + if len(shape_tensor.shape) > 1: + # shape_tensor need 1-dim in trt + shape_tensor_layer = network.add_shuffle(shape_tensor) + numel = 1 + for ele in shape_tensor.shape: + numel *= ele + shape_tensor_layer.reshape_dims = [numel] + shape_tensor = shape_tensor_layer.get_output(0) + return shape_tensor diff --git a/python/paddle/tensorrt/impls/common.py b/python/paddle/tensorrt/impls/common.py index ce6ac2ce9130a8..42f639cd856087 100644 --- a/python/paddle/tensorrt/impls/common.py +++ b/python/paddle/tensorrt/impls/common.py @@ -51,7 +51,6 @@ def dropout_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.bilinear_interp", trt_version="8.x") def bilinear_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") @@ -166,7 +165,6 @@ def bilinear_interp_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.nearest_interp", trt_version="8.x") def nearest_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") diff --git a/python/paddle/tensorrt/impls/creation.py b/python/paddle/tensorrt/impls/creation.py index 2505e1aa06e588..2c0f36a8d3293e 100644 --- a/python/paddle/tensorrt/impls/creation.py +++ b/python/paddle/tensorrt/impls/creation.py @@ -43,8 +43,13 @@ def full_int_array_converter(network, paddle_op, inputs): def full_converter(network, paddle_op, inputs): shape = paddle_op.attrs()["shape"] value = paddle_op.attrs().get("value", 1.0) + dtype = paddle_op.attrs().get("dtype") + if dtype == paddle.int32 or dtype == paddle.int64: + out_dtype = np.int32 + else: + out_dtype = np.float32 full_layer = network.add_constant( - shape, np.full(shape, value, dtype=np.float32) + shape, np.full(shape, value, dtype=out_dtype) ) return full_layer.get_output(0) @@ -120,7 +125,7 @@ def arange_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.full_like", trt_version="8.x") def full_like_converter(network, paddle_op, inputs): - shape = tuple(paddle_op.operands()[0].source().shape) + shape = inputs[0].shape ndims = len(shape) out_dtype = int(paddle_op.attrs().get("dtype", None)) diff --git a/python/paddle/tensorrt/impls/logic.py b/python/paddle/tensorrt/impls/logic.py index ebded660ce0306..4d38a06e980218 100644 --- a/python/paddle/tensorrt/impls/logic.py +++ b/python/paddle/tensorrt/impls/logic.py @@ -16,7 +16,6 @@ from paddle.tensorrt.converter_utils import ( add_elementwise_layer, - trt_cast, ) from paddle.tensorrt.register import converter_registry @@ -34,7 +33,7 @@ def logic_converter(network, paddle_op, inputs): layer_output = add_elementwise_layer( network, paddle_op, inputs, logic_type_map[paddle_op.name()] ) - return trt_cast(network, layer_output, inputs[0].dtype) + return layer_output @converter_registry.register("pd_op.not_equal", trt_version="8.x") @@ -44,4 +43,4 @@ def not_equal_converter(network, paddle_op, inputs): ) not_layer = network.add_unary(layer_output, trt.UnaryOperation.NOT) layer_output = not_layer.get_output(0) - return trt_cast(network, layer_output, inputs[0].dtype) + return layer_output diff --git a/python/paddle/tensorrt/impls/manipulation.py b/python/paddle/tensorrt/impls/manipulation.py index e78fbe933b8800..255104b51a17f7 100644 --- a/python/paddle/tensorrt/impls/manipulation.py +++ b/python/paddle/tensorrt/impls/manipulation.py @@ -13,7 +13,6 @@ # limitations under the License. -import numpy as np import tensorrt as trt from paddle.tensorrt.converter_utils import ( @@ -26,9 +25,11 @@ get_positive_dim, get_shape_tensor_element, has_dynamic_shape, + resize_to_1d, trt_concat, trt_expand, trt_floor_div, + trt_gather, trt_less, trt_max, trt_min, @@ -45,47 +46,51 @@ @converter_registry.register("pd_op.reshape", trt_version="8.x") def reshape_converter(network, paddle_op, inputs): - input_tensor, shape_tensor = inputs - input_shape = paddle_op.operands()[0].source().shape - - output_shape = paddle_op.results()[0].shape - if network.has_implicit_batch_dimension: - output_shape = output_shape[1:] + x = inputs[0] + is_constant_shape = False + shape_defining_op = paddle_op.operands()[1].source().get_defining_op() + if shape_defining_op.name() == "pd_op.full_int_array": + shape = shape_defining_op.attrs()["value"] + reshape_dim = shape + is_constant_shape = True + elif isinstance(inputs[1], list): + # shape tensor is a list value + shape_tensor = trt_concat(network, inputs[1]) + else: + # shape tensor is a value + shape_tensor = inputs[1] - if type(input_tensor) == trt.Weights: - input_tensor = network.add_constant( - input_shape, input_tensor - ).get_output(0) + if not is_constant_shape: + shape_tensor = resize_to_1d(network, shape_tensor) - shuffle_layer = network.add_shuffle(input_tensor) + layer = network.add_shuffle(x) + if is_constant_shape: + layer.reshape_dims = reshape_dim + else: + layer.set_input(1, shape_tensor) - try: - reshape_dims = ( - paddle_op.operands()[1].source().get_defining_op().attrs()["value"] - ) - shuffle_layer.reshape_dims = tuple(reshape_dims) - except Exception: - shuffle_layer.set_input(1, shape_tensor) + assert len(layer.get_output(0).shape) >= 0, ( + 'When convert reshape op to TRT reshape layer, the rank of trt reshape output dims is less than 0, ' + 'you should modify trt_config(a TensorRTConfig object) and set trt_config.disable_ops = ["pd_op.reshape"] to forbid this op.' + ) - return shuffle_layer.get_output(0) + return layer.get_output(0) @converter_registry.register("pd_op.gather_nd", trt_version="8.x") def gather_nd_converter(network, paddle_op, inputs): input_tensor, indices_tensor = inputs - shuffle_layer = network.add_shuffle(indices_tensor) - shuffle_layer.first_transpose = trt.Permutation([1, 0]) - # import pdb;pdb.set_trace() non_zero_layer = network.add_gather_v2( - input_tensor, shuffle_layer.get_output(0), trt.GatherMode.ND + input_tensor, indices_tensor, trt.GatherMode.ND ) + non_zero_layer.num_elementwise_dims = 0 return non_zero_layer.get_output(0) @converter_registry.register("pd_op.flatten", trt_version="8.x") def flatten_converter(network, paddle_op, inputs): input_val = inputs[0] - input_val_shape = input_val.shape + input_val_shape = paddle_op.operands()[0].source().shape dims = len(input_val_shape) start_axis = paddle_op.attrs().get("start_axis") @@ -185,20 +190,49 @@ def concat_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.unsqueeze", trt_version="8.x") @converter_registry.register("pd_op.unsqueeze_", trt_version="8.x") def unsqueeze_converter(network, paddle_op, inputs): - input_val = inputs[0] - input_shape = paddle_op.operands()[0].source().shape - input_shape_size = len(input_shape) + x = inputs[0] + input_dims = x.shape + axes = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] + assert len(axes) > 0, ( + "axes size should be > 0 in when convert unsqueeze op in TensorRT, but received len(axes) = %d." + % (len(axes)) + ) - if type(input_val) == trt.Weights: - input_val = network.add_constant(input_shape, input_val).get_output(0) - axis = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] - axis = axis[0] + should_unsqueeze = [False] * (len(input_dims) + len(axes)) + cur_out_rank = len(input_dims) + for i in range(len(axes)): + cur_out_rank += 1 + if axes[i] < 0: + axes[i] += cur_out_rank + + # axes[i] is relative to cur_out_rank + # we make [axes[i], cur_out_rank - 2] shift right + # and make (axes[i]) to true! + for j in range(cur_out_rank - 1, axes[i], -1): + should_unsqueeze[j] = should_unsqueeze[j - 1] + if axes[i] >= cur_out_rank: + should_unsqueeze[cur_out_rank - 1] = True + else: + should_unsqueeze[axes[i]] = True - axis = get_positive_dim(axis, input_shape_size + 1) - layer = network.add_shuffle(input_val) - layer.reshape_dims = ( - tuple(input_val.shape)[:axis] + (1,) + tuple(input_val.shape)[axis:] + gather_indices = [] + in_rank_i = 0 + for i in range(len(should_unsqueeze)): + if should_unsqueeze[i]: + gather_indices.append(len(input_dims)) + continue + gather_indices.append(in_rank_i) + in_rank_i += 1 + + layer = network.add_shuffle(x) + shape_tensor = trt_shape(network, x) + all_one = [1] * len(axes) + all_one_tensor = add_1D_constant_layer(network, all_one) + concat_inputs = [shape_tensor, all_one_tensor] + real_shape_tensor = trt_gather( + network, trt_concat(network, concat_inputs), gather_indices ) + layer.set_input(1, real_shape_tensor) return layer.get_output(0) @@ -206,7 +240,7 @@ def unsqueeze_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.squeeze_", trt_version="8.x") def squeeze_converter(network, paddle_op, inputs): input_val = inputs[0] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_val.shape input_shape_size = len(input_shape) if type(input_val) == trt.Weights: @@ -298,7 +332,6 @@ def cast_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.slice", trt_version="8.x") def slice_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape axes = paddle_op.attrs()["axes"] decrease_axis = paddle_op.attrs().get("decrease_axis") @@ -405,26 +438,23 @@ def slice_converter(network, paddle_op, inputs): output_tensor = slice_layer.get_output(0) # Handle decrease_axis - if decrease_axis: - output_shape = network.add_shape(output_tensor).get_output(0) - new_shape_dims = [] - for i in range(output_shape.shape[0]): - if i not in decrease_axis: - dim = network.add_slice(output_shape, [i], [1], [1]).get_output( - 0 - ) - new_shape_dims.append(dim) - if len(new_shape_dims) == 0: - new_shape_tensor = network.add_constant( - [1], np.array([1], dtype=np.int32) - ) + if len(decrease_axis) > 0: + gather_indices = [] + for i in range(input_rank): + if i in decrease_axis: + continue + gather_indices.append(i) + + if len(gather_indices) == 0: + # 0-dim tensor situation and shuffle layer will make its shape (1,) -> () + shuffle_layer = network.add_shuffle(output_tensor) + shuffle_layer.reshape_dims = () else: - new_shape_tensor = network.add_concatenation(new_shape_dims) - new_shape_tensor.axis = 0 + real_size_tensor = trt_gather(network, size_tensor, gather_indices) + shuffle_layer = network.add_shuffle(output_tensor) + shuffle_layer.set_input(1, real_size_tensor) - reshape_layer = network.add_shuffle(output_tensor) - reshape_layer.set_input(1, new_shape_tensor.get_output(0)) - output_tensor = reshape_layer.get_output(0) + output_tensor = shuffle_layer.get_output(0) return output_tensor @@ -508,7 +538,7 @@ def split_with_num_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.split", trt_version="8.x") def split_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_tensor.shape input_shape_size = len(input_shape) axis_op = paddle_op.operands()[2].source().get_defining_op() @@ -680,7 +710,7 @@ def stack_converter(network, paddle_op, inputs): @converter_registry.register("pd_op.tile", trt_version="8.x") def tile_converter(network, paddle_op, inputs): input = inputs[0] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input.shape input_shape_tensor = network.add_shape(input).get_output(0) rank = len(input_shape) @@ -691,6 +721,7 @@ def tile_converter(network, paddle_op, inputs): repeat_rank = len(repeat_times) else: repeat_tensor = inputs[1] + repeat_tensor = resize_to_1d(network, repeat_tensor) repeat_shape = paddle_op.operands()[1].source().shape repeat_rank = repeat_shape[0] @@ -750,7 +781,7 @@ def strided_slice_converter(network, paddle_op, inputs): if strides_op.name() == "pd_op.full_int_array": strides = strides_op.attrs()["value"] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_tensor.shape nchw_input_dims = len(input_shape) trt_start_dims = [0] * nchw_input_dims diff --git a/python/paddle/tensorrt/impls/math.py b/python/paddle/tensorrt/impls/math.py index b0ec0e29c9260b..22f4d7344b43ff 100644 --- a/python/paddle/tensorrt/impls/math.py +++ b/python/paddle/tensorrt/impls/math.py @@ -67,7 +67,7 @@ def scale_converter(network, paddle_op, inputs): def max_converter(network, paddle_op, inputs): input_tensor = inputs[0] axis = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_tensor.shape keepdim = paddle_op.attrs()["keepdim"] if network.has_implicit_batch_dimension: assert ( @@ -130,7 +130,7 @@ def _get_constant_or_expand_tensor( return expanded_tensor input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_tensor.shape rank = len(input_shape) input_shape_tensor = network.add_shape(input_tensor).get_output(0) @@ -158,7 +158,7 @@ def _get_constant_or_expand_tensor( @converter_registry.register("pd_op.remainder_", trt_version="8.x") def remainder_converter(network, paddle_op, inputs): weight_shape = paddle_op.operands()[1].source().shape - input_shape = paddle_op.operands()[0].source().shape + input_shape = inputs[0].shape weight_tensor = inputs[1] input_tensor = inputs[0] diff --git a/python/paddle/tensorrt/impls/search.py b/python/paddle/tensorrt/impls/search.py index 56588ba053d158..093e3fe8e04994 100644 --- a/python/paddle/tensorrt/impls/search.py +++ b/python/paddle/tensorrt/impls/search.py @@ -155,7 +155,7 @@ def where_converter(network, paddle_op, inputs): def topk_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape = paddle_op.operands()[0].source().shape + input_shape = input_tensor.shape axis = paddle_op.attrs().get("axis", -1) largest = paddle_op.attrs().get("largest", True) diff --git a/python/paddle/tensorrt/util.py b/python/paddle/tensorrt/util.py index dde376752bf49f..72f917a84bfd3b 100644 --- a/python/paddle/tensorrt/util.py +++ b/python/paddle/tensorrt/util.py @@ -131,4 +131,43 @@ def mark_buitlin_op(program): defining_op.has_attr("__l_trt__") and defining_op.attrs()["__l_trt__"] ): - enforce_op_lower_trt(program, op.name()) + op.set_bool_attr("__l_trt__", True) + if op.name() == "builtin.combine": + defining_op = op.results()[0].all_used_ops()[0] + if defining_op is not None: + if ( + defining_op.has_attr("__l_trt__") + and defining_op.attrs()["__l_trt__"] + ): + op.set_bool_attr("__l_trt__", True) + + +def weight_to_tensor(network, paddle_value, trt_tensor, use_op_name): + # the following op needn't cast trt.Weight to ITensor, because the layer need weight as input + forbid_cast_op = [ + "pd_op.depthwise_conv2d", + "pd_op.conv2d", + "pd_op.conv2d_transpose", + "pd_op.batch_norm", + "pd_op.batch_norm_", + "pd_op.layer_norm", + "pd_op.depthwise_conv2d_transpose", + ] + if use_op_name in forbid_cast_op: + return trt_tensor + input_shape = paddle_value.shape + if type(trt_tensor) == trt.Weights: + return network.add_constant(input_shape, trt_tensor).get_output(0) + return trt_tensor + + +def zero_dims_to_one_dims(network, trt_tensor): + if trt_tensor is None: + return None + if type(trt_tensor) == trt.Weights: + return trt_tensor + if len(trt_tensor.shape) != 0: + return trt_tensor + shuffle_layer = network.add_shuffle(trt_tensor) + shuffle_layer.reshape_dims = (1,) + return shuffle_layer.get_output(0) From 813999530224912c49353866aa6a4514dbd00b78 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Mon, 2 Dec 2024 14:53:55 +0800 Subject: [PATCH 082/288] some kernel support int64 input (#69829) * some kernel support int64 input --- paddle/phi/kernels/cpu/linspace_kernel.cc | 7 ++- .../phi/kernels/cpu/multiclass_nms3_kernel.cc | 18 ++++-- paddle/phi/kernels/cpu/roi_align_kernel.cc | 23 +++++-- .../funcs/distribute_fpn_proposals_functor.h | 36 ++++++++--- .../phi/kernels/funcs/interpolate_function.h | 62 +++++++++++++------ paddle/phi/kernels/gpu/roi_align_kernel.cu | 41 ++++++++---- paddle/phi/kernels/xpu/linspace_kernel.cc | 2 +- .../phi/kernels/xpu/multiclass_nms3_kernel.cc | 34 +++++++--- paddle/phi/kernels/xpu/roi_align_kernel.cc | 34 +++++++--- python/paddle/tensor/creation.py | 2 +- 10 files changed, 186 insertions(+), 73 deletions(-) diff --git a/paddle/phi/kernels/cpu/linspace_kernel.cc b/paddle/phi/kernels/cpu/linspace_kernel.cc index ef4d8c3ebfb405..9ec2d78a65a2f5 100644 --- a/paddle/phi/kernels/cpu/linspace_kernel.cc +++ b/paddle/phi/kernels/cpu/linspace_kernel.cc @@ -27,7 +27,12 @@ void LinspaceKernel(const Context& ctx, const DenseTensor& number, DataType dtype, DenseTensor* out) { - int32_t num = number.data()[0]; + int64_t num = 0; + if (number.dtype() == phi::DataType::INT64) { + num = number.data()[0]; + } else if (number.dtype() == phi::DataType::INT32) { + num = number.data()[0]; + } auto start_t = phi::funcs::TransDataType(ctx, start, dtype); auto stop_t = phi::funcs::TransDataType(ctx, stop, dtype); diff --git a/paddle/phi/kernels/cpu/multiclass_nms3_kernel.cc b/paddle/phi/kernels/cpu/multiclass_nms3_kernel.cc index 366f1d65cc8f0e..ceddca8b8a0d11 100644 --- a/paddle/phi/kernels/cpu/multiclass_nms3_kernel.cc +++ b/paddle/phi/kernels/cpu/multiclass_nms3_kernel.cc @@ -236,10 +236,20 @@ T PolyIoU(const T* box1, inline std::vector GetNmsLodFromRoisNum(const DenseTensor* rois_num) { std::vector rois_lod; - auto* rois_num_data = rois_num->data(); - rois_lod.push_back(static_cast(0)); - for (int i = 0; i < rois_num->numel(); ++i) { - rois_lod.push_back(rois_lod.back() + static_cast(rois_num_data[i])); + if (rois_num->dtype() == phi::DataType::INT64) { + auto* rois_num_data = rois_num->data(); + rois_lod.push_back(static_cast(0)); + for (int64_t i = 0; i < rois_num->numel(); ++i) { + rois_lod.push_back(rois_lod.back() + + static_cast(rois_num_data[i])); + } + } else if (rois_num->dtype() == phi::DataType::INT32) { + auto* rois_num_data = rois_num->data(); + rois_lod.push_back(static_cast(0)); + for (int i = 0; i < rois_num->numel(); ++i) { + rois_lod.push_back(rois_lod.back() + + static_cast(rois_num_data[i])); + } } return rois_lod; } diff --git a/paddle/phi/kernels/cpu/roi_align_kernel.cc b/paddle/phi/kernels/cpu/roi_align_kernel.cc index 7a0a00f82e7cd4..189d0c0efff272 100644 --- a/paddle/phi/kernels/cpu/roi_align_kernel.cc +++ b/paddle/phi/kernels/cpu/roi_align_kernel.cc @@ -217,13 +217,24 @@ void RoiAlignKernel(const Context& dev_ctx, "and the batch size of images is %d", boxes_batch_size, batch_size)); - auto* boxes_num_data = boxes_num->data(); - int start = 0; - for (int n = 0; n < boxes_batch_size; ++n) { - for (int i = start; i < start + boxes_num_data[n]; ++i) { - roi_batch_id_data[i] = n; + if (boxes_num->dtype() == phi::DataType::INT64) { + auto* boxes_num_data = boxes_num->data(); + int64_t start = 0; + for (int64_t n = 0; n < boxes_batch_size; ++n) { + for (int64_t i = start; i < start + boxes_num_data[n]; ++i) { + roi_batch_id_data[i] = n; + } + start += boxes_num_data[n]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + auto* boxes_num_data = boxes_num->data(); + int start = 0; + for (int n = 0; n < boxes_batch_size; ++n) { + for (int i = start; i < start + boxes_num_data[n]; ++i) { + roi_batch_id_data[i] = n; + } + start += boxes_num_data[n]; } - start += boxes_num_data[n]; } } else { auto lod = boxes.lod(); diff --git a/paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h b/paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h index 6042cafba3d05d..8f8b8ec39c07c4 100644 --- a/paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h +++ b/paddle/phi/kernels/funcs/distribute_fpn_proposals_functor.h @@ -33,16 +33,32 @@ template inline std::vector GetLodFromRoisNum(const Context& dev_ctx, const DenseTensor* rois_num) { std::vector rois_lod; - auto* rois_num_data = rois_num->data(); - DenseTensor cpu_tensor; - if (rois_num->place().GetType() == phi::AllocationType::GPU || - rois_num->place().GetType() == phi::AllocationType::XPU) { - Copy(dev_ctx, *rois_num, phi::CPUPlace(), true, &cpu_tensor); - rois_num_data = cpu_tensor.data(); - } - rois_lod.push_back(static_cast(0)); - for (int i = 0; i < rois_num->numel(); ++i) { - rois_lod.push_back(rois_lod.back() + static_cast(rois_num_data[i])); + if (rois_num->dtype() == phi::DataType::INT64) { + auto* rois_num_data = rois_num->data(); + DenseTensor cpu_tensor; + if (rois_num->place().GetType() == phi::AllocationType::GPU || + rois_num->place().GetType() == phi::AllocationType::XPU) { + Copy(dev_ctx, *rois_num, phi::CPUPlace(), true, &cpu_tensor); + rois_num_data = cpu_tensor.data(); + } + rois_lod.push_back(static_cast(0)); + for (int64_t i = 0; i < rois_num->numel(); ++i) { + rois_lod.push_back(rois_lod.back() + + static_cast(rois_num_data[i])); + } + } else if (rois_num->dtype() == phi::DataType::INT32) { + auto* rois_num_data = rois_num->data(); + DenseTensor cpu_tensor; + if (rois_num->place().GetType() == phi::AllocationType::GPU || + rois_num->place().GetType() == phi::AllocationType::XPU) { + Copy(dev_ctx, *rois_num, phi::CPUPlace(), true, &cpu_tensor); + rois_num_data = cpu_tensor.data(); + } + rois_lod.push_back(static_cast(0)); + for (int i = 0; i < rois_num->numel(); ++i) { + rois_lod.push_back(rois_lod.back() + + static_cast(rois_num_data[i])); + } } return rois_lod; } diff --git a/paddle/phi/kernels/funcs/interpolate_function.h b/paddle/phi/kernels/funcs/interpolate_function.h index bbfc54e5e2dc03..374602111c0949 100644 --- a/paddle/phi/kernels/funcs/interpolate_function.h +++ b/paddle/phi/kernels/funcs/interpolate_function.h @@ -94,28 +94,54 @@ inline std::vector get_new_shape( "The shape of dimension tensor should be [1] or []," "but received d%.", tensor->dims())); + if (tensor->dtype() == phi::DataType::INT64) { #ifdef PADDLE_WITH_CUSTOM_DEVICE - if (tensor->place().GetType() == phi::AllocationType::CUSTOM) { - DenseTensor temp; - phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); - vec_new_shape.push_back(static_cast(*temp.data())); - continue; - } + if (tensor->place().GetType() == phi::AllocationType::CUSTOM) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + continue; + } #endif #ifdef PADDLE_WITH_XPU - if (tensor->place().GetType() == phi::AllocationType::XPU) { - DenseTensor temp; - phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); - vec_new_shape.push_back(static_cast(*temp.data())); - continue; - } + if (tensor->place().GetType() == phi::AllocationType::XPU) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + continue; + } +#endif + if (tensor->place().GetType() == phi::AllocationType::GPU) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + } else { + vec_new_shape.push_back(static_cast(*tensor->data())); + } + } else if (tensor->dtype() == phi::DataType::INT32) { +#ifdef PADDLE_WITH_CUSTOM_DEVICE + if (tensor->place().GetType() == phi::AllocationType::CUSTOM) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + continue; + } +#endif +#ifdef PADDLE_WITH_XPU + if (tensor->place().GetType() == phi::AllocationType::XPU) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + continue; + } #endif - if (tensor->place().GetType() == phi::AllocationType::GPU) { - DenseTensor temp; - phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); - vec_new_shape.push_back(static_cast(*temp.data())); - } else { - vec_new_shape.push_back(static_cast(*tensor->data())); + if (tensor->place().GetType() == phi::AllocationType::GPU) { + DenseTensor temp; + phi::Copy(*dev_ctx, *tensor, phi::CPUPlace(), true, &temp); + vec_new_shape.push_back(static_cast(*temp.data())); + } else { + vec_new_shape.push_back(static_cast(*tensor->data())); + } } } diff --git a/paddle/phi/kernels/gpu/roi_align_kernel.cu b/paddle/phi/kernels/gpu/roi_align_kernel.cu index b04b36c954ed8e..b2de0d83f8917d 100644 --- a/paddle/phi/kernels/gpu/roi_align_kernel.cu +++ b/paddle/phi/kernels/gpu/roi_align_kernel.cu @@ -181,19 +181,36 @@ void RoiAlignKernel(const Context& dev_ctx, boxes_batch_size, batch_size)); - std::vector boxes_num_list(boxes_batch_size); - memory_utils::Copy(cplace, - boxes_num_list.data(), - gplace, - boxes_num->data(), - sizeof(int) * boxes_batch_size, - 0); - int start = 0; - for (int n = 0; n < boxes_batch_size; ++n) { - for (int i = start; i < start + boxes_num_list[n]; ++i) { - roi_batch_id_data[i] = n; + if (boxes_num->dtype() == phi::DataType::INT64) { + std::vector boxes_num_list(boxes_batch_size); + memory_utils::Copy(cplace, + boxes_num_list.data(), + gplace, + boxes_num->data(), + sizeof(int64_t) * boxes_batch_size, + 0); + int64_t start = 0; + for (int64_t n = 0; n < boxes_batch_size; ++n) { + for (int64_t i = start; i < start + boxes_num_list[n]; ++i) { + roi_batch_id_data[i] = n; + } + start += boxes_num_list[n]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + std::vector boxes_num_list(boxes_batch_size); + memory_utils::Copy(cplace, + boxes_num_list.data(), + gplace, + boxes_num->data(), + sizeof(int) * boxes_batch_size, + 0); + int start = 0; + for (int n = 0; n < boxes_batch_size; ++n) { + for (int i = start; i < start + boxes_num_list[n]; ++i) { + roi_batch_id_data[i] = n; + } + start += boxes_num_list[n]; } - start += boxes_num_list[n]; } } else { auto lod = boxes.lod(); diff --git a/paddle/phi/kernels/xpu/linspace_kernel.cc b/paddle/phi/kernels/xpu/linspace_kernel.cc index 63677b65a97cf6..c618a9022d5eb7 100644 --- a/paddle/phi/kernels/xpu/linspace_kernel.cc +++ b/paddle/phi/kernels/xpu/linspace_kernel.cc @@ -58,7 +58,7 @@ void LinspaceKernel(const Context& ctx, using XPUType = typename XPUTypeTrait::Type; T start_value = GetValueOfExpectedType(ctx, start); T stop_value = GetValueOfExpectedType(ctx, stop); - int32_t num = GetValueOfExpectedType(ctx, number); + int64_t num = GetValueOfExpectedType(ctx, number); PADDLE_ENFORCE_GT( num, diff --git a/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc b/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc index 2564454c838b25..6d2b53f1723565 100644 --- a/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc +++ b/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc @@ -63,16 +63,30 @@ void MultiClassNMSKernel(const Context& ctx, if (has_rois_num) { phi::DenseTensor rois_num_host; rois_num_host.Resize(rois_num.get_ptr()->dims()); - ctx.template HostAlloc(&rois_num_host); - phi::Copy(ctx, - *rois_num.get_ptr(), - rois_num_host.place(), - false, - &rois_num_host); - n = rois_num.get_ptr()->numel(); - for (int i = 0; i < n; i++) { - rois_num_vec.push_back(rois_num_host.data()[i]); - boxes_count += rois_num_host.data()[i]; + if (rois_num.get_ptr()->dtype() == phi::DataType::INT64) { + ctx.template HostAlloc(&rois_num_host); + phi::Copy(ctx, + *rois_num.get_ptr(), + rois_num_host.place(), + false, + &rois_num_host); + n = rois_num.get_ptr()->numel(); + for (int64_t i = 0; i < n; i++) { + rois_num_vec.push_back(rois_num_host.data()[i]); + boxes_count += rois_num_host.data()[i]; + } + } else if (rois_num.get_ptr()->dtype() == phi::DataType::INT32) { + ctx.template HostAlloc(&rois_num_host); + phi::Copy(ctx, + *rois_num.get_ptr(), + rois_num_host.place(), + false, + &rois_num_host); + n = rois_num.get_ptr()->numel(); + for (int i = 0; i < n; i++) { + rois_num_vec.push_back(rois_num_host.data()[i]); + boxes_count += rois_num_host.data()[i]; + } } } else { auto lod = bboxes.lod().back(); diff --git a/paddle/phi/kernels/xpu/roi_align_kernel.cc b/paddle/phi/kernels/xpu/roi_align_kernel.cc index 91c11d13bb6427..b79e7bde72fc07 100644 --- a/paddle/phi/kernels/xpu/roi_align_kernel.cc +++ b/paddle/phi/kernels/xpu/roi_align_kernel.cc @@ -64,16 +64,30 @@ void RoiAlignKernel(const Context& dev_ctx, rois_batch_size, batch_size)); - std::vector rois_num_list(rois_batch_size); - memory_utils::Copy(cplace, - rois_num_list.data(), - xplace, - boxes_num->data(), - sizeof(int) * rois_batch_size); - cpu_lod = new int[rois_batch_size + 1]; - cpu_lod[0] = 0; - for (int i = 0; i < rois_batch_size; i++) { - cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + if (boxes_num->dtype() == phi::DataType::INT64) { + std::vector rois_num_list(rois_batch_size); + memory_utils::Copy(cplace, + rois_num_list.data(), + xplace, + boxes_num->data(), + sizeof(int64_t) * rois_batch_size); + cpu_lod = new int[rois_batch_size + 1]; + cpu_lod[0] = 0; + for (int64_t i = 0; i < rois_batch_size; i++) { + cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + std::vector rois_num_list(rois_batch_size); + memory_utils::Copy(cplace, + rois_num_list.data(), + xplace, + boxes_num->data(), + sizeof(int) * rois_batch_size); + cpu_lod = new int[rois_batch_size + 1]; + cpu_lod[0] = 0; + for (int i = 0; i < rois_batch_size; i++) { + cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + } } } else { auto lod = boxes.lod(); diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index fb10bf2200ccca..b12d00fe09d12a 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -396,7 +396,7 @@ def linspace( else: check_type(stop, 'stop', (int, float), 'linspace') if isinstance(num, paddle.pir.Value): - check_dtype(num.dtype, 'num', ['int32'], 'linspace') + check_dtype(num.dtype, 'num', ['int32', 'int64'], 'linspace') check_dtype( dtype, 'dtype', From de1026d1770cb4c89f7b4d048a66e4395a5b9f41 Mon Sep 17 00:00:00 2001 From: Terry <38135104+TR666@users.noreply.github.com> Date: Mon, 2 Dec 2024 15:18:50 +0800 Subject: [PATCH 083/288] [XPU][PIR] add rms_norm_xpu_fuse_pass (#69727) --- .../inference/api/paddle_pass_builder.cc | 1 + paddle/fluid/pir/transforms/passes.h | 1 + .../pir/transforms/xpu/rms_norm_xpu_fuse.cc | 196 ++++++++++++++++++ .../transforms/xpu/rms_norm_xpu_fuse_pass.h | 26 +++ .../xpu/test_rms_norm_xpu_fuse_pass.py | 181 ++++++++++++++++ 5 files changed, 405 insertions(+) create mode 100644 paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse.cc create mode 100644 paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse_pass.h create mode 100644 test/ir/pir/fused_pass/xpu/test_rms_norm_xpu_fuse_pass.py diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc index bd4c7d4f29cab7..dcca713778c424 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.cc +++ b/paddle/fluid/inference/api/paddle_pass_builder.cc @@ -618,6 +618,7 @@ const std::vector kPirXpuPasses{ // Operator fusion pass "add_activation_xpu_fuse_pass", "add_layernorm_xpu_fuse_pass", + "rms_norm_xpu_fuse_pass", "conv2d_bn_xpu_fuse_pass", "conv2d_add_xpu_fuse_pass", "group_norm_silu_fuse_pass", diff --git a/paddle/fluid/pir/transforms/passes.h b/paddle/fluid/pir/transforms/passes.h index 37e085f92fd7b0..6556d9143b2c37 100644 --- a/paddle/fluid/pir/transforms/passes.h +++ b/paddle/fluid/pir/transforms/passes.h @@ -94,6 +94,7 @@ USE_PIR_PASS(cpu_bf16_quantize_squash_pass); #ifdef PADDLE_WITH_XPU USE_PIR_PASS(add_activation_xpu_fuse_pass); USE_PIR_PASS(add_layernorm_xpu_fuse_pass); +USE_PIR_PASS(rms_norm_xpu_fuse_pass); USE_PIR_PASS(conv2d_bn_xpu_fuse_pass); USE_PIR_PASS(conv2d_add_xpu_fuse_pass); USE_PIR_PASS(fc_xpu_fuse_pass); diff --git a/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse.cc b/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse.cc new file mode 100644 index 00000000000000..b6ff9e8e09ba5d --- /dev/null +++ b/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse.cc @@ -0,0 +1,196 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse_pass.h" + +#include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/fluid/pir/drr/include/drr_pattern_base.h" +#include "paddle/fluid/pir/utils/general_functions.h" + +#include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pass/pass_registry.h" + +/* +For example: +graph: + + x w + _ _ _ _ _| _ _ _ _ _ | + | | | + cast cast | + | | | + | | | + pow | | + | | | + mean epilson | | + \ / | | + rsqrt | | + | | | + \ / | + multiply | + | | + cast | + \ / + multiply + | + output +------------------------------------------------------ +After the pass is applied: + x w + \ / + | + rms_norm + | + | + Output +*/ + +namespace { + +class RmsNormFusePattern : public paddle::drr::DrrPatternBase { + private: + const bool is_half_weight_; + + public: + explicit RmsNormFusePattern(bool is_half_weight) + : is_half_weight_(is_half_weight) {} + + std::string name() const override { return "RmsNormFusePattern"; } + + void operator()(paddle::drr::DrrPatternContext *ctx) const override { + paddle::drr::SourcePattern pat = ctx->SourcePattern(); + const auto &pow = pat.Op(paddle::dialect::PowOp::name()); + const auto &mean = + pat.Op(paddle::dialect::MeanOp::name(), {{"axis", pat.Attr("axis")}}); + const auto &full = pat.Op(paddle::dialect::FullOp::name()); + const auto &scale = + pat.Op(paddle::dialect::ScaleOp::name(), {{"bias", pat.Attr("bias")}}); + const auto &rsqrt = pat.Op(paddle::dialect::RsqrtOp::name()); + const auto &multiply1 = pat.Op(paddle::dialect::MultiplyOp::name()); + const auto &multiply2 = pat.Op(paddle::dialect::MultiplyOp::name()); + if (is_half_weight_) { + const auto &cast1 = pat.Op(paddle::dialect::CastOp::name(), + {{"dtype", pat.Attr("cast_type_1")}}); + const auto &cast3 = pat.Op(paddle::dialect::CastOp::name(), + {{"dtype", pat.Attr("cast_type_1")}}); + pat.Tensor("cast_1_out") = cast1(pat.Tensor("x")); + pat.Tensor("cast_3_out") = cast3(pat.Tensor("x")); + pat.Tensor("pow_out") = pow(pat.Tensor("cast_1_out")); + pat.Tensor("mean_out") = mean(pat.Tensor("pow_out")); + pat.Tensor("scale_out") = scale(pat.Tensor("mean_out"), full()); + pat.Tensor("rsqrt_out") = rsqrt(pat.Tensor("scale_out")); + pat.Tensor("multiply_out1") = + multiply1(pat.Tensor("rsqrt_out"), pat.Tensor("cast_3_out")); + const auto &cast2 = pat.Op(paddle::dialect::CastOp::name(), + {{"dtype", pat.Attr("cast_type_2")}}); + pat.Tensor("cast_2_out") = cast2(pat.Tensor("multiply_out1")); + pat.Tensor("multiply_out2") = + multiply2(pat.Tensor("cast_2_out"), pat.Tensor("w")); + } else { + pat.Tensor("pow_out") = pow(pat.Tensor("x")); + pat.Tensor("mean_out") = mean(pat.Tensor("pow_out")); + pat.Tensor("scale_out") = scale(pat.Tensor("mean_out"), full()); + pat.Tensor("rsqrt_out") = rsqrt(pat.Tensor("scale_out")); + pat.Tensor("multiply_out1") = + multiply1(pat.Tensor("rsqrt_out"), pat.Tensor("x")); + pat.Tensor("multiply_out2") = + multiply2(pat.Tensor("multiply_out1"), pat.Tensor("w")); + } + + pat.AddConstraint([this](const paddle::drr::MatchContext &match_ctx) { + auto axis = match_ctx.Attr>("axis"); + if (axis.size() > 1) { + return false; + } + if (this->is_half_weight_) { + auto w_type = pir::GetDataTypeFromValue(match_ctx.Tensor("w")); + if (!(w_type.isa() || + w_type.isa())) { + return false; + } + + auto cast_type_1 = match_ctx.Attr("cast_type_1"); + auto cast_type_2 = match_ctx.Attr("cast_type_2"); + if (cast_type_1 != phi::DataType::FLOAT32) { + return false; + } + if (w_type.isa() && + cast_type_2 != phi::DataType::FLOAT16) { + return false; + } + if (w_type.isa() && + cast_type_2 != phi::DataType::BFLOAT16) { + return false; + } + } + return true; + }); + + paddle::drr::ResultPattern res = pat.ResultPattern(); + + const auto &begin_norm_axis = + res.ComputeAttr([](const paddle::drr::MatchContext &match_ctx) -> int { + const auto &axis = match_ctx.Attr>("axis"); + auto pow_out_shape = + pir::GetShapeFromValue(match_ctx.Tensor("pow_out")); + return axis[0] == -1 ? static_cast(pow_out_shape.size()) - 1 + : axis[0]; + }); + + const auto &rms_norm = res.Op(paddle::dialect::RmsNormOp::name(), + {{ + {"epsilon", pat.Attr("bias")}, + {"begin_norm_axis", begin_norm_axis}, + {"quant_scale", res.Float32Attr(-1.0)}, + {"quant_round_type", res.Int32Attr(0)}, + {"quant_max_bound", res.Float32Attr(0.0)}, + {"quant_min_bound", res.Float32Attr(0.0)}, + }}); + + rms_norm( + { + &res.Tensor("x"), + &res.InputNoneTensor(), + &res.InputNoneTensor(), + &res.Tensor("w"), + &res.InputNoneTensor(), + }, + {&res.Tensor("multiply_out2"), + &res.Tensor("residual_out"), + &res.Tensor("inv_var")}); + } +}; + +class RmsNormXpuFusePass : public pir::PatternRewritePass { + public: + RmsNormXpuFusePass() : pir::PatternRewritePass("rms_norm_xpu_fuse_pass", 2) {} + + pir::RewritePatternSet InitializePatterns(pir::IrContext *context) override { + pir::RewritePatternSet ps(context); + ps.Add(paddle::drr::Create(context, false)); + ps.Add(paddle::drr::Create(context, true)); + return ps; + } +}; + +} // namespace + +namespace pir { +std::unique_ptr CreateRmsNormXpuFusePass() { + return std::make_unique(); +} + +} // namespace pir + +REGISTER_IR_PASS(rms_norm_xpu_fuse_pass, RmsNormXpuFusePass); diff --git a/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse_pass.h b/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse_pass.h new file mode 100644 index 00000000000000..dc69eaaa328355 --- /dev/null +++ b/paddle/fluid/pir/transforms/xpu/rms_norm_xpu_fuse_pass.h @@ -0,0 +1,26 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include "paddle/pir/include/core/dll_decl.h" + +namespace pir { + +class Pass; + +IR_API std::unique_ptr CreateRmsNormXpuFusePass(); + +} // namespace pir diff --git a/test/ir/pir/fused_pass/xpu/test_rms_norm_xpu_fuse_pass.py b/test/ir/pir/fused_pass/xpu/test_rms_norm_xpu_fuse_pass.py new file mode 100644 index 00000000000000..f3b4dc5da4965c --- /dev/null +++ b/test/ir/pir/fused_pass/xpu/test_rms_norm_xpu_fuse_pass.py @@ -0,0 +1,181 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from pass_test import PassTest + +import paddle +from paddle.base import core +from paddle.pir.core import create_parameter + +paddle.enable_static() + + +class TestRmsNormXpuFusePattern(PassTest): + r""" + x x w + | | | + pow | | + | | | + mean epilson | | + \ / | | + rsqrt | | + | | | + \ / | + multiply | + | | + \ / + multiply + """ + + def is_program_valid(self, program=None): + return True + + def build_ir_program(self): + with paddle.pir_utils.IrGuard(): + main_prog = paddle.static.Program() + start_prog = paddle.static.Program() + with paddle.pir.core.program_guard(main_prog, start_prog): + x_shape = [2, 160, 40, 64] + x_type = 'float32' + w_shape = [64] + w_type = 'float32' + x = paddle.static.data(name='x', shape=x_shape, dtype=x_type) + w = create_parameter( + name="w", + shape=w_shape, + dtype=w_type, + initializer=paddle.nn.initializer.Assign( + np.random.random(w_shape).astype(w_type) + ), + ) + variance = x.pow(2).mean(-1, keepdim=True) + x = paddle.rsqrt(variance + 1e-6) * x + out = x * w + out = paddle.assign(out) + self.pass_attr_list = [{'rms_norm_xpu_fuse_pass': {}}] + self.feeds = { + "x": np.random.random(x_shape).astype("float32"), + } + self.fetch_list = [out] + self.valid_op_map = { + "pd_op.pow": 0, + "pd_op.mean": 0, + "pd_op.full": 0, + "pd_op.scale": 0, + "pd_op.rsqrt": 0, + "pd_op.multiply": 0, + "pd_op.rms_norm": 1, + } + + return [main_prog, start_prog] + + def sample_program(self): + pir_program = self.build_ir_program() + yield pir_program, False + + def setUp(self): + if core.is_compiled_with_xpu(): + self.places.append(paddle.device.XPUPlace(0)) + + def test_check_output(self): + self.check_pass_correct(atol=1e-3, rtol=1e-3) + + +# 因为无法使用numpy构造bfp16类型得数据，所以这里仅用fp16来测试半精度的case +class TestRmsNorm_FP16_XpuFusePattern(PassTest): + r""" + x w + _ _ _ _ _| _ _ _ _ _ | + | | | + cast cast | + | | | + | | | + pow | | + | | | + mean epilson | | + \ / | | + rsqrt | | + | | | + \ / | + multiply | + | | + cast | + \ / + multiply + | + output + """ + + def is_program_valid(self, program=None): + return True + + def build_ir_program(self): + with paddle.pir_utils.IrGuard(): + main_prog = paddle.static.Program() + start_prog = paddle.static.Program() + with paddle.pir.core.program_guard(main_prog, start_prog): + x_shape = [2, 160, 40, 64] + x_type = 'float16' + w_shape = [64] + w_type = 'float16' + x = paddle.static.data(name='x', shape=x_shape, dtype=x_type) + x_fp32_1 = paddle.cast(x, 'float32') + x_fp32_2 = paddle.cast(x, 'float32') + w = create_parameter( + name="w", + shape=w_shape, + dtype=w_type, + initializer=paddle.nn.initializer.Assign( + np.random.random(w_shape).astype(w_type) + ), + ) + variance = x_fp32_1.pow(2).mean(-1, keepdim=True) + x_fp32_1 = paddle.rsqrt(variance + 1e-6) * x_fp32_2 + x_float16 = paddle.cast(x_fp32_1, 'float16') + out = x_float16 * w + out = paddle.assign(out) + self.pass_attr_list = [{'rms_norm_xpu_fuse_pass': {}}] + self.feeds = { + "x": np.random.random(x_shape).astype("float16"), + } + self.fetch_list = [out] + self.valid_op_map = { + "pd_op.pow": 0, + "pd_op.mean": 0, + "pd_op.full": 0, + "pd_op.scale": 0, + "pd_op.rsqrt": 0, + "pd_op.multiply": 0, + "pd_op.rms_norm": 1, + } + + return [main_prog, start_prog] + + def sample_program(self): + pir_program = self.build_ir_program() + yield pir_program, False + + def test_check_output(self): + self.check_pass_correct(atol=1e-3, rtol=1e-3) + + def setUp(self): + if core.is_compiled_with_xpu(): + self.places.append(paddle.device.XPUPlace(0)) + + +if __name__ == "__main__": + unittest.main() From c8dc40a5c658bce4fd79ae997659c9849a6ee249 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Mon, 2 Dec 2024 15:20:22 +0800 Subject: [PATCH 084/288] add explicit error message of paddle.where/paddle.where_ (#69833) --- python/paddle/tensor/search.py | 17 ++++++++++++++++- test/legacy_test/test_where_op.py | 31 +++++++++++++++++++++++++++++++ 2 files changed, 47 insertions(+), 1 deletion(-) diff --git a/python/paddle/tensor/search.py b/python/paddle/tensor/search.py index 0d75bb92a38130..1bb5b4ff1512f4 100755 --- a/python/paddle/tensor/search.py +++ b/python/paddle/tensor/search.py @@ -730,7 +730,7 @@ def where( ``numpy.where(condition)`` is identical to ``paddle.nonzero(condition, as_tuple=True)``, please refer to :ref:`api_paddle_nonzero`. Args: - condition (Tensor): The condition to choose x or y. When True (nonzero), yield x, otherwise yield y. + condition (Tensor): The condition to choose x or y. When True (nonzero), yield x, otherwise yield y, must have a dtype of bool if used as mask. x (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is True with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. y (Tensor|scalar|None, optional): A Tensor or scalar to choose when the condition is False with data type of bfloat16, float16, float32, float64, int32 or int64. Either both or neither of x and y should be given. name (str|None, optional): For details, please refer to :ref:`api_guide_Name`. Generally, no setting is required. Default: None. @@ -777,6 +777,13 @@ def where( y_shape = list(y.shape) if in_dynamic_mode(): + # NOTE: `condition` must be a bool Tensor as required in + # https://data-apis.org/array-api/latest/API_specification/generated/array_api.where.html#array_api.where + if condition.dtype != paddle.bool: + raise ValueError( + "The `condition` is expected to be a boolean Tensor, " + f"but got a Tensor with dtype {condition.dtype}" + ) broadcast_shape = paddle.broadcast_shape(x_shape, y_shape) broadcast_shape = paddle.broadcast_shape( broadcast_shape, condition_shape @@ -866,6 +873,14 @@ def where_( if x is None or y is None: raise ValueError("either both or neither of x and y should be given") + # NOTE: `condition` must be a bool Tensor as required in + # https://data-apis.org/array-api/latest/API_specification/generated/array_api.where.html#array_api.where + if condition.dtype != paddle.bool: + raise ValueError( + "The `condition` is expected to be a boolean Tensor, " + f"but got a Tensor with dtype {condition.dtype}" + ) + condition_shape = list(condition.shape) x_shape = list(x.shape) y_shape = list(y.shape) diff --git a/test/legacy_test/test_where_op.py b/test/legacy_test/test_where_op.py index 626d98aabf4f1c..7208ba788d2e2a 100644 --- a/test/legacy_test/test_where_op.py +++ b/test/legacy_test/test_where_op.py @@ -943,6 +943,37 @@ def test_value_error(self): self.assertRaises(ValueError, paddle.where, cond, a) +class TestWhereDygraphAPINonBoolCondition(unittest.TestCase): + def test_condition_with_wrong_dtype(self): + with base.dygraph.guard(): + cond = paddle.to_tensor([True, False]) + + for dtype in [ + paddle.int64, + paddle.int32, + paddle.float32, + paddle.float64, + ]: + cond_wrong_dtype = cond.to(dtype) + with self.assertRaises(ValueError): + paddle.where(cond_wrong_dtype, 1, 0) + + def test_condition_inplace_with_wrong_dtype(self): + with base.dygraph.guard(): + cond = paddle.to_tensor([True, False]) + + x = paddle.zeros_like(cond).astype("float32") + for dtype in [ + paddle.int64, + paddle.int32, + paddle.float32, + paddle.float64, + ]: + cond_wrong_dtype = cond.to(dtype) + with self.assertRaises(ValueError): + x = x.where_(cond_wrong_dtype, x, x) + + if __name__ == "__main__": paddle.enable_static() unittest.main() From 4cc893f6f94160bc70d7d038277b2514e82e108b Mon Sep 17 00:00:00 2001 From: 0x3878f <37301539+0x3878f@users.noreply.github.com> Date: Mon, 2 Dec 2024 16:41:47 +0800 Subject: [PATCH 085/288] fix: unit test error test_mixed_extension_setup, test_custom_raw_op_kernel_op_deprecated (#69832) --- test/cpp_extension/test_mixed_extension_setup.py | 4 +++- .../custom_op/test_custom_raw_op_kernel_op_deprecated.py | 3 +++ 2 files changed, 6 insertions(+), 1 deletion(-) diff --git a/test/cpp_extension/test_mixed_extension_setup.py b/test/cpp_extension/test_mixed_extension_setup.py index 354291849683e7..574a218e062ecd 100644 --- a/test/cpp_extension/test_mixed_extension_setup.py +++ b/test/cpp_extension/test_mixed_extension_setup.py @@ -103,10 +103,12 @@ def setUp(self): cur_dir = os.path.dirname(os.path.abspath(__file__)) # install mixed custom_op and extension # compile, install the custom op egg into site-packages under background + site_dir = site.getsitepackages()[0] cmd = f'cd {cur_dir} && {sys.executable} mix_relu_and_extension_setup.py install' + if os.name != 'nt': + cmd += f' --install-lib={site_dir}' run_cmd(cmd) - site_dir = site.getsitepackages()[0] custom_egg_path = [ x for x in os.listdir(site_dir) if 'mix_relu_extension' in x ] diff --git a/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py b/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py index 9762d29c48c838..297f6e7f55b088 100644 --- a/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py +++ b/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py @@ -54,6 +54,9 @@ def setUpClass(cls): path = os.path.dirname(os.path.abspath(__file__)) path = os.path.join(path, "custom_raw_op_kernel_op_setup.py") cmd = [sys.executable, path, "install", "--force"] + if os.name != 'nt': + install_lib = f"--install-lib={site.getsitepackages()[0]}" + cmd.append(install_lib) cmd = " ".join([shlex.quote(c) for c in cmd]) os.environ['MODULE_NAME'] = MODULE_NAME assert os.system(cmd) == 0 From 420defe5b6d26822bbf74b252af1ad4f320e7b3f Mon Sep 17 00:00:00 2001 From: jiachengdai <144661430+jiachengdai@users.noreply.github.com> Date: Mon, 2 Dec 2024 16:50:47 +0800 Subject: [PATCH 086/288] =?UTF-8?q?[Docathon][Add=20API=20Legend=20No.19]+?= =?UTF-8?q?=E6=B7=BB=E5=8A=A0=E5=9B=BE=E4=BE=8B=E8=87=B3paddle.scatter=20?= =?UTF-8?q?=E8=8B=B1=E6=96=87=E6=96=87=E6=A1=A3=20(#69749)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add figure to scatter_en * update the description for scatter --- python/paddle/tensor/manipulation.py | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 6b48610fe28465..eccbbe6fc26e1d 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -4169,6 +4169,11 @@ def scatter( **Scatter Layer** Output is obtained by updating the input on selected indices based on updates. + As shown in the figure, when ``overwrite`` is set to ``True``, the output for the same index is updated in overwrite mode, where ``x[index[i]]`` is directly replaced with ``update[i]`` sequentially; When ``overwrite`` is set to ``False``, the output for the same index is updated in accumulation mode. In this mode, ``x[index[i]]`` is first initialized with elements set to 0. Then, ``update[i]`` is sequentially added to ``x[index[i]]`` to produce the output. + + .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/scatter.png + :alt: Legend - scatter behavior display + .. code-block:: python :name: scatter-example-1 From b1400c666a2280e61a3a5234e03a27b2c2f29d44 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Mon, 2 Dec 2024 18:25:43 +0800 Subject: [PATCH 087/288] [Eager] Fix Layer call quick path condition (#69867) --- python/paddle/nn/layer/layers.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/python/paddle/nn/layer/layers.py b/python/paddle/nn/layer/layers.py index 957aa24a580ef7..8b3d9e4769da5b 100644 --- a/python/paddle/nn/layer/layers.py +++ b/python/paddle/nn/layer/layers.py @@ -1524,11 +1524,10 @@ def __call__(self, *inputs: Any, **kwargs: Any) -> Any: (not in_to_static_mode()) and (not self._forward_pre_hooks) and (not self._forward_post_hooks) - and (not self._built) + and (self.__class__._build_once is Layer._build_once or self._built) and in_dygraph_mode() and (not in_profiler_mode() or in_sot_simulation_mode()) ): - self._build_once(*inputs, **kwargs) return self.forward(*inputs, **kwargs) else: return self._dygraph_call_func(*inputs, **kwargs) From 51dfb95eb753b6469d7de40f400fb8de865550da Mon Sep 17 00:00:00 2001 From: zyfncg Date: Mon, 2 Dec 2024 19:14:12 +0800 Subject: [PATCH 088/288] [PIR Pass] Support hook of value replace in pir pass (#69806) * support hook of value replace for pir pass * fix bug * refine code * fix bug --- paddle/fluid/pybind/pir.cc | 41 ++++++++++++------- .../dialect/shape/utils/shape_analysis.h | 3 ++ paddle/pir/include/pass/pass.h | 1 + paddle/pir/include/pass/pass_manager.h | 6 +++ .../include/pattern_rewrite/pattern_match.h | 2 + .../pattern_rewrite/pattern_rewrite_driver.h | 5 +++ .../src/dialect/shape/utils/shape_analysis.cc | 6 +++ paddle/pir/src/pass/pass.cc | 12 +++++- .../pir/src/pattern_rewrite/pattern_match.cc | 15 ++++++- .../pattern_rewrite/pattern_rewrite_driver.cc | 10 +++++ 10 files changed, 83 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index becb0b1f81f397..ef9e2df5e4c02a 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -85,6 +85,7 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_util.h" #include "paddle/cinn/hlir/dialect/operator/transforms/pir_to_py_code_converter.h" #include "paddle/cinn/hlir/framework/pir_compiler.h" +#include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #endif using paddle::dialect::ApiBuilder; @@ -2392,22 +2393,23 @@ void BindUtils(pybind11::module *m) { namespace { -#ifdef PADDLE_WITH_CINN -std::shared_ptr CreatePassManager() { - pir::IrContext *ctx = pir::IrContext::Instance(); - ctx->GetOrRegisterDialect(); - ctx->GetOrRegisterDialect(); - ctx->GetOrRegisterDialect(); - auto pass_manager = std::make_shared(ctx); - if (FLAGS_print_ir) { - pass_manager->EnableIRPrinting(); - } - return pass_manager; -} -#endif - void ApplyCinnPass(Program &program) { // NOLINT #ifdef PADDLE_WITH_CINN + auto CreatePassManager = [&]() -> std::shared_ptr { + pir::IrContext *ctx = pir::IrContext::Instance(); + ctx->GetOrRegisterDialect(); + ctx->GetOrRegisterDialect(); + ctx->GetOrRegisterDialect(); + auto pass_manager = std::make_shared(ctx); + if (FLAGS_print_ir) { + pass_manager->EnableIRPrinting(); + } + auto &shape_analysis = pir::ShapeAnalysisManager::Instance().Get(&program); + pass_manager->SetValueReplacedHook([&](pir::Value from, pir::Value to) { + shape_analysis.ShareShapeOrData(from, to); + }); + return pass_manager; + }; cinn::dialect::ir::ApplyCinnPass(&program, CreatePassManager); #else PADDLE_THROW(common::errors::Unimplemented( @@ -2418,6 +2420,17 @@ void ApplyCinnPass(Program &program) { // NOLINT void CheckInferSymbolicIfNeed(Program &program) { // NOLINT #ifdef PADDLE_WITH_CINN + auto CreatePassManager = [&]() -> std::shared_ptr { + pir::IrContext *ctx = pir::IrContext::Instance(); + ctx->GetOrRegisterDialect(); + ctx->GetOrRegisterDialect(); + ctx->GetOrRegisterDialect(); + auto pass_manager = std::make_shared(ctx); + if (FLAGS_print_ir) { + pass_manager->EnableIRPrinting(); + } + return pass_manager; + }; cinn::dialect::ir::CheckInferSymbolicIfNeed(&program, CreatePassManager); #else // Do nothing. diff --git a/paddle/pir/include/dialect/shape/utils/shape_analysis.h b/paddle/pir/include/dialect/shape/utils/shape_analysis.h index cdd72099648d5a..9b56c6f6a27ebb 100644 --- a/paddle/pir/include/dialect/shape/utils/shape_analysis.h +++ b/paddle/pir/include/dialect/shape/utils/shape_analysis.h @@ -196,6 +196,9 @@ class IR_API ShapeConstraintIRAnalysis final void SetShapeOrDataForValue(Value val, const symbol::ShapeOrDataDimExprs& shape_or_data); + // Set ShapeOrData of `to` value by ShapeOrData of `from` value. + void ShareShapeOrData(Value from, Value to); + bool IsEqual(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) const; bool IsGreatThanOne(const symbol::DimExpr& dim_expr) const; diff --git a/paddle/pir/include/pass/pass.h b/paddle/pir/include/pass/pass.h index 0cc31e124f663f..82d0aed7e43d83 100644 --- a/paddle/pir/include/pass/pass.h +++ b/paddle/pir/include/pass/pass.h @@ -76,6 +76,7 @@ class IR_API Pass { public: inline static const char kParamScopeAttr[] = "__param_scope__"; inline static const char kPlaceAttr[] = "__place__"; + inline static const char kValueReplaceHookAttr[] = "__value_replaced_hook__"; explicit Pass(const std::string& name, uint8_t opt_level, diff --git a/paddle/pir/include/pass/pass_manager.h b/paddle/pir/include/pass/pass_manager.h index fa78c14449c14e..894861aafae2e0 100644 --- a/paddle/pir/include/pass/pass_manager.h +++ b/paddle/pir/include/pass/pass_manager.h @@ -115,6 +115,10 @@ class IR_API PassManager { void AddInstrumentation(std::unique_ptr pi); + void SetValueReplacedHook(const VALUE_REPLACED_HOOK_FUNC &hook) { + value_replaced_hook_ = hook; + } + private: bool Initialize(IrContext *context); @@ -135,6 +139,8 @@ class IR_API PassManager { std::unique_ptr instrumentor_; + VALUE_REPLACED_HOOK_FUNC value_replaced_hook_ = nullptr; + // For access member of pass_adaptor_. friend class detail::PassAdaptor; }; diff --git a/paddle/pir/include/pattern_rewrite/pattern_match.h b/paddle/pir/include/pattern_rewrite/pattern_match.h index 214b78a917189b..15d8340c6a90b1 100644 --- a/paddle/pir/include/pattern_rewrite/pattern_match.h +++ b/paddle/pir/include/pattern_rewrite/pattern_match.h @@ -302,6 +302,8 @@ class RewriterBase : public Builder { virtual void NotifyOperationInserted(Operation* op) {} + virtual void NotifyValueReplaced(Value from, Value to) {} + virtual void StartRootUpdate(Operation* op) {} virtual void FinalizeRootUpdate(Operation* op) {} diff --git a/paddle/pir/include/pattern_rewrite/pattern_rewrite_driver.h b/paddle/pir/include/pattern_rewrite/pattern_rewrite_driver.h index 17f0c80544d61b..57d746c415b12c 100644 --- a/paddle/pir/include/pattern_rewrite/pattern_rewrite_driver.h +++ b/paddle/pir/include/pattern_rewrite/pattern_rewrite_driver.h @@ -19,6 +19,8 @@ namespace pir { +using VALUE_REPLACED_HOOK_FUNC = std::function; + class FrozenRewritePatternSet; /// This enum will control which ops will be added to the worklist during the @@ -57,6 +59,9 @@ class IR_API GreedyRewriteConfig { /// - ExistingOps: only pre-existing ops are added to the worklist. GreedyRewriteStrictness strict_mode = GreedyRewriteStrictness::AnyOp; + // Hook function for replacing the value. + VALUE_REPLACED_HOOK_FUNC value_replaced_hook = nullptr; + static constexpr int64_t kNoLimit = -1; }; diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index f60fee1fd75f88..add8193428af1a 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -613,6 +613,12 @@ void ShapeConstraintIRAnalysis::SetShapeOrDataForValue( context_.SetShapeOrDataForValue(val, shape_or_data); } +void ShapeConstraintIRAnalysis::ShareShapeOrData(Value from, Value to) { + if (context_.HasShapeOrDataForValue(from)) { + context_.SetShapeOrDataForValue(to, context_.GetShapeOrDataForValue(from)); + } +} + bool ShapeConstraintIRAnalysis::IsEqual(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) const { return context_.IsEqual(lhs, rhs); diff --git a/paddle/pir/src/pass/pass.cc b/paddle/pir/src/pass/pass.cc index 8d716c98a376ca..b9552f27e6b57c 100644 --- a/paddle/pir/src/pass/pass.cc +++ b/paddle/pir/src/pass/pass.cc @@ -89,8 +89,12 @@ GreedyRewriteConfig PatternRewritePass::InitializeConfig() { void PatternRewritePass::Run(Operation* op) { VLOG(4) << "Run PatternRewritePass: " << name(); - auto [_, num_rewrites] = - ApplyPatternsGreedily(op, patterns_, InitializeConfig()); + GreedyRewriteConfig config = InitializeConfig(); + if (Has(kValueReplaceHookAttr)) { + config.value_replaced_hook = + Get(kValueReplaceHookAttr); + } + auto [_, num_rewrites] = ApplyPatternsGreedily(op, patterns_, config); AddStatistics(num_rewrites); } @@ -202,6 +206,10 @@ bool PassManager::Run(Operation* op) { bool PassManager::Initialize(IrContext* context) { for (auto& pass : passes()) { if (!pass->Initialize(context)) return false; + if (value_replaced_hook_) { + pass->SetNotOwned(Pass::kValueReplaceHookAttr, + &value_replaced_hook_); + } } return true; diff --git a/paddle/pir/src/pattern_rewrite/pattern_match.cc b/paddle/pir/src/pattern_rewrite/pattern_match.cc index 9cf2585e97e444..c672d5364e626d 100644 --- a/paddle/pir/src/pattern_rewrite/pattern_match.cc +++ b/paddle/pir/src/pattern_rewrite/pattern_match.cc @@ -126,6 +126,9 @@ void RewriterBase::ReplaceOp(Operation* op, new_values.size(), common::errors::InvalidArgument("incorrect # of replacement values")); op->ReplaceAllUsesWith(new_values); + for (uint32_t i = 0; i < op->num_results(); ++i) { + NotifyValueReplaced(op->result(i), new_values[i]); + } NotifyOperationRemoved(op); op->Erase(); @@ -144,8 +147,10 @@ void RewriterBase::EraseOp(Operation* op) { // Find uses of `from` and replace it with `to`. void RewriterBase::ReplaceAllUsesWith(Value from, Value to) { - for (auto it = from.use_begin(); it != from.use_end();) + for (auto it = from.use_begin(); it != from.use_end();) { UpdateRootInplace(it.owner(), [&]() { (it++)->set_source(to); }); + } + NotifyValueReplaced(from, to); } // Find uses of `from` and replace them with `to` if the `functor` returns true. @@ -155,9 +160,15 @@ void RewriterBase::ReplaceUseIf(Value from, // Use post-increment operator for iterator since set_source() will change // `it`. // TODO(zhangbopd): Add unit test for this. + bool replaced = false; for (auto it = from.use_begin(); it != from.use_end();) { - if (functor(*it)) + if (functor(*it)) { UpdateRootInplace(it.owner(), [&]() { (it++)->set_source(to); }); + replaced = true; + } + } + if (replaced) { + NotifyValueReplaced(from, to); } } diff --git a/paddle/pir/src/pattern_rewrite/pattern_rewrite_driver.cc b/paddle/pir/src/pattern_rewrite/pattern_rewrite_driver.cc index 3a7161d5620c8c..57754f583b0450 100644 --- a/paddle/pir/src/pattern_rewrite/pattern_rewrite_driver.cc +++ b/paddle/pir/src/pattern_rewrite/pattern_rewrite_driver.cc @@ -53,6 +53,9 @@ class GreedyPatternRewriteDriver : public pir::PatternRewriter { } } } + if (config.value_replaced_hook) { + value_replaced_hook_fn_ = config.value_replaced_hook; + } } std::pair Simplify() { @@ -156,6 +159,12 @@ class GreedyPatternRewriteDriver : public pir::PatternRewriter { AddToWorklist(op); } + void NotifyValueReplaced(pir::Value from, pir::Value to) override { + if (value_replaced_hook_fn_) { + value_replaced_hook_fn_(from, to); + } + } + /// Add the given operation to the worklist. void AddToWorklist(pir::Operation* op) { if (config_.strict_mode == pir::GreedyRewriteStrictness::AnyOp || @@ -207,6 +216,7 @@ class GreedyPatternRewriteDriver : public pir::PatternRewriter { std::unordered_set strict_mode_filtered_ops_; pir::Region& region_; pir::PatternApplicator matcher_; + pir::VALUE_REPLACED_HOOK_FUNC value_replaced_hook_fn_ = nullptr; }; } // namespace From 2213b8729b29fa558fa8d33eff421823ac580185 Mon Sep 17 00:00:00 2001 From: blacksheep-Aristotle Date: Mon, 2 Dec 2024 19:21:42 +0800 Subject: [PATCH 089/288] [AutoParallel]:fix efficientattention_grad error when no use bias (#69824) --- paddle/phi/infermeta/backward.cc | 3 +++ 1 file changed, 3 insertions(+) diff --git a/paddle/phi/infermeta/backward.cc b/paddle/phi/infermeta/backward.cc index 43e0ef455ac26a..9ef2f3b73f0216 100644 --- a/paddle/phi/infermeta/backward.cc +++ b/paddle/phi/infermeta/backward.cc @@ -1165,6 +1165,9 @@ void MemoryEfficientAttentionGradInferMeta(const MetaTensor& query, bias_grad->share_lod(bias); bias_grad->set_dtype(bias.dtype()); bias_grad->set_layout(bias.layout()); + } else if (bias_grad) { + std::vector bias_grad_dims; + bias_grad->set_dims(common::make_ddim(bias_grad_dims)); } } From ef5b07af63ac9e95c87f47ede88cb92565866859 Mon Sep 17 00:00:00 2001 From: blacksheep-Aristotle Date: Mon, 2 Dec 2024 19:56:46 +0800 Subject: [PATCH 090/288] [AutoParallel]:fix auto parallel grad merge bug (#69805) * [AutoParallel]:fix auto parallel grad merge bug * [AutoParallel]:fix grad merge in auto parallel bug * [AutoParallel]:fix grad merge in auto parallel bug --- .../passes/auto_parallel_gradient_merge.py | 62 ++++++++++++------- 1 file changed, 40 insertions(+), 22 deletions(-) diff --git a/python/paddle/distributed/passes/auto_parallel_gradient_merge.py b/python/paddle/distributed/passes/auto_parallel_gradient_merge.py index b254fb8eaf0678..2d640bc3ac9ad3 100644 --- a/python/paddle/distributed/passes/auto_parallel_gradient_merge.py +++ b/python/paddle/distributed/passes/auto_parallel_gradient_merge.py @@ -269,6 +269,44 @@ def _append_gradient_merge_backward_op( return new_params_grads, grad_to_gradient_merge +def _move_used_grad_op(used_grad_op, grad): + move_to_opt_block_flag = True + move_to_opt_ops = [] + cannot_move_op = ["pd_op.send_v2", "pd_op.send"] + + def find_move_op(backward_op): + nonlocal move_to_opt_block_flag + if not move_to_opt_block_flag or backward_op in move_to_opt_ops: + return + if backward_op.name() in cannot_move_op: + move_to_opt_block_flag = False + return + if backward_op.num_operands() == 1: + move_to_opt_block_flag = True + move_to_opt_ops.append(backward_op) + elif backward_op.name() == "pd_op.slice": + move_to_opt_ops.append(backward_op) + for i in range(0, backward_op.num_operands()): + if not grad.is_same(backward_op.operand_source(i)): + move_to_opt_ops.append( + backward_op.operand_source(i).get_defining_op() + ) + move_to_opt_block_flag = True + else: + # NOTE(zhangwl):temp only consider one operand op + move_to_opt_block_flag = False + return + for op_result in backward_op.results(): + for next_op in op_result.all_used_ops(): + if next_op.op_role != int(OpRole.Optimize): + find_move_op(next_op) + + find_move_op(used_grad_op) + if move_to_opt_block_flag: + for move_op in move_to_opt_ops: + move_op.op_role = int(OpRole.Optimize) + + def _pir_append_gradient_merge_backward_op( main_program, startup_program, @@ -345,29 +383,9 @@ def _pir_append_gradient_merge_backward_op( ) new_gradient_merge_var_add_op.set_bool_attr("grad_merge_add", True) - # NOTE(zhangweilong): grad may in different device in auto_parallel, so need consider all_gather op + # NOTE(zhangweilong): grad may in different device in auto_parallel, so need consider all_gather/all_recdue/split/... op for used_grad_op in grad.all_used_ops(): - move_to_opt_block_flag = False - move_to_opt_ops = [] - if used_grad_op.num_operands() == 1: - move_to_opt_block_flag = True - move_to_opt_ops.append(used_grad_op) - elif used_grad_op.name() == "pd_op.slice": - move_to_opt_ops.append(used_grad_op) - for i in range(1, used_grad_op.num_operands()): - move_to_opt_ops.append( - used_grad_op.operand_source(i).get_defining_op() - ) - move_to_opt_block_flag = True - if move_to_opt_block_flag: - for used_op_result in used_grad_op.results(): - for used_op in used_op_result.all_used_ops(): - if used_op.op_role != int(OpRole.Optimize): - move_to_opt_block_flag = False - break - if move_to_opt_block_flag: - for move_op in move_to_opt_ops: - move_op.op_role = int(OpRole.Optimize) + _move_used_grad_op(used_grad_op, grad) opt_ops_use_grad = [ op From 1232cd55895d73dd2de49bff1623f69fdbc7f315 Mon Sep 17 00:00:00 2001 From: blacksheep-Aristotle Date: Mon, 2 Dec 2024 20:25:36 +0800 Subject: [PATCH 091/288] [AutoParallel]:fix vpp networking error (#69799) * [AutoParallel]:fix vpp networking error * [AutoParallel]:fix vpp networking bug * [AutoParallel]:fix vpp networking error * [AutoParallel]:fix vpp networking error * [AutoParallel]:fix vpp networking error * [AutoParallel]:fix vpp networking error --- paddle/fluid/pybind/pir.cc | 2 +- .../auto_parallel/static/pir_pass.py | 139 ++++++++++++++---- 2 files changed, 114 insertions(+), 27 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index ef9e2df5e4c02a..51fd3d79e092ae 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -1436,7 +1436,7 @@ void BindValue(py::module *m) { // The function will calculate the new local shape based on the global // shape and the dist_attr argument. .def("update_dist_attr", - [](Value &self, TensorDistAttribute dist_attr) { + [](Value &self, Attribute dist_attr) { self.set_type(dialect::CvtToPirDistType(self.type(), dist_attr)); }) .def("is_coalesced", diff --git a/python/paddle/distributed/auto_parallel/static/pir_pass.py b/python/paddle/distributed/auto_parallel/static/pir_pass.py index 167c1fd2c604fb..a60baa1ed04713 100644 --- a/python/paddle/distributed/auto_parallel/static/pir_pass.py +++ b/python/paddle/distributed/auto_parallel/static/pir_pass.py @@ -823,7 +823,7 @@ def _analyze_use_custom_mesh(ops, seg_method, pp_degree): return non_use_custom_mesh -def _set_process_mesh_and_chunk_id(op, process_mesh, chunk_id, set_mesh): +def _set_process_mesh_and_chunk_id(op, chunk_process_mesh, chunk_id, set_mesh): def set_var_origin_op_process_mesh(var_origin_op): var_origin_op_input_attr = var_origin_op.dist_attr.operands() var_origin_op_output_attr = var_origin_op.dist_attr.results() @@ -832,7 +832,7 @@ def set_var_origin_op_process_mesh(var_origin_op): ].as_tensor_dist_attr() var_origin_op_output_attr[0] = ( paddle.base.libpaddle.pir.create_tensor_dist_attribute( - process_mesh, + chunk_process_mesh, var_origin_op_output_attr[0].dims_mapping, var_origin_op_output_attr[0].partial_status, ) @@ -840,36 +840,105 @@ def set_var_origin_op_process_mesh(var_origin_op): var_origin_op.dist_attr = ( paddle.base.libpaddle.pir.create_op_dist_attribute( - process_mesh, + chunk_process_mesh, var_origin_op_input_attr, var_origin_op_output_attr, 0, ) ) - def set_process_mesh(vars, attrs): - for idx, (var, attr) in enumerate(zip(vars, attrs)): - var_dist_attr = var.dist_attr() - # Note(luchang): the var generated by builtin.combine will have mutilple dist_attr - if var_dist_attr and var_dist_attr.as_array_attr(): - var_array_attr = var_dist_attr.as_array_attr() - for i in range(len(var_array_attr)): - var_dist_attr = var_array_attr[i].as_tensor_dist_attr() + def get_var_process_mesh(var): + var_process_mesh = None + var_dist_attr = var.dist_attr() + + def get_attr_mesh(var_dist_attr): + if var_dist_attr: + if var_dist_attr.as_array_attr(): + var_array_attr = var_dist_attr.as_array_attr() + return var_array_attr[0].as_tensor_dist_attr().process_mesh + else: + return var_dist_attr.process_mesh + + if var_dist_attr: + var_process_mesh = get_attr_mesh(var_dist_attr) + elif var.is_combine(): + # NOTE(zhangwl): op var may is vec_type , need get var dist_attr one by one + var_list = var.type().as_vec_type() + var_list = var_list.as_list() if var_list is not None else var_list + var_attr_list = [] + for combine_var in var_list: + var_dist_attr = combine_var.as_dist_type().dist_attr() + var_process_mesh = get_attr_mesh(var_dist_attr) + if var_process_mesh is not None: + return var_process_mesh + + def get_var_attr_with_process_mesh( + var_dist_attr, var_origin_op, process_mesh + ): + # Note(luchang): the var generated by builtin.combine will have mutilple dist_attr + if var_dist_attr and var_dist_attr.as_array_attr(): + var_array_attr = var_dist_attr.as_array_attr() + for i in range(len(var_array_attr)): + var_dist_attr = var_array_attr[i].as_tensor_dist_attr() + if op_mesh is not None: if var_dist_attr.process_mesh == op_mesh: var_array_attr[i] = copy_dist_attr_with_new_member( var_dist_attr, new_process_mesh=process_mesh ) - var.update_dist_attr(var_array_attr) - elif var_dist_attr and var_dist_attr.process_mesh == op_mesh: - var.update_dist_attr( - copy_dist_attr_with_new_member( + else: + var_array_attr[i] = copy_dist_attr_with_new_member( var_dist_attr, new_process_mesh=process_mesh ) + return var_array_attr + elif var_dist_attr: + if op_mesh is not None: + if var_dist_attr.process_mesh == op_mesh: + if var_origin_op.name() in [ + "pd_op.data", + "builtin.parameter", + ]: + set_var_origin_op_process_mesh(var_origin_op) + var_attr = copy_dist_attr_with_new_member( + var_dist_attr, new_process_mesh=process_mesh + ) + return var_attr + else: + var_attr = copy_dist_attr_with_new_member( + var_dist_attr, new_process_mesh=process_mesh + ) + return var_attr + return var_dist_attr + + def set_var_process_mesh(var, process_mesh): + var_dist_attr = var.dist_attr() + var_origin_op = var.get_defining_op() + if var_dist_attr: + var_attr = get_var_attr_with_process_mesh( + var_dist_attr, var_origin_op, process_mesh + ) + if var_attr is not None: + var.update_dist_attr(var_attr) + elif var.is_combine(): + # NOTE(zhangwl): op var may is vec_type , need set var dist_attr one by one + var_list = var.type().as_vec_type() + var_list = var_list.as_list() if var_list is not None else var_list + var_attr_list = [] + for combine_var in var_list: + var_dist_attr = combine_var.as_dist_type().dist_attr() + var_attr_list.append( + get_var_attr_with_process_mesh( + var_dist_attr, var_origin_op, process_mesh + ) ) - var_origin_op = var.get_defining_op() - if var_origin_op.name() in ["pd_op.data", "builtin.parameter"]: - set_var_origin_op_process_mesh(var_origin_op) + var_array_attr = ( + paddle.base.libpaddle.pir.create_array_dist_attribute( + var_attr_list + ) + ) + var.update_dist_attr(var_array_attr) + def set_attrs_process_mesh(attrs, process_mesh): + for idx, attr in enumerate(attrs): if attr.as_array_attr(): array_attr = attr.as_array_attr() new_array_attr = [] @@ -892,17 +961,37 @@ def set_process_mesh(vars, attrs): tensor_attr, new_process_mesh=process_mesh ) + def set_process_mesh(vars, attrs, process_mesh): + if vars is not None: + for var in vars: + set_var_process_mesh(var, process_mesh) + if attrs is not None: + set_attrs_process_mesh(attrs, process_mesh) + + op_input_vars = op.operands_source() + op_output_vars = op.results() + # NOTE(zhangwl):dist_skip_op donnot have op_mesh + op_mesh = None + if op.name() in dist_skip_op_list: + input_var_process_mesh = None + # NOTE(zhangwl):dist_skip_op output_process_mesh must equal to input_process_mesh + for var in op_input_vars: + input_var_process_mesh = get_var_process_mesh(var) + if input_var_process_mesh is not None: + break + if input_var_process_mesh is not None: + set_process_mesh(op_output_vars, None, input_var_process_mesh) + return + op_dist_attr = op.dist_attr op_mesh = op_dist_attr.process_mesh op_input_attrs = op_dist_attr.operands() op_output_attrs = op_dist_attr.results() - op_input_vars = op.operands_source() - op_output_vars = op.results() - + # if op in seq_chunk , vpp need set var and op chunk_process_mesh and chunk_id if set_mesh: - set_process_mesh(op_input_vars, op_input_attrs) - set_process_mesh(op_output_vars, op_output_attrs) - op_mesh = process_mesh + set_process_mesh(op_input_vars, op_input_attrs, chunk_process_mesh) + set_process_mesh(op_output_vars, op_output_attrs, chunk_process_mesh) + op_mesh = chunk_process_mesh op.dist_attr = paddle.base.libpaddle.pir.create_op_dist_attribute( op_mesh, @@ -984,8 +1073,6 @@ def complete_chunk_id(dist_program, startup_program, pipeline_strategy): ) for idx in range(start_idx, end_idx): - if ops[idx].name() in dist_skip_op_list: - continue if ops[idx].name() == "dist_op.reshard": reshard_ops.append(ops[idx]) continue From f1c54e9f08135dfa17b0c5677ae5229587ec4cd3 Mon Sep 17 00:00:00 2001 From: liuruyan <44316842+liuruyan@users.noreply.github.com> Date: Mon, 2 Dec 2024 20:33:48 +0800 Subject: [PATCH 092/288] =?UTF-8?q?=E3=80=90CINN=E3=80=91Simplify=20specia?= =?UTF-8?q?l=20pattern=20(#69857)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add corner case * fix * fix --- paddle/cinn/common/CMakeLists.txt | 2 +- paddle/cinn/common/ir_util.cc | 121 +++++++++++++-- paddle/cinn/common/ir_util.h | 34 ++++- ...er_case.cc => simplify_special_pattern.cc} | 70 +++++++-- ...rner_case.h => simplify_special_pattern.h} | 0 paddle/cinn/ir/ir.cc | 140 +++--------------- test/cpp/pir/cinn/adt/index_expr_test.cc | 10 +- test/cpp/pir/cinn/adt/iter_simplify_test.cc | 1 - 8 files changed, 236 insertions(+), 142 deletions(-) rename paddle/cinn/common/{simplify_corner_case.cc => simplify_special_pattern.cc} (71%) rename paddle/cinn/common/{simplify_corner_case.h => simplify_special_pattern.h} (100%) diff --git a/paddle/cinn/common/CMakeLists.txt b/paddle/cinn/common/CMakeLists.txt index 5f69c98ff337ef..6731a908404132 100644 --- a/paddle/cinn/common/CMakeLists.txt +++ b/paddle/cinn/common/CMakeLists.txt @@ -26,7 +26,7 @@ gather_srcs( dim_expr_converter.cc broadcast_tree.cc iter_simplify.cc - simplify_corner_case.cc) + simplify_special_pattern.cc) cinn_cc_test(test_equation_graph_topo_walker SRCS equation_graph_topo_walker_test.cc DEPS gtest glog) diff --git a/paddle/cinn/common/ir_util.cc b/paddle/cinn/common/ir_util.cc index 6f272f88065520..bce89aedb3888c 100644 --- a/paddle/cinn/common/ir_util.cc +++ b/paddle/cinn/common/ir_util.cc @@ -19,7 +19,7 @@ #include #include "paddle/cinn/common/cas.h" -#include "paddle/cinn/common/simplify_corner_case.h" +#include "paddle/cinn/common/simplify_special_pattern.h" #include "paddle/cinn/ir/ir_mutator.h" #include "paddle/cinn/ir/ir_printer.h" #include "paddle/cinn/ir/op/ir_operators.h" @@ -176,7 +176,7 @@ Expr RampRelatedMul(Expr a, Expr b) { } // namespace static void MergeMulModInsertElements( - const std::vector &eles, + const std::vector &elems, std::list *mult_exprs, std::list> *mod_exprs, ir::IndexExpr *no_opt_sum, @@ -184,7 +184,7 @@ static void MergeMulModInsertElements( bool *has_mod) { *has_mult = false; *has_mod = false; - for (const ir::IndexExpr ele : eles) { + for (const ir::IndexExpr ele : elems) { auto mod_ptr = ele.As(); auto mult_ptr = ele.As(); if (mod_ptr) { @@ -234,6 +234,14 @@ static std::optional MergeMulModInner( } else if (inner_div_ptr) { ir::IndexExpr overall_mult = mult_inner.get() ? mult_inner * mult_outer : mult_outer; + VLOG(5) << "inner_div_ptr_b: " << inner_div_ptr->b().as_index(); + VLOG(5) << "overall_mult: " << overall_mult; + VLOG(5) << "mod_r_expr: " << mod_r_expr; + VLOG(5) << "inner_div_ptr_a - mod_l_expr: " + << inner_div_ptr->a().as_index() - mod_l_expr; + VLOG(5) << "ProveDivisible: " + << ProveDivisible(inner_div_ptr->a().as_index() - mod_l_expr, + mod_r_expr); if (overall_mult == inner_div_ptr->b().as_index() && overall_mult == mod_r_expr && ProveDivisible(inner_div_ptr->a().as_index() - mod_l_expr, @@ -273,14 +281,14 @@ static std::optional MergeMulModInner( ir::IndexExpr MergeMulMod(SymbolicExprAnalyzer *analyzer, const ir::IndexExpr &base) { ir::IndexExpr simplified_base = base.as_index().Normalize(); - std::vector eles = GetFlattenExprs(simplified_base); + std::vector elems = GetFlattenExprs(simplified_base); std::list mult_exprs; std::list> mod_exprs; ir::IndexExpr no_opt_sum; bool has_mult; bool has_mod; MergeMulModInsertElements( - eles, &mult_exprs, &mod_exprs, &no_opt_sum, &has_mult, &has_mod); + elems, &mult_exprs, &mod_exprs, &no_opt_sum, &has_mult, &has_mod); bool find_opt = false; std::list>::iterator search_mod_it = mod_exprs.begin(); @@ -297,9 +305,9 @@ ir::IndexExpr MergeMulMod(SymbolicExprAnalyzer *analyzer, ++search_mod_it; mod_exprs.erase(temp_mod_it); mult_exprs.erase(mult_it); - std::vector ret_eles = + std::vector ret_elems = GetFlattenExprs(ret.value()); - MergeMulModInsertElements(ret_eles, + MergeMulModInsertElements(ret_elems, &mult_exprs, &mod_exprs, &no_opt_sum, @@ -348,7 +356,7 @@ Expr IndiceToAbsOffset(const std::vector &shape, ::common::errors::InvalidArgument( "The size of shape should be less than or " "equal to the size of indices.")); - Expr res; + Expr res(0); ir::TryElevateInt32ToInt64(shape); common::cas_intervals_t var_intervals = common::CollectVarIntervalsOfExprs(indices); @@ -363,7 +371,8 @@ Expr IndiceToAbsOffset(const std::vector &shape, "the current data type of shape[{}] is {}", i, shape[i].type())); - Expr indice_cast = indices[i]; + + ir::IndexExpr indice_cast = indices[i]; optim::SimplifyCast(&indice_cast); if (res.defined()) { res = RampRelatedAdd(RampRelatedMul(res, shape[i]), indice_cast); @@ -373,6 +382,7 @@ Expr IndiceToAbsOffset(const std::vector &shape, } else { res = indice_cast; } + if (i > 0) { if (res.is_index()) { res = MergeMulMod(&analyzer, res.as_index()).as_index().Normalize(); @@ -660,6 +670,7 @@ bool ComparePriority(const ir::IndexExpr &lhs, const ir::IndexExpr &rhs) { bool IsSumPartialBySymbol(const ir::IndexExpr &expr, const ir::IndexExpr &symbol) { + if (expr == symbol) return true; // TODO(liujinnan): Check Ty switch (expr.node_type()) { case ir::IrNodeTy::IntImm: { @@ -690,10 +701,102 @@ bool IsSumPartialBySymbol(const ir::IndexExpr &expr, expr)); } } +ir::IndexExpr SimplifySymbolicAdd(const ir::IndexExpr &lhs, + const ir::IndexExpr &sym, + const ir::IndexExpr &outter_mul_factor) { + if (lhs == sym) return sym * (outter_mul_factor + ir::IndexExpr(1)); + switch (lhs.node_type()) { + case ir::IrNodeTy::IntImm: { + auto imm = lhs.As(); + if (imm->value != 0) + PADDLE_THROW(::common::errors::Fatal("Error in SimplifySymbolicAdd!")); + return ir::IndexExpr(0); + } + case ir::IrNodeTy::_Var_: { + return sym * (outter_mul_factor + ir::IndexExpr(1)); + } + case ir::IrNodeTy::Add: { + if (!common::IsSumPartialBySymbol(lhs->operand(0).as_index(), sym)) + return lhs->operand(0).as_index() + + SimplifySymbolicAdd( + lhs->operand(1).as_index(), sym, outter_mul_factor); + return SimplifySymbolicAdd( + lhs->operand(0).as_index(), sym, outter_mul_factor) + + lhs->operand(1).as_index(); + } + case ir::IrNodeTy::Mul: { + if (lhs->operand(1).is_constant() && + lhs->operand(1).get_constant() == -1) { + return SimplifySymbolicAdd( + lhs->operand(0).as_index(), sym, -outter_mul_factor) * + lhs->operand(1).as_index(); + } + if (lhs->operand(0).as_index() == sym) + return lhs->operand(0).as_index() * + (lhs->operand(1).as_index() + outter_mul_factor); + return (lhs->operand(0).as_index() + outter_mul_factor) * + lhs->operand(1).as_index(); + } + case ir::IrNodeTy::Mod: + PADDLE_THROW(::common::errors::Fatal("Error in SimplifySymbolicAdd!")); + case ir::IrNodeTy::Div: { + return SimplifySymbolicAdd( + lhs->operand(0).as_index(), + sym, + lhs->operand(1).as_index() * outter_mul_factor) / + lhs->operand(1).as_index(); + } + default: + PADDLE_THROW(::common::errors::InvalidArgument( + "Unsupported type of lhs in SimplifySymbolicAdd which is: %s", lhs)); + } +} + +ir::IndexExpr SimplifySymbolicDivide(const ir::IndexExpr &lhs, + const ir::IndexExpr &sym, + const ir::IrNodeTy &ty) { + if (lhs == sym) return ir::IndexExpr(1); + switch (lhs.node_type()) { + case ir::IrNodeTy::IntImm: { + auto imm = lhs.As(); + if (imm->value != 0) + PADDLE_THROW( + ::common::errors::Fatal("Error in SimplifySymbolicDivide!")); + return ir::IndexExpr(0); + } + case ir::IrNodeTy::_Var_: + return ir::IndexExpr(1); + case ir::IrNodeTy::Add: + return SimplifySymbolicDivide(lhs->operand(0).as_index(), sym, ty) + + SimplifySymbolicDivide(lhs->operand(1).as_index(), sym, ty); + case ir::IrNodeTy::Mul: { + if (!common::IsDivisiblieBySymbol(lhs->operand(0).as_index(), sym, ty)) + return lhs->operand(0).as_index() * + SimplifySymbolicDivide(lhs->operand(1).as_index(), sym, ty); + return SimplifySymbolicDivide(lhs->operand(0).as_index(), sym, ty) * + lhs->operand(1).as_index(); + } + case ir::IrNodeTy::Mod: + return SimplifySymbolicDivide( + lhs->operand(0).as_index(), sym, lhs.node_type()) % + SimplifySymbolicDivide( + lhs->operand(1).as_index(), sym, lhs.node_type()); + case ir::IrNodeTy::Div: { + return SimplifySymbolicDivide( + lhs->operand(0).as_index(), sym, lhs.node_type()) / + lhs->operand(1).as_index(); + } + default: + PADDLE_THROW(::common::errors::InvalidArgument( + "Unsupported type of lhs in SimplifySymbolicDivide which is: %s", + lhs)); + } +} bool IsDivisiblieBySymbol(const ir::IndexExpr &expr, const ir::IndexExpr &symbol, const ir::IrNodeTy &ty) { + if (expr == symbol) return true; // TODO(liujinnan): Check Ty switch (expr.node_type()) { case ir::IrNodeTy::IntImm: { diff --git a/paddle/cinn/common/ir_util.h b/paddle/cinn/common/ir_util.h index 1cf0e26814c650..c7df6a860fbfa9 100644 --- a/paddle/cinn/common/ir_util.h +++ b/paddle/cinn/common/ir_util.h @@ -262,6 +262,22 @@ bool ComparePriority(const ir::IndexExpr &lhs, const ir::IndexExpr &rhs); bool IsSumPartialBySymbol(const ir::IndexExpr &expr, const ir::IndexExpr &symbol); +/*! + * \brief Simplify the `lhs` by symbol `sym`. Usually run after + * `IsSumPartialBySymbol` + * + * \param lhs The expression to be simplified. + * \param sym The symbol to be checked. + * it may be `i, j ..` or `S0, S1 ..` or other symbolic expr. + * \param outter_mul_factor The scale of symbolic expr. + * e.g. `S0 * 4` ===> sym == S0, outter_mul_factor == 4 + * \return The expr after simplification. + */ +ir::IndexExpr SimplifySymbolicAdd( + const ir::IndexExpr &lhs, + const ir::IndexExpr &sym, + const ir::IndexExpr &outter_mul_factor = ir::IndexExpr(1)); + /*! * \brief Determines whether there are sub-parts in the `expr` that can be * simplified by `Div` operation with the input `symbol`. If true is returned, @@ -289,6 +305,20 @@ bool IsDivisiblieBySymbol(const ir::IndexExpr &expr, const ir::IndexExpr &symbol, const ir::IrNodeTy &ty); +/*! + * \brief Simplify the `lhs` by symbol `sym`. Usually run after + * `IsDivisiblieBySymbol` + * + * \param lhs The expression to be simplified. + * \param sym The symbol to be checked. + * it may be `i, j ..` or `S0, S1 ..` or other symbolic expr. + * \param ty ty is `Mod` or `Div`. + * \return The expr after simplification. + */ +ir::IndexExpr SimplifySymbolicDivide(const ir::IndexExpr &lhs, + const ir::IndexExpr &sym, + const ir::IrNodeTy &ty); + /*! * \brief Determine whether `lhs` is divisible by `rhs`, regardless of whether * `rhs` is a constant or a symbol. @@ -300,8 +330,8 @@ bool ProveDivisible(const ir::IndexExpr &lhs, const ir::IndexExpr &rhs); /*! * \brief Judge whether `candidate` is a negated index expression. - * \param lhs The expression to be checked. - * \param rhs The positive part + * \param candidate The expression to be checked. + * \param expr The positive part * \return A boolean value indicating whether `candidate` is negative. */ bool IsNegatedIndexExpr(const ir::IndexExpr &candidate, diff --git a/paddle/cinn/common/simplify_corner_case.cc b/paddle/cinn/common/simplify_special_pattern.cc similarity index 71% rename from paddle/cinn/common/simplify_corner_case.cc rename to paddle/cinn/common/simplify_special_pattern.cc index 97c652c32bef10..275c36e2c199cc 100644 --- a/paddle/cinn/common/simplify_corner_case.cc +++ b/paddle/cinn/common/simplify_special_pattern.cc @@ -13,7 +13,7 @@ // limitations under the License. #pragma once -#include "paddle/cinn/common/simplify_corner_case.h" +#include "paddle/cinn/common/simplify_special_pattern.h" #include #include #include @@ -58,6 +58,43 @@ std::optional DivMulAddModCornerCase(const ir::IndexExpr& lhs, return std::nullopt; } +// (S0 * 8 + S1 * 2 + S2) + (S1 * 2 + S2) * (-1) ===> 0 +std::optional AddMulCornerCase( + const ir::IndexExpr& lhs, + const ir::IndexExpr& rhs, + const ir::IndexExpr& scale = ir::IndexExpr(1)) { + auto rhsMul = rhs.As(); + if (!rhsMul) return std::nullopt; + if (!rhsMul->b().is_constant()) return std::nullopt; + + auto scale_ = scale * rhsMul->b().as_index(); + auto flatten = GetFlattenExprs(rhsMul->a()); + std::optional resOpt; + ir::IndexExpr res = lhs; + for (const auto& expr : flatten) { + if (auto innerMul = expr.As()) { + if (!innerMul->b().is_constant()) return std::nullopt; + auto resOpt = AddMulCornerCase(res, expr, scale_); + if (!resOpt.has_value()) + return std::nullopt; + else + res = resOpt.value(); + } else { + if (!IsSumPartialBySymbol(res, expr)) return std::nullopt; + } + } + + for (const auto& expr : flatten) { + if (expr.As()) continue; + if (expr.is_constant()) { + res = res + expr * scale_; + continue; + } + res = SimplifySymbolicAdd(res, expr, scale_); + } + return res; +} + // (S0 + S1 - (S0 + S1) % S2) % S2 == 0 // (S0 + S1 - (S0 + S1) % S2) / S2 == (S0 + S1) / S2 std::optional SubModCornerCase(const ir::IndexExpr& lhs, @@ -86,6 +123,20 @@ std::optional SubModCornerCase(const ir::IndexExpr& lhs, return isNeg ? innerMod->a().as_index() / rhs : -(innerMod->a().as_index() / rhs); } + + // For simplify mod case: ((S0 * 256 + S1) % 512 - S1) % 32 == 0 + if (!isDiv) { + auto diffBeforeNegation = diff; + auto isDiffNeg = IsNegatedIndexExpr(diff, diffBeforeNegation); + if (isDiffNeg) diff = diffBeforeNegation; + auto flatten_diff = GetFlattenExprs(diff); + bool isDivisible = true; + for (const auto& expr : flatten_diff) { + if (!isDivisible) break; + if (!ProveDivisible(expr, rhs)) isDivisible = false; + } + if (isDivisible) return ir::IndexExpr(0); + } } return std::nullopt; } @@ -125,9 +176,8 @@ std::optional SimplifyCornerCase(const ir::IndexExpr& expr) { std::optional SimplifyAddCornerCase(const ir::IndexExpr& lhs, const ir::IndexExpr& rhs) { - if (DivMulAddModCornerCase(lhs, rhs).has_value()) - return DivMulAddModCornerCase(lhs, rhs).value(); - + if (auto res = DivMulAddModCornerCase(lhs, rhs)) return res.value(); + if (auto res = AddMulCornerCase(lhs, rhs)) return res.value(); // Add other corner cases return std::nullopt; } @@ -140,21 +190,17 @@ std::optional SimplifyMulCornerCase(const ir::IndexExpr& lhs, std::optional SimplifyDivCornerCase(const ir::IndexExpr& lhs, const ir::IndexExpr& rhs) { - if (SubModCornerCase(lhs, rhs, true).has_value()) - return SubModCornerCase(lhs, rhs, true).value(); - if (MultiArgsDivAndMod(lhs, rhs, true).has_value()) - return MultiArgsDivAndMod(lhs, rhs, true).value(); + if (auto res = SubModCornerCase(lhs, rhs, true)) return res.value(); + if (auto res = MultiArgsDivAndMod(lhs, rhs, true)) return res.value(); // Add other corner cases return std::nullopt; } std::optional SimplifyModCornerCase(const ir::IndexExpr& lhs, const ir::IndexExpr& rhs) { - if (SubModCornerCase(lhs, rhs, false).has_value()) - return SubModCornerCase(lhs, rhs, false).value(); + if (auto res = SubModCornerCase(lhs, rhs, false)) return res.value(); // Add other corner cases - if (MultiArgsDivAndMod(lhs, rhs, false).has_value()) - return MultiArgsDivAndMod(lhs, rhs, false).value(); + if (auto res = MultiArgsDivAndMod(lhs, rhs, false)) return res.value(); return std::nullopt; } diff --git a/paddle/cinn/common/simplify_corner_case.h b/paddle/cinn/common/simplify_special_pattern.h similarity index 100% rename from paddle/cinn/common/simplify_corner_case.h rename to paddle/cinn/common/simplify_special_pattern.h diff --git a/paddle/cinn/ir/ir.cc b/paddle/cinn/ir/ir.cc index a2f5bbe22841ce..b9f70a22861f45 100644 --- a/paddle/cinn/ir/ir.cc +++ b/paddle/cinn/ir/ir.cc @@ -21,7 +21,7 @@ #include "paddle/cinn/common/cinn_value.h" #include "paddle/cinn/common/const_fold.h" #include "paddle/cinn/common/ir_util.h" -#include "paddle/cinn/common/simplify_corner_case.h" +#include "paddle/cinn/common/simplify_special_pattern.h" #include "paddle/cinn/ir/ir_printer.h" #include "paddle/cinn/ir/ir_utils.h" #include "paddle/cinn/ir/ir_visitor.h" @@ -1645,97 +1645,6 @@ IndexExpr ConstructIndexExprByNodeType(const IrNodeTy &ty, } } -IndexExpr SimplifySymbolicAdd( - const IndexExpr &lhs, - const IndexExpr &sym, - const IndexExpr &outter_mul_factor = IndexExpr(1)) { - switch (lhs.node_type()) { - case ir::IrNodeTy::IntImm: { - auto imm = lhs.As(); - if (imm->value != 0) - PADDLE_THROW(::common::errors::Fatal("Error in SimplifySymbolicAdd!")); - return IndexExpr(0); - } - case ir::IrNodeTy::_Var_: { - return sym * (outter_mul_factor + IndexExpr(1)); - } - case ir::IrNodeTy::Add: { - if (!common::IsSumPartialBySymbol(lhs->operand(0).as_index(), sym)) - return lhs->operand(0).as_index() + - SimplifySymbolicAdd( - lhs->operand(1).as_index(), sym, outter_mul_factor); - return SimplifySymbolicAdd( - lhs->operand(0).as_index(), sym, outter_mul_factor) + - lhs->operand(1).as_index(); - } - case ir::IrNodeTy::Mul: { - if (lhs->operand(1).is_constant() && - lhs->operand(1).get_constant() == -1) { - return SimplifySymbolicAdd( - lhs->operand(0).as_index(), sym, -outter_mul_factor) * - lhs->operand(1).as_index(); - } - if (lhs->operand(0).as_index() == sym) - return lhs->operand(0).as_index() * - (lhs->operand(1).as_index() + outter_mul_factor); - return (lhs->operand(0).as_index() + outter_mul_factor) * - lhs->operand(1).as_index(); - } - case ir::IrNodeTy::Mod: - PADDLE_THROW(::common::errors::Fatal("Error in SimplifySymbolicAdd!")); - case ir::IrNodeTy::Div: { - return SimplifySymbolicAdd( - lhs->operand(0).as_index(), - sym, - lhs->operand(1).as_index() * outter_mul_factor) / - lhs->operand(1).as_index(); - } - default: - PADDLE_THROW(::common::errors::InvalidArgument( - "Unsupported type of lhs in SimplifySymbolicAdd which is: %s", lhs)); - } -} - -IndexExpr SimplifySymbolicDivide(const IndexExpr &lhs, - const IndexExpr &sym, - const IrNodeTy &ty) { - switch (lhs.node_type()) { - case ir::IrNodeTy::IntImm: { - auto imm = lhs.As(); - if (imm->value != 0) - PADDLE_THROW( - ::common::errors::Fatal("Error in SimplifySymbolicDivide!")); - return IndexExpr(0); - } - case ir::IrNodeTy::_Var_: - return IndexExpr(1); - case ir::IrNodeTy::Add: - return SimplifySymbolicDivide(lhs->operand(0).as_index(), sym, ty) + - SimplifySymbolicDivide(lhs->operand(1).as_index(), sym, ty); - case ir::IrNodeTy::Mul: { - if (!common::IsDivisiblieBySymbol(lhs->operand(0).as_index(), sym, ty)) - return lhs->operand(0).as_index() * - SimplifySymbolicDivide(lhs->operand(1).as_index(), sym, ty); - return SimplifySymbolicDivide(lhs->operand(0).as_index(), sym, ty) * - lhs->operand(1).as_index(); - } - case ir::IrNodeTy::Mod: - return SimplifySymbolicDivide( - lhs->operand(0).as_index(), sym, lhs.node_type()) % - SimplifySymbolicDivide( - lhs->operand(1).as_index(), sym, lhs.node_type()); - case ir::IrNodeTy::Div: { - return SimplifySymbolicDivide( - lhs->operand(0).as_index(), sym, lhs.node_type()) / - lhs->operand(1).as_index(); - } - default: - PADDLE_THROW(::common::errors::InvalidArgument( - "Unsupported type of lhs in SimplifySymbolicDivide which is: %s", - lhs)); - } -} - IndexExpr Simplify(const IndexExpr &expr) { switch (expr.node_type()) { case ir::IrNodeTy::IntImm: @@ -1839,17 +1748,18 @@ static IndexExpr SimplifyAdd(const IndexExpr &lhs, const IndexExpr &rhs) { } // dynamic branch! - if (rhs.is_var() && common::IsSumPartialBySymbol(lhs, rhs)) - return SimplifySymbolicAdd(lhs, rhs); - if (auto rhs_mul = rhs.As()) { - if (rhs_mul->a().is_var() && rhs_mul->b().is_constant()) { - if (common::IsSumPartialBySymbol(lhs, rhs_mul->a().as_index())) { - return SimplifySymbolicAdd( - lhs, rhs_mul->a().as_index(), rhs_mul->b().as_index()); + if (!rhs.As()) { + if (common::IsSumPartialBySymbol(lhs, rhs)) + return cinn::common::SimplifySymbolicAdd(lhs, rhs); + if (auto rhs_mul = rhs.As()) { + if (rhs_mul->b().is_constant()) { + if (common::IsSumPartialBySymbol(lhs, rhs_mul->a().as_index())) { + return cinn::common::SimplifySymbolicAdd( + lhs, rhs_mul->a().as_index(), rhs_mul->b().as_index()); + } } } } - return Add::Make(lhs, rhs).as_index(); } @@ -1943,19 +1853,18 @@ static IndexExpr SimplifyDiv(const IndexExpr &lhs, const IndexExpr &rhs) { return lhsDiv->a().as_index() / (lrhs->value * rhsConst->value); } } + } else { + // dynamic branch! + if (common::IsDivisiblieBySymbol(lhs, rhs, ir::IrNodeTy::Div)) { + return cinn::common::SimplifySymbolicDivide(lhs, rhs, ir::IrNodeTy::Div); + } + // TODO(liujinnan): Deal dynamic shape, e.g. S0 / S1 / S2 ===> S0 / (S1 * + // S2). + // if (auto lhsDiv = lhs.As
()) { + // return lhsDiv->a().as_index() / (lhsDiv->b().as_index() * rhs); + // } } - // dynamic branch! - if (rhs.is_var() && - common::IsDivisiblieBySymbol(lhs, rhs, ir::IrNodeTy::Div)) { - return SimplifySymbolicDivide(lhs, rhs, ir::IrNodeTy::Div); - } - - // TODO(liujinnan): Deal dynamic shape, e.g. S0 / S1 / S2 ===> S0 / (S1 * S2). - // if (auto lhsDiv = lhs.As
()) { - // return lhsDiv->a().as_index() / (lhsDiv->b().as_index() * rhs); - // } - return Div::Make(lhs, rhs).as_index(); } @@ -1993,12 +1902,11 @@ static IndexExpr SimplifyMod(const IndexExpr &lhs, const IndexExpr &rhs) { if (llhsFactor % rhsConst->value == 0) return lhsMod->a().as_index() % rhsConst->value; } + } else { + // dynamic branch! + if (common::IsDivisiblieBySymbol(lhs, rhs, ir::IrNodeTy::Mod)) + return IndexExpr(0); } - - // dynamic branch! - if (rhs.is_var() && common::IsDivisiblieBySymbol(lhs, rhs, ir::IrNodeTy::Mod)) - return IndexExpr(0); - return Mod::Make(lhs, rhs).as_index(); } IndexExpr IndexExpr::operator-() const { return *this * IndexExpr(-1); } diff --git a/test/cpp/pir/cinn/adt/index_expr_test.cc b/test/cpp/pir/cinn/adt/index_expr_test.cc index 166b63344f54aa..15850511964fae 100644 --- a/test/cpp/pir/cinn/adt/index_expr_test.cc +++ b/test/cpp/pir/cinn/adt/index_expr_test.cc @@ -15,7 +15,7 @@ #include #include #include "paddle/cinn/common/integer_set.h" -#include "paddle/cinn/common/simplify_corner_case.h" +#include "paddle/cinn/common/simplify_special_pattern.h" #include "paddle/cinn/ir/ir.h" #include "paddle/cinn/ir/ir_base.h" #include "paddle/cinn/ir/ir_mutator.h" @@ -145,6 +145,11 @@ TEST(IndexExpr, IndexExpr_3) { ir::Expr q4 = (S4 + S5) / (S6 + S7) * (S6 + S7) + (S4 + S5) % (S6 + S7); ir::Expr q5 = (S4 + S5) / 5 * 5 + (S4 + S5) * 11 % 5; ir::Expr q14 = (S4 + S5) / (S6 * S7) * S6 * S7 + (S4 + S5) % (S6 * S7); + ir::Expr q15 = + (S4 * 256 + S5 + S6 * 1024) % 25088 / 512 * 512 + (S4 * 256 + S5) % 512; + ir::Expr q16 = + ((S4 * 256 + S5) / S6 / S7 * S7 + (S4 * 256 + S5) / S6 % S7) * S6 + + (S4 * 256 + S5) % S6; // `Div` corner cases ir::Expr q6 = (S4 % S5 - S4) / S5; @@ -172,6 +177,9 @@ TEST(IndexExpr, IndexExpr_3) { EXPECT_EQ(q12.as_index().Normalize(), ir::IndexExpr(0)); EXPECT_EQ(q13.as_index().Normalize(), ir::IndexExpr(0)); EXPECT_EQ(q14.as_index().Normalize(), ir::IndexExpr(S4 + S5)); + EXPECT_EQ(q15.as_index().Normalize(), + ir::IndexExpr((S4 * 256 + S5 + S6 * 1024)) % 25088); + EXPECT_EQ(q16.as_index().Normalize(), ir::IndexExpr(S4 * 256 + S5)); } } // namespace common } // namespace cinn diff --git a/test/cpp/pir/cinn/adt/iter_simplify_test.cc b/test/cpp/pir/cinn/adt/iter_simplify_test.cc index cc66685a1c37a4..4d142950eab6c8 100644 --- a/test/cpp/pir/cinn/adt/iter_simplify_test.cc +++ b/test/cpp/pir/cinn/adt/iter_simplify_test.cc @@ -259,7 +259,6 @@ TEST_F(TestIterSimplify, div) { TEST_EXPR(e1, gt1, i_j_k_fused / 8); TEST_EXPR(e2, gt2, i_j_k_fused / 32); TEST_EXPR(e3, gt3, i_j_k_fused); - TEST_EXPR(e4, gt4, i_j_k_fused * 2); TEST_EXPR(e5, gt5, i_j_k_fused / 2); TEST_EXPR(e6, gt6, (i_j_k_fused + 8) / 16); From c44e0406ad71b143151b74efea66c3def7139bda Mon Sep 17 00:00:00 2001 From: houj04 <35131887+houj04@users.noreply.github.com> Date: Mon, 2 Dec 2024 20:50:15 +0800 Subject: [PATCH 093/288] [XPU] fix fleet unittests (#68542) * [XPU] fix fleet unittests * [XPU] fix fleet unittests * refine: use new default parameter * revert unnecessary modifications. * revert unnecessary modifications. * fix cmakelist * revert unnecessary modifications. * fix cmakelist for recompute ut. --- paddle/phi/api/lib/data_transform.cc | 32 +++++++++++++++++++ .../group_sharded_optimizer_stage2.py | 8 ++++- .../sharding/group_sharded_stage2.py | 3 +- .../sharding/group_sharded_stage3.py | 10 ++++-- .../sharding/group_sharded_utils.py | 2 ++ test/collective/fleet/CMakeLists.txt | 19 ++++++++--- .../fleet/dygraph_group_sharded_stage2.py | 16 +++++++--- ...graph_group_sharded_stage2_comm_overlap.py | 21 +++++++++--- .../dygraph_group_sharded_stage2_offload.py | 5 ++- .../fleet/dygraph_group_sharded_stage3.py | 5 ++- .../dygraph_group_sharded_stage3_offload.py | 5 ++- .../fleet/hybrid_parallel_mp_bf16.py | 4 ++- .../test_parallel_dygraph_tensor_parallel.py | 6 +++- .../test_parallel_dygraph_dataparallel.py | 3 +- 14 files changed, 111 insertions(+), 28 deletions(-) diff --git a/paddle/phi/api/lib/data_transform.cc b/paddle/phi/api/lib/data_transform.cc index 5d39d9b2e13668..4f7cd1fd2db75d 100644 --- a/paddle/phi/api/lib/data_transform.cc +++ b/paddle/phi/api/lib/data_transform.cc @@ -143,6 +143,33 @@ phi::DenseTensor CastDataType(const phi::GPUContext& dev_ctx, } #endif +#ifdef PADDLE_WITH_XPU +phi::DenseTensor CastDataType(const phi::XPUContext& dev_ctx, + const phi::DenseTensor& tensor, + DataType dtype) { + switch (tensor.dtype()) { + case DataType::FLOAT32: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::FLOAT64: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::INT32: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::INT64: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::FLOAT16: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::BOOL: + return phi::Cast(dev_ctx, tensor, dtype); + case DataType::UINT8: + return phi::Cast(dev_ctx, tensor, dtype); + default: + PADDLE_THROW(common::errors::Unimplemented( + "Data type (%s) is not supported when casting data type.", + tensor.dtype())); + } +} +#endif + inline phi::DenseTensor TransDataType(const phi::DenseTensor& tensor, DataType dtype) { auto& pool = phi::DeviceContextPool::Instance(); @@ -161,6 +188,11 @@ inline phi::DenseTensor TransDataType(const phi::DenseTensor& tensor, auto* dev_ctx = static_cast(pool.Get(tensor.place())); return CastDataType(*dev_ctx, tensor, dtype); #endif +#ifdef PADDLE_WITH_XPU + } else if (tensor.place().GetType() == phi::AllocationType::XPU) { + auto* dev_ctx = static_cast(pool.Get(tensor.place())); + return CastDataType(*dev_ctx, tensor, dtype); +#endif #ifdef PADDLE_WITH_CUSTOM_DEVICE } else if (tensor.place().GetType() == phi::AllocationType::CUSTOM) { phi::DenseTensor out; diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py index 3873234b737466..d01e6680bd0bb6 100644 --- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py +++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_optimizer_stage2.py @@ -74,7 +74,7 @@ def __init__( optim, group=None, offload=False, - device="gpu", + device="xpu" if core.is_compiled_with_xpu() else "gpu", pretrain_sync_models=True, dp_group=None, **kw, @@ -590,6 +590,12 @@ def _step(self): ) .cast(dtype=param.dtype) ) + elif self._default_device == "xpu": + param.set_value( + self._master_params[param.name] + .to("xpu:" + str(self.dev_id)) + .cast(dtype=param.dtype) + ) else: param.set_value( self._master_params[param.name] diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py index 1baa0d815a4aa3..a75d6d1be843b7 100644 --- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py +++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage2.py @@ -31,6 +31,7 @@ from paddle import nn from paddle.distributed import collective from paddle.distributed.utils.log_utils import get_logger +from paddle.framework import core from .group_sharded_optimizer_stage2 import GroupShardedOptimizerStage2 from .group_sharded_storage import GradStorage @@ -66,7 +67,7 @@ def __init__( sync_buffers=False, buffer_max_size=2**23, # 8MB auto_refresh_trainable=True, - device="gpu", + device="xpu" if core.is_compiled_with_xpu() else "gpu", dp_group=None, ): super().__init__() diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py index b879ebd9c5f350..738f50f3111f15 100644 --- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py +++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_stage3.py @@ -104,7 +104,7 @@ def __init__( optimizer, group=None, sync_buffers=False, - device="gpu", + device="xpu" if core.is_compiled_with_xpu() else "gpu", segment_size=2**20, pretrain_sync_models=True, offload=False, @@ -310,7 +310,10 @@ def _clear_gradients(self): paddle.CustomPlace(self._default_device, DEV_ID), True ) else: - tmp_var = param.cuda(DEV_ID) + # both GPU and XPU + tmp_var = param.to( + self._default_device + ":" + (str)(DEV_ID) + ) if ( tmp_var.dtype == Type.fp32.value @@ -1197,7 +1200,8 @@ def _cpu2device(param): if DEV in paddle.device.get_all_custom_device_type(): tmp_p = param.fw_storage._copy_to(paddle.CustomPlace(DEV, DEV_ID), True) else: - tmp_p = param.fw_storage.cuda(DEV_ID) + # both GPU and XPU + tmp_p = param.fw_storage.to(DEV + ":" + (str)(DEV_ID)) if ( tmp_p.dtype == Type.fp32.value and param2dtype[param.name] == Type.fp16.value diff --git a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py index 552d36afb1ddab..eace26f3fecb9e 100644 --- a/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py +++ b/python/paddle/distributed/fleet/meta_parallel/sharding/group_sharded_utils.py @@ -167,6 +167,8 @@ def _dygraph_clip(self, params_grads): global_norm_var = global_norm_var._copy_to( paddle.CustomPlace(dev_type, dev_id), True ) + elif dev_type == "xpu": + global_norm_var = global_norm_var.to(self._device) else: global_norm_var = global_norm_var.cuda(dev_id) diff --git a/test/collective/fleet/CMakeLists.txt b/test/collective/fleet/CMakeLists.txt index 0318ac32984082..cae2ded0162f8a 100644 --- a/test/collective/fleet/CMakeLists.txt +++ b/test/collective/fleet/CMakeLists.txt @@ -61,8 +61,13 @@ if((WITH_ROCM) AND LOCAL_ALL_PLAT) "PADDLE_DIST_UT_PORT=21204;http_proxy=;https_proxy=;PYTHONPATH=../..:${PADDLE_BINARY_DIR}/python" ) endif() -if(WITH_NCCL OR WITH_RCCL) - if((WITH_GPU OR WITH_ROCM) AND LOCAL_ALL_PLAT) +if(WITH_NCCL + OR WITH_RCCL + OR WITH_XPU_BKCL) + if((WITH_GPU + OR WITH_ROCM + OR WITH_XPU) + AND LOCAL_ALL_PLAT) bash_test_modules( test_parallel_dygraph_mp_layers START_BASH @@ -608,13 +613,19 @@ if((WITH_GPU OR WITH_ROCM) AND LOCAL_ALL_PLAT) set_tests_properties(test_imperative_auto_mixed_precision_for_eager PROPERTIES TIMEOUT "300" LABELS "RUN_TYPE=DIST") endif() -if((WITH_GPU OR WITH_ROCM) AND LOCAL_ALL_PLAT) +if((WITH_GPU + OR WITH_ROCM + OR WITH_XPU) + AND LOCAL_ALL_PLAT) py_test_modules( test_dygraph_recompute_for_eager MODULES test_dygraph_recompute_for_eager ENVS "http_proxy=;https_proxy=;PYTHONPATH=../..:${PADDLE_BINARY_DIR}/python") endif() -if((WITH_GPU OR WITH_ROCM) AND LOCAL_ALL_PLAT) +if((WITH_GPU + OR WITH_ROCM + OR WITH_XPU) + AND LOCAL_ALL_PLAT) py_test_modules( test_dygraph_recompute MODULES test_dygraph_recompute ENVS "http_proxy=;https_proxy=;PYTHONPATH=../..:${PADDLE_BINARY_DIR}/python") diff --git a/test/collective/fleet/dygraph_group_sharded_stage2.py b/test/collective/fleet/dygraph_group_sharded_stage2.py index 46b0b8a449069d..61033956bbaeaa 100644 --- a/test/collective/fleet/dygraph_group_sharded_stage2.py +++ b/test/collective/fleet/dygraph_group_sharded_stage2.py @@ -99,7 +99,9 @@ def train_mlp( scale_fn_test=False, ): if sharding_stage != "dp": - group = paddle.distributed.new_group([0, 1], backend="nccl") + group = paddle.distributed.new_group( + [0, 1], backend="bkcl" if paddle.is_compiled_with_xpu() else "nccl" + ) if opt_group: optimizer = optimizer_setting( model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group @@ -149,7 +151,7 @@ def train_mlp( ) if sharding_stage == 2: - model.to(device="gpu") + model.to(device="xpu" if paddle.is_compiled_with_xpu() else "gpu") for eop in range(epoch): model.train() @@ -210,7 +212,10 @@ def test_dp_stage2(): ) for i in range(len(dp_params)): np.testing.assert_allclose( - dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6 + dp_params[i].numpy(), + stage2_params[i].numpy(), + rtol=1e-6, + atol=1e-8 if paddle.is_compiled_with_xpu() else 0, ) # stage2 accumulate grad @@ -232,7 +237,10 @@ def test_dp_stage2(): ) for i in range(len(dp_params)): np.testing.assert_allclose( - dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6 + dp_params[i].numpy(), + stage2_params[i].numpy(), + rtol=1e-6, + atol=1e-8 if paddle.is_compiled_with_xpu() else 0, ) # save/load model diff --git a/test/collective/fleet/dygraph_group_sharded_stage2_comm_overlap.py b/test/collective/fleet/dygraph_group_sharded_stage2_comm_overlap.py index 83196efd89e022..573caa86eaa943 100644 --- a/test/collective/fleet/dygraph_group_sharded_stage2_comm_overlap.py +++ b/test/collective/fleet/dygraph_group_sharded_stage2_comm_overlap.py @@ -98,7 +98,9 @@ def train_mlp( test_minimize=False, ): if sharding_stage != "dp": - group = paddle.distributed.new_group([0, 1], backend="nccl") + group = paddle.distributed.new_group( + [0, 1], backend="bkcl" if paddle.is_compiled_with_xpu() else "nccl" + ) if opt_group: optimizer = optimizer_setting( model=model, use_pure_fp16=use_pure_fp16, opt_group=opt_group @@ -140,7 +142,7 @@ def train_mlp( ) if sharding_stage == 2: - model.to(device="gpu") + model.to(device="xpu" if paddle.is_compiled_with_xpu() else "gpu") for eop in range(epoch): model.train() @@ -166,7 +168,10 @@ def train_mlp( optimizer.step() optimizer.clear_grad() - paddle.device.cuda.synchronize() + if paddle.is_compiled_with_xpu(): + paddle.device.xpu.synchronize() + else: + paddle.device.cuda.synchronize() if save_model: return model, optimizer @@ -201,7 +206,10 @@ def test_dp_stage2(): ) for i in range(len(dp_params)): np.testing.assert_allclose( - dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6 + dp_params[i].numpy(), + stage2_params[i].numpy(), + rtol=1e-6, + atol=1e-8 if paddle.is_compiled_with_xpu() else 0, ) # stage2 accumulate grad @@ -223,7 +231,10 @@ def test_dp_stage2(): ) for i in range(len(dp_params)): np.testing.assert_allclose( - dp_params[i].numpy(), stage2_params[i].numpy(), rtol=1e-6 + dp_params[i].numpy(), + stage2_params[i].numpy(), + rtol=1e-6, + atol=1e-8 if paddle.is_compiled_with_xpu() else 0, ) # save/load model diff --git a/test/collective/fleet/dygraph_group_sharded_stage2_offload.py b/test/collective/fleet/dygraph_group_sharded_stage2_offload.py index aa36631caa9bf4..0f07fc30852951 100644 --- a/test/collective/fleet/dygraph_group_sharded_stage2_offload.py +++ b/test/collective/fleet/dygraph_group_sharded_stage2_offload.py @@ -94,7 +94,10 @@ def train_mlp(model, offload=False, test=False): for dtype in optimizer.param_storages: for dst_rank, param_storage in optimizer.param_storages[dtype].items(): - param_storage.to(device="gpu", dtype=dtype) + param_storage.to( + device="xpu" if paddle.is_compiled_with_xpu() else "gpu", + dtype=dtype, + ) return model.parameters() diff --git a/test/collective/fleet/dygraph_group_sharded_stage3.py b/test/collective/fleet/dygraph_group_sharded_stage3.py index 39f3b1d55b07bf..648acbe189e828 100644 --- a/test/collective/fleet/dygraph_group_sharded_stage3.py +++ b/test/collective/fleet/dygraph_group_sharded_stage3.py @@ -366,10 +366,9 @@ def test_stage2_stage3(): ) # bfp16 - nccl_version = core.nccl_version() - if ( - nccl_version >= 21000 + paddle.is_compiled_with_xpu() + or core.nccl_version() >= 21000 and paddle.device.cuda.get_device_properties().major >= 8 ): stage2_params = train_mlp( diff --git a/test/collective/fleet/dygraph_group_sharded_stage3_offload.py b/test/collective/fleet/dygraph_group_sharded_stage3_offload.py index 315b252722d673..466ff7888ff8bf 100644 --- a/test/collective/fleet/dygraph_group_sharded_stage3_offload.py +++ b/test/collective/fleet/dygraph_group_sharded_stage3_offload.py @@ -216,9 +216,8 @@ def test_stage3_offload(): ) # bfp16 offload - nccl_version = core.nccl_version() - if ( - nccl_version >= 21000 + if paddle.is_compiled_with_xpu() or ( + core.nccl_version() >= 21000 and paddle.device.cuda.get_device_properties().major >= 8 ): stage3_params = train_mlp(mlp7, use_pure_fp16=True, use_bfp16=True) diff --git a/test/collective/fleet/hybrid_parallel_mp_bf16.py b/test/collective/fleet/hybrid_parallel_mp_bf16.py index 2ddf1868dd0e51..6aa40babdce1f3 100644 --- a/test/collective/fleet/hybrid_parallel_mp_bf16.py +++ b/test/collective/fleet/hybrid_parallel_mp_bf16.py @@ -60,7 +60,9 @@ def train_batch(self, batch, model, optimizer, is_mp): if __name__ == "__main__": - if ( + if paddle.is_compiled_with_xpu(): + unittest.main() + elif ( check_nccl_version_for_bf16() and paddle.device.cuda.get_device_properties().major >= 8 ): diff --git a/test/collective/fleet/test_parallel_dygraph_tensor_parallel.py b/test/collective/fleet/test_parallel_dygraph_tensor_parallel.py index 79aa2042aca968..184857f55ad0b8 100644 --- a/test/collective/fleet/test_parallel_dygraph_tensor_parallel.py +++ b/test/collective/fleet/test_parallel_dygraph_tensor_parallel.py @@ -18,6 +18,8 @@ TestMultipleAccelerators, ) +from paddle.framework import core + class TestHybridParallel(TestMultipleAccelerators): def test_hybrid_parallel_mp_random(self): @@ -35,7 +37,9 @@ def test_hybrid_parallel_mp_fp16(self): self.run_mnist_2accelerators('hybrid_parallel_mp_fp16.py') def test_hybrid_parallel_mp_bf16(self): - self.run_mnist_2accelerators('hybrid_parallel_mp_bf16.py') + # XPU will use its own fast_paddle lib for bf16 training, therefore skip ordinary ut here. + if not core.is_compiled_with_xpu(): + self.run_mnist_2accelerators('hybrid_parallel_mp_bf16.py') def test_hybrid_parallel_mp_clip_grad(self): self.run_mnist_2accelerators('hybrid_parallel_mp_clip_grad.py') diff --git a/test/legacy_test/test_parallel_dygraph_dataparallel.py b/test/legacy_test/test_parallel_dygraph_dataparallel.py index 497464cd281104..7672a29be76ea1 100644 --- a/test/legacy_test/test_parallel_dygraph_dataparallel.py +++ b/test/legacy_test/test_parallel_dygraph_dataparallel.py @@ -164,7 +164,7 @@ def run_mnist_2accelerators( target_file_name, allocator_strategy="auto_growth", need_envs={}, - accelerator_type="gpu", + accelerator_type="xpu" if base.core.is_compiled_with_xpu() else "gpu", ): if accelerator_type == "gpu": if ( @@ -198,6 +198,7 @@ def run_mnist_2accelerators( training_script=target_file_name, training_script_args=[], need_envs=need_envs, + accelerator_type=accelerator_type, ) while True: From 035be36f62e29c725d73ffa5dbd529cf7f332fb1 Mon Sep 17 00:00:00 2001 From: Ruibiao Chen Date: Mon, 2 Dec 2024 21:26:15 +0800 Subject: [PATCH 094/288] Upgrade openblas version (#69791) --- cmake/external/openblas.cmake | 13 ++++--------- 1 file changed, 4 insertions(+), 9 deletions(-) diff --git a/cmake/external/openblas.cmake b/cmake/external/openblas.cmake index 5c9112a4d4e893..2a58fbe7a0e4fd 100644 --- a/cmake/external/openblas.cmake +++ b/cmake/external/openblas.cmake @@ -19,16 +19,11 @@ set(CBLAS_INSTALL_DIR ${THIRD_PARTY_PATH}/install/openblas) set(CBLAS_SOURCE_DIR ${PADDLE_SOURCE_DIR}/third_party/openblas) set(CBLAS_TAG v0.3.7) -# Why use v0.3.18? The IDG business line encountered a random openblas error, -# which can be resolved after upgrading openblas. -# And why compile when gcc>8.2? Please refer to -# https://github.com/spack/spack/issues/19932#issuecomment-733452619 -# v0.3.18 only support gcc>=8.3 or gcc>=7.4 -if((CMAKE_CXX_COMPILER_ID STREQUAL "GNU") - AND CMAKE_CXX_COMPILER_VERSION VERSION_GREATER 8.2 +if(UNIX + AND NOT APPLE + AND NOT WITH_ROCM AND NOT WITH_XPU) - # We only compile with openblas 0.3.18 when gcc >= 8.3 - set(CBLAS_TAG v0.3.18) + set(CBLAS_TAG v0.3.28) endif() if(APPLE AND WITH_ARM) From 239715ad74fdc191911b36cc81339fc71a192c28 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Mon, 2 Dec 2024 22:34:51 +0800 Subject: [PATCH 095/288] [Dy2St] Optimize `range_block_do` performance (#69834) --- paddle/fluid/pybind/pir.cc | 35 +++++++++---------- .../jit/dy2static/pir_partial_program.py | 4 +-- 2 files changed, 18 insertions(+), 21 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 51fd3d79e092ae..2b7f60eebc0765 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -1690,30 +1690,27 @@ void BindInsertionPoint(pybind11::module *m) { return_value_policy::reference); } -std::list::const_iterator list_offset(const Block *block, - int start_idx) { - auto it = block->begin(); - while (it != block->end() && start_idx--) ++it; - return it; -} - template void range_block_do(const Block *block, - std::vector range, + std::pair range, F fn, S skip_fn) { - for (auto it = list_offset(block, range[0]); - it != list_offset(block, range[1]); - ++it) { - if (skip_fn(*it)) { + auto [start, end] = range; + if (start >= end) { + return; + } + auto it = block->begin(); + std::advance(it, start); + for (size_t i = start; i < end && it != block->end(); ++i, ++it) { + if (skip_fn(it)) { continue; } - fn(*it); + fn(it); } } template -void range_block_do(const Block *block, std::vector range, F fn) { +void range_block_do(const Block *block, std::pair range, F fn) { range_block_do(block, range, fn, [](Operation *op) { return false; }); } @@ -1754,8 +1751,8 @@ std::pair, std::unordered_set> AnalysisMiddleVariable(const Program &program, const std::vector &forward_inputs, const std::vector &backward_outputs, - const std::vector &forward_range, - const std::vector &backward_range) { + const std::pair &forward_range, + const std::pair &backward_range) { std::vector middle_values; std::unordered_set backward_used_values; @@ -1811,7 +1808,7 @@ using SplitedAttribute = std::map>; using SplitedResult = std::pair; static auto GetNoNeedBufferValue(const ::pir::Block *whole_block, - std::vector range) { + std::pair range) { // filter no need buffer values. std::unordered_set<::pir::Value> need_buffer_values; std::unordered_set<::pir::Value> no_need_buffer_values; @@ -1926,8 +1923,8 @@ SplitedResult SplitForwardBackward( const std::vector &forward_inputs_grads, const std::vector &forward_params_grads, const std::vector &forward_outputs_grads, - const std::vector &forward_range, - const std::vector &backward_range) { + const std::pair &forward_range, + const std::pair &backward_range) { std::vector forward_in_out_values; for (auto &v : std::vector({&forward_inputs, &forward_outputs, &forward_params})) { diff --git a/python/paddle/jit/dy2static/pir_partial_program.py b/python/paddle/jit/dy2static/pir_partial_program.py index 18cdd93e5a4072..6c4c323506f45c 100644 --- a/python/paddle/jit/dy2static/pir_partial_program.py +++ b/python/paddle/jit/dy2static/pir_partial_program.py @@ -260,8 +260,8 @@ def split_forward_backward(self): self.x_grad_values, self.param_grad_values, self.out_grad_values, - list(self.forward_range), - list(self.backward_range), + self.forward_range, + self.backward_range, ) return [fwd_prog, bwd_prog], prog_attr From b4e2d5d60656f7d995f47b1e5479927ef19aa785 Mon Sep 17 00:00:00 2001 From: Zhan Rongrui <46243324+zrr1999@users.noreply.github.com> Date: Mon, 2 Dec 2024 22:35:21 +0800 Subject: [PATCH 096/288] [SOT] Extract `FrameProxy` to separate file `frame_proxy.h` and `frame_proxy.c` (#69837) --------- Co-authored-by: SigureMo --- paddle/fluid/pybind/CMakeLists.txt | 1 + paddle/fluid/pybind/jit.cc | 4 + paddle/fluid/pybind/sot/cpython_internals.c | 8 +- paddle/fluid/pybind/sot/eval_frame.c | 120 +------------------ paddle/fluid/pybind/sot/eval_frame_tools.h | 12 +- paddle/fluid/pybind/sot/frame_proxy.c | 122 ++++++++++++++++++++ paddle/fluid/pybind/sot/frame_proxy.h | 63 ++++++++++ paddle/fluid/pybind/sot/guards.cc | 2 +- 8 files changed, 200 insertions(+), 132 deletions(-) create mode 100644 paddle/fluid/pybind/sot/frame_proxy.c create mode 100644 paddle/fluid/pybind/sot/frame_proxy.h diff --git a/paddle/fluid/pybind/CMakeLists.txt b/paddle/fluid/pybind/CMakeLists.txt index 1a29bf8f861d22..ca6a895b397e76 100755 --- a/paddle/fluid/pybind/CMakeLists.txt +++ b/paddle/fluid/pybind/CMakeLists.txt @@ -140,6 +140,7 @@ set(PYBIND_SRCS auto_parallel_py.cc sot/eval_frame_tools.cc sot/cpython_internals.c + sot/frame_proxy.c sot/eval_frame.c sot/guards.cc op_callstack_utils.cc diff --git a/paddle/fluid/pybind/jit.cc b/paddle/fluid/pybind/jit.cc index f78068e11feb1c..3dde422b554be4 100644 --- a/paddle/fluid/pybind/jit.cc +++ b/paddle/fluid/pybind/jit.cc @@ -22,6 +22,7 @@ limitations under the License. */ #include "paddle/fluid/jit/serializer.h" #include "paddle/fluid/pybind/sot/eval_frame.h" #include "paddle/fluid/pybind/sot/eval_frame_tools.h" +#include "paddle/fluid/pybind/sot/frame_proxy.h" #include "paddle/fluid/pybind/sot/guards.h" #include "paddle/fluid/pybind/sot/macros.h" #include "paddle/phi/common/data_type.h" @@ -119,6 +120,9 @@ void BindGuard(pybind11::module *m) { void BindSot(pybind11::module *m) { #if SOT_IS_SUPPORTED PyInit__eval_frame(); +#if PY_3_11_PLUS + PyInit__frame_proxy(); +#endif m->def( "set_eval_frame", [](const py::object &py_func) { diff --git a/paddle/fluid/pybind/sot/cpython_internals.c b/paddle/fluid/pybind/sot/cpython_internals.c index 6c997399e4066a..12b4d750b36100 100644 --- a/paddle/fluid/pybind/sot/cpython_internals.c +++ b/paddle/fluid/pybind/sot/cpython_internals.c @@ -14,10 +14,12 @@ limitations under the License. */ #include "paddle/fluid/pybind/sot/cpython_internals.h" -#include - #if SOT_IS_SUPPORTED +#if !PY_3_11_PLUS +#include +#endif + #if PY_3_11_PLUS #include #include @@ -682,7 +684,7 @@ static void Internal_take_ownership(PyFrameObject *f, } else { f->f_back = (PyFrameObject *)Py_NewRef(back); } -#if PY_VERSION_HEX < PY_3_12_0_HEX +#if !PY_3_12_PLUS frame->previous = NULL; #endif } diff --git a/paddle/fluid/pybind/sot/eval_frame.c b/paddle/fluid/pybind/sot/eval_frame.c index e156fff0de64a8..073d0d3780d429 100644 --- a/paddle/fluid/pybind/sot/eval_frame.c +++ b/paddle/fluid/pybind/sot/eval_frame.c @@ -18,16 +18,16 @@ limitations under the License. */ #include "paddle/fluid/pybind/sot/cpython_internals.h" #include "paddle/fluid/pybind/sot/eval_frame_tools.h" +#include "paddle/fluid/pybind/sot/frame_proxy.h" #include -#include -#if PY_3_8_PLUS && PY_VERSION_HEX < PY_3_9_0_HEX +#if PY_3_8_PLUS && !PY_3_9_PLUS #define Py_BUILD_CORE // internal/pycore_pymem.h need this macro #include #undef Py_BUILD_CORE #endif -#if PY_VERSION_HEX < PY_3_11_0_HEX +#if !PY_3_11_PLUS #include #endif @@ -35,115 +35,8 @@ limitations under the License. */ #include #if PY_3_11_PLUS -// To avoid the error: undefined symbol: _PyFrame_GetFrameObject, all we need is -// to redefine this function based source code in python3.11. The advantage is -// that we don't need any modification in eval_frame functions. -typedef _PyInterpreterFrame FrameObject; #define CALL_STAT_INC(name) ((void)0) -// clang-format off -// Define a proxy PyObject to access _PyInterpreterFrame's properties. -// It will be passed as an argument to the eval frame's callback. -typedef struct PyInterpreterFrameProxy { - PyObject_HEAD - _PyInterpreterFrame *frame; - #if PY_3_13_PLUS - PyObject* locals; - #endif -} PyInterpreterFrameProxy; -// clang-format on - -#define DECLARE_PROXY_PROPERTY(name) \ - static PyObject *PyInterpreterFrameProxy_property_##name( \ - PyInterpreterFrameProxy *self, void *closure) { \ - Py_XINCREF(self->frame->name); \ - return (PyObject *)self->frame->name; \ - } - -// clang-format off -#define REGISTER_PROXY_PROPERTY(property_name, func_name) \ - { #property_name, (getter)PyInterpreterFrameProxy_property_##func_name, NULL, NULL, NULL } -// clang-format on - -#if PY_3_13_PLUS -DECLARE_PROXY_PROPERTY(f_executable) -#else -DECLARE_PROXY_PROPERTY(f_code) -#endif -#if PY_3_13_PLUS -static PyObject *PyInterpreterFrameProxy_property_f_locals( - PyInterpreterFrameProxy *self, void *closure) { - Py_XINCREF(self->locals); - return self->locals; -} -#else -DECLARE_PROXY_PROPERTY(f_locals) -#endif -DECLARE_PROXY_PROPERTY(f_globals) -DECLARE_PROXY_PROPERTY(f_builtins) - -// Refer to -// https://github.com/python/cpython/blob/9414ddf91898892f3f6a672ae946931ee4b3ceb7/Objects/frameobject.c#L953-L961 -static PyObject *PyInterpreterFrameProxy_method_repr( - PyInterpreterFrameProxy *self) { -#if PY_3_13_PLUS - int lineno = Internal_PyUnstable_InterpreterFrame_GetLine(self->frame); -#else - int lineno = Internal_PyInterpreterFrame_GetLine(self->frame); -#endif - PyCodeObject *code = PyFrame_GET_CODE(self->frame); - return PyUnicode_FromFormat( - "", - self, - code->co_filename, - lineno, - code->co_name); -} - -static PyGetSetDef PyInterpreterFrameProxy_properties[] = { -#if PY_3_13_PLUS - REGISTER_PROXY_PROPERTY(f_code, f_executable), -#else - REGISTER_PROXY_PROPERTY(f_code, f_code), -#endif - REGISTER_PROXY_PROPERTY(f_locals, f_locals), - REGISTER_PROXY_PROPERTY(f_globals, f_globals), - REGISTER_PROXY_PROPERTY(f_builtins, f_builtins), - {NULL} /* Sentinel */ -}; - -// clang-format off -static PyTypeObject PyInterpreterFrameProxyType = { - PyVarObject_HEAD_INIT(NULL, 0) - .tp_name = "paddle.framework.core.PyInterpreterFrameProxy", - .tp_doc = PyDoc_STR("A proxy object for _PyInterpreterFrame, " - "it's only define all properties we need."), - .tp_repr = (reprfunc)PyInterpreterFrameProxy_method_repr, - .tp_basicsize = sizeof(PyInterpreterFrameProxy), - .tp_itemsize = 0, - .tp_flags = Py_TPFLAGS_DEFAULT, - .tp_getset = PyInterpreterFrameProxy_properties, -}; -// clang-format on - -PyInterpreterFrameProxy *PyInterpreterFrameProxy_New( - _PyInterpreterFrame *frame) { - PyTypeObject *type = &PyInterpreterFrameProxyType; - PyInterpreterFrameProxy *self = - (PyInterpreterFrameProxy *)type->tp_alloc(type, 0); - if (!self) { - // VLOG(7) << "Failed to allocate PyInterpreterFrameProxy"; - return NULL; - } - self->frame = frame; -#if PY_3_13_PLUS - self->locals = NULL; -#endif - return self; -} - -#else -typedef PyFrameObject FrameObject; #endif #ifdef _WIN32 @@ -556,13 +449,6 @@ PyMODINIT_FUNC PyInit__eval_frame() { Py_INCREF(Py_None); eval_frame_callback_set(Py_None); -#if PY_3_11_PLUS - if (PyType_Ready(&PyInterpreterFrameProxyType) < 0) { - // VLOG(7) << "PyInterpreterFrameProxyType has not been ready!"; - } - Py_INCREF(&PyInterpreterFrameProxyType); -#endif - return NULL; } diff --git a/paddle/fluid/pybind/sot/eval_frame_tools.h b/paddle/fluid/pybind/sot/eval_frame_tools.h index 4fcb9f9b75597b..417a4a5ed89777 100644 --- a/paddle/fluid/pybind/sot/eval_frame_tools.h +++ b/paddle/fluid/pybind/sot/eval_frame_tools.h @@ -19,21 +19,11 @@ extern "C" { #endif #include -#include +#include "paddle/fluid/pybind/sot/frame_proxy.h" #include "paddle/fluid/pybind/sot/macros.h" #if SOT_IS_SUPPORTED -#if PY_3_11_PLUS -#if PY_3_13_PLUS -#define Py_BUILD_CORE -#endif -#include -typedef _PyInterpreterFrame FrameObject; -#else -typedef PyFrameObject FrameObject; -#endif - int need_skip(FrameObject* frame); int is_code_without_graph(PyCodeObject* code); diff --git a/paddle/fluid/pybind/sot/frame_proxy.c b/paddle/fluid/pybind/sot/frame_proxy.c new file mode 100644 index 00000000000000..7b75c5f9931840 --- /dev/null +++ b/paddle/fluid/pybind/sot/frame_proxy.c @@ -0,0 +1,122 @@ +/* Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ + +#include "paddle/fluid/pybind/sot/frame_proxy.h" +#include "paddle/fluid/pybind/sot/macros.h" + +#if SOT_IS_SUPPORTED +#include + +#if PY_3_11_PLUS + +#define DECLARE_PROXY_PROPERTY(name) \ + static PyObject *PyInterpreterFrameProxy_property_##name( \ + PyInterpreterFrameProxy *self, void *closure) { \ + Py_XINCREF(self->frame->name); \ + return (PyObject *)self->frame->name; \ + } + +// clang-format off +#define REGISTER_PROXY_PROPERTY(property_name, func_name) \ + { #property_name, (getter)PyInterpreterFrameProxy_property_##func_name, NULL, NULL, NULL } +// clang-format on + +#if PY_3_13_PLUS +DECLARE_PROXY_PROPERTY(f_executable) +#else +DECLARE_PROXY_PROPERTY(f_code) +#endif +#if PY_3_13_PLUS +static PyObject *PyInterpreterFrameProxy_property_f_locals( + PyInterpreterFrameProxy *self, void *closure) { + Py_XINCREF(self->locals); + return self->locals; +} +#else +DECLARE_PROXY_PROPERTY(f_locals) +#endif +DECLARE_PROXY_PROPERTY(f_globals) +DECLARE_PROXY_PROPERTY(f_builtins) + +// Refer to +// https://github.com/python/cpython/blob/9414ddf91898892f3f6a672ae946931ee4b3ceb7/Objects/frameobject.c#L953-L961 +static PyObject *PyInterpreterFrameProxy_method_repr( + PyInterpreterFrameProxy *self) { +#if PY_3_13_PLUS + int lineno = Internal_PyUnstable_InterpreterFrame_GetLine(self->frame); +#else + int lineno = Internal_PyInterpreterFrame_GetLine(self->frame); +#endif + PyCodeObject *code = PyFrame_GET_CODE(self->frame); + return PyUnicode_FromFormat( + "", + self, + code->co_filename, + lineno, + code->co_name); +} + +static PyGetSetDef PyInterpreterFrameProxy_properties[] = { +#if PY_3_13_PLUS + REGISTER_PROXY_PROPERTY(f_code, f_executable), +#else + REGISTER_PROXY_PROPERTY(f_code, f_code), +#endif + REGISTER_PROXY_PROPERTY(f_locals, f_locals), + REGISTER_PROXY_PROPERTY(f_globals, f_globals), + REGISTER_PROXY_PROPERTY(f_builtins, f_builtins), + {NULL} /* Sentinel */ +}; + +// clang-format off +static PyTypeObject PyInterpreterFrameProxyType = { + PyVarObject_HEAD_INIT(NULL, 0) + .tp_name = "paddle.framework.core.PyInterpreterFrameProxy", + .tp_doc = PyDoc_STR("A proxy object for _PyInterpreterFrame, " + "it's only define all properties we need."), + .tp_repr = (reprfunc)PyInterpreterFrameProxy_method_repr, + .tp_basicsize = sizeof(PyInterpreterFrameProxy), + .tp_itemsize = 0, + .tp_flags = Py_TPFLAGS_DEFAULT, + .tp_getset = PyInterpreterFrameProxy_properties, +}; +// clang-format on + +PyInterpreterFrameProxy *PyInterpreterFrameProxy_New( + _PyInterpreterFrame *frame) { + PyTypeObject *type = &PyInterpreterFrameProxyType; + PyInterpreterFrameProxy *self = + (PyInterpreterFrameProxy *)type->tp_alloc(type, 0); + if (!self) { + // VLOG(7) << "Failed to allocate PyInterpreterFrameProxy"; + return NULL; + } + self->frame = frame; +#if PY_3_13_PLUS + self->locals = NULL; +#endif + return self; +} + +PyMODINIT_FUNC PyInit__frame_proxy() { + if (PyType_Ready(&PyInterpreterFrameProxyType) < 0) { + // VLOG(7) << "PyInterpreterFrameProxyType has not been ready!"; + } + Py_INCREF(&PyInterpreterFrameProxyType); + return NULL; +} + +#endif + +#endif diff --git a/paddle/fluid/pybind/sot/frame_proxy.h b/paddle/fluid/pybind/sot/frame_proxy.h new file mode 100644 index 00000000000000..a8b83d9f9fca42 --- /dev/null +++ b/paddle/fluid/pybind/sot/frame_proxy.h @@ -0,0 +1,63 @@ +/* Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. + +Licensed under the Apache License, Version 2.0 (the "License"); +you may not use this file except in compliance with the License. +You may obtain a copy of the License at + + http://www.apache.org/licenses/LICENSE-2.0 + +Unless required by applicable law or agreed to in writing, software +distributed under the License is distributed on an "AS IS" BASIS, +WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +See the License for the specific language governing permissions and +limitations under the License. */ +#pragma once + +#ifdef __cplusplus +extern "C" { +#endif + +#include +#include "paddle/fluid/pybind/sot/cpython_internals.h" +#include "paddle/fluid/pybind/sot/macros.h" + +#if SOT_IS_SUPPORTED + +#if !PY_3_11_PLUS +#include +#endif + +#if PY_3_11_PLUS + +#if PY_3_13_PLUS +#define Py_BUILD_CORE +#endif +#include + +typedef _PyInterpreterFrame FrameObject; + +// clang-format off +// Define a proxy PyObject to access _PyInterpreterFrame's properties. +// It will be passed as an argument to the eval frame's callback. +typedef struct PyInterpreterFrameProxy { + PyObject_HEAD + _PyInterpreterFrame *frame; + #if PY_3_13_PLUS + PyObject* locals; + #endif +} PyInterpreterFrameProxy; +// clang-format on + +PyInterpreterFrameProxy *PyInterpreterFrameProxy_New( + _PyInterpreterFrame *frame); +PyMODINIT_FUNC PyInit__frame_proxy(); + +#else +typedef PyFrameObject FrameObject; +#endif + +#endif + +#ifdef __cplusplus +} +#endif diff --git a/paddle/fluid/pybind/sot/guards.cc b/paddle/fluid/pybind/sot/guards.cc index 6abef8ce9172e2..50415b5a70c811 100644 --- a/paddle/fluid/pybind/sot/guards.cc +++ b/paddle/fluid/pybind/sot/guards.cc @@ -20,7 +20,7 @@ limitations under the License. */ #include #include -#if !defined(PyObject_CallOneArg) && PY_VERSION_HEX < PY_3_9_0_HEX +#if !defined(PyObject_CallOneArg) && !PY_3_9_PLUS static inline PyObject* PyObject_CallOneArg(PyObject* func, PyObject* arg) { return PyObject_CallFunctionObjArgs(func, arg, NULL); } From d808ec647121b8e7574df99e73293970b78c37ac Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Tue, 3 Dec 2024 07:30:11 +0800 Subject: [PATCH 097/288] [Auto Parallel] fix bug for transpose spmd (#69862) --- paddle/phi/infermeta/spmd_rules/transpose.cc | 3 ++- 1 file changed, 2 insertions(+), 1 deletion(-) diff --git a/paddle/phi/infermeta/spmd_rules/transpose.cc b/paddle/phi/infermeta/spmd_rules/transpose.cc index 06796b42eb72f1..cbafade6ea9ca6 100644 --- a/paddle/phi/infermeta/spmd_rules/transpose.cc +++ b/paddle/phi/infermeta/spmd_rules/transpose.cc @@ -87,13 +87,14 @@ SpmdInfo TransposeInferSpmd(const DistMetaTensor& x, GetDimsMappingForAxes(out_axes, axis_to_dim_map); auto x_dist_attr_dst = CopyTensorDistAttrForOutput(x_dist_attr_src); + x_dist_attr_dst.set_partial_status(x_dist_attr_src.partial_status()); x_dist_attr_dst.set_dims_mapping(x_dims_mapping); // initialize output dist_attr's process_mesh, batch_dim and dynamic dims with // input dist_attr. TensorDistAttr out_dist_attr = CopyTensorDistAttrForOutput(x_dist_attr_src); out_dist_attr.set_dims_mapping(out_dims_mapping); - out_dist_attr.set_partial_status(x_dist_attr_src.partial_status()); + out_dist_attr.set_partial_status(x_dist_attr_dst.partial_status()); VLOG(4) << "TransposeInferSpmd:"; VLOG(4) << "Input: shape: [" << str_join(x_shape) << "] " From c7ed2aa6a8d0d5d9bfbe6465c9be2587e443c3a4 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 3 Dec 2024 09:20:18 +0800 Subject: [PATCH 098/288] [fluid_ops] Remove lod_rank_table (#69812) * Fix * Fix --- paddle/fluid/operators/lod_rank_table_op.cc | 101 ------------------ paddle/fluid/operators/max_sequence_len_op.cc | 85 --------------- 2 files changed, 186 deletions(-) delete mode 100644 paddle/fluid/operators/lod_rank_table_op.cc delete mode 100644 paddle/fluid/operators/max_sequence_len_op.cc diff --git a/paddle/fluid/operators/lod_rank_table_op.cc b/paddle/fluid/operators/lod_rank_table_op.cc deleted file mode 100644 index 4782e2b751063b..00000000000000 --- a/paddle/fluid/operators/lod_rank_table_op.cc +++ /dev/null @@ -1,101 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ -#include "paddle/fluid/framework/op_registry.h" - -namespace paddle { -namespace framework { -class InferShapeContext; -class OpDesc; -class Scope; -template -class EmptyGradOpMaker; -} // namespace framework -namespace imperative { -class OpBase; -} // namespace imperative -} // namespace paddle - -namespace paddle { -namespace operators { - -class LoDRankTableOp : public framework::OperatorBase { - public: - LoDRankTableOp(const std::string &type, - const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorBase(type, inputs, outputs, attrs) {} - - private: - void RunImpl(const framework::Scope &scope, - const phi::Place &dev_place) const override { - auto x = scope.FindVar(Input("X"))->Get(); - auto *out = - scope.FindVar(Output("Out"))->GetMutable(); - VLOG(10) << "Level = " << static_cast(Attr("level")); - out->Reset(x.lod(), static_cast(Attr("level"))); - VLOG(10) << Input("X") << "'s lod information is " << *out; - } -}; - -class LoDRankTableOpProtoMaker : public framework::OpProtoAndCheckerMaker { - public: - void Make() override { - AddInput( - "X", - "(phi::DenseTensor) input lod tensor, must contain lod information."); - AddOutput("Out", "(LoDRankTable) The rank table of specific level."); - AddAttr("level", "(int) the specific lod level to rank.") - .SetDefault(0) - .EqualGreaterThan(0); - AddComment(R"DOC(Create LoDRanTable by phi::DenseTensor - -LoD Rank Table stores the `level` of `lod` which is ordered by sequence -length in descending order. It is useful when implement dynamic RNN and is -shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice -output operators. -)DOC"); - } -}; - -class LoDRankTableInferShape : public framework::InferShapeBase { - public: - void operator()(framework::InferShapeContext *context) const override { - PADDLE_ENFORCE_EQ( - context->HasInput("X"), - true, - common::errors::NotFound("LoDRankTable must have input X.")); - } -}; - -class LoDRankTableInferVarType : public framework::VarTypeInference { - public: - void operator()(framework::InferVarTypeContext *ctx) const override { - ctx->SetOutputType("Out", - framework::proto::VarType::LOD_RANK_TABLE, - framework::ALL_ELEMENTS); - } -}; - -} // namespace operators -} // namespace paddle - -REGISTER_OPERATOR( - lod_rank_table, - paddle::operators::LoDRankTableOp, - paddle::operators::LoDRankTableOpProtoMaker, - paddle::operators::LoDRankTableInferShape, - paddle::operators::LoDRankTableInferVarType, - paddle::framework::EmptyGradOpMaker, - paddle::framework::EmptyGradOpMaker); diff --git a/paddle/fluid/operators/max_sequence_len_op.cc b/paddle/fluid/operators/max_sequence_len_op.cc deleted file mode 100644 index 21c2b8ef948818..00000000000000 --- a/paddle/fluid/operators/max_sequence_len_op.cc +++ /dev/null @@ -1,85 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/framework/operator.h" - -namespace paddle { -namespace framework { -class InferShapeContext; -class OpDesc; -class Scope; -template -class EmptyGradOpMaker; -} // namespace framework -namespace imperative { -class OpBase; -} // namespace imperative -} // namespace paddle - -namespace paddle { -namespace operators { - -class MaxSequenceLenOp : public framework::OperatorBase { - public: - MaxSequenceLenOp(const std::string &type, - const framework::VariableNameMap &inputs, - const framework::VariableNameMap &outputs, - const framework::AttributeMap &attrs) - : OperatorBase(type, inputs, outputs, attrs) {} - - private: - void RunImpl(const framework::Scope &scope, - const phi::Place &dev_place) const override { - auto &rank_table = - scope.FindVar(Input("RankTable"))->Get(); - auto *out = scope.FindVar(Output("Out"))->GetMutable(); - int64_t *out_ptr = out->mutable_data({1}, phi::CPUPlace()); - *out_ptr = rank_table.items()[0].length; // NOLINT - } -}; - -class MaxSequenceLenOpProtoMaker : public framework::OpProtoAndCheckerMaker { - public: - void Make() override { - AddInput("RankTable", "Input variable which is a LoDRankTable object"); - AddOutput("Out", "The max sequence length"); - AddComment(R"DOC( - Given a LoDRankTable object, this layer returns the max length of - a batch of sequences. In fact, a LoDRankTable object contains a list of - tuples() and the list is already sorted by - sequence length in descending order, so the operator just returns the - sequence length of the first tuple element -)DOC"); - } -}; - -class MaxSequenceLenInferShape : public framework::InferShapeBase { - public: - void operator()(framework::InferShapeContext *context) const override { - OP_INOUT_CHECK( - context->HasInput("RankTable"), "Input", "RankTable", "MaxSequenceLen"); - context->SetOutputDim("Out", {1}); - } -}; -} // namespace operators -} // namespace paddle - -REGISTER_OPERATOR( - max_sequence_len, - paddle::operators::MaxSequenceLenOp, - paddle::operators::MaxSequenceLenOpProtoMaker, - paddle::operators::MaxSequenceLenInferShape, - paddle::framework::EmptyGradOpMaker, - paddle::framework::EmptyGradOpMaker); From a61ce21192ae593a8fcb4f045fc61a87e05758cd Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 3 Dec 2024 09:21:08 +0800 Subject: [PATCH 099/288] Fix (#69840) --- paddle/fluid/operators/seed_op.cc | 3 +- paddle/fluid/operators/seed_op.h | 58 ------------------------------- 2 files changed, 2 insertions(+), 59 deletions(-) delete mode 100644 paddle/fluid/operators/seed_op.h diff --git a/paddle/fluid/operators/seed_op.cc b/paddle/fluid/operators/seed_op.cc index 9eb18da45e0334..c82db1fdb064dd 100644 --- a/paddle/fluid/operators/seed_op.cc +++ b/paddle/fluid/operators/seed_op.cc @@ -12,7 +12,8 @@ // See the License for the specific language governing permissions and // limitations under the License. -#include "paddle/fluid/operators/seed_op.h" +#include "paddle/fluid/framework/op_registry.h" +#include "paddle/fluid/framework/op_version_registry.h" namespace paddle { namespace operators { diff --git a/paddle/fluid/operators/seed_op.h b/paddle/fluid/operators/seed_op.h deleted file mode 100644 index b9cbb81dd2db3a..00000000000000 --- a/paddle/fluid/operators/seed_op.h +++ /dev/null @@ -1,58 +0,0 @@ -// Copyright (c) 2019 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. -#pragma once - -#include "paddle/fluid/framework/op_registry.h" -#include "paddle/fluid/framework/op_version_registry.h" -#include "paddle/phi/core/generator.h" - -namespace paddle { -namespace operators { - -static int get_seed(const framework::ExecutionContext& context) { - int user_seed = context.Attr("seed"); - bool deterministic = context.Attr("deterministic"); - - int seed = 0; - if (!deterministic) { - // NOTE: fixed seed should only be used in unittest or for debug. - // Guarantee to use random seed in training. - if (user_seed != 0) { - seed = user_seed; - } else { - std::random_device rnd; - seed = rnd(); - } - } else { - std::string name = context.Attr("rng_name"); - auto rng = phi::GetRandomSeedGenerator(name); - do { // NOTE(wangxi): cpu dropout will use random seed if seed == 0 - seed = static_cast(rng->Random64()); - } while (seed == 0); - } - return seed; -} - -template -class CPUSeedKernel : public framework::OpKernel { - public: - void Compute(const framework::ExecutionContext& context) const override { - auto* out = context.Output("Out"); - auto* out_data = out->mutable_data(context.GetPlace()); - out_data[0] = get_seed(context); - } -}; - -} // namespace operators -} // namespace paddle From a2d91524c9daa8ca476496440f3400ff9f975046 Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Tue, 3 Dec 2024 10:35:19 +0800 Subject: [PATCH 100/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?TensorRT=20No.36=E3=80=91Add=20`pd=5Fop.flip`=20converter=20(#6?= =?UTF-8?q?9724)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add * fix codestyle * fix codestyle * fix codestyle * update * fix codestyle * add --- .../transforms/tensorrt/trt_op_marker_pass.cc | 2 + python/paddle/tensorrt/impls/linalg.py | 43 +++++++++++++ test/tensorrt/test_converter_linalg.py | 60 +++++++++++++++++++ 3 files changed, 105 insertions(+) diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index e6887de9618de5..6f852b540311ea 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -86,6 +86,7 @@ DEFINE_GENERAL_PATTERN(Floor, paddle::dialect::FloorOp) DEFINE_GENERAL_PATTERN(Roll, paddle::dialect::RollOp) DEFINE_GENERAL_PATTERN(Softplus, paddle::dialect::SoftplusOp) DEFINE_GENERAL_PATTERN(ThresholdedRelu, paddle::dialect::ThresholdedReluOp) +DEFINE_GENERAL_PATTERN(Flip, paddle::dialect::FlipOp) #undef DEFINE_GENERAL_PATTERN @@ -2140,6 +2141,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ADD_PATTERN(Roll) ADD_PATTERN(Softplus) ADD_PATTERN(ThresholdedRelu) + ADD_PATTERN(Flip) #if IS_TRT_VERSION_GE(8600) ADD_PATTERN(Layer_norm) #endif diff --git a/python/paddle/tensorrt/impls/linalg.py b/python/paddle/tensorrt/impls/linalg.py index 768afb45057a64..90d8db58077b19 100644 --- a/python/paddle/tensorrt/impls/linalg.py +++ b/python/paddle/tensorrt/impls/linalg.py @@ -16,7 +16,11 @@ import tensorrt as trt from paddle.tensorrt.converter_utils import ( + add_1D_constant_layer, broadcast, + get_shape_tensor_element, + trt_shape, + trt_sum, ) from paddle.tensorrt.register import converter_registry @@ -71,3 +75,42 @@ def bmm_converter(network, paddle_op, inputs): inputs[0], trt.MatrixOperation.NONE, inputs[1], trt.MatrixOperation.NONE ) return out.get_output(0) + + +@converter_registry.register("pd_op.flip", trt_version="8.x") +def flip_converter(network, paddle_op, inputs): + input_tensor = inputs[0] + input_dims = input_tensor.shape + rank = len(input_dims) + axis = paddle_op.attrs()["axis"] + axis = [a + rank if a < 0 else a for a in axis] + shape_tensor = trt_shape(network, input_tensor) + + def get_axis_length(axis_idx): + dim_val = input_dims[axis_idx] + if dim_val >= 0: + return add_1D_constant_layer(network, [dim_val], is_scalar=True) + else: + return get_shape_tensor_element( + network, shape_tensor, axis_idx, is_scalar=True + ) + + for axis_idx in axis: + loop_layer = network.add_loop() + trip_limit = get_axis_length(axis_idx) + loop_layer.add_trip_limit(trip_limit, trt.TripLimit.COUNT) + iterator = loop_layer.add_iterator(input_tensor, axis_idx, reverse=True) + zero_tensor = add_1D_constant_layer(network, [0]) + one_tensor = add_1D_constant_layer(network, [1]) + iRec_layer = loop_layer.add_recurrence(zero_tensor) + iCur = iRec_layer.get_output(0) + iNext_layer = trt_sum(network, iCur, one_tensor) + iRec_layer.set_input(1, iNext_layer) + loop_out_layer = loop_layer.add_loop_output( + iterator.get_output(0), trt.LoopOutput.CONCATENATE, axis_idx + ) + loop_out_layer.set_input(1, trip_limit) + input_tensor = loop_out_layer.get_output(0) + + identity_layer = network.add_identity(input_tensor) + return identity_layer.get_output(0) diff --git a/test/tensorrt/test_converter_linalg.py b/test/tensorrt/test_converter_linalg.py index 20605e03fd51ea..28162d1da0359b 100644 --- a/test/tensorrt/test_converter_linalg.py +++ b/test/tensorrt/test_converter_linalg.py @@ -67,5 +67,65 @@ def test_trt_result(self): self.check_trt_result() +class TestFlipTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.flip + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("float32"), + "axis": [0, 2], + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestFlipNegAxisTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.flip + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("float32"), + "axis": [-1, -3], + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestFlipIntTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.flip + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("int64"), + "axis": [0, 2], + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestFlipIntNegAxisTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.flip + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("int64"), + "axis": [-1, -3], + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + if __name__ == '__main__': unittest.main() From 30defc0177e83d1aa5800ec1ac778e72628a7aeb Mon Sep 17 00:00:00 2001 From: zyfncg Date: Tue, 3 Dec 2024 10:42:34 +0800 Subject: [PATCH 101/288] add value_replaced_hook in inference pass for cinn (#69888) --- paddle/fluid/inference/api/analysis_predictor.cc | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 1eeabed005e232..1c5114b5253a14 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -102,6 +102,7 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_util.h" #include "paddle/pir/include/dialect/shape/ir/shape_dialect.h" +#include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #endif #include "paddle/common/flags.h" @@ -843,6 +844,11 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { std::make_unique( ir_printing_conditions, ir_printing_conditions)); } + auto &shape_analysis = + pir::ShapeAnalysisManager::Instance().Get(pir_program_.get()); + pass_manager->SetValueReplacedHook([&](pir::Value from, pir::Value to) { + shape_analysis.ShareShapeOrData(from, to); + }); return pass_manager; }; From f8c16f4efe9b2feb99be6ba40de82ee853fa6e00 Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Tue, 3 Dec 2024 10:49:18 +0800 Subject: [PATCH 102/288] [CodeStyle][Typos][C-[32-38]] Fix typos (`compatiblity`,`compability`,`compitable`,`Compitable`,`compatable`,`compling`,`comple`,`complition`,`complext`,`compsite`) (#69847) --- _typos.toml | 10 ------ paddle/cinn/ir/ir.cc | 4 +-- paddle/cinn/ir/ir_utils.h | 4 +-- paddle/cinn/ir/stmt.cc | 2 +- paddle/cinn/ir/tensor.cc | 12 +++---- paddle/cinn/lang/placeholder.cc | 4 +-- .../pir_graph_analyzing/dim_relation.cc | 2 +- .../pir_graph_analyzing/fusion_iters.cc | 2 +- .../shardable_axes_base.cc | 32 +++++++++---------- paddle/cinn/operator_fusion/utils.cc | 2 +- paddle/cinn/operator_fusion/utils.h | 6 ++-- .../framework/ir/op_compat_sensible_pass.cc | 4 +-- .../framework/ir/op_compat_sensible_pass.h | 8 ++--- paddle/fluid/pybind/tensor_py.h | 4 +-- paddle/phi/backends/gpu/gpu_resources.cc | 2 +- paddle/phi/kernels/gpu/flash_attn_utils.h | 2 +- .../auto_parallel/static/engine.py | 2 +- python/paddle/tensor/creation.py | 2 +- test/legacy_test/test_hapi_amp.py | 4 +-- test/legacy_test/test_model.py | 2 +- .../test_comp_eager_reshape_double_grad.py | 2 +- 21 files changed, 51 insertions(+), 61 deletions(-) diff --git a/_typos.toml b/_typos.toml index 7d549381267022..608ba391bdaf60 100644 --- a/_typos.toml +++ b/_typos.toml @@ -61,16 +61,6 @@ cann = 'cann' vart = 'vart' checkings = 'checkings' childs = 'childs' -compability = 'compability' -compatiblity = 'compatiblity' -Compitable = 'Compitable' -compatable = 'compatable' -compitable = 'compitable' -compling = 'compling' -comple = 'comple' -complition = 'complition' -complext = 'complext' -compsite = 'compsite' comsume = 'comsume' Continer = 'Continer' contenst = 'contenst' diff --git a/paddle/cinn/ir/ir.cc b/paddle/cinn/ir/ir.cc index b9f70a22861f45..71ee0e74036c9c 100644 --- a/paddle/cinn/ir/ir.cc +++ b/paddle/cinn/ir/ir.cc @@ -602,7 +602,7 @@ Expr Store::Make(Expr tensor, Expr value, const std::vector &indices) { node->tensor = tensor; node->value = value; node->indices = - utils::GetCompitableStoreLoadIndices(tensor.as_tensor_ref(), indices); + utils::GetCompatibleStoreLoadIndices(tensor.as_tensor_ref(), indices); if (tensor->type() != Void()) { node->set_type( @@ -904,7 +904,7 @@ Expr Load::Make(Expr tensor, const std::vector &origin_indices) { true, ::common::errors::InvalidArgument("The tensor type is not valid. " "A valid tensor type is required.")); - const auto indices = utils::GetCompitableStoreLoadIndices( + const auto indices = utils::GetCompatibleStoreLoadIndices( tensor.as_tensor_ref(), origin_indices); PADDLE_ENFORCE_EQ( !indices.empty(), diff --git a/paddle/cinn/ir/ir_utils.h b/paddle/cinn/ir/ir_utils.h index 25cf9cdf0a49cf..15fed88e6fe35f 100644 --- a/paddle/cinn/ir/ir_utils.h +++ b/paddle/cinn/ir/ir_utils.h @@ -19,7 +19,7 @@ namespace cinn::ir::utils { // FIXME(Aurelius84): Return [Expr(1)] for 0D Tensor as the shape. -static inline std::vector GetCompitableShape( +static inline std::vector GetCompatibleShape( const std::vector& shape) { return shape.empty() ? std::vector({Expr(1)}) : shape; } @@ -32,7 +32,7 @@ static inline bool MaybeZeroRankTensor(const Tensor& tensor) { } // FIXME(Aurelius84): Return [Expr(0)] for 0D Tensor as the indices. -static inline std::vector GetCompitableStoreLoadIndices( +static inline std::vector GetCompatibleStoreLoadIndices( const Tensor& tensor, const std::vector& indices) { const bool should_fill_zero = indices.empty() && MaybeZeroRankTensor(tensor); return should_fill_zero ? std::vector({Expr(0)}) : indices; diff --git a/paddle/cinn/ir/stmt.cc b/paddle/cinn/ir/stmt.cc index 3485c928b6aae4..2129f44786c0db 100644 --- a/paddle/cinn/ir/stmt.cc +++ b/paddle/cinn/ir/stmt.cc @@ -80,7 +80,7 @@ Store _Store_::Make(Expr tensor, Expr value, const std::vector &indices) { ref->set_tensor(tensor); ref->set_value(value); ref->set_indices( - utils::GetCompitableStoreLoadIndices(tensor.as_tensor_ref(), indices)); + utils::GetCompatibleStoreLoadIndices(tensor.as_tensor_ref(), indices)); if (tensor->type() != Void()) { ref->set_type( diff --git a/paddle/cinn/ir/tensor.cc b/paddle/cinn/ir/tensor.cc index 1f64709435ce28..b272e4da03f84a 100644 --- a/paddle/cinn/ir/tensor.cc +++ b/paddle/cinn/ir/tensor.cc @@ -51,7 +51,7 @@ Tensor _Tensor_::Make(const std::string &name, "Required tensor name shall not be empty.")); auto n = make_shared<_Tensor_>(); n->name = name; - n->shape = utils::GetCompitableShape(shape); + n->shape = utils::GetCompatibleShape(shape); n->domain = domain; n->reduce_axis = reduce_axis; n->set_type(dtype); @@ -71,7 +71,7 @@ Tensor _Tensor_::Make(const std::string &name, "Required tensor name shall not be empty.")); auto n = make_shared<_Tensor_>(); n->name = name; - n->shape = utils::GetCompitableShape(shape); + n->shape = utils::GetCompatibleShape(shape); n->domain = domain; n->reduce_axis = reduce_axis; n->operation = PlaceholderOp::Make(n->name, n->shape, Float(32)); @@ -178,14 +178,14 @@ Expr Tensor::operator()(const std::vector &indices) const { ::common::errors::PreconditionNotMet( "Required tensor shall not be tuple type.")); auto *node = operator->(); - const auto compitable_indices = - utils::GetCompitableStoreLoadIndices(*this, indices); + const auto compatible_indices = + utils::GetCompatibleStoreLoadIndices(*this, indices); - PADDLE_ENFORCE_EQ(compitable_indices.size(), + PADDLE_ENFORCE_EQ(compatible_indices.size(), ndims(), ::common::errors::PreconditionNotMet( "number of indices not match the dimension")); - return Load::Make(*this, compitable_indices); + return Load::Make(*this, compatible_indices); } Expr _Tensor_::inline_expanded(const std::vector &indices) { diff --git a/paddle/cinn/lang/placeholder.cc b/paddle/cinn/lang/placeholder.cc index 995b8c7d69eba7..da9f0ab93de518 100644 --- a/paddle/cinn/lang/placeholder.cc +++ b/paddle/cinn/lang/placeholder.cc @@ -32,7 +32,7 @@ ir::Tensor CreatePlaceHolder(const std::vector &shape, expr_shape.push_back(Expr(s)); } return CreatePlaceHolder( - ir::utils::GetCompitableShape(expr_shape), type, name); + ir::utils::GetCompatibleShape(expr_shape), type, name); } ir::Tensor CreatePlaceHolder(const std::vector &shape, @@ -75,7 +75,7 @@ ir::Tensor CreatePlaceHolder(const std::vector &shape, ir::Tensor CreatePlaceHolder(const std::vector &origin_shape, Type type, const std::string &name) { - const auto shape = ir::utils::GetCompitableShape(origin_shape); + const auto shape = ir::utils::GetCompatibleShape(origin_shape); if (type.is_float(32)) { return Placeholder(name, shape); } else if (type.is_float(64)) { diff --git a/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc b/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc index 9aefabc6880dfc..00b3cc816c7931 100644 --- a/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc +++ b/paddle/cinn/operator_fusion/pir_graph_analyzing/dim_relation.cc @@ -109,7 +109,7 @@ static DimUsageRelation CreateOpRelativenessForBroadcast(pir::Operation* op) { static DimUsageRelation CreateOpRelativenessForReduce(pir::Operation* op) { const auto& reduce_axis_idx = GetReduceAxisIdx(op); DimUsageRelation res; - const size_t input_rank = GetCompitableRank(op->operand_source(0)); + const size_t input_rank = GetCompatibleRank(op->operand_source(0)); int out_idx = 0; bool keep_dim = GetReduceOpKeepDims(op); for (size_t i = 0; i < input_rank; i++) { diff --git a/paddle/cinn/operator_fusion/pir_graph_analyzing/fusion_iters.cc b/paddle/cinn/operator_fusion/pir_graph_analyzing/fusion_iters.cc index e46e2b9241b4fe..7e89e967eb3b23 100644 --- a/paddle/cinn/operator_fusion/pir_graph_analyzing/fusion_iters.cc +++ b/paddle/cinn/operator_fusion/pir_graph_analyzing/fusion_iters.cc @@ -134,7 +134,7 @@ FusionItersSignature FusionItersManager::GetItersSignature(pir::Operation* op) { if (axes.reduce_size > 0) { PADDLE_ENFORCE_LE( axes.reduce_size, - GetCompitableRank(op->operand(0).source()), + GetCompatibleRank(op->operand(0).source()), ::common::errors::InvalidArgument("The number of reduce_axis should be " "no more than output value ranks.")); } diff --git a/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc b/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc index 5e874fa0be3fb3..03aea53b8ddebd 100644 --- a/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc +++ b/paddle/cinn/operator_fusion/pir_graph_analyzing/shardable_axes_base.cc @@ -77,11 +77,11 @@ ShardableAxesSignature CreateDefaultSignature(pir::Operation* op) { ShardableAxesSignature result = ShardableAxesSignature(); for (int i = 0; i < op->num_operands(); ++i) { result.inputs.emplace_back( - CreateNewNamesWithRank(GetCompitableRank(op->operand_source(i)))); + CreateNewNamesWithRank(GetCompatibleRank(op->operand_source(i)))); } for (int i = 0; i < op->num_results(); ++i) { result.outputs.emplace_back( - CreateNewNamesWithRank(GetCompitableRank(op->result(i)))); + CreateNewNamesWithRank(GetCompatibleRank(op->result(i)))); } return result; } @@ -109,7 +109,7 @@ ShardableAxesSignature CreateSignatureForReduce(pir::Operation* reduce_op) { 1, ::common::errors::PreconditionNotMet( "Required reduce_op->num_results() shall be equal 1.")); - const size_t input_rank = GetCompitableRank(reduce_op->operand_source(0)); + const size_t input_rank = GetCompatibleRank(reduce_op->operand_source(0)); auto input_axes = CreateNewNamesWithRank(input_rank); const std::vector reduce_axis_idx = GetReduceAxisIdx(reduce_op); @@ -152,12 +152,12 @@ ShardableAxesSignature CreateSignatureForReduce(pir::Operation* reduce_op) { ShardableAxesSignature CreateSignatureForElementWise(pir::Operation* op) { ShardableAxesSignature result = ShardableAxesSignature(); - int64_t rank = GetCompitableRank(op->result(0)); + int64_t rank = GetCompatibleRank(op->result(0)); auto same_axes = CreateNewNamesWithRank(rank); for (int i = 0; i < op->num_operands(); ++i) { PADDLE_ENFORCE_EQ(rank, - GetCompitableRank(op->operand_source(i)), + GetCompatibleRank(op->operand_source(i)), ::common::errors::PreconditionNotMet( "Required all inputs rank shall be equal output in " "elementwise op.")); @@ -165,7 +165,7 @@ ShardableAxesSignature CreateSignatureForElementWise(pir::Operation* op) { } for (int i = 0; i < op->num_results(); ++i) { PADDLE_ENFORCE_EQ(rank, - GetCompitableRank(op->result(i)), + GetCompatibleRank(op->result(i)), ::common::errors::PreconditionNotMet( "Required all outputs rank shall be equal each other " "in elementwise op.")); @@ -188,7 +188,7 @@ ShardableAxesSignature CreateSignatureForTranspose(pir::Operation* op) { "Required transpose_op->num_results() shall be equal 1.")); const auto input_axes = - CreateNewNamesWithRank(GetCompitableRank(op->operand_source(0))); + CreateNewNamesWithRank(GetCompatibleRank(op->operand_source(0))); std::vector perm = GetInt32ArrayAttributeData(op->attributes().at("perm")); @@ -224,7 +224,7 @@ ShardableAxesSignature CreateSignatureForSlice( "Required slice_op->num_results() shall be equal 1.")); const auto input_axes = - CreateNewNamesWithRank(GetCompitableRank(op->operand_source(0))); + CreateNewNamesWithRank(GetCompatibleRank(op->operand_source(0))); const auto [slice_axis, keepdim] = GetSliceAxis(op); const auto output_axes = [&]() -> decltype(auto) { @@ -266,8 +266,8 @@ ShardableAxesSignature CreateSignatureForBroadcast( "Required broad_cast_value is not empty.")); const auto& [input_value, output_value] = broad_cast_value.value(); - const int input_rank = GetCompitableRank(input_value); - const int output_rank = GetCompitableRank(output_value); + const int input_rank = GetCompatibleRank(input_value); + const int output_rank = GetCompatibleRank(output_value); PADDLE_ENFORCE_GE( output_rank, input_rank, @@ -278,7 +278,7 @@ ShardableAxesSignature CreateSignatureForBroadcast( // output. for (int i = 0; i < op->num_operands(); ++i) { result.inputs.emplace_back( - CreateNewNamesWithRank(GetCompitableRank(op->operand_source(i)))); + CreateNewNamesWithRank(GetCompatibleRank(op->operand_source(i)))); } // Create output axes. Compare axis one by one, from back to front. @@ -309,8 +309,8 @@ ShardableAxesSignature CreateSignatureForReshape( pir::ShapeConstraintIRAnalysis* shape_analysis) { const auto input_value = op->operand_source(0); const auto output_value = op->result(0); - const auto input_rank = GetCompitableRank(op->operand_source(0)); - const auto output_rank = GetCompitableRank(op->result(0)); + const auto input_rank = GetCompatibleRank(op->operand_source(0)); + const auto output_rank = GetCompatibleRank(op->result(0)); const auto in_shape = GetDimExprsFromValue(input_value); const auto out_shape = GetDimExprsFromValue(output_value); @@ -320,7 +320,7 @@ ShardableAxesSignature CreateSignatureForReshape( if (op->name() == "pd_op.reshape" && op->num_operands() == 2) { result.inputs.emplace_back( - CreateNewNamesWithRank(GetCompitableRank(op->operand_source(1)))); + CreateNewNamesWithRank(GetCompatibleRank(op->operand_source(1)))); } if (GetRank(input_value) == 0 || GetRank(output_value) == 0) { @@ -387,7 +387,7 @@ ShardableAxesSignature CreateSignatureForReshape( ShardableAxesSignature CreateSignatureForConcat( pir::Operation* op, ShardableAxesInfoManager* axes_manager) { - size_t rank = GetCompitableRank(op->result(0)); + size_t rank = GetCompatibleRank(op->result(0)); const auto same_axes = CreateNewNamesWithRank(rank - 1); const auto axis_attr = @@ -406,7 +406,7 @@ ShardableAxesSignature CreateSignatureForConcat( ShardableAxesSignature result = ShardableAxesSignature(); for (int i = 0; i < op->num_operands(); ++i) { PADDLE_ENFORCE_EQ(rank, - GetCompitableRank(op->operand_source(i)), + GetCompatibleRank(op->operand_source(i)), ::common::errors::PreconditionNotMet( "Required all inputs rank shall be equal output in " "concat op.")); diff --git a/paddle/cinn/operator_fusion/utils.cc b/paddle/cinn/operator_fusion/utils.cc index ec25f2aaf15e30..ec346a76c258f5 100644 --- a/paddle/cinn/operator_fusion/utils.cc +++ b/paddle/cinn/operator_fusion/utils.cc @@ -55,7 +55,7 @@ std::vector GetInt32ArrayAttributeData( } std::vector GetReduceAxisIdx(pir::Operation* reduce_op) { - const size_t input_rank = GetCompitableRank(reduce_op->operand_source(0)); + const size_t input_rank = GetCompatibleRank(reduce_op->operand_source(0)); const auto& attr_val = reduce_op->attributes().at("axis"); PADDLE_ENFORCE_EQ(attr_val.isa<::pir::ArrayAttribute>(), true, diff --git a/paddle/cinn/operator_fusion/utils.h b/paddle/cinn/operator_fusion/utils.h index da5b6da2b3e861..1e37e85292c26e 100644 --- a/paddle/cinn/operator_fusion/utils.h +++ b/paddle/cinn/operator_fusion/utils.h @@ -61,9 +61,9 @@ static size_t GetRank(pir::Value value) { return value.type().dyn_cast().dims().size(); } -// FIXME(Aurelius84): 0D Tensor is not compitable with other rank. +// FIXME(Aurelius84): 0D Tensor is not compatible with other rank. // So we need to add a special case for 0D Tensor. -static size_t GetCompitableRank(pir::Value value) { +static size_t GetCompatibleRank(pir::Value value) { size_t rank = GetRank(value); return rank == 0 ? 1 : rank; } @@ -404,7 +404,7 @@ struct ValueDim { static std::vector GetAllValueDimFromValue(const pir::Value& v) { std::vector value_dims; - size_t rank = GetCompitableRank(v); + size_t rank = GetCompatibleRank(v); for (size_t i = 0; i < rank; ++i) { value_dims.emplace_back(v, i); } diff --git a/paddle/fluid/framework/ir/op_compat_sensible_pass.cc b/paddle/fluid/framework/ir/op_compat_sensible_pass.cc index b186d65aac5406..1f1ceaddad98fb 100644 --- a/paddle/fluid/framework/ir/op_compat_sensible_pass.cc +++ b/paddle/fluid/framework/ir/op_compat_sensible_pass.cc @@ -220,7 +220,7 @@ bool OpCompat::Judge(const OpDesc& op_desc, const std::string& pass_name) { LOG(WARNING) << " Attribute(" << attr_compat.first << ") of Op(" << op_name_ << ") is not defined in opProto or is in extra set!" - << "The compatable check for this attribute is not use." + << "The compatible check for this attribute is not use." << " Please remove it from the precondition of pass: " << pass_name.c_str(); } @@ -298,7 +298,7 @@ OpCompat& OpCompatSensiblePass::AddOpCompat(OpCompat&& op_compat) { return *(op_compat_judgers_[name]); } -//! Tell the Op compability of a subgraph. +//! Tell the Op compatibility of a subgraph. bool OpCompatSensiblePass::IsCompat( const GraphPatternDetector::subgraph_t& subgraph, Graph*) const { PADDLE_ENFORCE_EQ(op_compat_judgers_.empty(), diff --git a/paddle/fluid/framework/ir/op_compat_sensible_pass.h b/paddle/fluid/framework/ir/op_compat_sensible_pass.h index 4c941e169b89e7..9c720c88b8efa9 100644 --- a/paddle/fluid/framework/ir/op_compat_sensible_pass.h +++ b/paddle/fluid/framework/ir/op_compat_sensible_pass.h @@ -154,7 +154,7 @@ class OpCompat { /** * OpCompatSensiblePass is a base class for all the passes thouse is sensitive * to Op update. - * There are two methods to help tell the compability of an Op + * There are two methods to help tell the compatibility of an Op * bool IsCompat(const GraphPatternDetector::subgraph_t& subgraph, Graph* g); * bool IsCompat(const OpDesc& op_desc); * @@ -172,7 +172,7 @@ class OpCompat { * class FcFusePass : public OpCompatSensiblePass { * public: * FcFusePass() { - * // define Mul op compatiblity. + * // define Mul op compatibility. * AddOpCompat(OpCompat("Mul")) * .AddInput("Input").IsTensor().End() * .AddAttr("in_num_col_dims").IsNumGE(1); @@ -195,12 +195,12 @@ class OpCompatSensiblePass : public Pass { /** * Developer should push the compatibility `teller` for each kind of Op in the * subgraph. - * NOTE One should add all the related op compatiblity in the construct so + * NOTE One should add all the related op compatibility in the construct so * that all the following methods are valid. */ OpCompat& AddOpCompat(OpCompat&& op_compat); - //! Tell the Op compability of a subgraph. + //! Tell the Op compatibility of a subgraph. bool IsCompat(const GraphPatternDetector::subgraph_t& subgraph, Graph* g) const; diff --git a/paddle/fluid/pybind/tensor_py.h b/paddle/fluid/pybind/tensor_py.h index 072871f594e484..9c60552d87e69b 100644 --- a/paddle/fluid/pybind/tensor_py.h +++ b/paddle/fluid/pybind/tensor_py.h @@ -196,7 +196,7 @@ struct npy_format_descriptor> { // print '{0:14s} : {1:40s}'.format(str(k), v) return "F"; } - static constexpr auto name = _("complext64"); + static constexpr auto name = _("complex64"); }; template <> @@ -214,7 +214,7 @@ struct npy_format_descriptor> { // print '{0:14s} : {1:40s}'.format(str(k), v) return "D"; } - static constexpr auto name = _("complext128"); + static constexpr auto name = _("complex128"); }; template <> diff --git a/paddle/phi/backends/gpu/gpu_resources.cc b/paddle/phi/backends/gpu/gpu_resources.cc index aa3e89e08a8fde..8553bc42026d80 100644 --- a/paddle/phi/backends/gpu/gpu_resources.cc +++ b/paddle/phi/backends/gpu/gpu_resources.cc @@ -166,7 +166,7 @@ void InitGpuProperties(Place place, << get_cudnn_major(cudnn_dso_ver) << "." << get_cudnn_minor(cudnn_dso_ver) << "."; - // Check CUDA/CUDNN version compatiblity + // Check CUDA/CUDNN version compatibility auto local_cuda_version = (*driver_version / 1000) * 10 + (*driver_version % 100) / 10; auto compile_cuda_version = diff --git a/paddle/phi/kernels/gpu/flash_attn_utils.h b/paddle/phi/kernels/gpu/flash_attn_utils.h index 7a0cbaae7be06a..d5e56fe7aaceaa 100644 --- a/paddle/phi/kernels/gpu/flash_attn_utils.h +++ b/paddle/phi/kernels/gpu/flash_attn_utils.h @@ -391,7 +391,7 @@ static void CheckFlashAttnStatus(const bool status) { static void RaiseNotSupportedError(int version = 2) { PADDLE_THROW(common::errors::Unimplemented( "FlashAttentio%d is unsupported, please check " - "the GPU compability and CUDA Version.", + "the GPU compatibility and CUDA Version.", version)); } diff --git a/python/paddle/distributed/auto_parallel/static/engine.py b/python/paddle/distributed/auto_parallel/static/engine.py index 9b1ca75b63a438..f8499e5deec395 100644 --- a/python/paddle/distributed/auto_parallel/static/engine.py +++ b/python/paddle/distributed/auto_parallel/static/engine.py @@ -810,7 +810,7 @@ def _parallel_pir(self, mode): # and all the Pass in this Part should be optional to allow consistence in dynamic and static mode. if self._strategy.auto_mode == "semi-auto": # TODO(xxxx) Step 2.1 Entire Graph Completion in Pir. - # dist_program = apply_complition_pass(dist_program) + # dist_program = apply_completion_pass(dist_program) pass elif self._strategy.auto_mode == "random" or "full_random": # TODO(caozhou) Step 2.3 Basic Random / MCMC Algorithm for Fully Auto Parallel Search. diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index b12d00fe09d12a..24697cf78367f6 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -1394,7 +1394,7 @@ def _check_attr(attr, message): 'int32', 'int64', 'complex64', - 'comple128', + 'complex128', ], 'eye', ) diff --git a/test/legacy_test/test_hapi_amp.py b/test/legacy_test/test_hapi_amp.py index 7ef944a7f91e81..1590267e1c8ad6 100644 --- a/test/legacy_test/test_hapi_amp.py +++ b/test/legacy_test/test_hapi_amp.py @@ -144,7 +144,7 @@ def test_dynamic_check_input(self): "O3", ] if not base.is_compiled_with_cuda(): - self.skipTest('module not tested when ONLY_CPU compling') + self.skipTest('module not tested when ONLY_CPU compiling') paddle.set_device('gpu') net = LeNet() model = Model(net) @@ -171,7 +171,7 @@ def test_static_check_input(self): paddle.enable_static() amp_configs = {"level": "O2", "use_pure_fp16": True} if not base.is_compiled_with_cuda(): - self.skipTest('module not tested when ONLY_CPU compling') + self.skipTest('module not tested when ONLY_CPU compiling') paddle.set_device('gpu') net = LeNet() diff --git a/test/legacy_test/test_model.py b/test/legacy_test/test_model.py index 6493623e255ac5..60edb53a87503f 100644 --- a/test/legacy_test/test_model.py +++ b/test/legacy_test/test_model.py @@ -188,7 +188,7 @@ class TestModel(unittest.TestCase): @classmethod def setUpClass(cls): if not base.is_compiled_with_cuda(): - cls().skipTest('module not tested when ONLY_CPU compling') + cls().skipTest('module not tested when ONLY_CPU compiling') cls.device = paddle.set_device('gpu') base.enable_dygraph(cls.device) diff --git a/test/prim/prim/vjp/eager/test_comp_eager_reshape_double_grad.py b/test/prim/prim/vjp/eager/test_comp_eager_reshape_double_grad.py index 212d79564d4fe2..0d53141100f44d 100644 --- a/test/prim/prim/vjp/eager/test_comp_eager_reshape_double_grad.py +++ b/test/prim/prim/vjp/eager/test_comp_eager_reshape_double_grad.py @@ -44,7 +44,7 @@ def test_reshape_double_grad_comp(self): def actual(primal0, shape): core.set_prim_eager_enabled(True) paddle.disable_static() - # diable rshape_grad to trigger the compsite double_grad + # diable rshape_grad to trigger the composite double_grad core._set_prim_backward_blacklist("reshape_grad") x = paddle.to_tensor(primal0, dtype='float32', stop_gradient=False) From 0f90243955d18a0c23cae3f6056fdcf5498d69f2 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Tue, 3 Dec 2024 10:50:17 +0800 Subject: [PATCH 103/288] [SOT] Use tuple `InputSpec` to avoid type check error (#69853) --- .../jit/sot/opcode_translator/executor/function_graph.py | 2 +- python/paddle/jit/sot/symbolic/compile_cache.py | 9 ++++++--- python/paddle/jit/sot/symbolic/symbolic_context.py | 4 +++- 3 files changed, 10 insertions(+), 5 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py index 73e568cd9843d5..ecce8d92e2506e 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py +++ b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py @@ -433,7 +433,7 @@ def compile_graph(self, *ret_vars: VariableBase) -> CompileGraphResult: symbolic_inputs = self._find_tensor_inputs(input_names) compiled_fn = self.sir_ctx.compile_fn( statement_ir.name, - [var.meta.to_input_spec() for var in symbolic_inputs], + tuple(var.meta.to_input_spec() for var in symbolic_inputs), **self._kwargs, ) return compiled_fn, (statement_ir, symbolic_inputs, symbolic_outputs) diff --git a/python/paddle/jit/sot/symbolic/compile_cache.py b/python/paddle/jit/sot/symbolic/compile_cache.py index cebe1ac110d187..55446ca6d4f2a3 100644 --- a/python/paddle/jit/sot/symbolic/compile_cache.py +++ b/python/paddle/jit/sot/symbolic/compile_cache.py @@ -104,7 +104,10 @@ def amp_cast_inputs(self, args, kwargs): def graph_size(self): if self.partial_program is None: input_spec = convert_meta_to_input_spec( - [self.SIR.symbol_meta_map[symbol] for symbol in self.SIR.inputs] + tuple( + self.SIR.symbol_meta_map[symbol] + for symbol in self.SIR.inputs + ) ) ( self.concrete_program, @@ -181,7 +184,7 @@ def key_fn( self, context: SymbolicTraceContext, sir_name: str, - input_spec: list[InputSpec], + input_spec: tuple[InputSpec, ...], **kwargs, ): """ @@ -204,7 +207,7 @@ def value_fn( self, context: SymbolicTraceContext, sir_name: str, - input_spec: list[InputSpec], + input_spec: tuple[InputSpec, ...], **kwargs, ): """ diff --git a/python/paddle/jit/sot/symbolic/symbolic_context.py b/python/paddle/jit/sot/symbolic/symbolic_context.py index d7c83f75962226..5cbf497cc7151f 100644 --- a/python/paddle/jit/sot/symbolic/symbolic_context.py +++ b/python/paddle/jit/sot/symbolic/symbolic_context.py @@ -156,7 +156,9 @@ def graph_size(self): return DummyFunc() - def compile_fn(self, sir_name: str, input_spec: list[InputSpec], **kwargs): + def compile_fn( + self, sir_name: str, input_spec: tuple[InputSpec, ...], **kwargs + ): """ start compile and return the python function, which must can be to_static without errors. """ From 8c5e85456b1f7872db1f656280203870a1283e0a Mon Sep 17 00:00:00 2001 From: LiYuRio <63526175+LiYuRio@users.noreply.github.com> Date: Tue, 3 Dec 2024 10:50:34 +0800 Subject: [PATCH 104/288] increase timeout for unit case (#69880) --- test/collective/fleet/CMakeLists.txt | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/test/collective/fleet/CMakeLists.txt b/test/collective/fleet/CMakeLists.txt index cae2ded0162f8a..4d86203f6c64eb 100644 --- a/test/collective/fleet/CMakeLists.txt +++ b/test/collective/fleet/CMakeLists.txt @@ -361,14 +361,14 @@ if(LOCAL_ALL_ARCH AND LOCAL_ALL_PLAT) START_BASH ../../legacy_test/dist_test.sh TIMEOUT - "120" + "220" LABELS "RUN_TYPE=DIST" ENVS "PADDLE_DIST_UT_PORT=21242;http_proxy=;https_proxy=;PYTHONPATH=../..:${PADDLE_BINARY_DIR}/python" ) set_tests_properties(test_parallel_dygraph_sharding_parallel - PROPERTIES TIMEOUT "120") + PROPERTIES TIMEOUT "220") endif() if(LOCAL_ALL_ARCH AND LOCAL_ALL_PLAT) bash_test_modules( From 8464c04b11000c2d3b0fd55d9236adad7f57b9d5 Mon Sep 17 00:00:00 2001 From: Ayakouji <148307532+aquagull@users.noreply.github.com> Date: Tue, 3 Dec 2024 10:52:24 +0800 Subject: [PATCH 105/288] =?UTF-8?q?=E3=80=90Paddle=20Tensor=20No.26?= =?UTF-8?q?=E3=80=91fix=20svdvals=20(#69820)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix svdvals * fix * fix * fix * fix bug in op_gen * fix svdvals and restore op_gen --- paddle/phi/kernels/cpu/svdvals_kernel.cc | 2 +- python/paddle/tensor/linalg.py | 15 +------------ test/legacy_test/test_svdvals_op.py | 26 ++++++++++++---------- test/white_list/op_threshold_white_list.py | 1 + 4 files changed, 17 insertions(+), 27 deletions(-) diff --git a/paddle/phi/kernels/cpu/svdvals_kernel.cc b/paddle/phi/kernels/cpu/svdvals_kernel.cc index b8c59ae1e615aa..f15bf66adb1b6e 100644 --- a/paddle/phi/kernels/cpu/svdvals_kernel.cc +++ b/paddle/phi/kernels/cpu/svdvals_kernel.cc @@ -107,7 +107,7 @@ void SvdvalsKernel(const Context& dev_ctx, 0, phi::errors::InvalidArgument("The batch size of Input(X) must be > 0.")); DDim s_dims; - if (batches == 1) { + if (x_dims.size() <= 2) { s_dims = {k}; } else { s_dims = {batches, k}; diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index 6a5e4070d0d0e4..f070246493cbd6 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -3025,20 +3025,7 @@ def svdvals(x: Tensor, name: str | None = None) -> Tensor: Tensor(shape=[2], dtype=float32, place=Place(cpu), stop_gradient=True, [8.14753819, 0.78589684]) """ - if in_dynamic_or_pir_mode(): - return _C_ops.svdvals(x) - else: - check_variable_and_dtype(x, 'dtype', ['float32', 'float64'], 'svdvals') - helper = LayerHelper('svdvals', **locals()) - s = helper.create_variable_for_type_inference(dtype=x.dtype) - attrs = {} - helper.append_op( - type='svdvals', - inputs={'X': [x]}, - outputs={'S': s}, - attrs=attrs, - ) - return s + return _C_ops.svdvals(x) def _conjugate(x): diff --git a/test/legacy_test/test_svdvals_op.py b/test/legacy_test/test_svdvals_op.py index 3e8c264b6dc955..89a987a7c90e07 100644 --- a/test/legacy_test/test_svdvals_op.py +++ b/test/legacy_test/test_svdvals_op.py @@ -1,4 +1,4 @@ -# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -15,7 +15,7 @@ import unittest import numpy as np -from op_test import OpTest, skip_check_grad_ci +from op_test import OpTest from utils import dygraph_guard, static_guard import paddle @@ -69,13 +69,10 @@ def init_data(self): self.outputs = {"s": self._output_data} -@skip_check_grad_ci( - reason="'check_grad' on singular values is not required for svdvals." -) class TestSvdvalsBigMatrix(TestSvdvalsOp): def init_data(self): """Generate large input matrix.""" - self._input_shape = (200, 300) + self._input_shape = (40, 40) self._input_data = np.random.random(self._input_shape).astype("float64") self._output_data = np.linalg.svd( self._input_data, compute_uv=False, hermitian=False @@ -84,7 +81,13 @@ def init_data(self): self.outputs = {'s': self._output_data} def test_check_grad(self): - pass + self.check_grad( + ['x'], + ['s'], + numeric_grad_delta=0.001, + max_relative_error=1e-5, + check_pir=True, + ) class TestSvdvalsAPI(unittest.TestCase): @@ -103,8 +106,7 @@ def test_dygraph_api(self): # Test dynamic graph for svdvals s = paddle.linalg.svdvals(x) np_s = np.linalg.svd(self.x_np, compute_uv=False, hermitian=False) - self.assertTrue(np.allclose(np_s, s.numpy(), rtol=1e-6)) - + np.testing.assert_allclose(np_s, s.numpy(), rtol=1e-6) # Test with reshaped input x_reshaped = x.reshape([-1, 12, 10]) s_reshaped = paddle.linalg.svdvals(x_reshaped) @@ -114,8 +116,8 @@ def test_dygraph_api(self): for matrix in self.x_np.reshape([-1, 12, 10]) ] ) - self.assertTrue( - np.allclose(np_s_reshaped, s_reshaped.numpy(), rtol=1e-6) + np.testing.assert_allclose( + np_s_reshaped, s_reshaped.numpy(), rtol=1e-6 ) def test_static_api(self): @@ -130,7 +132,7 @@ def test_static_api(self): np_s = np.linalg.svd(self.x_np, compute_uv=False, hermitian=False) for r in res: - self.assertTrue(np.allclose(np_s, r, rtol=1e-6)) + np.testing.assert_allclose(np_s, r, rtol=1e-6) def test_error(self): """Test invalid inputs for svdvals""" diff --git a/test/white_list/op_threshold_white_list.py b/test/white_list/op_threshold_white_list.py index 324c8defbb0719..f7e888a3615bbf 100644 --- a/test/white_list/op_threshold_white_list.py +++ b/test/white_list/op_threshold_white_list.py @@ -48,6 +48,7 @@ 'lgamma', 'sparse_attention', 'svd', + 'svdvals', 'matrix_power', 'cholesky_solve', 'solve', From 29b048b63415ab7bcfc14b8c983129f4fd42dabb Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 3 Dec 2024 10:53:10 +0800 Subject: [PATCH 106/288] [Lod][fluid_ops] LegacyLoD (#69822) --- paddle/common/ddim.h | 3 +- .../distributed/fleet_executor/dist_model.cc | 2 +- .../distributed/ps/service/brpc_utils.cc | 4 +- paddle/fluid/distributed/ps/wrapper/fleet.cc | 2 +- paddle/fluid/distributed/ps/wrapper/fleet.h | 2 +- .../fluid/distributed/test/brpc_utils_test.cc | 4 +- paddle/fluid/framework/data_feed.cc | 18 +++--- paddle/fluid/framework/data_feed.cu | 4 +- paddle/fluid/framework/downpour_worker.cc | 2 +- paddle/fluid/framework/fleet/fleet_wrapper.cc | 2 +- paddle/fluid/framework/fleet/fleet_wrapper.h | 2 +- paddle/fluid/framework/fleet/heter_wrapper.cc | 2 +- paddle/fluid/framework/infershape_utils.h | 2 +- paddle/fluid/framework/lod_rank_table.cc | 2 +- paddle/fluid/framework/lod_rank_table.h | 6 +- paddle/fluid/framework/lod_tensor.cc | 14 ++--- paddle/fluid/framework/lod_tensor.h | 14 ++--- .../instruction/instruction_base.cc | 4 +- .../new_executor/interpreter/static_build.cc | 2 +- paddle/fluid/framework/operator.cc | 8 +-- paddle/fluid/framework/operator.h | 2 +- paddle/fluid/framework/tensor.h | 3 +- paddle/fluid/framework/tensor_util.cc | 4 +- paddle/fluid/framework/tensor_util.h | 2 +- .../fluid/inference/api/analysis_predictor.cc | 2 +- paddle/fluid/inference/api/api_impl.cc | 2 +- .../inference/api/details/zero_copy_tensor.cc | 2 +- paddle/fluid/inference/api/helper.cc | 4 +- .../ir_adaptor/translator/op_translator.cc | 2 +- .../ir_adaptor/translator/type_translator.cc | 10 ++-- .../operators/beam_search_decode_op_def.h | 2 +- .../fluid/operators/controlflow/while_op.cc | 2 +- paddle/fluid/operators/lod_reset_op.h | 2 +- .../pir/dialect/distributed/ir/dist_type.h | 4 +- .../pir/dialect/kernel/ir/kernel_type.cc | 4 +- .../fluid/pir/dialect/kernel/ir/kernel_type.h | 8 +-- .../pir/dialect/operator/ir/ir_meta_tensor.cc | 2 +- .../pir/dialect/operator/ir/ir_meta_tensor.h | 2 +- .../dialect/operator/ir/ir_selected_rows.cc | 2 +- .../dialect/operator/ir/ir_selected_rows.h | 10 ++-- .../dialect/operator/ir/ir_sparse_tensor.h | 2 +- .../pir/dialect/operator/ir/ir_tensor.cc | 2 +- .../fluid/pir/dialect/operator/ir/ir_tensor.h | 10 ++-- .../pir/dialect/operator/ir/op_dialect.cc | 4 +- .../fluid/pir/dialect/operator/ir/op_type.cc | 2 +- .../fluid/pir/dialect/operator/ir/op_type.h | 4 +- .../pir/dialect/operator/ir/tensorrt_op.cc | 2 +- .../pir/dialect/operator/ir/type_storage.h | 10 ++-- .../pir/transforms/pd_op_to_kernel_pass.cc | 2 +- .../fluid/pybind/manual_static_op_function.h | 4 +- paddle/fluid/pybind/tensor.cc | 41 ++++++------- paddle/phi/capi/include/kernel_registry.h | 2 +- paddle/phi/capi/include/wrapper_base.h | 9 +-- paddle/phi/capi/lib/c_tensor.cc | 2 +- paddle/phi/core/dense_tensor.cc | 2 +- paddle/phi/core/dense_tensor.h | 4 +- paddle/phi/core/dense_tensor.inl | 4 +- paddle/phi/core/dense_tensor_impl.cc | 4 +- .../core/framework/dense_tensor_serialize.cc | 2 +- paddle/phi/core/lod_utils.cc | 17 +++--- paddle/phi/core/lod_utils.h | 16 ++--- paddle/phi/core/meta_tensor.cc | 6 +- paddle/phi/core/meta_tensor.h | 6 +- paddle/phi/core/tensor_meta.cc | 2 +- paddle/phi/core/tensor_meta.h | 7 ++- paddle/phi/kernels/cpu/concat_kernel.cc | 4 +- .../cpu/distribute_fpn_proposals_kernel.cc | 2 +- .../cpu/fusion_seqpool_concat_kernel.cc | 2 +- .../kernels/cpu/generate_proposals_kernel.cc | 2 +- .../kernels/cpu/match_matrix_tensor_kernel.cc | 2 +- paddle/phi/kernels/cpu/pyramid_hash_kernel.cc | 4 +- paddle/phi/kernels/funcs/beam_search_decode.h | 2 +- paddle/phi/kernels/funcs/math/beam_search.cc | 6 +- paddle/phi/kernels/funcs/math/beam_search.cu | 4 +- paddle/phi/kernels/funcs/math/beam_search.h | 4 +- .../phi/kernels/funcs/math/beam_search_xpu.cc | 6 +- paddle/phi/kernels/funcs/sequence2batch.h | 2 +- paddle/phi/kernels/funcs/sequence_scale.cc | 2 +- paddle/phi/kernels/funcs/tensor_formatter.cc | 2 +- .../cpu/fusion_seqpool_cvm_concat_kernel.cc | 2 +- .../fusion/xpu/sequance_unpad_xpu_kernerl.cc | 2 +- paddle/phi/kernels/gpu/box_clip_kernel.cu | 2 +- .../gpu/collect_fpn_proposals_kernel.cu | 2 +- paddle/phi/kernels/gpu/concat_kernel.cu | 4 +- paddle/phi/kernels/gpu/ctc_align_kernel.cu | 2 +- .../gpu/distribute_fpn_proposals_kernel.cu | 2 +- .../kernels/gpu/generate_proposals_kernel.cu | 2 +- .../impl/collect_fpn_proposals_kernel_impl.h | 2 +- .../phi/kernels/impl/ctc_align_kernel_impl.h | 2 +- .../kernels/impl/im2sequence_kernel_impl.h | 4 +- .../phi/kernels/impl/lod_reset_kernel_impl.h | 2 +- .../kernels/impl/sequence_pool_kernel_impl.h | 2 +- .../cpu/legacy_generate_proposals_kernel.cc | 2 +- .../legacy/cpu/multiclass_nms_kernel.cc | 2 +- .../gpu/legacy_generate_proposals_kernel.cu | 2 +- paddle/phi/kernels/xpu/concat_kernel.cc | 4 +- .../kernels/xpu/generate_proposals_kernel.cc | 2 +- .../phi/kernels/xpu/multiclass_nms3_kernel.cc | 2 +- paddle/pir/include/core/builtin_type.h | 6 +- .../pir/include/core/builtin_type_storage.h | 12 ++-- paddle/pir/src/core/builtin_type.cc | 2 +- test/cpp/fluid/beam_search_decode_op_test.cc | 6 +- test/cpp/fluid/beam_search_op_test_xpu.cc | 2 +- test/cpp/fluid/benchmark/op_tester.cc | 2 +- .../cpp/fluid/framework/device_worker_test.cc | 4 +- test/cpp/fluid/framework/lod_tensor_test.cc | 58 +++++++++---------- test/cpp/fluid/framework/lod_tensor_test.cu | 4 +- test/cpp/fluid/framework/operator_test.cc | 9 +-- test/cpp/fluid/math/beam_search_test.cc | 2 +- test/cpp/fluid/platform/bfloat16_test.cc | 2 +- test/cpp/fluid/platform/float16_test.cc | 2 +- test/cpp/fluid/save_load_combine_op_test.cc | 40 ++++++------- .../fluid/save_load_combine_op_test_xpu.cc | 18 +++--- test/cpp/fluid/save_load_op_test.cc | 6 +- test/cpp/fluid/save_load_op_test_xpu.cc | 2 +- test/cpp/inference/api/api_impl_tester.cc | 4 +- test/cpp/inference/api/tester_helper.h | 2 +- test/cpp/inference/test_helper.h | 4 +- .../standalone_executor_pir_test.cc | 2 +- test/cpp/phi/core/test_dense_tensor.cc | 8 +-- test/cpp/phi/core/test_tensor_array.cc | 4 +- test/cpp/phi/kernels/sequence_padding_test.cc | 10 ++-- test/cpp/phi/kernels/sequence_pooling_test.cc | 10 ++-- .../cinn/add_broadcast_to_elementwise_test.cc | 2 +- test/cpp/pir/cinn/pir_all_path_test.cc | 2 +- test/cpp/pir/cinn/symbolic_lower_test.cc | 2 +- test/cpp/pir/core/ir_op_test.cc | 12 ++-- test/cpp/pir/core/type_interface_test.cc | 2 +- test/cpp/pir/core/type_test.cc | 8 +-- test/cpp/pir/distributed/dist_dialect_test.cc | 6 +- .../pattern_rewrite/pattern_rewrite_test.cc | 2 +- test/cpp/pir/tools/test_pir_utils.h | 2 +- .../test_recommender_system_deprecated.py | 2 +- .../test_trt_multiclass_nms3_op_deprecated.py | 2 +- test/legacy_test/op_test.py | 8 +-- .../xpu/cpp/beam_search_decode_op_xpu_test.cc | 6 +- 136 files changed, 354 insertions(+), 347 deletions(-) diff --git a/paddle/common/ddim.h b/paddle/common/ddim.h index 831635f36a7b9e..5af0ae660c1b78 100644 --- a/paddle/common/ddim.h +++ b/paddle/common/ddim.h @@ -243,7 +243,8 @@ TEST_API DDim ComputeCompatibleDim(const DDim& dim1, const DDim& dim2); namespace pir { using DDim = common::DDim; -using LoD = std::vector>; +using LegacyLoD = std::vector>; +using LoD = LegacyLoD; } // namespace pir namespace std { diff --git a/paddle/fluid/distributed/fleet_executor/dist_model.cc b/paddle/fluid/distributed/fleet_executor/dist_model.cc index 0cfdc01b0a6b53..c073dcbd19a37c 100644 --- a/paddle/fluid/distributed/fleet_executor/dist_model.cc +++ b/paddle/fluid/distributed/fleet_executor/dist_model.cc @@ -125,7 +125,7 @@ bool LoadDataFromDistModelTensor(const DistModelTensor &input_data, "DistModel only supports CPU and GPU and XPU and CustomDevice.")); } - phi::LoD dst_lod; + phi::LegacyLoD dst_lod; for (auto &src_lod : input_data.lod) { dst_lod.emplace_back(src_lod); } diff --git a/paddle/fluid/distributed/ps/service/brpc_utils.cc b/paddle/fluid/distributed/ps/service/brpc_utils.cc index 3f53a5ddb00e34..21ce06030c71f3 100644 --- a/paddle/fluid/distributed/ps/service/brpc_utils.cc +++ b/paddle/fluid/distributed/ps/service/brpc_utils.cc @@ -91,7 +91,7 @@ void SerializeDenseTensor(framework::Variable* var, butil::IOBuf* iobuf) { auto* tensor = var->GetMutable(); var_msg->set_type(::paddle::distributed::DENSE_TENSOR); - const phi::LoD lod = tensor->lod(); + const phi::LegacyLoD lod = tensor->lod(); if (!lod.empty()) { var_msg->set_lod_level(lod.size()); for (auto& each : lod) { @@ -231,7 +231,7 @@ void DeserializeDenseTensor(framework::Variable* var, } tensor->Resize(common::make_ddim(vec_dim)); - phi::LoD lod; + phi::LegacyLoD lod; for (int i = 0; i < msg.lod_level(); ++i) { phi::Vector v; for (int j = 0; j < msg.lod(i).lod_data_size(); ++j) { diff --git a/paddle/fluid/distributed/ps/wrapper/fleet.cc b/paddle/fluid/distributed/ps/wrapper/fleet.cc index 48999c71cdb263..36468dcc51ff1a 100644 --- a/paddle/fluid/distributed/ps/wrapper/fleet.cc +++ b/paddle/fluid/distributed/ps/wrapper/fleet.cc @@ -1016,7 +1016,7 @@ std::default_random_engine& FleetWrapper::LocalRandomEngine() { size_t FleetWrapper::GetAbsoluteSum(size_t start, size_t end, size_t level, - const phi::LoD& lod) { + const phi::LegacyLoD& lod) { if (level >= lod.size() - 1) { return end - start; } diff --git a/paddle/fluid/distributed/ps/wrapper/fleet.h b/paddle/fluid/distributed/ps/wrapper/fleet.h index b81183cb4195b4..4fa2dba2fdfc27 100644 --- a/paddle/fluid/distributed/ps/wrapper/fleet.h +++ b/paddle/fluid/distributed/ps/wrapper/fleet.h @@ -335,7 +335,7 @@ class FleetWrapper { size_t GetAbsoluteSum(size_t start, size_t end, size_t level, - const phi::LoD& lod); + const phi::LegacyLoD& lod); protected: static bool is_initialized_; diff --git a/paddle/fluid/distributed/test/brpc_utils_test.cc b/paddle/fluid/distributed/test/brpc_utils_test.cc index 8941a31477edf5..3b68bfbe29bd6c 100644 --- a/paddle/fluid/distributed/test/brpc_utils_test.cc +++ b/paddle/fluid/distributed/test/brpc_utils_test.cc @@ -33,7 +33,7 @@ void CreateVarsOnScope(framework::Scope* scope, framework::Variable* var1 = scope->Var("x1"); auto* tensor1 = var1->GetMutable(); tensor1->Resize(common::make_ddim({512, 8, 4, 2})); - phi::LoD lod1; + phi::LegacyLoD lod1; lod1.push_back(phi::Vector({1, 3, 8})); tensor1->set_lod(lod1); tensor1->mutable_data(*place); @@ -43,7 +43,7 @@ void CreateVarsOnScope(framework::Scope* scope, framework::Variable* var2 = scope->Var("x2"); auto* tensor2 = var2->GetMutable(); tensor2->Resize(common::make_ddim({1000, 64})); - phi::LoD lod2; + phi::LegacyLoD lod2; lod2.push_back(phi::Vector({1, 1})); tensor2->set_lod(lod2); tensor2->mutable_data(*place); diff --git a/paddle/fluid/framework/data_feed.cc b/paddle/fluid/framework/data_feed.cc index 6c324e6fae4813..788d4d2e3b4971 100644 --- a/paddle/fluid/framework/data_feed.cc +++ b/paddle/fluid/framework/data_feed.cc @@ -1053,7 +1053,7 @@ void MultiSlotDataFeed::PutToFeedVec( } if (!use_slots_is_dense_[i]) { - LoD data_lod{offset}; + LegacyLoD data_lod{offset}; feed_vec_[i]->set_lod(data_lod); } if (use_slots_is_dense_[i]) { @@ -1446,7 +1446,7 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec(const Record* ins_vec, int num) { } auto& slot_offset = offset_[i]; if (this->input_type_ == 0) { - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[i]->set_lod(data_lod); } else if (this->input_type_ == 1) { if (!use_slots_is_dense_[i]) { @@ -1463,7 +1463,7 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec(const Record* ins_vec, int num) { tmp_offset.emplace_back(k); } slot_offset = tmp_offset; - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[i]->set_lod(data_lod); } } @@ -1545,7 +1545,7 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec( auto& slot_offset = offset_[i]; if (this->input_type_ == 0) { if (!use_slots_is_dense_[i]) { - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[i]->set_lod(data_lod); } } else if (this->input_type_ == 1) { @@ -1563,7 +1563,7 @@ void MultiSlotInMemoryDataFeed::PutToFeedVec( tmp_offset.emplace_back(k); } slot_offset = tmp_offset; - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[i]->set_lod(data_lod); } } @@ -1600,7 +1600,7 @@ void PrivateInstantDataFeed::PutToFeedVec() { tensor_ptr, &feasign[0], total_instance * sizeof(int64_t)); } - LoD data_lod{offset}; + LegacyLoD data_lod{offset}; feed_vec_[i]->set_lod(data_lod); if (use_slots_is_dense_[i]) { int64_t total_dims = 1; @@ -2048,7 +2048,7 @@ void PaddleBoxDataFeed::PutToFeedVec(const std::vector& ins_vec) { CopyToFeedTensor(tensor_ptr, feasign, total_instance * sizeof(int64_t)); } auto& slot_offset = offset_[i]; - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[i]->set_lod(data_lod); if (use_slots_is_dense_[i]) { if (inductive_shape_index_[i] != -1) { @@ -2704,7 +2704,7 @@ void SlotRecordInMemoryDataFeed::PutToFeedVec(const SlotRecord* ins_vec, } feed->Resize(common::make_ddim(info.local_shape)); } else { - LoD data_lod{slot_offset}; + LegacyLoD data_lod{slot_offset}; feed_vec_[j]->set_lod(data_lod); } } @@ -3117,7 +3117,7 @@ void SlotRecordInMemoryDataFeed::PackToScope(MiniBatchGpuPack* pack, } feed->Resize(common::make_ddim(info.local_shape)); } else { - LoD& lod = (*feed->mutable_lod()); + LegacyLoD& lod = (*feed->mutable_lod()); lod.resize(1); lod[0].resize(offset_cols_size); phi::MixVector mixv_lod(&lod[0]); diff --git a/paddle/fluid/framework/data_feed.cu b/paddle/fluid/framework/data_feed.cu index 90e2631832a883..fc4053cbd54555 100644 --- a/paddle/fluid/framework/data_feed.cu +++ b/paddle/fluid/framework/data_feed.cu @@ -1285,14 +1285,14 @@ int GraphDataGenerator::GenerateBatch() { } sage_batch_count_ += 1; } - LoD lod{offset_}; + LegacyLoD lod{offset_}; if (conf_.accumulate_num >= 2) { offset_.clear(); offset_.push_back(0); offset_.push_back(uniq_instance_vec_[sage_batch_count_ * 2]); } - LoD lod2{offset_}; + LegacyLoD lod2{offset_}; if (conf_.accumulate_num == 1) { for (int tensor_pair_idx = 0; tensor_pair_idx < conf_.tensor_pair_num; diff --git a/paddle/fluid/framework/downpour_worker.cc b/paddle/fluid/framework/downpour_worker.cc index ac0d844781d300..a67bd42db9b07e 100644 --- a/paddle/fluid/framework/downpour_worker.cc +++ b/paddle/fluid/framework/downpour_worker.cc @@ -232,7 +232,7 @@ void DownpourWorker::FillSparseValue(size_t table_idx) { phi::CPUPlace()); memset(ptr, 0, sizeof(float) * len * table.emb_dim()); auto& tensor_lod = tensor->lod()[0]; - LoD data_lod{tensor_lod}; + LegacyLoD data_lod{tensor_lod}; tensor_emb->set_lod(data_lod); bool is_nid = (adjust_ins_weight_config_.need_adjust() && diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.cc b/paddle/fluid/framework/fleet/fleet_wrapper.cc index 3681ed31b097e9..76f9ee993bef2a 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.cc +++ b/paddle/fluid/framework/fleet/fleet_wrapper.cc @@ -1983,7 +1983,7 @@ int32_t FleetWrapper::CopyTableByFeasign( size_t FleetWrapper::GetAbsoluteSum(size_t start, size_t end, size_t level, - const phi::LoD& lod) { + const phi::LegacyLoD& lod) { if (level >= lod.size() - 1) { return end - start; } diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.h b/paddle/fluid/framework/fleet/fleet_wrapper.h index 5015d04b9083ac..53aa21c4c533b5 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.h +++ b/paddle/fluid/framework/fleet/fleet_wrapper.h @@ -420,7 +420,7 @@ class FleetWrapper { size_t GetAbsoluteSum(size_t start, size_t end, size_t level, - const phi::LoD& lod); + const phi::LegacyLoD& lod); protected: static bool is_initialized_; diff --git a/paddle/fluid/framework/fleet/heter_wrapper.cc b/paddle/fluid/framework/fleet/heter_wrapper.cc index d186fa0779abd3..b6c0492b7079eb 100644 --- a/paddle/fluid/framework/fleet/heter_wrapper.cc +++ b/paddle/fluid/framework/fleet/heter_wrapper.cc @@ -98,7 +98,7 @@ void HeterWrapper::SerializeToReq(const std::string& varname, for (auto& dim : common::vectorize(tensor->dims())) { req_var->add_dims(dim); } - const phi::LoD lod = tensor->lod(); + const phi::LegacyLoD lod = tensor->lod(); if (lod.size() > 0) { req_var->set_lod_level(lod.size()); for (auto& each : lod) { diff --git a/paddle/fluid/framework/infershape_utils.h b/paddle/fluid/framework/infershape_utils.h index d0398bbaf322ba..8df408e6256c93 100644 --- a/paddle/fluid/framework/infershape_utils.h +++ b/paddle/fluid/framework/infershape_utils.h @@ -70,7 +70,7 @@ class CompatMetaTensor : public phi::MetaTensor { bool operator!() const override { return !initialized_; } private: - const LoD& GetRuntimeLoD() const { + const LegacyLoD& GetRuntimeLoD() const { auto* var = PADDLE_GET_CONST(Variable*, var_); return var->Get().lod(); } diff --git a/paddle/fluid/framework/lod_rank_table.cc b/paddle/fluid/framework/lod_rank_table.cc index 7dff8a0c2aad0b..6e04ea4582b045 100644 --- a/paddle/fluid/framework/lod_rank_table.cc +++ b/paddle/fluid/framework/lod_rank_table.cc @@ -19,7 +19,7 @@ limitations under the License. */ namespace paddle { namespace framework { -void LoDRankTable::Reset(const LoD& lod, size_t level) { +void LoDRankTable::Reset(const LegacyLoD& lod, size_t level) { this->coarse_lod_.clear(); this->items_.clear(); PADDLE_ENFORCE_LT( diff --git a/paddle/fluid/framework/lod_rank_table.h b/paddle/fluid/framework/lod_rank_table.h index 9a7c1285e305cf..e69f217aa49562 100644 --- a/paddle/fluid/framework/lod_rank_table.h +++ b/paddle/fluid/framework/lod_rank_table.h @@ -41,16 +41,16 @@ class LoDRankTable { LoDRankTable() {} - void Reset(const LoD& lod, size_t level); + void Reset(const LegacyLoD& lod, size_t level); const std::vector& items() const { return this->items_; } - const LoD& coarse_lod() const { return this->coarse_lod_; } + const LegacyLoD& coarse_lod() const { return this->coarse_lod_; } size_t level() const { return coarse_lod_.size(); } private: - LoD coarse_lod_; + LegacyLoD coarse_lod_; std::vector items_; }; diff --git a/paddle/fluid/framework/lod_tensor.cc b/paddle/fluid/framework/lod_tensor.cc index 05dfbed223b2f9..7fafc5841415b3 100644 --- a/paddle/fluid/framework/lod_tensor.cc +++ b/paddle/fluid/framework/lod_tensor.cc @@ -21,13 +21,13 @@ limitations under the License. */ namespace paddle::framework { -std::string LoDToString(const LoD &lod) { +std::string LegacyLoDToString(const LegacyLoD &lod) { std::ostringstream stream; stream << lod; return stream.str(); } -bool operator==(const LoD &a, const LoD &b) { +bool operator==(const LegacyLoD &a, const LegacyLoD &b) { if (a.size() != b.size()) { return false; } @@ -47,7 +47,7 @@ bool operator==(const LoD &a, const LoD &b) { return true; } -bool CheckLoD(const LoD &in, int tensor_height) { +bool CheckLegacyLoD(const LegacyLoD &in, int tensor_height) { if (in.empty()) return true; for (const auto &level : in) { // check: there should be more than 2 offsets existing in each level. @@ -67,16 +67,16 @@ bool CheckLoD(const LoD &in, int tensor_height) { // check: the higher level's last offset should equals the lower level's // size-1. - // NOTE LoD store the levels from top to bottom, so the higher level goes - // first. + // NOTE LegacyLoD store the levels from top to bottom, so the higher level + // goes first. for (size_t level = 0; level < in.size() - 1; level++) { if (in[level].back() != in[level + 1].size() - 1) return false; } return true; } -LoD ConvertToOffsetBasedLoD(const LoD &length_lod) { - LoD offset_lod; +LegacyLoD ConvertToOffsetBasedLegacyLoD(const LegacyLoD &length_lod) { + LegacyLoD offset_lod; offset_lod.reserve(length_lod.size()); for (const auto &item : length_lod) { std::vector level; diff --git a/paddle/fluid/framework/lod_tensor.h b/paddle/fluid/framework/lod_tensor.h index a41a68911bbecf..26a7ff342a33df 100644 --- a/paddle/fluid/framework/lod_tensor.h +++ b/paddle/fluid/framework/lod_tensor.h @@ -34,7 +34,7 @@ namespace paddle { namespace framework { /* - * LoD is short for Level of Details. + * LegacyLoD is short for Level of Details. * * - in a level, each element indicates relative offset of the lower level * - the first element should be 0 and that indicates that this sequence start @@ -42,17 +42,17 @@ namespace framework { * - each sequence's begin and end(no-inclusive) is level[id, id+1] * * For example: - * 3-level LoD stores + * 3-level LegacyLoD stores * * 0 2 3 * 0 2 4 7 * 0 2 5 7 10 12 15 20 */ -using LoD = std::vector>; +using LegacyLoD = std::vector>; -std::string LoDToString(const LoD& lod); +std::string LegacyLoDToString(const LegacyLoD& lod); -TEST_API bool operator==(const LoD& a, const LoD& b); +TEST_API bool operator==(const LegacyLoD& a, const LegacyLoD& b); /* * Check whether this lod's format is valid. @@ -70,9 +70,9 @@ TEST_API bool operator==(const LoD& a, const LoD& b); * tensor_height>0. */ -TEST_API bool CheckLoD(const LoD& in, int tensor_height = -1); +TEST_API bool CheckLegacyLoD(const LegacyLoD& in, int tensor_height = -1); -TEST_API LoD ConvertToOffsetBasedLoD(const LoD& length_lod); +TEST_API LegacyLoD ConvertToOffsetBasedLegacyLoD(const LegacyLoD& length_lod); } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/new_executor/instruction/instruction_base.cc b/paddle/fluid/framework/new_executor/instruction/instruction_base.cc index e50d466d88407f..0acd423665fb0e 100644 --- a/paddle/fluid/framework/new_executor/instruction/instruction_base.cc +++ b/paddle/fluid/framework/new_executor/instruction/instruction_base.cc @@ -127,9 +127,9 @@ static int GetRowSize(const Scope& scope, const std::string& name) { return -1; } -static LoD GetLoDDebug(const Scope& scope, const std::string& name) { +static LegacyLoD GetLoDDebug(const Scope& scope, const std::string& name) { Variable* var = scope.FindVar(name); - auto default_lod = LoD({{}}); + auto default_lod = LegacyLoD({{}}); if (var == nullptr) { return default_lod; diff --git a/paddle/fluid/framework/new_executor/interpreter/static_build.cc b/paddle/fluid/framework/new_executor/interpreter/static_build.cc index 32bc2dfc406650..b40418aff9b07d 100644 --- a/paddle/fluid/framework/new_executor/interpreter/static_build.cc +++ b/paddle/fluid/framework/new_executor/interpreter/static_build.cc @@ -620,7 +620,7 @@ void RunWhileBlockPreStaticBuild(const framework::Scope& scope, if (var->IsType()) { // Clear all lod information for all lod_tensors. auto* t = var->GetMutable(); - phi::LoD empty_lod; + phi::LegacyLoD empty_lod; t->set_lod(empty_lod); } else if (var->IsType()) { // Clear elements of all tensor arrays. diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index 14772bc28c8868..0464b0f5267308 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -193,9 +193,9 @@ static int GetRowSize(const Scope& scope, const std::string& name) { return -1; } -static LoD GetLoDDebug(const Scope& scope, const std::string& name) { +static LegacyLoD GetLoDDebug(const Scope& scope, const std::string& name) { Variable* var = scope.FindVar(name); - auto default_lod = LoD({{}}); + auto default_lod = LegacyLoD({{}}); if (var == nullptr) { return default_lod; @@ -644,12 +644,12 @@ bool RuntimeInferShapeContext::HasRuntimeAttributes() const { return is_runtime; } -std::vector RuntimeInferShapeContext::GetOutputsLod( +std::vector RuntimeInferShapeContext::GetOutputsLod( const std::string& out) const { auto out_it = ctx_.outputs.find(out); auto& out_var_list = out_it->second; - std::vector ret; + std::vector ret; for (auto* out_var : out_var_list) { if (out_var != nullptr) { auto* out_tensor = out_var->GetMutable(); diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h index 9cfa3d51d6b106..cfb26188979440 100644 --- a/paddle/fluid/framework/operator.h +++ b/paddle/fluid/framework/operator.h @@ -230,7 +230,7 @@ class RuntimeInferShapeContext : public InferShapeContext { void SetSkipLoD(bool skip); - std::vector GetOutputsLod(const std::string& out) const; + std::vector GetOutputsLod(const std::string& out) const; std::vector GetOutputsDim(const std::string& name) const; diff --git a/paddle/fluid/framework/tensor.h b/paddle/fluid/framework/tensor.h index d581838f6dbd22..608a42855b25d3 100644 --- a/paddle/fluid/framework/tensor.h +++ b/paddle/fluid/framework/tensor.h @@ -23,7 +23,8 @@ limitations under the License. */ namespace paddle { namespace framework { -using LoD = std::vector>; +using LegacyLoD = std::vector>; +using LoD = LegacyLoD; } // namespace framework } // namespace paddle diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index 9d5fd908ae4530..4a959bc96c721b 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -1067,7 +1067,7 @@ std::ostream& print_tensor>( return os; } -std::ostream& operator<<(std::ostream& os, const LoD& lod) { +std::ostream& operator<<(std::ostream& os, const LegacyLoD& lod) { // NOTE(xiongkun): // https://stackoverflow.com/questions/5195512/namespaces-and-operator-resolution // if we don't redefine, the operator << of phi / framework LoD is not found. @@ -1080,7 +1080,7 @@ std::ostream& operator<<(std::ostream& os, const LoD& lod) { namespace phi { -std::ostream& operator<<(std::ostream& os, const LoD& lod) { +std::ostream& operator<<(std::ostream& os, const LegacyLoD& lod) { paddle::string::operator<<(os, lod); return os; } diff --git a/paddle/fluid/framework/tensor_util.h b/paddle/fluid/framework/tensor_util.h index 25e39cd9ada4ae..26ef35de213e92 100644 --- a/paddle/fluid/framework/tensor_util.h +++ b/paddle/fluid/framework/tensor_util.h @@ -421,7 +421,7 @@ inline void TensorToVector(const phi::DenseTensor& src, delete[] array; } -std::ostream& operator<<(std::ostream& os, const LoD& lod); +std::ostream& operator<<(std::ostream& os, const LegacyLoD& lod); template inline T GetValue(const phi::DenseTensor* x) { diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 1c5114b5253a14..9d18e1bb1d9acb 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -354,7 +354,7 @@ bool PaddleTensorToDenseTensor(const PaddleTensor &pt, "now.")); } // TODO(Superjomn) Low performance, need optimization for heavy LoD copy. - phi::LoD lod; + phi::LegacyLoD lod; for (auto &level : pt.lod) { lod.emplace_back(level); } diff --git a/paddle/fluid/inference/api/api_impl.cc b/paddle/fluid/inference/api/api_impl.cc index 55e01fe773aea1..e7132613531891 100644 --- a/paddle/fluid/inference/api/api_impl.cc +++ b/paddle/fluid/inference/api/api_impl.cc @@ -281,7 +281,7 @@ bool NativePaddlePredictor::SetFeed(const std::vector &inputs, } // TODO(Superjomn) Low performance, need optimization for heavy LoD copy. - phi::LoD lod; + phi::LegacyLoD lod; for (auto &level : inputs[i].lod) { lod.emplace_back(level); } diff --git a/paddle/fluid/inference/api/details/zero_copy_tensor.cc b/paddle/fluid/inference/api/details/zero_copy_tensor.cc index ab586471c25501..8a3ae4ab94131d 100644 --- a/paddle/fluid/inference/api/details/zero_copy_tensor.cc +++ b/paddle/fluid/inference/api/details/zero_copy_tensor.cc @@ -747,7 +747,7 @@ std::vector Tensor::shape() const { void Tensor::SetLoD(const std::vector> &x) { EAGER_GET_TENSOR(phi::DenseTensor); - phi::LoD lod; + phi::LegacyLoD lod; for (auto &level : x) { lod.emplace_back(level); } diff --git a/paddle/fluid/inference/api/helper.cc b/paddle/fluid/inference/api/helper.cc index 25e9835b17e9be..eb74825abba894 100644 --- a/paddle/fluid/inference/api/helper.cc +++ b/paddle/fluid/inference/api/helper.cc @@ -381,7 +381,7 @@ void RegisterAllCustomOperator(bool use_pir) { auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; out_types.push_back(paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), @@ -398,7 +398,7 @@ void RegisterAllCustomOperator(bool use_pir) { auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; auto out_type = paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), diff --git a/paddle/fluid/ir_adaptor/translator/op_translator.cc b/paddle/fluid/ir_adaptor/translator/op_translator.cc index 76e4e532767268..314d0645a33a9f 100644 --- a/paddle/fluid/ir_adaptor/translator/op_translator.cc +++ b/paddle/fluid/ir_adaptor/translator/op_translator.cc @@ -3243,7 +3243,7 @@ struct RandIntOpTranscriber : public OpTranscriber { common::make_ddim(var->GetShape()); paddle::dialect::DenseTensorTypeStorage::DataLayout layout = paddle::dialect::DenseTensorTypeStorage::DataLayout::NCHW; - paddle::dialect::DenseTensorTypeStorage::LoD lod = {}; + paddle::dialect::DenseTensorTypeStorage::LegacyLoD lod = {}; size_t offset = 0; pir::Type translated_var_type = paddle::dialect::DenseTensorType::get( ctx, dtype, dim, layout, lod, offset); diff --git a/paddle/fluid/ir_adaptor/translator/type_translator.cc b/paddle/fluid/ir_adaptor/translator/type_translator.cc index 61422444c09d58..129eeeae114fa0 100644 --- a/paddle/fluid/ir_adaptor/translator/type_translator.cc +++ b/paddle/fluid/ir_adaptor/translator/type_translator.cc @@ -34,7 +34,7 @@ using SparseCooTensorTypeStorage = paddle::dialect::SparseCooTensorTypeStorage; using SparseCsrTensorType = paddle::dialect::SparseCsrTensorType; using SparseCsrTensorTypeStorage = paddle::dialect::SparseCsrTensorTypeStorage; using DataLayout = DenseTensorTypeStorage::DataLayout; -using LoD = DenseTensorTypeStorage::LoD; +using LegacyLoD = DenseTensorTypeStorage::LegacyLoD; TypeTranslator::TypeTranslator() { const auto& HandleTensor = [&](pir::IrContext* ctx, @@ -45,7 +45,7 @@ TypeTranslator::TypeTranslator() { this->operator[](var_desc.GetDataType())(ctx, var_desc); const auto dim = common::make_ddim(var_desc.GetShape()); const auto layout = DataLayout::NCHW; - const LoD lod = {}; + const LegacyLoD lod = {}; const size_t offset = 0; return DenseTensorType::get(ctx, dtype, dim, layout, lod, offset); }; @@ -68,7 +68,7 @@ TypeTranslator::TypeTranslator() { this->operator[](var_desc.GetDataType())(ctx, var_desc); const auto dim = common::make_ddim(var_desc.GetShape()); const auto layout = DataLayout::NCHW; - const LoD lod = {}; + const LegacyLoD lod = {}; const size_t offset = 0; pir::Type SelectedRows = SelectedRowsType::get(ctx, dtype, dim, layout, lod, offset); @@ -82,7 +82,7 @@ TypeTranslator::TypeTranslator() { const pir::Type dtype = this->operator[](var_desc.GetDataType())(ctx, var_desc); const auto dim = common::make_ddim(var_desc.GetShape()); - const LoD lod = {}; + const LegacyLoD lod = {}; const size_t offset = 0; bool coalesced = false; const auto non_zero_dims = common::make_ddim(var_desc.GetShape()); @@ -109,7 +109,7 @@ TypeTranslator::TypeTranslator() { const pir::Type dtype = this->operator[](var_desc.GetDataType())(ctx, var_desc); const auto dim = common::make_ddim(var_desc.GetShape()); - const LoD lod = {}; + const LegacyLoD lod = {}; const size_t offset = 0; const auto layout = DataLayout::NCHW; pir::DenseTensorType non_zero_crows = diff --git a/paddle/fluid/operators/beam_search_decode_op_def.h b/paddle/fluid/operators/beam_search_decode_op_def.h index 9d4a40111626f4..0cea29a9b48d5d 100644 --- a/paddle/fluid/operators/beam_search_decode_op_def.h +++ b/paddle/fluid/operators/beam_search_decode_op_def.h @@ -138,7 +138,7 @@ void BeamSearchDecoder::ConvertSentenceVectorToDenseTensor( auto cpu_place = std::unique_ptr(new phi::CPUPlace()); phi::CPUContext cpu_ctx(*cpu_place); - phi::LoD lod; + phi::LegacyLoD lod; lod.push_back(source_level_lod); lod.push_back(sentence_level_lod); diff --git a/paddle/fluid/operators/controlflow/while_op.cc b/paddle/fluid/operators/controlflow/while_op.cc index 290cae5a385884..0dd6137dbaeb7b 100644 --- a/paddle/fluid/operators/controlflow/while_op.cc +++ b/paddle/fluid/operators/controlflow/while_op.cc @@ -257,7 +257,7 @@ class WhileOp : public framework::OperatorBase { if (var->IsType()) { // Clear all lod information for all lod_tensors. auto *t = var->GetMutable(); - phi::LoD empty_lod; + phi::LegacyLoD empty_lod; t->set_lod(empty_lod); } else if (var->IsType()) { // Clear elements of all tensor arrays. diff --git a/paddle/fluid/operators/lod_reset_op.h b/paddle/fluid/operators/lod_reset_op.h index 4389bba7a37077..6b6d73520d1578 100644 --- a/paddle/fluid/operators/lod_reset_op.h +++ b/paddle/fluid/operators/lod_reset_op.h @@ -116,7 +116,7 @@ class LoDResetKernel : public framework::OpKernel { auto* out_lod = out->mutable_lod(); out_lod->push_back(ulevel0); } else { - phi::LoD target_lod; + phi::LegacyLoD target_lod; target_lod.push_back(ulevel0); out->set_lod(target_lod); } diff --git a/paddle/fluid/pir/dialect/distributed/ir/dist_type.h b/paddle/fluid/pir/dialect/distributed/ir/dist_type.h index 031f3ace52e11a..2a8a89ea9a57ce 100644 --- a/paddle/fluid/pir/dialect/distributed/ir/dist_type.h +++ b/paddle/fluid/pir/dialect/distributed/ir/dist_type.h @@ -34,7 +34,7 @@ class DistDenseTensorType DistTypeInterface> { public: using Base::Base; - using LoD = pir::DenseTensorTypeStorage::LoD; + using LegacyLoD = pir::DenseTensorTypeStorage::LegacyLoD; static std::string name() { return "t_dist_dtensor"; } @@ -44,7 +44,7 @@ class DistDenseTensorType const common::DDim& local_ddim() const; Type dtype() const { return dense_tensor_type().dtype(); } DataLayout data_layout() const { return dense_tensor_type().data_layout(); } - const LoD& lod() const { return dense_tensor_type().lod(); } + const LegacyLoD& lod() const { return dense_tensor_type().lod(); } size_t offset() const { return dense_tensor_type().offset(); } pir::DenseTensorType prim_type() { return dense_tensor_type(); } diff --git a/paddle/fluid/pir/dialect/kernel/ir/kernel_type.cc b/paddle/fluid/pir/dialect/kernel/ir/kernel_type.cc index 924111aa7d77ff..c826e370a14ed9 100644 --- a/paddle/fluid/pir/dialect/kernel/ir/kernel_type.cc +++ b/paddle/fluid/pir/dialect/kernel/ir/kernel_type.cc @@ -36,7 +36,7 @@ phi::DataLayout AllocatedDenseTensorType::data_layout() const { return storage()->dense_tensor_type_.data_layout(); } -const phi::LoD& AllocatedDenseTensorType::lod() const { +const phi::LegacyLoD& AllocatedDenseTensorType::lod() const { return storage()->dense_tensor_type_.lod(); } @@ -64,7 +64,7 @@ phi::DataLayout AllocatedSelectedRowsType::data_layout() const { return storage()->selected_rows_type_.data_layout(); } -const phi::LoD& AllocatedSelectedRowsType::lod() const { +const phi::LegacyLoD& AllocatedSelectedRowsType::lod() const { return storage()->selected_rows_type_.lod(); } diff --git a/paddle/fluid/pir/dialect/kernel/ir/kernel_type.h b/paddle/fluid/pir/dialect/kernel/ir/kernel_type.h index bb7cb78edb3b64..3277cba2c7915b 100644 --- a/paddle/fluid/pir/dialect/kernel/ir/kernel_type.h +++ b/paddle/fluid/pir/dialect/kernel/ir/kernel_type.h @@ -41,7 +41,7 @@ class AllocatedDenseTensorType const pir::Type &dtype, const phi::DDim &dims, const phi::DataLayout &layout, - const phi::LoD &lod, + const phi::LegacyLoD &lod, size_t offset) { dialect::DenseTensorType dense_tensor_type = dialect::DenseTensorType::get(ctx, dtype, dims, layout, lod, offset); @@ -60,7 +60,7 @@ class AllocatedDenseTensorType phi::DataLayout data_layout() const; - const phi::LoD &lod() const; + const phi::LegacyLoD &lod() const; size_t offset() const; }; @@ -85,7 +85,7 @@ class AllocatedSelectedRowsType const pir::Type &dtype, const phi::DDim &dims, const phi::DataLayout &layout, - const phi::LoD &lod, + const phi::LegacyLoD &lod, size_t offset) { dialect::SelectedRowsType type = dialect::SelectedRowsType::get(ctx, dtype, dims, layout, lod, offset); @@ -104,7 +104,7 @@ class AllocatedSelectedRowsType phi::DataLayout data_layout() const; - const phi::LoD &lod() const; + const phi::LegacyLoD &lod() const; size_t offset() const; }; diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.cc b/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.cc index e98775d8e946ae..921274e1bae7eb 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.cc +++ b/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.cc @@ -45,7 +45,7 @@ phi::DataLayout IrMetaTensor::layout() const { return tensor_->layout(); } -const phi::LoD& IrMetaTensor::lod() const { +const phi::LegacyLoD& IrMetaTensor::lod() const { ValidCheck(*this); return static_cast(tensor_)->lod(); } diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.h b/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.h index f2af1eb66bf3c0..f0b5355468b45a 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.h +++ b/paddle/fluid/pir/dialect/operator/ir/ir_meta_tensor.h @@ -31,7 +31,7 @@ class IrMetaTensor : public phi::MetaTensor { phi::DataLayout layout() const override; - const phi::LoD& lod() const; + const phi::LegacyLoD& lod() const; void set_dims(const phi::DDim& dims) override; diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.cc b/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.cc index 7a54da19e6e4a1..49dda1e6d623df 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.cc +++ b/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.cc @@ -21,7 +21,7 @@ namespace paddle::dialect { IrSelectedRows::IrSelectedRows(phi::DataType dtype, const phi::DDim& dims, phi::DataLayout layout, - LoD lod, + LegacyLoD lod, size_t offset) : dims_(dims), dtype_(dtype), diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.h b/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.h index f1e06713eba132..c083a51485e50a 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.h +++ b/paddle/fluid/pir/dialect/operator/ir/ir_selected_rows.h @@ -23,7 +23,7 @@ namespace paddle { namespace dialect { -using LoD = std::vector>; +using LegacyLoD = std::vector>; class IrSelectedRows : public phi::TensorBase, @@ -34,7 +34,7 @@ class IrSelectedRows IrSelectedRows(phi::DataType dtype, const phi::DDim& dims, phi::DataLayout layout, - LoD lod, + LegacyLoD lod, size_t offset = 0); IrSelectedRows(IrSelectedRows&& other) = default; @@ -66,9 +66,9 @@ class IrSelectedRows void SetLayout(phi::DataLayout layout) { layout_ = layout; } - const LoD& lod() const noexcept { return lod_; } + const LegacyLoD& lod() const noexcept { return lod_; } - void SetLod(LoD lod) { lod_ = lod; } + void SetLod(LegacyLoD lod) { lod_ = lod; } size_t offset() const noexcept { return offset_; } @@ -90,7 +90,7 @@ class IrSelectedRows phi::DDim dims_; phi::DataType dtype_{phi::DataType::FLOAT32}; phi::DataLayout layout_{phi::DataLayout::ANY}; - LoD lod_; + LegacyLoD lod_; size_t offset_{0}; }; diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_sparse_tensor.h b/paddle/fluid/pir/dialect/operator/ir/ir_sparse_tensor.h index 7f5be23294feab..29fea50fce234e 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_sparse_tensor.h +++ b/paddle/fluid/pir/dialect/operator/ir/ir_sparse_tensor.h @@ -179,7 +179,7 @@ inline SparseCooTensorType CvtToSparseCooTensorType( pir::IrContext* ctx = pir::IrContext::Instance(); pir::Type fp32_dtype = pir::Float32Type::get(ctx); phi::DataLayout data_layout = phi::DataLayout::UNDEFINED; - phi::LoD lod = {}; + phi::LegacyLoD lod = {}; phi::DDim dims = {}; size_t offset = 0; pir::DenseTensorType non_zero_indices = pir::DenseTensorType::get( diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_tensor.cc b/paddle/fluid/pir/dialect/operator/ir/ir_tensor.cc index ea9a9d8b4b20f4..1d59c6bf64f2a5 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_tensor.cc +++ b/paddle/fluid/pir/dialect/operator/ir/ir_tensor.cc @@ -22,7 +22,7 @@ namespace paddle::dialect { IrTensor::IrTensor(phi::DataType dtype, const phi::DDim& dims, phi::DataLayout layout, - LoD lod, + LegacyLoD lod, size_t offset) : dims_(dims), dtype_(dtype), diff --git a/paddle/fluid/pir/dialect/operator/ir/ir_tensor.h b/paddle/fluid/pir/dialect/operator/ir/ir_tensor.h index 4b1f0e5e8037fc..bf931ab03fecb3 100644 --- a/paddle/fluid/pir/dialect/operator/ir/ir_tensor.h +++ b/paddle/fluid/pir/dialect/operator/ir/ir_tensor.h @@ -23,7 +23,7 @@ namespace paddle { namespace dialect { -using LoD = std::vector>; +using LegacyLoD = std::vector>; class IrTensor : public phi::TensorBase, public phi::TypeInfoTraits { @@ -33,7 +33,7 @@ class IrTensor : public phi::TensorBase, IrTensor(phi::DataType dtype, const phi::DDim& dims, phi::DataLayout layout, - LoD lod, + LegacyLoD lod, size_t offset = 0); IrTensor(IrTensor&& other) = default; @@ -65,9 +65,9 @@ class IrTensor : public phi::TensorBase, void SetLayout(phi::DataLayout layout) { layout_ = layout; } - const LoD& lod() const noexcept { return lod_; } + const LegacyLoD& lod() const noexcept { return lod_; } - void SetLod(LoD lod) { lod_ = lod; } + void SetLod(LegacyLoD lod) { lod_ = lod; } size_t offset() const noexcept { return offset_; } @@ -89,7 +89,7 @@ class IrTensor : public phi::TensorBase, phi::DDim dims_; phi::DataType dtype_{phi::DataType::FLOAT32}; phi::DataLayout layout_{phi::DataLayout::NCHW}; - LoD lod_; + LegacyLoD lod_; size_t offset_{0}; }; diff --git a/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc b/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc index 68fa94c8f1b516..d477a7e9718d5d 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc +++ b/paddle/fluid/pir/dialect/operator/ir/op_dialect.cc @@ -914,7 +914,7 @@ struct CustomOpVjpInterfaceModel : public VjpInterface::Concept { auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; auto type = paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), @@ -943,7 +943,7 @@ struct CustomOpVjpInterfaceModel : public VjpInterface::Concept { auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; auto out_type = paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), diff --git a/paddle/fluid/pir/dialect/operator/ir/op_type.cc b/paddle/fluid/pir/dialect/operator/ir/op_type.cc index 2991b672e5cd22..7e7f0910025285 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_type.cc +++ b/paddle/fluid/pir/dialect/operator/ir/op_type.cc @@ -24,7 +24,7 @@ const phi::DataLayout& SelectedRowsType::data_layout() const { return storage()->layout_; } -const phi::LoD& SelectedRowsType::lod() const { return storage()->lod_; } +const phi::LegacyLoD& SelectedRowsType::lod() const { return storage()->lod_; } const size_t& SelectedRowsType::offset() const { return storage()->offset_; } diff --git a/paddle/fluid/pir/dialect/operator/ir/op_type.h b/paddle/fluid/pir/dialect/operator/ir/op_type.h index 18b98154b86042..b16421092b9bd3 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_type.h +++ b/paddle/fluid/pir/dialect/operator/ir/op_type.h @@ -41,7 +41,7 @@ class TEST_API SelectedRowsType const phi::DataLayout &data_layout() const; - const phi::LoD &lod() const; + const phi::LegacyLoD &lod() const; const size_t &offset() const; @@ -57,7 +57,7 @@ class TEST_API SelectedRowsType Type dtype, const phi::DDim &dims, DataLayout layout = DataLayout::kNCHW, - const phi::LoD &lod = {}, + const phi::LegacyLoD &lod = {}, size_t offset = 0u) { return Base::get(ctx, dtype, dims, layout, lod, offset); } diff --git a/paddle/fluid/pir/dialect/operator/ir/tensorrt_op.cc b/paddle/fluid/pir/dialect/operator/ir/tensorrt_op.cc index 871e2c5c6920a1..5dd3c66c5f7024 100644 --- a/paddle/fluid/pir/dialect/operator/ir/tensorrt_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/tensorrt_op.cc @@ -193,7 +193,7 @@ void TensorRTEngineOp::Build(pir::Builder &builder, // NOLINT TransToIrDataType(outputs_dtype[i]), phi::DDim(outputs_shape[i].data(), outputs_shape[i].size()), phi::DataLayout::kNCHW, - phi::LoD(), + phi::LegacyLoD(), 0)); } } diff --git a/paddle/fluid/pir/dialect/operator/ir/type_storage.h b/paddle/fluid/pir/dialect/operator/ir/type_storage.h index 95b68a3370714b..05c1c18c8cae7b 100644 --- a/paddle/fluid/pir/dialect/operator/ir/type_storage.h +++ b/paddle/fluid/pir/dialect/operator/ir/type_storage.h @@ -30,17 +30,17 @@ using DenseTensorTypeStorage = pir::DenseTensorTypeStorage; struct SelectedRowsTypeStorage : public pir::TypeStorage { using DataLayout = phi::DataLayout; using Dim = phi::DDim; - using LoD = std::vector>; + using LegacyLoD = std::vector>; /// /// \brief Declare ParamKey according to parameter type. /// using ParamKey = - std::tuple; + std::tuple; SelectedRowsTypeStorage(const pir::Type& dtype, const phi::DDim& dims, const phi::DataLayout& layout, - const phi::LoD& lod, + const phi::LegacyLoD& lod, size_t offset) : dtype_(dtype), dims_(dims), @@ -79,7 +79,7 @@ struct SelectedRowsTypeStorage : public pir::TypeStorage { std::get<2>(key)))); // hash lod hash_value = pir::detail::hash_combine( - hash_value, std::hash()(std::get<3>(key))); + hash_value, std::hash()(std::get<3>(key))); // hash offset hash_value = pir::detail::hash_combine( hash_value, std::hash()(std::get<4>(key))); @@ -104,7 +104,7 @@ struct SelectedRowsTypeStorage : public pir::TypeStorage { pir::Type dtype_; phi::DDim dims_; phi::DataLayout layout_; - phi::LoD lod_; + phi::LegacyLoD lod_; size_t offset_; }; diff --git a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc index ee36ccd7cb8c9c..84da8212c66290 100644 --- a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc +++ b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc @@ -2266,7 +2266,7 @@ void PushBackOutputTypes(pir::IrContext* ctx, data_layout = phi::DataLayout::ONEDNN; } #endif - phi::LoD lod = {{}}; + phi::LegacyLoD lod = {{}}; size_t offset = 0; auto dense_tensor_dtype = DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); diff --git a/paddle/fluid/pybind/manual_static_op_function.h b/paddle/fluid/pybind/manual_static_op_function.h index 0436fd420e6dce..25f0dba0bfbe83 100644 --- a/paddle/fluid/pybind/manual_static_op_function.h +++ b/paddle/fluid/pybind/manual_static_op_function.h @@ -857,7 +857,7 @@ static PyObject *static_api_run_custom_op(PyObject *self, auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; auto type = paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), @@ -885,7 +885,7 @@ static PyObject *static_api_run_custom_op(PyObject *self, auto ddims = phi::make_ddim(output_shapes[value_index]); auto dtype = output_dtypes[value_index]; phi::DataLayout layout{DataLayout::NCHW}; - phi::LoD lod; + phi::LegacyLoD lod; auto out_type = paddle::dialect::DenseTensorType::get( pir::IrContext::Instance(), paddle::dialect::TransToIrDataType(dtype), diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index 8336dbbc725bf5..c31775c9cd8ae2 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -492,19 +492,19 @@ void BindTensor(pybind11::module &m) { // NOLINT }) /* ------ End of original Tensor ------ */ .def(py::init([](const std::vector> &recursive_sequence_lengths) { - LoD new_lod; + LegacyLoD new_lod; new_lod.reserve(recursive_sequence_lengths.size()); std::copy(recursive_sequence_lengths.begin(), recursive_sequence_lengths.end(), std::back_inserter(new_lod)); - LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod); + LegacyLoD new_offset_lod = ConvertToOffsetBasedLegacyLoD(new_lod); PADDLE_ENFORCE_EQ( - CheckLoD(new_offset_lod, -1), + CheckLegacyLoD(new_offset_lod, -1), true, common::errors::InvalidArgument( "The provided recursive_sequence_lengths info is " "invalid, " - "the LoD converted by recursive_sequence_lengths is %s", + "the LegacyLoD converted by recursive_sequence_lengths is %s", new_lod)); return std::make_unique(new_offset_lod); })) @@ -520,19 +520,20 @@ void BindTensor(pybind11::module &m) { // NOLINT [](phi::DenseTensor &self, const std::vector> &lod) { // the input lod is offset-based level-of-detail info - LoD new_lod; + LegacyLoD new_lod; new_lod.reserve(lod.size()); std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); PADDLE_ENFORCE_EQ( - CheckLoD(new_lod, common::vectorize(self.dims()).front()), + CheckLegacyLoD(new_lod, common::vectorize(self.dims()).front()), true, common::errors::InvalidArgument( - "The provided LoD is invalid, the LoD is %s", new_lod)); + "The provided LegacyLoD is invalid, the LegacyLoD is %s", + new_lod)); self.set_lod(new_lod); }, py::arg("lod"), R"DOC( - Set LoD of the Tensor. + Set LegacyLoD of the Tensor. Args: lod (list[list[int]]): The lod to set. @@ -559,27 +560,27 @@ void BindTensor(pybind11::module &m) { // NOLINT &recursive_sequence_lengths) { // the input recursive_sequence_lengths is length-based // level-of-detail info - LoD new_lod; + LegacyLoD new_lod; new_lod.reserve(recursive_sequence_lengths.size()); std::copy(recursive_sequence_lengths.begin(), recursive_sequence_lengths.end(), std::back_inserter(new_lod)); - LoD new_offset_lod = ConvertToOffsetBasedLoD(new_lod); + LegacyLoD new_offset_lod = ConvertToOffsetBasedLegacyLoD(new_lod); PADDLE_ENFORCE_EQ( - CheckLoD(new_offset_lod, - common::vectorize(self.dims()).front()), + CheckLegacyLoD(new_offset_lod, + common::vectorize(self.dims()).front()), true, common::errors::InvalidArgument( "The provided recursive_sequence_lengths info is " "invalid, " - "the LoD converted by recursive_sequence_lengths is " + "the LegacyLoD converted by recursive_sequence_lengths is " "%s", new_lod)); self.set_lod(new_offset_lod); }, py::arg("recursive_sequence_lengths"), R"DOC( - Set LoD of the Tensor according to recursive sequence lengths. + Set LegacyLoD of the Tensor according to recursive sequence lengths. For example, if recursive_sequence_lengths=[[2, 3]], which means there are two sequences with length 2 and 3 respectively, the @@ -609,14 +610,14 @@ void BindTensor(pybind11::module &m) { // NOLINT "lod", [](phi::DenseTensor &self) -> std::vector> { // output the offset-based lod info - LoD lod = self.lod(); + LegacyLoD lod = self.lod(); std::vector> new_lod; new_lod.reserve(lod.size()); std::copy(lod.begin(), lod.end(), std::back_inserter(new_lod)); return new_lod; }, R"DOC( - Return the LoD of the Tensor. + Return the LegacyLoD of the Tensor. Returns: list[list[int]]: The lod of the Tensor. @@ -682,7 +683,7 @@ void BindTensor(pybind11::module &m) { // NOLINT auto dtype = static_cast(t[1].cast()); auto dims = common::make_ddim(t[2].cast>()); - auto lod_info = t[3].cast(); + auto lod_info = t[3].cast(); auto device_id = t[4].cast(); auto shared_reader_holder = @@ -793,7 +794,7 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_reader_holder, static_cast(t[3].cast())); tensor.Resize(common::make_ddim(t[4].cast>())); - tensor.set_lod(t[5].cast()); + tensor.set_lod(t[5].cast()); return tensor; }, @@ -928,7 +929,7 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_holder, static_cast(t[3].cast())); tensor.Resize(common::make_ddim(t[4].cast>())); - tensor.set_lod(t[5].cast()); + tensor.set_lod(t[5].cast()); return tensor; }, @@ -1016,7 +1017,7 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_reader_holder, static_cast(t[2].cast())); tensor.Resize(common::make_ddim(t[3].cast>())); - tensor.set_lod(t[4].cast()); + tensor.set_lod(t[4].cast()); return tensor; })); diff --git a/paddle/phi/capi/include/kernel_registry.h b/paddle/phi/capi/include/kernel_registry.h index 71fcc76c2c213f..5f605d38eb60e2 100644 --- a/paddle/phi/capi/include/kernel_registry.h +++ b/paddle/phi/capi/include/kernel_registry.h @@ -553,7 +553,7 @@ CPP_TYPE_TO_PD_ARG_TYPE_REGISTER(PD_ARG_TYPE_TO_CPP_TYPE) } // namespace capi -using LoD = capi::LoD; +using LegacyLoD = capi::LegacyLoD; using Context = capi::DeviceContext; using DenseTensor = capi::DenseTensor; using Scalar = capi::Scalar; diff --git a/paddle/phi/capi/include/wrapper_base.h b/paddle/phi/capi/include/wrapper_base.h index 75f3e2d9e350eb..3e617e3b1e6e98 100644 --- a/paddle/phi/capi/include/wrapper_base.h +++ b/paddle/phi/capi/include/wrapper_base.h @@ -43,7 +43,8 @@ namespace phi { namespace capi { -using LoD = std::vector>; +using LegacyLoD = std::vector>; +using LoD = LegacyLoD; template static inline PD_List PDListFromVector(std::vector* vec) { @@ -209,12 +210,12 @@ class DenseTensor : public WrapperBase { return byte_size; } - LoD lod() const { + LegacyLoD lod() const { PD_List data, offset; C_Status status; PD_TensorGetLoD(raw_data(), &data, &offset, &status); PD_CHECK_STATUS(status); - LoD lod_; + LegacyLoD lod_; auto ptr = static_cast(data.data); auto offset_ptr = static_cast(offset.data); for (size_t i = 0; i < offset.size - 1; ++i) { @@ -225,7 +226,7 @@ class DenseTensor : public WrapperBase { return lod_; } - void ResetLoD(const LoD& lod) { + void ResetLoD(const LegacyLoD& lod) { std::vector data, offset; offset.push_back(0); for (const auto& item : lod) { diff --git a/paddle/phi/capi/lib/c_tensor.cc b/paddle/phi/capi/lib/c_tensor.cc index eb8c8c6f4eb47d..5f24aaae8d1ecd 100644 --- a/paddle/phi/capi/lib/c_tensor.cc +++ b/paddle/phi/capi/lib/c_tensor.cc @@ -315,7 +315,7 @@ void PD_TensorResetLoD(PD_Tensor* tensor, *status = C_SUCCESS; } - phi::LoD lod; + phi::LegacyLoD lod; auto offset_ptr = static_cast(offset.data); auto data_ptr = static_cast(data.data); diff --git a/paddle/phi/core/dense_tensor.cc b/paddle/phi/core/dense_tensor.cc index 8fafa6fa62eca1..38706cdbc1d15b 100644 --- a/paddle/phi/core/dense_tensor.cc +++ b/paddle/phi/core/dense_tensor.cc @@ -255,7 +255,7 @@ void DenseTensor::ResizeAndAllocate(const DDim& dims) { } } -void DenseTensor::ResetLoD(const LoD& legacy_lod) { +void DenseTensor::ResetLoD(const LegacyLoD& legacy_lod) { meta_.legacy_lod = legacy_lod; } diff --git a/paddle/phi/core/dense_tensor.h b/paddle/phi/core/dense_tensor.h index 44bf8a24eff72c..d47cb412b5cf70 100644 --- a/paddle/phi/core/dense_tensor.h +++ b/paddle/phi/core/dense_tensor.h @@ -95,7 +95,7 @@ class TEST_API DenseTensor : public TensorBase, /// \brief Returns the lod of the tensor. /// \return The lod of the tensor. - const LoD& lod() const noexcept { return meta_.legacy_lod; } + const LegacyLoD& lod() const noexcept { return meta_.legacy_lod; } /// \brief Returns the data type of the tensor. /// \return The data type of the tensor. @@ -154,7 +154,7 @@ class TEST_API DenseTensor : public TensorBase, /// \brief Change the lod information in the metadata. /// \param legacy_lod The new lod of the dense tensor. - void ResetLoD(const LoD& legacy_lod); + void ResetLoD(const LegacyLoD& legacy_lod); /// \brief Returns the actual allocation size occupied by tensor, may be /// larger diff --git a/paddle/phi/core/dense_tensor.inl b/paddle/phi/core/dense_tensor.inl index fecdd47e7310dc..a834c0f653717d 100644 --- a/paddle/phi/core/dense_tensor.inl +++ b/paddle/phi/core/dense_tensor.inl @@ -118,11 +118,11 @@ void set_mem_desc(const dnnl::memory::desc& mem_desc); Will be adjusted/removed/moved in the near future */ public: -explicit DenseTensor(const LoD& lod); +explicit DenseTensor(const LegacyLoD& lod); void set_lod(const LoD& lod); -LoD* mutable_lod(); +LegacyLoD* mutable_lod(); /* * Get the start offset and end offset of an element from LoD. diff --git a/paddle/phi/core/dense_tensor_impl.cc b/paddle/phi/core/dense_tensor_impl.cc index 79084a014275fe..0c155abb0a853b 100644 --- a/paddle/phi/core/dense_tensor_impl.cc +++ b/paddle/phi/core/dense_tensor_impl.cc @@ -227,11 +227,11 @@ LEGACY_DATA_MEMBER_FUNC_INSTANTIATION(::phi::dtype::complex) /* From phi::DenseTensor */ /* ------------------------------ */ -DenseTensor::DenseTensor(const LoD& legacy_lod) : DenseTensor() { +DenseTensor::DenseTensor(const LegacyLoD& legacy_lod) : DenseTensor() { meta_.legacy_lod = legacy_lod; } -void DenseTensor::set_lod(const LoD& legacy_lod) { +void DenseTensor::set_lod(const LegacyLoD& legacy_lod) { meta_.legacy_lod = legacy_lod; } diff --git a/paddle/phi/core/framework/dense_tensor_serialize.cc b/paddle/phi/core/framework/dense_tensor_serialize.cc index 647c2956fd4bf9..cc117f41c6cf3f 100644 --- a/paddle/phi/core/framework/dense_tensor_serialize.cc +++ b/paddle/phi/core/framework/dense_tensor_serialize.cc @@ -37,7 +37,7 @@ void SerializeToStream(std::ostream &os, os.write(reinterpret_cast(&size), sizeof(size)); for (auto &each : lod) { - size = each.size() * sizeof(phi::LoD::value_type::value_type); + size = each.size() * sizeof(phi::LegacyLoD::value_type::value_type); os.write(reinterpret_cast(&size), sizeof(size)); os.write(reinterpret_cast(each.data()), static_cast(size)); diff --git a/paddle/phi/core/lod_utils.cc b/paddle/phi/core/lod_utils.cc index 0075977af8d984..f49d9b4d79d119 100644 --- a/paddle/phi/core/lod_utils.cc +++ b/paddle/phi/core/lod_utils.cc @@ -18,10 +18,10 @@ namespace phi { -LoD ToAbsOffset(const LoD &in) { +LegacyLoD ToAbsOffset(const LegacyLoD &in) { // the lowest level stores relative offsets if (in.empty() || in.size() == 1) return in; - LoD result = in; + LegacyLoD result = in; for (auto level = static_cast(in.size() - 2); level >= 0; level--) { for (size_t i = 0; i < in[level].size(); ++i) { size_t index = in[level][i]; @@ -31,20 +31,21 @@ LoD ToAbsOffset(const LoD &in) { return result; } -void AppendLoD(LoD *lod, const LoD &lod_length) { +void AppendLegacyLoD(LegacyLoD *lod, const LegacyLoD &lod_length) { PADDLE_ENFORCE_EQ( (lod->empty() || lod->size() == lod_length.size()), true, common::errors::InvalidArgument( - "The input LoD length should be equal to the appended LoD size, but " - "received input LoD length is %d, actual LoD size is %d.", + "The input LegacyLoD length should be equal to the appended " + "LegacyLoD size, but " + "received input LegacyLoD length is %d, actual LegacyLoD size is %d.", lod_length.size(), lod->size())); if (lod->empty()) { for (size_t i = 0; i < lod_length.size(); ++i) { lod->emplace_back(1, 0); // size = 1, value = 0; } - *lod = LoD(lod_length.size(), std::vector({0})); + *lod = LegacyLoD(lod_length.size(), std::vector({0})); } for (size_t i = 0; i < lod->size(); ++i) { auto &level = (*lod)[i]; @@ -54,8 +55,8 @@ void AppendLoD(LoD *lod, const LoD &lod_length) { } } -LoD ConvertToLengthBasedLoD(const LoD &offset_lod) { - LoD length_lod; +LegacyLoD ConvertToLengthBasedLegacyLoD(const LegacyLoD &offset_lod) { + LegacyLoD length_lod; length_lod.reserve(offset_lod.size()); for (const auto &item : offset_lod) { std::vector level; diff --git a/paddle/phi/core/lod_utils.h b/paddle/phi/core/lod_utils.h index 2f092df4d620cf..5f0f1c64625135 100644 --- a/paddle/phi/core/lod_utils.h +++ b/paddle/phi/core/lod_utils.h @@ -19,25 +19,25 @@ #include "paddle/utils/test_macros.h" namespace phi { -using LoD = std::vector>; +using LegacyLoD = std::vector>; /* - * Transform an LoD from relative offsets to absolute offsets. + * Transform an LegacyLoD from relative offsets to absolute offsets. */ -LoD ToAbsOffset(const LoD& in); +LegacyLoD ToAbsOffset(const LegacyLoD& in); -TEST_API void AppendLoD(LoD* lod, const LoD& lod_length); +TEST_API void AppendLegacyLoD(LegacyLoD* lod, const LegacyLoD& lod_length); /* - * Convert between length-based LoD and offset-based LoD. - * The implementation of DenseTensor class use offset-based LoD. + * Convert between length-based LegacyLoD and offset-based LegacyLoD. + * The implementation of DenseTensor class use offset-based LegacyLoD. * However, we want to expose the more user-friendly length-based - * LoD to the Python side instead. + * LegacyLoD to the Python side instead. * * Example: * If offset_lod = [[0, 2, 3],[0, 3, 5, 9]] * then length_lod = [[2, 1], [3, 2, 4]] */ -TEST_API LoD ConvertToLengthBasedLoD(const LoD& offset_lod); +TEST_API LegacyLoD ConvertToLengthBasedLegacyLoD(const LegacyLoD& offset_lod); } // namespace phi diff --git a/paddle/phi/core/meta_tensor.cc b/paddle/phi/core/meta_tensor.cc index fbc5bcf79f3caf..53ef7a04303e89 100644 --- a/paddle/phi/core/meta_tensor.cc +++ b/paddle/phi/core/meta_tensor.cc @@ -220,7 +220,7 @@ void MetaTensor::share_lod(const MetaTensor& meta_tensor) { } } -void MetaTensor::share_lod(const LoD& legacy_lod) { +void MetaTensor::share_lod(const LegacyLoD& legacy_lod) { ValidCheck(*this); if (phi::SparseCooTensor::classof(tensor_) || phi::SparseCsrTensor::classof(tensor_) || @@ -339,7 +339,7 @@ bool MetaTensor::initialized() const { return tensor_ != nullptr; } // Private Member Methods -const LoD& MetaTensor::lod() const { +const LegacyLoD& MetaTensor::lod() const { if (phi::DenseTensor::classof(tensor_)) { return static_cast(tensor_)->lod(); } else if (phi::SelectedRows::classof(tensor_)) { @@ -354,7 +354,7 @@ const LoD& MetaTensor::lod() const { } } -const LoD& MetaTensor::lod(int64_t index) const { +const LegacyLoD& MetaTensor::lod(int64_t index) const { ValidCheck(*this); PADDLE_ENFORCE_EQ( is_tensor_array(), diff --git a/paddle/phi/core/meta_tensor.h b/paddle/phi/core/meta_tensor.h index c31274966c7972..570f519870a841 100644 --- a/paddle/phi/core/meta_tensor.h +++ b/paddle/phi/core/meta_tensor.h @@ -75,7 +75,7 @@ class TEST_API MetaTensor { virtual void set_strides(const DDim& strides); virtual void share_lod(const MetaTensor& meta_tensor); - void share_lod(const LoD& legacy_lod); + void share_lod(const LegacyLoD& legacy_lod); void share_lod(const MetaTensor& meta_tensor, int64_t index); virtual void share_meta(const MetaTensor& meta_tensor); virtual void share_dims(const MetaTensor& meta_tensor); @@ -105,8 +105,8 @@ class TEST_API MetaTensor { protected: // Because the lod in compiletime and runtime is different, // so `LoD` cannot in public methods - const LoD& lod() const; - const LoD& lod(int64_t index) const; + const LegacyLoD& lod() const; + const LegacyLoD& lod(int64_t index) const; TensorBase* tensor() const; TensorBase* tensor_ = nullptr; diff --git a/paddle/phi/core/tensor_meta.cc b/paddle/phi/core/tensor_meta.cc index 8fa10eb4d3cae8..3bd82c888a2acc 100644 --- a/paddle/phi/core/tensor_meta.cc +++ b/paddle/phi/core/tensor_meta.cc @@ -148,7 +148,7 @@ DenseTensorMeta::DenseTensorMeta(DataType dtype, DenseTensorMeta::DenseTensorMeta(DataType dtype, const DDim& dims, DataLayout layout, - const LoD& legacy_lod, + const LegacyLoD& legacy_lod, size_t offset) : dims(dims), dtype(dtype), diff --git a/paddle/phi/core/tensor_meta.h b/paddle/phi/core/tensor_meta.h index 5135f2efdcb672..1e3cf0f84da0c5 100644 --- a/paddle/phi/core/tensor_meta.h +++ b/paddle/phi/core/tensor_meta.h @@ -41,7 +41,8 @@ namespace phi { * 0 2 4 7 * 0 2 5 7 10 12 15 20 */ -using LoD = std::vector>; +using LegacyLoD = std::vector>; +using LoD = LegacyLoD; /// \brief The meta data of dense tensor. Take the structure type /// and use all default operations. @@ -57,7 +58,7 @@ struct TEST_API DenseTensorMeta { DenseTensorMeta(DataType dtype, const DDim& dims, DataLayout layout, - const LoD& legacy_lod, + const LegacyLoD& legacy_lod, size_t offset = 0); DenseTensorMeta(const DenseTensorMeta& other); @@ -80,7 +81,7 @@ struct TEST_API DenseTensorMeta { DDim dims; DataType dtype{DataType::UNDEFINED}; DataLayout layout{DataLayout::NCHW}; - LoD legacy_lod; + LegacyLoD legacy_lod; size_t offset{0}; DDim strides; }; diff --git a/paddle/phi/kernels/cpu/concat_kernel.cc b/paddle/phi/kernels/cpu/concat_kernel.cc index 1aed8463cf317e..580a89643828f3 100644 --- a/paddle/phi/kernels/cpu/concat_kernel.cc +++ b/paddle/phi/kernels/cpu/concat_kernel.cc @@ -71,8 +71,8 @@ void ConcatKernel(const Context& dev_ctx, if (lod_size) { auto* out_lod = out->mutable_lod(); for (size_t i = 1; i < x.size(); ++i) { - auto in_lod = phi::ConvertToLengthBasedLoD(x[i]->lod()); - phi::AppendLoD(out_lod, in_lod); + auto in_lod = phi::ConvertToLengthBasedLegacyLoD(x[i]->lod()); + phi::AppendLegacyLoD(out_lod, in_lod); } } } diff --git a/paddle/phi/kernels/cpu/distribute_fpn_proposals_kernel.cc b/paddle/phi/kernels/cpu/distribute_fpn_proposals_kernel.cc index 28248b1a188794..c1c13e1539bdb9 100644 --- a/paddle/phi/kernels/cpu/distribute_fpn_proposals_kernel.cc +++ b/paddle/phi/kernels/cpu/distribute_fpn_proposals_kernel.cc @@ -132,7 +132,7 @@ void DistributeFpnProposalsKernel( } // merge lod information into DenseTensor for (int i = 0; i < num_level; ++i) { - LoD lod; + LegacyLoD lod; lod.emplace_back(multi_fpn_rois_lod0[i]); multi_fpn_rois[i]->set_lod(lod); } diff --git a/paddle/phi/kernels/cpu/fusion_seqpool_concat_kernel.cc b/paddle/phi/kernels/cpu/fusion_seqpool_concat_kernel.cc index 23121b8ccfc0fc..7a6da56f8510fa 100644 --- a/paddle/phi/kernels/cpu/fusion_seqpool_concat_kernel.cc +++ b/paddle/phi/kernels/cpu/fusion_seqpool_concat_kernel.cc @@ -32,7 +32,7 @@ void FusionSeqPoolConcatKernel(const Context& dev_ctx, const auto& y_dims = out->dims(); size_t bs = x0_lod[0].size() - 1; out->Resize({static_cast(bs), y_dims[1]}); - phi::LoD y_lod(1); + phi::LegacyLoD y_lod(1); y_lod[0].resize(bs + 1); for (size_t i = 0; i <= bs; ++i) { y_lod[0][i] = i; diff --git a/paddle/phi/kernels/cpu/generate_proposals_kernel.cc b/paddle/phi/kernels/cpu/generate_proposals_kernel.cc index 2980b9b1ae06d8..69ff203e2fead5 100644 --- a/paddle/phi/kernels/cpu/generate_proposals_kernel.cc +++ b/paddle/phi/kernels/cpu/generate_proposals_kernel.cc @@ -321,7 +321,7 @@ void GenerateProposalsKernel(const Context& ctx, trans(ctx, bbox_deltas, &bbox_deltas_swap, axis); trans(ctx, scores, &scores_swap, axis); - phi::LoD lod; + phi::LegacyLoD lod; lod.resize(1); auto& lod0 = lod[0]; lod0.push_back(0); diff --git a/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc b/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc index 2d6e3cda65bb65..2bc95d0e75f112 100644 --- a/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc +++ b/paddle/phi/kernels/cpu/match_matrix_tensor_kernel.cc @@ -183,7 +183,7 @@ void CPUMatchMatrixTensorOPKernel(const Context& dev_ctx, } } - phi::LoD out_lod; + phi::LegacyLoD out_lod; out_lod.push_back(top_offset); out->set_lod(out_lod); diff --git a/paddle/phi/kernels/cpu/pyramid_hash_kernel.cc b/paddle/phi/kernels/cpu/pyramid_hash_kernel.cc index 7250fd50228f0a..e8cd1dd4828aeb 100644 --- a/paddle/phi/kernels/cpu/pyramid_hash_kernel.cc +++ b/paddle/phi/kernels/cpu/pyramid_hash_kernel.cc @@ -179,13 +179,13 @@ void CPUPyramidHashOPKernel(const Context& dev_ctx, int top_l = static_cast(top_offset[top_offset.size() - 1]); - phi::LoD top_lod; + phi::LegacyLoD top_lod; top_lod.push_back(top_offset); top->set_lod(top_lod); top->Resize(common::make_ddim({top_l, _num_emb})); auto* top_data = dev_ctx.template Alloc(top); - phi::LoD drop_pos_lod; + phi::LegacyLoD drop_pos_lod; drop_pos_lod.push_back(drop_pos_offset); drop_pos->set_lod(drop_pos_lod); diff --git a/paddle/phi/kernels/funcs/beam_search_decode.h b/paddle/phi/kernels/funcs/beam_search_decode.h index fd505d19dbe211..1e3fef7c21b5d8 100644 --- a/paddle/phi/kernels/funcs/beam_search_decode.h +++ b/paddle/phi/kernels/funcs/beam_search_decode.h @@ -136,7 +136,7 @@ void BeamSearchDecoder::ConvertSentenceVectorToDenseTensor( sentence_vector_list[src_idx].size()); } - phi::LoD lod; + phi::LegacyLoD lod; lod.push_back(source_level_lod); lod.push_back(sentence_level_lod); diff --git a/paddle/phi/kernels/funcs/math/beam_search.cc b/paddle/phi/kernels/funcs/math/beam_search.cc index d56d468130359c..88736c4ef47f9c 100644 --- a/paddle/phi/kernels/funcs/math/beam_search.cc +++ b/paddle/phi/kernels/funcs/math/beam_search.cc @@ -93,10 +93,10 @@ class BeamSearchFunctor { low_level.push_back(low_offset); // fill lod - phi::LoD lod(2); + phi::LegacyLoD lod(2); lod[0].assign(high_level.begin(), high_level.end()); lod[1].assign(low_level.begin(), low_level.end()); - if (!CheckLoD(lod)) { + if (!CheckLegacyLoD(lod)) { PADDLE_THROW(common::errors::InvalidArgument( "lod %s is not right in" " beam_search, please check your code.", @@ -155,7 +155,7 @@ class BeamSearchFunctor { * since the end tokens must be writed out. */ void PruneEndBeams(const phi::DenseTensor *pre_ids, - const phi::LoD &abs_lod, + const phi::LegacyLoD &abs_lod, std::vector> *items, size_t lod_level, int end_id) { diff --git a/paddle/phi/kernels/funcs/math/beam_search.cu b/paddle/phi/kernels/funcs/math/beam_search.cu index 1af08f42f71c96..40b4dbbda7ad85 100644 --- a/paddle/phi/kernels/funcs/math/beam_search.cu +++ b/paddle/phi/kernels/funcs/math/beam_search.cu @@ -443,7 +443,7 @@ class BeamSearchFunctor { int* parent_idx_data = parent_idx ? context.template Alloc(parent_idx) : nullptr; - phi::LoD selected_lod(2); + phi::LegacyLoD selected_lod(2); selected_lod[0].assign(abs_lod[level].begin(), abs_lod[level].end()); selected_lod[1].resize(scores->dims()[0] + 1); phi::MixVector mix_vector(&selected_lod[1]); @@ -511,7 +511,7 @@ class BeamSearchFunctor { context.Wait(); mix_vector.CopyToCPU(); - if (!CheckLoD(selected_lod)) { + if (!CheckLegacyLoD(selected_lod)) { PADDLE_THROW(common::errors::InvalidArgument( "lod %s is not right in" " beam_search, please check your code.", diff --git a/paddle/phi/kernels/funcs/math/beam_search.h b/paddle/phi/kernels/funcs/math/beam_search.h index 92c88430b2a0a6..40b8d39b01cb1b 100644 --- a/paddle/phi/kernels/funcs/math/beam_search.h +++ b/paddle/phi/kernels/funcs/math/beam_search.h @@ -25,7 +25,7 @@ namespace phi { namespace math { -static inline std::string LoDToString(const LoD& lod) { +static inline std::string LoDToString(const LegacyLoD& lod) { std::ostringstream stream; for (const auto& row : lod) { for (const auto& element : row) { @@ -36,7 +36,7 @@ static inline std::string LoDToString(const LoD& lod) { return stream.str(); } -static inline bool CheckLoD(const LoD& in, int tensor_height = -1) { +static inline bool CheckLegacyLoD(const LoD& in, int tensor_height = -1) { if (in.empty()) return true; for (const auto& level : in) { // check: there should be more than 2 offsets existing in each level. diff --git a/paddle/phi/kernels/funcs/math/beam_search_xpu.cc b/paddle/phi/kernels/funcs/math/beam_search_xpu.cc index 327f31900f75c3..bfbe024b5fd188 100644 --- a/paddle/phi/kernels/funcs/math/beam_search_xpu.cc +++ b/paddle/phi/kernels/funcs/math/beam_search_xpu.cc @@ -122,10 +122,10 @@ class BeamSearchFunctor { low_level.push_back(low_offset); // fill lod - phi::LoD lod(2); + phi::LegacyLoD lod(2); lod[0].assign(high_level.begin(), high_level.end()); lod[1].assign(low_level.begin(), low_level.end()); - if (!CheckLoD(lod)) { + if (!CheckLegacyLoD(lod)) { PADDLE_THROW(common::errors::InvalidArgument( "lod %s is not right in" " beam_search, please check your code.", @@ -180,7 +180,7 @@ class BeamSearchFunctor { * since the end tokens must be writed out. */ void PruneEndBeams(const phi::DenseTensor *pre_ids, - const phi::LoD &abs_lod, + const phi::LegacyLoD &abs_lod, std::vector> *items, size_t lod_level, int end_id, diff --git a/paddle/phi/kernels/funcs/sequence2batch.h b/paddle/phi/kernels/funcs/sequence2batch.h index 0b2518511a0d7f..2c42a76aa1b1a4 100644 --- a/paddle/phi/kernels/funcs/sequence2batch.h +++ b/paddle/phi/kernels/funcs/sequence2batch.h @@ -131,7 +131,7 @@ class LoDTensor2BatchFunctor { // The max_seqlen represents batch size after rearranging the // input DenseTensor. It is also the maximum length of input sequence. - phi::LoD batch_lods; + phi::LegacyLoD batch_lods; batch_lods.emplace_back(std::vector{0}); batch_lods.emplace_back(std::vector{0}); batch_lods.emplace_back(std::vector{0}); diff --git a/paddle/phi/kernels/funcs/sequence_scale.cc b/paddle/phi/kernels/funcs/sequence_scale.cc index 5feec87ff3e873..5f05af1c6bcd6e 100644 --- a/paddle/phi/kernels/funcs/sequence_scale.cc +++ b/paddle/phi/kernels/funcs/sequence_scale.cc @@ -32,7 +32,7 @@ class ScaleLoDTensorFunctor { auto lod = seq->lod(); const size_t num_seq = lod[level].size() - 1; size_t seq_width = seq->dims()[1]; - phi::LoD abs_offset_lod = phi::ToAbsOffset(lod); + phi::LegacyLoD abs_offset_lod = phi::ToAbsOffset(lod); T* seq_data = context.template Alloc(seq); for (size_t i = 0; i < num_seq; ++i) { diff --git a/paddle/phi/kernels/funcs/tensor_formatter.cc b/paddle/phi/kernels/funcs/tensor_formatter.cc index c1b4dfee839dce..20463053af6e97 100644 --- a/paddle/phi/kernels/funcs/tensor_formatter.cc +++ b/paddle/phi/kernels/funcs/tensor_formatter.cc @@ -65,7 +65,7 @@ std::string TensorFormatter::Format(const phi::DenseTensor& print_tensor, if (print_tensor_lod_) { log_stream << " - lod: {"; - const phi::LoD& lod = print_tensor.lod(); + const phi::LegacyLoD& lod = print_tensor.lod(); for (auto const& level : lod) { log_stream << "{"; bool is_first = true; diff --git a/paddle/phi/kernels/fusion/cpu/fusion_seqpool_cvm_concat_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_seqpool_cvm_concat_kernel.cc index f9a4c71ee8647f..14e483617390b0 100644 --- a/paddle/phi/kernels/fusion/cpu/fusion_seqpool_cvm_concat_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fusion_seqpool_cvm_concat_kernel.cc @@ -36,7 +36,7 @@ void FusionSeqPoolCVMConcatKernel(const Context& dev_ctx, const auto& y_dims = out->dims(); size_t bs = x0_lod[0].size() - 1; out->Resize({static_cast(bs), y_dims[1]}); - phi::LoD y_lod(1); + phi::LegacyLoD y_lod(1); y_lod[0].resize(bs + 1); for (size_t i = 0; i <= bs; ++i) { y_lod[0][i] = i; diff --git a/paddle/phi/kernels/fusion/xpu/sequance_unpad_xpu_kernerl.cc b/paddle/phi/kernels/fusion/xpu/sequance_unpad_xpu_kernerl.cc index df8f93df2258d6..394df10b032ddf 100644 --- a/paddle/phi/kernels/fusion/xpu/sequance_unpad_xpu_kernerl.cc +++ b/paddle/phi/kernels/fusion/xpu/sequance_unpad_xpu_kernerl.cc @@ -32,7 +32,7 @@ void SequenceUnpadXPUKernel(const Context& ctx, for (int64_t i = 0; i < batch_size; ++i) { out_lod0[i + 1] = out_lod0[i] + seq_len_ptr[i]; } - phi::LoD out_lod; + phi::LegacyLoD out_lod; out_lod.push_back(out_lod0); int64_t out_dim0 = out_lod0.back(); diff --git a/paddle/phi/kernels/gpu/box_clip_kernel.cu b/paddle/phi/kernels/gpu/box_clip_kernel.cu index 4c432abdd79688..2cb120a820c8bc 100644 --- a/paddle/phi/kernels/gpu/box_clip_kernel.cu +++ b/paddle/phi/kernels/gpu/box_clip_kernel.cu @@ -56,7 +56,7 @@ void GPUBoxClipKernel(const Context &dev_ctx, const int64_t num = input_p->dims()[0]; const int64_t bbox_width = input_p->numel() / num; auto lod = input_p->lod(); - phi::LoD abs_offset_lod = phi::ToAbsOffset(lod); + phi::LegacyLoD abs_offset_lod = phi::ToAbsOffset(lod); auto stream = dev_ctx.stream(); const size_t batch_size = lod.back().size() - 1; diff --git a/paddle/phi/kernels/gpu/collect_fpn_proposals_kernel.cu b/paddle/phi/kernels/gpu/collect_fpn_proposals_kernel.cu index 8d368d622a8819..2c3c3cdb550a8f 100644 --- a/paddle/phi/kernels/gpu/collect_fpn_proposals_kernel.cu +++ b/paddle/phi/kernels/gpu/collect_fpn_proposals_kernel.cu @@ -271,7 +271,7 @@ void GPUCollectFpnProposalsOpKernel( dev_ctx.stream()); } - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(offset); fpn_rois->set_lod(lod); } diff --git a/paddle/phi/kernels/gpu/concat_kernel.cu b/paddle/phi/kernels/gpu/concat_kernel.cu index dbf3b4eff5c497..aa6a1ce9d19576 100644 --- a/paddle/phi/kernels/gpu/concat_kernel.cu +++ b/paddle/phi/kernels/gpu/concat_kernel.cu @@ -70,8 +70,8 @@ void ConcatKernel(const Context& dev_ctx, if (lod_size) { auto* out_lod = out->mutable_lod(); for (size_t i = 1; i < x.size(); ++i) { - auto in_lod = phi::ConvertToLengthBasedLoD(x[i]->lod()); - phi::AppendLoD(out_lod, in_lod); + auto in_lod = phi::ConvertToLengthBasedLegacyLoD(x[i]->lod()); + phi::AppendLegacyLoD(out_lod, in_lod); } } } diff --git a/paddle/phi/kernels/gpu/ctc_align_kernel.cu b/paddle/phi/kernels/gpu/ctc_align_kernel.cu index b0b96a841df3fc..ffb04ae4f0ed27 100644 --- a/paddle/phi/kernels/gpu/ctc_align_kernel.cu +++ b/paddle/phi/kernels/gpu/ctc_align_kernel.cu @@ -142,7 +142,7 @@ void CTCAlignOpCUDAKernel(const Context& dev_ctx, // set output lod std::vector host_out_lod0(dev_out_lod0.begin(), dev_out_lod0.end()); - phi::LoD out_lod; + phi::LegacyLoD out_lod; out_lod.push_back(host_out_lod0); output->set_lod(out_lod); diff --git a/paddle/phi/kernels/gpu/distribute_fpn_proposals_kernel.cu b/paddle/phi/kernels/gpu/distribute_fpn_proposals_kernel.cu index 092d2428640c89..95b70da2ff4830 100644 --- a/paddle/phi/kernels/gpu/distribute_fpn_proposals_kernel.cu +++ b/paddle/phi/kernels/gpu/distribute_fpn_proposals_kernel.cu @@ -253,7 +253,7 @@ void DistributeFpnProposalsKernel( Copy(dev_ctx, sub_lod, dev_ctx.GetPlace(), true, rois_num_t); rois_num_t->Resize({lod_size}); } - LoD lod; + LegacyLoD lod; lod.emplace_back(offset); multi_fpn_rois[i]->set_lod(lod); } diff --git a/paddle/phi/kernels/gpu/generate_proposals_kernel.cu b/paddle/phi/kernels/gpu/generate_proposals_kernel.cu index ce2f8dc2467ed0..a4fddb7b51df5a 100644 --- a/paddle/phi/kernels/gpu/generate_proposals_kernel.cu +++ b/paddle/phi/kernels/gpu/generate_proposals_kernel.cu @@ -572,7 +572,7 @@ void GenerateProposalsKernel(const Context &ctx, ctx.stream()); rpn_rois_num->Resize(common::make_ddim({num})); } - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(offset); rpn_rois->Resize(common::make_ddim({num_proposals, 4})); rpn_roi_probs->Resize(common::make_ddim({num_proposals, 1})); diff --git a/paddle/phi/kernels/impl/collect_fpn_proposals_kernel_impl.h b/paddle/phi/kernels/impl/collect_fpn_proposals_kernel_impl.h index 5bc9d7733bb6f8..761fc62e20b263 100644 --- a/paddle/phi/kernels/impl/collect_fpn_proposals_kernel_impl.h +++ b/paddle/phi/kernels/impl/collect_fpn_proposals_kernel_impl.h @@ -192,7 +192,7 @@ void CollectFpnProposalsOpKernel( } } lod0.emplace_back(post_nms_topn); - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(lod0); fpn_rois->set_lod(lod); } diff --git a/paddle/phi/kernels/impl/ctc_align_kernel_impl.h b/paddle/phi/kernels/impl/ctc_align_kernel_impl.h index 927c4c27488104..3a97634ddac744 100644 --- a/paddle/phi/kernels/impl/ctc_align_kernel_impl.h +++ b/paddle/phi/kernels/impl/ctc_align_kernel_impl.h @@ -97,7 +97,7 @@ void CTCAlignKernel(const Context& dev_ctx, } // set output lod - phi::LoD output_lod; + phi::LegacyLoD output_lod; output_lod.push_back(output_lod0); output->set_lod(output_lod); // resize output dims diff --git a/paddle/phi/kernels/impl/im2sequence_kernel_impl.h b/paddle/phi/kernels/impl/im2sequence_kernel_impl.h index 30429154646dcd..a6265e5b30836f 100644 --- a/paddle/phi/kernels/impl/im2sequence_kernel_impl.h +++ b/paddle/phi/kernels/impl/im2sequence_kernel_impl.h @@ -101,7 +101,7 @@ void Im2SequenceKernel(const Context& dev_ctx, phi::funcs::Im2ColFunctor f; f(dev_ctx, src, dilations, strides, paddings, &dst); } - phi::LoD lod(1); + phi::LegacyLoD lod(1); lod[0].reserve(batch_size + 1); int offset = 0; lod[0].push_back(offset); @@ -132,7 +132,7 @@ void Im2SequenceKernel(const Context& dev_ctx, f(dev_ctx, src, dilations, strides, paddings, &dst); } out->Resize(out_dims); - phi::LoD lod(1); + phi::LegacyLoD lod(1); lod[0].reserve(batch_size + 1); int offset = 0; lod[0].push_back(offset); diff --git a/paddle/phi/kernels/impl/lod_reset_kernel_impl.h b/paddle/phi/kernels/impl/lod_reset_kernel_impl.h index a69ce56304fa8b..cde4fb584f8c57 100644 --- a/paddle/phi/kernels/impl/lod_reset_kernel_impl.h +++ b/paddle/phi/kernels/impl/lod_reset_kernel_impl.h @@ -105,7 +105,7 @@ void LodResetKernel(const Context& dev_ctx, auto* out_lod = out->mutable_lod(); out_lod->push_back(ulevel0); } else { - phi::LoD target_lod; + phi::LegacyLoD target_lod; target_lod.push_back(ulevel0); out->set_lod(target_lod); } diff --git a/paddle/phi/kernels/impl/sequence_pool_kernel_impl.h b/paddle/phi/kernels/impl/sequence_pool_kernel_impl.h index e448820516afe1..2925685fc8b7cc 100644 --- a/paddle/phi/kernels/impl/sequence_pool_kernel_impl.h +++ b/paddle/phi/kernels/impl/sequence_pool_kernel_impl.h @@ -54,7 +54,7 @@ void SequencePoolKernel(const Context& ctx, lod[0][lod[0].size() - 1], lod[1].size() - 1, errors::InvalidArgument("The input lod information is illegal.")); - phi::LoD out_lod; + phi::LegacyLoD out_lod; out_lod.push_back(lod[0]); out->set_lod(out_lod); } diff --git a/paddle/phi/kernels/legacy/cpu/legacy_generate_proposals_kernel.cc b/paddle/phi/kernels/legacy/cpu/legacy_generate_proposals_kernel.cc index 9837e3ef80de7d..5e6249249b1ee9 100644 --- a/paddle/phi/kernels/legacy/cpu/legacy_generate_proposals_kernel.cc +++ b/paddle/phi/kernels/legacy/cpu/legacy_generate_proposals_kernel.cc @@ -175,7 +175,7 @@ void GenerateProposalsKernel(const Context &dev_ctx, trans(dev_ctx, *bbox_deltas, &bbox_deltas_swap, axis); trans(dev_ctx, *scores, &scores_swap, axis); - phi::LoD lod; + phi::LegacyLoD lod; lod.resize(1); auto &lod0 = lod[0]; lod0.push_back(0); diff --git a/paddle/phi/kernels/legacy/cpu/multiclass_nms_kernel.cc b/paddle/phi/kernels/legacy/cpu/multiclass_nms_kernel.cc index ca634faf68275d..0c7023344b7017 100644 --- a/paddle/phi/kernels/legacy/cpu/multiclass_nms_kernel.cc +++ b/paddle/phi/kernels/legacy/cpu/multiclass_nms_kernel.cc @@ -366,7 +366,7 @@ void MulticlassNMSv1Kernel(const Context& dev_ctx, } } - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(batch_starts); outs->set_lod(lod); } diff --git a/paddle/phi/kernels/legacy/gpu/legacy_generate_proposals_kernel.cu b/paddle/phi/kernels/legacy/gpu/legacy_generate_proposals_kernel.cu index 3d8a6af1ac452f..c7630a3717a41f 100644 --- a/paddle/phi/kernels/legacy/gpu/legacy_generate_proposals_kernel.cu +++ b/paddle/phi/kernels/legacy/gpu/legacy_generate_proposals_kernel.cu @@ -253,7 +253,7 @@ void CUDAGenerateProposalsKernel(const Context &dev_ctx, dev_ctx.stream()); rpn_rois_num->Resize({num}); } - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(offset); rpn_rois->set_lod(lod); rpn_roi_probs->set_lod(lod); diff --git a/paddle/phi/kernels/xpu/concat_kernel.cc b/paddle/phi/kernels/xpu/concat_kernel.cc index a704ad4f5c4fad..2834b143f4cad4 100644 --- a/paddle/phi/kernels/xpu/concat_kernel.cc +++ b/paddle/phi/kernels/xpu/concat_kernel.cc @@ -84,8 +84,8 @@ void ConcatKernel(const Context& dev_ctx, if (lod_size) { auto* out_lod = out->mutable_lod(); for (size_t i = 1; i < x.size(); ++i) { - auto in_lod = phi::ConvertToLengthBasedLoD(x[i]->lod()); - phi::AppendLoD(out_lod, in_lod); + auto in_lod = phi::ConvertToLengthBasedLegacyLoD(x[i]->lod()); + phi::AppendLegacyLoD(out_lod, in_lod); } } } diff --git a/paddle/phi/kernels/xpu/generate_proposals_kernel.cc b/paddle/phi/kernels/xpu/generate_proposals_kernel.cc index d95b030946e44b..4c9619e4ada214 100644 --- a/paddle/phi/kernels/xpu/generate_proposals_kernel.cc +++ b/paddle/phi/kernels/xpu/generate_proposals_kernel.cc @@ -401,7 +401,7 @@ void GenerateProposalsKernel(const Context& dev_ctx, place, num_data, cpu_place, &tmp_num[0], sizeof(int) * num); } - phi::LoD lod; + phi::LegacyLoD lod; lod.emplace_back(offset); rpn_rois->set_lod(lod); rpn_roi_probs->set_lod(lod); diff --git a/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc b/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc index 6d2b53f1723565..49c0cfd4378e44 100644 --- a/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc +++ b/paddle/phi/kernels/xpu/multiclass_nms3_kernel.cc @@ -211,7 +211,7 @@ void MultiClassNMSKernel(const Context& ctx, } phi::Copy(ctx, nms_rois_num_cpu, nms_rois_num->place(), true, nms_rois_num); } - LoD lod; + LegacyLoD lod; if (num_kept == 0) { batch_starts[batch_starts.size() - 1] = 1; } diff --git a/paddle/pir/include/core/builtin_type.h b/paddle/pir/include/core/builtin_type.h index 9391fdd82da910..21bb482251d3ae 100644 --- a/paddle/pir/include/core/builtin_type.h +++ b/paddle/pir/include/core/builtin_type.h @@ -60,12 +60,12 @@ class IR_API DenseTensorType : public Type::TypeBase>; - using ParamKey = std::tuple; + using LegacyLoD = std::vector>; + using ParamKey = std::tuple; DenseTensorTypeStorage(Type dtype, const pir::DDim& dims, DataLayout layout, - const LoD& lod, + const LegacyLoD& lod, size_t offset) : dtype_(dtype), dims_(dims), @@ -83,8 +83,8 @@ struct DenseTensorTypeStorage : public pir::TypeStorage { static_cast::type>( std::get<2>(key)))); // hash lod - hash_value = - detail::hash_combine(hash_value, std::hash()(std::get<3>(key))); + hash_value = detail::hash_combine(hash_value, + std::hash()(std::get<3>(key))); // hash offset hash_value = detail::hash_combine(hash_value, std::hash()(std::get<4>(key))); @@ -109,7 +109,7 @@ struct DenseTensorTypeStorage : public pir::TypeStorage { pir::Type dtype_; pir::DDim dims_; DataLayout layout_; - LoD lod_; + LegacyLoD lod_; size_t offset_; }; diff --git a/paddle/pir/src/core/builtin_type.cc b/paddle/pir/src/core/builtin_type.cc index 57c3da6ccae44b..95eec98e3cf87f 100644 --- a/paddle/pir/src/core/builtin_type.cc +++ b/paddle/pir/src/core/builtin_type.cc @@ -25,7 +25,7 @@ const DenseTensorType::Dim& DenseTensorType::dims() const { DataLayout DenseTensorType::data_layout() const { return storage()->layout_; } -const DenseTensorType::LoD& DenseTensorType::lod() const { +const DenseTensorType::LegacyLoD& DenseTensorType::lod() const { return storage()->lod_; } diff --git a/test/cpp/fluid/beam_search_decode_op_test.cc b/test/cpp/fluid/beam_search_decode_op_test.cc index e79cadb99d06d5..602036024f2dda 100644 --- a/test/cpp/fluid/beam_search_decode_op_test.cc +++ b/test/cpp/fluid/beam_search_decode_op_test.cc @@ -17,7 +17,7 @@ limitations under the License. */ #include "gtest/gtest.h" using CPUPlace = phi::CPUPlace; -using LoD = phi::LoD; +using LegacyLoD = phi::LegacyLoD; using DenseTensorArray = phi::TensorArray; template @@ -53,7 +53,7 @@ void GenerateExample(const std::vector& level_0, CPUPlace place; - LoD lod; + LegacyLoD lod; lod.push_back(level_0); lod.push_back(level_1); @@ -127,7 +127,7 @@ void BeamSearchDecodeTestFrame() { phi::DenseTensor score_tensor; helper.Backtrace(ids, scores, &id_tensor, &score_tensor); - LoD lod = id_tensor.lod(); + LegacyLoD lod = id_tensor.lod(); std::vector expect_source_lod = {0, 2, 4}; EXPECT_EQ(lod[0], expect_source_lod); std::vector expect_sentence_lod = {0, 4, 7, 12, 17}; diff --git a/test/cpp/fluid/beam_search_op_test_xpu.cc b/test/cpp/fluid/beam_search_op_test_xpu.cc index ad016618c20d67..1267025cab95cc 100644 --- a/test/cpp/fluid/beam_search_op_test_xpu.cc +++ b/test/cpp/fluid/beam_search_op_test_xpu.cc @@ -25,7 +25,7 @@ void PrepareCPUTensors(phi::DenseTensor* ids, phi::DenseTensor* pre_ids, phi::DenseTensor* pre_scores) { // lod - phi::LoD lod; + phi::LegacyLoD lod; std::vector level0({0, 2, 4}); std::vector level1({0, 1, 2, 3, 4}); lod.push_back(level0); diff --git a/test/cpp/fluid/benchmark/op_tester.cc b/test/cpp/fluid/benchmark/op_tester.cc index c7204de1b48a87..d59a08d8d5c8f6 100644 --- a/test/cpp/fluid/benchmark/op_tester.cc +++ b/test/cpp/fluid/benchmark/op_tester.cc @@ -377,7 +377,7 @@ void OpTester::CreateVariables(framework::Scope *scope) { VLOG(3) << "Set lod for tensor " << var_name; std::vector> &lod_vec = item.second.lod; - phi::LoD lod; + phi::LegacyLoD lod; for (auto &item : lod_vec) { lod.push_back(item); } diff --git a/test/cpp/fluid/framework/device_worker_test.cc b/test/cpp/fluid/framework/device_worker_test.cc index 0197b27cb647c2..ff5f764672fcab 100644 --- a/test/cpp/fluid/framework/device_worker_test.cc +++ b/test/cpp/fluid/framework/device_worker_test.cc @@ -84,7 +84,7 @@ TEST(DenseTensor, PrintDenseTensor) { } TEST(DenseTensor, GetTensorBound) { - LoD lod{{0, 2}}; + LegacyLoD lod{{0, 2}}; phi::DenseTensor tensor; tensor.set_lod(lod); tensor.Resize({2, 1}); @@ -97,7 +97,7 @@ TEST(DenseTensor, GetTensorBound) { } TEST(DenseTensor, CheckValidOutput) { - LoD lod{{0, 1, 2}}; + LegacyLoD lod{{0, 1, 2}}; phi::DenseTensor tensor; tensor.set_lod(lod); tensor.Resize({2, 1}); diff --git a/test/cpp/fluid/framework/lod_tensor_test.cc b/test/cpp/fluid/framework/lod_tensor_test.cc index 228533e17b09dc..4d8b3488cfc1ae 100644 --- a/test/cpp/fluid/framework/lod_tensor_test.cc +++ b/test/cpp/fluid/framework/lod_tensor_test.cc @@ -22,7 +22,7 @@ namespace paddle { namespace framework { -TEST(LoD, PrintLoDTensor) { +TEST(LegacyLoD, PrintLoDTensor) { phi::DenseTensor tensor1; tensor1.Resize({2}); tensor1.mutable_data(phi::CPUPlace()); @@ -38,8 +38,8 @@ TEST(LoD, PrintLoDTensor) { LOG(INFO) << tensor2; } -TEST(LoD, data) { - LoD lod{{0, 1, 2}}; +TEST(LegacyLoD, data) { + LegacyLoD lod{{0, 1, 2}}; lod.push_back({0, 2, 4, 5}); lod.push_back(std::vector({0, 1, 6, 8, 10, 11})); @@ -49,20 +49,20 @@ TEST(LoD, data) { } } -TEST(LoD, AppendLoD) { - LoD lod_lens; +TEST(LegacyLoD, AppendLegacyLoD) { + LegacyLoD lod_lens; lod_lens.push_back(std::vector({2})); lod_lens.push_back(std::vector({2, 2})); lod_lens.push_back(std::vector({2, 3, 4, 2})); - LoD origin; + LegacyLoD origin; origin.push_back(std::vector({0, 2})); origin.push_back(std::vector({0, 1, 6})); origin.push_back(std::vector({0, 2, 5, 7, 10, 12, 15})); - phi::AppendLoD(&origin, lod_lens); + phi::AppendLegacyLoD(&origin, lod_lens); - LoD expected; + LegacyLoD expected; expected.push_back(std::vector({0, 2, 4})); expected.push_back(std::vector({0, 1, 6, 8, 10})); expected.push_back( @@ -70,46 +70,46 @@ TEST(LoD, AppendLoD) { EXPECT_EQ(origin, expected); } -TEST(LoD, CheckLoD) { - LoD relative_lod; +TEST(LegacyLoD, CheckLegacyLoD) { + LegacyLoD relative_lod; relative_lod.push_back(std::vector({0, 2})); relative_lod.push_back(std::vector({0, 1, 3})); relative_lod.push_back(std::vector({0, 2, 4, 5})); // check compatible - ASSERT_TRUE(CheckLoD(relative_lod)); + ASSERT_TRUE(CheckLegacyLoD(relative_lod)); relative_lod[1].back()++; - ASSERT_FALSE(CheckLoD(relative_lod)); + ASSERT_FALSE(CheckLegacyLoD(relative_lod)); relative_lod[1].back()--; // recover it // check empty - LoD empty_lod; - ASSERT_TRUE(CheckLoD(empty_lod)); + LegacyLoD empty_lod; + ASSERT_TRUE(CheckLegacyLoD(empty_lod)); // check less than 2 offsets in a level - LoD some_lod0; + LegacyLoD some_lod0; some_lod0.push_back(std::vector({0})); - ASSERT_FALSE(CheckLoD(some_lod0)); + ASSERT_FALSE(CheckLegacyLoD(some_lod0)); // check with underlying tensor storage. - ASSERT_TRUE(CheckLoD(relative_lod, 5)); - ASSERT_FALSE(CheckLoD(relative_lod, 9)); + ASSERT_TRUE(CheckLegacyLoD(relative_lod, 5)); + ASSERT_FALSE(CheckLegacyLoD(relative_lod, 9)); // check whether lod is ascending-sorted (allow same items) - ASSERT_TRUE(CheckLoD({{0, 1, 2, 3, 4, 5}}, 5)); - ASSERT_TRUE(CheckLoD({{0, 1, 3, 3, 4, 5}}, 5)); - ASSERT_FALSE(CheckLoD({{0, 1, 3, 2, 5}}, 5)); + ASSERT_TRUE(CheckLegacyLoD({{0, 1, 2, 3, 4, 5}}, 5)); + ASSERT_TRUE(CheckLegacyLoD({{0, 1, 3, 3, 4, 5}}, 5)); + ASSERT_FALSE(CheckLegacyLoD({{0, 1, 3, 2, 5}}, 5)); } -TEST(LoD, ConvertToLengthBasedLoD) { - LoD offset_lod; +TEST(LegacyLoD, ConvertToLengthBasedLegacyLoD) { + LegacyLoD offset_lod; offset_lod.push_back(std::vector({0, 2})); offset_lod.push_back(std::vector({0, 1, 3})); offset_lod.push_back(std::vector({0, 2, 4, 5})); - LoD length_lod = phi::ConvertToLengthBasedLoD(offset_lod); + LegacyLoD length_lod = phi::ConvertToLengthBasedLegacyLoD(offset_lod); - LoD expected; + LegacyLoD expected; expected.push_back(std::vector({2})); expected.push_back(std::vector({1, 2})); expected.push_back(std::vector({2, 2, 1})); @@ -117,15 +117,15 @@ TEST(LoD, ConvertToLengthBasedLoD) { EXPECT_EQ(length_lod, expected); } -TEST(LoD, ConvertToOffsetBasedLoD) { - LoD length_lod; +TEST(LegacyLoD, ConvertToOffsetBasedLegacyLoD) { + LegacyLoD length_lod; length_lod.push_back(std::vector({2})); length_lod.push_back(std::vector({1, 2})); length_lod.push_back(std::vector({2, 2, 1})); - LoD offset_lod = ConvertToOffsetBasedLoD(length_lod); + LegacyLoD offset_lod = ConvertToOffsetBasedLegacyLoD(length_lod); - LoD expected; + LegacyLoD expected; expected.push_back(std::vector({0, 2})); expected.push_back(std::vector({0, 1, 3})); expected.push_back(std::vector({0, 2, 4, 5})); diff --git a/test/cpp/fluid/framework/lod_tensor_test.cu b/test/cpp/fluid/framework/lod_tensor_test.cu index ee984795cafd95..50d902b58e6a27 100644 --- a/test/cpp/fluid/framework/lod_tensor_test.cu +++ b/test/cpp/fluid/framework/lod_tensor_test.cu @@ -26,7 +26,7 @@ __global__ void test(size_t* a, int size) { TEST(LoD, data) { paddle::framework::InitDevices(); - phi::LoD lod{{0, 1, 2}}; + phi::LegacyLoD lod{{0, 1, 2}}; lod.push_back({0, 2, 4, 5}); lod.push_back(std::vector({0, 1, 6, 8, 10, 11})); @@ -58,7 +58,7 @@ TEST(DenseTensor, LoDInGPU) { phi::DenseTensor lod_tensor; phi::GPUPlace place(0); - phi::LoD src_lod; + phi::LegacyLoD src_lod; src_lod.push_back(std::vector{0, 2, 4, 6, 8, 10, 12, 14}); lod_tensor.Resize({14, 16}); diff --git a/test/cpp/fluid/framework/operator_test.cc b/test/cpp/fluid/framework/operator_test.cc index fef432045613ca..6bdac948ec5320 100644 --- a/test/cpp/fluid/framework/operator_test.cc +++ b/test/cpp/fluid/framework/operator_test.cc @@ -489,10 +489,11 @@ class GetLoDLevelTest : public OperatorWithKernel { OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "GetLoDLevelTest"); auto lod_level = ctx->GetLoDLevel("X"); - PADDLE_ENFORCE_GT(lod_level, - 0, - common::errors::InvalidArgument( - "The LoD level Input(X) should be larger than 0.")); + PADDLE_ENFORCE_GT( + lod_level, + 0, + common::errors::InvalidArgument( + "The LegacyLoD level Input(X) should be larger than 0.")); } }; diff --git a/test/cpp/fluid/math/beam_search_test.cc b/test/cpp/fluid/math/beam_search_test.cc index 065d816171600c..66e1f0ce21c8d7 100644 --- a/test/cpp/fluid/math/beam_search_test.cc +++ b/test/cpp/fluid/math/beam_search_test.cc @@ -25,7 +25,7 @@ void PrepareCPUTensors(phi::DenseTensor* ids, phi::DenseTensor* pre_ids, phi::DenseTensor* pre_scores) { // lod - phi::LoD lod; + phi::LegacyLoD lod; std::vector level0({0, 2, 4}); std::vector level1({0, 1, 2, 3, 4}); lod.push_back(level0); diff --git a/test/cpp/fluid/platform/bfloat16_test.cc b/test/cpp/fluid/platform/bfloat16_test.cc index ac7dccd0572b39..38e4da8d3d67e0 100644 --- a/test/cpp/fluid/platform/bfloat16_test.cc +++ b/test/cpp/fluid/platform/bfloat16_test.cc @@ -114,7 +114,7 @@ TEST(bfloat16, dense_tensor_cpu) { EXPECT_EQ(input_data[3].x, 0x0000); dense_tensor.Resize({4, 1}); - dense_tensor.set_lod(phi::LoD({{0, 2, 4}})); + dense_tensor.set_lod(phi::LegacyLoD({{0, 2, 4}})); bfloat16* data_ptr = dense_tensor.mutable_data(CPUPlace()); EXPECT_NE(data_ptr, nullptr); diff --git a/test/cpp/fluid/platform/float16_test.cc b/test/cpp/fluid/platform/float16_test.cc index ad996c4b2bc0d1..3172546dfbd56f 100644 --- a/test/cpp/fluid/platform/float16_test.cc +++ b/test/cpp/fluid/platform/float16_test.cc @@ -122,7 +122,7 @@ TEST(float16, lod_tensor_cpu) { EXPECT_EQ(input_data[3].x, 0x0000); lod_tensor.Resize({4, 1}); - lod_tensor.set_lod(phi::LoD({{0, 2, 4}})); + lod_tensor.set_lod(phi::LegacyLoD({{0, 2, 4}})); float16* data_ptr = lod_tensor.mutable_data(CPUPlace()); EXPECT_NE(data_ptr, nullptr); diff --git a/test/cpp/fluid/save_load_combine_op_test.cc b/test/cpp/fluid/save_load_combine_op_test.cc index 6455de950f4aeb..1e4c7d62f8b441 100644 --- a/test/cpp/fluid/save_load_combine_op_test.cc +++ b/test/cpp/fluid/save_load_combine_op_test.cc @@ -29,7 +29,7 @@ T* CreateForSaveCombineOp(int x, std::string var_name, const phi::CPUPlace& place, paddle::framework::Scope* scope, - phi::LoD* expect_lod) { + phi::LegacyLoD* expect_lod) { auto var = scope->Var(var_name); auto tensor = var->GetMutable(); tensor->Resize({x, y}); @@ -56,7 +56,7 @@ phi::DenseTensor* GeneratePlaceholderBeforeLoad( template T* GetValuesAfterLoadCombineOp(phi::DenseTensor* target, const paddle::framework::Scope& scope, - phi::LoD* actual_lod) { + phi::LegacyLoD* actual_lod) { T* actual = target->data(); *actual_lod = target->lod(); return actual; @@ -65,8 +65,8 @@ T* GetValuesAfterLoadCombineOp(phi::DenseTensor* target, template void CheckValues(T* expect, U* actual, - const phi::LoD& expect_lod, - const phi::LoD& actual_lod, + const phi::LegacyLoD& expect_lod, + const phi::LegacyLoD& actual_lod, const int& numel) { for (int i = 0; i < numel; ++i) { EXPECT_EQ(expect[i], static_cast(actual[i])); @@ -88,25 +88,25 @@ void SaveLoadCombineOp() { std::vector lod1 = {0, 1, 2, 3, 10}; int numel1 = 100; - phi::LoD expect_lod1; + phi::LegacyLoD expect_lod1; T* expect1 = CreateForSaveCombineOp( 10, 10, lod1, "test_var1", place, &scope, &expect_lod1); std::vector lod2 = {0, 2, 5, 10}; int numel2 = 200; - phi::LoD expect_lod2; + phi::LegacyLoD expect_lod2; T* expect2 = CreateForSaveCombineOp( 10, 20, lod2, "test_var2", place, &scope, &expect_lod2); std::vector lod3 = {0, 2, 3, 20}; int numel3 = 4000; - phi::LoD expect_lod3; + phi::LegacyLoD expect_lod3; T* expect3 = CreateForSaveCombineOp( 20, 200, lod3, "test_var3", place, &scope, &expect_lod3); std::vector lod4 = {0, 1, 20}; int numel4 = 1000; - phi::LoD expect_lod4; + phi::LegacyLoD expect_lod4; T* expect4 = CreateForSaveCombineOp( 20, 50, lod4, "test_var4", place, &scope, &expect_lod4); @@ -137,7 +137,7 @@ void SaveLoadCombineOp() { attrs); load_combine_op->Run(scope, place); - phi::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; + phi::LegacyLoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; U* actual1 = GetValuesAfterLoadCombineOp(target1, scope, &actual_lod1); U* actual2 = GetValuesAfterLoadCombineOp(target2, scope, &actual_lod2); U* actual3 = GetValuesAfterLoadCombineOp(target3, scope, &actual_lod3); @@ -163,25 +163,25 @@ TEST(SaveCombineFP16Op, CPU) { std::vector lod1 = {0, 1, 2, 3, 10}; int numel1 = 100; - phi::LoD expect_lod1; + phi::LegacyLoD expect_lod1; float* expect1 = CreateForSaveCombineOp( 10, 10, lod1, "test_var1", place, &scope, &expect_lod1); std::vector lod2 = {0, 2, 5, 10}; int numel2 = 200; - phi::LoD expect_lod2; + phi::LegacyLoD expect_lod2; float* expect2 = CreateForSaveCombineOp( 10, 20, lod2, "test_var2", place, &scope, &expect_lod2); std::vector lod3 = {0, 20}; int numel3 = 4000; - phi::LoD expect_lod3; + phi::LegacyLoD expect_lod3; float* expect3 = CreateForSaveCombineOp( 20, 200, lod3, "test_var3", place, &scope, &expect_lod3); std::vector lod4 = {0, 1, 20}; int numel4 = 1000; - phi::LoD expect_lod4; + phi::LegacyLoD expect_lod4; float* expect4 = CreateForSaveCombineOp( 20, 50, lod4, "test_var4", place, &scope, &expect_lod4); @@ -213,7 +213,7 @@ TEST(SaveCombineFP16Op, CPU) { attrs); load_combine_op->Run(scope, place); - phi::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; + phi::LegacyLoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; phi::dtype::float16* actual1 = GetValuesAfterLoadCombineOp( target1, scope, &actual_lod1); @@ -245,25 +245,25 @@ TEST(LoadCombineFP16Op, CPU) { std::vector lod1 = {0, 1, 2, 3, 10}; int numel1 = 100; - phi::LoD expect_lod1; + phi::LegacyLoD expect_lod1; float* expect1 = CreateForSaveCombineOp( 10, 10, lod1, "test_var1", place, &scope, &expect_lod1); std::vector lod2 = {0, 2, 5, 10}; int numel2 = 200; - phi::LoD expect_lod2; + phi::LegacyLoD expect_lod2; float* expect2 = CreateForSaveCombineOp( 10, 20, lod2, "test_var2", place, &scope, &expect_lod2); std::vector lod3 = {0, 20}; int numel3 = 4000; - phi::LoD expect_lod3; + phi::LegacyLoD expect_lod3; float* expect3 = CreateForSaveCombineOp( 20, 200, lod3, "test_var3", place, &scope, &expect_lod3); std::vector lod4 = {0, 1, 20}; int numel4 = 1000; - phi::LoD expect_lod4; + phi::LegacyLoD expect_lod4; float* expect4 = CreateForSaveCombineOp( 20, 50, lod4, "test_var4", place, &scope, &expect_lod4); @@ -300,7 +300,7 @@ TEST(LoadCombineFP16Op, CPU) { auto* target3 = load_var3->GetMutable(); auto* target4 = load_var4->GetMutable(); - phi::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; + phi::LegacyLoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; phi::dtype::float16* actual1 = GetValuesAfterLoadCombineOp( target1, scope, &actual_lod1); @@ -332,7 +332,7 @@ TEST(SaveLoadTestWithCombineOp, CPU) { auto var = scope.Var("test_var"); auto tensor = var->GetMutable(); tensor->Resize({3, 4000}); - phi::LoD expect_lod; + phi::LegacyLoD expect_lod; expect_lod.resize(1); expect_lod[0].push_back(0); expect_lod[0].push_back(1); diff --git a/test/cpp/fluid/save_load_combine_op_test_xpu.cc b/test/cpp/fluid/save_load_combine_op_test_xpu.cc index 3ef238145189da..29fa097d0d4494 100644 --- a/test/cpp/fluid/save_load_combine_op_test_xpu.cc +++ b/test/cpp/fluid/save_load_combine_op_test_xpu.cc @@ -25,7 +25,7 @@ T* CreateForSaveCombineOp(int x, std::string var_name, const Place& place, paddle::framework::Scope* scope, - phi::LoD* expect_lod) { + phi::LegacyLoD* expect_lod) { phi::CPUPlace cpu_place; std::vector ground_truth_cpu(x * y); for (int i = 0; i < x * y; ++i) { @@ -59,7 +59,7 @@ phi::DenseTensor* GeneratePlaceholderBeforeLoad( template T* GetValuesAfterLoadCombineOp(phi::DenseTensor* target, const paddle::framework::Scope& scope, - phi::LoD* actual_lod) { + phi::LegacyLoD* actual_lod) { T* actual = target->data(); *actual_lod = target->lod(); return actual; @@ -68,8 +68,8 @@ T* GetValuesAfterLoadCombineOp(phi::DenseTensor* target, template void CheckValues(T* expect, U* actual, - const phi::LoD& expect_lod, - const phi::LoD& actual_lod, + const phi::LegacyLoD& expect_lod, + const phi::LegacyLoD& actual_lod, const int& numel) { for (int i = 0; i < numel; ++i) { EXPECT_EQ(expect[i], static_cast(actual[i])); @@ -89,25 +89,25 @@ int SaveLoadCombineOpTest(Place place) { std::vector lod1 = {0, 1, 2, 3, 10}; int numel1 = 100; - phi::LoD expect_lod1; + phi::LegacyLoD expect_lod1; T* expect1 = CreateForSaveCombineOp( 10, 10, lod1, "test_var1", place, &scope, &expect_lod1); std::vector lod2 = {0, 2, 5, 10}; int numel2 = 200; - phi::LoD expect_lod2; + phi::LegacyLoD expect_lod2; T* expect2 = CreateForSaveCombineOp( 10, 20, lod2, "test_var2", place, &scope, &expect_lod2); std::vector lod3 = {0, 2, 3, 20}; int numel3 = 4000; - phi::LoD expect_lod3; + phi::LegacyLoD expect_lod3; T* expect3 = CreateForSaveCombineOp( 20, 200, lod3, "test_var3", place, &scope, &expect_lod3); std::vector lod4 = {0, 1, 20}; int numel4 = 1000; - phi::LoD expect_lod4; + phi::LegacyLoD expect_lod4; T* expect4 = CreateForSaveCombineOp( 20, 50, lod4, "test_var4", place, &scope, &expect_lod4); @@ -134,7 +134,7 @@ int SaveLoadCombineOpTest(Place place) { attrs); load_combine_op->Run(scope, place); - phi::LoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; + phi::LegacyLoD actual_lod1, actual_lod2, actual_lod3, actual_lod4; U* actual1 = GetValuesAfterLoadCombineOp(target1, scope, &actual_lod1); U* actual2 = GetValuesAfterLoadCombineOp(target2, scope, &actual_lod2); U* actual3 = GetValuesAfterLoadCombineOp(target3, scope, &actual_lod3); diff --git a/test/cpp/fluid/save_load_op_test.cc b/test/cpp/fluid/save_load_op_test.cc index 6f9146ec4af455..eb6ecab9131f73 100644 --- a/test/cpp/fluid/save_load_op_test.cc +++ b/test/cpp/fluid/save_load_op_test.cc @@ -28,7 +28,7 @@ TEST(SaveLoadOp, CPU) { auto var = scope.Var("test_var"); auto tensor = var->GetMutable(); tensor->Resize({3, 10}); - phi::LoD expect_lod; + phi::LegacyLoD expect_lod; expect_lod.resize(1); expect_lod[0].push_back(0); expect_lod[0].push_back(1); @@ -109,7 +109,7 @@ TEST(SaveFP16Op, CPU) { auto var = scope.Var("test_var"); auto tensor = var->GetMutable(); tensor->Resize({3, 10}); - phi::LoD expect_lod; + phi::LegacyLoD expect_lod; expect_lod.resize(1); expect_lod[0].push_back(0); expect_lod[0].push_back(1); @@ -156,7 +156,7 @@ TEST(LoadFP16Op, CPU) { auto tensor = var->GetMutable(); tensor->Resize({3, 10}); - phi::LoD expect_lod; + phi::LegacyLoD expect_lod; expect_lod.resize(1); expect_lod[0].push_back(0); expect_lod[0].push_back(1); diff --git a/test/cpp/fluid/save_load_op_test_xpu.cc b/test/cpp/fluid/save_load_op_test_xpu.cc index 12a3c52ec7229d..a0eb3f5697a9fa 100644 --- a/test/cpp/fluid/save_load_op_test_xpu.cc +++ b/test/cpp/fluid/save_load_op_test_xpu.cc @@ -31,7 +31,7 @@ int SaveLoadOpTest(Place place, int dim_1, int dim_2) { auto var = scope.Var("test_var"); auto tensor = var->GetMutable(); tensor->Resize({dim_1, dim_2}); - phi::LoD expect_lod; + phi::LegacyLoD expect_lod; expect_lod.resize(1); for (int i = 0; i < dim_1; i++) { expect_lod[0].push_back(i); diff --git a/test/cpp/inference/api/api_impl_tester.cc b/test/cpp/inference/api/api_impl_tester.cc index 16b4153a2a2814..c9275c44c05f16 100644 --- a/test/cpp/inference/api/api_impl_tester.cc +++ b/test/cpp/inference/api/api_impl_tester.cc @@ -74,7 +74,7 @@ void MainWord2Vec(const ::paddle::PaddlePlace& place) { config.use_xpu = ::paddle::xpu_place_used(place); phi::DenseTensor first_word, second_word, third_word, fourth_word; - phi::LoD lod{{0, 1}}; + phi::LegacyLoD lod{{0, 1}}; int64_t dict_size = 2073; // The size of dictionary SetupLoDTensor(&first_word, lod, static_cast(0), dict_size - 1); @@ -178,7 +178,7 @@ void MainThreadsWord2Vec(const ::paddle::PaddlePlace& place) { // each job has 4 words jobs[i].resize(4); for (size_t j = 0; j < 4; ++j) { - phi::LoD lod{{0, 1}}; + phi::LegacyLoD lod{{0, 1}}; int64_t dict_size = 2073; // The size of dictionary SetupLoDTensor(&jobs[i][j], lod, static_cast(0), dict_size - 1); paddle_tensor_feeds[i].push_back(LodTensorToPaddleTensor(&jobs[i][j])); diff --git a/test/cpp/inference/api/tester_helper.h b/test/cpp/inference/api/tester_helper.h index 12b0ba71f3d342..14491b02f1e047 100644 --- a/test/cpp/inference/api/tester_helper.h +++ b/test/cpp/inference/api/tester_helper.h @@ -1065,7 +1065,7 @@ std::string LoDTensorSummary(const phi::DenseTensor &tensor) { return ss.str(); } -static bool CompareLoD(const phi::LoD &a, const phi::LoD &b) { +static bool CompareLoD(const phi::LegacyLoD &a, const phi::LegacyLoD &b) { if (a.size() != b.size()) { LOG(ERROR) << string::Sprintf( "lod size not match %d != %d", a.size(), b.size()); diff --git a/test/cpp/inference/test_helper.h b/test/cpp/inference/test_helper.h index c0b02838ae7a79..755fda6c3add69 100644 --- a/test/cpp/inference/test_helper.h +++ b/test/cpp/inference/test_helper.h @@ -69,7 +69,7 @@ void SetupTensor(phi::DenseTensor* input, template void SetupLoDTensor(phi::DenseTensor* input, - const phi::LoD& lod, + const phi::LegacyLoD& lod, T lower, T upper) { input->set_lod(lod); @@ -80,7 +80,7 @@ void SetupLoDTensor(phi::DenseTensor* input, template void SetupLoDTensor(phi::DenseTensor* input, phi::DDim dims, - const phi::LoD lod, + const phi::LegacyLoD lod, const std::vector& data) { const size_t level = lod.size() - 1; PADDLE_ENFORCE_EQ(dims[0], diff --git a/test/cpp/new_executor/standalone_executor_pir_test.cc b/test/cpp/new_executor/standalone_executor_pir_test.cc index dade2c75c37ea2..af1321470d0ddb 100644 --- a/test/cpp/new_executor/standalone_executor_pir_test.cc +++ b/test/cpp/new_executor/standalone_executor_pir_test.cc @@ -154,7 +154,7 @@ TEST(StandaloneExecutor, run_feed_tensor) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); phi::DDim dims = {1}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0}}; + phi::LegacyLoD lod = {{0}}; size_t offset = 0; pir::Type dense_tensor_dtype = paddle::dialect::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); diff --git a/test/cpp/phi/core/test_dense_tensor.cc b/test/cpp/phi/core/test_dense_tensor.cc index e4364c42a8f499..133f84e05131d7 100644 --- a/test/cpp/phi/core/test_dense_tensor.cc +++ b/test/cpp/phi/core/test_dense_tensor.cc @@ -25,7 +25,7 @@ TEST(dense_tensor, meta) { const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; // TODO(Shixiaowei02): need to check the lod is valid. - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta_0; PADDLE_ENFORCE_EQ(meta_0.valid(), @@ -209,7 +209,7 @@ TEST(dense_tensor, ctor) { const DDim dims({1, 2}); const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta(dtype, dims, layout, lod); auto fancy_allocator = std::unique_ptr(new FancyAllocator); @@ -239,7 +239,7 @@ TEST(dense_tensor, resize) { const DDim dims({1, 2}); const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta(dtype, dims, layout, lod); auto fancy_allocator = std::unique_ptr(new FancyAllocator); @@ -265,7 +265,7 @@ TEST(dense_tensor, shallow_copy) { const DDim dims({1, 2}); const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta(dtype, dims, layout, lod); auto fancy_allocator = std::unique_ptr(new FancyAllocator); diff --git a/test/cpp/phi/core/test_tensor_array.cc b/test/cpp/phi/core/test_tensor_array.cc index c126f3862a8079..6fc5d8deb0d034 100644 --- a/test/cpp/phi/core/test_tensor_array.cc +++ b/test/cpp/phi/core/test_tensor_array.cc @@ -32,7 +32,7 @@ TEST(tensor_array, tensor_array_not_init) { const DDim dims({1, 2}); const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta(dtype, dims, layout, lod); DenseTensor tensor_0; tensor_0.set_meta(meta); @@ -94,7 +94,7 @@ TEST(tensor_array, tensor_array_init) { const DDim dims2({1, 2, 3}); const DataType dtype{DataType::INT8}; const DataLayout layout{DataLayout::NHWC}; - const LoD lod{}; + const LegacyLoD lod{}; DenseTensorMeta meta1(dtype, dims1, layout, lod); DenseTensorMeta meta2(dtype, dims2, layout, lod); diff --git a/test/cpp/phi/kernels/sequence_padding_test.cc b/test/cpp/phi/kernels/sequence_padding_test.cc index dab519337536e3..c1d8c47d14eca4 100644 --- a/test/cpp/phi/kernels/sequence_padding_test.cc +++ b/test/cpp/phi/kernels/sequence_padding_test.cc @@ -20,7 +20,7 @@ limitations under the License. */ template void TestSequencePadding(const DeviceContext &context, - const phi::LoD &lod, + const phi::LegacyLoD &lod, const size_t sequence_width) { phi::DenseTensor cpu_seq; phi::DenseTensor cpu_seq_back; @@ -107,11 +107,11 @@ TEST(Seq2BatchPadding, CPU) { auto *context = static_cast( phi::DeviceContextPool::Instance().Get(place)); - phi::LoD lod1; + phi::LegacyLoD lod1; lod1.push_back(std::vector{0, 10}); TestSequencePadding(*context, lod1, 16); - phi::LoD lod2; + phi::LegacyLoD lod2; lod2.push_back(std::vector{0, 2, 7, 10}); TestSequencePadding(*context, lod2, 128); } @@ -122,11 +122,11 @@ TEST(SequencePadding, CUDA) { auto *context = static_cast( phi::DeviceContextPool::Instance().Get(place)); - phi::LoD lod1; + phi::LegacyLoD lod1; lod1.push_back(std::vector{0, 10}); TestSequencePadding(*context, lod1, 16); - phi::LoD lod2; + phi::LegacyLoD lod2; lod2.push_back(std::vector{0, 2, 7, 10}); TestSequencePadding(*context, lod2, 128); } diff --git a/test/cpp/phi/kernels/sequence_pooling_test.cc b/test/cpp/phi/kernels/sequence_pooling_test.cc index c0b8937f7dc5c7..e924d5ffbc9d23 100644 --- a/test/cpp/phi/kernels/sequence_pooling_test.cc +++ b/test/cpp/phi/kernels/sequence_pooling_test.cc @@ -21,7 +21,7 @@ limitations under the License. */ template void TestSequencePoolingSum(const DeviceContext &context, - const phi::LoD &lod, + const phi::LegacyLoD &lod, const int64_t second_dim) { phi::DenseTensor cpu_out_grad; phi::DenseTensor cpu_in_grad; @@ -122,11 +122,11 @@ TEST(SequencePoolingGrad, CPU_SUM) { auto *context = static_cast( phi::DeviceContextPool::Instance().Get(place)); - phi::LoD lod1; + phi::LegacyLoD lod1; lod1.push_back(std::vector{0, 10}); TestSequencePoolingSum(*context, lod1, 128); - phi::LoD lod2; + phi::LegacyLoD lod2; lod2.push_back(std::vector{0, 2, 7, 10}); TestSequencePoolingSum(*context, lod2, 128); } @@ -137,11 +137,11 @@ TEST(SequencePoolingGrad, CUDA_SUM) { auto *context = static_cast( phi::DeviceContextPool::Instance().Get(place)); - phi::LoD lod1; + phi::LegacyLoD lod1; lod1.push_back(std::vector{0, 10}); TestSequencePoolingSum(*context, lod1, 128); - phi::LoD lod2; + phi::LegacyLoD lod2; lod2.push_back(std::vector{0, 2, 7, 10}); TestSequencePoolingSum(*context, lod2, 128); } diff --git a/test/cpp/pir/cinn/add_broadcast_to_elementwise_test.cc b/test/cpp/pir/cinn/add_broadcast_to_elementwise_test.cc index 5aec5a94d95285..afce380feb0022 100644 --- a/test/cpp/pir/cinn/add_broadcast_to_elementwise_test.cc +++ b/test/cpp/pir/cinn/add_broadcast_to_elementwise_test.cc @@ -32,7 +32,7 @@ std::vector CreateDenseTensorTypes(const phi::DDim &dims) { pir::IrContext *ctx = ::pir::IrContext::Instance(); pir::Type fp32_dtype = ::pir::Float32Type::get(ctx); phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {}; + phi::LegacyLoD lod = {}; size_t offset = 0; std::vector<::pir::Type> op_output_types = {::pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset)}; diff --git a/test/cpp/pir/cinn/pir_all_path_test.cc b/test/cpp/pir/cinn/pir_all_path_test.cc index ce465972565a09..9f3fe59a812525 100644 --- a/test/cpp/pir/cinn/pir_all_path_test.cc +++ b/test/cpp/pir/cinn/pir_all_path_test.cc @@ -47,7 +47,7 @@ std::vector<::pir::Type> CreateDenseTensorTypes(const phi::DDim& dims) { ::pir::IrContext* ctx = ::pir::IrContext::Instance(); ::pir::Type fp32_dtype = ::pir::Float32Type::get(ctx); phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {}; + phi::LegacyLoD lod = {}; size_t offset = 0; std::vector<::pir::Type> op_output_types = {::pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset)}; diff --git a/test/cpp/pir/cinn/symbolic_lower_test.cc b/test/cpp/pir/cinn/symbolic_lower_test.cc index 9a4254f52c7ca1..810e0bb230f100 100644 --- a/test/cpp/pir/cinn/symbolic_lower_test.cc +++ b/test/cpp/pir/cinn/symbolic_lower_test.cc @@ -48,7 +48,7 @@ std::vector<::pir::Type> CreateDenseTensorTypes(const phi::DDim& dims) { ::pir::IrContext* ctx = ::pir::IrContext::Instance(); ::pir::Type fp32_dtype = ::pir::Float32Type::get(ctx); phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {}; + phi::LegacyLoD lod = {}; size_t offset = 0; std::vector<::pir::Type> op_output_types = {::pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset)}; diff --git a/test/cpp/pir/core/ir_op_test.cc b/test/cpp/pir/core/ir_op_test.cc index 7c780d268f0c1e..d0d364e03f1b4e 100644 --- a/test/cpp/pir/core/ir_op_test.cc +++ b/test/cpp/pir/core/ir_op_test.cc @@ -130,7 +130,7 @@ TEST(op_test, op_traits_test) { pir::Type dtype = pir::Float32Type::get(ctx); phi::DDim dims = {2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype = @@ -183,7 +183,7 @@ TEST(op_test, same_operands_shape_trait_test2) { phi::DDim dims2 = {2, 2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype = @@ -256,7 +256,7 @@ TEST(op_test, same_operands_and_result_shape_trait_test3) { phi::DDim dims2 = {2, 2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype = @@ -301,7 +301,7 @@ TEST(op_test, same_operands_element_type_trait_test2) { phi::DDim dims = {2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype = @@ -372,7 +372,7 @@ TEST(op_test, same_operands_and_result_element_type_trait_test3) { phi::DDim dims2 = {2, 2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype1 = @@ -454,7 +454,7 @@ TEST(op_test, same_operands_and_result_type_trait_test3) { phi::DDim dims2 = {2, 2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_dtype1 = diff --git a/test/cpp/pir/core/type_interface_test.cc b/test/cpp/pir/core/type_interface_test.cc index 71ba055ffb56f0..d37e140d5d6d30 100644 --- a/test/cpp/pir/core/type_interface_test.cc +++ b/test/cpp/pir/core/type_interface_test.cc @@ -32,7 +32,7 @@ TEST(shape_dtype_test, shape_dtype_test) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); phi::DDim dims = {2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_type = pir::DenseTensorType::get( diff --git a/test/cpp/pir/core/type_test.cc b/test/cpp/pir/core/type_test.cc index fc8415db8c11c4..b92e4ea173e2f3 100644 --- a/test/cpp/pir/core/type_test.cc +++ b/test/cpp/pir/core/type_test.cc @@ -256,7 +256,7 @@ TEST(type_test, sparse_coo) { common::DDim dims = {4, 4}; common::DDim non_zero_dims = {4, 1}; common::DataLayout data_layout = common::DataLayout::NCHW; - pir::LoD lod = {{0, 1, 2}}; + pir::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType none_zero_indices = pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); @@ -304,7 +304,7 @@ TEST(type_test, pd_op_dialect) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); phi::DDim dims = {2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; paddle::dialect::SelectedRowsType select_rows_dtype = paddle::dialect::SelectedRowsType::get( @@ -322,7 +322,7 @@ TEST(type_test, sparse_csr) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); common::DDim dims = {4, 4}; common::DataLayout data_layout = common::DataLayout::NCHW; - pir::LoD lod = {{0, 1, 2}}; + pir::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType non_zero_crows = pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); @@ -369,7 +369,7 @@ TEST(type_test, type_util) { phi::DDim dims1 = {2, 2}; phi::DDim dims2 = {2, 2, 3}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; paddle::dialect::SelectedRowsType select_rows_dtype1 = diff --git a/test/cpp/pir/distributed/dist_dialect_test.cc b/test/cpp/pir/distributed/dist_dialect_test.cc index e5028ca973e030..a1816b6b10f005 100644 --- a/test/cpp/pir/distributed/dist_dialect_test.cc +++ b/test/cpp/pir/distributed/dist_dialect_test.cc @@ -115,7 +115,7 @@ TEST(dist_dense_tensor_type_test, base) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); common::DDim dims = {2, 2}; common::DataLayout data_layout = common::DataLayout::NCHW; - pir::LoD lod = {{0, 1, 2}}; + pir::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_type = pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); @@ -154,7 +154,7 @@ TEST(dist_dense_tensor_type_test, warp_type_interface) { pir::Type fp32_dtype = pir::Float32Type::get(ctx); common::DDim dims = {2, 2}; common::DataLayout data_layout = common::DataLayout::NCHW; - pir::LoD lod = {{0, 1, 2}}; + pir::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_type = pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); @@ -189,7 +189,7 @@ TEST(dist_dense_tensor_type_test, dist_interface) { common::DDim dims = {4, 8}; common::DDim local_dims = {2, 8}; common::DataLayout data_layout = common::DataLayout::NCHW; - pir::LoD lod = {{0, 1, 2}}; + pir::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::DenseTensorType dense_tensor_type = pir::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); diff --git a/test/cpp/pir/pattern_rewrite/pattern_rewrite_test.cc b/test/cpp/pir/pattern_rewrite/pattern_rewrite_test.cc index 8cdcbeb94c6936..bbfbcbdb528b40 100644 --- a/test/cpp/pir/pattern_rewrite/pattern_rewrite_test.cc +++ b/test/cpp/pir/pattern_rewrite/pattern_rewrite_test.cc @@ -439,7 +439,7 @@ void BuildConstantFoldingProgram(pir::Program *program, pir::Type fp32_dtype = pir::Float32Type::get(ctx); phi::DDim dims = {2, 2}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; pir::Type dense_tensor_dtype = paddle::dialect::DenseTensorType::get( ctx, fp32_dtype, dims, data_layout, lod, offset); diff --git a/test/cpp/pir/tools/test_pir_utils.h b/test/cpp/pir/tools/test_pir_utils.h index 3b105896706734..7b664135df4b46 100644 --- a/test/cpp/pir/tools/test_pir_utils.h +++ b/test/cpp/pir/tools/test_pir_utils.h @@ -41,7 +41,7 @@ pir::Operation *CreateDenseTensorOp( pir::Float32Type::get(pir::IrContext::Instance())) { std::vector op_inputs = {}; phi::DataLayout data_layout = phi::DataLayout::NCHW; - phi::LoD lod = {{0, 1, 2}}; + phi::LegacyLoD lod = {{0, 1, 2}}; size_t offset = 0; std::vector op_output_types = { paddle::dialect::DenseTensorType::get( diff --git a/test/deprecated/book/test_recommender_system_deprecated.py b/test/deprecated/book/test_recommender_system_deprecated.py index 8a11d6f35a6dc0..b1ee42c8f8c1c0 100644 --- a/test/deprecated/book/test_recommender_system_deprecated.py +++ b/test/deprecated/book/test_recommender_system_deprecated.py @@ -320,7 +320,7 @@ def infer(use_cuda, save_dirname=None): # Use the first data from paddle.dataset.movielens.test() as input assert feed_target_names[0] == "user_id" # Use create_lod_tensor(data, recursive_sequence_lengths, place) API - # to generate LoD Tensor where `data` is a list of sequences of index + # to generate LegacyLoD Tensor where `data` is a list of sequences of index # numbers, `recursive_sequence_lengths` is the length-based level of detail # (lod) info associated with `data`. # For example, data = [[10, 2, 3], [2, 3]] means that it contains diff --git a/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py b/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py index 2f140036e4f7c1..218050f7813e5d 100644 --- a/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py +++ b/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py @@ -105,7 +105,7 @@ class number or A 2-D DenseTensor with shape [No, 10] represents the detections. Each row has 10 values: [label, confidence, x1, y1, x2, y2, x3, y3, x4, y4]. No is the total number of detections. - If all images have not detected results, all elements in LoD will be + If all images have not detected results, all elements in LegacyLoD will be 0, and output tensor is empty (None). Index: Only return when return_index is True. A 2-D DenseTensor with shape [No, 1] represents the selected index which type is Integer. diff --git a/test/legacy_test/op_test.py b/test/legacy_test/op_test.py index 922050ede5608e..e1b677834f97dd 100644 --- a/test/legacy_test/op_test.py +++ b/test/legacy_test/op_test.py @@ -902,13 +902,13 @@ def _create_var_from_numpy(self, value): return paddle.to_tensor(value) def get_sequence_batch_size_1_input(self, lod=None, shape=None): - """Get LoD input data whose batch size is 1. + """Get LegacyLoD input data whose batch size is 1. All sequence related OP unittests should call this function to contain the case of batch size = 1. Args: lod (list[list of int], optional): Length-based LoD, length of lod[0] should be 1. Default: [[13]]. shape (list, optional): Shape of input, shape[0] should be equals to lod[0][0]. Default: [13, 23]. Returns: - tuple (ndarray, lod) : LoD input data whose batch size is 1. + tuple (ndarray, lod) : LegacyLoD input data whose batch size is 1. """ if lod is None: lod = [[13]] @@ -937,13 +937,13 @@ def lod_has_continuous_zero(self, lod): return False def get_sequence_instance_size_0_input(self, lod=None, shape=None): - """Get LoD input data whose instance size is 0. + """Get LegacyLoD input data whose instance size is 0. All sequence related OP unittests should call this function to contain the case of instance size is 0. Args: lod (list[list of int], optional): Length-based LoD, lod[0]'s size must at least eight, lod[0] must at least two zeros at the beginning and at least two zeros at the end, the middle position of lod[0] contains a single zero and multiple zero. Default: [[0, 0, 4, 0, 3, 0, 0, 5, 0, 0]]. shape (list, optional): Shape of input, shape[0] should be equals to lod[0][0]. Default: [13, 23]. Returns: - tuple (ndarray, lod): LoD input data whose instance size is 0. + tuple (ndarray, lod): LegacyLoD input data whose instance size is 0. """ if lod is None: lod = [[0, 0, 4, 0, 3, 0, 0, 5, 0, 0]] diff --git a/test/xpu/cpp/beam_search_decode_op_xpu_test.cc b/test/xpu/cpp/beam_search_decode_op_xpu_test.cc index 9f64e29ba3f0e7..3511122dda14f8 100644 --- a/test/xpu/cpp/beam_search_decode_op_xpu_test.cc +++ b/test/xpu/cpp/beam_search_decode_op_xpu_test.cc @@ -20,7 +20,7 @@ limitations under the License. */ using CPUPlace = phi::CPUPlace; using XPUPlace = phi::XPUPlace; -using LoD = phi::LoD; +using LegacyLoD = phi::LegacyLoD; using DenseTensorArray = phi::TensorArray; template @@ -59,7 +59,7 @@ void GenerateXPUExample(const std::vector& level_0, XPUPlace xpu_place(XPU_PlaceNo); - LoD lod; + LegacyLoD lod; lod.push_back(level_0); lod.push_back(level_1); @@ -182,7 +182,7 @@ void BeamSearchDecodeTestByXPUFrame() { ids, scores, &id_tensor_cpu, &score_tensor_cpu, 2, 1); bs_xpu.apply_xpu(); - LoD lod = id_tensor_cpu.lod(); + LegacyLoD lod = id_tensor_cpu.lod(); std::vector expect_source_lod = {0, 2, 4}; ASSERT_EQ(lod[0], expect_source_lod); From 163ee6be9600f2b1c9e5aeff4727cdf8aeb6f20a Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Tue, 3 Dec 2024 11:24:58 +0800 Subject: [PATCH 107/288] [SOT][3.13] Temporary disable SOT in Python 3.13 (#69887) --- python/paddle/jit/api.py | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/python/paddle/jit/api.py b/python/paddle/jit/api.py index 2769cce31414c4..a2a7886621be5e 100644 --- a/python/paddle/jit/api.py +++ b/python/paddle/jit/api.py @@ -292,9 +292,9 @@ def decorated(python_func): flag = ENV_ENABLE_SOT.get() full_graph = not flag - if sys.version_info >= (3, 14) and not full_graph: + if sys.version_info >= (3, 13) and not full_graph: warnings.warn( - "full_graph=False is not supported in Python 3.14+. Set full_graph=True automatically" + "full_graph=False is not supported in Python 3.13+. Set full_graph=True automatically" ) full_graph = True From a4f5446f1ef7fa3cb6fc7b196deccda3061395da Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Tue, 3 Dec 2024 11:25:16 +0800 Subject: [PATCH 108/288] [CodeStyle][Typos][A-46,A-47,N-15] Fix typos (`axises`, `aixs`, `numberic`) (#69856) --- _typos.toml | 4 ---- paddle/cinn/common/axis.cc | 16 ++++++++-------- .../operator/transforms/reduce_as_to_sum_pass.cc | 2 +- paddle/cinn/lang/compute.cc | 4 ++-- .../fluid/inference/tensorrt/convert/slice_op.cc | 10 +++++----- .../tensorrt/convert/strided_slice_op.cc | 10 +++++----- python/paddle/tensor/stat.py | 6 +++--- test/custom_op/test_custom_concat.py | 10 +++++----- test/legacy_test/test_kthvalue_op.py | 6 +++--- test/legacy_test/test_mode_op.py | 6 +++--- test/legacy_test/test_reduce_op.py | 8 ++++---- tools/gen_pybind11_stub.py | 2 +- 12 files changed, 40 insertions(+), 44 deletions(-) diff --git a/_typos.toml b/_typos.toml index 608ba391bdaf60..8052ec5a4a6611 100644 --- a/_typos.toml +++ b/_typos.toml @@ -26,9 +26,6 @@ UE = "UE" unpacket = "unpacket" # These words need to be fixed -axises = 'axises' -Axises = 'Axises' -aixs = 'aixs' beacuse = 'beacuse' becasue = 'becasue' Becasue = 'Becasue' @@ -382,7 +379,6 @@ Normlized = 'Normlized' normlize = 'normlize' noraml = 'noraml' numer = 'numer' -Numberic = 'Numberic' occured = 'occured' Ocurred = 'Ocurred' occures = 'occures' diff --git a/paddle/cinn/common/axis.cc b/paddle/cinn/common/axis.cc index 9ae10ea6f60d2e..dc3dbdcc7541c4 100644 --- a/paddle/cinn/common/axis.cc +++ b/paddle/cinn/common/axis.cc @@ -23,7 +23,7 @@ namespace cinn { namespace common { -static const std::vector kAxises({ +static const std::vector kAxes({ "i", // level 0 "j", // level 1 "k", // level 2 @@ -49,12 +49,12 @@ static const std::vector kAxises({ }); std::string axis_name(int level) { - if (level < kAxises.size()) { - return kAxises[level]; + if (level < kAxes.size()) { + return kAxes[level]; } // upper level - int repeat_num = 1 + (level / kAxises.size()); - const auto& base_axis = kAxises[level % kAxises.size()]; + int repeat_num = 1 + (level / kAxes.size()); + const auto& base_axis = kAxes[level % kAxes.size()]; // if the level greater than kAxis, repeat the axis, like: // level == 22 ==> axis = "ii" @@ -89,7 +89,7 @@ std::vector GenDefaultAxisAsExpr(int naxis) { } static const std::set& axis_set() { - static std::set x(kAxises.begin(), kAxises.end()); + static std::set x(kAxes.begin(), kAxes.end()); return x; } @@ -102,13 +102,13 @@ bool IsAxisNameReserved(const std::string& x) { return true; } if (!axis_set().count(std::string(1, x[0]))) { - // all char in axis should in kAxises + // all char in axis should in kAxes return false; } bool is_repeat_axis = true; for (int i = 1; i < x.size(); ++i) { if (x[i] != x[0]) { - // the axis are repeat with the char in kAxises + // the axis are repeat with the char in kAxes is_repeat_axis = false; break; } diff --git a/paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.cc index 25af0fdc6feb32..6eb6adf13dddb6 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.cc @@ -152,7 +152,7 @@ class ReduceAsOpPattern size_t x_rank = x_shape.size(); size_t y_rank = y_shape.size(); - // Get reduc aixs and + // Get reduc axis and int64_t compare_offset = x_rank - y_rank; for (size_t i = 0; i < y_rank; ++i) { diff --git a/paddle/cinn/lang/compute.cc b/paddle/cinn/lang/compute.cc index 0eb71309c374ad..0fea7f91daa9b0 100644 --- a/paddle/cinn/lang/compute.cc +++ b/paddle/cinn/lang/compute.cc @@ -155,9 +155,9 @@ ir::Tensor Compute(const std::vector &domain, std::function &)> fn, const std::string &name, const std::vector &shape) { - auto axises = cinn::common::GenDefaultAxis(domain.size()); + auto axes = cinn::common::GenDefaultAxis(domain.size()); std::vector _axis; - for (auto &x : axises) _axis.push_back(x); + for (auto &x : axes) _axis.push_back(x); Expr fn_body = fn(_axis); std::vector reduce_axis; diff --git a/paddle/fluid/inference/tensorrt/convert/slice_op.cc b/paddle/fluid/inference/tensorrt/convert/slice_op.cc index 8ce51a66a1914e..0d135085a4ec75 100644 --- a/paddle/fluid/inference/tensorrt/convert/slice_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/slice_op.cc @@ -39,7 +39,7 @@ class SliceOpConverter : public OpConverter { PADDLE_GET_CONST(std::vector, op_desc.GetAttr("starts")); std::vector ends = PADDLE_GET_CONST(std::vector, op_desc.GetAttr("ends")); - std::vector decrease_axises = + std::vector decrease_axes = PADDLE_GET_CONST(std::vector, op_desc.GetAttr("decrease_axis")); auto input_dims = input->getDimensions(); nvinfer1::ILayer* layer = nullptr; @@ -139,15 +139,15 @@ class SliceOpConverter : public OpConverter { layer->setInput(1, *start_tensor); layer->setInput(2, *size_tensor); - if (!decrease_axises.empty()) { + if (!decrease_axes.empty()) { std::vector gather_indices; for (int i = 0; i < trt_size_dims.nbDims; i++) { - if (decrease_axises.end() != - std::find(decrease_axises.begin(), decrease_axises.end(), i)) + if (decrease_axes.end() != + std::find(decrease_axes.begin(), decrease_axes.end(), i)) continue; gather_indices.push_back(i); } - if (gather_indices.empty()) gather_indices.push_back(decrease_axises[0]); + if (gather_indices.empty()) gather_indices.push_back(decrease_axes[0]); auto real_size_tensor = Gather(size_tensor, gather_indices); layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0)); layer->setInput(1, *real_size_tensor); diff --git a/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc b/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc index 7f69f66f1446c5..a0a9ad2b981f92 100644 --- a/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc @@ -39,7 +39,7 @@ class StridedSliceOpConverter : public OpConverter { PADDLE_GET_CONST(std::vector, op_desc.GetAttr("ends")); std::vector strides = PADDLE_GET_CONST(std::vector, op_desc.GetAttr("strides")); - std::vector decrease_axises = + std::vector decrease_axes = PADDLE_GET_CONST(std::vector, op_desc.GetAttr("decrease_axis")); nvinfer1::ILayer* layer = nullptr; @@ -96,15 +96,15 @@ class StridedSliceOpConverter : public OpConverter { layer->setInput(2, *size_tensor); layer->setInput(3, *step_tensor); - if (!decrease_axises.empty()) { + if (!decrease_axes.empty()) { std::vector gather_indices; for (int i = 0; i < trt_size_dims.nbDims; i++) { - if (decrease_axises.end() != - std::find(decrease_axises.begin(), decrease_axises.end(), i)) + if (decrease_axes.end() != + std::find(decrease_axes.begin(), decrease_axes.end(), i)) continue; gather_indices.push_back(i); } - if (gather_indices.empty()) gather_indices.push_back(decrease_axises[0]); + if (gather_indices.empty()) gather_indices.push_back(decrease_axes[0]); auto real_size_tensor = Gather(size_tensor, gather_indices); layer = TRT_ENGINE_ADD_LAYER(engine_, Shuffle, *layer->getOutput(0)); layer->setInput(1, *real_size_tensor); diff --git a/python/paddle/tensor/stat.py b/python/paddle/tensor/stat.py index e8abfe53acbfe2..b2589409ed4ab8 100644 --- a/python/paddle/tensor/stat.py +++ b/python/paddle/tensor/stat.py @@ -721,7 +721,7 @@ def _compute_quantile( axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. - If ``axis`` is a list, quantile is calculated over all elements of given axises. + If ``axis`` is a list, quantile is calculated over all elements of given axes. If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of @@ -905,7 +905,7 @@ def quantile( axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. - If ``axis`` is a list, quantile is calculated over all elements of given axises. + If ``axis`` is a list, quantile is calculated over all elements of given axes. If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of @@ -989,7 +989,7 @@ def nanquantile( axis (int|list, optional): The axis along which to calculate quantile. ``axis`` should be int or list of int. ``axis`` should be in range [-D, D), where D is the dimensions of ``x`` . If ``axis`` is less than 0, it works the same way as :math:`axis + D`. - If ``axis`` is a list, quantile is calculated over all elements of given axises. + If ``axis`` is a list, quantile is calculated over all elements of given axes. If ``axis`` is None, quantile is calculated over all elements of ``x``. Default is None. keepdim (bool, optional): Whether to reserve the reduced dimension(s) in the output Tensor. If ``keepdim`` is True, the dimensions of diff --git a/test/custom_op/test_custom_concat.py b/test/custom_op/test_custom_concat.py index 29bbd3517702fd..db7b5f134c14cc 100644 --- a/test/custom_op/test_custom_concat.py +++ b/test/custom_op/test_custom_concat.py @@ -119,7 +119,7 @@ def setUp(self): np.array([[1, 2, 3], [4, 5, 6]]), np.array([[11, 12, 13], [14, 15, 16]]), ] - self.axises = [0, 1] + self.axes = [0, 1] def check_output(self, out, pd_out, name): np.testing.assert_array_equal( @@ -130,7 +130,7 @@ def check_output(self, out, pd_out, name): def test_dynamic(self): for dtype in self.dtypes: - for axis in self.axises: + for axis in self.axes: out, grad_inputs = concat_dynamic( custom_ops.custom_concat, dtype, self.np_inputs, axis ) @@ -144,7 +144,7 @@ def test_dynamic(self): def test_static(self): for dtype in self.dtypes: - for axis in self.axises: + for axis in self.axes: out, x1_grad, x2_grad = concat_static( custom_ops.custom_concat, dtype, self.np_inputs, axis ) @@ -158,7 +158,7 @@ def test_static(self): def test_dynamic_with_attr(self): for dtype in self.dtypes: - for axis in self.axises: + for axis in self.axes: out, grad_inputs = concat_dynamic( custom_ops.custom_concat_with_attr, dtype, @@ -176,7 +176,7 @@ def test_dynamic_with_attr(self): def test_static_with_attr(self): for dtype in self.dtypes: - for axis in self.axises: + for axis in self.axes: out, x1_grad, x2_grad = concat_static( custom_ops.custom_concat_with_attr, dtype, diff --git a/test/legacy_test/test_kthvalue_op.py b/test/legacy_test/test_kthvalue_op.py index 16d865e1d21e92..94df240453413e 100644 --- a/test/legacy_test/test_kthvalue_op.py +++ b/test/legacy_test/test_kthvalue_op.py @@ -121,7 +121,7 @@ def init_dtype(self): class TestKthvalueOpKernels(unittest.TestCase): def setUp(self): - self.axises = [2, -1] + self.axes = [2, -1] def test_kthvalue_op(self): paddle.disable_static() @@ -132,7 +132,7 @@ def test_cpu_kernel(): paddle.set_device('cpu') inputs = np.random.random(shape) tensor = paddle.to_tensor(inputs) - for axis in self.axises: + for axis in self.axes: value_expect, indice_expect = cal_kthvalue(inputs, k, axis) v, inds = paddle.kthvalue(tensor, k, axis) np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05) @@ -146,7 +146,7 @@ def test_gpu_kernel(): paddle.set_device('gpu') inputs = np.random.random(shape) tensor = paddle.to_tensor(inputs) - for axis in self.axises: + for axis in self.axes: value_expect, indice_expect = cal_kthvalue(inputs, k, axis) v, inds = paddle.kthvalue(tensor, k, axis) np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05) diff --git a/test/legacy_test/test_mode_op.py b/test/legacy_test/test_mode_op.py index ea2aab39b0ba10..3d49d77e11120b 100644 --- a/test/legacy_test/test_mode_op.py +++ b/test/legacy_test/test_mode_op.py @@ -187,7 +187,7 @@ def init_args(self): class TestModeOpKernels(unittest.TestCase): def setUp(self): - self.axises = [-1, 1] + self.axes = [-1, 1] np.random.seed(666) self.inputs = np.ceil(np.random.rand(2, 10, 10) * 1000) @@ -195,7 +195,7 @@ def test_mode_op(self): def test_cpu_kernel(): paddle.set_device('cpu') tensor = paddle.to_tensor(self.inputs) - for axis in self.axises: + for axis in self.axes: value_expect, indice_expect = cal_mode(self.inputs, axis) v, inds = paddle.mode(tensor, axis) np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05) @@ -209,7 +209,7 @@ def test_cpu_kernel(): def test_gpu_kernel(): paddle.set_device('gpu') tensor = paddle.to_tensor(self.inputs) - for axis in self.axises: + for axis in self.axes: value_expect, indice_expect = cal_mode(self.inputs, axis) v, inds = paddle.mode(tensor, axis) np.testing.assert_allclose(v.numpy(), value_expect, rtol=1e-05) diff --git a/test/legacy_test/test_reduce_op.py b/test/legacy_test/test_reduce_op.py index ee36d18251bcf9..96332ddd77c859 100644 --- a/test/legacy_test/test_reduce_op.py +++ b/test/legacy_test/test_reduce_op.py @@ -1331,7 +1331,7 @@ def test_check_grad(self): reason="reduce_max is discontinuous non-derivable function," " its gradient check is not supported by unittest framework." ) -class TestReduceMaxOpMultiAxises(OpTest): +class TestReduceMaxOpMultiAxes(OpTest): """Remove Max with subgradient from gradient check to confirm the success of CI.""" def setUp(self): @@ -1363,7 +1363,7 @@ def test_check_grad(self): reason="reduce_min is discontinuous non-derivable function," " its gradient check is not supported by unittest framework." ) -class TestReduceMinOpMultiAxises(OpTest): +class TestReduceMinOpMultiAxes(OpTest): """Remove Min with subgradient from gradient check to confirm the success of CI.""" def setUp(self): @@ -1379,7 +1379,7 @@ def test_check_output(self): self.check_output() -class TestKeepDimReduceSumMultiAxises(OpTest): +class TestKeepDimReduceSumMultiAxes(OpTest): def setUp(self): self.op_type = "reduce_sum" self.python_api = paddle.sum @@ -1404,7 +1404,7 @@ def test_check_grad(self): self.check_grad(['X'], 'Out', check_prim=True) -class TestKeepDimReduceSumMultiAxisesForEager(OpTest): +class TestKeepDimReduceSumMultiAxesForEager(OpTest): def setUp(self): self.op_type = "reduce_sum" self.python_api = reduce_sum_wrapper2 diff --git a/tools/gen_pybind11_stub.py b/tools/gen_pybind11_stub.py index 22d7620bfd772d..116cdcbdee1cb7 100644 --- a/tools/gen_pybind11_stub.py +++ b/tools/gen_pybind11_stub.py @@ -84,7 +84,7 @@ 'TensorLike': 'paddle._typing.TensorLike', 'DTypeLike': 'paddle._typing.DTypeLike', 'ShapeLike': 'paddle._typing.ShapeLike', - 'Numberic': 'paddle._typing.Numberic', + 'Numeric': 'paddle._typing.Numeric', 'TypeGuard': 'typing_extensions.TypeGuard', '_Interpolation': 'paddle.tensor.stat._Interpolation', 'ParamAttrLike': 'paddle._typing.ParamAttrLike', From e7044801a05e24825c62271b75d34aec35353523 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 3 Dec 2024 12:51:01 +0800 Subject: [PATCH 109/288] [Ehance & Fix] Support any slice interval for indexing(`__getitem__`) in eager/static mode (#69827) * support any slice interval * fix bug * fix more bug --- paddle/fluid/pybind/slice_utils.h | 13 +- paddle/phi/kernels/funcs/slice_utils.h | 233 ++++++++++++++---- paddle/phi/kernels/funcs/strided_slice.h | 73 +++--- paddle/phi/kernels/stride/slice_kernel.cc | 5 +- .../kernels/stride/strided_slice_kernel.cc | 49 ++-- python/paddle/base/variable_index.py | 5 +- test/indexing/test_getitem.py | 172 +++++++++++++ test/legacy_test/test_multinomial_op.py | 5 - 8 files changed, 410 insertions(+), 145 deletions(-) diff --git a/paddle/fluid/pybind/slice_utils.h b/paddle/fluid/pybind/slice_utils.h index 8925580950a09e..3a42f954538311 100644 --- a/paddle/fluid/pybind/slice_utils.h +++ b/paddle/fluid/pybind/slice_utils.h @@ -31,6 +31,7 @@ #include "paddle/phi/core/compat/convert_utils.h" #include "paddle/phi/core/dense_tensor.h" #include "paddle/phi/kernels/funcs/common_infer_shape_functions.h" +#include "paddle/phi/kernels/funcs/strided_slice.h" #include "pybind11/numpy.h" #include "pybind11/pybind11.h" #include "pybind11/stl.h" @@ -143,11 +144,9 @@ static int _PySlice_GetIndices(PySliceObject* r, "tensor(int) and numpy(int) in slice item, but received %s.", std::string(Py_TYPE(r->start)->tp_name))); } - if (*start < 0) *start += length; - *start = std::max(*start, static_cast(0)); } if (r->stop == Py_None) { - *stop = *step < 0 ? -1 : length; + *stop = *step < 0 ? -length - 1 : length; } else { if (PyCheckInteger(r->stop) || IsNumpyType(r->stop)) { *stop = PyLong_AsLong(r->stop); @@ -159,9 +158,13 @@ static int _PySlice_GetIndices(PySliceObject* r, "tensor(int) and numpy(int) in slice item, but received %s.", std::string(Py_TYPE(r->stop)->tp_name))); } - if (0 < *step && *stop < 0) *stop += length; - *stop = std::min(*stop, length); } + + // normalize start and stop + bool dummy_zero_dim_out = false; + phi::funcs::normalize_interval( + *start, *stop, *step, length, start, stop, &dummy_zero_dim_out); + // return value below seems to be useless... if (*stop > length) return -1; if (*start >= length) return -1; if (*step == 0) return -1; diff --git a/paddle/phi/kernels/funcs/slice_utils.h b/paddle/phi/kernels/funcs/slice_utils.h index 14cda4f9016fe0..c24ce7d51681c4 100644 --- a/paddle/phi/kernels/funcs/slice_utils.h +++ b/paddle/phi/kernels/funcs/slice_utils.h @@ -23,6 +23,164 @@ namespace phi { namespace funcs { +/** + * @brief Normalizes the slice interval [st, ed) with a given step and dimension + * size. + * + * This function adjusts the interval [st, ed) to fit within the bounds defined + * by the dimension size, taking into account the specified step. It handles + * both positive and negative steps and accounts for negative indices by + * converting them to equivalent positive indices within the dimension size. + * + * @tparam T The data type of the input parameters, which can be an integer or + * floating-point type. + * @param st The starting index of the interval. + * @param ed The ending index of the interval (exclusive). + * @param step The step size for iterating through the interval, which can be + * positive or negative. + * @param dim_size The size of the dimension, serving as the upper bound for + * valid indices. + * @param st_out Pointer to store the normalized starting index. + * @param ed_out Pointer to store the normalized ending index. + * @param zero_dim_out Pointer to a boolean flag that is set to true if the + * resulting interval is empty. + * + * @details + * - If `step > 0`, the function ensures that `st` and `ed` are adjusted to be + * within the range [0, dim_size). + * - If `step < 0`, the function adjusts `st` and `ed` to accommodate the + * reverse traversal of the interval. + * - Handles special cases where `st` and `ed` may be out of bounds or where + * `dim_size` is zero. + * - Uses pointer parameters for output to modify the values directly. + * - The function also handles scenarios involving negative indices, converting + * them appropriately. + * + * @example + * T st_out, ed_out; + * bool zero_dim; + * normalize_interval(-3, -2, 1, 4, &st_out, &ed_out, &zero_dim); + * // Results in: st_out = 1, ed_out = 2, zero_dim = false + * + * @note The function assumes that the pointers provided for output parameters + * are valid and non-null. + */ +template +void normalize_interval( + T st, T ed, T step, T dim_size, T* st_out, T* ed_out, bool* zero_dim_out) { + /* Normalize slice interval [st, ed) with given step and dim_size. + e.g. if given st = -3, ed = -2, step = 1, dim_size = 4, + then normalized st_out = 1(-3+4), st_ed = 2(-2+4). + + This function is general enough and applicable + for both step > 0 and step < 0 scenarios. + + Indicices dipicted as below: + + =============================================================== + | 0 1 2 3 ... D-1 | D D+1 ... + ... -D-2 -D-1 | -D -D+1 -D+2 -D+3 ... -1 | + =============================================================== + */ + // 0 dim size, just return + if (dim_size <= 0) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + if (step > 0) { + /* positive step */ + // 0 dim size case 1 + if (st >= dim_size) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + // 0 dim size case 2 + if (ed <= -dim_size) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + // make st belongs: (-inf, -D-1)∪[0, D) + if (-dim_size <= st && st < 0) { + st += dim_size; + } + // make st belongs: [0, D) + st = std::max(st, static_cast(0)); + + // make ed belongs: [0, +inf) + if (-dim_size <= ed && ed < 0) { + ed += dim_size; + } + // make ed belongs: [0, D] + ed = std::min(ed, dim_size); + + // 0 dim size case 3 + if (st >= ed) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + *st_out = st; + *ed_out = ed; + return; + + } else { + /* negative step */ + // 0 dim size case 1 + if (st <= -dim_size - 1) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + // 0 dim size case 2 + if (ed >= dim_size - 1) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + // make st belongs: [0, D)∪[0, +inf) + if (-dim_size <= st && st < 0) { + st += dim_size; + } + // make st belongs: [0, D) + st = std::min(st, dim_size - 1); + + // make ed belongs: [-inf, -D)∪[0, D) + if (-dim_size <= ed && ed < 0) { + ed += dim_size; + } + // make ed belongs: [-D-1, -D)∪[0, D) ==> {-D-1}∪[0, D) + ed = std::max(ed, -dim_size - 1); + + if (ed == -dim_size - 1) { + // When ed=-D-1, it is symmetrical to when step is greater than 0 and + // ed=D. + *st_out = st; + *ed_out = ed; + return; + } + + // now only remain the case that ed belongs to: [0, D) + // 0 dim size case 3 + if (ed >= st) { + *st_out = *ed_out = 0; + *zero_dim_out = true; + return; + } + + *st_out = st; + *ed_out = ed; + return; + } +} + template inline void CheckAndUpdateSliceAttrs(const DDim in_dims, const std::vector& axes, @@ -56,41 +214,17 @@ inline void CheckAndUpdateSliceAttrs(const DDim in_dims, common::errors::InvalidArgument( "Step should not be 0, but received step = %d.", step)); - T start = (*starts)[i] < 0 ? ((*starts)[i] + dim_value) : (*starts)[i]; - start = std::max(start, static_cast(0)); - - T end = - 0 < step && (*ends)[i] < 0 ? ((*ends)[i] + dim_value) : (*ends)[i]; - end = std::min(end, dim_value); - - if (step > 0) { - start = std::min(start, dim_value); - end = std::max(end, static_cast(0)); - PADDLE_ENFORCE_GE( - end, - start, - common::errors::InvalidArgument( - "When step > 0, end should be greater than start, but " - "received end = %d, start = %d.", - end, - start)); - } else { - // NOTE(liym27): When step < 0, start should less and equal to - // dim_value-1 - // "end is -1" means contain the 0-th element of this axis. - start = std::min(start, dim_value - 1); - if (end < -1) { - end += dim_value; - } - end = std::max(end, static_cast(-1)); - PADDLE_ENFORCE_GE( - start, - end, - common::errors::InvalidArgument( - "When step < 0, start should be greater than end, but " - "received start = %d, end = %d.", - start, - end)); + T start, end; + bool dummy_zero_out_dim = false; + normalize_interval((*starts)[i], + (*ends)[i], + step, + dim_value, + &start, + &end, + &dummy_zero_out_dim); + if (end == -dim_value - 1) { + end = -1; } (*starts)[i] = start; @@ -117,24 +251,17 @@ inline void UpdateSliceAttrs(const DDim in_dims, T dim_value = in_dims[axis]; if (dim_value > 0) { T step = steps == nullptr ? 1 : (*steps)[i]; - T start = (*starts)[i] < 0 ? ((*starts)[i] + dim_value) : (*starts)[i]; - start = std::max(start, static_cast(0)); - T end = - 0 < step && (*ends)[i] < 0 ? ((*ends)[i] + dim_value) : (*ends)[i]; - end = std::min(end, dim_value); - - if (step > 0) { - start = std::min(start, dim_value); - end = std::max(end, static_cast(0)); - } else { - // NOTE: When step < 0, start should less and equal to - // dim_value-1 - // "end is -1" means contain the 0-th element of this axis. - start = std::min(start, dim_value - 1); - if (end < -1) { - end += dim_value; - } - end = std::max(end, static_cast(-1)); + T start = (*starts)[i]; + T end = (*ends)[i]; + + bool dummy_zero_out_dim = false; + normalize_interval( + start, end, step, dim_value, &start, &end, &dummy_zero_out_dim); + + // manually set the end to -1 when step < 0, + // which indicates that it can extend to the left endpoint. + if (end == -dim_value - 1 && step < 0) { + end = -1; } (*starts)[i] = start; (*ends)[i] = end; diff --git a/paddle/phi/kernels/funcs/strided_slice.h b/paddle/phi/kernels/funcs/strided_slice.h index 46342175a8213b..f14f2f5990b9fb 100644 --- a/paddle/phi/kernels/funcs/strided_slice.h +++ b/paddle/phi/kernels/funcs/strided_slice.h @@ -25,6 +25,7 @@ #include "paddle/phi/kernels/funcs/eigen/common.h" #include "paddle/phi/kernels/funcs/eigen/eigen_function.h" #include "paddle/phi/kernels/funcs/math_function.h" +#include "paddle/phi/kernels/funcs/slice_utils.h" namespace phi { namespace funcs { @@ -73,39 +74,26 @@ static void StridedSliceOutDims(const std::vector& starts, continue; } - if (start_index < 0) { - start_index = start_index + axis_size; - start_index = std::max(start_index, 0); - } - if (end_index < 0) { - if (!(end_index == -1 && stride_index < 0)) { // skip None stop condition - end_index = end_index + axis_size; - if (end_index < 0) { - end_index = 0; - } - } + bool neg_dim_condition = false; + normalize_interval(start_index, + end_index, + stride_index, + axis_size, + &start_index, + &end_index, + &neg_dim_condition); + if (end_index == -axis_size - 1) { + end_index = -1; } - if (stride_index < 0) { - start_index = start_index + 1; - end_index = end_index + 1; + int64_t out_dims_index; + if (neg_dim_condition) { + out_dims_index = 0; + } else { + int64_t step_size = std::abs(stride_index); + out_dims_index = + (std::abs(end_index - start_index) + step_size - 1) / step_size; } - - bool neg_dim_condition = ((stride_index < 0 && (start_index < end_index)) || - (stride_index > 0 && (start_index > end_index))); - PADDLE_ENFORCE_EQ(neg_dim_condition, - false, - errors::InvalidArgument( - "The start index and end index are invalid for their " - "corresponding stride.")); - - int64_t left = - std::max(static_cast(0), std::min(start_index, end_index)); - int64_t right = std::min(axis_size, std::max(start_index, end_index)); - int64_t step = std::abs(stride_index); - - auto out_dims_index = (std::abs(right - left) + step - 1) / step; - out_dims_vector[axes_index] = out_dims_index; } } @@ -136,19 +124,18 @@ static void StridedSliceFunctor(int64_t* starts, decrease_axis_affect = true; } } - // stride must not be zero - if (starts[axis_index] < 0) { - starts[axis_index] = starts[axis_index] + axis_size; - starts[axis_index] = std::max(starts[axis_index], 0); - } - if (ends[axis_index] < 0) { - if (!(ends[axis_index] == -1 && - strides[axis_index] < 0)) { // skip None stop condition - ends[axis_index] = ends[axis_index] + axis_size; - if (ends[axis_index] < 0) { - ends[axis_index] = 0; - } - } + bool dummy_zero_dim_out = false; + normalize_interval(starts[axis_index], + ends[axis_index], + strides[axis_index], + axis_size, + &starts[axis_index], + &ends[axis_index], + &dummy_zero_dim_out); + if (ends[axis_index] == -axis_size - 1) { + // manually set the end to -1 when step < 0, + // which indicates that it can extend to the left endpoint. + ends[axis_index] = -1; } if (decrease_axis_affect) { if (strides[axis_index] < 0) { diff --git a/paddle/phi/kernels/stride/slice_kernel.cc b/paddle/phi/kernels/stride/slice_kernel.cc index 71eaec4fd98d9e..fe65a002b67df6 100644 --- a/paddle/phi/kernels/stride/slice_kernel.cc +++ b/paddle/phi/kernels/stride/slice_kernel.cc @@ -49,7 +49,8 @@ void SliceStridedKernel(const Context& ctx, item = std::max(int64_t(0), item + int64_t(in_dims.size())); } } - + // axis = 0, dim_value = 3, st[0]=0, ed[0]=4 + // The step seems to be regarded as 1 here phi::funcs::CheckAndUpdateSliceAttrs( in_dims, new_axes, &starts, &ends, nullptr, nullptr); @@ -62,7 +63,7 @@ void SliceStridedKernel(const Context& ctx, output_offset = static_cast( output_offset + starts[i] * output_stride[new_axes[i]] * SizeOf(out->dtype())); - output_dims[new_axes[i]] = ends[i] - starts[i]; + output_dims[new_axes[i]] = std::abs(ends[i] - starts[i]); } std::vector decrease_flag(output_dims.size(), 0); diff --git a/paddle/phi/kernels/stride/strided_slice_kernel.cc b/paddle/phi/kernels/stride/strided_slice_kernel.cc index 69183b8b9a69c8..4779930fbd6ff7 100644 --- a/paddle/phi/kernels/stride/strided_slice_kernel.cc +++ b/paddle/phi/kernels/stride/strided_slice_kernel.cc @@ -15,6 +15,7 @@ #include "paddle/phi/kernels/strided_slice_kernel.h" #include "glog/logging.h" +#include "paddle/phi/kernels/funcs/slice_utils.h" #include "paddle/common/flags.h" #include "paddle/phi/backends/all_context.h" @@ -53,47 +54,25 @@ void StridedSliceRawStridedKernel(const Context& dev_ctx, if (axis_size < 0) { continue; } - - if (starts[i] < 0) { - starts[i] = starts[i] + axis_size; - starts[i] = std::max(starts[i], 0); - } - if (ends[i] < 0) { - if (!(ends[i] == -1 && strides[i] < 0)) { // skip None stop condition - ends[i] = ends[i] + axis_size; - if (ends[i] < 0) { - ends[i] = 0; - } - } + bool dummy_zero_dim_out = false; + funcs::normalize_interval(starts[i], + ends[i], + strides[i], + axis_size, + &starts[i], + &ends[i], + &dummy_zero_dim_out); + if (ends[i] == -axis_size - 1) { + ends[i] = -1; } - int64_t left = 0; - int64_t right = 0; - - if (strides[i] < 0) { - left = std::max(static_cast(-1), ends[i]); - right = std::min(axis_size - 1, starts[i]); - } else { - left = std::max(static_cast(0), starts[i]); - right = std::min(axis_size, ends[i]); - } - int64_t step = std::abs(strides[i]); + int64_t step_size = std::abs(strides[i]); - auto dim = (std::abs(right - left) + step - 1) / step; - - if (dim <= 0) { - dim = 0; - strides[i] = 1; - starts[i] = 0; - } - - if (starts[i] >= axis_size) { - starts[i] = (strides[i] < 0) ? axis_size - 1 : axis_size; - } + auto out_dim = (std::abs(ends[i] - starts[i]) + step_size - 1) / step_size; output_offset += static_cast(starts[i] * output_stride[axes[i]] * SizeOf(out->dtype())); - output_dims[axes[i]] = dim; + output_dims[axes[i]] = out_dim; output_stride[axes[i]] *= strides[i]; } diff --git a/python/paddle/base/variable_index.py b/python/paddle/base/variable_index.py index 09191281a46835..cd06712477aa4f 100644 --- a/python/paddle/base/variable_index.py +++ b/python/paddle/base/variable_index.py @@ -20,6 +20,7 @@ from . import core, unique_name MAX_INTEGER = 2**31 - 1 +MIN_INTEGER = -(2**31) def replace_ellipsis(var, item): @@ -335,7 +336,7 @@ def parse_index(x, indices): if start is None: start = 0 if step > 0 else MAX_INTEGER if end is None: - end = MAX_INTEGER if step > 0 else -1 + end = MAX_INTEGER if step > 0 else MIN_INTEGER if not ( is_tensor_array @@ -343,7 +344,7 @@ def parse_index(x, indices): or isinstance(step, (paddle.base.Variable, paddle.pir.Value)) ): if x.shape[dim] != -1 and end >= x.shape[dim]: - end = MAX_INTEGER if step > 0 else -1 + end = MAX_INTEGER if step > 0 else x.shape[dim] estimated_dim += 1 dim += 1 diff --git a/test/indexing/test_getitem.py b/test/indexing/test_getitem.py index 86fe0919ffd6b4..685927af685274 100644 --- a/test/indexing/test_getitem.py +++ b/test/indexing/test_getitem.py @@ -410,6 +410,76 @@ def test_indexing_is_boolean_false(self): np.testing.assert_allclose(y.numpy(), np_res) +class TestMultipleIndexing(TestGetitemInDygraph): + def test_indexing_with_all_possible_start_end_step_dygraph(self): + np_data = np.arange(5 * 4 * 3 * 2).reshape((5, 4, 3, 2)) + dim_size = np_data.shape[3] + for st in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for step in list(range(-dim_size - 1, dim_size + 2)): + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res_out = pd_data[:, :, st:ed:step, :] + self.assertEqual( + pd_res_out.shape, + list(np_res.shape), + f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + np.testing.assert_allclose(pd_res_out.numpy(), np_res) + + def test_indexing_with_all_possible_start_end_step_dygraph_0_size(self): + np_data = np.arange(0 * 4 * 3 * 2).reshape((0, 4, 3, 2)) + dim_size = np_data.shape[3] + for st in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for step in list(range(-dim_size - 1, dim_size + 2)): + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res_out = pd_data[:, :, st:ed:step, :] + self.assertEqual( + pd_res_out.shape, + list(np_res.shape), + f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + np.testing.assert_allclose(pd_res_out.numpy(), np_res) + + def test_indexing_with_all_possible_start_end_step_dygraph_0_size_self( + self, + ): + np_data = np.arange(5 * 4 * 0 * 2).reshape((5, 4, 0, 2)) + dim_size = np_data.shape[3] + for st in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for ed in [*list(range(-dim_size - 1, dim_size + 2)), None]: + for step in list(range(-dim_size - 1, dim_size + 2)): + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res_out = pd_data[:, :, st:ed:step, :] + self.assertEqual( + pd_res_out.shape, + list(np_res.shape), + f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + np.testing.assert_allclose(pd_res_out.numpy(), np_res) + + @unittest.skipIf( not core.is_compiled_with_cuda() or not core.is_float16_supported(core.CUDAPlace(0)), @@ -1028,6 +1098,108 @@ def test_combined_index_12(self): np.testing.assert_allclose(res[0], np_res) + def test_indexing_with_all_possible_start_end_step(self): + np_data = np.arange(5 * 4 * 3 * 2).reshape((5, 4, 3, 2)) + dim_size = np_data.shape[3] + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + for st in [-dim_size - 1, dim_size + 1, 0, None]: + for ed in [-dim_size - 1, dim_size + 1, 0, None]: + for step in [-dim_size - 1, dim_size + 1, 0]: + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res = _getitem_static( + pd_data, + ( + slice(None), + slice(None), + slice(st, ed, step), + slice(None), + ), + ) + (pd_res_out,) = self.exe.run(fetch_list=[pd_res]) + + np.testing.assert_allclose( + pd_res_out, + np_res, + err_msg=f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + + def test_indexing_with_all_possible_start_end_step_0_size(self): + np_data = np.arange(0 * 4 * 3 * 2).reshape((0, 4, 3, 2)) + dim_size = np_data.shape[3] + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + for st in [-dim_size - 1, dim_size + 1, 0, None]: + for ed in [-dim_size - 1, dim_size + 1, 0, None]: + for step in [-dim_size - 1, dim_size + 1, 0]: + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res = _getitem_static( + pd_data, + ( + slice(None), + slice(None), + slice(st, ed, step), + slice(None), + ), + ) + (pd_res_out,) = self.exe.run(fetch_list=[pd_res]) + + np.testing.assert_allclose( + pd_res_out, + np_res, + err_msg=f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + + def test_indexing_with_all_possible_start_end_step_0_size_self(self): + np_data = np.arange(5 * 4 * 0 * 2).reshape((5, 4, 0, 2)) + dim_size = np_data.shape[3] + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + for st in [-dim_size - 1, dim_size + 1, 0, None]: + for ed in [-dim_size - 1, dim_size + 1, 0, None]: + for step in [-dim_size - 1, dim_size + 1, 0]: + if step == 0: + continue + try: + np_res = np_data[:, :, st:ed:step, :] + except Exception as e: + # skip the invalid case use try-except strategy + continue + pd_data = paddle.to_tensor(np_data) + pd_res = _getitem_static( + pd_data, + ( + slice(None), + slice(None), + slice(st, ed, step), + slice(None), + ), + ) + (pd_res_out,) = self.exe.run(fetch_list=[pd_res]) + + np.testing.assert_allclose( + pd_res_out, + np_res, + err_msg=f"Failed indexing test in case: x.shape={np_data.shape}, slice=({st},{ed},{step})", + ) + def test_index_has_range(self): # only one bool tensor with all False np_data = np.arange(3 * 4 * 5 * 6).reshape((3, 4, 5, 6)) diff --git a/test/legacy_test/test_multinomial_op.py b/test/legacy_test/test_multinomial_op.py index 50333f2a602c40..c863bffad3b763 100644 --- a/test/legacy_test/test_multinomial_op.py +++ b/test/legacy_test/test_multinomial_op.py @@ -384,11 +384,6 @@ def test_dim_less_than_1(): with self.assertRaises(ValueError): y = paddle.multinomial(paddle.to_tensor([1.0, 2.0, -3.0])) - with self.assertRaises(ValueError): - prob = paddle.rand([20, 1000]) - prob[1:0] = 0 - y = paddle.multinomial(prob) - class TestRandomValue(unittest.TestCase): def test_fixed_random_number(self): From 642f52d0c6d3485ac845a38c20fbc19446c3c7a0 Mon Sep 17 00:00:00 2001 From: ZhenxingLi Date: Tue, 3 Dec 2024 13:31:19 +0800 Subject: [PATCH 110/288] =?UTF-8?q?=E3=80=90Auto-Parallel=E3=80=91Fix=5Fck?= =?UTF-8?q?pt=5Foom=20(#69764)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../distributed/checkpoint/load_state_dict.py | 32 ++++++++++++------- 1 file changed, 20 insertions(+), 12 deletions(-) diff --git a/python/paddle/distributed/checkpoint/load_state_dict.py b/python/paddle/distributed/checkpoint/load_state_dict.py index c508b8111b423e..6747ab216e5d39 100644 --- a/python/paddle/distributed/checkpoint/load_state_dict.py +++ b/python/paddle/distributed/checkpoint/load_state_dict.py @@ -587,6 +587,12 @@ def load_state_dict( offload, ) + for flat_key, keys in mapping.items(): + tmp = state_dict + for key in keys[:-1]: + tmp = tmp[key] + tmp[keys[-1]] = flat_state_dict[flat_key] + def _load_state_dict( target_state_dict, @@ -597,13 +603,6 @@ def _load_state_dict( offload=False, ) -> None: with paddle.base.dygraph.guard(): - - state_dict_in_cpu = {} - for k, v in target_state_dict.items(): - if v.place.is_cpu_place(): - state_dict_in_cpu[k] = v - target_state_dict[k] = v.cuda() - use_dist = True if paddle.distributed.get_world_size() > 1 else False local_load_files = list(source_state_dict.keys()) @@ -616,7 +615,14 @@ def _load_state_dict( read_items = get_read_items( metadata_list, target_state_dict, process_group, use_dist ) + state_dict_in_cpu = [] + idx = 0 for item in read_items: + key = item.local_tensor_index.tensor_key + if key in target_state_dict: + if target_state_dict[key].place.is_cpu_place(): + state_dict_in_cpu.append(key) + target_state_dict[key] = target_state_dict[key].cuda() assert ( item.local_tensor_index in load_infos ), f"read item:{item}, load_infos:{load_infos}" @@ -716,11 +722,13 @@ def _load_state_dict( tmp_tensor, src=src_rank, group=process_group ) paddle.assign(tmp_tensor, cur_chunk_tensor) - - for k, v in target_state_dict.items(): - if k in state_dict_in_cpu: - value = state_dict_in_cpu[k] - paddle.assign(v.cpu(), value) + if ( + key in state_dict_in_cpu + and idx + 1 < len(read_items) + and read_items[idx + 1].local_tensor_index.tensor_key != key + ): + target_state_dict[key] = target_state_dict[key].cpu() + idx = idx + 1 if use_dist: paddle.distributed.barrier(process_group) From 52734e88f32a149111cb2c3e3d0d545eab4d6c9c Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Tue, 3 Dec 2024 14:12:22 +0800 Subject: [PATCH 111/288] Optimize recompute algorithm analyze runtime (#69886) --- paddle/fluid/pybind/pir.cc | 17 ++++++++ python/paddle/decomposition/recompute.py | 52 +++++++++++++++--------- 2 files changed, 49 insertions(+), 20 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 2b7f60eebc0765..88118f00231fb4 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -687,6 +687,23 @@ void BindBlock(py::module *m) { .def("add_arg", &Block::AddArg) .def("add_kwarg", &Block::AddKwarg) .def("erase_kwarg", &Block::EraseKwarg) + .def("get_value_from_op_idxs", + [](Block &self, const py::list &op_idxs) -> py::list { + py::list value_list; + auto it = self.begin(); + std::set idxs_set; + for (py::handle item : op_idxs) { + idxs_set.insert(item.cast()); + } + for (int i = 0; it != self.end(); ++i, ++it) { + if (idxs_set.find(i) != idxs_set.end()) { + for (uint32_t j = 0; j < it->num_results(); ++j) { + value_list.append(static_cast(it->result(j))); + } + } + } + return value_list; + }) .def("remove_op", [](Block &self, const Operation &op) { self.erase(op); }) .def( diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index ca4be198d9e860..75b7ee78685df5 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -14,8 +14,10 @@ from __future__ import annotations import functools +import logging import math import os +import time from collections import deque from typing import TYPE_CHECKING @@ -378,6 +380,8 @@ def auto_recompute( # 1.1 classify value nodes import networkx as nx + start_time = time.time() + # model value as graph's node, op as graph's edge ( required_fw_value_nodes, @@ -629,6 +633,10 @@ def _ban_recomputation(value_node): backward_op_start_idx, ) DebugPrint("program after recompute:", program_after_recompute) + end_time = time.time() + logging.info( + f"Time of auto recompute program: ***** [ {end_time - start_time} ] ***** seconds." + ) return program_after_recompute, fwd_op_end_idx_after_recompute @@ -828,36 +836,40 @@ def getIdx(program, op): def classify_value_node(program, grad_outputs, fwd_op_end_idx): all_ops = program.global_block().ops - required_fw_value_nodes = backward_utils.ValueSet() required_fw_ops = set(all_ops[: fwd_op_end_idx + 1]) - for required_fw_op in required_fw_ops: - fw_op_outputs = required_fw_op.results() - required_fw_value_nodes = ( - required_fw_value_nodes | backward_utils.ValueSet(fw_op_outputs) - ) - required_bw_value_nodes = backward_utils.ValueSet() + + required_fw_op_idxs = list(range(0, fwd_op_end_idx + 1)) + required_fw_value_nodes = backward_utils.ValueSet( + program.global_block().get_value_from_op_idxs(required_fw_op_idxs) + ) + required_bw_ops = set() for grad_output in grad_outputs: required_bw_ops = required_bw_ops | find_child_ops(grad_output) required_bw_ops.add(grad_output.get_defining_op()) - for required_bw_op in required_bw_ops: - bw_op_outputs = ( - required_bw_op.results() if required_bw_op is not None else [] - ) - required_bw_value_nodes = ( - required_bw_value_nodes | backward_utils.ValueSet(bw_op_outputs) - ) - unclaimed_value_nodes = backward_utils.ValueSet() + + required_bw_op_idxs = [] + for idx, op in enumerate(all_ops): + if op in required_bw_ops: + required_bw_op_idxs.append(idx) + required_bw_value_nodes = backward_utils.ValueSet( + program.global_block().get_value_from_op_idxs(required_bw_op_idxs) + ) + unclaimed_ops = { op for op in all_ops if op not in required_fw_ops and op not in required_bw_ops } - for unclaimed_op in unclaimed_ops: - unclaimed_op_outputs = unclaimed_op.results() - unclaimed_value_nodes = unclaimed_value_nodes | backward_utils.ValueSet( - unclaimed_op_outputs - ) + + unclaimed_op_idxs = [] + for idx, op in enumerate(all_ops): + if op in unclaimed_ops: + unclaimed_op_idxs.append(idx) + unclaimed_value_nodes = backward_utils.ValueSet( + program.global_block().get_value_from_op_idxs(unclaimed_op_idxs) + ) + return ( required_fw_value_nodes, required_bw_value_nodes | unclaimed_value_nodes, From d4a02d65fe25cf021e7c2cf53cacb71a8d7afb55 Mon Sep 17 00:00:00 2001 From: xiaoguoguo626807 <100397923+xiaoguoguo626807@users.noreply.github.com> Date: Tue, 3 Dec 2024 14:12:38 +0800 Subject: [PATCH 112/288] =?UTF-8?q?=E3=80=90pir=E3=80=91Add=20more=20TrueS?= =?UTF-8?q?topgradient=20op=20(#69821)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * modify logic op stopgradient * add true stopgradient --- .../fluid/pir/dialect/op_generator/op_build_gen.py | 13 +++++++++++++ 1 file changed, 13 insertions(+) diff --git a/paddle/fluid/pir/dialect/op_generator/op_build_gen.py b/paddle/fluid/pir/dialect/op_generator/op_build_gen.py index 34f23bb0c4e661..7e8abc0f9884e0 100644 --- a/paddle/fluid/pir/dialect/op_generator/op_build_gen.py +++ b/paddle/fluid/pir/dialect/op_generator/op_build_gen.py @@ -144,6 +144,19 @@ 'LogicalNotOp', 'LogicalNot_Op', 'LogicalXorOp', + 'GreaterEqualOp', + 'GreaterEqual_Op', + 'GreaterThanOp', + 'GreaterThan_Op', + 'LessEqualOp', + 'LessEqual_Op', + 'LessThanOp', + 'LessThan_Op', + 'EqualOp', + 'Equal_Op', + 'EqualAllOp', + 'NotEqualOp', + 'NotEqual_Op', } OP_BUILD_TEMPLATE = """ void {op_name}::Build({build_args}) {{ From 90edb427eb09c96c667eb8e19de758eb2c9bd2cc Mon Sep 17 00:00:00 2001 From: liuruyan <44316842+liuruyan@users.noreply.github.com> Date: Tue, 3 Dec 2024 14:18:31 +0800 Subject: [PATCH 113/288] fix comment (#69858) * fix comment * fix comment --- .../ir/schedule/impl/loop_transformation.cc | 104 ++++++------------ 1 file changed, 31 insertions(+), 73 deletions(-) diff --git a/paddle/cinn/ir/schedule/impl/loop_transformation.cc b/paddle/cinn/ir/schedule/impl/loop_transformation.cc index cb2b7120174ad4..4e7424bfa9252f 100644 --- a/paddle/cinn/ir/schedule/impl/loop_transformation.cc +++ b/paddle/cinn/ir/schedule/impl/loop_transformation.cc @@ -39,6 +39,32 @@ namespace cinn { namespace ir { +void SimplifyBindingsInStaticShape(const cinn::ir::DyScheduleImpl* sch, + const Expr& loop, + const std::string& sch_name, + Expr* stmt) { + // Get outter loops of current loops. + Expr root = sch->GetRootBlock(loop); + std::vector outter_loops = GetLoopsOfExpr(loop, root); + + // TODO(liujinnan): Deal dynamic shape. + if (!ContainDynamicShape(root)) { + // Create an analyzer of outter loops and new fused loop. + std::vector combine_loops = outter_loops; + combine_loops.push_back(*stmt); + common::cas_intervals_t var_intervals_t = + common::CollectVarIntervalsOfExprs(combine_loops); + common::SymbolicExprAnalyzer ana{var_intervals_t}; + + // Simplify the bindings of new loop. + VLOG(4) << "Before SimplifyBindings in " << sch_name << ", ir is:\n" + << *stmt; + common::SimplifyBlockBinding::SimplifyBindings(*stmt, outter_loops, ana); + VLOG(4) << "After SimplifyBindings in " << sch_name << ", ir is:\n" + << *stmt; + } +} + std::vector DyScheduleImpl::Split(const Expr& loop, const std::vector& factors) { CINN_IR_SCHEDULE_BEGIN(); @@ -119,25 +145,8 @@ std::vector DyScheduleImpl::Split(const Expr& loop, splited_loops[i] = new_node; } - // Get outter loops of current loops. - Expr root = this->GetRootBlock(loop); - std::vector outter_loops = GetLoopsOfExpr(loop, root); - - // TODO(liujinnan): Deal dynamic shape. - if (!ContainDynamicShape(root)) { - // Create an analyzer of outter loops and new fused loop. - std::vector combine_loops = outter_loops; - combine_loops.push_back(new_node); - common::cas_intervals_t var_intervals_t = - common::CollectVarIntervalsOfExprs(combine_loops); - common::SymbolicExprAnalyzer ana{var_intervals_t}; - - // Simplify the bindings of new fused loop. - VLOG(4) << "Before SimplifyBindings in split, ir is:\n" << new_node; - common::SimplifyBlockBinding::SimplifyBindings( - new_node, outter_loops, ana); - VLOG(4) << "After SimplifyBindings in split, ir is:\n" << new_node; - } + SimplifyBindingsInStaticShape(this, loop, "split", &new_node); + this->Replace(loop, new_node); VLOG(3) << "After Split, ir is:\n" << splited_loops.at(0); return splited_loops; @@ -231,24 +240,7 @@ std::vector DyScheduleImpl::Split(const Expr& loop, splited_loops[i] = new_node; } - // Get outter loops of current loops. - Expr root = this->GetRootBlock(loop); - std::vector outter_loops = GetLoopsOfExpr(loop, root); - - // TODO(liujinnan): Deal dynamic shape. - if (!ContainDynamicShape(root)) { - // Create an analyzer of outter loops and new fused loop. - std::vector combine_loops = outter_loops; - combine_loops.push_back(new_node); - common::cas_intervals_t var_intervals_t = - common::CollectVarIntervalsOfExprs(combine_loops); - common::SymbolicExprAnalyzer ana{var_intervals_t}; - - // Simplify the bindings of new fused loop. - VLOG(4) << "Before SimplifyBindings in split, ir is:\n" << new_node; - common::SimplifyBlockBinding::SimplifyBindings(new_node, outter_loops, ana); - VLOG(4) << "After SimplifyBindings in split, ir is:\n" << new_node; - } + SimplifyBindingsInStaticShape(this, loop, "split", &new_node); this->Replace(loop, new_node); VLOG(3) << "After Split, ir is:\n" << splited_loops.at(0); @@ -354,24 +346,7 @@ std::vector DyScheduleImpl::Split(const Expr& loop, splited_loops[i] = new_node; } - // Get outter loops of current loops. - Expr root = this->GetRootBlock(loop); - std::vector outter_loops = GetLoopsOfExpr(loop, root); - - // TODO(liujinnan): Deal dynamic shape. - if (!ContainDynamicShape(root)) { - // Create an analyzer of outter loops and new fused loop. - std::vector combine_loops = outter_loops; - combine_loops.push_back(new_node); - common::cas_intervals_t var_intervals_t = - common::CollectVarIntervalsOfExprs(combine_loops); - common::SymbolicExprAnalyzer ana{var_intervals_t}; - - // Simplify the bindings of new fused loop. - VLOG(4) << "Before SimplifyBindings in split, ir is:\n" << new_node; - common::SimplifyBlockBinding::SimplifyBindings(new_node, outter_loops, ana); - VLOG(4) << "After SimplifyBindings in split, ir is:\n" << new_node; - } + SimplifyBindingsInStaticShape(this, loop, "split", &new_node); this->Replace(loop, new_node); VLOG(3) << "After Split, ir is:\n" << splited_loops.at(0); @@ -476,24 +451,7 @@ Expr DyScheduleImpl::Fuse(const std::vector& loops) { for_nodes[0]->device_api, fused_body); - // Get outter loops of current loops. - Expr root = this->GetRootBlock(loops[0]); - std::vector outter_loops = GetLoopsOfExpr(loops[0], root); - - // TODO(liujinnan): Deal dynamic shape. - if (!ContainDynamicShape(root)) { - // Create an analyzer of outter loops and new fused loop. - std::vector combine_loops = outter_loops; - combine_loops.push_back(new_stmt); - common::cas_intervals_t var_intervals_t = - common::CollectVarIntervalsOfExprs(combine_loops); - common::SymbolicExprAnalyzer ana{var_intervals_t}; - - // Simplify the bindings of new fused loop. - VLOG(4) << "Before SimplifyBindings in Fuse, ir is:\n" << new_stmt; - common::SimplifyBlockBinding::SimplifyBindings(new_stmt, outter_loops, ana); - VLOG(4) << "After SimplifyBindings in Fuse, ir is:\n" << new_stmt; - } + SimplifyBindingsInStaticShape(this, loops[0], "fuse", &new_stmt); this->Replace(loops[0], new_stmt); From 6e2ca4c7f4cc06576ccd2cdaf5bb99d6bb180842 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 3 Dec 2024 14:35:24 +0800 Subject: [PATCH 114/288] [Lod][fluid_ops] LodRanktable (#69828) --- .../event_garbage_collector.cc | 3 -- .../fast_garbage_collector.cc | 3 -- .../no_event_garbage_collector.cc | 3 -- paddle/fluid/framework/var_type.h | 4 --- paddle/fluid/framework/var_type_traits.cc | 1 - paddle/fluid/framework/var_type_traits.h | 3 -- paddle/fluid/framework/variable_helper.cc | 3 -- paddle/fluid/imperative/var_helper.cc | 3 -- paddle/fluid/pybind/compiled_program.cc | 1 - paddle/fluid/pybind/place.cc | 1 - paddle/fluid/pybind/pybind.cc | 14 ---------- paddle/fluid/pybind/tensor.cc | 1 - test/cpp/fluid/framework/operator_test.cc | 28 ------------------- .../fluid/framework/var_type_traits_test.cc | 3 -- test/cpp/imperative/test_eager.cc | 11 +------- 15 files changed, 1 insertion(+), 81 deletions(-) diff --git a/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc index 34b6c7cf37132b..964ade8f80dc2f 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/event_garbage_collector.cc @@ -95,9 +95,6 @@ void InterpreterCoreEventGarbageCollector::Add(Variable* var, OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? - } else if (var->IsType()) { - // TODO(xiongkun03) in old executor, this type of variable is not support - // eager deletion. so we just leave it here ? } else if (var->IsType()) { Add(var->GetMutable() ->mutable_value() diff --git a/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc index 0133d8d0313344..83ab2d73015dd8 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.cc @@ -40,9 +40,6 @@ void InterpreterCoreFastGarbageCollector::Add(Variable* var) { OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? - } else if (var->IsType()) { - // TODO(xiongkun03) in old executor, this type of variable is not support - // eager deletion. so we just leave it here ? } else if (var->IsType()) { Add(var->GetMutable() ->mutable_value() diff --git a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc index 6b4ac89038475d..dbf89671b55c37 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc @@ -55,9 +55,6 @@ void InterpreterCoreNoEventGarbageCollector::Add( OrderedMultiDeviceDenseTensorBlockingQueueHolder>()) { // NOLINT // TODO(xiongkun03) in old executor, this type of variable is not support // eager deletion. so we just leave it here ? - } else if (var->IsType()) { - // TODO(xiongkun03) in old executor, this type of variable is not support - // eager deletion. so we just leave it here ? } else if (var->IsType()) { Add(var->GetMutable() ->mutable_value() diff --git a/paddle/fluid/framework/var_type.h b/paddle/fluid/framework/var_type.h index 570083b07b4a00..6c2a5da0021b63 100644 --- a/paddle/fluid/framework/var_type.h +++ b/paddle/fluid/framework/var_type.h @@ -14,7 +14,6 @@ limitations under the License. */ #pragma once #include "paddle/fluid/framework/dense_tensor_array.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/selected_rows_utils.h" #include "paddle/fluid/framework/var_type_traits.h" @@ -51,9 +50,6 @@ inline void VisitVarType(const framework::Variable& var, Visitor visitor) { case proto::VarType::DENSE_TENSOR: visitor(var.Get()); return; - case proto::VarType::LOD_RANK_TABLE: - visitor(var.Get()); - return; case proto::VarType::DENSE_TENSOR_ARRAY: visitor(var.Get()); return; diff --git a/paddle/fluid/framework/var_type_traits.cc b/paddle/fluid/framework/var_type_traits.cc index 696b1df185231f..596974f622ec2a 100644 --- a/paddle/fluid/framework/var_type_traits.cc +++ b/paddle/fluid/framework/var_type_traits.cc @@ -15,7 +15,6 @@ #include "paddle/fluid/framework/var_type_traits.h" #include "paddle/common/macros.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/scope.h" #include "paddle/phi/core/framework/reader.h" #include "paddle/phi/core/operators/reader/dense_tensor_blocking_queue.h" diff --git a/paddle/fluid/framework/var_type_traits.h b/paddle/fluid/framework/var_type_traits.h index b2931ada75c4fc..319d614756277b 100644 --- a/paddle/fluid/framework/var_type_traits.h +++ b/paddle/fluid/framework/var_type_traits.h @@ -76,7 +76,6 @@ class BKCLCommunicator; } // namespace platform namespace framework { -class LoDRankTable; class Scope; class ReaderHolder; class Scope; @@ -180,7 +179,6 @@ using VarTypeRegistry = detail::VarTypeRegistryImpl< phi::SparseCooTensor, phi::SparseCsrTensor, std::vector, - LoDRankTable, Strings, phi::TensorArray, phi::PlaceList, @@ -240,7 +238,6 @@ struct VarTypeTrait { REG_PROTO_VAR_TYPE_TRAIT(phi::DenseTensor, proto::VarType::DENSE_TENSOR); REG_PROTO_VAR_TYPE_TRAIT(phi::SelectedRows, proto::VarType::SELECTED_ROWS); REG_PROTO_VAR_TYPE_TRAIT(std::vector, proto::VarType::STEP_SCOPES); -REG_PROTO_VAR_TYPE_TRAIT(LoDRankTable, proto::VarType::LOD_RANK_TABLE); REG_PROTO_VAR_TYPE_TRAIT(phi::TensorArray, proto::VarType::DENSE_TENSOR_ARRAY); REG_PROTO_VAR_TYPE_TRAIT(phi::PlaceList, proto::VarType::PLACE_LIST); REG_PROTO_VAR_TYPE_TRAIT(ReaderHolder, proto::VarType::READER); diff --git a/paddle/fluid/framework/variable_helper.cc b/paddle/fluid/framework/variable_helper.cc index e061da398ba04b..822701425dcba1 100644 --- a/paddle/fluid/framework/variable_helper.cc +++ b/paddle/fluid/framework/variable_helper.cc @@ -16,7 +16,6 @@ limitations under the License. */ #include "paddle/fluid/framework/dense_tensor_array.h" #include "paddle/fluid/framework/feed_fetch_type.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows_utils.h" @@ -37,8 +36,6 @@ void InitializeVariable(Variable *var, proto::VarType::Type var_type) { var->GetMutable(); } else if (var_type == proto::VarType::STEP_SCOPES) { var->GetMutable>(); - } else if (var_type == proto::VarType::LOD_RANK_TABLE) { - var->GetMutable(); } else if (var_type == proto::VarType::DENSE_TENSOR_ARRAY) { var->GetMutable(); } else if (var_type == proto::VarType::STRINGS) { diff --git a/paddle/fluid/imperative/var_helper.cc b/paddle/fluid/imperative/var_helper.cc index 45f8c0dca4447c..117b958168f88c 100644 --- a/paddle/fluid/imperative/var_helper.cc +++ b/paddle/fluid/imperative/var_helper.cc @@ -18,7 +18,6 @@ #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/dense_tensor_array.h" #include "paddle/fluid/framework/feed_fetch_type.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/tensor.h" @@ -55,8 +54,6 @@ void InitializeVariable(paddle::framework::Variable *var, var->GetMutable(); } else if (var_type == paddle::framework::proto::VarType::STEP_SCOPES) { var->GetMutable>(); - } else if (var_type == paddle::framework::proto::VarType::LOD_RANK_TABLE) { - var->GetMutable(); } else if (var_type == paddle::framework::proto::VarType::DENSE_TENSOR_ARRAY) { var->GetMutable(); diff --git a/paddle/fluid/pybind/compiled_program.cc b/paddle/fluid/pybind/compiled_program.cc index d884c6db5f1f8a..d3d9aa92c8dd48 100755 --- a/paddle/fluid/pybind/compiled_program.cc +++ b/paddle/fluid/pybind/compiled_program.cc @@ -50,7 +50,6 @@ #include "paddle/fluid/framework/ir/cost_model.h" #include "paddle/fluid/framework/ir/generate_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/new_executor/executor_statistics.h" #include "paddle/fluid/framework/new_executor/standalone_executor.h" #include "paddle/fluid/framework/op_info.h" diff --git a/paddle/fluid/pybind/place.cc b/paddle/fluid/pybind/place.cc index 2a6b0de9b713dd..52e2619fd7fc1d 100644 --- a/paddle/fluid/pybind/place.cc +++ b/paddle/fluid/pybind/place.cc @@ -49,7 +49,6 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/cost_model.h" #include "paddle/fluid/framework/ir/generate_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/new_executor/executor_statistics.h" #include "paddle/fluid/framework/new_executor/standalone_executor.h" #include "paddle/fluid/framework/op_info.h" diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 43f39912c8f9ff..18b0761ae1705a 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -54,7 +54,6 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/cost_model.h" #include "paddle/fluid/framework/ir/generate_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/new_executor/collect_shape_manager.h" #include "paddle/fluid/framework/new_executor/executor_statistics.h" #include "paddle/fluid/framework/new_executor/interpreter/job.h" @@ -1511,10 +1510,6 @@ All parameter, weight, gradient are variables in Paddle. "get_map_tensor", [](Variable &self) { return self.GetMutable(); }, py::return_value_policy::reference) - .def( - "get_lod_rank_table", - [](Variable &self) { return self.GetMutable(); }, - py::return_value_policy::reference) .def( "get_selected_rows", [](Variable &self) -> phi::SelectedRows * { @@ -2497,15 +2492,6 @@ All parameter, weight, gradient are variables in Paddle. BindAutoParallel(&m); BindJitProperty(&m); - py::class_(m, "LodRankTable") - .def("items", [](framework::LoDRankTable &table) { - std::vector> res; - for (auto &item : table.items()) { - res.push_back({item.index, item.length}); - } - return res; - }); - py::class_ pydensetensorarray(m, "DenseTensorArray", R"DOC( DenseTensorArray is array of DenseTensor, it supports operator[], len() and for-loop iteration. diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index c31775c9cd8ae2..319884d26cc858 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -49,7 +49,6 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/cost_model.h" #include "paddle/fluid/framework/ir/generate_pass.h" #include "paddle/fluid/framework/ir/pass_builder.h" -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/new_executor/executor_statistics.h" #include "paddle/fluid/framework/new_executor/standalone_executor.h" #include "paddle/fluid/framework/op_info.h" diff --git a/test/cpp/fluid/framework/operator_test.cc b/test/cpp/fluid/framework/operator_test.cc index 6bdac948ec5320..64230dc174adf9 100644 --- a/test/cpp/fluid/framework/operator_test.cc +++ b/test/cpp/fluid/framework/operator_test.cc @@ -414,34 +414,6 @@ REGISTER_OP_CPU_KERNEL( indicate_other_data_type_test, paddle::framework::EmptyTestKernel); -TEST(IndicateVarDataTypeTest, other) { - paddle::framework::InitDevices(); - paddle::framework::proto::OpDesc op_desc; - op_desc.set_type("indicate_other_data_type_test"); - BuildVar("Other", {"lod_rank_table_1"}, op_desc.add_inputs()); - - phi::CPUPlace cpu_place; - paddle::framework::Scope scope; - - auto op = paddle::framework::OpRegistry::CreateOp(op_desc); - auto* var = scope.Var("lod_rank_table_1"); - var->GetMutable(); - - bool caught = false; - try { - op->Run(scope, cpu_place); - } catch (paddle::platform::EnforceNotMet& err) { - caught = true; - std::string ex_msg = err.what(); - EXPECT_TRUE(ex_msg.find("The Input Variable(Other) of " - "(indicate_other_data_type_test) Operator used to " - "determine kernel data type " - "is empty or not phi::DenseTensor or SelectedRows " - "or DenseTensorArray.") != std::string::npos); - } - ASSERT_TRUE(caught); -} - TEST(ExecutionContextAttrAndInOut, new_api) { paddle::framework::InitDevices(); paddle::framework::proto::OpDesc op_desc; diff --git a/test/cpp/fluid/framework/var_type_traits_test.cc b/test/cpp/fluid/framework/var_type_traits_test.cc index d90d7f0c1ee1d8..c34e8df4523530 100644 --- a/test/cpp/fluid/framework/var_type_traits_test.cc +++ b/test/cpp/fluid/framework/var_type_traits_test.cc @@ -16,7 +16,6 @@ #include -#include "paddle/fluid/framework/lod_rank_table.h" #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/selected_rows_utils.h" #include "paddle/phi/core/framework/reader.h" @@ -94,7 +93,6 @@ TEST(var_type_traits, check_proto_type_id) { ASSERT_TRUE(CheckVarId(proto::VarType::DENSE_TENSOR)); ASSERT_TRUE(CheckVarId(proto::VarType::SELECTED_ROWS)); ASSERT_TRUE(CheckVarId>(proto::VarType::STEP_SCOPES)); - ASSERT_TRUE(CheckVarId(proto::VarType::LOD_RANK_TABLE)); ASSERT_TRUE(CheckVarId(proto::VarType::DENSE_TENSOR_ARRAY)); ASSERT_TRUE(CheckVarId(proto::VarType::PLACE_LIST)); ASSERT_TRUE(CheckVarId(proto::VarType::READER)); @@ -104,7 +102,6 @@ TEST(var_type_traits, check_proto_type_id) { ASSERT_EQ(proto::VarType_Type_DENSE_TENSOR, proto::VarType::DENSE_TENSOR); ASSERT_EQ(proto::VarType_Type_SELECTED_ROWS, proto::VarType::SELECTED_ROWS); ASSERT_EQ(proto::VarType_Type_STEP_SCOPES, proto::VarType::STEP_SCOPES); - ASSERT_EQ(proto::VarType_Type_LOD_RANK_TABLE, proto::VarType::LOD_RANK_TABLE); ASSERT_EQ(proto::VarType_Type_DENSE_TENSOR_ARRAY, proto::VarType::DENSE_TENSOR_ARRAY); ASSERT_EQ(proto::VarType_Type_PLACE_LIST, proto::VarType::PLACE_LIST); diff --git a/test/cpp/imperative/test_eager.cc b/test/cpp/imperative/test_eager.cc index 1d0fc70c017498..94ec4ab8d0cfc9 100644 --- a/test/cpp/imperative/test_eager.cc +++ b/test/cpp/imperative/test_eager.cc @@ -68,10 +68,9 @@ TEST(test_create_node, eager_node) { {}); } TEST(test_var_helper, eager_var_helper) { - framework::Variable var0, var1, var2, var3, var4, var5, var6, var7, var8; + framework::Variable var0, var1, var3, var4, var5, var6, var7, var8; InitializeVariable(&var0, paddle::framework::proto::VarType::FEED_MINIBATCH); InitializeVariable(&var1, paddle::framework::proto::VarType::STEP_SCOPES); - InitializeVariable(&var2, paddle::framework::proto::VarType::LOD_RANK_TABLE); InitializeVariable(&var3, paddle::framework::proto::VarType::DENSE_TENSOR_ARRAY); InitializeVariable(&var4, paddle::framework::proto::VarType::STRINGS); @@ -82,12 +81,10 @@ TEST(test_var_helper, eager_var_helper) { InitializeVariable(&var8, paddle::framework::proto::VarType::FP64)); auto egr_tensor = std::make_shared(); - auto egr_tensor2 = std::make_shared(); egr_tensor->MutableVar() ->GetMutable() ->mutable_value() ->mutable_data(phi::CPUPlace()); - egr_tensor2->MutableVar()->GetMutable(); VLOG(6) << "egr_tensor create with "; ASSERT_TRUE(phi::is_cpu_place(GetPlace(egr_tensor))); ASSERT_TRUE(GetDataType(egr_tensor) == @@ -96,12 +93,6 @@ TEST(test_var_helper, eager_var_helper) { phi::KernelKey(phi::Backend::CPU, phi::DataLayout::ALL_LAYOUT, phi::DataType::FLOAT32)); - SetCachedValue(egr_tensor, - phi::KernelKey(phi::Backend::CPU, - phi::DataLayout::ALL_LAYOUT, - phi::DataType::FLOAT32), - egr_tensor2); - ASSERT_ANY_THROW(GetPlace(egr_tensor2)); ASSERT_ANY_THROW(SetType( egr_tensor, paddle::framework::proto::VarType::DENSE_TENSOR_ARRAY)); } From dc2e55266c0670c397e874c53ec3e2d13873867d Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Tue, 3 Dec 2024 14:46:38 +0800 Subject: [PATCH 115/288] fix ceil bug (#69870) --- .../infer_symbolic_shape/same_operands_result.cc | 15 +++++++++++++-- 1 file changed, 13 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc index 39e788f520c647..62dfa12ef01802 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/same_operands_result.cc @@ -49,8 +49,6 @@ OP_SAME_OPERANDS_AND_RESULT(Hardtanh_) OP_SAME_OPERANDS_AND_RESULT(Bernoulli) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot) OP_SAME_OPERANDS_AND_RESULT(BitwiseNot_) -OP_SAME_OPERANDS_AND_RESULT(Ceil) -OP_SAME_OPERANDS_AND_RESULT(Ceil_) OP_SAME_OPERANDS_AND_RESULT(Celu) OP_SAME_OPERANDS_AND_RESULT(Clip) OP_SAME_OPERANDS_AND_RESULT(Clip_) @@ -284,6 +282,19 @@ bool ArgsortOpInferSymbolicShape( return true; } +bool CeilOpInferSymbolicShape(pir::Operation *op, + pir::InferSymbolicShapeContext *infer_context) { + const symbol::ShapeOrDataDimExprs &operand_shape_or_data = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + infer_context->SetShapeOrDataForValue(op->result(0), operand_shape_or_data); + return true; +} + +bool Ceil_OpInferSymbolicShape(pir::Operation *op, + pir::InferSymbolicShapeContext *infer_context) { + return CeilOpInferSymbolicShape(op, infer_context); +} + } // namespace paddle::dialect namespace cinn::dialect {} // namespace cinn::dialect From a76839d91a0f59d3d49adc61f1e27fd8a8f0d303 Mon Sep 17 00:00:00 2001 From: yinfan98 <1106310035@qq.com> Date: Tue, 3 Dec 2024 14:51:42 +0800 Subject: [PATCH 116/288] =?UTF-8?q?=E3=80=90Hackathon=207th=20No.32?= =?UTF-8?q?=E3=80=91=E4=B8=BA=20paddle.nn.functional.scaled=5Fdot=5Fproduc?= =?UTF-8?q?t=5Fattention=20=E8=BF=9B=E8=A1=8C=E5=8A=9F=E8=83=BD=E5=A2=9E?= =?UTF-8?q?=E5=BC=BA=20(#69099)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix scaled dot product attention * add test & fix * fix * fix ci * fix ci * Update flash_attention.py * Update flash_attention.py * assert paddle sdpa accuracy * Update flash_attention.py * fix alignment for sdpa * add some torch test * delete same expected output * Update flash_attention.py * Update flash_attention.py * Update flash_attention.py * Update test_flash_attention.py * Update flash_attention.py --- .../paddle/nn/functional/flash_attention.py | 302 +++++++++++++++--- test/legacy_test/test_flash_attention.py | 294 +++++++++++++++++ 2 files changed, 549 insertions(+), 47 deletions(-) diff --git a/python/paddle/nn/functional/flash_attention.py b/python/paddle/nn/functional/flash_attention.py index 76314d775f3d8b..7203a037deaf5c 100644 --- a/python/paddle/nn/functional/flash_attention.py +++ b/python/paddle/nn/functional/flash_attention.py @@ -16,12 +16,15 @@ from typing import TYPE_CHECKING, Literal, overload +import numpy as np + import paddle import paddle.nn.functional as F from paddle import _C_ops, in_dynamic_mode from paddle.base.framework import in_dynamic_or_pir_mode from paddle.base.layer_helper import LayerHelper from paddle.base.wrapped_decorator import signature_safe_contextmanager +from paddle.device.cuda import get_device_capability g_enable_math = None g_enable_flash = None @@ -33,6 +36,116 @@ from paddle import Tensor +def _get_arch_info(): + # Get SMVersion from device. + cuda_version = paddle.version.cuda() + if ( + cuda_version is not None and cuda_version != 'False' + ) or paddle.is_compiled_with_rocm(): + major, minor = get_device_capability() + arch = int(major * 10 + minor) + return arch + else: + raise ValueError( + "Paddle is not compiled with CUDA, we cannot get SMVersion from device, please try to compile Paddle with CUDA" + ) + + +def check_flash_head_dim_constraints(query, dropout_p=0.0): + arch = _get_arch_info() + is_sm86_to_sm89 = 86 <= arch <= 89 + + if not is_sm86_to_sm89: + return True + + head_dim = query.shape[-1] + requires_grad = not query.stop_gradient + + if not requires_grad: + return True + + is_head_dim_gt192 = head_dim > 192 + is_head_dim_lte224 = head_dim <= 224 + is_dropout = dropout_p > 0.0 + + cond1 = is_head_dim_gt192 and is_head_dim_lte224 + cond2 = head_dim > 224 and is_dropout + + if cond1 or cond2: + return False + return True + + +def check_flash_causal_non_square_seqlens(query, key, is_causal=False): + if not is_causal: + return True + + seqlen_q = query.shape[-3] + seqlen_k = key.shape[-3] + + if seqlen_q != seqlen_k: + return False + return True + + +def check_dtypes_low_precision(query, debug=False): + arch = _get_arch_info() + dtype = query.dtype + + if arch >= 80: + supported_dtypes = [paddle.float16, paddle.bfloat16] + else: + supported_dtypes = [paddle.float16] + + return dtype in supported_dtypes + + +def can_use_flash_attn(query, key, attn_mask, dropout, is_causal) -> bool: + # sdpa flash check + # step1 check tensor place on cuda + # step2 check tensor shape, flash attn only support shape == 4 + # step3 check attn_mask, some diff with torch version + # step4 check head_dim <= 256 + # step5 check arch_info > sm80 + # step5 check specify sm head dim constraint + # step6 check causal qk + # step7 check sm dtype support + if "gpu" not in paddle.get_device(): + return False + if query.ndim != 4: + return False + if attn_mask is not None and attn_mask.dtype not in [ + paddle.bool, + paddle.float32, + ]: + return False + if query.shape[-1] >= 256: + return False + if _get_arch_info() < 80: + return False + if not check_flash_head_dim_constraints(query, dropout): + return False + if not check_flash_causal_non_square_seqlens(query, key, is_causal): + return False + if not check_dtypes_low_precision(query): + return False + return True + + +def can_use_efficient(query) -> bool: + # sdpa efficient check + # step1 check tensor place on cuda + # step2 check arch_info in [sm50, sm90] + # step3 check tensor shape, mem efficient only support shape == 4 + if "gpu" not in paddle.get_device(): + return False + if _get_arch_info() < 50 and _get_arch_info() > 90: + return False + if query.ndim != 4: + return False + return True + + @signature_safe_contextmanager def sdp_kernel( enable_math: bool = False, @@ -73,6 +186,7 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, + mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: Literal[False] = ..., @@ -85,6 +199,7 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, + mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: Literal[True] = ..., @@ -97,6 +212,7 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, + mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: bool = ..., @@ -108,6 +224,7 @@ def _math_attention( query, key, value, + mask=None, dropout_rate=0.0, causal=False, return_softmax=False, @@ -123,6 +240,9 @@ def _math_attention( value = paddle.transpose(value, [0, 2, 1, 3]) product = paddle.matmul(x=query * (head_dim**-0.5), y=key, transpose_y=True) + if mask is not None: + product = product + mask + if not causal: weights = F.softmax(product) else: @@ -146,6 +266,7 @@ def _math_attention( def _select_sdp_cuda(head_dim: int) -> str: + if head_dim <= 256: return "flash_attn" else: @@ -191,6 +312,54 @@ def _select_sdp(head_dim: int) -> str: return "mem_efficient" +def _select_sdp_for_sdpa(query, key, attn_mask, dropout, is_causal) -> str: + r""" + this select sdpa is alignment for torch version + """ + place = paddle.get_device() + if "xpu" in place: + return "flash_attn" + + # not use sdp_kernel + if ( + g_enable_flash is None + and g_enable_math is None + and g_enable_mem_efficient is None + ): + # test flash attn usage + use_flash = can_use_flash_attn( + query, key, attn_mask, dropout, is_causal + ) + use_efficient = can_use_efficient(query) + use_math = True + if use_flash: + return "flash_attn" + elif use_efficient: + return "mem_efficient" + elif use_math: + return "math" + + if ( + g_enable_math is False + and g_enable_flash is False + and g_enable_mem_efficient is False + ): + raise AssertionError( + "No available backend for scaled_dot_product_attention was found." + ) + + if g_enable_math is True: + if g_enable_flash is False and g_enable_mem_efficient is False: + return "math" + if "gpu" not in place: + return "math" + if g_enable_flash is True and g_enable_mem_efficient is True: + return _select_sdp_cuda(query.shape[-1]) + if g_enable_flash is True: + return "flash_attn" + return "mem_efficient" + + @overload def flash_attention( query: Tensor, @@ -1035,64 +1204,103 @@ def scaled_dot_product_attention( >>> # doctest: -SKIP """ + head_dim = query.shape[3] + sdp_func_name = _select_sdp_for_sdpa( + query, key, attn_mask, dropout_p, is_causal + ) + if attn_mask is None: # downgraded to ordinary flash attention implementation out, _ = flash_attention(query, key, value, dropout_p, is_causal) return out else: - if in_dynamic_or_pir_mode(): - fixed_seed_offset = None - return_softmax = False - rng_name = "" - out, _, _, _ = _C_ops.flash_attn( + if sdp_func_name == "flash_attn": + if in_dynamic_or_pir_mode(): + fixed_seed_offset = None + return_softmax = False + rng_name = "" + out, _, _, _ = _C_ops.flash_attn( + query, + key, + value, + fixed_seed_offset, + attn_mask, + dropout_p, + is_causal, + return_softmax, + not training, + rng_name, + ) + return out + else: + helper = LayerHelper('flash_attn', **locals()) + dtype = helper.input_dtype(input_param_name='q') + out = helper.create_variable_for_type_inference(dtype) + softmax = helper.create_variable_for_type_inference(dtype) + softmax_lse = helper.create_variable_for_type_inference( + paddle.float32 + ) + seed_offset = helper.create_variable_for_type_inference( + paddle.int64 + ) + inputs = { + 'q': query, + 'k': key, + 'v': value, + 'attn_mask': attn_mask, + } + outputs = { + 'out': out, + 'softmax': softmax, + 'softmax_lse': softmax_lse, + 'seed_offset': seed_offset, + } + helper.append_op( + type='flash_attn', + inputs=inputs, + outputs=outputs, + attrs={ + 'dropout': dropout_p, + 'causal': is_causal, + 'return_softmax': False, + 'is_test': not training, + 'rng_name': '', + }, + ) + return out + elif sdp_func_name == "mem_efficient": + from paddle.incubate.nn.functional.variable_length_memory_efficient_attention import ( + variable_length_memory_efficient_attention, + ) + + seq_lens = paddle.to_tensor( + [query.shape[1]] * query.shape[0], dtype='int32' + ) + + scale = 1.0 / np.sqrt(query.shape[-1]) + + query = query.transpose([0, 2, 1, 3]) + key = key.transpose([0, 2, 1, 3]) + value = value.transpose([0, 2, 1, 3]) + + output = variable_length_memory_efficient_attention( + query, key, value, seq_lens, seq_lens, attn_mask, scale + ) + + output = output.transpose([0, 2, 1, 3]) + + return output + elif sdp_func_name == "math": + return _math_attention( query, key, value, - fixed_seed_offset, attn_mask, dropout_p, is_causal, - return_softmax, - not training, - rng_name, - ) - return out - else: - helper = LayerHelper('flash_attn', **locals()) - dtype = helper.input_dtype(input_param_name='q') - out = helper.create_variable_for_type_inference(dtype) - softmax = helper.create_variable_for_type_inference(dtype) - softmax_lse = helper.create_variable_for_type_inference( - paddle.float32 - ) - seed_offset = helper.create_variable_for_type_inference( - paddle.int64 - ) - inputs = { - 'q': query, - 'k': key, - 'v': value, - 'attn_mask': attn_mask, - } - outputs = { - 'out': out, - 'softmax': softmax, - 'softmax_lse': softmax_lse, - 'seed_offset': seed_offset, - } - helper.append_op( - type='flash_attn', - inputs=inputs, - outputs=outputs, - attrs={ - 'dropout': dropout_p, - 'causal': is_causal, - 'return_softmax': False, - 'is_test': not training, - 'rng_name': '', - }, - ) - return out + False, + training, + )[0] def flashmask_attention( diff --git a/test/legacy_test/test_flash_attention.py b/test/legacy_test/test_flash_attention.py index 5c5cf6808c8a4b..4a3ab1f4763354 100644 --- a/test/legacy_test/test_flash_attention.py +++ b/test/legacy_test/test_flash_attention.py @@ -23,6 +23,7 @@ import paddle.nn.functional as F from paddle import base from paddle.base import core +from paddle.nn.functional import sdp_kernel from paddle.nn.functional.flash_attention import ( calc_reduced_attention_scores, flash_attention, @@ -484,6 +485,41 @@ def setUp(self): self.causal = False +# cpu case +class TestSDPAttentionWithMaskAPITest(TestFlashAttentionWithMaskAPI): + def setUp(self): + self.place = paddle.CPUPlace() + self.shape = (8, 1024, 16, 128) + self.dtype = 'float32' + self.dropout = 0.0 + self.causal = False + + +# fp32 case +class TestSDPAttentionWithMaskAPITest2(TestFlashAttentionWithMaskAPI): + def setUp(self): + self.place = paddle.CUDAPlace(0) + self.shape = (8, 1024, 16, 128) + self.dtype = 'float32' + self.dropout = 0.0 + self.causal = False + + +# low sm case +@unittest.skipIf( + is_sm_supported, + "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" + "and device's compute capability must be 7.5 or 8.x", +) +class TestSDPAttentionWithMaskAPITest3(TestFlashAttentionWithMaskAPI): + def setUp(self): + self.place = paddle.CUDAPlace(0) + self.shape = (8, 1024, 16, 128) + self.dtype = 'float16' + self.dropout = 0.0 + self.causal = False + + @unittest.skipIf( not is_flashattn_supported(), "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" @@ -798,6 +834,9 @@ def unpad(self, x, cu_seqlen): return unpad_x def test_main(self): + # test dynamic + paddle.disable_static() + for causal in [False, True]: for use_unpadded in [False, True]: ( @@ -1549,5 +1588,260 @@ def setUp(self): self.dtype = 'bfloat16' +@unittest.skipIf( + not is_flashattn_supported(), + "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" + "and device's compute capability must be 7.5 or 8.x", +) +class TestFlashAttentionAlignment(unittest.TestCase): + def setUp(self): + paddle.disable_static() + self.bs = 1 + self.seq_len = 8 + self.num_head = 1 + self.head_dim = 8 + self.dtype = 'float16' + self.query = np.array( + [ # batch_size = 1 + [[0.3, -0.7, 0.2, 0.5, -0.4, 0.8, -0.2, 0.1]], # seq position 0 + [ + [-0.5, 0.4, 0.7, -0.3, 0.6, -0.8, 0.3, -0.1] + ], # seq position 1 + [[0.2, 0.8, -0.4, 0.1, -0.6, 0.3, 0.7, -0.5]], # seq position 2 + [[-0.8, 0.1, 0.6, 0.4, -0.2, -0.7, 0.5, 0.3]], # seq position 3 + [[0.7, -0.3, -0.5, 0.8, 0.2, 0.4, -0.6, 0.1]], # seq position 4 + [[-0.2, 0.5, 0.3, -0.7, 0.8, 0.1, -0.4, 0.6]], # seq position 5 + [[0.4, -0.6, 0.8, -0.1, 0.3, 0.5, -0.8, 0.2]], # seq position 6 + [[-0.4, 0.2, -0.8, 0.6, 0.1, -0.3, 0.7, 0.5]], # seq position 7 + ], + dtype=np.float16, + ).reshape(1, 8, 1, 8) + self.key = np.array( + [ # batch_size = 1 + [[0.6, -0.2, 0.8, -0.4, 0.3, 0.1, -0.7, 0.5]], # seq position 0 + [[-0.3, 0.7, 0.1, 0.5, -0.8, 0.4, -0.2, 0.6]], # seq position 1 + [[0.8, -0.5, 0.3, -0.1, 0.6, 0.2, -0.4, 0.7]], # seq position 2 + [[-0.6, 0.4, -0.2, 0.7, 0.1, -0.8, 0.3, 0.5]], # seq position 3 + [[0.2, 0.8, -0.6, 0.3, 0.5, -0.1, 0.7, -0.4]], # seq position 4 + [[-0.7, 0.3, 0.5, 0.1, -0.4, 0.8, -0.2, 0.6]], # seq position 5 + [[0.5, -0.8, 0.2, 0.6, -0.3, 0.7, 0.1, -0.5]], # seq position 6 + [[-0.1, 0.6, 0.4, -0.7, 0.2, 0.5, -0.8, 0.3]], # seq position 7 + ], + dtype=np.float16, + ).reshape(1, 8, 1, 8) + self.value = np.array( + [ # batch_size = 1 + [[-0.4, 0.8, -0.1, 0.3, 0.6, -0.5, 0.2, 0.7]], # seq position 0 + [[0.5, -0.3, 0.7, 0.2, -0.6, 0.4, -0.8, 0.1]], # seq position 1 + [[-0.2, 0.6, 0.4, -0.7, 0.3, 0.8, -0.1, 0.5]], # seq position 2 + [[0.7, -0.4, 0.1, 0.5, -0.8, 0.2, 0.6, -0.3]], # seq position 3 + [[-0.5, 0.3, 0.8, -0.2, 0.4, 0.1, -0.7, 0.6]], # seq position 4 + [[0.2, -0.6, 0.3, 0.7, -0.1, 0.5, -0.4, 0.8]], # seq position 5 + [[-0.8, 0.1, 0.5, -0.3, 0.7, 0.4, -0.2, 0.6]], # seq position 6 + [[0.3, -0.7, 0.2, 0.6, -0.4, 0.8, -0.5, 0.1]], # seq position 7 + ], + dtype=np.float16, + ).reshape(1, 8, 1, 8) + self.mask = paddle.zeros( + [1, 1, self.seq_len, self.seq_len], dtype='float16' + ) + for i in range(self.bs): + seq_len = self.seq_len + mask = ( + paddle.tril( + paddle.ones(shape=(seq_len, seq_len), dtype=paddle.float32) + ) + - 1 + ) + self.mask[i, 0, :seq_len, :seq_len] = mask * 1e4 + self.rtol = 1e-3 + self.atol = 1e-3 + + self.expected_output = np.array( + [ + [ + [ + [ + -3.9990e-01, + 7.9980e-01, + -9.9976e-02, + 3.0005e-01, + 6.0010e-01, + -5.0000e-01, + 1.9995e-01, + 7.0020e-01, + ] + ], + [ + [ + -6.1798e-03, + 3.1860e-01, + 2.5000e-01, + 2.5610e-01, + 7.5012e-02, + -1.0626e-01, + -2.3743e-01, + 4.3750e-01, + ] + ], + [ + [ + 1.0028e-01, + 1.9958e-01, + 4.2505e-01, + 5.3787e-04, + -7.5317e-02, + 2.7441e-01, + -3.7524e-01, + 3.4985e-01, + ] + ], + [ + [ + 2.9224e-01, + 1.6373e-02, + 2.7368e-01, + 1.8188e-01, + -3.0298e-01, + 2.2412e-01, + 3.4210e-02, + 1.2610e-01, + ] + ], + [ + [ + -1.6998e-02, + 2.5220e-01, + 3.7939e-01, + -3.7048e-02, + 3.0151e-02, + 2.3108e-01, + -1.6772e-01, + 3.5327e-01, + ] + ], + [ + [ + 1.1948e-02, + 1.2378e-01, + 3.2935e-01, + 1.2390e-01, + 2.6123e-02, + 2.3279e-01, + -1.6919e-01, + 4.4019e-01, + ] + ], + [ + [ + -1.6162e-01, + 1.9812e-01, + 3.2544e-01, + 1.8021e-02, + 2.0081e-01, + 2.5586e-01, + -1.5466e-01, + 5.0635e-01, + ] + ], + [ + [ + 5.0873e-02, + -7.4219e-02, + 3.9502e-01, + 1.5466e-01, + -8.6182e-02, + 3.1958e-01, + -2.1179e-01, + 3.1714e-01, + ] + ], + ] + ], + dtype=np.float16, + ) + + def test_flash_attention(self): + paddle.disable_static() + query = paddle.to_tensor(self.query) + key = paddle.to_tensor(self.key) + value = paddle.to_tensor(self.value) + mask = paddle.to_tensor(self.mask) + + with sdp_kernel( + enable_flash=True, enable_math=False, enable_mem_efficient=False + ): + output = paddle.nn.functional.scaled_dot_product_attention( + query, + key, + value, + attn_mask=mask, + dropout_p=0.0, + is_causal=False, + ) + + np.testing.assert_allclose( + output.numpy(), + self.expected_output, + rtol=self.rtol, + atol=self.atol, + err_msg='Flash attention output does not match expected values', + ) + + def test_math_attention(self): + paddle.disable_static() + query = paddle.to_tensor(self.query) + key = paddle.to_tensor(self.key) + value = paddle.to_tensor(self.value) + mask = paddle.to_tensor(self.mask) + + with sdp_kernel( + enable_flash=False, enable_math=True, enable_mem_efficient=False + ): + output = paddle.nn.functional.scaled_dot_product_attention( + query, + key, + value, + attn_mask=mask, + dropout_p=0.0, + is_causal=False, + ) + + np.testing.assert_allclose( + output.numpy(), + self.expected_output, + rtol=self.rtol, + atol=self.atol, + err_msg='Math attention output does not match expected values', + ) + + def test_mem_efficient_attention(self): + paddle.disable_static() + query = paddle.to_tensor(self.query) + key = paddle.to_tensor(self.key) + value = paddle.to_tensor(self.value) + mask = paddle.to_tensor(self.mask) + + with sdp_kernel( + enable_flash=False, enable_math=False, enable_mem_efficient=True + ): + output = paddle.nn.functional.scaled_dot_product_attention( + query, + key, + value, + attn_mask=mask, + dropout_p=0.0, + is_causal=False, + ) + + np.testing.assert_allclose( + output.numpy(), + self.expected_output, + rtol=self.rtol, + atol=self.atol, + err_msg='Memory efficient attention output does not match expected values', + ) + + if __name__ == '__main__': unittest.main() From b89d9ffa438f65f0a040f84d044f6436e3b38c05 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 3 Dec 2024 14:54:29 +0800 Subject: [PATCH 117/288] [Lod][fluid_ops] detection_map (#69742) --- .../phi/kernels/cpu/detection_map_kernel.cc | 534 ------------------ paddle/phi/kernels/detection_map_kernel.h | 38 -- paddle/phi/ops/yaml/op_compat.yaml | 7 - paddle/phi/ops/yaml/ops.yaml | 15 - test/legacy_test/CMakeLists.txt | 4 +- test/legacy_test/test_detection_map_op.py | 360 ------------ 6 files changed, 1 insertion(+), 957 deletions(-) delete mode 100644 paddle/phi/kernels/cpu/detection_map_kernel.cc delete mode 100644 paddle/phi/kernels/detection_map_kernel.h delete mode 100644 test/legacy_test/test_detection_map_op.py diff --git a/paddle/phi/kernels/cpu/detection_map_kernel.cc b/paddle/phi/kernels/cpu/detection_map_kernel.cc deleted file mode 100644 index 9e67d99aeddded..00000000000000 --- a/paddle/phi/kernels/cpu/detection_map_kernel.cc +++ /dev/null @@ -1,534 +0,0 @@ -// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "paddle/phi/kernels/detection_map_kernel.h" -#include -#include -#include -#include -#include - -#include "paddle/phi/core/kernel_registry.h" -#include "paddle/phi/kernels/funcs/eigen/common.h" - -namespace phi { - -enum APType { kNone = 0, kIntegral, k11point }; - -APType GetAPType(const std::string& str) { - if (str == "integral") { - return APType::kIntegral; - } else if (str == "11point") { - return APType::k11point; - } else { - return APType::kNone; - } -} - -template -inline bool SortScorePairDescend(const std::pair& pair1, - const std::pair& pair2) { - return pair1.first > pair2.first; -} - -template -inline void GetAccumulation(std::vector> in_pairs, - std::vector* accu_vec) { - std::stable_sort(in_pairs.begin(), in_pairs.end(), SortScorePairDescend); - accu_vec->clear(); - size_t sum = 0; - for (size_t i = 0; i < in_pairs.size(); ++i) { - auto count = in_pairs[i].second; - sum += count; - accu_vec->push_back(sum); - } -} - -template -struct Box { - Box(T xmin, T ymin, T xmax, T ymax) - : xmin(xmin), ymin(ymin), xmax(xmax), ymax(ymax), is_difficult(false) {} - - T xmin, ymin, xmax, ymax; - bool is_difficult; -}; - -template -inline T JaccardOverlap(const Box& box1, const Box& box2) { - if (box2.xmin > box1.xmax || box2.xmax < box1.xmin || box2.ymin > box1.ymax || - box2.ymax < box1.ymin) { - return 0.0; - } else { - T inter_xmin = std::max(box1.xmin, box2.xmin); - T inter_ymin = std::max(box1.ymin, box2.ymin); - T inter_xmax = std::min(box1.xmax, box2.xmax); - T inter_ymax = std::min(box1.ymax, box2.ymax); - - T inter_width = inter_xmax - inter_xmin; - T inter_height = inter_ymax - inter_ymin; - T inter_area = inter_width * inter_height; - - T bbox_area1 = (box1.xmax - box1.xmin) * (box1.ymax - box1.ymin); - T bbox_area2 = (box2.xmax - box2.xmin) * (box2.ymax - box2.ymin); - - return inter_area / (bbox_area1 + bbox_area2 - inter_area); - } -} - -template -inline void ClipBBox(const Box& bbox, Box* clipped_bbox) { - T one = static_cast(1.0); - T zero = static_cast(0.0); - clipped_bbox->xmin = std::max(std::min(bbox.xmin, one), zero); - clipped_bbox->ymin = std::max(std::min(bbox.ymin, one), zero); - clipped_bbox->xmax = std::max(std::min(bbox.xmax, one), zero); - clipped_bbox->ymax = std::max(std::min(bbox.ymax, one), zero); -} - -template -void GetOutputPos( - const Context& dev_ctx, - const std::map& label_pos_count, - const std::map>>& true_pos, - const std::map>>& false_pos, - phi::DenseTensor* output_pos_count, - phi::DenseTensor* output_true_pos, - phi::DenseTensor* output_false_pos, - const int class_num) { - int true_pos_count = 0; - int false_pos_count = 0; - for (auto it = true_pos.begin(); it != true_pos.end(); ++it) { - auto tp = it->second; - true_pos_count += tp.size(); - } - for (auto it = false_pos.begin(); it != false_pos.end(); ++it) { - auto fp = it->second; - false_pos_count += fp.size(); - } - - output_pos_count->Resize(common::make_ddim({class_num, 1})); - int* pos_count_data = dev_ctx.template Alloc(output_pos_count); - - output_true_pos->Resize(common::make_ddim({true_pos_count, 2})); - T* true_pos_data = dev_ctx.template Alloc(output_true_pos); - output_false_pos->Resize(common::make_ddim({false_pos_count, 2})); - T* false_pos_data = dev_ctx.template Alloc(output_false_pos); - true_pos_count = 0; - false_pos_count = 0; - std::vector true_pos_starts = {0}; - std::vector false_pos_starts = {0}; - for (int i = 0; i < class_num; ++i) { - auto it_count = label_pos_count.find(i); - pos_count_data[i] = 0; - if (it_count != label_pos_count.end()) { - pos_count_data[i] = it_count->second; - } - auto it_true_pos = true_pos.find(i); - if (it_true_pos != true_pos.end()) { - const std::vector>& true_pos_vec = it_true_pos->second; - for (const std::pair& tp : true_pos_vec) { - true_pos_data[true_pos_count * 2] = tp.first; - true_pos_data[true_pos_count * 2 + 1] = static_cast(tp.second); - true_pos_count++; - } - } - true_pos_starts.push_back(true_pos_count); - - auto it_false_pos = false_pos.find(i); - if (it_false_pos != false_pos.end()) { - const std::vector>& false_pos_vec = - it_false_pos->second; - for (const std::pair& fp : false_pos_vec) { - false_pos_data[false_pos_count * 2] = fp.first; - false_pos_data[false_pos_count * 2 + 1] = static_cast(fp.second); - false_pos_count++; - } - } - false_pos_starts.push_back(false_pos_count); - } - - phi::LoD true_pos_lod; - true_pos_lod.emplace_back(true_pos_starts); - phi::LoD false_pos_lod; - false_pos_lod.emplace_back(false_pos_starts); - - output_true_pos->set_lod(true_pos_lod); - output_false_pos->set_lod(false_pos_lod); -} - -template -void GetInputPos(const phi::DenseTensor& input_pos_count, - const phi::DenseTensor& input_true_pos, - const phi::DenseTensor& input_false_pos, - std::map* label_pos_count, - std::map>>* true_pos, - std::map>>* false_pos, - const int class_num) { - const int* pos_count_data = input_pos_count.data(); - for (int i = 0; i < class_num; ++i) { - (*label_pos_count)[i] = pos_count_data[i]; - } - - auto SetData = [](const phi::DenseTensor& pos_tensor, - std::map>>& pos) { - const T* pos_data = pos_tensor.data(); - auto& pos_data_lod = pos_tensor.lod()[0]; - for (size_t i = 0; i < pos_data_lod.size() - 1; ++i) { - for (size_t j = pos_data_lod[i]; j < pos_data_lod[i + 1]; ++j) { - T score = pos_data[j * 2]; - int flag = pos_data[j * 2 + 1]; - pos[i].push_back(std::make_pair(score, flag)); - } - } - }; - - SetData(input_true_pos, *true_pos); - SetData(input_false_pos, *false_pos); - return; -} - -template -void CalcTrueAndFalsePositive( - const std::vector>>>& gt_boxes, - const std::vector>>>>& - detect_boxes, - bool evaluate_difficult, - float overlap_threshold, - std::map* label_pos_count, - std::map>>* true_pos, - std::map>>* false_pos) { - int batch_size = gt_boxes.size(); - for (int n = 0; n < batch_size; ++n) { - auto& image_gt_boxes = gt_boxes[n]; - for (auto& image_gt_box : image_gt_boxes) { - size_t count = 0; - auto& labeled_bboxes = image_gt_box.second; - if (evaluate_difficult) { - count = labeled_bboxes.size(); - } else { - for (auto& box : labeled_bboxes) { - if (!box.is_difficult) { - ++count; - } - } - } - if (count == 0) { - continue; - } - int label = image_gt_box.first; - if (label_pos_count->find(label) == label_pos_count->end()) { - (*label_pos_count)[label] = count; - } else { - (*label_pos_count)[label] += count; - } - } - } - - for (size_t n = 0; n < detect_boxes.size(); ++n) { - auto image_gt_boxes = gt_boxes[n]; - auto detections = detect_boxes[n]; - - if (image_gt_boxes.size() == 0) { - for (auto it = detections.begin(); it != detections.end(); ++it) { - auto pred_boxes = it->second; - int label = it->first; - for (size_t i = 0; i < pred_boxes.size(); ++i) { - auto score = pred_boxes[i].first; - (*true_pos)[label].push_back(std::make_pair(score, 0)); - (*false_pos)[label].push_back(std::make_pair(score, 1)); - } - } - continue; - } - - for (auto it = detections.begin(); it != detections.end(); ++it) { - int label = it->first; - auto pred_boxes = it->second; - if (image_gt_boxes.find(label) == image_gt_boxes.end()) { - for (size_t i = 0; i < pred_boxes.size(); ++i) { - auto score = pred_boxes[i].first; - (*true_pos)[label].push_back(std::make_pair(score, 0)); - (*false_pos)[label].push_back(std::make_pair(score, 1)); - } - continue; - } - - auto matched_bboxes = image_gt_boxes.find(label)->second; - std::vector visited(matched_bboxes.size(), false); - // Sort detections in descend order based on scores - std::sort( - pred_boxes.begin(), pred_boxes.end(), SortScorePairDescend>); - for (size_t i = 0; i < pred_boxes.size(); ++i) { - T max_overlap = -1.0; - size_t max_idx = 0; - auto score = pred_boxes[i].first; - for (size_t j = 0; j < matched_bboxes.size(); ++j) { - Box& pred_box = pred_boxes[i].second; - ClipBBox(pred_box, &pred_box); - T overlap = JaccardOverlap(pred_box, matched_bboxes[j]); - if (overlap > max_overlap) { - max_overlap = overlap; - max_idx = j; - } - } - if (max_overlap > overlap_threshold) { - bool match_evaluate_difficult = - evaluate_difficult || - (!evaluate_difficult && !matched_bboxes[max_idx].is_difficult); - if (match_evaluate_difficult) { - if (!visited[max_idx]) { - (*true_pos)[label].push_back(std::make_pair(score, 1)); - (*false_pos)[label].push_back(std::make_pair(score, 0)); - visited[max_idx] = true; - } else { - (*true_pos)[label].push_back(std::make_pair(score, 0)); - (*false_pos)[label].push_back(std::make_pair(score, 1)); - } - } - } else { - (*true_pos)[label].push_back(std::make_pair(score, 0)); - (*false_pos)[label].push_back(std::make_pair(score, 1)); - } - } - } - } -} - -template -T CalcMAP(APType ap_type, - const std::map& label_pos_count, - const std::map>>& true_pos, - const std::map>>& false_pos, - const int background_label) { - T mAP = 0.0; - int count = 0; - for (auto it = label_pos_count.begin(); it != label_pos_count.end(); ++it) { - int label = it->first; - int label_num_pos = it->second; - if (label_num_pos == background_label) { - continue; - } - if (true_pos.find(label) == true_pos.end()) { - count++; - continue; - } - auto label_true_pos = true_pos.find(label)->second; - auto label_false_pos = false_pos.find(label)->second; - // Compute average precision. - std::vector tp_sum; - GetAccumulation(label_true_pos, &tp_sum); - std::vector fp_sum; - GetAccumulation(label_false_pos, &fp_sum); - std::vector precision, recall; - size_t num = tp_sum.size(); - // Compute Precision. - for (size_t i = 0; i < num; ++i) { - precision.push_back(static_cast(tp_sum[i]) / - static_cast(tp_sum[i] + fp_sum[i])); - recall.push_back(static_cast(tp_sum[i]) / label_num_pos); - } - // VOC2007 style - if (ap_type == APType::k11point) { - std::vector max_precisions(11, 0.0); - int start_idx = num - 1; - for (int j = 10; j >= 0; --j) - for (int i = start_idx; i >= 0; --i) { - if (recall[i] < j / 10.) { - start_idx = i; - if (j > 0) max_precisions[j - 1] = max_precisions[j]; - break; - } else { - if (max_precisions[j] < precision[i]) - max_precisions[j] = precision[i]; - } - } - for (int j = 10; j >= 0; --j) mAP += max_precisions[j] / 11; - ++count; - } else if (ap_type == APType::kIntegral) { - // Nature integral - float average_precisions = 0.; - float prev_recall = 0.; - for (size_t i = 0; i < num; ++i) { - if (fabs(recall[i] - prev_recall) > 1e-6) - average_precisions += precision[i] * fabs(recall[i] - prev_recall); - prev_recall = recall[i]; - } - mAP += average_precisions; - ++count; - } else { - PADDLE_THROW(common::errors::Unimplemented( - "Unknown ap version %s. Now only supports integral and l1point.", - ap_type)); - } - } - if (count != 0) mAP /= count; - return mAP; -} - -template -void GetBoxes(const phi::DenseTensor& input_label, - const phi::DenseTensor& input_detect, - std::vector>>>* gt_boxes, - std::vector>>>>& - detect_boxes) { - auto labels = phi::EigenTensor::From(input_label); - auto detect = phi::EigenTensor::From(input_detect); - - auto& label_lod = input_label.lod(); - auto& detect_lod = input_detect.lod(); - - int batch_size = label_lod[0].size() - 1; - auto& label_index = label_lod[0]; - - for (int n = 0; n < batch_size; ++n) { - std::map>> boxes; - for (size_t i = label_index[n]; i < label_index[n + 1]; ++i) { - int label = labels(i, 0); - if (input_label.dims()[1] == 6) { - Box box(labels(i, 2), labels(i, 3), labels(i, 4), labels(i, 5)); - auto is_difficult = labels(i, 1); - if (std::abs(is_difficult - 0.0) < 1e-6) - box.is_difficult = false; - else - box.is_difficult = true; - boxes[label].push_back(box); - } else { - PADDLE_ENFORCE_EQ( - input_label.dims()[1], - 5, - common::errors::InvalidArgument( - "The input label width" - " must be 5, but received %d, please check your input data", - input_label.dims()[1])); - Box box(labels(i, 1), labels(i, 2), labels(i, 3), labels(i, 4)); - boxes[label].push_back(box); - } - } - gt_boxes->push_back(boxes); - } - - auto detect_index = detect_lod[0]; - for (int n = 0; n < batch_size; ++n) { - std::map>>> boxes; - for (size_t i = detect_index[n]; i < detect_index[n + 1]; ++i) { - Box box(detect(i, 2), detect(i, 3), detect(i, 4), detect(i, 5)); - int label = detect(i, 0); - auto score = detect(i, 1); - boxes[label].push_back(std::make_pair(score, box)); - } - detect_boxes.push_back(boxes); - } -} - -template -void DetectionMAPOpKernel(const Context& dev_ctx, - const DenseTensor& detect_res, - const DenseTensor& label, - const paddle::optional& has_state, - const paddle::optional& pos_count, - const paddle::optional& true_pos, - const paddle::optional& false_pos, - int class_num, - int background_label, - float overlap_threshold, - bool evaluate_difficult, - const std::string& ap_type, - DenseTensor* accum_pos_count, - DenseTensor* accum_true_pos, - DenseTensor* accum_false_pos, - DenseTensor* m_ap) { - auto* in_detect = &detect_res; - auto* in_label = &label; - auto* out_map = m_ap; - - auto* in_pos_count = pos_count.get_ptr(); - auto* in_true_pos = true_pos.get_ptr(); - auto* in_false_pos = false_pos.get_ptr(); - - auto* out_pos_count = accum_pos_count; - auto* out_true_pos = accum_true_pos; - auto* out_false_pos = accum_false_pos; - - auto& label_lod = in_label->lod(); - auto& detect_lod = in_detect->lod(); - PADDLE_ENFORCE_EQ( - label_lod.size(), - 1UL, - common::errors::InvalidArgument("Only support DenseTensor of lod_level " - "with 1 in label, but received %d.", - label_lod.size())); - PADDLE_ENFORCE_EQ(label_lod[0].size(), - detect_lod[0].size(), - common::errors::InvalidArgument( - "The batch_size of input(Label) and input(Detection) " - "must be the same, but received %d:%d", - label_lod[0].size(), - detect_lod[0].size())); - - std::vector>>> gt_boxes; - std::vector>>>> detect_boxes; - - GetBoxes(*in_label, *in_detect, >_boxes, detect_boxes); - - std::map label_pos_count; - std::map>> true_pos_map; - std::map>> false_pos_map; - - auto* has_state_p = has_state.get_ptr(); - int state = 0; - if (has_state_p != nullptr) { - state = has_state_p->data()[0]; - } - - if (in_pos_count != nullptr && state) { - GetInputPos(*in_pos_count, - *in_true_pos, - *in_false_pos, - &label_pos_count, - &true_pos_map, - &false_pos_map, - class_num); - } - - CalcTrueAndFalsePositive(gt_boxes, - detect_boxes, - evaluate_difficult, - overlap_threshold, - &label_pos_count, - &true_pos_map, - &false_pos_map); - - auto ap_type_enum = GetAPType(ap_type); - T map = CalcMAP(ap_type_enum, - label_pos_count, - true_pos_map, - false_pos_map, - background_label); - - GetOutputPos(dev_ctx, - label_pos_count, - true_pos_map, - false_pos_map, - out_pos_count, - out_true_pos, - out_false_pos, - class_num); - - T* map_data = dev_ctx.template Alloc(out_map); - map_data[0] = map; -} - -} // namespace phi -PD_REGISTER_KERNEL( - detection_map, CPU, ALL_LAYOUT, phi::DetectionMAPOpKernel, float, double) {} diff --git a/paddle/phi/kernels/detection_map_kernel.h b/paddle/phi/kernels/detection_map_kernel.h deleted file mode 100644 index 7f0b892de66789..00000000000000 --- a/paddle/phi/kernels/detection_map_kernel.h +++ /dev/null @@ -1,38 +0,0 @@ -// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#pragma once -#include "paddle/phi/backends/all_context.h" -#include "paddle/phi/core/dense_tensor.h" -namespace phi { - -template -void DetectionMAPOpKernel(const Context& dev_ctx, - const DenseTensor& detect_res, - const DenseTensor& label, - const paddle::optional& has_state, - const paddle::optional& pos_count, - const paddle::optional& true_pos, - const paddle::optional& false_pos, - int class_num, - int background_label, - float overlap_threshold, - bool evaluate_difficult, - const std::string& ap_type, - DenseTensor* accum_pos_count, - DenseTensor* accum_true_pos, - DenseTensor* accum_false_pos, - DenseTensor* m_ap); - -} // namespace phi diff --git a/paddle/phi/ops/yaml/op_compat.yaml b/paddle/phi/ops/yaml/op_compat.yaml index 0506bd4f7c51de..e187c1eeee4fae 100755 --- a/paddle/phi/ops/yaml/op_compat.yaml +++ b/paddle/phi/ops/yaml/op_compat.yaml @@ -4224,13 +4224,6 @@ outputs: out : Out -- op: detection_map - backward: detection_map_grad - inputs: - {detect_res : DetectRes, label : Label, has_state : HasState, pos_count : PosCount, true_pos : TruePos, false_pos : FalsePos} - outputs: - {accum_pos_count : AccumPosCount, accum_true_pos : AccumTruePos, accum_false_pos : AccumFalsePos, m_ap : MAP} - - op: dgc inputs: {u: U, v: V, grad: Grad} diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index a651956df126a1..3ebc02318276de 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -1388,21 +1388,6 @@ backward : det_grad interfaces : paddle::dialect::InferSymbolicShapeInterface -- op : detection_map - args: (Tensor detect_res, Tensor label, Tensor has_state, Tensor pos_count, Tensor - true_pos, Tensor false_pos, int class_num, int background_label = 0, float overlap_threshold - = .5f, bool evaluate_difficult = true, str ap_type = "integral") - output: Tensor (accum_pos_count), Tensor (accum_true_pos), Tensor (accum_false_pos), - Tensor (m_ap) - infer_meta: - func: DetectionMapInferMeta - kernel: - func: detection_map - data_type: detect_res - optional: has_state, pos_count, true_pos, false_pos - interfaces : paddle::dialect::InferSymbolicShapeInterface - traits : paddle::dialect::ForwardOnlyTrait - - op : dgc args : (Tensor u, Tensor v, Tensor grad, Tensor param, Tensor current_step, Tensor nranks, float m=0.9, bool use_nesterov=true, float[] sparsity={}, float rampup_begin_step=0.0, float rampup_step=0.0, float regular_coeff=0.0, int regular_type=0) output : Tensor(u_out), Tensor(v_out), Tensor(encode_grad), Tensor(grad_out), Tensor(k), Tensor(gather_buff) diff --git a/test/legacy_test/CMakeLists.txt b/test/legacy_test/CMakeLists.txt index bc7bb48b1b8aa8..844724820fa2a5 100644 --- a/test/legacy_test/CMakeLists.txt +++ b/test/legacy_test/CMakeLists.txt @@ -233,10 +233,8 @@ if(APPLE) endif() message( WARNING - "These tests has been disabled in OSX before being fixed: \n test_detection_map_op \n test_dist_se_resnext_*" + "These tests has been disabled in OSX before being fixed: \n test_dist_se_resnext_*" ) - # this op is not support on mac - list(REMOVE_ITEM TEST_OPS test_detection_map_op) endif() if(NOT WITH_MKL OR NOT WITH_AVX) diff --git a/test/legacy_test/test_detection_map_op.py b/test/legacy_test/test_detection_map_op.py deleted file mode 100644 index 376b9876cd46a9..00000000000000 --- a/test/legacy_test/test_detection_map_op.py +++ /dev/null @@ -1,360 +0,0 @@ -# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved. -# -# Licensed under the Apache License, Version 2.0 (the "License"); -# you may not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# http://www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an "AS IS" BASIS, -# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. - -import collections -import math -import unittest - -import numpy as np -from op_test import OpTest - - -class TestDetectionMAPOp(OpTest): - def set_data(self): - self.class_num = 4 - self.init_test_case() - self.mAP = [self.calc_map(self.tf_pos, self.tf_pos_lod)] - self.label = np.array(self.label).astype('float32') - self.detect = np.array(self.detect).astype('float32') - self.mAP = np.array(self.mAP).astype('float32') - - if len(self.class_pos_count) > 0: - self.class_pos_count = np.array(self.class_pos_count).astype( - 'int32' - ) - self.true_pos = np.array(self.true_pos).astype('float32') - self.false_pos = np.array(self.false_pos).astype('float32') - self.has_state = np.array([1]).astype('int32') - - self.inputs = { - 'Label': (self.label, self.label_lod), - 'DetectRes': (self.detect, self.detect_lod), - 'HasState': self.has_state, - 'PosCount': self.class_pos_count, - 'TruePos': (self.true_pos, self.true_pos_lod), - 'FalsePos': (self.false_pos, self.false_pos_lod), - } - else: - self.inputs = { - 'Label': (self.label, self.label_lod), - 'DetectRes': (self.detect, self.detect_lod), - } - - self.attrs = { - 'overlap_threshold': self.overlap_threshold, - 'evaluate_difficult': self.evaluate_difficult, - 'ap_type': self.ap_type, - 'class_num': self.class_num, - } - - self.out_class_pos_count = np.array(self.out_class_pos_count).astype( - 'int' - ) - self.out_true_pos = np.array(self.out_true_pos).astype('float32') - self.out_false_pos = np.array(self.out_false_pos).astype('float32') - - self.outputs = { - 'MAP': self.mAP, - 'AccumPosCount': self.out_class_pos_count, - 'AccumTruePos': (self.out_true_pos, self.out_true_pos_lod), - 'AccumFalsePos': (self.out_false_pos, self.out_false_pos_lod), - } - - def init_test_case(self): - self.overlap_threshold = 0.3 - self.evaluate_difficult = True - self.ap_type = "integral" - - self.label_lod = [[2, 2]] - # label difficult xmin ymin xmax ymax - self.label = [ - [1, 0, 0.1, 0.1, 0.3, 0.3], - [1, 1, 0.6, 0.6, 0.8, 0.8], - [2, 0, 0.3, 0.3, 0.6, 0.5], - [1, 0, 0.7, 0.1, 0.9, 0.3], - ] - - # label score xmin ymin xmax ymax difficult - self.detect_lod = [[3, 4]] - self.detect = [ - [1, 0.3, 0.1, 0.0, 0.4, 0.3], - [1, 0.7, 0.0, 0.1, 0.2, 0.3], - [1, 0.9, 0.7, 0.6, 0.8, 0.8], - [2, 0.8, 0.2, 0.1, 0.4, 0.4], - [2, 0.1, 0.4, 0.3, 0.7, 0.5], - [1, 0.2, 0.8, 0.1, 1.0, 0.3], - [3, 0.2, 0.8, 0.1, 1.0, 0.3], - ] - - # label score true_pos false_pos - self.tf_pos_lod = [[3, 4]] - self.tf_pos = [ - [1, 0.9, 1, 0], - [1, 0.7, 1, 0], - [1, 0.3, 0, 1], - [1, 0.2, 1, 0], - [2, 0.8, 0, 1], - [2, 0.1, 1, 0], - [3, 0.2, 0, 1], - ] - - self.class_pos_count = [] - self.true_pos_lod = [[]] - self.true_pos = [[]] - self.false_pos_lod = [[]] - self.false_pos = [[]] - - def calc_map(self, tf_pos, tf_pos_lod): - mAP = 0.0 - count = 0 - - def get_input_pos( - class_pos_count, true_pos, true_pos_lod, false_pos, false_pos_lod - ): - class_pos_count_dict = collections.Counter() - true_pos_dict = collections.defaultdict(list) - false_pos_dict = collections.defaultdict(list) - for i, count in enumerate(class_pos_count): - class_pos_count_dict[i] = count - - cur_pos = 0 - for i in range(len(true_pos_lod[0])): - start = cur_pos - cur_pos += true_pos_lod[0][i] - end = cur_pos - for j in range(start, end): - true_pos_dict[i].append(true_pos[j]) - - cur_pos = 0 - for i in range(len(false_pos_lod[0])): - start = cur_pos - cur_pos += false_pos_lod[0][i] - end = cur_pos - for j in range(start, end): - false_pos_dict[i].append(false_pos[j]) - - return class_pos_count_dict, true_pos_dict, false_pos_dict - - def get_output_pos(label_count, true_pos, false_pos): - label_number = self.class_num - - out_class_pos_count = [] - out_true_pos_lod = [] - out_true_pos = [] - out_false_pos_lod = [] - out_false_pos = [] - - for i in range(label_number): - out_class_pos_count.append([label_count[i]]) - true_pos_list = true_pos[i] - out_true_pos += true_pos_list - out_true_pos_lod.append(len(true_pos_list)) - false_pos_list = false_pos[i] - out_false_pos += false_pos_list - out_false_pos_lod.append(len(false_pos_list)) - - return ( - out_class_pos_count, - out_true_pos, - [out_true_pos_lod], - out_false_pos, - [out_false_pos_lod], - ) - - def get_accumulation(pos_list): - sorted_list = sorted(pos_list, key=lambda pos: pos[0], reverse=True) - sum = 0 - accu_list = [] - for score, count in sorted_list: - sum += count - accu_list.append(sum) - return accu_list - - label_count, true_pos, false_pos = get_input_pos( - self.class_pos_count, - self.true_pos, - self.true_pos_lod, - self.false_pos, - self.false_pos_lod, - ) - for v in self.label: - label = v[0] - difficult = False if len(v) == 5 else v[1] - if self.evaluate_difficult: - label_count[label] += 1 - elif not difficult: - label_count[label] += 1 - - for label, score, tp, fp in tf_pos: - true_pos[label].append([score, tp]) - false_pos[label].append([score, fp]) - - for label, label_pos_num in label_count.items(): - if label_pos_num == 0: - continue - if label not in true_pos: - count += 1 - continue - label_true_pos = true_pos[label] - label_false_pos = false_pos[label] - - accu_tp_sum = get_accumulation(label_true_pos) - accu_fp_sum = get_accumulation(label_false_pos) - - precision = [] - recall = [] - - for i in range(len(accu_tp_sum)): - precision.append( - float(accu_tp_sum[i]) - / float(accu_tp_sum[i] + accu_fp_sum[i]) - ) - recall.append(float(accu_tp_sum[i]) / label_pos_num) - - if self.ap_type == "11point": - max_precisions = [0.0] * 11 - start_idx = len(accu_tp_sum) - 1 - for j in range(10, -1, -1): - for i in range(start_idx, -1, -1): - if recall[i] < float(j) / 10.0: - start_idx = i - if j > 0: - max_precisions[j - 1] = max_precisions[j] - break - else: - if max_precisions[j] < precision[i]: - max_precisions[j] = precision[i] - for j in range(10, -1, -1): - mAP += max_precisions[j] / 11 - count += 1 - elif self.ap_type == "integral": - average_precisions = 0.0 - prev_recall = 0.0 - for i in range(len(accu_tp_sum)): - if math.fabs(recall[i] - prev_recall) > 1e-6: - average_precisions += precision[i] * math.fabs( - recall[i] - prev_recall - ) - prev_recall = recall[i] - - mAP += average_precisions - count += 1 - pcnt, tp, tp_lod, fp, fp_lod = get_output_pos( - label_count, true_pos, false_pos - ) - self.out_class_pos_count = pcnt - self.out_true_pos = tp - self.out_true_pos_lod = tp_lod - self.out_false_pos = fp - self.out_false_pos_lod = fp_lod - if count != 0: - mAP /= count - return mAP - - def setUp(self): - self.op_type = "detection_map" - self.set_data() - - def test_check_output(self): - # NODE(yjjiang11): This op will be deprecated. - self.check_output(check_dygraph=False) - - -class TestDetectionMAPOpSkipDiff(TestDetectionMAPOp): - def init_test_case(self): - super().init_test_case() - - self.evaluate_difficult = False - - self.tf_pos_lod = [[2, 4]] - # label score true_pos false_pos - self.tf_pos = [ - [1, 0.7, 1, 0], - [1, 0.3, 0, 1], - [1, 0.2, 1, 0], - [2, 0.8, 0, 1], - [2, 0.1, 1, 0], - [3, 0.2, 0, 1], - ] - - -class TestDetectionMAPOpWithoutDiff(TestDetectionMAPOp): - def init_test_case(self): - super().init_test_case() - - # label xmin ymin xmax ymax - self.label = [ - [1, 0.1, 0.1, 0.3, 0.3], - [1, 0.6, 0.6, 0.8, 0.8], - [2, 0.3, 0.3, 0.6, 0.5], - [1, 0.7, 0.1, 0.9, 0.3], - ] - - -class TestDetectionMAPOp11Point(TestDetectionMAPOp): - def init_test_case(self): - super().init_test_case() - - self.ap_type = "11point" - - -class TestDetectionMAPOpMultiBatch(TestDetectionMAPOp): - def init_test_case(self): - super().init_test_case() - self.class_pos_count = [0, 2, 1, 0] - self.true_pos_lod = [[0, 3, 2]] - self.true_pos = [ - [0.7, 1.0], - [0.3, 0.0], - [0.2, 1.0], - [0.8, 0.0], - [0.1, 1.0], - ] - self.false_pos_lod = [[0, 3, 2]] - self.false_pos = [ - [0.7, 0.0], - [0.3, 1.0], - [0.2, 0.0], - [0.8, 1.0], - [0.1, 0.0], - ] - - -class TestDetectionMAPOp11PointWithClassNoTP(TestDetectionMAPOp): - def init_test_case(self): - self.overlap_threshold = 0.3 - self.evaluate_difficult = True - self.ap_type = "11point" - - self.label_lod = [[2]] - # label difficult xmin ymin xmax ymax - self.label = [[2, 0, 0.3, 0.3, 0.6, 0.5], [1, 0, 0.7, 0.1, 0.9, 0.3]] - - # label score xmin ymin xmax ymax difficult - self.detect_lod = [[1]] - self.detect = [[1, 0.2, 0.8, 0.1, 1.0, 0.3]] - - # label score true_pos false_pos - self.tf_pos_lod = [[3, 4]] - self.tf_pos = [[1, 0.2, 1, 0]] - - self.class_pos_count = [] - self.true_pos_lod = [[]] - self.true_pos = [[]] - self.false_pos_lod = [[]] - self.false_pos = [[]] - - -if __name__ == '__main__': - unittest.main() From 34c353809cd6d40221b8c7a732b1725eab7fbc3f Mon Sep 17 00:00:00 2001 From: Ruibiao Chen Date: Tue, 3 Dec 2024 14:56:36 +0800 Subject: [PATCH 118/288] Fix vpp warmup step bug (#69894) --- .../passes/pipeline_scheduler_pass/pipeline_vpp.py | 9 ++++++--- 1 file changed, 6 insertions(+), 3 deletions(-) diff --git a/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py b/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py index 711943f54bcb2b..15495de3cd635d 100644 --- a/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py +++ b/python/paddle/distributed/passes/pipeline_scheduler_pass/pipeline_vpp.py @@ -84,9 +84,12 @@ def _get_virtual_pp_rank(micro_step, forward): return virtual_pp_stage total_num_steps = accumulate_steps * num_model_chunks - warmup_steps = (num_stages - stage_id - 1) * 2 - warmup_steps += (num_model_chunks - 1) * num_stages - warmup_steps = min(warmup_steps, total_num_steps) + if accumulate_steps == num_stages: + warmup_steps = total_num_steps + else: + warmup_steps = (num_stages - stage_id - 1) * 2 + warmup_steps += (num_model_chunks - 1) * num_stages + warmup_steps = min(warmup_steps, total_num_steps) steady_steps = total_num_steps - warmup_steps real_split_backward = ( From 16dbf479ac273183ade95ce7c7370fc9462bbfd2 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Tue, 3 Dec 2024 15:26:47 +0800 Subject: [PATCH 119/288] [CINN] Add InferSymbolicShape Interface for `add_n_array` op (#69698) * add add_n_array op * add notes * fix compile * update * refine logic * refine logic * revert tmp test * apply review * revert * apply review --- .../pir/dialect/operator/ir/manual_op.cc | 28 ++++++++++ .../fluid/pir/dialect/operator/ir/manual_op.h | 9 ++-- .../pir/dialect/operator/ir/op_dialect.cc | 52 +++++++++++++------ 3 files changed, 71 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc index 6b8fbaaf105a15..b232c1b79a41d0 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.cc @@ -592,6 +592,27 @@ std::vector AddNArrayOp::InferMeta( return argument_outputs; } +bool AddNArrayOp::InferSymbolicShape( + pir::InferSymbolicShapeContext *infer_context) { + // The inputs for add_n_array op is defined by builtin.combine. + // We use the combine op's inputs to infer the output shape. + pir::CombineOp combine_op = + inputs().defining_op()->dyn_cast(); + // Try to get the infer result as much as possible. + for (size_t i = 0; i < combine_op.num_operands(); i++) { + if (infer_context->HasShapeOrDataForValue(combine_op.operand_source(i))) { + auto out_shape_or_data = + infer_context->GetShapeOrDataForValue(combine_op.operand_source(i)) + .dyn_cast(); + infer_context->SetShapeOrDataForValue( + out(), symbol::ShapeOrDataDimExprs{out_shape_or_data}); + return true; + } + } + PADDLE_THROW(common::errors::InvalidArgument( + "At least one operand of CombineOp should have shape or data.")); +} + const char *FusedGemmEpilogueOp::attributes_name[3] = { // NOLINT "trans_x", "trans_y", @@ -1495,6 +1516,7 @@ std::vector CreateArrayOp::InferMeta( bool CreateArrayOp::InferSymbolicShape( pir::InferSymbolicShapeContext *infer_context) { + // TODO(ooooo): Try to use output type's dims to decide. infer_context->SetShapeOrDataForValue( out(), symbol::ShapeOrDataDimExprs{symbol::RankedTensorArrayShapeOrDataDimExprs( @@ -2167,6 +2189,12 @@ bool ArrayWrite_Op::InferSymbolicShape( out(), symbol::ShapeOrDataDimExprs{ symbol::RankedTensorArrayShapeOrDataDimExprs(x_shape)}); + // update array's shape as x's shape. + // TOOD(ooooo) Do not change if shape is set by custom, similar to infer_meta + infer_context->SetShapeOrDataForValue( + array(), + symbol::ShapeOrDataDimExprs{ + symbol::RankedTensorArrayShapeOrDataDimExprs(x_shape)}); return true; } diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op.h b/paddle/fluid/pir/dialect/operator/ir/manual_op.h index 41fac710e28e21..69ab21519d5719 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op.h +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op.h @@ -61,9 +61,11 @@ class TEST_API AddN_Op bool InferSymbolicShape(pir::InferSymbolicShapeContext *infer_context); }; -class AddNArrayOp : public pir::Op { +class AddNArrayOp + : public pir::Op { public: using Op::Op; static const char *name() { return "pd_op.add_n_array"; } @@ -82,6 +84,7 @@ class AddNArrayOp : public pir::Op InferMeta( const std::vector &input_values, pir::AttributeMap *p_attributes); + bool InferSymbolicShape(pir::InferSymbolicShapeContext *infer_context); }; class FusedGemmEpilogueOp : public pir::Opoperand(0).type().dyn_cast()) { + const auto shape_data_list = [&] { + symbol::TensorListShapeOrDataDimExprs shape_data_list; + for (size_t i = 0; i < op->num_operands(); ++i) { + PADDLE_ENFORCE_NOT_NULL( + op->operand(i).type().dyn_cast(), + common::errors::InvalidArgument( + "The operand at index %d must be a DenseTensorArray. " + "Currently InferSymbolicShape of CombineOp only accepts " + "inputs that are either all DenseTensors or all " + "DenseTensorArrays.", + i)); + shape_data_list.emplace_back( + infer_context->GetShapeOrDataForValue(op->operand_source(i)) + .dyn_cast()); + } + return shape_data_list; + }(); + symbol::ShapeOrDataDimExprs shape_data{shape_data_list}; + infer_context->SetShapeOrDataForValue(op->result(0), shape_data); + return true; + } else if (op->operand(0).type().dyn_cast()) { + // Note: Return NullShapeOrDataDimExpr for CombineOp with all + // DenseTensorArrayType. The logic is designed for add_n_array op. + // TODO(ooooo): Actually RankedTensorArrayListShapeOrDataDimExprs is + // better. for (size_t i = 0; i < op->num_operands(); ++i) { PADDLE_ENFORCE_NOT_NULL( - op->operand(i).type().dyn_cast(), + op->operand(i).type().dyn_cast(), common::errors::InvalidArgument( - "Currently InferSymbolicShape of CombineOp only support " - "DenseTensorType.")); - - shape_data_list.emplace_back( - infer_context->GetShapeOrDataForValue(op->operand_source(i)) - .dyn_cast()); + "The operand at index %d must be a DenseTensorArray. Currently " + "InferSymbolicShape of CombineOp only accepts inputs that are " + "either all DenseTensors or all DenseTensorArrays.", + i)); } - return shape_data_list; - }(); - - symbol::ShapeOrDataDimExprs shape_data{shape_data_list}; - infer_context->SetShapeOrDataForValue(op->result(0), shape_data); - return true; + return true; + } else { + PADDLE_THROW(common::errors::InvalidArgument( + "Currently InferSymbolicShape of CombineOp only accepts " + "inputs that are either all DenseTensors or all DenseTensorArrays.")); + } } CombineOpInferSymbolicShapeInterfaceModel() From 1dcab8211ba5270eb9f435279e8a641e910f0aef Mon Sep 17 00:00:00 2001 From: xuxinyi389 <104957571+xuxinyi389@users.noreply.github.com> Date: Tue, 3 Dec 2024 15:28:46 +0800 Subject: [PATCH 120/288] Support PyLayer's output as None (#69674) * tmp * fix_range * fix_test_part_I * support_none * polish --- .../control_flow/pylayer_instruction.cc | 7 ++++--- .../control_flow/yield_instruction.cc | 11 ++++++++--- .../pir/dialect/operator/ir/manual_pylayer_op.cc | 9 ++++++--- .../fluid/pir/transforms/pd_op_to_kernel_pass.cc | 8 ++++++-- paddle/fluid/pybind/pybind.cc | 12 +++++------- paddle/fluid/pybind/python_callable_registry.cc | 16 +++++++++++----- python/paddle/autograd/ir_backward.py | 6 ------ test/dygraph_to_static/test_pylayer.py | 14 +++----------- 8 files changed, 43 insertions(+), 40 deletions(-) diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc index f8cecafa312414..b5fdbf17da90c6 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc @@ -58,10 +58,11 @@ PyLayerInstruction::PyLayerInstruction( SetKernelType(AnalyseOpFuncType(op, place)); VLOG(6) << "finish process analyse kernel type"; - for (size_t i = 0; i < pylayer_op.num_results(); ++i) { - output_vars_.push_back(value_exec_info->GetScope()->GetVar( - value_exec_info->GetValue2VarName().at(pylayer_op.result(i)))); + if (pylayer_op.result(i) && pylayer_op.result(i).type()) { + output_vars_.push_back(value_exec_info->GetScope()->GetVar( + value_exec_info->GetValue2VarName().at(pylayer_op.result(i)))); + } } VLOG(6) << "finish process output_vars"; diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/yield_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/yield_instruction.cc index 8b2d7e6e355d7e..3b8c23f7cb2c2d 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/yield_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/yield_instruction.cc @@ -38,13 +38,18 @@ YieldInstruction::YieldInstruction(size_t id, continue; } auto in = op->operand_source(i); - inputs.emplace(in, GetValueIds(in, *value_exe_info)); - input_vars_.push_back(value_exe_info->GetVarByValue(in)); + if (in && in.type()) { + inputs.emplace(in, GetValueIds(in, *value_exe_info)); + input_vars_.push_back(value_exe_info->GetVarByValue(in)); + } } SetInputs(inputs); for (size_t i = 0; i < parent_op->num_results(); ++i) { - output_vars_.push_back(value_exe_info->GetVarByValue(parent_op->result(i))); + if (parent_op->result(i) && parent_op->result(i).type()) { + output_vars_.push_back( + value_exe_info->GetVarByValue(parent_op->result(i))); + } } PADDLE_ENFORCE_EQ( diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc index e6a9f06a3ed302..3a85b535a4e0e1 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc @@ -84,14 +84,17 @@ void PyLayerOp::Build(pir::Builder &builder, // NOLINT auto &op = fwd_block->back(); - std::vector outs_stop_gradient; + auto outs_stop_gradient_attr = true; for (size_t i = 0; i < op.num_operands(); ++i) { argument.AddOutput(op.operand(i).type()); auto bool_attr = op.operand_source(i).attribute( pir::kStopGradientAttrName); - outs_stop_gradient.push_back(bool_attr ? bool_attr - : builder.bool_attr(false)); + if (!bool_attr || (bool_attr && !bool_attr.data())) { + outs_stop_gradient_attr = false; + } } + std::vector outs_stop_gradient( + op.num_operands(), builder.bool_attr(outs_stop_gradient_attr)); argument.AddAttribute( kBackwardFunctionIdAttrName, diff --git a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc index 84da8212c66290..0b0326e1b31b5e 100644 --- a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc +++ b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc @@ -1524,8 +1524,12 @@ void HandleForPyLayerOp( auto old_pylayerop = op_item->dyn_cast(); std::vector new_pylayerop_outputs; for (size_t i = 0; i < old_pylayerop.num_results(); ++i) { - new_pylayerop_outputs.push_back( - ConvertOpTypeToKernelType(ctx, old_pylayerop.result(i).type(), place)); + if (!static_cast(old_pylayerop.result(i).type())) { + new_pylayerop_outputs.push_back(old_pylayerop.result(i).type()); + } else { + new_pylayerop_outputs.push_back(ConvertOpTypeToKernelType( + ctx, old_pylayerop.result(i).type(), place)); + } } // Create PyLayerOp and insert to kernel dialect program diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 18b0761ae1705a..5b4fbc7041c014 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -755,10 +755,11 @@ static std::vector> GenerateBackwardBlockForPyLayerOp( // 1. construct pylayer grad op VLOG(6) << "Prepare Outputs for pylayer_grad"; std::vector output_types; + // NOTE: the last input of pylayer op is create_stack when called + // save_for_backward, whose stop_gradient is always True for (size_t i = 0; i < inputs_.size(); ++i) { - if (!stop_gradients[i][0]) { - output_types.push_back(inputs_[i][0].type()); - } + if (inputs_[i][0].type().isa()) break; + output_types.push_back(inputs_[i][0].type()); } VLOG(6) << "Prepare Inputs for pylayer_grad"; @@ -837,12 +838,9 @@ static std::vector> GenerateBackwardBlockForPyLayerOp( VLOG(6) << "Update pylayer_grad op finished"; std::vector> res{inputs_.size()}; - int grad_op_result_index = 0; for (size_t i = 0; i < res.size(); ++i) { res[i].resize(1); - res[i][0] = !stop_gradients[i][0] - ? pylayer_grad->result(grad_op_result_index++) - : pir::Value(); + res[i][0] = !stop_gradients[i][0] ? pylayer_grad->result(i) : pir::Value(); } return res; } diff --git a/paddle/fluid/pybind/python_callable_registry.cc b/paddle/fluid/pybind/python_callable_registry.cc index efe0629a515a85..fc6207b0410941 100644 --- a/paddle/fluid/pybind/python_callable_registry.cc +++ b/paddle/fluid/pybind/python_callable_registry.cc @@ -69,11 +69,17 @@ void PirCallPythonFunc(py::object *callable, for (size_t i = 0; i < out_num; ++i) { try { - auto py_out_value = py::cast(ret_tuple[i]); - PADDLE_ENFORCE_NOT_NULL(py_out_value.impl(), - common::errors::InvalidArgument( - "Output value %d should not be nullptr", i)); - (*outs)[i] = py_out_value; + if (ret_tuple[i].is_none()) { + VLOG(6) << "Set Output( " << i << " ) value as fake_value"; + (*outs)[i] = pir::Value(nullptr); + } else { + auto py_out_value = py::cast(ret_tuple[i]); + PADDLE_ENFORCE_NOT_NULL( + py_out_value.impl(), + common::errors::InvalidArgument( + "Output value %d should not be nullptr", i)); + (*outs)[i] = py_out_value; + } } catch (py::cast_error &) { PADDLE_THROW(common::errors::InvalidArgument( "pybind11::cast to pir::Value error. The %d-th output exception is " diff --git a/python/paddle/autograd/ir_backward.py b/python/paddle/autograd/ir_backward.py index d1f2774bb42543..810abfdac4be25 100644 --- a/python/paddle/autograd/ir_backward.py +++ b/python/paddle/autograd/ir_backward.py @@ -873,12 +873,6 @@ def append_yield( # create grad_op before_ops_num = len(bwd_block.ops) - # TODO(MarioLulab): `PyLayer.backward` has not supported return `None` yet. Will be supported soon. - if any(zero_flag): - raise ValueError( - "pylayer_op.backward have not supported return `None` yet. Will be supported soon." - ) - with dynamic_shape_prim_vjp_guard(op, inputs): input_grads = paddle.framework.core.call_vjp( op, diff --git a/test/dygraph_to_static/test_pylayer.py b/test/dygraph_to_static/test_pylayer.py index 7f7724d5db2361..d5a00a34075aa5 100644 --- a/test/dygraph_to_static/test_pylayer.py +++ b/test/dygraph_to_static/test_pylayer.py @@ -108,7 +108,6 @@ def backward(ctx, dy): class cus_tanh_3(PyLayer): @staticmethod def forward(ctx, x1, x2, func1, func2=paddle.square): - ctx.func = func2 y1 = func1(x1) y2 = func1(x2) ctx.save_for_backward(y1, y2) @@ -117,7 +116,7 @@ def forward(ctx, x1, x2, func1, func2=paddle.square): @staticmethod def backward(ctx, dy1, dy2): y1, y2 = ctx.saved_tensor() - re1 = dy1 * (1 - ctx.func(y1)) + re1 = dy1 * (1 - paddle.square(y1)) re2 = dy2 * (1 - paddle.square(y2)) return re1, None @@ -510,15 +509,8 @@ def test_func(input1, input2): self.run_in_pir = False self._run_and_compare(input1, input2) - - # TODO(MarioLulab): pylayer_op.backward have not supported return `None` yet. Will be supported soon. - with self.assertRaises(Exception) as e: - self.run_in_pir = True - self._run_and_compare(input1, input2) - self.assertTrue( - "pylayer_op.backward have not supported return `None` yet. Will be supported soon." - in str(e.exception) - ) + self.run_in_pir = True + self._run_and_compare(input1, input2) def test_simple_pylayer_return_none(self): @paddle.jit.to_static(full_graph=True) From 458ff21b58a2336eb2cf2d6ece4138f664845860 Mon Sep 17 00:00:00 2001 From: risemeup1 <62429225+risemeup1@users.noreply.github.com> Date: Tue, 3 Dec 2024 17:23:11 +0800 Subject: [PATCH 121/288] fix bug (#69883) --- cmake/cinn.cmake | 1 + cmake/cinn/core.cmake | 4 ---- 2 files changed, 1 insertion(+), 4 deletions(-) diff --git a/cmake/cinn.cmake b/cmake/cinn.cmake index 46e673d50ccbf3..a3708676ffd5d6 100644 --- a/cmake/cinn.cmake +++ b/cmake/cinn.cmake @@ -160,6 +160,7 @@ cinn_cc_library( ${cinnapi_src} DEPS glog + python ${llvm_libs} param_proto auto_schedule_proto diff --git a/cmake/cinn/core.cmake b/cmake/cinn/core.cmake index 59aaf7266460b6..5df41f06dc6712 100644 --- a/cmake/cinn/core.cmake +++ b/cmake/cinn/core.cmake @@ -19,10 +19,6 @@ function(cinn_cc_library TARGET_NAME) endif() if(cinn_cc_library_DEPS) - if("${cinn_cc_library_DEPS};" MATCHES "python;") - list(REMOVE_ITEM cinn_cc_library_DEPS python) - add_dependencies(${TARGET_NAME} python) - endif() target_link_libraries(${TARGET_NAME} ${cinn_cc_library_DEPS}) add_dependencies(${TARGET_NAME} ${cinn_cc_library_DEPS}) endif() From 66f879776a4801a194febe66a9a12432b69cf01b Mon Sep 17 00:00:00 2001 From: Lucas Date: Tue, 3 Dec 2024 18:33:46 +0800 Subject: [PATCH 122/288] [XPU] Update xhpc to 1203 (#68740) --- cmake/external/xpu.cmake | 2 +- paddle/phi/kernels/xpu/flash_attn_grad_kernel.cc | 10 ++++++++-- paddle/phi/kernels/xpu/flash_attn_kernel.cc | 8 ++++++-- 3 files changed, 15 insertions(+), 5 deletions(-) diff --git a/cmake/external/xpu.cmake b/cmake/external/xpu.cmake index 78157615a5a68e..132f3145749306 100644 --- a/cmake/external/xpu.cmake +++ b/cmake/external/xpu.cmake @@ -30,7 +30,7 @@ set(XPU_XFA_LIB_NAME "libxpu_flash_attention.so") set(XPU_XPUDNN_LIB_NAME "libxpu_dnn.so") if(NOT DEFINED XPU_XHPC_BASE_DATE) - set(XPU_XHPC_BASE_DATE "dev/20241128") + set(XPU_XHPC_BASE_DATE "dev/20241203") endif() set(XPU_XCCL_BASE_VERSION "3.0.1.1") # For XRE5 if(NOT DEFINED XPU_XFT_BASE_VERSION) diff --git a/paddle/phi/kernels/xpu/flash_attn_grad_kernel.cc b/paddle/phi/kernels/xpu/flash_attn_grad_kernel.cc index 15c4c9d55ecc19..adefa40abe9ad5 100644 --- a/paddle/phi/kernels/xpu/flash_attn_grad_kernel.cc +++ b/paddle/phi/kernels/xpu/flash_attn_grad_kernel.cc @@ -143,7 +143,12 @@ void FlashAttnGradKernel(const Context& ctx, // const TACCUM* bias = nullptr, const float* q_maxptr = nullptr, const float* // k_maxptr = nullptr, const float* v_maxptr = nullptr, const float* o_maxptr // = nullptr, float* dq_maxptr = nullptr, float* dk_maxptr = nullptr, float* - // dv_maxptr = nullptr, const float* do_maxptr = nullptr); + // dv_maxptr = nullptr, const float* do_maxptr = nullptr, const bool + // is_qkv_fusion = false, const bool is_dqkv_fusion = false, const int64_t + // qkv_layout = AttnQKVLayout_t::ATTN_BLHD, const float* alibi_slopes = + // nullptr, const std::vector& alibi_slopes_shape = {}, int + // window_size_left = -1, int window_size_right = -1, int64_t v_head_dim = + // -1); int r = flash_attention_grad_kernel( ctx.x_context(), dout_data, // dout @@ -182,7 +187,8 @@ void FlashAttnGradKernel(const Context& ctx, nullptr, // alibi_slopes {}, // alibi_slopes_shape -1, // window_size_left - -1 // window_size_right + -1, // window_size_right + -1 // v_head_dim ); PADDLE_ENFORCE_XDNN_SUCCESS(r, "mha_varlen_bwd"); #else diff --git a/paddle/phi/kernels/xpu/flash_attn_kernel.cc b/paddle/phi/kernels/xpu/flash_attn_kernel.cc index 15634712821cca..bb756a16b9e71d 100644 --- a/paddle/phi/kernels/xpu/flash_attn_kernel.cc +++ b/paddle/phi/kernels/xpu/flash_attn_kernel.cc @@ -290,7 +290,10 @@ void FlashAttnKernel(const Context& ctx, // 0x45678901, const bool is_causal = true, const TACCUM* attn_mask = nullptr, // const TACCUM* bias = nullptr, const float* q_maxptr = nullptr, const float* // k_maxptr = nullptr, const float* v_maxptr = nullptr, float* o_maxptr = - // nullptr); + // nullptr, const bool is_qkv_fusion = false, const int64_t qkv_layout = + // AttnQKVLayout_t::ATTN_BLHD, const float* alibi_slopes = nullptr, const + // std::vector& alibi_slopes_shape = {}, int window_size_left = -1, + // int window_size_right = -1, int64_t v_head_dim = -1); int fa_tgemm = get_flash_attn_tgemm(); auto flash_attention_kernel = baidu::xpu::xfa::mha_varlen_fwd; @@ -330,7 +333,8 @@ void FlashAttnKernel(const Context& ctx, nullptr, // alibi_slopes {}, // alibi_slopes_shape -1, // window_size_left - -1 // window_size_right + -1, // window_size_right + -1 // v_head_dim ); PADDLE_ENFORCE_XDNN_SUCCESS(r, "mha_varlen_fwd"); #else From 87f63604e60633af02d7124b430d67805bb72fe6 Mon Sep 17 00:00:00 2001 From: linkk08 <124329195+linkk08@users.noreply.github.com> Date: Tue, 3 Dec 2024 19:00:23 +0800 Subject: [PATCH 123/288] add xpu backend (#69535) (#69537) --- .../paddle/incubate/jit/inference_decorator.py | 17 +++++++++++++++-- 1 file changed, 15 insertions(+), 2 deletions(-) diff --git a/python/paddle/incubate/jit/inference_decorator.py b/python/paddle/incubate/jit/inference_decorator.py index 5c7f3d74f772b9..db628bd6001480 100644 --- a/python/paddle/incubate/jit/inference_decorator.py +++ b/python/paddle/incubate/jit/inference_decorator.py @@ -25,6 +25,7 @@ from typing_extensions import ParamSpec import paddle +from paddle.base.framework import use_pir_api from paddle.inference import Config, PrecisionType, create_predictor from paddle.nn import Layer from paddle.static import InputSpec @@ -371,11 +372,14 @@ def get_input_tensor_lists(self, *args, **kwargs): # why we need input_tensor_lists? this is for TensorRT max/min/opt shape. def create_predictor(self, input_tensor_lists): # create predictor - model_file = os.path.join(self.save_model_dir, "infer.pdmodel") + if use_pir_api(): + model_file = os.path.join(self.save_model_dir, "infer.json") + else: + model_file = os.path.join(self.save_model_dir, "infer.pdmodel") params_file = os.path.join(self.save_model_dir, "infer.pdiparams") config = Config(model_file, params_file) - config.enable_memory_optim() + config.enable_memory_optim(False) config.switch_ir_debug(self.switch_ir_debug) config.switch_ir_optim(self.switch_ir_optim) if self.exp_enable_use_cutlass: @@ -392,6 +396,15 @@ def create_predictor(self, input_tensor_lists): gpu_id, get_inference_precision(self.precision_mode), ) + elif 'xpu' in device_num: + config.enable_xpu() + device_id = int(device_num.split(':')[1]) + config.set_xpu_device_id(device_id) + xpu_config = paddle.inference.XpuConfig() + xpu_config.device_id = device_id + xpu_config.l3_size = 0 + xpu_config.conv_autotune_level = 0 + config.set_xpu_config(xpu_config) if self.with_trt: dynamic_names = [] From 6043c7f382021b2ce1c1c0a9fd8153dedb18556c Mon Sep 17 00:00:00 2001 From: lizexu123 <39205361+lizexu123@users.noreply.github.com> Date: Tue, 3 Dec 2024 19:05:14 +0800 Subject: [PATCH 124/288] [Paddle TensorRT] fix pd_op.pool2d (#69864) * fix pool2d * fix --- .../transforms/tensorrt/trt_op_marker_pass.cc | 54 +++++ python/paddle/tensorrt/impls/pooling.py | 159 ++++++++++----- test/tensorrt/CMakeLists.txt | 2 +- test/tensorrt/test_converter_pooling.py | 185 ++++++++++++++++++ 4 files changed, 355 insertions(+), 45 deletions(-) create mode 100644 test/tensorrt/test_converter_pooling.py diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index 6f852b540311ea..8e2889f3e865de 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -331,6 +331,60 @@ class Pool2dOpPattern } } } + + auto ceil_mode = op->attribute("ceil_mode").data(); + auto global_pooling = + op->attribute("global_pooling").data(); + std::string padding_algorithm = + op->attribute("padding_algorithm").AsString(); + // TODO(Lizexu): The general plugin approach for entering TensorRT has not + // been supported yet. + auto adaptive = op->attribute("adaptive").data(); + if (adaptive) { + VLOG(3) + << "The adaptive is true pd_op.pool2d is not supported by trt now"; + return false; + } + // TODO(Lizexu): This piece of code exists in the old IR-TRT implementation + // but is not covered by unit tests, raising suspicions about its + // correctness. In the PIR-TRT implementation, following the same approach + // causes precision issues. For now, we will exclude it from entering + // TensorRT. + pir::Value input = op.operand_source(0); + auto kernel_size_attr = + full_int_array_op->attribute("value"); + std::vector kernel_size; + for (const auto &attr : kernel_size_attr.AsVector()) { + kernel_size.push_back(attr.dyn_cast().data()); + } + + auto input_type = input.type().dyn_cast(); + auto input_dims = input_type.dims(); + int g_post_pad_h = 0; + int g_post_pad_w = 0; + int input_height = input_dims[input_dims.size() - 2]; + int input_width = input_dims[input_dims.size() - 1]; + std::vector strides; + auto strides_attr = op->attribute("strides"); + for (const auto &attr : strides_attr.AsVector()) { + strides.push_back(attr.dyn_cast().data()); + } + if (input_height > 0 && + input_height - kernel_size[0] + 2 * paddings[0] < 0) { + g_post_pad_h = strides[0] - 1; + } + if (input_width > 0 && input_width - kernel_size[1] + 2 * paddings[1] < 0) { + g_post_pad_w = strides[1] - 1; + } + if (!adaptive && !global_pooling && !ceil_mode) { + if (padding_algorithm != "SAME" && + ((g_post_pad_h > 0 && input_height > 0) || + (g_post_pad_w > 0 && input_width > 0))) { + VLOG(3) << "The pool2d op meets the condition that may cause precision " + "issues in TRT. Skip TRT conversion."; + return false; + } + } op->set_attribute(kCanRunTrtAttr, rewriter.bool_attr(true)); return true; } diff --git a/python/paddle/tensorrt/impls/pooling.py b/python/paddle/tensorrt/impls/pooling.py index df7f7b1a5e0e96..33a28dae4ea71a 100644 --- a/python/paddle/tensorrt/impls/pooling.py +++ b/python/paddle/tensorrt/impls/pooling.py @@ -21,68 +21,139 @@ @converter_registry.register("pd_op.pool2d", trt_version="8.x") def pool2d_converter(network, paddle_op, inputs): input_tensor = inputs[0] - pooling_type = paddle_op.attrs().get("pooling_type", "max") - padding = paddle_op.attrs().get("paddings", [0, 0]) - stride = paddle_op.attrs().get("strides", [1, 1]) - ceil_mode = paddle_op.attrs().get("ceil_mode", False) - exclusive = paddle_op.attrs().get("exclusive") - adaptive = paddle_op.attrs().get("adaptive") - padding_algorithm = paddle_op.attrs().get("padding_algorithm") - input_shape = input_tensor.shape + input_shape = paddle_op.operands()[0].source().shape + input_dims = len(input_shape) + + global_pooling = paddle_op.attrs().get("global_pooling", False) + pool_type = paddle_op.attrs().get("pooling_type") + strides = paddle_op.attrs().get("strides") + paddings = paddle_op.attrs().get("paddings") + exclusive = paddle_op.attrs().get("exclusive", True) + ceil_mode = paddle_op.attrs().get("ceil_mode", False) + adaptive = paddle_op.attrs().get("adaptive", False) + padding_algorithm = paddle_op.attrs().get("padding_algorithm", "EXPLICIT") - # TODO attention for these codes if not paddle_op.attrs().get("kernel_size") and len(inputs) == 2: - # the size of pool2d inputs is 2, means kernel size is the second input. - # kernel_size_tensor = inputs[1] full_int_op = paddle_op.operands()[1].source().get_defining_op() if full_int_op.name() == "pd_op.full_int_array": kernel_size = full_int_op.attrs().get("value") else: raise Exception( - "the defining op of kernel size must be pd_op.full_int_array" + "The defining op of kernel size must be pd_op.full_int_array" ) else: kernel_size = paddle_op.attrs().get("kernel_size") - if len(stride) == 0 or stride[0] is None: - stride = kernel_size + nv_pool_type = trt.PoolingType.MAX + reduce_operation = trt.ReduceOperation.MAX + if pool_type == "max": + nv_pool_type = trt.PoolingType.MAX + reduce_operation = trt.ReduceOperation.MAX + elif pool_type == "avg": + nv_pool_type = trt.PoolingType.AVERAGE + reduce_operation = trt.ReduceOperation.AVG - if pooling_type == "max": - pooling_type = trt.PoolingType.MAX - elif pooling_type == "avg": - pooling_type = trt.PoolingType.AVERAGE - else: - raise ValueError(f"Unsupported pooling type: {pooling_type}") + if global_pooling or adaptive: + paddings = [0] * len(paddings) if padding_algorithm == "VALID": - padding = [0, 0] - - if adaptive: - output_size = kernel_size - stride = tuple(input_shape[-2 + i] // output_size[i] for i in range(2)) - kernel_size = tuple( - input_shape[-2 + i] - (output_size[i] - 1) * stride[i] - for i in range(2) + paddings = [0] * len(paddings) + + nv_paddings = trt.DimsHW(paddings[0], paddings[1]) + nv_ksize = trt.DimsHW(kernel_size[0], kernel_size[1]) + nv_strides = trt.DimsHW(strides[0], strides[1]) + + layer = None + g_pre_pad = trt.DimsHW(0, 0) + g_post_pad = trt.DimsHW(0, 0) + + if ( + input_shape[input_dims - 2] > 0 + and input_shape[input_dims - 2] - kernel_size[0] + 2 * paddings[0] < 0 + ): + g_post_pad.h = strides[0] - 1 + if ( + input_shape[input_dims - 1] > 0 + and input_shape[input_dims - 1] - kernel_size[1] + 2 * paddings[1] < 0 + ): + g_post_pad.w = strides[1] - 1 + + real_paddings = paddings.copy() + for i in range(2): + copy_pad = paddings[i] + real_paddings.insert(2 * i + 1, copy_pad) + + if padding_algorithm == "SAME": + for i in range(2): + copy_pad = paddings[2 * i] + paddings.insert(2 * i + 1, copy_pad) + + for i in range(2): + out_size = (input_shape[2 + i] + strides[i] - 1) // strides[i] + pad_sum = max( + (out_size - 1) * strides[i] + + kernel_size[i] + - input_shape[2 + i], + 0, + ) + pad_0 = pad_sum // 2 + pad_1 = pad_sum - pad_0 + paddings[2 * i] = pad_0 + paddings[2 * i + 1] = pad_1 + real_paddings = paddings.copy() + + paddings = [paddings[i] for i in range(len(paddings)) if i % 2 == 0] + + if padding_algorithm == "VALID": + read_paddings = [0] * len(real_paddings) + + if not adaptive and not global_pooling and not ceil_mode: + if padding_algorithm != "SAME" and ( + (g_post_pad.h > 0 and input_shape[input_dims - 2] > 0) + or (g_post_pad.w > 0 and input_shape[input_dims - 1] > 0) + ): + pad_layer = network.add_padding_nd( + input=input_tensor, + pre_padding=tuple(g_pre_pad), + post_padding=tuple(g_post_pad), + ) + input_tensor = pad_layer.get_output(0) + pooling_layer = network.add_pooling_nd( + input=input_tensor, type=nv_pool_type, window_size=nv_ksize ) + pooling_layer.stride_nd = nv_strides + pooling_layer.padding_nd = nv_paddings + pooling_layer.average_count_excludes_padding = exclusive + if padding_algorithm == "SAME": + pooling_layer.padding_mode = trt.PaddingMode.SAME_UPPER - pool_layer = network.add_pooling_nd( - input_tensor, pooling_type, window_size=kernel_size + layer = pooling_layer + elif not adaptive and not global_pooling and ceil_mode: + pooling_layer = network.add_pooling_nd( + input=input_tensor, type=nv_pool_type, window_size=nv_ksize + ) + pooling_layer.stride_nd = nv_strides + pooling_layer.padding_nd = nv_paddings + pooling_layer.average_count_excludes_padding = exclusive + if padding_algorithm == "SAME": + pooling_layer.padding_mode = trt.PaddingMode.SAME_UPPER + else: + pooling_layer.padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP + layer = pooling_layer + elif global_pooling and not adaptive: + reduce_axes = (1 << (input_dims - 2)) | (1 << (input_dims - 1)) + reduce_layer = network.add_reduce( + input=input_tensor, + op=reduce_operation, + axes=reduce_axes, + keep_dims=True, ) - pool_layer.stride_nd = stride - if pooling_type == "max": - pool_layer.padding_nd = padding + layer = reduce_layer else: - pool_layer = network.add_pooling( - input_tensor, pooling_type, window_size=kernel_size + raise NotImplementedError( + "The combination of attributes is not supported yet." ) - pool_layer.stride = stride - pool_layer.padding = padding - if exclusive: - pool_layer.average_count_excludes_padding = True - else: - pool_layer.average_count_excludes_padding = False - if ceil_mode: - pool_layer.padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP - return pool_layer.get_output(0) + output_tensor = layer.get_output(0) + return output_tensor diff --git a/test/tensorrt/CMakeLists.txt b/test/tensorrt/CMakeLists.txt index 087643f06fee64..ebb96be4f00b2b 100644 --- a/test/tensorrt/CMakeLists.txt +++ b/test/tensorrt/CMakeLists.txt @@ -28,5 +28,5 @@ if(NOT WIN32 AND TENSORRT_FOUND) set_tests_properties(test_converter_linalg PROPERTIES TIMEOUT "100") set_tests_properties(test_converter_search PROPERTIES TIMEOUT "300") set_tests_properties(test_converter_logic PROPERTIES TIMEOUT "300") - + set_tests_properties(test_converter_pooling PROPERTIES TIMEOUT "300") endif() diff --git a/test/tensorrt/test_converter_pooling.py b/test/tensorrt/test_converter_pooling.py new file mode 100644 index 00000000000000..e3191b5a6a4c1c --- /dev/null +++ b/test/tensorrt/test_converter_pooling.py @@ -0,0 +1,185 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from tensorrt_test_base import TensorRTBaseTest + +import paddle + + +def pool2d_api( + x, + ksize=[], + strides=[], + paddings=[], + ceil_mode=False, + exclusive=True, + data_format="NCHW", + pooling_type="max", + global_pooling=False, + adaptive=False, + padding_algorithm="EXPLICIT", +): + return paddle._C_ops.pool2d( + x, + ksize, + strides, + paddings, + ceil_mode, + exclusive, + data_format, + pooling_type, + global_pooling, + adaptive, + padding_algorithm, + ) + + +class TestPoolingTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.AvgPool2D(kernel_size=2, stride=1) + self.api_args = { + "x": np.random.randn(1, 1, 2, 3).astype("float32"), + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 2, 3]} + self.max_shape = {"x": [5, 1, 2, 3]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTCase1Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 1, 2, 3).astype("float32"), + "ksize": [2, 3], + "strides": [1, 2], + "paddings": [0, 0], + "ceil_mode": False, + "exclusive": False, + "data_format": "NCHW", + "pooling_type": "avg", + "global_pooling": False, + "adaptive": False, + "padding_algorithm": "VALID", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 2, 3]} + self.max_shape = {"x": [5, 1, 2, 3]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTCase2Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 1, 2, 3).astype("float32"), + "ksize": [2, 3], + "strides": [1, 2], + "paddings": [0, 0], + "ceil_mode": True, + "exclusive": True, + "data_format": "NCHW", + "pooling_type": "max", + "global_pooling": False, + "adaptive": False, + "padding_algorithm": "SAME", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 2, 3]} + self.max_shape = {"x": [5, 1, 2, 3]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTCase3Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 1, 2, 3).astype("float32"), + "ksize": [2, 3], + "strides": [1, 2], + "paddings": [0, 0], + "ceil_mode": True, + "exclusive": True, + "data_format": "NCHW", + "pooling_type": "max", + "global_pooling": True, + "adaptive": False, + "padding_algorithm": "SAME", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 2, 3]} + self.max_shape = {"x": [5, 1, 2, 3]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTCase4Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 1, 5, 5).astype("float32"), + "ksize": [3, 3], + "strides": [1, 1], + "paddings": [0, 0], + "ceil_mode": False, + "exclusive": False, + "data_format": "NCHW", + "pooling_type": "avg", + "global_pooling": True, + "adaptive": False, + "padding_algorithm": "SAME", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 5, 5]} + self.max_shape = {"x": [5, 1, 5, 5]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTMarker(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 3, 5, 5).astype("float32"), + "ksize": [6, 6], + "strides": [2, 2], + "paddings": [0, 0], + "ceil_mode": False, + "exclusive": False, + "data_format": "NCHW", + "pooling_type": "avg", + "global_pooling": False, + "adaptive": False, + "padding_algorithm": "EXPLICIT", + } + self.program_config = {"feed_list": ["x"]} + self.target_marker_op = "pd_op.pool2d" + + def test_trt_result(self): + self.check_marker(expected_result=False) + + +if __name__ == '__main__': + unittest.main() From cf5ce024baf289de035c6e9dd7c63ed2243c768f Mon Sep 17 00:00:00 2001 From: co63oc Date: Wed, 4 Dec 2024 09:28:21 +0800 Subject: [PATCH 125/288] [Lod][fluid_ops]Change lod_level to legacy_lod_level in framework.proto (#69893) * Fix * Fix * Fix * Fix * Fix --- paddle/fluid/framework/framework.proto | 4 +- paddle/fluid/framework/var_desc.cc | 71 ++++++++++++++--------- paddle/fluid/framework/var_desc.h | 9 +++ paddle/fluid/pybind/protobuf.cc | 6 ++ paddle/phi/core/framework/framework.proto | 4 +- 5 files changed, 64 insertions(+), 30 deletions(-) diff --git a/paddle/fluid/framework/framework.proto b/paddle/fluid/framework/framework.proto index f9f0210bd34ee6..efe8253c345ff4 100644 --- a/paddle/fluid/framework/framework.proto +++ b/paddle/fluid/framework/framework.proto @@ -197,13 +197,13 @@ message VarType { message DenseTensorDesc { required TensorDesc tensor = 1; - optional int32 lod_level = 2 [ default = 0 ]; + optional int32 legacy_lod_level = 2 [ default = 0 ]; } optional DenseTensorDesc dense_tensor = 3; message DenseTensorArrayDesc { required TensorDesc tensor = 1; - optional int32 lod_level = 2 [ default = 0 ]; + optional int32 legacy_lod_level = 2 [ default = 0 ]; } optional DenseTensorArrayDesc tensor_array = 4; diff --git a/paddle/fluid/framework/var_desc.cc b/paddle/fluid/framework/var_desc.cc index 497b1c636088ec..971c5949e65f95 100644 --- a/paddle/fluid/framework/var_desc.cc +++ b/paddle/fluid/framework/var_desc.cc @@ -159,77 +159,96 @@ std::vector VarDesc::GetDataTypes() const { return res; } -void VarDesc::SetLoDLevel(int32_t lod_level) { +void VarDesc::SetLegacyLoDLevel(int32_t legacy_lod_level) { switch (desc_.type().type()) { case proto::VarType::DENSE_TENSOR: - desc_.mutable_type()->mutable_dense_tensor()->set_lod_level(lod_level); + desc_.mutable_type()->mutable_dense_tensor()->set_legacy_lod_level( + legacy_lod_level); break; case proto::VarType::DENSE_TENSOR_ARRAY: - desc_.mutable_type()->mutable_tensor_array()->set_lod_level(lod_level); + desc_.mutable_type()->mutable_tensor_array()->set_legacy_lod_level( + legacy_lod_level); break; default: - PADDLE_THROW(common::errors::Unavailable( - "Setting 'lod_level' is not supported by the %s type variable.", - this->Name())); + PADDLE_THROW( + common::errors::Unavailable("Setting 'legacy_lod_level' is not " + "supported by the %s type variable.", + this->Name())); } need_updated_ = true; } -void VarDesc::SetLoDLevels(const std::vector &multiple_lod_level) { - if (multiple_lod_level.size() != GetTensorDescNum()) { - VLOG(3) << "WARNING: The number of given lod_levels(" - << multiple_lod_level.size() +void VarDesc::SetLegacyLoDLevels( + const std::vector &multiple_legacy_lod_level) { + if (multiple_legacy_lod_level.size() != GetTensorDescNum()) { + VLOG(3) << "WARNING: The number of given legacy_lod_levels(" + << multiple_legacy_lod_level.size() << ") doesn't match the existing tensor number(" << GetTensorDescNum() << "). The Reader is going to be reinitialized."; - SetTensorDescNum(multiple_lod_level.size()); + SetTensorDescNum(multiple_legacy_lod_level.size()); } switch (desc_.type().type()) { case proto::VarType::READER: { size_t i = 0; for (auto &dense_tensor : *desc_.mutable_type()->mutable_reader()->mutable_dense_tensor()) { - dense_tensor.set_lod_level(multiple_lod_level[i++]); + dense_tensor.set_legacy_lod_level(multiple_legacy_lod_level[i++]); } } break; default: - PADDLE_THROW(common::errors::Unavailable( - "Setting 'lod_levels' is not supported by the %s type variable", - this->Name())); + PADDLE_THROW( + common::errors::Unavailable("Setting 'legacy_lod_levels' is not " + "supported by the %s type variable", + this->Name())); } need_updated_ = true; } -int32_t VarDesc::GetLoDLevel() const { +int32_t VarDesc::GetLegacyLoDLevel() const { switch (desc_.type().type()) { case proto::VarType::DENSE_TENSOR: - return desc_.type().dense_tensor().lod_level(); + return desc_.type().dense_tensor().legacy_lod_level(); case proto::VarType::DENSE_TENSOR_ARRAY: - return desc_.type().tensor_array().lod_level(); + return desc_.type().tensor_array().legacy_lod_level(); default: - PADDLE_THROW(common::errors::Unavailable( - "Getting 'lod_level' is not supported by the %s type variable.", - this->Name())); + PADDLE_THROW( + common::errors::Unavailable("Getting 'legacy_lod_level' is not " + "supported by the %s type variable.", + this->Name())); } } -std::vector VarDesc::GetLoDLevels() const { +std::vector VarDesc::GetLegacyLoDLevels() const { std::vector res; switch (desc_.type().type()) { case proto::VarType::READER: res.reserve(desc_.type().reader().dense_tensor_size()); for (auto &dense_tensor : desc_.type().reader().dense_tensor()) { - res.push_back(dense_tensor.lod_level()); + res.push_back(dense_tensor.legacy_lod_level()); } return res; break; default: - PADDLE_THROW(common::errors::Unavailable( - "Getting 'lod_levels' is not supported by the %s type variable.", - this->Name())); + PADDLE_THROW( + common::errors::Unavailable("Getting 'legacy_lod_levels' is not " + "supported by the %s type variable.", + this->Name())); } } +void VarDesc::SetLoDLevel(int32_t lod_level) { SetLegacyLoDLevel(lod_level); } + +void VarDesc::SetLoDLevels(const std::vector &multiple_lod_level) { + SetLegacyLoDLevels(multiple_lod_level); +} + +int32_t VarDesc::GetLoDLevel() const { return GetLegacyLoDLevel(); } + +std::vector VarDesc::GetLoDLevels() const { + return GetLegacyLoDLevels(); +} + const proto::VarType::TensorDesc &VarDesc::tensor_desc() const { PADDLE_ENFORCE_EQ( desc_.has_type(), diff --git a/paddle/fluid/framework/var_desc.h b/paddle/fluid/framework/var_desc.h index 45629eeede9238..13af5f76d57664 100644 --- a/paddle/fluid/framework/var_desc.h +++ b/paddle/fluid/framework/var_desc.h @@ -135,6 +135,15 @@ class TEST_API VarDesc { std::vector GetLoDLevels() const; + void SetLegacyLoDLevel(int32_t legacy_lod_level); + + void SetLegacyLoDLevels( + const std::vector &multiple_legacy_lod_level); + + int32_t GetLegacyLoDLevel() const; + + std::vector GetLegacyLoDLevels() const; + proto::VarType::Type GetType() const; void SetType(proto::VarType::Type type); diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index 5076b7b3408237..1b5f649e8399d4 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -251,6 +251,12 @@ void BindVarDesc(pybind11::module *m) { pybind11::return_value_policy::reference) .def("set_lod_level", &pd::VarDesc::SetLoDLevel) .def("set_lod_levels", &pd::VarDesc::SetLoDLevels) + .def("legacy_lod_level", &pd::VarDesc::GetLegacyLoDLevel) + .def("legacy_lod_levels", + &pd::VarDesc::GetLegacyLoDLevels, + pybind11::return_value_policy::reference) + .def("set_legacy_lod_level", &pd::VarDesc::SetLegacyLoDLevel) + .def("set_legacy_lod_levels", &pd::VarDesc::SetLegacyLoDLevels) .def("type", &pd::VarDesc::GetType) .def("set_type", &pd::VarDesc::SetType) .def("serialize_to_string", SerializeMessage) diff --git a/paddle/phi/core/framework/framework.proto b/paddle/phi/core/framework/framework.proto index 4a27346ceb9f54..83f8f488cde08a 100644 --- a/paddle/phi/core/framework/framework.proto +++ b/paddle/phi/core/framework/framework.proto @@ -197,13 +197,13 @@ message VarType { message DenseTensorDesc { required TensorDesc tensor = 1; - optional int32 lod_level = 2 [ default = 0 ]; + optional int32 legacy_lod_level = 2 [ default = 0 ]; } optional DenseTensorDesc dense_tensor = 3; message DenseTensorArrayDesc { required TensorDesc tensor = 1; - optional int32 lod_level = 2 [ default = 0 ]; + optional int32 legacy_lod_level = 2 [ default = 0 ]; } optional DenseTensorArrayDesc tensor_array = 4; From 31ca1d998971072e185190981d90f480ca281013 Mon Sep 17 00:00:00 2001 From: cubehan3 Date: Wed, 4 Dec 2024 09:41:20 +0800 Subject: [PATCH 126/288] fix bugs in mean_grad (#69912) --- .../primitive/decomp_rule/decomp_vjp/details.h | 18 ++++++++++-------- 1 file changed, 10 insertions(+), 8 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index c79137ed004d30..5904cc118620a1 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -287,21 +287,23 @@ void mean_grad(const Tensor& x, axis_data.push_back(i); } } + + for (int64_t& idx : axis_data) { + if (idx < 0) { + idx += x_dim.size(); + } + } + if (has_dynamic_shape(x_dim, axis_data)) { auto x_shape = shape64(x); - factor_tensor = - slice(x_shape, {0}, {axis_data[0]}, {axis_data[0] + 1}, {1}, {0}); - for (size_t i = 1; i < axis_data.size(); ++i) { - factor_tensor = - factor_tensor * - slice( - x_shape, {0}, {axis_data[i]}, {axis_data[i] + 1}, {1}, {0}); + factor_tensor = full({1}, 1.0, x_shape.dtype(), x_shape.place()); + for (int64_t idx : axis_data) { + factor_tensor = factor_tensor * get_slice(x_shape, idx); } factor_tensor = cast(factor_tensor, x.dtype()); } else { int64_t factor = 1; for (int64_t idx : axis_data) { - if (idx < 0) idx += x_dim.size(); factor *= x_dim[idx]; } factor_tensor = From 33549df1fc75335c045b8d374b23551930e5340e Mon Sep 17 00:00:00 2001 From: cubehan3 Date: Wed, 4 Dec 2024 09:59:38 +0800 Subject: [PATCH 127/288] [Prim][Pir] Remove arange_with_tensor (#69873) * remove arange_with_tensor * polish eager prim file --- .../manual/manual_eager_prim_backend.cc | 10 +++++----- .../backend/manual/manual_prim_backend.h | 7 ------- .../manual/manual_static_prim_backend.cc | 18 ------------------ .../decomp_rule/decomp_rule/composite.h | 11 +++++------ .../primitive/decomp_rule/decomp_vjp/details.h | 2 +- 5 files changed, 11 insertions(+), 37 deletions(-) diff --git a/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc b/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc index 0a71b3f8e47d46..393ae879d34521 100644 --- a/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc +++ b/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc @@ -36,11 +36,11 @@ Tensor full(const IntArray& shape, } template <> -Tensor arange_with_tensor(const Tensor& start, - const Tensor& end, - const Tensor& step, - DataType dtype, - Place place) { +Tensor arange(const Tensor& start, + const Tensor& end, + const Tensor& step, + DataType dtype, + Place place) { VLOG(4) << "Eager Prim API arange_ad_func call"; return ::arange_ad_func(start, end, step, dtype, place); } diff --git a/paddle/fluid/primitive/backend/manual/manual_prim_backend.h b/paddle/fluid/primitive/backend/manual/manual_prim_backend.h index b62ca4a2dbb9d8..0dfc9c63b7d08c 100644 --- a/paddle/fluid/primitive/backend/manual/manual_prim_backend.h +++ b/paddle/fluid/primitive/backend/manual/manual_prim_backend.h @@ -35,13 +35,6 @@ Tensor full_with_tensor(const Tensor& shape, DataType dtype = DataType::FLOAT32, Place place = Place()); -template -Tensor arange_with_tensor(const Tensor& start, - const Tensor& end, - const Tensor& step, - DataType dtype = DataType::FLOAT64, - Place place = CPUPlace()); - } // namespace backend } // namespace primitive } // namespace paddle diff --git a/paddle/fluid/primitive/backend/manual/manual_static_prim_backend.cc b/paddle/fluid/primitive/backend/manual/manual_static_prim_backend.cc index d304203ba858cb..2f0ea6b2f0a403 100644 --- a/paddle/fluid/primitive/backend/manual/manual_static_prim_backend.cc +++ b/paddle/fluid/primitive/backend/manual/manual_static_prim_backend.cc @@ -48,24 +48,6 @@ Tensor full_with_tensor(const Tensor& shape, return out; } -template <> -Tensor arange_with_tensor(const Tensor& start, - const Tensor& end, - const Tensor& step, - DataType dtype, - Place place) { - pir::Value start_val = - std::static_pointer_cast(start.impl())->value(); - pir::Value end_val = - std::static_pointer_cast(end.impl())->value(); - pir::Value step_val = - std::static_pointer_cast(step.impl())->value(); - auto op_res = - paddle::dialect::arange(start_val, end_val, step_val, dtype, place); - Tensor out(std::make_shared(op_res)); - return out; -} - } // namespace backend } // namespace primitive } // namespace paddle diff --git a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h index 8f4f5aa5a51c46..8c690a4ece2bbb 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h @@ -183,8 +183,8 @@ template Tensor one_hot_decomp(const Tensor& x, const Tensor& num_classes) { auto start = full({1}, 0, x.dtype(), x.place()); auto step = full({1}, 1, x.dtype(), x.place()); - auto arange_class = backend::arange_with_tensor( - start, num_classes, step, x.dtype(), x.place()); + auto arange_class = + backend::arange(start, num_classes, step, x.dtype(), x.place()); auto reshape_x = backend::unsqueeze(x, {-1}); auto equal_res = backend::equal(reshape_x, arange_class); return cast(equal_res, phi::DataType::FLOAT32); @@ -1223,10 +1223,9 @@ Tensor index_sample_decomp(const Tensor& x, const Tensor& index) { auto index_dim = get_slice(shape64(index), 0); auto start = full({1}, 0, index_dim.dtype()); auto step = full({1}, 1, index_dim.dtype()); - auto arange_tmp = - reshape(backend::arange_with_tensor( - start, index_dim, step, index.dtype(), index.place()), - tmp_shape); + auto arange_tmp = reshape( + backend::arange(start, index_dim, step, index.dtype(), index.place()), + tmp_shape); auto index_res = reshape(backend::expand(arange_tmp, shape64(index)), tmp_shape); diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index 5904cc118620a1..97272c05c35dcb 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1256,7 +1256,7 @@ void masked_select_grad(const Tensor& x, auto end = full({1}, x_num, x.dtype(), x.place()); auto start = full({1}, 0, x.dtype(), x.place()); auto step = full({1}, 1, x.dtype(), x.place()); - auto x_arange = backend::arange_with_tensor( + auto x_arange = backend::arange( start, end, step, promoted_x.dtype(), promoted_x.place()); auto x_arange_reshape = reshape(x_arange, promoted_x.shape()); From 31bb97db099c563b16088c989eacc84229d86772 Mon Sep 17 00:00:00 2001 From: doggy-tao <3160391266@qq.com> Date: Wed, 4 Dec 2024 10:30:35 +0800 Subject: [PATCH 128/288] [Prim][PIR] Decomp eye op (#69838) * decomp eye op * replace arange_with_tensor with arange * add eye to the dynamic_shape_blacklist --- .../decomp_interface_gen_op_list.py | 2 ++ paddle/fluid/primitive/base/decomp_trans.cc | 2 +- .../decomp_rule/decomp_rule/composite.h | 23 +++++++++++++++++++ test/legacy_test/test_eye_op.py | 23 +++++++++++++++---- 4 files changed, 44 insertions(+), 6 deletions(-) diff --git a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py index 42148cdd16b3b7..ff860addad9709 100644 --- a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py +++ b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py @@ -28,6 +28,7 @@ "bmm", "clip", "dropout", + "eye", "elu", "embedding", "flatten", @@ -81,6 +82,7 @@ "bce_loss", "bmm", "dropout", + "eye", "elu", "embedding", "flatten", diff --git a/paddle/fluid/primitive/base/decomp_trans.cc b/paddle/fluid/primitive/base/decomp_trans.cc index 19a3e93cc27112..2e8f4c99888c52 100644 --- a/paddle/fluid/primitive/base/decomp_trans.cc +++ b/paddle/fluid/primitive/base/decomp_trans.cc @@ -51,7 +51,7 @@ std::unordered_set decomp_op_contain_none = { // std::unordered_set dynamic_shape_blacklist = { - "pd_op.squeeze", "pd_op.unsqueeze", "pd_op.flatten"}; + "pd_op.squeeze", "pd_op.unsqueeze", "pd_op.flatten", "pd_op.eye"}; namespace { std::set StringSplit(const std::string& str) { diff --git a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h index 8c690a4ece2bbb..2a670c3485719f 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h @@ -1412,6 +1412,29 @@ Tensor addmm_decomp(const Tensor& input, full_scalar(beta, input.dtype()) * input; } +template +Tensor eye_decomp(const paddle::Scalar& num_rows, + const paddle::Scalar& num_columns, + const DataType dtype, + const Place& place) { + int32_t min_num = std::min(num_rows.to(), num_columns.to()); + Tensor zero_tensor = + full({num_rows.to(), num_columns.to()}, 0, dtype, place); + auto zero_tensor_cast = ConverToMT(zero_tensor); + Tensor diag_one = unsqueeze(full({min_num}, 1, dtype, place), {1}); + auto diag_one_cast = ConverToMT(diag_one); + + auto start = full({1}, 0, dtype, place); + auto stop = full({1}, min_num, dtype, place); + auto step = full({1}, 1, dtype, place); + Tensor index = unsqueeze( + backend::arange(start, stop, step, DataType::INT32, place), {1}); + + auto index_cast = ConverToMT(index); + Tensor res = put_along_axis(zero_tensor_cast, index, diag_one_cast, 1); + + return ConverToOrig(res, dtype); +} } // namespace details } // namespace primitive diff --git a/test/legacy_test/test_eye_op.py b/test/legacy_test/test_eye_op.py index 02a36f4a630c89..92992296cf77fa 100644 --- a/test/legacy_test/test_eye_op.py +++ b/test/legacy_test/test_eye_op.py @@ -35,6 +35,8 @@ def setUp(self): ''' self.python_api = paddle.eye self.op_type = "eye" + self.prim_op_type = "comp" + self.public_python_api = paddle.eye self.init_dtype() self.init_attrs() @@ -49,7 +51,10 @@ def setUp(self): } def test_check_output(self): - self.check_output(check_pir=True) + if self.dtype == np.complex64 or self.dtype == np.complex128: + self.check_output(check_pir=True) + else: + self.check_output(check_pir=True, check_prim_pir=True) def init_dtype(self): self.dtype = np.int32 @@ -66,13 +71,15 @@ def setUp(self): ''' self.python_api = paddle.eye self.op_type = "eye" + self.prim_op_type = "comp" + self.public_python_api = paddle.eye self.inputs = {} self.attrs = {'num_rows': 50} self.outputs = {'Out': np.eye(50, dtype=float)} def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_prim_pir=True) class TestEyeOp2(OpTest): @@ -82,13 +89,15 @@ def setUp(self): ''' self.python_api = paddle.eye self.op_type = "eye" + self.prim_op_type = "comp" + self.public_python_api = paddle.eye self.inputs = {} self.attrs = {'num_rows': 99, 'num_columns': 1} self.outputs = {'Out': np.eye(99, 1, dtype=float)} def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_prim_pir=True) class TestEyeOp3(OpTest): @@ -98,13 +107,15 @@ def setUp(self): ''' self.python_api = paddle.eye self.op_type = "eye" + self.prim_op_type = "comp" + self.public_python_api = paddle.eye self.inputs = {} self.attrs = {'num_rows': np.int32(99), 'num_columns': np.int32(1)} self.outputs = {'Out': np.eye(99, 1, dtype=float)} def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_prim_pir=True) class API_TestTensorEye(unittest.TestCase): @@ -242,6 +253,8 @@ def setUp(self): self.op_type = "eye" self.dtype = np.uint16 self.python_api = paddle.eye + self.prim_op_type = "comp" + self.public_python_api = paddle.eye self.inputs = {} self.attrs = { 'num_rows': 219, @@ -251,7 +264,7 @@ def setUp(self): def test_check_output(self): place = core.CUDAPlace(0) - self.check_output_with_place(place, check_pir=True) + self.check_output_with_place(place, check_pir=True, check_prim_pir=True) if __name__ == "__main__": From 251dcfcaad90aaff1451455ffee6d04eba25f168 Mon Sep 17 00:00:00 2001 From: co63oc Date: Wed, 4 Dec 2024 10:31:00 +0800 Subject: [PATCH 129/288] [Lod][fluid_ops] Remove lod_level=0 in test/ (#69665) --- test/collective/fleet/test_dgc_optimizer.py | 8 +- .../ir/test_ir_fc_fuse_pass_deprecated.py | 2 +- ...reln_residual_bias_fuse_pass_deprecated.py | 16 +-- .../test_ir_skip_layernorm_pass_deprecated.py | 8 +- test/deprecated/legacy_test/dist_fleet_ctr.py | 1 - test/deprecated/legacy_test/test_dataset.py | 8 +- ...t_infer_no_need_buffer_slots_deprecated.py | 4 +- .../legacy_test/test_layers_deprecated.py | 4 +- .../legacy_test/test_optimizer_deprecated.py | 98 ++++++------------- .../test_regularizer_deprecated.py | 22 ++--- .../legacy_test/test_sgd_op_deprecated.py | 7 +- .../legacy_test/test_variable_deprecated.py | 2 +- test/legacy_test/dist_ctr.py | 1 - test/legacy_test/dist_fleet_ctr.py | 1 - .../dist_fleet_heter_pipeline_ctr.py | 1 - .../dist_fleet_sparse_embedding_ctr.py | 1 - test/legacy_test/fleet_heter_ps_training.py | 1 - test/legacy_test/test_boxps.py | 8 +- .../test_dataset_consistency_inspection.py | 1 - test/legacy_test/test_debugger.py | 12 +-- .../test_dist_fleet_sparse_embedding_ctr.py | 1 - .../test_imperative_ocr_attention_model.py | 4 +- test/legacy_test/test_initializer.py | 22 ----- test/legacy_test/test_initializer_nn.py | 16 --- test/legacy_test/test_operator_desc.py | 26 ++--- .../legacy_test/test_optimizer_for_varbase.py | 2 - test/legacy_test/test_variable.py | 12 +-- 27 files changed, 75 insertions(+), 214 deletions(-) diff --git a/test/collective/fleet/test_dgc_optimizer.py b/test/collective/fleet/test_dgc_optimizer.py index 15afec928f2422..4addb5b76f0eea 100644 --- a/test/collective/fleet/test_dgc_optimizer.py +++ b/test/collective/fleet/test_dgc_optimizer.py @@ -45,7 +45,6 @@ def check_dgc_momentum_optimizer( mul_x = block.create_parameter( dtype="float32", shape=[dims[0], dims[1]], - lod_level=0, name="mul.x", optimize_attr={'learning_rate': 1.1}, regularizer=( @@ -55,12 +54,11 @@ def check_dgc_momentum_optimizer( ), ) mul_y = block.create_var( - dtype="float32", shape=[dims[1], dims[2]], lod_level=0, name="mul.y" + dtype="float32", shape=[dims[1], dims[2]], name="mul.y" ) mul_out = block.create_var( dtype="float32", shape=[dims[0], dims[2]], - lod_level=0, name="mul.out", ) block.append_op( @@ -94,9 +92,7 @@ def check_dgc_momentum_optimizer( dgc_momentum_optimizer._optimizer.get_velocity_str ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) diff --git a/test/deprecated/ir/test_ir_fc_fuse_pass_deprecated.py b/test/deprecated/ir/test_ir_fc_fuse_pass_deprecated.py index 79aa2f6efc9eb3..831b5cc194603f 100644 --- a/test/deprecated/ir/test_ir_fc_fuse_pass_deprecated.py +++ b/test/deprecated/ir/test_ir_fc_fuse_pass_deprecated.py @@ -31,7 +31,7 @@ class FCFusePassTest(PassTest): def setUp(self): with base.program_guard(self.main_program, self.startup_program): data = paddle.static.data( - name="data", shape=[32, 128], dtype="float32", lod_level=0 + name="data", shape=[32, 128], dtype="float32" ) tmp_0 = paddle.static.nn.fc( x=data, size=128, num_flatten_dims=1, activation="relu" diff --git a/test/deprecated/ir/test_ir_preln_residual_bias_fuse_pass_deprecated.py b/test/deprecated/ir/test_ir_preln_residual_bias_fuse_pass_deprecated.py index 56b70182276483..26ac1c8d6b7005 100644 --- a/test/deprecated/ir/test_ir_preln_residual_bias_fuse_pass_deprecated.py +++ b/test/deprecated/ir/test_ir_preln_residual_bias_fuse_pass_deprecated.py @@ -27,13 +27,9 @@ def setUp(self): with paddle.static.program_guard( self.main_program, self.startup_program ): - x = paddle.static.data( - name="x", shape=[128, 768], dtype="float32", lod_level=0 - ) + x = paddle.static.data(name="x", shape=[128, 768], dtype="float32") bias = paddle.static.create_parameter(shape=[768], dtype='float32') - y = paddle.static.data( - name="y", shape=[128, 768], dtype="float32", lod_level=0 - ) + y = paddle.static.data(name="y", shape=[128, 768], dtype="float32") x = x + bias elementwise_out = x + y out = paddle.static.nn.layer_norm(input=elementwise_out) @@ -63,12 +59,8 @@ def setUp(self): with paddle.static.program_guard( self.main_program, self.startup_program ): - x = paddle.static.data( - name="x", shape=[128, 768], dtype="float32", lod_level=0 - ) - y = paddle.static.data( - name="y", shape=[128, 768], dtype="float32", lod_level=0 - ) + x = paddle.static.data(name="x", shape=[128, 768], dtype="float32") + y = paddle.static.data(name="y", shape=[128, 768], dtype="float32") elementwise_out = x + y out = paddle.static.nn.layer_norm(input=elementwise_out) diff --git a/test/deprecated/ir/test_ir_skip_layernorm_pass_deprecated.py b/test/deprecated/ir/test_ir_skip_layernorm_pass_deprecated.py index 2ef0394fdada6f..dd0b88fac553d9 100644 --- a/test/deprecated/ir/test_ir_skip_layernorm_pass_deprecated.py +++ b/test/deprecated/ir/test_ir_skip_layernorm_pass_deprecated.py @@ -27,12 +27,8 @@ class SkipLayerNormFusePassTest(PassTest): def setUp(self): paddle.enable_static() with base.program_guard(self.main_program, self.startup_program): - x = paddle.static.data( - name="x", shape=[128, 768], dtype="float32", lod_level=0 - ) - y = paddle.static.data( - name="y", shape=[128, 768], dtype="float32", lod_level=0 - ) + x = paddle.static.data(name="x", shape=[128, 768], dtype="float32") + y = paddle.static.data(name="y", shape=[128, 768], dtype="float32") elementwise_out = paddle.add(x=x, y=y) out = paddle.static.nn.layer_norm(input=elementwise_out) diff --git a/test/deprecated/legacy_test/dist_fleet_ctr.py b/test/deprecated/legacy_test/dist_fleet_ctr.py index c5739e13c81fa0..8e8eab9fe909fb 100644 --- a/test/deprecated/legacy_test/dist_fleet_ctr.py +++ b/test/deprecated/legacy_test/dist_fleet_ctr.py @@ -79,7 +79,6 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/deprecated/legacy_test/test_dataset.py b/test/deprecated/legacy_test/test_dataset.py index 8c0f8d799d4367..80813f75685ecf 100644 --- a/test/deprecated/legacy_test/test_dataset.py +++ b/test/deprecated/legacy_test/test_dataset.py @@ -444,16 +444,16 @@ def test_in_memory_dataset_masterpatch1(self): startup_program = base.Program() with base.program_guard(train_program, startup_program): var1 = paddle.static.data( - name="slot1", shape=[-1, 1], dtype="int64", lod_level=0 + name="slot1", shape=[-1, 1], dtype="int64" ) var2 = paddle.static.data( - name="slot2", shape=[-1, 1], dtype="int64", lod_level=0 + name="slot2", shape=[-1, 1], dtype="int64" ) var3 = paddle.static.data( - name="slot3", shape=[-1, 1], dtype="float32", lod_level=0 + name="slot3", shape=[-1, 1], dtype="float32" ) var4 = paddle.static.data( - name="slot4", shape=[-1, 1], dtype="float32", lod_level=0 + name="slot4", shape=[-1, 1], dtype="float32" ) slots_vars = [var1, var2, var3, var4] diff --git a/test/deprecated/legacy_test/test_infer_no_need_buffer_slots_deprecated.py b/test/deprecated/legacy_test/test_infer_no_need_buffer_slots_deprecated.py index d1286cbd02aba1..94d24bca3bcdf2 100644 --- a/test/deprecated/legacy_test/test_infer_no_need_buffer_slots_deprecated.py +++ b/test/deprecated/legacy_test/test_infer_no_need_buffer_slots_deprecated.py @@ -26,12 +26,12 @@ def net(self): x1 = ( base.default_main_program() .global_block() - .create_var(dtype="float32", shape=[1], lod_level=0, name="x1") + .create_var(dtype="float32", shape=[1], name="x1") ) x2 = ( base.default_main_program() .global_block() - .create_var(dtype="float32", shape=[1], lod_level=0, name="x2") + .create_var(dtype="float32", shape=[1], name="x2") ) x = paddle.add(x1, x2) return x diff --git a/test/deprecated/legacy_test/test_layers_deprecated.py b/test/deprecated/legacy_test/test_layers_deprecated.py index 4f2492f30591a0..e3b5a1b2a167f3 100644 --- a/test/deprecated/legacy_test/test_layers_deprecated.py +++ b/test/deprecated/legacy_test/test_layers_deprecated.py @@ -1400,9 +1400,7 @@ def test_simple_conv2d(self): def test_shuffle_batch(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): - x = paddle.static.data( - name='X', shape=[-1, 4, 50], dtype='float32', lod_level=0 - ) + x = paddle.static.data(name='X', shape=[-1, 4, 50], dtype='float32') out1 = shuffle_batch(x) paddle.seed(1000) out2 = shuffle_batch(x) diff --git a/test/deprecated/legacy_test/test_optimizer_deprecated.py b/test/deprecated/legacy_test/test_optimizer_deprecated.py index f87f348d456ae7..b36aa8cf9ce1f6 100644 --- a/test/deprecated/legacy_test/test_optimizer_deprecated.py +++ b/test/deprecated/legacy_test/test_optimizer_deprecated.py @@ -37,18 +37,17 @@ def check_sgd_optimizer(optimizer_attr): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr=optimizer_attr, ) mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" + dtype="float32", shape=[10, 8], name="mul.y" ) mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" + dtype="float32", shape=[1], name="mean.out" ) block.append_op( type="mul", @@ -81,18 +80,17 @@ def check_sgd_optimizer(optimizer_attr): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr=optimizer_attr, ) mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" + dtype="float32", shape=[10, 8], name="mul.y" ) mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" + dtype="float32", shape=[1], name="mean.out" ) block.append_op( type="mul", @@ -133,15 +131,12 @@ def test_vanilla_momentum_optimizer(self): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr={'learning_rate': 1.1}, ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) block.append_op( type="mul", @@ -153,9 +148,7 @@ def test_vanilla_momentum_optimizer(self): momentum_optimizer = self.MockMomentum( learning_rate=learning_rate, momentum=0.2 ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) @@ -192,15 +185,12 @@ def test_nesterov_momentum_optimizer(self): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr={'learning_rate': 1.1}, ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) block.append_op( type="mul", @@ -208,9 +198,7 @@ def test_nesterov_momentum_optimizer(self): outputs={"Out": mul_out}, attrs={"x_num_col_dims": 1}, ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) @@ -263,15 +251,12 @@ def test_adam_optimizer(self): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr={'learning_rate': 1.1}, ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) block.append_op( type="mul", @@ -279,9 +264,7 @@ def test_adam_optimizer(self): outputs={"Out": mul_out}, attrs={"x_num_col_dims": 1}, ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) @@ -321,45 +304,32 @@ def net(self, return_input=False, with_dropout=False, with_seed=False): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x" - ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" + dtype="float32", shape=[5, 10], name="mul.x" ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) if with_dropout is True: mul_out_drop = block.create_var( dtype="float32", shape=[5, 8], - lod_level=0, name="mul.out.dropout", ) mul_out_mask = block.create_var( - dtype="uint8", shape=[5, 8], lod_level=0, name="mul.out.mask" + dtype="uint8", shape=[5, 8], name="mul.out.mask" ) if with_seed is True: seed_out = block.create_var( dtype="int32", shape=[1], name="seed.out" ) - b1 = block.create_parameter( - dtype="float32", shape=[5, 8], lod_level=0, name="b1" - ) - b1_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="b1_out" - ) - b2 = block.create_parameter( - dtype="float32", shape=[5, 8], lod_level=0, name="b2" - ) - b2_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="b2_out" - ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + b1 = block.create_parameter(dtype="float32", shape=[5, 8], name="b1") + b1_out = block.create_var(dtype="float32", shape=[5, 8], name="b1_out") + b2 = block.create_parameter(dtype="float32", shape=[5, 8], name="b2") + b2_out = block.create_var(dtype="float32", shape=[5, 8], name="b2_out") + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mul", inputs={"X": mul_x, "Y": mul_y}, @@ -927,23 +897,15 @@ def net(self): program = framework.Program() block = program.global_block() mul_x = block.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x" - ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" + dtype="float32", shape=[5, 10], name="mul.x" ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" - ) - b1 = block.create_parameter( - dtype="float32", shape=[5, 8], lod_level=0, name="b1" - ) - b1_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="b1_out" - ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" + dtype="float32", shape=[5, 8], name="mul.out" ) + b1 = block.create_parameter(dtype="float32", shape=[5, 8], name="b1") + b1_out = block.create_var(dtype="float32", shape=[5, 8], name="b1_out") + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mul", inputs={"X": mul_x, "Y": mul_y}, diff --git a/test/deprecated/legacy_test/test_regularizer_deprecated.py b/test/deprecated/legacy_test/test_regularizer_deprecated.py index 6cce66ca4ff242..fd6fbeb044f206 100644 --- a/test/deprecated/legacy_test/test_regularizer_deprecated.py +++ b/test/deprecated/legacy_test/test_regularizer_deprecated.py @@ -34,17 +34,14 @@ def test_l2decay_regularizer(self): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", regularizer=regularizer.L2Decay(0.5), ) self.assertIsNotNone(mul_x.regularizer) self.assertTrue(isinstance(mul_x.regularizer, regularizer.L2Decay)) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) block.append_op( type="mul", @@ -52,9 +49,7 @@ def test_l2decay_regularizer(self): outputs={"Out": mul_out}, attrs={"x_num_col_dims": 1}, ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) @@ -77,17 +72,14 @@ def test_l2decay_regularizer(self): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", regularizer=regularizer.L1Decay(0.5), ) self.assertIsNotNone(mul_x.regularizer) self.assertTrue(isinstance(mul_x.regularizer, regularizer.L1Decay)) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) block.append_op( type="mul", @@ -95,9 +87,7 @@ def test_l2decay_regularizer(self): outputs={"Out": mul_out}, attrs={"x_num_col_dims": 1}, ) - mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" - ) + mean_out = block.create_var(dtype="float32", shape=[1], name="mean.out") block.append_op( type="mean", inputs={"X": mul_out}, outputs={"Out": mean_out} ) diff --git a/test/deprecated/legacy_test/test_sgd_op_deprecated.py b/test/deprecated/legacy_test/test_sgd_op_deprecated.py index 11d899f755526e..0f76edd33e3233 100644 --- a/test/deprecated/legacy_test/test_sgd_op_deprecated.py +++ b/test/deprecated/legacy_test/test_sgd_op_deprecated.py @@ -67,18 +67,17 @@ def check_sgd_optimizer(optimizer_attr): mul_x = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="mul.x", optimize_attr=optimizer_attr, ) mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" + dtype="float32", shape=[10, 8], name="mul.y" ) mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) mean_out = block.create_var( - dtype="float32", shape=[1], lod_level=0, name="mean.out" + dtype="float32", shape=[1], name="mean.out" ) block.append_op( type="mul", diff --git a/test/deprecated/legacy_test/test_variable_deprecated.py b/test/deprecated/legacy_test/test_variable_deprecated.py index 3cbf074bd0addd..56702f137a4ae3 100644 --- a/test/deprecated/legacy_test/test_variable_deprecated.py +++ b/test/deprecated/legacy_test/test_variable_deprecated.py @@ -33,7 +33,7 @@ def setUp(self): def _test_slice(self, place): b = default_main_program().current_block() - w = b.create_var(dtype="float64", shape=[784, 100, 100], lod_level=0) + w = b.create_var(dtype="float64", shape=[784, 100, 100]) for i in range(3): nw = w[i] diff --git a/test/legacy_test/dist_ctr.py b/test/legacy_test/dist_ctr.py index 560ad6a7d3bba0..dc296ef19c7db6 100644 --- a/test/legacy_test/dist_ctr.py +++ b/test/legacy_test/dist_ctr.py @@ -47,7 +47,6 @@ def get_model(self, batch_size=2): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) # build dnn model diff --git a/test/legacy_test/dist_fleet_ctr.py b/test/legacy_test/dist_fleet_ctr.py index d042d2ad794095..e944c024e41946 100644 --- a/test/legacy_test/dist_fleet_ctr.py +++ b/test/legacy_test/dist_fleet_ctr.py @@ -77,7 +77,6 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py index b1bc1396b7f00b..fd812745825c31 100644 --- a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py +++ b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py @@ -64,7 +64,6 @@ def net(self, args, batch_size=4, lr=0.01): name="click", shape=[-1, 1], dtype="float32", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py index b1f6cf9587d232..77a5375901a7b2 100644 --- a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py @@ -68,7 +68,6 @@ def net(self, args, batch_size=4, lr=0.01): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/legacy_test/fleet_heter_ps_training.py b/test/legacy_test/fleet_heter_ps_training.py index 565c814fe95113..74ced0bd2b0df2 100644 --- a/test/legacy_test/fleet_heter_ps_training.py +++ b/test/legacy_test/fleet_heter_ps_training.py @@ -57,7 +57,6 @@ def net(batch_size=4, lr=0.01): name="click", shape=[-1, 1], dtype="float32", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/legacy_test/test_boxps.py b/test/legacy_test/test_boxps.py index 7bd93df456269b..0330dc2fffd731 100644 --- a/test/legacy_test/test_boxps.py +++ b/test/legacy_test/test_boxps.py @@ -99,12 +99,8 @@ def test_pull_box_sparse_op(self): with paddle.pir_utils.OldIrGuard(): program = base.Program() with base.program_guard(program): - x = paddle.static.data( - name='x', shape=[-1, 1], dtype='int64', lod_level=0 - ) - y = paddle.static.data( - name='y', shape=[-1, 1], dtype='int64', lod_level=0 - ) + x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64') + y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64') emb_x, emb_y = _pull_box_sparse([x, y], size=1) diff --git a/test/legacy_test/test_dataset_consistency_inspection.py b/test/legacy_test/test_dataset_consistency_inspection.py index 1842413d2a8b56..f33a1610b91571 100644 --- a/test/legacy_test/test_dataset_consistency_inspection.py +++ b/test/legacy_test/test_dataset_consistency_inspection.py @@ -413,7 +413,6 @@ def test_var_consistency_insepection(self): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) slot_data.append(label) diff --git a/test/legacy_test/test_debugger.py b/test/legacy_test/test_debugger.py index 75ee1c3707e82a..58fb2c07105209 100644 --- a/test/legacy_test/test_debugger.py +++ b/test/legacy_test/test_debugger.py @@ -40,15 +40,9 @@ def test_DrawBlockGraphviz_str(self): ) # operator - mul_x = b.create_parameter( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x" - ) - mul_y = b.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) - mul_out = b.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" - ) + mul_x = b.create_parameter(dtype="float32", shape=[5, 10], name="mul.x") + mul_y = b.create_var(dtype="float32", shape=[10, 8], name="mul.y") + mul_out = b.create_var(dtype="float32", shape=[5, 8], name="mul.out") b.append_op( type="mul", inputs={"X": mul_x, "Y": mul_y}, diff --git a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py index c678a62fde2016..6795ff96858a0c 100644 --- a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py @@ -205,7 +205,6 @@ def net(): name="click", shape=[-1, 1], dtype="int64", - lod_level=0, ) datas = [dnn_data, lr_data, label] diff --git a/test/legacy_test/test_imperative_ocr_attention_model.py b/test/legacy_test/test_imperative_ocr_attention_model.py index ddb7bd77ae7878..de8198ce73e113 100644 --- a/test/legacy_test/test_imperative_ocr_attention_model.py +++ b/test/legacy_test/test_imperative_ocr_attention_model.py @@ -577,12 +577,12 @@ def run_dygraph(): if not paddle.framework.use_pir_api(): images.desc.set_need_check_feed(False) static_label_in = paddle.static.data( - name='label_in', shape=[-1, 1], dtype='int64', lod_level=0 + name='label_in', shape=[-1, 1], dtype='int64' ) if not paddle.framework.use_pir_api(): static_label_in.desc.set_need_check_feed(False) static_label_out = paddle.static.data( - name='label_out', shape=[-1, 1], dtype='int64', lod_level=0 + name='label_out', shape=[-1, 1], dtype='int64' ) if not paddle.framework.use_pir_api(): static_label_out.desc.set_need_check_feed(False) diff --git a/test/legacy_test/test_initializer.py b/test/legacy_test/test_initializer.py index 084e89da35938b..ece4acf2f2e10d 100644 --- a/test/legacy_test/test_initializer.py +++ b/test/legacy_test/test_initializer.py @@ -78,7 +78,6 @@ def test_constant_initializer_default_value(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.Constant(), ) @@ -98,7 +97,6 @@ def test_constant_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.Constant(2.3), ) @@ -132,7 +130,6 @@ def test_uniform_initializer_default_value(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.Uniform(), ) @@ -155,14 +152,12 @@ def test_uniform_initializer_random_seed(self): block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param1", initializer=paddle.nn.initializer.Uniform(), ) block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param2", initializer=paddle.nn.initializer.UniformInitializer( seed=456 @@ -182,7 +177,6 @@ def test_uniform_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.UniformInitializer( -4.2, 3.1, 123 @@ -206,7 +200,6 @@ def test_uniform_initializer_two_op(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.UniformInitializer( -4.2, float(i), 123 @@ -399,7 +392,6 @@ def test_normal_initializer_default_value(self): block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.Normal(), ) @@ -419,7 +411,6 @@ def test_normal_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.NormalInitializer( 2.3, 1.9, 123 @@ -443,7 +434,6 @@ def test_normal_initializer_complex(self, dtype="complex64"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.NormalInitializer( 2.2 + 2.2j, 1.9, 123 @@ -487,7 +477,6 @@ def test_uniform_xavier_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.XavierUniform(), ) @@ -510,7 +499,6 @@ def test_uniform_xavier_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=paddle.nn.initializer.XavierUniform(), ) @@ -536,7 +524,6 @@ def test_normal_xavier_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.XavierNormal(), ) @@ -559,7 +546,6 @@ def test_normal_xavier_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=paddle.nn.initializer.XavierNormal(), ) @@ -585,7 +571,6 @@ def test_xavier_initializer_supplied_arguments( block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.XavierInitializer( uniform=uniform, @@ -879,7 +864,6 @@ def test_uniform_msra_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.KaimingUniform(), ) @@ -902,7 +886,6 @@ def test_uniform_msra_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=paddle.nn.initializer.KaimingUniform(), ) @@ -926,7 +909,6 @@ def test_normal_msra_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.KaimingNormal(), ) @@ -949,7 +931,6 @@ def test_normal_msra_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=paddle.nn.initializer.KaimingNormal(), ) @@ -971,7 +952,6 @@ def test_msra_initializer_supplied_arguments(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=paddle.nn.initializer.MSRAInitializer( fan_in=12, seed=134 @@ -1236,7 +1216,6 @@ def test_bilinear_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[8, 1, 3, 3], - lod_level=0, name="param", initializer=paddle.nn.initializer.Bilinear(), ) @@ -1406,7 +1385,6 @@ def test_numpy_array_initializer(self, dtype="float32"): block.create_parameter( dtype=np_array.dtype, shape=np_array.shape, - lod_level=0, name="param", initializer=paddle.nn.initializer.Assign(np_array), ) diff --git a/test/legacy_test/test_initializer_nn.py b/test/legacy_test/test_initializer_nn.py index 94c13c109c9a88..abbd7f1830b851 100644 --- a/test/legacy_test/test_initializer_nn.py +++ b/test/legacy_test/test_initializer_nn.py @@ -52,7 +52,6 @@ def static_test_constant_initializer_common( block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=init_inst, ) @@ -134,7 +133,6 @@ def static_test_kaiming_initializer_common( param = block.create_parameter( dtype="float32", shape=shape_mat, - lod_level=0, name="param", initializer=init_inst, ) @@ -228,7 +226,6 @@ def test_uniform_common(self, dtype="float32", seed=0): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Uniform(), ) @@ -257,7 +254,6 @@ def test_uniform_initializer_default_value( block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Uniform(), ) @@ -286,7 +282,6 @@ def test_uniform_initializer( block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Uniform(min_value, max_value), ) @@ -314,7 +309,6 @@ def test_uniform_initializer_two_op( block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Uniform(min_value, float(i)), ) @@ -377,7 +371,6 @@ def test_normal_initializer_default_value(self): block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Normal(), ) @@ -400,7 +393,6 @@ def test_normal_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Normal(2.3, 1.9), ) @@ -425,7 +417,6 @@ def test_normal_initializer_complex(self, dtype="complex64"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.Normal(2.3 + 2.3j, 1.9), ) @@ -480,7 +471,6 @@ def test_truncated_normal_initializer_default_value(self): block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=initializer.TruncatedNormal(), ) @@ -505,7 +495,6 @@ def test_truncated_normal_initializer(self, dtype="float32"): block.create_parameter( dtype=dtype, shape=[5, 10], - lod_level=0, name="param", initializer=initializer.TruncatedNormal(2.3, 1.9), ) @@ -594,7 +583,6 @@ def test_xavier_uniform_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=initializer.XavierUniform(), ) @@ -620,7 +608,6 @@ def test_xavier_uniform_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=initializer.XavierUniform(), ) @@ -659,7 +646,6 @@ def test_xavier_normal_initializer(self): param = block.create_parameter( dtype="float32", shape=[5, 10], - lod_level=0, name="param", initializer=initializer.XavierNormal(), ) @@ -685,7 +671,6 @@ def test_xavier_normal_initializer_conv(self): param = block.create_parameter( dtype="float32", shape=[5, 10, 15, 20], - lod_level=0, name="param", initializer=initializer.XavierNormal(), ) @@ -728,7 +713,6 @@ def test_assign_initializer(self, dtype="float32"): block.create_parameter( dtype=np_array.dtype, shape=np_array.shape, - lod_level=0, name="param", initializer=initializer.Assign(np_array), ) diff --git a/test/legacy_test/test_operator_desc.py b/test/legacy_test/test_operator_desc.py index c4fc5e1ad24cb8..f0ef230ede2564 100644 --- a/test/legacy_test/test_operator_desc.py +++ b/test/legacy_test/test_operator_desc.py @@ -41,14 +41,10 @@ def test_error_type(self): def test_op_desc_creation(self): program = Program() block = program.current_block() - mul_x = block.create_var( - dtype="float32", shape=[5, 10], lod_level=0, name="mul.x" - ) - mul_y = block.create_var( - dtype="float32", shape=[10, 8], lod_level=0, name="mul.y" - ) + mul_x = block.create_var(dtype="float32", shape=[5, 10], name="mul.x") + mul_y = block.create_var(dtype="float32", shape=[10, 8], name="mul.y") mul_out = block.create_var( - dtype="float32", shape=[5, 8], lod_level=0, name="mul.out" + dtype="float32", shape=[5, 8], name="mul.out" ) mul_op = block.append_op( type="mul", @@ -91,18 +87,10 @@ def test_op_desc_creation(self): def test_mult_input(self): program = Program() block = program.current_block() - sum_x1 = block.create_var( - dtype="int", shape=[3, 4], lod_level=0, name="sum.x1" - ) - sum_x2 = block.create_var( - dtype="int", shape=[3, 4], lod_level=0, name="sum.x2" - ) - sum_x3 = block.create_var( - dtype="int", shape=[3, 4], lod_level=0, name="sum.x3" - ) - sum_out = block.create_var( - dtype="int", shape=[3, 4], lod_level=0, name="sum.out" - ) + sum_x1 = block.create_var(dtype="int", shape=[3, 4], name="sum.x1") + sum_x2 = block.create_var(dtype="int", shape=[3, 4], name="sum.x2") + sum_x3 = block.create_var(dtype="int", shape=[3, 4], name="sum.x3") + sum_out = block.create_var(dtype="int", shape=[3, 4], name="sum.out") sum_op = block.append_op( type="sum", inputs={"X": [sum_x1, sum_x2, sum_x3]}, diff --git a/test/legacy_test/test_optimizer_for_varbase.py b/test/legacy_test/test_optimizer_for_varbase.py index abeebc2ec3e04e..308a1caa8fb11c 100644 --- a/test/legacy_test/test_optimizer_for_varbase.py +++ b/test/legacy_test/test_optimizer_for_varbase.py @@ -91,7 +91,6 @@ def test_create_param_lr_with_1_for_coverage(self): x = paddle.base.framework.EagerParamBase( dtype="float32", shape=[5, 10], - lod_level=0, name="x", optimize_attr={'learning_rate': 1.0}, ) @@ -110,7 +109,6 @@ def test_create_param_lr_with_no_1_value_for_coverage(self): x = paddle.base.framework.EagerParamBase( dtype="float32", shape=[5, 10], - lod_level=0, name="x", optimize_attr={'learning_rate': 0.12}, ) diff --git a/test/legacy_test/test_variable.py b/test/legacy_test/test_variable.py index b83c4b1aa43797..ef4d1b5dec1da1 100644 --- a/test/legacy_test/test_variable.py +++ b/test/legacy_test/test_variable.py @@ -48,9 +48,7 @@ def test_np_dtype_convert(self): def test_var(self): b = default_main_program().current_block() - w = b.create_var( - dtype="float64", shape=[784, 100], lod_level=0, name="fc.w" - ) + w = b.create_var(dtype="float64", shape=[784, 100], name="fc.w") w_dtype = w.dtype if paddle.framework.use_pir_api() and isinstance( w_dtype, paddle.base.libpaddle.VarDesc.VarType @@ -293,11 +291,11 @@ def test_slice(self): def _tostring(self): b = default_main_program().current_block() - w = b.create_var(dtype="float64", lod_level=0) + w = b.create_var(dtype="float64") self.assertTrue(isinstance(str(w), str)) if core.is_compiled_with_cuda(): - wc = b.create_var(dtype="int", lod_level=0) + wc = b.create_var(dtype="int") self.assertTrue(isinstance(str(wc), str)) def test_tostring(self): @@ -308,7 +306,7 @@ def test_tostring(self): def test_fake_interface_only_api(self): b = default_main_program().current_block() - var = b.create_var(dtype="float64", lod_level=0) + var = b.create_var(dtype="float64") with base.dygraph.guard(): self.assertRaises(AssertionError, var.numpy) self.assertRaises(AssertionError, var.backward) @@ -369,7 +367,7 @@ def test_size(self): def test_detach(self): b = default_main_program().current_block() - x = b.create_var(shape=[2, 3, 5], dtype="float64", lod_level=0) + x = b.create_var(shape=[2, 3, 5], dtype="float64") detach_x = x.detach() self.assertEqual(x.persistable, detach_x.persistable) self.assertEqual(x.shape, detach_x.shape) From 46fbb3b10ef401b3c5d5f71cbfc58822f4195527 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Wed, 4 Dec 2024 10:34:47 +0800 Subject: [PATCH 130/288] [CINN] fix some cinn bug (#69906) * fix some cinn bug --- .../operator/transforms/check_infer_symbolic_pass.cc | 2 +- python/paddle/decomposition/recompute.py | 5 ++++- python/paddle/jit/dy2static/pir_partial_program.py | 9 +++++---- 3 files changed, 10 insertions(+), 6 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc index 133928e0cfc421..bf3d194f263912 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_pass.cc @@ -45,7 +45,7 @@ std::unordered_set SKIP_CHECK_OPS = { "pd_op.less_than", "pd_op.less_equal", "pd_op.greater_than", "pd_op.greater_equal", "pd_op.equal", "pd_op.not_equal", "pd_op.logical_and", "pd_op.logical_or", "pd_op.logical_xor", - "pd_op.shape"}; + "pd_op.shape", "pd_op.shape64"}; class BlockDimExprsAsserter { public: diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index 75b7ee78685df5..f06122f40d206a 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -76,6 +76,7 @@ "pd_op.where", "pd_op.pow", "pd_op.shape", + "pd_op.shape64", "pd_op.slice", "pd_op.squeeze", "pd_op.unsqueeze", @@ -729,6 +730,8 @@ def getIdx(program, op): if len(op_inputs) == 0 and define_op.name() not in [ "pd_op.full", "pd_op.full_int_array", + "builtin.parameter", + "pd_op.data", ]: def getIdx(program, op): @@ -738,7 +741,7 @@ def getIdx(program, op): raise RuntimeError("op not found in program") raise Exception( - f"Every path to recompute value {recompute_value} must have saved value or starting point of the path is one of op in [pd_op.full, pd_op.full_int_array], but find {define_op.name()} op" + f"Every path to recompute value {recompute_value} must have saved value or starting point of the path is one of op in [pd_op.full, pd_op.full_int_array], but find {define_op.name()} op, op ir is {define_op}" ) for op_input in op_inputs: if op_input in saved_values: diff --git a/python/paddle/jit/dy2static/pir_partial_program.py b/python/paddle/jit/dy2static/pir_partial_program.py index 6c4c323506f45c..9b228c348516f1 100644 --- a/python/paddle/jit/dy2static/pir_partial_program.py +++ b/python/paddle/jit/dy2static/pir_partial_program.py @@ -771,7 +771,7 @@ def get_kwargs_forward_matched_value(kw_name, kw_value): elif kw_name in forward_name_value_map: return forward_name_value_map[kw_name] else: - raise Exception(f"kw_args: {kw_name} not found") + return None for [kw_name, kw_value] in ( backward_program.global_block().kwargs().items() @@ -779,9 +779,10 @@ def get_kwargs_forward_matched_value(kw_name, kw_value): forward_matched_value = ( get_kwargs_forward_matched_value(kw_name, kw_value) ) - share_symbol_shape_from_forward_to_backward( - forward_matched_value, kw_value - ) + if forward_matched_value is not None: + share_symbol_shape_from_forward_to_backward( + forward_matched_value, kw_value + ) if cse_is_enabled(): paddle.base.libpaddle.pir.apply_cse_pass(forward_program) From 18f5daab1d89cda40ac8e45bc2159370a64caed1 Mon Sep 17 00:00:00 2001 From: huangjiyi <43315610+huangjiyi@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:35:34 +0800 Subject: [PATCH 131/288] [CINN] Fix reshape dynamic shape to static shape fusion (#69879) --- .../shape/utils/constraints_manager.cc | 14 +++- .../symbolic/test_reshape_dyshape2stshape.py | 74 +++++++++++++++++++ 2 files changed, 87 insertions(+), 1 deletion(-) create mode 100644 test/ir/pir/cinn/symbolic/test_reshape_dyshape2stshape.py diff --git a/paddle/pir/src/dialect/shape/utils/constraints_manager.cc b/paddle/pir/src/dialect/shape/utils/constraints_manager.cc index b5bada17f30819..5b06cbcd32a8be 100644 --- a/paddle/pir/src/dialect/shape/utils/constraints_manager.cc +++ b/paddle/pir/src/dialect/shape/utils/constraints_manager.cc @@ -59,7 +59,15 @@ std::pair EliminateCommonFactor(const OpT& lhs, lhs_diffs->push_back(lhs_dim_expr); } } - if (lhs_diffs->empty() || rhs_diffs->empty()) return std::pair(lhs, rhs); + if (lhs_diffs->empty() && rhs_diffs->empty()) return std::pair(lhs, lhs); + + bool opt_is_add = DimExpr(lhs).isa>(); + if (lhs_diffs->empty()) { + lhs_diffs->push_back(opt_is_add ? DimExpr(0) : DimExpr(1)); + } + if (rhs_diffs->empty()) { + rhs_diffs->push_back(opt_is_add ? DimExpr(0) : DimExpr(1)); + } auto lhs_diff = lhs_diffs->size() == 1 ? lhs_diffs->at(0) : OpT{lhs_diffs}; auto rhs_diff = @@ -78,6 +86,10 @@ std::pair SimplifyEqCstr(const DimExpr& lhs, const Mul& rhs) -> std::pair { return EliminateCommonFactor(lhs, rhs); }, + [](const Mul& lhs, int64_t rhs) -> std::pair { + Mul mul_rhs{List{rhs}}; + return EliminateCommonFactor(lhs, mul_rhs); + }, [](const auto& lhs, const auto& rhs) -> std::pair { return std::make_pair(DimExpr(lhs), DimExpr(rhs)); }}; diff --git a/test/ir/pir/cinn/symbolic/test_reshape_dyshape2stshape.py b/test/ir/pir/cinn/symbolic/test_reshape_dyshape2stshape.py new file mode 100644 index 00000000000000..3651790fd3817b --- /dev/null +++ b/test/ir/pir/cinn/symbolic/test_reshape_dyshape2stshape.py @@ -0,0 +1,74 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle +from paddle.static import InputSpec + + +class ReshapeCase(paddle.nn.Layer): + def __init__(self): + super().__init__() + + def forward( + self, + var_0, + ): + var_0 = var_0 + 1.0 + var_1 = paddle.sqrt(var_0) + var_2 = paddle.reshape(var_1, shape=[1, 32, 64]) + var_3 = var_2 * 2 + var_4 = var_0 / 2 + return var_3, var_4 + + +class TestSigmoid(unittest.TestCase): + def setUp(self): + self.inputs = (paddle.rand(shape=[1, 32, 16, 4], dtype=paddle.float32),) + self.net = ReshapeCase() + + def train(self, net, to_static, with_cinn=False): + if to_static: + if with_cinn: + build_strategy = paddle.static.BuildStrategy() + build_strategy.build_cinn_pass = True + input_spec = [ + InputSpec(shape=[None, 32, 16, 4], dtype='float32') + ] + net = paddle.jit.to_static( + net, + build_strategy=build_strategy, + input_spec=input_spec, + full_graph=True, + ) + else: + net = paddle.jit.to_static(net, full_graph=True) + paddle.seed(123) + outs = net(*self.inputs) + return outs + + def test_ast_prim_cinn(self): + st_out = self.train(self.net, to_static=True) + cinn_out = self.train(self.net, to_static=True, with_cinn=True) + for st, cinn in zip( + paddle.utils.flatten(st_out), paddle.utils.flatten(cinn_out) + ): + np.testing.assert_allclose(st.numpy(), cinn.numpy(), atol=1e-8) + + +if __name__ == '__main__': + unittest.main() From 9932611863b414ac5001598837f8cbcb08da777b Mon Sep 17 00:00:00 2001 From: tianshuo78520a <707759223@qq.com> Date: Wed, 4 Dec 2024 10:46:23 +0800 Subject: [PATCH 132/288] Add gcc-13 python13 (#69491) * Add gcc-13 python13 * fix gcc13 * Fix gcc * Fix install gcc13 * Fix python3.13 * Fix pip3.13t * Fix * Fix pip install * Fix * Fix * Fix * Update * Update pip * Update * Fix * Fix pip * Fix pip * Fix pip * Update Dockerfile.ubuntu20 fix * Update Dockerfile.ubuntu20 test * Update * Update * Fix * Fix * Fix * fix test --- python/unittest_py/requirements.txt | 5 ++- .../test_semi_auto_parallel_custom_op.py | 6 +-- .../cpp_extension/test_cpp_extension_setup.py | 4 +- test/custom_op/test_custom_relu_op_setup.py | 5 +-- tools/dockerfile/Dockerfile.ubuntu20 | 44 ++++++++++--------- tools/dockerfile/ci_dockerfile.sh | 6 ++- 6 files changed, 40 insertions(+), 30 deletions(-) diff --git a/python/unittest_py/requirements.txt b/python/unittest_py/requirements.txt index 7d27554c431f7d..fb461ef1131764 100644 --- a/python/unittest_py/requirements.txt +++ b/python/unittest_py/requirements.txt @@ -5,9 +5,10 @@ gymnasium>=1.0.0a1 hypothesis opencv-python>= 4.10.0.84 visualdl==2.5.3 -paddle2onnx>=0.9.6 +paddle2onnx>=0.9.6; python_version < "3.13" scipy>=1.6, !=1.7.2, !=1.7.3 ; platform_system == "Windows" -scipy>=1.13 ; platform_system != "Windows" +scipy == 1.10.1; python_version == "3.8" +scipy > 1.13; python_version > "3.8" prettytable distro autograd==1.4 diff --git a/test/auto_parallel/custom_op/test_semi_auto_parallel_custom_op.py b/test/auto_parallel/custom_op/test_semi_auto_parallel_custom_op.py index eee9ddb25e17e7..fe6a0c98be804f 100644 --- a/test/auto_parallel/custom_op/test_semi_auto_parallel_custom_op.py +++ b/test/auto_parallel/custom_op/test_semi_auto_parallel_custom_op.py @@ -13,6 +13,7 @@ # limitations under the License. import os +import site import sys import unittest @@ -31,9 +32,8 @@ def setUp(self): if os.name == 'nt': cmd = f'cd /d {cur_dir} && python custom_relu_setup.py install' else: - cmd = ( - f'cd {cur_dir} && {sys.executable} custom_relu_setup.py install' - ) + site_dir = site.getsitepackages()[0] + cmd = f'cd {cur_dir} && {sys.executable} custom_relu_setup.py install --install-lib={site_dir}' run_cmd(cmd) # test dynamic auto parallel run diff --git a/test/cpp_extension/test_cpp_extension_setup.py b/test/cpp_extension/test_cpp_extension_setup.py index fae59ea689993c..56db08ae0f0902 100644 --- a/test/cpp_extension/test_cpp_extension_setup.py +++ b/test/cpp_extension/test_cpp_extension_setup.py @@ -33,10 +33,12 @@ def setUp(self): cur_dir = os.path.dirname(os.path.abspath(__file__)) # install general extension # compile, install the custom op egg into site-packages under background + site_dir = site.getsitepackages()[0] cmd = f'cd {cur_dir} && {sys.executable} cpp_extension_setup.py install' + if os.name != 'nt': + cmd += f' --install-lib={site_dir}' run_cmd(cmd) - site_dir = site.getsitepackages()[0] custom_egg_path = [ x for x in os.listdir(site_dir) if 'custom_cpp_extension' in x ] diff --git a/test/custom_op/test_custom_relu_op_setup.py b/test/custom_op/test_custom_relu_op_setup.py index 2b59782a7a6645..d63c8633844ea0 100644 --- a/test/custom_op/test_custom_relu_op_setup.py +++ b/test/custom_op/test_custom_relu_op_setup.py @@ -149,9 +149,8 @@ def setUp(self): if os.name == 'nt': cmd = f'cd /d {cur_dir} && python custom_relu_setup.py install' else: - cmd = ( - f'cd {cur_dir} && {sys.executable} custom_relu_setup.py install' - ) + site_dir = site.getsitepackages()[0] + cmd = f'cd {cur_dir} && {sys.executable} custom_relu_setup.py install --install-lib={site_dir}' run_cmd(cmd) # NOTE(Aurelius84): Normally, it's no need to add following codes for users. diff --git a/tools/dockerfile/Dockerfile.ubuntu20 b/tools/dockerfile/Dockerfile.ubuntu20 index 63f68d03101e43..3b948df08ded2b 100644 --- a/tools/dockerfile/Dockerfile.ubuntu20 +++ b/tools/dockerfile/Dockerfile.ubuntu20 @@ -63,23 +63,12 @@ RUN apt-get update && \ python3.9 python3.9-dev python3.9-distutils \ python3.10 python3.10-dev python3.10-distutils \ python3.11 python3.11-dev python3.11-distutils \ - python3.12 python3.12-dev python3.12-distutils && \ + python3.12 python3.12-dev python3.12-distutils \ + python3.13 python3.13-dev python3.13-nogil && \ apt-get install python-is-python3 && \ rm /usr/bin/python && ln -s /usr/bin/python3.9 /usr/bin/python && \ rm /usr/bin/python3 && ln -s /usr/bin/python3.9 /usr/bin/python3 -WORKDIR /home -RUN wget -q https://files.pythonhosted.org/packages/a7/e0/30642b9c2df516506d40b563b0cbd080c49c6b3f11a70b4c7a670f13a78b/setuptools-50.3.2.zip && unzip setuptools-50.3.2.zip -WORKDIR /home/setuptools-50.3.2 -RUN python3.9 setup.py build && python3.9 setup.py install && \ - python3.8 setup.py build && python3.8 setup.py install - -WORKDIR /home -RUN wget -q https://files.pythonhosted.org/packages/ef/cc/93f7213b2ab5ed383f98ce8020e632ef256b406b8569606c3f160ed8e1c9/setuptools-68.2.2.tar.gz && tar xf setuptools-68.2.2.tar.gz -WORKDIR /home/setuptools-68.2.2 -RUN python3.10 setup.py build && python3.10 setup.py install && \ - python3.11 setup.py build && python3.11 setup.py install && \ - python3.12 setup.py build && python3.12 setup.py install WORKDIR /home RUN wget -q https://bootstrap.pypa.io/get-pip.py @@ -89,10 +78,19 @@ RUN python3.9 get-pip.py && \ python3.11 get-pip.py && \ python3.12 get-pip.py -WORKDIR /home -RUN rm setuptools-50.3.2.zip setuptools-68.2.2.tar.gz && \ - rm -r setuptools-50.3.2 setuptools-68.2.2 get-pip.py +RUN python3.13t get-pip.py && \ + mv /usr/local/bin/pip3.13 /usr/local/bin/pip3.13t && \ + python3.13 get-pip.py + +RUN python3.8 -m pip install setuptools==69.5.1 && \ + python3.9 -m pip install setuptools==69.5.1 && \ + python3.10 -m pip install setuptools==69.5.1 && \ + python3.11 -m pip install setuptools==69.5.1 && \ + python3.12 -m pip install --break-system-packages setuptools==69.5.1 && \ + python3.13 -m pip install setuptools==69.5.1 && \ + python3.13t -m pip install setuptools==69.5.1 +WORKDIR /home # remove them when apt-get support 2.27 and higher version RUN wget -q https://ftp.gnu.org/gnu/binutils/binutils-2.33.1.tar.gz && \ tar -xzf binutils-2.33.1.tar.gz && \ @@ -130,14 +128,18 @@ RUN pip3.8 --no-cache-dir install ipython==5.3.0 && \ pip3.11 --no-cache-dir install ipython==5.3.0 && \ pip3.11 --no-cache-dir install ipykernel==4.6.0 wheel && \ pip3.12 --no-cache-dir install ipython==5.3.0 && \ - pip3.12 --no-cache-dir install ipykernel==4.6.0 wheel + pip3.12 --no-cache-dir install ipykernel==4.6.0 wheel && \ + pip3.13 --no-cache-dir install ipython==5.3.0 && \ + pip3.13 --no-cache-dir install ipykernel==4.6.0 wheel && \ + pip3.13t --no-cache-dir install wheel # For PaddleTest CE RUN pip3.8 --no-cache-dir install pytest && \ pip3.9 --no-cache-dir install pytest && \ pip3.10 --no-cache-dir install pytest && \ pip3.11 --no-cache-dir install pytest && \ - pip3.12 --no-cache-dir install pytest + pip3.12 --no-cache-dir install pytest && \ + pip3.13 --no-cache-dir install pytest RUN pip3.8 --no-cache-dir install pre-commit==2.17.0 && \ pip3.9 --no-cache-dir install pre-commit==2.17.0 && \ @@ -146,7 +148,8 @@ RUN pip3.8 --no-cache-dir install pre-commit==2.17.0 && \ pip3.9 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 && \ pip3.10 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 && \ pip3.11 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 && \ - pip3.12 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 + pip3.12 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 && \ + pip3.13 --no-cache-dir install cpplint==1.6.0 clang-format==13.0.0 COPY ./python/requirements.txt /root/ COPY ./python/unittest_py/requirements.txt /home/ @@ -158,7 +161,8 @@ RUN pip3.8 --no-cache-dir install -r /root/requirements.txt && \ pip3.11 --no-cache-dir install -r /root/requirements.txt && \ pip3.11 --no-cache-dir install -r /home/requirements.txt && \ pip3.12 --no-cache-dir install -r /root/requirements.txt && \ - pip3.12 --no-cache-dir install -r /home/requirements.txt + pip3.12 --no-cache-dir install -r /home/requirements.txt && \ + pip3.13 --no-cache-dir install -r /root/requirements.txt # ccache 4.2.0 diff --git a/tools/dockerfile/ci_dockerfile.sh b/tools/dockerfile/ci_dockerfile.sh index dbe28723a3adbe..2c63e7abc4a492 100644 --- a/tools/dockerfile/ci_dockerfile.sh +++ b/tools/dockerfile/ci_dockerfile.sh @@ -44,6 +44,10 @@ function make_cpu_dockerfile(){ ./configure --with-openssl --with-curl --prefix=/usr/local \&\& \ make -j8 \&\& make install " ${dockerfile_name} sed -i 's##RUN apt-get install -y gcc g++ make#g' ${dockerfile_name} + sed -i 's#RUN bash /build_scripts/install_gcc.sh gcc121#RUN add-apt-repository ppa:ubuntu-toolchain-r/test \&\& apt-get update \&\& apt-get install -y gcc-13 g++-13#g' ${dockerfile_name} + sed -i 's#/usr/local/gcc-12.1/bin/gcc#/usr/bin/gcc-13#g' ${dockerfile_name} + sed -i 's#/usr/local/gcc-12.1/bin/g++#/usr/bin/g++-13#g' ${dockerfile_name} + sed -i 's#ENV PATH=/usr/local/gcc-12.1/bin:$PATH##g' ${dockerfile_name} } @@ -79,7 +83,7 @@ function make_ubuntu20_cu12_dockerfile(){ dockerfile_line=$(wc -l ${dockerfile_name}|awk '{print $1}') sed -i "${dockerfile_line}i RUN wget --no-check-certificate -q https://paddle-edl.bj.bcebos.com/hadoop-2.7.7.tar.gz \&\& \ tar -xzf hadoop-2.7.7.tar.gz && mv hadoop-2.7.7 /usr/local/" ${dockerfile_name} - sed -i "${dockerfile_line}i RUN apt remove git -y \&\& apt update \&\& apt install -y libcurl4-openssl-dev gettext pigz zstd ninja-build \&\& wget -q https://paddle-ci.gz.bcebos.com/git-2.17.1.tar.gz \&\& \ + sed -i "${dockerfile_line}i RUN apt remove git -y \&\& apt update \&\& apt install -y libcurl4-openssl-dev gettext pigz zstd ninja-build \&\& wget -q https://paddle-ci.gz.bcebos.com/git-2.17.1.tar.gz \&\& \ tar -xvf git-2.17.1.tar.gz \&\& \ cd git-2.17.1 \&\& \ ./configure --with-openssl --with-curl --prefix=/usr/local \&\& \ From d70704410e430c34d2bc85103d28f0dabdcd25c7 Mon Sep 17 00:00:00 2001 From: houj04 <35131887+houj04@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:47:02 +0800 Subject: [PATCH 133/288] [XPU] bf16 for multinomial op (#69898) --- paddle/phi/backends/xpu/xpu2_op_list.cc | 4 +++- paddle/phi/backends/xpu/xpu3_op_list.cc | 4 +++- paddle/phi/kernels/xpu/multinomial_kernel.cc | 3 ++- test/xpu/test_multinomial_op_xpu.py | 12 ++++++++---- 4 files changed, 16 insertions(+), 7 deletions(-) diff --git a/paddle/phi/backends/xpu/xpu2_op_list.cc b/paddle/phi/backends/xpu/xpu2_op_list.cc index b8e6d04efbeeb9..a296607bcb9db9 100644 --- a/paddle/phi/backends/xpu/xpu2_op_list.cc +++ b/paddle/phi/backends/xpu/xpu2_op_list.cc @@ -713,7 +713,9 @@ XPUOpMap& get_kl2_ops() { XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"multiclass_nms3", XPUKernelSet({phi::DataType::FLOAT32})}, {"multinomial", - XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, + XPUKernelSet({phi::DataType::FLOAT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16})}, {"nearest_interp_v2", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index ce6c464ebb21a8..f1ea5e21b3d1b3 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -813,7 +813,9 @@ XPUOpMap& get_kl3_ops() { XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"multiclass_nms3", XPUKernelSet({phi::DataType::FLOAT32})}, {"multinomial", - XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, + XPUKernelSet({phi::DataType::FLOAT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16})}, {"nearest_interp_v2", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, diff --git a/paddle/phi/kernels/xpu/multinomial_kernel.cc b/paddle/phi/kernels/xpu/multinomial_kernel.cc index 3874c44b9b6e8a..64d0cadb9c3355 100644 --- a/paddle/phi/kernels/xpu/multinomial_kernel.cc +++ b/paddle/phi/kernels/xpu/multinomial_kernel.cc @@ -79,6 +79,7 @@ PD_REGISTER_KERNEL(multinomial, ALL_LAYOUT, phi::MultinomialKernel, float, - phi::dtype::float16) { + phi::dtype::float16, + phi::dtype::bfloat16) { kernel->OutputAt(0).SetDataType(phi::DataType::INT64); } diff --git a/test/xpu/test_multinomial_op_xpu.py b/test/xpu/test_multinomial_op_xpu.py index 283f6bb19dae28..c700216e0d16c8 100644 --- a/test/xpu/test_multinomial_op_xpu.py +++ b/test/xpu/test_multinomial_op_xpu.py @@ -21,6 +21,7 @@ create_test_class, get_xpu_op_support_types, ) +from op_test import convert_float_to_uint16 from op_test_xpu import XPUOpTest import paddle @@ -61,11 +62,14 @@ def setUp(self): self.op_type = "multinomial" self.python_api = paddle.multinomial self.init_data() - self.inputs = {"X": self.input_np} + if self.in_type == np.uint16: + self.inputs = {"X": convert_float_to_uint16(self.input_np)} + else: + self.inputs = {"X": self.input_np.astype(self.dtype)} def init_data(self): # input probability is a vector, and replacement is True - self.input_np = np.random.rand(4).astype(self.dtype) + self.input_np = np.random.rand(4).astype(np.float32) self.outputs = {"Out": np.zeros(100000).astype("int64")} self.attrs = {"num_samples": 100000, "replacement": True} @@ -95,7 +99,7 @@ def verify_output(self, outs): class TestMultinomialOp2(TestMultinomialOp): def init_data(self): # input probability is a matrix - self.input_np = np.random.rand(3, 4).astype(self.dtype) + self.input_np = np.random.rand(3, 4).astype(np.float32) self.outputs = {"Out": np.zeros((3, 100000)).astype("int64")} self.attrs = {"num_samples": 100000, "replacement": True} @@ -105,7 +109,7 @@ def sample_output(self, out): class TestMultinomialOp3(TestMultinomialOp): def init_data(self): # replacement is False. number of samples must be less than number of categories. - self.input_np = np.random.rand(1000).astype(self.dtype) + self.input_np = np.random.rand(1000).astype(np.float32) self.outputs = {"Out": np.zeros(100).astype("int64")} self.attrs = {"num_samples": 100, "replacement": False} From 1faef3bf5c8c0273ddc7d2ecef105fcc211dc9ae Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:55:45 +0800 Subject: [PATCH 134/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-2=20(#64757)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../fleet_executor/compute_interceptor.cc | 6 ++---- .../distributed/test/dense_table_test.cc | 6 ++---- .../test/memory_sparse_table_test.cc | 6 ++---- paddle/fluid/framework/dataset_factory.cc | 6 ++---- .../fluid/framework/details/op_handle_base.cc | 8 ++------ paddle/fluid/framework/fleet/gloo_wrapper.cc | 18 ++++++----------- ...ptive_pool2d_convert_global_pass_tester.cc | 8 ++------ .../framework/ir/add_support_int8_pass.cc | 8 ++------ .../ir/fc_elementwise_layernorm_fuse_pass.cc | 20 ++++++------------- .../framework/ir/fuse_elewise_add_act_pass.cc | 8 ++------ .../framework/ir/fuse_gemm_epilogue_pass.cc | 8 ++------ .../ir/fusion_group/code_generator_tester.cc | 10 ++-------- .../ir/onednn/fc_act_onednn_fuse_pass.cc | 8 ++------ .../ir/repeated_fc_relu_fuse_pass_tester.cc | 14 ++++--------- .../framework/ir/trans_layernorm_fuse_pass.cc | 20 ++++++------------- .../control_flow/if_instruction.cc | 6 ++---- .../control_flow/select_output_instruction.cc | 6 ++---- paddle/fluid/imperative/basic_engine.cc | 6 ++---- .../tensorrt/convert/nearest_interp_v2_op.cc | 8 ++------ .../inference/tensorrt/convert/pad3d_op.cc | 8 ++------ .../tensorrt/convert/shuffle_channel_op.cc | 8 ++------ .../inference/tensorrt/convert/softmax_op.cc | 8 ++------ paddle/fluid/jit/function.cc | 6 ++---- paddle/fluid/operators/activation_op.cc | 6 ++---- .../detection/distribute_fpn_proposals_op.cc | 6 ++---- .../operators/pscore/listen_and_serv_op.cc | 16 ++++++--------- .../fluid/pir/dialect/operator/ir/op_type.cc | 6 ++---- .../prim/api/manual_prim/utils/eager_utils.cc | 6 ++---- .../manual/manual_eager_prim_backend.cc | 8 ++------ paddle/fluid/pybind/crypto.cc | 6 ++---- paddle/fluid/pybind/custom_device_py.cc | 6 ++---- paddle/phi/backends/stream.cc | 6 ++---- .../distributed/auto_parallel/dist_tensor.cc | 6 ++---- .../reshard/r_to_p_reshard_function.cc | 6 ++---- .../reshard/s_to_p_reshard_function.cc | 6 ++---- .../phi/core/distributed/gloo_comm_context.cc | 6 ++---- .../core/memory/allocation/buddy_allocator.cc | 8 ++------ .../phi/core/platform/gen_comm_id_helper.cc | 6 ++---- .../core/platform/profiler/cpu_utilization.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/expand_as.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/numel.cc | 7 +++---- paddle/phi/infermeta/spmd_rules/triu.cc | 6 ++---- paddle/phi/kernels/funcs/sequence_scale.cc | 6 ++---- .../phi/kernels/sparse/cpu/coalesce_kernel.cc | 6 ++---- .../kernels/sparse/cpu/elementwise_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/pool_kernel.cc | 6 ++---- test/cpp/fluid/platform/complex_test.cc | 6 ++---- 47 files changed, 113 insertions(+), 256 deletions(-) diff --git a/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc b/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc index c9be8264ee7329..1c29559d4424fa 100644 --- a/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc +++ b/paddle/fluid/distributed/fleet_executor/compute_interceptor.cc @@ -21,8 +21,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/jit/serializer.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { ComputeInterceptor::ComputeInterceptor(int64_t interceptor_id, TaskNode* node) : Interceptor(interceptor_id, node), @@ -398,5 +397,4 @@ void ComputeInterceptor::Compute(const InterceptorMessage& msg) { REGISTER_INTERCEPTOR(Compute, ComputeInterceptor); -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/test/dense_table_test.cc b/paddle/fluid/distributed/test/dense_table_test.cc index 8b021e2c9624e3..1a5d6ff5f6c3fb 100644 --- a/paddle/fluid/distributed/test/dense_table_test.cc +++ b/paddle/fluid/distributed/test/dense_table_test.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/fluid/distributed/ps/table/memory_dense_table.h" #include "paddle/fluid/distributed/the_one_ps.pb.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { // MemoryDenseTable + Adam class Table; @@ -227,5 +226,4 @@ TEST(MemoryDenseTable, SGD) { } } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/test/memory_sparse_table_test.cc b/paddle/fluid/distributed/test/memory_sparse_table_test.cc index 391d387b76c9aa..33a4d97ba2164f 100644 --- a/paddle/fluid/distributed/test/memory_sparse_table_test.cc +++ b/paddle/fluid/distributed/test/memory_sparse_table_test.cc @@ -25,8 +25,7 @@ limitations under the License. */ #include "paddle/fluid/distributed/ps/table/table.h" #include "paddle/fluid/distributed/the_one_ps.pb.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { TEST(MemorySparseTable, SGD) { int emb_dim = 8; @@ -152,5 +151,4 @@ TEST(MemorySparseTable, SGD) { } } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/dataset_factory.cc b/paddle/fluid/framework/dataset_factory.cc index 38200927c5586f..25f2a1e7095219 100644 --- a/paddle/fluid/framework/dataset_factory.cc +++ b/paddle/fluid/framework/dataset_factory.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "glog/logging.h" -namespace paddle { -namespace framework { +namespace paddle::framework { typedef std::unique_ptr (*CreateDatasetFunction)(); typedef std::unordered_map datasetMap; datasetMap g_dataset_map; @@ -62,5 +61,4 @@ std::unique_ptr DatasetFactory::CreateDataset( REGISTER_DATASET_CLASS(MultiSlotDataset); REGISTER_DATASET_CLASS(SlotRecordDataset); -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/details/op_handle_base.cc b/paddle/fluid/framework/details/op_handle_base.cc index 8e7256105215fb..ded8d606af3aee 100644 --- a/paddle/fluid/framework/details/op_handle_base.cc +++ b/paddle/fluid/framework/details/op_handle_base.cc @@ -13,9 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/details/op_handle_base.h" -namespace paddle { -namespace framework { -namespace details { +namespace paddle::framework::details { std::string OpHandleBase::DebugString() const { std::stringstream ss; ss << Name() << "("; @@ -315,6 +313,4 @@ void OpHandleBase::SetLocalExecScopes( } } -} // namespace details -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::details diff --git a/paddle/fluid/framework/fleet/gloo_wrapper.cc b/paddle/fluid/framework/fleet/gloo_wrapper.cc index deb7752554d72c..d35500caef4dd3 100644 --- a/paddle/fluid/framework/fleet/gloo_wrapper.cc +++ b/paddle/fluid/framework/fleet/gloo_wrapper.cc @@ -14,14 +14,11 @@ limitations under the License. */ #include "paddle/fluid/framework/io/fs.h" #include "paddle/utils/string/string_helper.h" -namespace gloo { -namespace transport { +namespace gloo::transport { class Device; -} // namespace transport -} // namespace gloo +} // namespace gloo::transport -namespace gloo { -namespace rendezvous { +namespace gloo::rendezvous { class HTTPStore; class Store; @@ -300,11 +297,9 @@ void ParallelConnectContext::connectFullMesh( VLOG(0) << "ParallelConnectContext::connectFullMesh() is over"; } #endif -} // namespace rendezvous -} // namespace gloo +} // namespace gloo::rendezvous -namespace paddle { -namespace framework { +namespace paddle::framework { void GlooWrapper::Init() { if (is_initialized_) { @@ -377,5 +372,4 @@ template std::vector GlooWrapper::AllGather( template std::vector GlooWrapper::AllGather( double& input); // NOLINT -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass_tester.cc b/paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass_tester.cc index dcc4d9defd9624..ae6dc3ebc0c996 100644 --- a/paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass_tester.cc +++ b/paddle/fluid/framework/ir/adaptive_pool2d_convert_global_pass_tester.cc @@ -19,9 +19,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { TEST(AdaptivePool2dConvertGlobalPass, basic) { Layers layers; @@ -63,8 +61,6 @@ TEST(AdaptivePool2dConvertGlobalPass, pass_op_version_check) { .IsPassCompatible("adaptive_pool2d_convert_global_pass")); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(adaptive_pool2d_convert_global_pass); diff --git a/paddle/fluid/framework/ir/add_support_int8_pass.cc b/paddle/fluid/framework/ir/add_support_int8_pass.cc index 5dedfe59f6900a..bde6690ed5bcea 100644 --- a/paddle/fluid/framework/ir/add_support_int8_pass.cc +++ b/paddle/fluid/framework/ir/add_support_int8_pass.cc @@ -14,9 +14,7 @@ #include "paddle/fluid/framework/ir/add_support_int8_pass.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { #define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern); #define GET_NODES GET_IR_NODE(quant_op); @@ -89,8 +87,6 @@ void AddSupportInt8Pass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(add_support_int8_pass, paddle::framework::ir::AddSupportInt8Pass); diff --git a/paddle/fluid/framework/ir/fc_elementwise_layernorm_fuse_pass.cc b/paddle/fluid/framework/ir/fc_elementwise_layernorm_fuse_pass.cc index 6c919b6db903e7..979693ae3ee317 100644 --- a/paddle/fluid/framework/ir/fc_elementwise_layernorm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/fc_elementwise_layernorm_fuse_pass.cc @@ -19,18 +19,11 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { struct FCElementwiseLayerNorm : public PatternBase { FCElementwiseLayerNorm(PDPattern *pattern, const std::string &name_scope) @@ -122,7 +115,8 @@ PDNode *FCElementwiseLayerNorm::operator()(PDNode *x) { return layer_norm_out_var; } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { template static bool IsEqual(const std::vector &x, const std::vector &y) { @@ -333,9 +327,7 @@ void FCElementwiseLayerNormFusePass::ApplyImpl(ir::Graph *graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fc_elementwise_layernorm_fuse_pass, paddle::framework::ir::FCElementwiseLayerNormFusePass); diff --git a/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc b/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc index f47c1a9c5f5ef7..01844d6e0473b5 100644 --- a/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc +++ b/paddle/fluid/framework/ir/fuse_elewise_add_act_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void FuseElewiseAddActPass::ApplyImpl(ir::Graph *graph) const { std::unordered_set act_types = {"relu", "scale", "tanh"}; @@ -527,9 +525,7 @@ std::vector FuseElewiseAddActPass::RemoveNode( static_cast(std::distance(new_list.begin(), end_iter))); return new_list; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fuse_elewise_add_act_pass, paddle::framework::ir::FuseElewiseAddActPass); diff --git a/paddle/fluid/framework/ir/fuse_gemm_epilogue_pass.cc b/paddle/fluid/framework/ir/fuse_gemm_epilogue_pass.cc index 8b715339166adf..ff9f480ec3cebe 100644 --- a/paddle/fluid/framework/ir/fuse_gemm_epilogue_pass.cc +++ b/paddle/fluid/framework/ir/fuse_gemm_epilogue_pass.cc @@ -20,9 +20,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { static void GetTransposeAttrsFromOp(const OpDesc &op, bool *trans_x, @@ -511,9 +509,7 @@ bool FuseGemmEpiloguePass::IsGemmFromLinear_( return (w_shape.size() == 2 && x_shape.size() >= 2); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fuse_gemm_epilogue_pass, paddle::framework::ir::FuseGemmEpiloguePass); diff --git a/paddle/fluid/framework/ir/fusion_group/code_generator_tester.cc b/paddle/fluid/framework/ir/fusion_group/code_generator_tester.cc index f24028068528a5..6fd7a075988ab8 100644 --- a/paddle/fluid/framework/ir/fusion_group/code_generator_tester.cc +++ b/paddle/fluid/framework/ir/fusion_group/code_generator_tester.cc @@ -29,10 +29,7 @@ class DenseTensor; #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) -namespace paddle { -namespace framework { -namespace ir { -namespace fusion_group { +namespace paddle::framework::ir::fusion_group { // relu inline float relu(float x) { return x > 0 ? x : 0.; } // NOLINT @@ -164,10 +161,7 @@ void SetupRandomCPUTensor(phi::DenseTensor* tensor) { } } -} // namespace fusion_group -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir::fusion_group namespace fusion_group = paddle::framework::ir::fusion_group; diff --git a/paddle/fluid/framework/ir/onednn/fc_act_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/fc_act_onednn_fuse_pass.cc index 7f702be2cd54d2..8c7220b89ab404 100644 --- a/paddle/fluid/framework/ir/onednn/fc_act_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/fc_act_onednn_fuse_pass.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -66,9 +64,7 @@ void FuseFCActOneDNNPass::FuseFCAct(Graph *graph, "--- fused %d fc with %s activation", found_fc_act_count, act_type); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fc_act_onednn_fuse_pass, paddle::framework::ir::FuseFCActOneDNNPass); diff --git a/paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass_tester.cc b/paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass_tester.cc index 55b863f80b866b..ee031938ef2465 100644 --- a/paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass_tester.cc @@ -17,15 +17,11 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/ir/repeated_fc_relu_fuse_pass.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class VarDesc; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void TestMain(int num_fc) { // inputs operator output @@ -80,8 +76,6 @@ TEST(RepeatedFCReluFusePass, basic_3) { TestMain(3); } TEST(RepeatedFCReluFusePass, basic_9) { TestMain(9); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(repeated_fc_relu_fuse_pass); diff --git a/paddle/fluid/framework/ir/trans_layernorm_fuse_pass.cc b/paddle/fluid/framework/ir/trans_layernorm_fuse_pass.cc index 23825ded580566..d8dd917ac22fe9 100644 --- a/paddle/fluid/framework/ir/trans_layernorm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/trans_layernorm_fuse_pass.cc @@ -17,18 +17,11 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { struct TransLayernormPattern : public PatternBase { TransLayernormPattern(PDPattern *pattern, const std::string &name_scope) : PatternBase(pattern, name_scope, "trans_layernorm") {} @@ -71,7 +64,8 @@ void TransLayernormPattern::operator()(PDNode *x) { layernorm->LinksFrom({reshape_output, layernorm_scale, layernorm_bias}) .LinksTo({layernorm_output}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { // this pass make a fusion as below: // @@ -199,9 +193,7 @@ void TransLayernormFusePass::ApplyImpl(ir::Graph *graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(trans_layernorm_fuse_pass, paddle::framework::ir::TransLayernormFusePass); diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc index d3417759afe0a0..bbbcaf9c64815a 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc @@ -41,8 +41,7 @@ #include "paddle/fluid/platform/onednn_helper.h" #endif -namespace paddle { -namespace framework { +namespace paddle::framework { IfInstruction::IfInstruction(size_t id, const phi::Place& place, @@ -266,5 +265,4 @@ void IfInstruction::Run() { // copy output } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc index 2f913ca4847906..54f9df4d72b14f 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/select_output_instruction.cc @@ -17,8 +17,7 @@ #include "paddle/fluid/framework/new_executor/new_executor_defs.h" #include "paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.h" -namespace paddle { -namespace framework { +namespace paddle::framework { SelectOutputInstruction::SelectOutputInstruction( size_t id, @@ -138,5 +137,4 @@ void SelectOutputInstruction::Run() { VisitVarType(*input_, AssignFunctor(selected)); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/imperative/basic_engine.cc b/paddle/fluid/imperative/basic_engine.cc index 78eaf8012f2d4a..9062f014484914 100644 --- a/paddle/fluid/imperative/basic_engine.cc +++ b/paddle/fluid/imperative/basic_engine.cc @@ -36,8 +36,7 @@ COMMON_DECLARE_bool(sort_sum_gradient); -namespace paddle { -namespace imperative { +namespace paddle::imperative { void BasicEngine::Init( const std::vector>& tensors, @@ -683,5 +682,4 @@ void BasicEngine::Clear() { leaf_accumulators_.clear(); } -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative diff --git a/paddle/fluid/inference/tensorrt/convert/nearest_interp_v2_op.cc b/paddle/fluid/inference/tensorrt/convert/nearest_interp_v2_op.cc index 528c1d45ca4482..5d548960e3412e 100644 --- a/paddle/fluid/inference/tensorrt/convert/nearest_interp_v2_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/nearest_interp_v2_op.cc @@ -12,9 +12,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_layout.h" #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class NearestInterpolateV2OpConverter : public OpConverter { public: @@ -129,8 +127,6 @@ class NearestInterpolateV2OpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(nearest_interp_v2, NearestInterpolateV2OpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/pad3d_op.cc b/paddle/fluid/inference/tensorrt/convert/pad3d_op.cc index 043b2723991f17..3179ae6b760055 100644 --- a/paddle/fluid/inference/tensorrt/convert/pad3d_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pad3d_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Pad3dOp. @@ -175,8 +173,6 @@ class Pad3dOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(pad3d, Pad3dOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc b/paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc index de33802f93a21f..4ae525d13d25f0 100644 --- a/paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/shuffle_channel_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * ConcatOp @@ -64,8 +62,6 @@ class ShuffleChannelOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(shuffle_channel, ShuffleChannelOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/softmax_op.cc b/paddle/fluid/inference/tensorrt/convert/softmax_op.cc index e136e21bcf03c7..2becf9e042a259 100644 --- a/paddle/fluid/inference/tensorrt/convert/softmax_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/softmax_op.cc @@ -16,9 +16,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * SoftMaxOp, ISoftMaxLayer in TRT. This Layer doesn't has weights. @@ -91,9 +89,7 @@ class SoftMaxOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt USE_OP_ITSELF(softmax); REGISTER_TRT_OP_CONVERTER(softmax, SoftMaxOpConverter); diff --git a/paddle/fluid/jit/function.cc b/paddle/fluid/jit/function.cc index 29522214cda3dd..da387ea93558de 100644 --- a/paddle/fluid/jit/function.cc +++ b/paddle/fluid/jit/function.cc @@ -23,8 +23,7 @@ #include "paddle/fluid/jit/engine/base_engine.h" #include "paddle/fluid/jit/function_utils.h" -namespace paddle { -namespace jit { +namespace paddle::jit { Function::Function(BaseEngine* engine) : engine_(engine) {} @@ -47,5 +46,4 @@ std::vector Function::operator()( return (*engine_)(inputs); } -} // namespace jit -} // namespace paddle +} // namespace paddle::jit diff --git a/paddle/fluid/operators/activation_op.cc b/paddle/fluid/operators/activation_op.cc index 7117559249f0b1..9bb4212743d8ef 100644 --- a/paddle/fluid/operators/activation_op.cc +++ b/paddle/fluid/operators/activation_op.cc @@ -31,8 +31,7 @@ limitations under the License. */ COMMON_DECLARE_bool(use_mkldnn); -namespace paddle { -namespace operators { +namespace paddle::operators { template static constexpr bool CanInplaceAct() { @@ -290,8 +289,7 @@ DECLARE_INPLACE_OP_INFERER(ActivationTripleGradOpInplaceInferer, DECLARE_INPLACE_OP_INFERER(ActFwdInplaceInferer, {"X", "Out"}); -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc b/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc index 9fa761abcfabc2..ca09ae648230d3 100644 --- a/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc +++ b/paddle/fluid/operators/detection/distribute_fpn_proposals_op.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class DistributeFpnProposalsOp : public framework::OperatorWithKernel { public: @@ -80,8 +79,7 @@ we return an array which indicate the original index of rois in )DOC"); } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/pscore/listen_and_serv_op.cc b/paddle/fluid/operators/pscore/listen_and_serv_op.cc index a5ba7a34825a0b..7a30990408b9cf 100644 --- a/paddle/fluid/operators/pscore/listen_and_serv_op.cc +++ b/paddle/fluid/operators/pscore/listen_and_serv_op.cc @@ -21,21 +21,18 @@ constexpr char kPrefetchVarNameToBlockId[] = constexpr char kOptimizeBlocks[] = "optimize_blocks"; // NOLINT constexpr char kSparseGradToParam[] = "sparse_grad_to_param"; // NOLINT -namespace paddle { -namespace framework { +namespace paddle::framework { class InferShapeContext; class OpDesc; class Scope; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class ListenAndServOp : public framework::OperatorBase { public: @@ -107,8 +104,7 @@ class ListenAndServOpShapeInference : public framework::InferShapeBase { void operator()(framework::InferShapeContext* ctx) const override {} }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/pir/dialect/operator/ir/op_type.cc b/paddle/fluid/pir/dialect/operator/ir/op_type.cc index 7e7f0910025285..4d8e75d312864d 100644 --- a/paddle/fluid/pir/dialect/operator/ir/op_type.cc +++ b/paddle/fluid/pir/dialect/operator/ir/op_type.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/pir/dialect/operator/ir/op_type.h" -namespace paddle { -namespace dialect { +namespace paddle::dialect { const pir::Type& SelectedRowsType::dtype() const { return storage()->dtype_; } const phi::DDim& SelectedRowsType::dims() const { return storage()->dims_; } @@ -163,8 +162,7 @@ SparseCsrTensorType SparseCsrTensorType::dyn_cast_impl(Type type) { } return nullptr; } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::SelectedRowsType) IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::DenseTensorArrayType) diff --git a/paddle/fluid/prim/api/manual_prim/utils/eager_utils.cc b/paddle/fluid/prim/api/manual_prim/utils/eager_utils.cc index b760b903bf434c..22731428151e53 100644 --- a/paddle/fluid/prim/api/manual_prim/utils/eager_utils.cc +++ b/paddle/fluid/prim/api/manual_prim/utils/eager_utils.cc @@ -16,8 +16,7 @@ #include "paddle/fluid/prim/api/manual_prim/utils/utils.h" #include "paddle/phi/api/include/tensor.h" -namespace paddle { -namespace prim { +namespace paddle::prim { template <> Tensor empty(const paddle::experimental::IntArray& shape, @@ -50,5 +49,4 @@ void by_pass(const paddle::Tensor& x, Tensor* out) { set_output(x, out); } -} // namespace prim -} // namespace paddle +} // namespace paddle::prim diff --git a/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc b/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc index 393ae879d34521..6aef7ee8c7bdc4 100644 --- a/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc +++ b/paddle/fluid/primitive/backend/manual/manual_eager_prim_backend.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/primitive/backend/generated/generated_backend.h" #include "paddle/fluid/primitive/backend/manual/manual_prim_backend.h" -namespace paddle { -namespace primitive { -namespace backend { +namespace paddle::primitive::backend { template <> Tensor full(const IntArray& shape, @@ -45,6 +43,4 @@ Tensor arange(const Tensor& start, return ::arange_ad_func(start, end, step, dtype, place); } -} // namespace backend -} // namespace primitive -} // namespace paddle +} // namespace paddle::primitive::backend diff --git a/paddle/fluid/pybind/crypto.cc b/paddle/fluid/pybind/crypto.cc index dd7787501ecb93..8553cc08a9a438 100644 --- a/paddle/fluid/pybind/crypto.cc +++ b/paddle/fluid/pybind/crypto.cc @@ -23,8 +23,7 @@ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { using paddle::framework::AESCipher; using paddle::framework::Cipher; @@ -145,5 +144,4 @@ void BindCrypto(py::module* m) { BindAESCipher(m); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/custom_device_py.cc b/paddle/fluid/pybind/custom_device_py.cc index e072466b5ed4ea..fb36983d9090c4 100644 --- a/paddle/fluid/pybind/custom_device_py.cc +++ b/paddle/fluid/pybind/custom_device_py.cc @@ -24,8 +24,7 @@ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindCustomDevicePy(py::module *m_ptr) { auto &m = *m_ptr; // Bind Methods @@ -580,5 +579,4 @@ void BindCustomDevicePy(py::module *m_ptr) { #endif }); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/stream.cc b/paddle/phi/backends/stream.cc index 5f6cd24ed34e12..6244751ee65b13 100644 --- a/paddle/phi/backends/stream.cc +++ b/paddle/phi/backends/stream.cc @@ -21,8 +21,7 @@ #include "paddle/phi/backends/device_guard.h" #include "paddle/phi/backends/event.h" -namespace phi { -namespace stream { +namespace phi::stream { std::list g_streams; std::mutex g_streams_mutex; @@ -119,5 +118,4 @@ void Stream::Synchronize() const { device_->SynchronizeStream(this); } const Place& Stream::GetPlace() const { return place_; } -} // namespace stream -} // namespace phi +} // namespace phi::stream diff --git a/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc b/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc index e6644985176bd3..cc22d17867ef96 100644 --- a/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc +++ b/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc @@ -22,8 +22,7 @@ #include "paddle/phi/core/distributed/auto_parallel/reshard/reshard_utils.h" #include "paddle/phi/core/distributed/store/store_utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { inline void check_defined(const DistTensor& dist_tensor, std::string method_hint) { @@ -332,5 +331,4 @@ void DistTensor::clear() { } } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/r_to_p_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/r_to_p_reshard_function.cc index e53a2f9009803a..dc19c04e6c2102 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/r_to_p_reshard_function.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/r_to_p_reshard_function.cc @@ -24,8 +24,7 @@ #include "paddle/phi/kernels/assign_kernel.h" #include "paddle/phi/kernels/full_kernel.h" -namespace phi { -namespace distributed { +namespace phi::distributed { bool RToPReshardFunction::IsSuitable(const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -121,5 +120,4 @@ void RToPReshardFunctionCrossMesh::Eval(phi::DeviceContext* dev_ctx, } } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/s_to_p_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/s_to_p_reshard_function.cc index fdaa562e334e5a..ca8af19d9b1aea 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/s_to_p_reshard_function.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/s_to_p_reshard_function.cc @@ -24,8 +24,7 @@ #include "paddle/phi/kernels/reduce_scatter_kernel.h" #include "paddle/phi/kernels/transpose_kernel.h" -namespace phi { -namespace distributed { +namespace phi::distributed { bool SToPReshardFunction::IsSuitable(const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -111,5 +110,4 @@ void SToPReshardFunctionCrossMesh::Eval(DeviceContext* dev_ctx, } } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/gloo_comm_context.cc b/paddle/phi/core/distributed/gloo_comm_context.cc index 84197e47af2705..792d16505cbf64 100644 --- a/paddle/phi/core/distributed/gloo_comm_context.cc +++ b/paddle/phi/core/distributed/gloo_comm_context.cc @@ -29,8 +29,7 @@ #include "paddle/phi/core/distributed/check/static_check.h" #include "paddle/phi/core/enforce.h" -namespace phi { -namespace distributed { +namespace phi::distributed { GlooCommContext::GlooCommContext( int rank, @@ -168,5 +167,4 @@ void GlooCommContext::Recv(phi::DenseTensor* out_tensor, send_recv(&opts); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/memory/allocation/buddy_allocator.cc b/paddle/phi/core/memory/allocation/buddy_allocator.cc index e8e6f09de83299..e2909ef6d750be 100644 --- a/paddle/phi/core/memory/allocation/buddy_allocator.cc +++ b/paddle/phi/core/memory/allocation/buddy_allocator.cc @@ -27,9 +27,7 @@ COMMON_DECLARE_uint64(reallocate_gpu_memory_in_mb); #include "paddle/phi/common/place.h" #include "paddle/phi/core/platform/device/device_wrapper.h" -namespace paddle { -namespace memory { -namespace detail { +namespace paddle::memory::detail { BuddyAllocator::BuddyAllocator( std::unique_ptr system_allocator, @@ -382,6 +380,4 @@ size_t BuddyAllocator::DeviceAllocateSize( return allocate_bytes; } -} // namespace detail -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::detail diff --git a/paddle/phi/core/platform/gen_comm_id_helper.cc b/paddle/phi/core/platform/gen_comm_id_helper.cc index 999e47c3c127ba..a8cb386a10d019 100644 --- a/paddle/phi/core/platform/gen_comm_id_helper.cc +++ b/paddle/phi/core/platform/gen_comm_id_helper.cc @@ -40,8 +40,7 @@ limitations under the License. */ COMMON_DECLARE_int32(get_host_by_name_time); -namespace paddle { -namespace platform { +namespace paddle::platform { std::once_flag SocketServer::init_flag_; @@ -509,7 +508,6 @@ INSTANT_TEMPLATE(BKCLUniqueId) #ifdef PADDLE_WITH_CUSTOM_DEVICE INSTANT_TEMPLATE(phi::ccl::CCLRootId) #endif -} // namespace platform -} // namespace paddle +} // namespace paddle::platform #endif diff --git a/paddle/phi/core/platform/profiler/cpu_utilization.cc b/paddle/phi/core/platform/profiler/cpu_utilization.cc index 20be9d700f7b6b..354d119e709b41 100644 --- a/paddle/phi/core/platform/profiler/cpu_utilization.cc +++ b/paddle/phi/core/platform/profiler/cpu_utilization.cc @@ -17,8 +17,7 @@ #include #include "glog/logging.h" -namespace paddle { -namespace platform { +namespace paddle::platform { #ifdef _MSC_VER static uint64_t FileTimeToUint64(FILETIME time) { @@ -186,5 +185,4 @@ float CpuUtilization::GetCpuCurProcessUtilization() { return cpu_process_utilization; } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/phi/infermeta/spmd_rules/expand_as.cc b/paddle/phi/infermeta/spmd_rules/expand_as.cc index ea26fe7b54c262..9600ff0c0e57b4 100644 --- a/paddle/phi/infermeta/spmd_rules/expand_as.cc +++ b/paddle/phi/infermeta/spmd_rules/expand_as.cc @@ -15,8 +15,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { std::tuple AlignExpandAsDistAttrs( const DistMetaTensor& x, const DistMetaTensor& y) { @@ -82,5 +81,4 @@ SpmdInfo ExpandAsGradInferSpmd(const DistMetaTensor& x, return {{x_dist_attr, y_dist_attr}, {x_grad_dist_attr}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/numel.cc b/paddle/phi/infermeta/spmd_rules/numel.cc index 76c41ba881ae4c..6c77fb8fc5bde0 100644 --- a/paddle/phi/infermeta/spmd_rules/numel.cc +++ b/paddle/phi/infermeta/spmd_rules/numel.cc @@ -19,8 +19,8 @@ limitations under the License. */ #include "paddle/phi/core/enforce.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { +using phi::distributed::auto_parallel::str_join; SpmdInfo NumelInferSpmd(const DistMetaTensor& x) { std::string alphabet = "abcdefghijklmnopqrstuvwxyz"; @@ -49,5 +49,4 @@ SpmdInfo NumelInferSpmd(const DistMetaTensor& x) { return SpmdInfo({x_dist_attr_src}, {out_dist_attr}); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/triu.cc b/paddle/phi/infermeta/spmd_rules/triu.cc index 0e5f4b268fef9a..c228832a62ded2 100644 --- a/paddle/phi/infermeta/spmd_rules/triu.cc +++ b/paddle/phi/infermeta/spmd_rules/triu.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/core/enforce.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; SpmdInfo TriuInferSpmdBase(const DistMetaTensor& x) { @@ -176,5 +175,4 @@ SpmdInfo TrilTriuInferSpmdReverse(const DistMetaTensor& x, bool lower) { return TriuInferSpmdReverseBase(x, out); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/sequence_scale.cc b/paddle/phi/kernels/funcs/sequence_scale.cc index 5f05af1c6bcd6e..8b4da0a33e966b 100644 --- a/paddle/phi/kernels/funcs/sequence_scale.cc +++ b/paddle/phi/kernels/funcs/sequence_scale.cc @@ -19,8 +19,7 @@ namespace phi { class DenseTensor; } // namespace phi -namespace phi { -namespace funcs { +namespace phi::funcs { template class ScaleLoDTensorFunctor { @@ -48,5 +47,4 @@ class ScaleLoDTensorFunctor { template class ScaleLoDTensorFunctor; template class ScaleLoDTensorFunctor; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/sparse/cpu/coalesce_kernel.cc b/paddle/phi/kernels/sparse/cpu/coalesce_kernel.cc index 4e582b35618ea9..bc6a7db6b381bc 100644 --- a/paddle/phi/kernels/sparse/cpu/coalesce_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/coalesce_kernel.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/phi/core/visit_type.h" #include "paddle/phi/kernels/funcs/sparse/flatten_indices.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void CoalesceCooCPUKernel(const CPUContext& dev_ctx, @@ -104,8 +103,7 @@ void CoalesceCooKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(coalesce_coo, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc b/paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc index 1ac45ebea449a5..3b04652701835a 100644 --- a/paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/elementwise_kernel.cc @@ -26,8 +26,7 @@ limitations under the License. */ #include "paddle/phi/kernels/sparse/empty_kernel.h" #include "paddle/phi/kernels/sparse/sparse_utils_kernel.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template struct BinaryOPWithZeroCompareFunctor { @@ -327,8 +326,7 @@ DEFINE_COO_ELEMENTWISE_KERNEL(Subtract) DEFINE_COO_ELEMENTWISE_KERNEL(Multiply) DEFINE_COO_ELEMENTWISE_KERNEL(Divide) -} // namespace sparse -} // namespace phi +} // namespace phi::sparse using complex64 = ::phi::dtype::complex; using complex128 = ::phi::dtype::complex; diff --git a/paddle/phi/kernels/sparse/cpu/pool_kernel.cc b/paddle/phi/kernels/sparse/cpu/pool_kernel.cc index ecd25f269dcba7..4cd2ef2dbfb329 100644 --- a/paddle/phi/kernels/sparse/cpu/pool_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/pool_kernel.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/kernels/funcs/sparse/convolution.h" #include "paddle/phi/kernels/sparse/cpu/conv.h" -namespace phi { -namespace sparse { +namespace phi::sparse { /** * x: (N, D, H, W, C) @@ -125,8 +124,7 @@ void MaxPoolCooKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(maxpool_coo, CPU, diff --git a/test/cpp/fluid/platform/complex_test.cc b/test/cpp/fluid/platform/complex_test.cc index fe41c64d841a15..c180f39a8e0892 100644 --- a/test/cpp/fluid/platform/complex_test.cc +++ b/test/cpp/fluid/platform/complex_test.cc @@ -23,8 +23,7 @@ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace platform { +namespace paddle::platform { template using complex = phi::dtype::complex; @@ -325,5 +324,4 @@ TEST(complex, isnan) { EXPECT_EQ(std::isnan(c1), true); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform From 183dcd986a0da77253fcf7e7cb83b04068edf733 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:57:25 +0800 Subject: [PATCH 135/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-4=20(#64759)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../spmd_rules/dist_tensor_spec.cc | 8 ++------ .../fleet_executor/fleet_executor.cc | 6 ++---- .../distributed/fleet_executor/task_node.cc | 6 ++---- paddle/fluid/framework/attribute.cc | 6 ++---- .../framework/ir/attention_lstm_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/fuse_pass_base.cc | 14 ++++--------- .../operator_unsqueeze2_onednn_fuse_pass.cc | 8 ++------ .../framework/ir/op_compat_sensible_pass.cc | 8 ++------ .../ir/set_transformer_input_convert_pass.cc | 12 ++++------- ...t_embedding_eltwise_layernorm_fuse_pass.cc | 20 ++++++------------- .../control_flow/while_instruction.cc | 6 ++---- .../instruction/phi_kernel_instruction.cc | 6 ++---- .../new_executor/interpreter/static_build.cc | 8 ++------ paddle/fluid/framework/trainer_factory.cc | 6 ++---- paddle/fluid/framework/var_desc.cc | 6 ++---- paddle/fluid/imperative/tracer.cc | 6 ++---- .../passes/inference_op_replace_pass.cc | 8 ++------ .../inference/tensorrt/convert/arg_max_op.cc | 8 ++------ .../inference/tensorrt/convert/clip_op.cc | 8 ++------ .../inference/tensorrt/convert/cumsum_op.cc | 8 ++------ .../tensorrt/convert/deformable_conv_op.cc | 8 ++------ .../tensorrt/convert/dequantize_linear_op.cc | 8 ++------ .../tensorrt/convert/fill_any_like_op.cc | 8 ++------ .../tensorrt/convert/logsigmoid_op.cc | 8 ++------ .../inference/tensorrt/convert/one_hot_op.cc | 18 ++++++----------- .../operators/controlflow/while_op_helper.cc | 12 ++++------- .../operators/fused/fused_gemm_epilogue_op.cc | 6 ++---- .../pscore/distributed_push_sparse_op.cc | 6 ++---- .../operators/reader/create_py_reader_op.cc | 8 ++------ .../operators/reduce_ops/reduce_mean_op.cc | 6 ++---- .../decomp_utils/decomp_static_utils.cc | 6 ++---- paddle/fluid/pybind/communication.cc | 6 ++---- paddle/fluid/pybind/eager.cc | 6 ++---- paddle/phi/backends/gpu/cuda/cuda_info.cc | 8 ++------ .../distributed/auto_parallel/device_mesh.cc | 8 ++------ .../auto_parallel/placement_types.cc | 6 ++---- .../memory/allocation/buffered_allocator.cc | 8 ++------ .../core/memory/allocation/retry_allocator.cc | 8 ++------ .../allocation/thread_local_allocator.cc | 8 ++------ paddle/phi/core/memory/memcpy.cc | 6 ++---- paddle/phi/core/platform/collective_helper.cc | 6 ++---- .../platform/device/gpu/gpu_resource_pool.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/dim_trans.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/layer_norm.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/scale.cc | 6 ++---- .../phi/kernels/selected_rows/scale_kernel.cc | 6 ++---- paddle/pir/src/core/op_operand_impl.cc | 6 ++---- 47 files changed, 108 insertions(+), 258 deletions(-) diff --git a/paddle/fluid/distributed/auto_parallel/spmd_rules/dist_tensor_spec.cc b/paddle/fluid/distributed/auto_parallel/spmd_rules/dist_tensor_spec.cc index 36b6322836b20c..4652ebafbe312c 100644 --- a/paddle/fluid/distributed/auto_parallel/spmd_rules/dist_tensor_spec.cc +++ b/paddle/fluid/distributed/auto_parallel/spmd_rules/dist_tensor_spec.cc @@ -16,9 +16,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/utils.h" -namespace paddle { -namespace distributed { -namespace auto_parallel { +namespace paddle::distributed::auto_parallel { DistTensorSpec::DistTensorSpec(const std::vector& shape, const TensorDistAttr& dist_attr) { @@ -82,6 +80,4 @@ std::string DistTensorSpec::to_string() const { return spec_str; } -} // namespace auto_parallel -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed::auto_parallel diff --git a/paddle/fluid/distributed/fleet_executor/fleet_executor.cc b/paddle/fluid/distributed/fleet_executor/fleet_executor.cc index 5adb1ec4b854f9..0e5b6f53e433c7 100644 --- a/paddle/fluid/distributed/fleet_executor/fleet_executor.cc +++ b/paddle/fluid/distributed/fleet_executor/fleet_executor.cc @@ -28,8 +28,7 @@ #include "paddle/fluid/framework/program_desc.h" #include "paddle/fluid/framework/variable.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { FleetExecutor::FleetExecutor(const std::string& exe_desc_str) : carrier_ids_() { bool parse_flag = exe_desc_.ParseFromString(exe_desc_str); @@ -290,5 +289,4 @@ void FleetExecutor::Run(const std::string& carrier_id) { carrier->Start(); } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/fleet_executor/task_node.cc b/paddle/fluid/distributed/fleet_executor/task_node.cc index c855e954abca16..2bfcab55fa615c 100644 --- a/paddle/fluid/distributed/fleet_executor/task_node.cc +++ b/paddle/fluid/distributed/fleet_executor/task_node.cc @@ -18,8 +18,7 @@ #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/operator.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { namespace { using OperatorBase = TaskNode::OperatorBase; } @@ -186,5 +185,4 @@ void TaskNode::SetSendDownPerSteps(int64_t value) { send_down_per_steps_ = value; } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/attribute.cc b/paddle/fluid/framework/attribute.cc index 7fb8b716d38bcb..60d6c2f55ccb0e 100644 --- a/paddle/fluid/framework/attribute.cc +++ b/paddle/fluid/framework/attribute.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/phi/common/scalar.h" #include "paddle/utils/blank.h" -namespace paddle { -namespace framework { +namespace paddle::framework { paddle::any GetAttrValue(const Attribute& attr) { switch (AttrTypeID(attr)) { @@ -319,5 +318,4 @@ void CanonicalizeScalarAttrs(const proto::OpProto& op_proto, } } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc index c500169b1d657e..bd3458e3a879ff 100644 --- a/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/attention_lstm_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/ir/graph_viz_pass.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { AttentionLSTMFusePass::AttentionLSTMFusePass() { AddOpCompat(OpCompat("while")) @@ -377,9 +375,7 @@ void AttentionLSTMFusePass::ApplyImpl(ir::Graph* graph) const { FindWhileOp(graph); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(attention_lstm_fuse_pass, paddle::framework::ir::AttentionLSTMFusePass); diff --git a/paddle/fluid/framework/ir/fuse_pass_base.cc b/paddle/fluid/framework/ir/fuse_pass_base.cc index 359f998effbeb0..df14105a181353 100644 --- a/paddle/fluid/framework/ir/fuse_pass_base.cc +++ b/paddle/fluid/framework/ir/fuse_pass_base.cc @@ -16,15 +16,11 @@ #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; @@ -77,6 +73,4 @@ FuseOptions FusePassBase::FindFuseOption(const Node& node1, #endif } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/onednn/operator_unsqueeze2_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/operator_unsqueeze2_onednn_fuse_pass.cc index 696d5cdb4fd4e3..8a1f61d02052ed 100644 --- a/paddle/fluid/framework/ir/onednn/operator_unsqueeze2_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/operator_unsqueeze2_onednn_fuse_pass.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -109,9 +107,7 @@ void FuseOperatorUnsqueeze2OneDNNPass::FuseUnsqueeze2( op_type); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(operator_unsqueeze2_onednn_fuse_pass, paddle::framework::ir::FuseOperatorUnsqueeze2OneDNNPass); diff --git a/paddle/fluid/framework/ir/op_compat_sensible_pass.cc b/paddle/fluid/framework/ir/op_compat_sensible_pass.cc index 1f1ceaddad98fb..01ffdc65f14759 100644 --- a/paddle/fluid/framework/ir/op_compat_sensible_pass.cc +++ b/paddle/fluid/framework/ir/op_compat_sensible_pass.cc @@ -38,9 +38,7 @@ std::unordered_set global_extra_attrs = { "with_quant_attr"}; } // namespace -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { AttrCompat& AttrCompat::IsStringEQ(const std::string& value) { conditions_.emplace_back([value](const Attribute& attr) -> bool { @@ -325,6 +323,4 @@ bool OpCompatSensiblePass::IsCompat( return true; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/set_transformer_input_convert_pass.cc b/paddle/fluid/framework/ir/set_transformer_input_convert_pass.cc index 8f2f394aee8632..0734eba11142fc 100644 --- a/paddle/fluid/framework/ir/set_transformer_input_convert_pass.cc +++ b/paddle/fluid/framework/ir/set_transformer_input_convert_pass.cc @@ -18,10 +18,7 @@ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { void SetTransformerInputConvert::operator()(const std::string &pos_id) { std::unordered_set lookup_table_ops{"lookup_table", @@ -49,7 +46,8 @@ void MultiheadMatmulOP::operator()() { // links nodes. multihead_matmul_out->LinksFrom({multihead_matmul}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { void SetTransformerInputConvertPass::ApplyImpl(ir::Graph *graph) const { bool with_dynamic_shape = Get("with_dynamic_shape"); @@ -152,9 +150,7 @@ void SetTransformerInputConvertPass::ApplyImpl(ir::Graph *graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(set_transformer_input_convert_pass, paddle::framework::ir::SetTransformerInputConvertPass); diff --git a/paddle/fluid/framework/ir/trt_embedding_eltwise_layernorm_fuse_pass.cc b/paddle/fluid/framework/ir/trt_embedding_eltwise_layernorm_fuse_pass.cc index 71da146ad2f943..d9ead1a699084b 100644 --- a/paddle/fluid/framework/ir/trt_embedding_eltwise_layernorm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/trt_embedding_eltwise_layernorm_fuse_pass.cc @@ -18,18 +18,11 @@ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { static PDNode* create_emb_vars(PDPattern* pattern, const std::string& name, @@ -141,7 +134,8 @@ void TrtSkipLayerNorm::operator()() { .LinksTo({layer_norm_out, layer_norm_mean_var, layer_norm_variance_var}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { int TrtEmbeddingEltwiseLayerNormFusePass::BuildFusion( Graph* graph, const std::string& name_scope @@ -485,9 +479,7 @@ void TrtEmbeddingEltwiseLayerNormFusePass::ApplyImpl(Graph* graph) const { AddStatis(fusion_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(trt_embedding_eltwise_layernorm_fuse_pass, paddle::framework::ir::TrtEmbeddingEltwiseLayerNormFusePass); diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc index 3b47335760e18d..bdd6c97e61631d 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc @@ -42,8 +42,7 @@ #include "paddle/fluid/platform/onednn_helper.h" #endif -namespace paddle { -namespace framework { +namespace paddle::framework { WhileInstruction::WhileInstruction( size_t id, @@ -237,5 +236,4 @@ void WhileInstruction::Run() { VLOG(6) << "while instruction run done"; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/phi_kernel_instruction.cc b/paddle/fluid/framework/new_executor/instruction/phi_kernel_instruction.cc index dcc39a95e4115a..1f1f3d2a6682dd 100644 --- a/paddle/fluid/framework/new_executor/instruction/phi_kernel_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/phi_kernel_instruction.cc @@ -38,8 +38,7 @@ PHI_DEFINE_EXPORTED_bool(print_kernel_run_info, false, "Whether print kernel run info."); -namespace paddle { -namespace framework { +namespace paddle::framework { PhiKernelInstruction::PhiKernelInstruction( size_t id, @@ -233,5 +232,4 @@ void PhiKernelInstruction::Run() { VLOG(6) << "End run op " << phi_op_name_ << " kernel."; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/interpreter/static_build.cc b/paddle/fluid/framework/new_executor/interpreter/static_build.cc index b40418aff9b07d..d229613813a0bb 100644 --- a/paddle/fluid/framework/new_executor/interpreter/static_build.cc +++ b/paddle/fluid/framework/new_executor/interpreter/static_build.cc @@ -62,9 +62,7 @@ std::set StaticBuildBlackList = { "sparse_sparse_coo_tensor" /*: to handle sparse output*/, "distributed_fused_lamb_init"}; -namespace paddle { -namespace framework { -namespace interpreter { +namespace paddle::framework::interpreter { using InterpreterCore = framework::InterpreterCore; @@ -984,6 +982,4 @@ void FakeInitializeOutputsForStructureKernel( } } -} // namespace interpreter -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::interpreter diff --git a/paddle/fluid/framework/trainer_factory.cc b/paddle/fluid/framework/trainer_factory.cc index 81b2df6efc723d..61ba78c8147cda 100644 --- a/paddle/fluid/framework/trainer_factory.cc +++ b/paddle/fluid/framework/trainer_factory.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "glog/logging.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class TrainerBase; @@ -85,5 +84,4 @@ REGISTER_TRAINER_CLASS(PSGPUTrainer); #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) REGISTER_TRAINER_CLASS(PipelineTrainer); #endif -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/var_desc.cc b/paddle/fluid/framework/var_desc.cc index 971c5949e65f95..4a42a4ec9c468c 100644 --- a/paddle/fluid/framework/var_desc.cc +++ b/paddle/fluid/framework/var_desc.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_type.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { VarDesc::VarDesc(const VarDesc &other) : desc_(other.desc_), @@ -478,5 +477,4 @@ bool operator==(const VarDesc &left, const VarDesc &right) { right.Proto()->SerializeAsString(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/imperative/tracer.cc b/paddle/fluid/imperative/tracer.cc index c6911dba0029e8..c7cc830cb3825b 100644 --- a/paddle/fluid/imperative/tracer.cc +++ b/paddle/fluid/imperative/tracer.cc @@ -40,8 +40,7 @@ COMMON_DECLARE_string(tracer_onednn_ops_on); COMMON_DECLARE_string(tracer_onednn_ops_off); COMMON_DECLARE_bool(use_stride_kernel); -namespace paddle { -namespace imperative { +namespace paddle::imperative { thread_local std::string Tracer::python_stack_ = ""; thread_local bool Tracer::use_layout_autotune_ = false; @@ -649,5 +648,4 @@ phi::KernelSignature Tracer::GetExpectedKernelSignature( } } -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative diff --git a/paddle/fluid/inference/analysis/passes/inference_op_replace_pass.cc b/paddle/fluid/inference/analysis/passes/inference_op_replace_pass.cc index 993ab2e8618f47..85daf816db00bf 100644 --- a/paddle/fluid/inference/analysis/passes/inference_op_replace_pass.cc +++ b/paddle/fluid/inference/analysis/passes/inference_op_replace_pass.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/inference/analysis/argument.h" -namespace paddle { -namespace inference { -namespace analysis { +namespace paddle::inference::analysis { void InferenceOpReplacePass::RunImpl(Argument* argument) { if (argument->use_pir()) { @@ -47,6 +45,4 @@ std::string InferenceOpReplacePass::repr() const { return "inference_op_replace_pass"; } -} // namespace analysis -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::analysis diff --git a/paddle/fluid/inference/tensorrt/convert/arg_max_op.cc b/paddle/fluid/inference/tensorrt/convert/arg_max_op.cc index dc89f3b83722b2..d2e0a7bb04bcbf 100644 --- a/paddle/fluid/inference/tensorrt/convert/arg_max_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/arg_max_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ArgMaxOpConverter : public OpConverter { public: @@ -58,8 +56,6 @@ class ArgMaxOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(arg_max, ArgMaxOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/clip_op.cc b/paddle/fluid/inference/tensorrt/convert/clip_op.cc index f5caed8f232a6c..f8789c552e12c5 100644 --- a/paddle/fluid/inference/tensorrt/convert/clip_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/clip_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * ClipOp @@ -59,8 +57,6 @@ class ClipOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(clip, ClipOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/cumsum_op.cc b/paddle/fluid/inference/tensorrt/convert/cumsum_op.cc index a18d16bd012fb8..a6de4bce94a25d 100644 --- a/paddle/fluid/inference/tensorrt/convert/cumsum_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/cumsum_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Cumsum Op @@ -169,8 +167,6 @@ class CumsumOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(cumsum, CumsumOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/deformable_conv_op.cc b/paddle/fluid/inference/tensorrt/convert/deformable_conv_op.cc index 6eb9be6d2c0ece..862f366f171fa2 100644 --- a/paddle/fluid/inference/tensorrt/convert/deformable_conv_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/deformable_conv_op.cc @@ -18,9 +18,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/deformable_conv_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class DeformableConvOpConverter : public OpConverter { void operator()(const framework::proto::OpDesc& op, @@ -109,8 +107,6 @@ class DeformableConvOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(deformable_conv, DeformableConvOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/dequantize_linear_op.cc b/paddle/fluid/inference/tensorrt/convert/dequantize_linear_op.cc index 7466520e702cbc..e5515a82406733 100644 --- a/paddle/fluid/inference/tensorrt/convert/dequantize_linear_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/dequantize_linear_op.cc @@ -11,9 +11,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class DequantizeLinearOpConverter : public OpConverter { public: @@ -59,8 +57,6 @@ class DequantizeLinearOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(dequantize_linear, DequantizeLinearOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/fill_any_like_op.cc b/paddle/fluid/inference/tensorrt/convert/fill_any_like_op.cc index 2b3efea9bd7bd0..1295746e375867 100644 --- a/paddle/fluid/inference/tensorrt/convert/fill_any_like_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/fill_any_like_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class FillAnyLikeOpConverter : public OpConverter { public: @@ -81,8 +79,6 @@ class FillAnyLikeOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(fill_any_like, FillAnyLikeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc b/paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc index 993a46f050f8f1..cf6a3589654a82 100644 --- a/paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/logsigmoid_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class LogSigmoidOpConverter : public OpConverter { public: @@ -58,8 +56,6 @@ class LogSigmoidOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(logsigmoid, LogSigmoidOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/one_hot_op.cc b/paddle/fluid/inference/tensorrt/convert/one_hot_op.cc index 8d6d6895385778..1c5e3589d32241 100644 --- a/paddle/fluid/inference/tensorrt/convert/one_hot_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/one_hot_op.cc @@ -14,19 +14,15 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -namespace proto { +} // namespace paddle::framework +namespace paddle::framework::proto { class OpDesc; -} // namespace proto -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::proto -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * OneHot Op @@ -86,9 +82,7 @@ class OneHotOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(one_hot, OneHotOpConverter); REGISTER_TRT_OP_CONVERTER(one_hot_v2, OneHotOpConverter); diff --git a/paddle/fluid/operators/controlflow/while_op_helper.cc b/paddle/fluid/operators/controlflow/while_op_helper.cc index 5f5420204f1393..e56eb0848b2b53 100644 --- a/paddle/fluid/operators/controlflow/while_op_helper.cc +++ b/paddle/fluid/operators/controlflow/while_op_helper.cc @@ -18,14 +18,11 @@ #include "paddle/utils/string/string_helper.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class BlockDesc; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace operators { +namespace paddle::operators { // Set skip variables of while_op and while_grad_op // These variables should be skipped when eager deletion enables. @@ -283,5 +280,4 @@ void TransferVariablePlace(const framework::Scope *scope, << " place: " << new_t->place(); } -} // namespace operators -} // namespace paddle +} // namespace paddle::operators diff --git a/paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc b/paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc index fc3500cbd90c69..cc70380b9d334b 100644 --- a/paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc +++ b/paddle/fluid/operators/fused/fused_gemm_epilogue_op.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/core/infermeta_utils.h" #include "paddle/phi/infermeta/fusion.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class FusedGemmEpilogueOp : public framework::OperatorWithKernel { public: @@ -176,8 +175,7 @@ class FusedGemmEpilogueOpGradMaker : public framework::SingleGradOpMaker { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; DECLARE_INFER_SHAPE_FUNCTOR(fused_gemm_epilogue, diff --git a/paddle/fluid/operators/pscore/distributed_push_sparse_op.cc b/paddle/fluid/operators/pscore/distributed_push_sparse_op.cc index ee546595121097..d1a75211ff1c94 100644 --- a/paddle/fluid/operators/pscore/distributed_push_sparse_op.cc +++ b/paddle/fluid/operators/pscore/distributed_push_sparse_op.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_registry.h" #include "paddle/phi/kernels/funcs/math_function.h" -namespace paddle { -namespace operators { +namespace paddle::operators { constexpr int64_t kNoPadding = -1; @@ -125,8 +124,7 @@ random value and set the value into the table for the next looking up. )DOC"); } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/reader/create_py_reader_op.cc b/paddle/fluid/operators/reader/create_py_reader_op.cc index 472de92a88630d..7d52ea56d7889e 100644 --- a/paddle/fluid/operators/reader/create_py_reader_op.cc +++ b/paddle/fluid/operators/reader/create_py_reader_op.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/operators/reader/reader_op_registry.h" #include "paddle/phi/core/operators/reader/py_reader.h" -namespace paddle { -namespace operators { -namespace reader { +namespace paddle::operators::reader { class CreatePyReaderOp : public framework::OperatorBase { public: @@ -116,9 +114,7 @@ class CreatePyReaderOpMaker : public FileReaderMakerBase { } }; -} // namespace reader -} // namespace operators -} // namespace paddle +} // namespace paddle::operators::reader namespace reader = ::paddle::operators::reader; diff --git a/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc b/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc index 868690c02bc19c..afa4407c19320e 100644 --- a/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc +++ b/paddle/fluid/operators/reduce_ops/reduce_mean_op.cc @@ -23,8 +23,7 @@ #include "paddle/phi/infermeta/unary.h" namespace ops = paddle::operators; -namespace paddle { -namespace operators { +namespace paddle::operators { class ReduceBaseOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -336,8 +335,7 @@ If reduce_all is true, just reduce along all dimensions and output a scalar. virtual std::string GetName() const = 0; virtual std::string GetOpType() const = 0; }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators class __reduce_meanMaker__ : public ops::ReduceBaseOpMaker { protected: diff --git a/paddle/fluid/primitive/decomp_utils/decomp_static_utils.cc b/paddle/fluid/primitive/decomp_utils/decomp_static_utils.cc index cd2302394e2f58..649a29e303b929 100644 --- a/paddle/fluid/primitive/decomp_utils/decomp_static_utils.cc +++ b/paddle/fluid/primitive/decomp_utils/decomp_static_utils.cc @@ -15,8 +15,7 @@ #include "paddle/fluid/primitive/base/lazy_tensor.h" #include "paddle/fluid/primitive/decomp_utils/decomp_utils.h" -namespace paddle { -namespace primitive { +namespace paddle::primitive { template <> void set_output(const paddle::Tensor& x_tmp, paddle::Tensor* x) { x->set_impl(x_tmp.impl()); @@ -72,5 +71,4 @@ std::vector> ConstructVjpResultByStopGradients( return vjp_results; } -} // namespace primitive -} // namespace paddle +} // namespace paddle::primitive diff --git a/paddle/fluid/pybind/communication.cc b/paddle/fluid/pybind/communication.cc index 5e202a2b79d2e6..12e1182ab5e45b 100644 --- a/paddle/fluid/pybind/communication.cc +++ b/paddle/fluid/pybind/communication.cc @@ -34,8 +34,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindCommContextManager(py::module *m) { auto P2POption = py::class_(*m, "P2POption") @@ -142,5 +141,4 @@ void BindTCPStore(py::module *m) { &phi::distributed::CreateOrGetGlobalTCPStore); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/eager.cc b/paddle/fluid/pybind/eager.cc index 941b4c2f90ca8d..5ef4cfb58d1b79 100644 --- a/paddle/fluid/pybind/eager.cc +++ b/paddle/fluid/pybind/eager.cc @@ -54,8 +54,7 @@ using phi::distributed::ProcessMesh; using phi::distributed::TensorDistAttr; using phi::distributed::auto_parallel::str_join; -namespace paddle { -namespace pybind { +namespace paddle::pybind { namespace py = ::pybind11; @@ -1573,5 +1572,4 @@ void BindEagerStringTensor(pybind11::module* module) { } } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/gpu/cuda/cuda_info.cc b/paddle/phi/backends/gpu/cuda/cuda_info.cc index af8c38be531a10..cf73f9bf07d8c8 100644 --- a/paddle/phi/backends/gpu/cuda/cuda_info.cc +++ b/paddle/phi/backends/gpu/cuda/cuda_info.cc @@ -22,9 +22,7 @@ static std::once_flag g_device_props_size_init_flag; static std::vector> g_device_props_init_flags; static std::vector g_device_props; -namespace phi { -namespace backends { -namespace gpu { +namespace phi::backends::gpu { int DnnVersion() { if (!dynload::HasCUDNN()) return -1; @@ -361,6 +359,4 @@ bool IsGPUManagedMemoryOversubscriptionSupported(int dev_id) { #endif } -} // namespace gpu -} // namespace backends -} // namespace phi +} // namespace phi::backends::gpu diff --git a/paddle/phi/core/distributed/auto_parallel/device_mesh.cc b/paddle/phi/core/distributed/auto_parallel/device_mesh.cc index 32030b05b55fdc..03e234bb713ac8 100644 --- a/paddle/phi/core/distributed/auto_parallel/device_mesh.cc +++ b/paddle/phi/core/distributed/auto_parallel/device_mesh.cc @@ -18,9 +18,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/device_mesh.h" #include "paddle/phi/core/distributed/auto_parallel/proto_helper.h" #include "paddle/phi/core/distributed/auto_parallel/utils.h" -namespace phi { -namespace distributed { -namespace auto_parallel { +namespace phi::distributed::auto_parallel { std::string DeviceCapability::to_string() const { std::string str; @@ -385,6 +383,4 @@ bool operator==(const DeviceMesh &lhs, const DeviceMesh &rhs) { return true; } -} // namespace auto_parallel -} // namespace distributed -} // namespace phi +} // namespace phi::distributed::auto_parallel diff --git a/paddle/phi/core/distributed/auto_parallel/placement_types.cc b/paddle/phi/core/distributed/auto_parallel/placement_types.cc index 30931e848ec4dc..344c3369803572 100644 --- a/paddle/phi/core/distributed/auto_parallel/placement_types.cc +++ b/paddle/phi/core/distributed/auto_parallel/placement_types.cc @@ -14,8 +14,7 @@ #include "paddle/phi/core/distributed/auto_parallel/placement_types.h" -namespace phi { -namespace distributed { +namespace phi::distributed { int64_t DistTensorMeta::num_shard() const { int64_t num_shard = 1; @@ -57,5 +56,4 @@ bool DistTensorMeta::is_replicated() const { [](const auto& p) { return p->is_replicated(); }); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/memory/allocation/buffered_allocator.cc b/paddle/phi/core/memory/allocation/buffered_allocator.cc index 92af4c0856069e..ec2765e5f8d842 100644 --- a/paddle/phi/core/memory/allocation/buffered_allocator.cc +++ b/paddle/phi/core/memory/allocation/buffered_allocator.cc @@ -18,9 +18,7 @@ REGISTER_FILE_SYMBOLS(buffered_allocator); -namespace paddle { -namespace memory { -namespace allocation { +namespace paddle::memory::allocation { BufferedAllocator::BufferedAllocator(std::shared_ptr allocator) : underlying_allocator_(std::move(allocator)) { @@ -75,6 +73,4 @@ phi::Allocation *BufferedAllocator::AllocateImpl(size_t size) { } } -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation diff --git a/paddle/phi/core/memory/allocation/retry_allocator.cc b/paddle/phi/core/memory/allocation/retry_allocator.cc index 1071e5e2238f44..8f29551f9c5e48 100644 --- a/paddle/phi/core/memory/allocation/retry_allocator.cc +++ b/paddle/phi/core/memory/allocation/retry_allocator.cc @@ -16,9 +16,7 @@ #include "glog/logging.h" -namespace paddle { -namespace memory { -namespace allocation { +namespace paddle::memory::allocation { class WaitedAllocateSizeGuard { public: @@ -97,6 +95,4 @@ phi::Allocation* RetryAllocator::AllocateImpl(size_t size) { } } -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation diff --git a/paddle/phi/core/memory/allocation/thread_local_allocator.cc b/paddle/phi/core/memory/allocation/thread_local_allocator.cc index 736bd49010d6ae..b724b1c099d59a 100644 --- a/paddle/phi/core/memory/allocation/thread_local_allocator.cc +++ b/paddle/phi/core/memory/allocation/thread_local_allocator.cc @@ -14,9 +14,7 @@ #include "paddle/phi/core/memory/allocation/thread_local_allocator.h" -namespace paddle { -namespace memory { -namespace allocation { +namespace paddle::memory::allocation { ThreadLocalAllocatorImpl::ThreadLocalAllocatorImpl(const phi::Place& p) : place_(p) { @@ -76,6 +74,4 @@ uint64_t ThreadLocalAllocatorImpl::ReleaseImpl() { return buddy_allocator_->Release(); } -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation diff --git a/paddle/phi/core/memory/memcpy.cc b/paddle/phi/core/memory/memcpy.cc index cf6a871896d494..d2da5d72405073 100644 --- a/paddle/phi/core/memory/memcpy.cc +++ b/paddle/phi/core/memory/memcpy.cc @@ -24,8 +24,7 @@ limitations under the License. */ #include "paddle/phi/backends/xpu/xpu_header.h" #endif -namespace paddle { -namespace memory { +namespace paddle::memory { #ifdef PADDLE_WITH_CUSTOM_DEVICE template <> @@ -875,5 +874,4 @@ TEST_API void Copy(phi::Place dst_place, } #endif -} // namespace memory -} // namespace paddle +} // namespace paddle::memory diff --git a/paddle/phi/core/platform/collective_helper.cc b/paddle/phi/core/platform/collective_helper.cc index 0c1802960d50de..e1cf2f82fc6b48 100644 --- a/paddle/phi/core/platform/collective_helper.cc +++ b/paddle/phi/core/platform/collective_helper.cc @@ -22,8 +22,7 @@ #include "paddle/phi/core/platform/device/device_wrapper.h" #include "paddle/phi/core/platform/device/gpu/gpu_resource_pool.h" -namespace paddle { -namespace platform { +namespace paddle::platform { #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) class NCCLCommImpl : public NCCLComm { public: @@ -612,5 +611,4 @@ void XCCLCommContext::ReleaseXCCLComms() { } #endif -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/phi/core/platform/device/gpu/gpu_resource_pool.cc b/paddle/phi/core/platform/device/gpu/gpu_resource_pool.cc index 07a1ff45bdeaae..019740a377cccd 100644 --- a/paddle/phi/core/platform/device/gpu/gpu_resource_pool.cc +++ b/paddle/phi/core/platform/device/gpu/gpu_resource_pool.cc @@ -17,8 +17,7 @@ #include "paddle/phi/backends/gpu/gpu_info.h" -namespace paddle { -namespace platform { +namespace paddle::platform { CudaStreamResourcePool::CudaStreamResourcePool() { int dev_cnt = phi::backends::gpu::GetGPUDeviceCount(); @@ -123,7 +122,6 @@ std::shared_ptr CudaEventResourcePool::New(int dev_idx) { return pool_[dev_idx]->New(); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform #endif diff --git a/paddle/phi/infermeta/spmd_rules/dim_trans.cc b/paddle/phi/infermeta/spmd_rules/dim_trans.cc index 0575de6d1562ed..02a8358821b385 100644 --- a/paddle/phi/infermeta/spmd_rules/dim_trans.cc +++ b/paddle/phi/infermeta/spmd_rules/dim_trans.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.h" #include "paddle/phi/core/enforce.h" -namespace phi { -namespace distributed { +namespace phi::distributed { DimTrans::DimTrans(Type type) : type_(type) {} @@ -376,5 +375,4 @@ std::vector> InferFromDimTrans( return {new_input_dims_mapping, out_dims_mapping}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/layer_norm.cc b/paddle/phi/infermeta/spmd_rules/layer_norm.cc index f5ede839c988d6..a30b766ce24aaa 100644 --- a/paddle/phi/infermeta/spmd_rules/layer_norm.cc +++ b/paddle/phi/infermeta/spmd_rules/layer_norm.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/utils.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -472,5 +471,4 @@ SpmdInfo FastLnGradInferSpmd(const DistMetaTensor& x, return spmd_info; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/scale.cc b/paddle/phi/infermeta/spmd_rules/scale.cc index 040e7979ddcfa9..a945d56d508fbd 100644 --- a/paddle/phi/infermeta/spmd_rules/scale.cc +++ b/paddle/phi/infermeta/spmd_rules/scale.cc @@ -12,13 +12,11 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/scale.h" #include "paddle/phi/infermeta/spmd_rules/elementwise.h" -namespace phi { -namespace distributed { +namespace phi::distributed { SpmdInfo ScaleInferSpmd(const DistMetaTensor& x, const Scalar& scale, const Scalar& bias, bool bias_after_scale) { return ElementwiseUnaryInferSpmd(x); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/selected_rows/scale_kernel.cc b/paddle/phi/kernels/selected_rows/scale_kernel.cc index 6eded1219b2830..5a226f0d198526 100644 --- a/paddle/phi/kernels/selected_rows/scale_kernel.cc +++ b/paddle/phi/kernels/selected_rows/scale_kernel.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/common/bfloat16.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/scale_kernel.h" -namespace phi { -namespace sr { +namespace phi::sr { template void ScaleKernel(const Context& dev_ctx, @@ -38,8 +37,7 @@ void ScaleKernel(const Context& dev_ctx, dev_ctx, x.value(), scale, bias, bias_after_scale, out->mutable_value()); } -} // namespace sr -} // namespace phi +} // namespace phi::sr PD_REGISTER_KERNEL(scale_sr, CPU, diff --git a/paddle/pir/src/core/op_operand_impl.cc b/paddle/pir/src/core/op_operand_impl.cc index 35827ad1efa175..6977e1278df56a 100644 --- a/paddle/pir/src/core/op_operand_impl.cc +++ b/paddle/pir/src/core/op_operand_impl.cc @@ -16,8 +16,7 @@ #include "paddle/pir/include/core/operation.h" #include "paddle/pir/src/core/value_impl.h" -namespace pir { -namespace detail { +namespace pir::detail { pir::Operation *OpOperandImpl::owner() const { return owner_; } @@ -79,5 +78,4 @@ void OpOperandImpl::RemoveFromUdChain() { OpOperandImpl::~OpOperandImpl() { RemoveFromUdChain(); } -} // namespace detail -} // namespace pir +} // namespace pir::detail From ad5400495c168c8a3d8b52a74a198a8dacd52b60 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 10:58:59 +0800 Subject: [PATCH 136/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-7=20(#64762)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 7 * format --- .../framework/details/nan_inf_utils_detail.cc | 8 ++------ .../fluid/framework/ir/cudnn_placement_pass.cc | 8 ++------ .../framework/ir/delete_assign_op_pass.cc | 18 ++++++------------ .../fluid/framework/ir/fuse_adamw_op_pass.cc | 8 ++------ .../ir/gpu_cpu_map_matmul_to_mul_pass.cc | 8 ++------ .../framework/ir/identity_op_clean_pass.cc | 13 ++++--------- .../fluid/framework/ir/layer_norm_fuse_pass.cc | 8 ++------ .../framework/ir/onednn/cpu_bfloat16_pass.cc | 8 ++------ .../matmul_elementwise_add_onednn_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/pass_builder.cc | 8 ++------ .../framework/ir/runtime_context_cache_pass.cc | 8 ++------ .../framework/ir/set_subgraph_edge_pass.cc | 8 ++------ .../ir/trt_cross_multihead_matmul_fuse_pass.cc | 18 ++++++------------ .../ir/trt_flash_multihead_matmul_fuse_pass.cc | 18 ++++++------------ .../garbage_collector/garbage_collector.cc | 6 ++---- .../instruction/onednn/onednn_instruction.cc | 6 ++---- .../pir_adaptor/pir_adaptor_util.cc | 6 ++---- .../analysis/passes/memory_optimize_pass.cc | 16 ++++------------ .../fluid/inference/analysis/passes/passes.cc | 8 ++------ .../inference/tensorrt/convert/arg_min_op.cc | 8 ++------ .../inference/tensorrt/convert/conv2d_op.cc | 18 ++++++------------ .../tensorrt/convert/layer_norm_op.cc | 8 ++------ .../inference/tensorrt/convert/mish_op.cc | 8 ++------ .../inference/tensorrt/trt_int8_calibrator.cc | 8 ++------ .../translator/attribute_translator.cc | 6 ++---- .../operators/collective/c_reduce_sum_op.cc | 16 ++++++---------- .../c_softmax_with_cross_entropy_op.cc | 6 ++---- .../collective/c_sync_comm_stream_op.cc | 6 ++---- .../fused/fused_multi_transformer_int8_op.cc | 6 ++---- paddle/fluid/operators/load_combine_op.cc | 6 ++---- paddle/fluid/operators/nccl/nccl_gpu_common.cc | 6 ++---- paddle/fluid/operators/quantize_linear_op.cc | 6 ++---- paddle/fluid/operators/save_combine_op.cc | 6 ++---- .../pir/dialect/operator/ir/api_builder.cc | 6 ++---- .../operator/ir/manual_op_decomp_rule.cc | 6 ++---- paddle/fluid/pir/drr/src/match_context.cc | 6 ++---- paddle/fluid/platform/init.cc | 6 ++---- .../reshard/same_status_reshard_function.cc | 6 ++---- .../phi/core/distributed/store/gloo_store.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/scatter.cc | 6 ++---- paddle/phi/kernels/funcs/jit/gen_base.cc | 6 ++---- .../funcs/jit/more/intrinsic/layer_norm.cc | 10 ++-------- paddle/phi/kernels/funcs/lstm_compute.cc | 6 ++---- paddle/phi/kernels/funcs/pooling.cc | 6 ++---- .../kernels/sparse/cpu/reshape_grad_kernel.cc | 6 ++---- .../kernels/sparse/cpu/softmax_grad_kernel.cc | 6 ++---- .../kernels/strings/strings_empty_kernel.cc | 6 ++---- 47 files changed, 118 insertions(+), 273 deletions(-) diff --git a/paddle/fluid/framework/details/nan_inf_utils_detail.cc b/paddle/fluid/framework/details/nan_inf_utils_detail.cc index 828d43ab3bf98f..713e8c70d32d38 100644 --- a/paddle/fluid/framework/details/nan_inf_utils_detail.cc +++ b/paddle/fluid/framework/details/nan_inf_utils_detail.cc @@ -23,9 +23,7 @@ #include "paddle/fluid/framework/convert_utils.h" #include "paddle/phi/kernels/funcs/eigen/extensions.h" -namespace paddle { -namespace framework { -namespace details { +namespace paddle::framework::details { struct DebugTools { DebugTools() = default; std::string path = ""; @@ -288,6 +286,4 @@ void CheckOpHasNanOrInf(const framework::OperatorBase& op, } } -} // namespace details -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::details diff --git a/paddle/fluid/framework/ir/cudnn_placement_pass.cc b/paddle/fluid/framework/ir/cudnn_placement_pass.cc index e7d0e50404c59d..8468d49f7dce3f 100644 --- a/paddle/fluid/framework/ir/cudnn_placement_pass.cc +++ b/paddle/fluid/framework/ir/cudnn_placement_pass.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/cudnn_placement_pass.h" #include "paddle/fluid/framework/operator.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { bool CUDNNPlacementPass::IsSupport(const Node* op) const { std::string attr_name = GetAttrName(); @@ -40,9 +38,7 @@ bool CUDNNPlacementPass::IsSupport(const Node* op) const { return false; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(cudnn_placement_pass, paddle::framework::ir::CUDNNPlacementPass) .RequirePassAttr("cudnn_enabled_op_types"); diff --git a/paddle/fluid/framework/ir/delete_assign_op_pass.cc b/paddle/fluid/framework/ir/delete_assign_op_pass.cc index 00a5855a33121b..ebe9353063bb18 100644 --- a/paddle/fluid/framework/ir/delete_assign_op_pass.cc +++ b/paddle/fluid/framework/ir/delete_assign_op_pass.cc @@ -23,16 +23,11 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { struct AssignWithSameInputOutputNamePattern : public PatternBase { AssignWithSameInputOutputNamePattern(PDPattern* pattern, @@ -54,7 +49,8 @@ AssignWithSameInputOutputNamePattern::AssignWithSameInputOutputNamePattern( }); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { /* Delete "assign" if its input and output is same. @@ -90,9 +86,7 @@ void DeleteAssignOpPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(delete_assign_op_pass, paddle::framework::ir::DeleteAssignOpPass); diff --git a/paddle/fluid/framework/ir/fuse_adamw_op_pass.cc b/paddle/fluid/framework/ir/fuse_adamw_op_pass.cc index 37259de68fd5d8..79d12a66022c8d 100644 --- a/paddle/fluid/framework/ir/fuse_adamw_op_pass.cc +++ b/paddle/fluid/framework/ir/fuse_adamw_op_pass.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { std::vector GetNodeNames(const std::vector &node_vector) { std::vector out_vector; @@ -312,8 +310,6 @@ ir::Graph *FuseAdamWPass::FuseAdamWFun(ir::Graph *graph, return graph; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fuse_adamw_op_pass, paddle::framework::ir::FuseAdamWPass); diff --git a/paddle/fluid/framework/ir/gpu_cpu_map_matmul_to_mul_pass.cc b/paddle/fluid/framework/ir/gpu_cpu_map_matmul_to_mul_pass.cc index 6a19962862eda6..c68b36fb6db59d 100644 --- a/paddle/fluid/framework/ir/gpu_cpu_map_matmul_to_mul_pass.cc +++ b/paddle/fluid/framework/ir/gpu_cpu_map_matmul_to_mul_pass.cc @@ -22,9 +22,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; @@ -782,9 +780,7 @@ void GpuCpuFlatten2MatmulFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(gpu_cpu_map_matmul_to_mul_pass, paddle::framework::ir::GpuCpuMapMatmul2MulPass); diff --git a/paddle/fluid/framework/ir/identity_op_clean_pass.cc b/paddle/fluid/framework/ir/identity_op_clean_pass.cc index 08fa1f387f1a7b..27738e65713c1a 100644 --- a/paddle/fluid/framework/ir/identity_op_clean_pass.cc +++ b/paddle/fluid/framework/ir/identity_op_clean_pass.cc @@ -17,11 +17,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { - -namespace patterns { +namespace paddle::framework::ir::patterns { // pre_op -> useless_op_in -> useless_op -> useless_op_out // -> @@ -151,7 +147,8 @@ FindTwoCastOpPattern::FindTwoCastOpPattern(PDPattern* pattern, cast_op_1->LinksFrom({pre_op_out}).LinksTo({cast_op_1_out}); cast_op_2->LinksFrom({cast_op_1_out}).LinksTo({cast_op_2_out}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { int IdentityOpCleanPass::CleanUselessOp(ir::Graph* graph) const { GraphPatternDetector gpd; @@ -279,9 +276,7 @@ void IdentityOpCleanPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(identity_op_clean_pass, paddle::framework::ir::IdentityOpCleanPass); diff --git a/paddle/fluid/framework/ir/layer_norm_fuse_pass.cc b/paddle/fluid/framework/ir/layer_norm_fuse_pass.cc index f5e5a038ff5fa4..20f130cb37208e 100644 --- a/paddle/fluid/framework/ir/layer_norm_fuse_pass.cc +++ b/paddle/fluid/framework/ir/layer_norm_fuse_pass.cc @@ -24,9 +24,7 @@ #include "paddle/utils/string/pretty_log.h" #include "paddle/utils/string/printf.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { // cpplint complaints (wrong!) for not included header in below line. using string::PrettyLogDetail; // NOLINT @@ -428,9 +426,7 @@ void LayerNormFusePass::ApplyImpl(Graph* graph) const { found_layer_norm_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir #undef CHECK_TRUE #undef EXPECT_TRUE diff --git a/paddle/fluid/framework/ir/onednn/cpu_bfloat16_pass.cc b/paddle/fluid/framework/ir/onednn/cpu_bfloat16_pass.cc index 9766f3628190a1..7b621a98491dc8 100644 --- a/paddle/fluid/framework/ir/onednn/cpu_bfloat16_pass.cc +++ b/paddle/fluid/framework/ir/onednn/cpu_bfloat16_pass.cc @@ -20,9 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { namespace { class Quanter { @@ -268,9 +266,7 @@ void CPUBFloat16Pass::ApplyImpl(ir::Graph* graph) const { dequantize_counter); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(cpu_bfloat16_pass, paddle::framework::ir::CPUBFloat16Pass); diff --git a/paddle/fluid/framework/ir/onednn/matmul_elementwise_add_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/matmul_elementwise_add_onednn_fuse_pass.cc index 8d80eb57e50328..98695c3e7f6bca 100644 --- a/paddle/fluid/framework/ir/onednn/matmul_elementwise_add_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/matmul_elementwise_add_onednn_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -163,9 +161,7 @@ MatmulElementwiseAddMKLDNNFusePass::MatmulElementwiseAddMKLDNNFusePass() { .End(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(matmul_elementwise_add_onednn_fuse_pass, paddle::framework::ir::MatmulElementwiseAddMKLDNNFusePass); diff --git a/paddle/fluid/framework/ir/pass_builder.cc b/paddle/fluid/framework/ir/pass_builder.cc index e6d2ebaa7457f1..94f6488d415478 100644 --- a/paddle/fluid/framework/ir/pass_builder.cc +++ b/paddle/fluid/framework/ir/pass_builder.cc @@ -19,9 +19,7 @@ limitations under the License. */ #include "glog/logging.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Pass; @@ -54,6 +52,4 @@ std::shared_ptr PassBuilder::InsertPass(size_t idx, return passes_[idx]; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/runtime_context_cache_pass.cc b/paddle/fluid/framework/ir/runtime_context_cache_pass.cc index 408d21d645db41..e330797165f59b 100644 --- a/paddle/fluid/framework/ir/runtime_context_cache_pass.cc +++ b/paddle/fluid/framework/ir/runtime_context_cache_pass.cc @@ -17,9 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/graph_helper.h" #include "paddle/fluid/framework/operator.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void RuntimeContextCachePass::ApplyImpl(ir::Graph* graph) const { static constexpr char kNotAllowInferShapeCache[] = // NOLINT @@ -48,9 +46,7 @@ void RuntimeContextCachePass::ApplyImpl(ir::Graph* graph) const { } } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(runtime_context_cache_pass, paddle::framework::ir::RuntimeContextCachePass); diff --git a/paddle/fluid/framework/ir/set_subgraph_edge_pass.cc b/paddle/fluid/framework/ir/set_subgraph_edge_pass.cc index cf6f5f30643d6c..915c565a8c699e 100644 --- a/paddle/fluid/framework/ir/set_subgraph_edge_pass.cc +++ b/paddle/fluid/framework/ir/set_subgraph_edge_pass.cc @@ -20,9 +20,7 @@ #include #include -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { #define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern); #define GET_NODES GET_IR_NODE(ops); @@ -132,8 +130,6 @@ void SetSubgraphEdge::ApplyImpl(Graph *graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(set_subgraph_edge_pass, paddle::framework::ir::SetSubgraphEdge); diff --git a/paddle/fluid/framework/ir/trt_cross_multihead_matmul_fuse_pass.cc b/paddle/fluid/framework/ir/trt_cross_multihead_matmul_fuse_pass.cc index c60d1d1bdf0daa..2bb30602dcc3de 100644 --- a/paddle/fluid/framework/ir/trt_cross_multihead_matmul_fuse_pass.cc +++ b/paddle/fluid/framework/ir/trt_cross_multihead_matmul_fuse_pass.cc @@ -22,16 +22,11 @@ #ifdef PADDLE_WITH_TENSORRT #include "paddle/fluid/inference/tensorrt/helper.h" #endif -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { // input_q input_kv // |q |k v @@ -207,7 +202,8 @@ PDNode* TrtCrossMultiHeadMatmulPattern::operator()() { return reshape2_qkv_out_var; } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { TrtCrossMultiHeadMatmulFusePass::TrtCrossMultiHeadMatmulFusePass() { AddOpCompat(OpCompat("reshape2")) @@ -521,9 +517,7 @@ void TrtCrossMultiHeadMatmulFusePass::ApplyImpl(Graph* graph) const { AddStatis(fusion_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(trt_cross_multihead_matmul_fuse_pass, paddle::framework::ir::TrtCrossMultiHeadMatmulFusePass); diff --git a/paddle/fluid/framework/ir/trt_flash_multihead_matmul_fuse_pass.cc b/paddle/fluid/framework/ir/trt_flash_multihead_matmul_fuse_pass.cc index b95ded46fcfffd..44c19de295f22a 100644 --- a/paddle/fluid/framework/ir/trt_flash_multihead_matmul_fuse_pass.cc +++ b/paddle/fluid/framework/ir/trt_flash_multihead_matmul_fuse_pass.cc @@ -23,16 +23,11 @@ #include "paddle/fluid/inference/tensorrt/helper.h" #include "paddle/phi/backends/gpu/gpu_info.h" #endif -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { // input // |q k v @@ -206,7 +201,8 @@ PDNode* TrtFlashMultiHeadMatmulPattern::operator()() { return reshape2_qkv_out_var; } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { TrtFlashMultiHeadMatmulFusePass::TrtFlashMultiHeadMatmulFusePass() { AddOpCompat(OpCompat("reshape2")) @@ -534,9 +530,7 @@ void TrtFlashMultiHeadMatmulFusePass::ApplyImpl(Graph* graph) const { AddStatis(fusion_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(trt_flash_multihead_matmul_fuse_pass, paddle::framework::ir::TrtFlashMultiHeadMatmulFusePass); diff --git a/paddle/fluid/framework/new_executor/garbage_collector/garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/garbage_collector.cc index ed4f6ecd63db52..10801822c85ff2 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/garbage_collector.cc @@ -18,8 +18,7 @@ #include "paddle/fluid/framework/new_executor/garbage_collector/fast_garbage_collector.h" #include "paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.h" -namespace paddle { -namespace framework { +namespace paddle::framework { InterpreterCoreGarbageCollector::InterpreterCoreGarbageCollector() : garbages_(std::make_unique()) { @@ -86,5 +85,4 @@ CreateInterpreterCoreGarbageCollector( } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_instruction.cc b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_instruction.cc index 09f4b7c2a7b583..5d78e73949fd36 100644 --- a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_instruction.cc @@ -40,8 +40,7 @@ #include "paddle/phi/backends/onednn/onednn_helper.h" #include "paddle/phi/kernels/funcs/data_layout_transform.h" -namespace paddle { -namespace framework { +namespace paddle::framework { static phi::Attribute ConvertPirAttribute2RuntimeAttribute( pir::Attribute attr, @@ -516,5 +515,4 @@ void OneDNNPhiKernelInstruction::Run() { one_dnn_ctx->ClearDnnAttr(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.cc b/paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.cc index 34f248fc132af5..7f6864c63c3604 100644 --- a/paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.cc +++ b/paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.cc @@ -50,8 +50,7 @@ #include "paddle/pir/include/dialect/control_flow/ir/cf_op.h" #include "paddle/pir/include/dialect/control_flow/ir/cf_type.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::shared_ptr ValueExecutionInfo::NewChild(Scope* scope) { std::shared_ptr info = std::make_shared(scope); @@ -1087,5 +1086,4 @@ std::shared_ptr BuildOperatorBase( return res; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc index aaa5207de3f692..9871aeb6e78f72 100644 --- a/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc +++ b/paddle/fluid/inference/analysis/passes/memory_optimize_pass.cc @@ -24,18 +24,12 @@ #include "paddle/fluid/inference/analysis/pass_result_info.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; class Node; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir -namespace paddle { -namespace inference { -namespace analysis { +namespace paddle::inference::analysis { using framework::ir::Graph; using framework::ir::Node; @@ -294,6 +288,4 @@ void MemoryOptimizePass::RunImpl(Argument* argument) { return; } -} // namespace analysis -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::analysis diff --git a/paddle/fluid/inference/analysis/passes/passes.cc b/paddle/fluid/inference/analysis/passes/passes.cc index e2b01d17044e1e..754ef80d01a752 100644 --- a/paddle/fluid/inference/analysis/passes/passes.cc +++ b/paddle/fluid/inference/analysis/passes/passes.cc @@ -23,9 +23,7 @@ #include "paddle/fluid/inference/analysis/passes/memory_optimize_pass.h" #include "paddle/fluid/inference/analysis/passes/save_optimized_model_pass.h" -namespace paddle { -namespace inference { -namespace analysis { +namespace paddle::inference::analysis { PassRegistry::PassRegistry() { // NOLINT // Register manually to avoid the trivial `USE_OP` like macro for easier use @@ -46,6 +44,4 @@ PassRegistry::PassRegistry() { // NOLINT std::make_unique()); } -} // namespace analysis -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::analysis diff --git a/paddle/fluid/inference/tensorrt/convert/arg_min_op.cc b/paddle/fluid/inference/tensorrt/convert/arg_min_op.cc index e998bb41a3654d..6a8852d9703269 100644 --- a/paddle/fluid/inference/tensorrt/convert/arg_min_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/arg_min_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ArgMinOpConverter : public OpConverter { public: @@ -58,8 +56,6 @@ class ArgMinOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(arg_min, ArgMinOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc b/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc index 5c2bf9c2463cc7..680e12a5055e33 100644 --- a/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/conv2d_op.cc @@ -16,19 +16,15 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/engine.h" #include "paddle/phi/common/data_type.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -namespace proto { +} // namespace paddle::framework +namespace paddle::framework::proto { class OpDesc; -} // namespace proto -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::proto -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { template void ConvertConv2d(TensorRTEngine* engine, @@ -260,9 +256,7 @@ class Deconv2dOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(conv2d, Conv2dOpConverter); REGISTER_TRT_OP_CONVERTER(fused_conv2d_add_act, Conv2dOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc b/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc index ea78560c887221..49f8697a0eac02 100644 --- a/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/layer_norm_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class LayerNormOpConverter : public OpConverter { public: @@ -116,8 +114,6 @@ class LayerNormOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(layer_norm, LayerNormOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/mish_op.cc b/paddle/fluid/inference/tensorrt/convert/mish_op.cc index be0ab973b3991f..5dff91421c77d3 100644 --- a/paddle/fluid/inference/tensorrt/convert/mish_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/mish_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/mish_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Mish OP @@ -50,8 +48,6 @@ class MishOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(mish, MishOpConverter); diff --git a/paddle/fluid/inference/tensorrt/trt_int8_calibrator.cc b/paddle/fluid/inference/tensorrt/trt_int8_calibrator.cc index b40eb159c0dacb..747cbe59701b31 100644 --- a/paddle/fluid/inference/tensorrt/trt_int8_calibrator.cc +++ b/paddle/fluid/inference/tensorrt/trt_int8_calibrator.cc @@ -17,9 +17,7 @@ #include "glog/logging.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { // set the batch size before constructing the thread to execute engine int TRTInt8Calibrator::getBatchSize() const TRT_NOEXCEPT { return batch_size_; } @@ -159,6 +157,4 @@ TRTInt8Calibrator::~TRTInt8Calibrator() { VLOG(4) << "Destroying calibrator for " << engine_name_; } -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt diff --git a/paddle/fluid/ir_adaptor/translator/attribute_translator.cc b/paddle/fluid/ir_adaptor/translator/attribute_translator.cc index 021d03be857f7b..817886d7f5af30 100644 --- a/paddle/fluid/ir_adaptor/translator/attribute_translator.cc +++ b/paddle/fluid/ir_adaptor/translator/attribute_translator.cc @@ -27,8 +27,7 @@ #include "paddle/phi/core/utils/data_type.h" #include "paddle/utils/variant.h" -namespace paddle { -namespace translator { +namespace paddle::translator { class AttributeVisitor { public: @@ -297,5 +296,4 @@ pir::Attribute AttributeTranslator::operator()( return paddle::visit(*(special_visitors.at(target_type)), attr); } -} // namespace translator -} // namespace paddle +} // namespace paddle::translator diff --git a/paddle/fluid/operators/collective/c_reduce_sum_op.cc b/paddle/fluid/operators/collective/c_reduce_sum_op.cc index ccc73d50d258ed..9c92cd9a9dd733 100644 --- a/paddle/fluid/operators/collective/c_reduce_sum_op.cc +++ b/paddle/fluid/operators/collective/c_reduce_sum_op.cc @@ -14,19 +14,16 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_reduce_op.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class CReduceSumOpMaker : public CReduceOpMaker { protected: @@ -35,8 +32,7 @@ class CReduceSumOpMaker : public CReduceOpMaker { DEFINE_C_REDUCE_CPU_KERNEL(CReduceSum, kRedSum) -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_softmax_with_cross_entropy_op.cc b/paddle/fluid/operators/collective/c_softmax_with_cross_entropy_op.cc index 8239d827641ccb..bc188576682402 100644 --- a/paddle/fluid/operators/collective/c_softmax_with_cross_entropy_op.cc +++ b/paddle/fluid/operators/collective/c_softmax_with_cross_entropy_op.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_softmax_with_cross_entropy_op.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class CSoftmaxWithCrossEntropyOp : public framework::OperatorWithKernel { public: @@ -186,8 +185,7 @@ DECLARE_INPLACE_OP_INFERER(CSoftmaxWithCrossEntropyInplaceInferer, DECLARE_INPLACE_OP_INFERER(CSoftmaxWithCrossEntropyGradInplaceInferer, {"Softmax", framework::GradVarName("Logits")}); -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_sync_comm_stream_op.cc b/paddle/fluid/operators/collective/c_sync_comm_stream_op.cc index c1fa342ad7c1d2..bd1aacc5e49259 100644 --- a/paddle/fluid/operators/collective/c_sync_comm_stream_op.cc +++ b/paddle/fluid/operators/collective/c_sync_comm_stream_op.cc @@ -13,8 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class CSyncCommStreamOp : public framework::OperatorWithKernel { public: @@ -44,8 +43,7 @@ Call communication stream synchronization. } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/fused/fused_multi_transformer_int8_op.cc b/paddle/fluid/operators/fused/fused_multi_transformer_int8_op.cc index 34c002c6a1fab7..28a87239f37693 100644 --- a/paddle/fluid/operators/fused/fused_multi_transformer_int8_op.cc +++ b/paddle/fluid/operators/fused/fused_multi_transformer_int8_op.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_registry.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class FusedMultiTransformerINT8Op : public framework::OperatorWithKernel { private: @@ -367,8 +366,7 @@ class FusedMultiTransformerINT8OpMaker } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; REGISTER_OPERATOR( diff --git a/paddle/fluid/operators/load_combine_op.cc b/paddle/fluid/operators/load_combine_op.cc index 4961fc6ce3ffc4..8900ab47fc2585 100644 --- a/paddle/fluid/operators/load_combine_op.cc +++ b/paddle/fluid/operators/load_combine_op.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include #include -namespace paddle { -namespace operators { +namespace paddle::operators { class LoadCombineOp : public framework::OperatorWithKernel { public: @@ -73,8 +72,7 @@ that were saved using the SaveCombine operator. } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; // NOLINT diff --git a/paddle/fluid/operators/nccl/nccl_gpu_common.cc b/paddle/fluid/operators/nccl/nccl_gpu_common.cc index 8e3e153d0398aa..bc12430471d3df 100644 --- a/paddle/fluid/operators/nccl/nccl_gpu_common.cc +++ b/paddle/fluid/operators/nccl/nccl_gpu_common.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" -namespace paddle { -namespace platform { +namespace paddle::platform { namespace { // TODO(panyx0718): Where to destroy them. std::unique_ptr> global_comms; @@ -60,5 +59,4 @@ const std::vector& Communicator::comms() const { return *global_comms; } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/fluid/operators/quantize_linear_op.cc b/paddle/fluid/operators/quantize_linear_op.cc index b356a7a19e727d..c78ca321bca66a 100644 --- a/paddle/fluid/operators/quantize_linear_op.cc +++ b/paddle/fluid/operators/quantize_linear_op.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/binary.h" #include "paddle/phi/infermeta/multiary.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class QuantizeLinearOp : public framework::OperatorWithKernel { public: using framework::OperatorWithKernel::OperatorWithKernel; @@ -118,8 +117,7 @@ In above three formulas, the range value of c is as follow: )DOC"); } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/save_combine_op.cc b/paddle/fluid/operators/save_combine_op.cc index 61a2b7f384042b..ebcfa0026b3d10 100644 --- a/paddle/fluid/operators/save_combine_op.cc +++ b/paddle/fluid/operators/save_combine_op.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/common/bfloat16.h" #include "paddle/phi/core/kernel_registry.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class SaveCombineOp : public framework::OperatorWithKernel { public: @@ -94,8 +93,7 @@ class SaveCombineOpInferVarType : public framework::VarTypeInference { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/pir/dialect/operator/ir/api_builder.cc b/paddle/fluid/pir/dialect/operator/ir/api_builder.cc index 1417fc8018ff6c..ca875b357d8278 100644 --- a/paddle/fluid/pir/dialect/operator/ir/api_builder.cc +++ b/paddle/fluid/pir/dialect/operator/ir/api_builder.cc @@ -16,8 +16,7 @@ #include "paddle/common/enforce.h" #include "paddle/pir/include/core/ir_context.h" -namespace paddle { -namespace dialect { +namespace paddle::dialect { ApiBuilder::ApiBuilder() : ctx_(pir::IrContext::Instance()), @@ -64,5 +63,4 @@ void ApiBuilder::LoadInsertionPoint() { insertion_point_stack_.pop(); } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op_decomp_rule.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op_decomp_rule.cc index 58d871ec758738..454095bd4fa9c0 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op_decomp_rule.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op_decomp_rule.cc @@ -25,8 +25,7 @@ // TODO(chenzhuo) // this file will be generated in pd_op_decomp.cc -namespace paddle { -namespace dialect { +namespace paddle::dialect { using IntArray = paddle::experimental::IntArray; std::vector> BatchNormOp::Decomp(pir::Operation* op) { @@ -279,5 +278,4 @@ std::vector> OneHotOp::Decomp(pir::Operation* op) { return res; } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect diff --git a/paddle/fluid/pir/drr/src/match_context.cc b/paddle/fluid/pir/drr/src/match_context.cc index 527504b6fda2d8..04344de0b463d6 100644 --- a/paddle/fluid/pir/drr/src/match_context.cc +++ b/paddle/fluid/pir/drr/src/match_context.cc @@ -19,8 +19,7 @@ #include "paddle/fluid/pir/drr/src/match_context_impl.h" #include "paddle/phi/common/data_type.h" -namespace paddle { -namespace drr { +namespace paddle::drr { MatchContext::MatchContext(std::shared_ptr impl) : impl_(std::move(impl)) {} @@ -48,5 +47,4 @@ template phi::DataType MatchContext::Attr( const std::string&) const; template phi::Place MatchContext::Attr(const std::string&) const; -} // namespace drr -} // namespace paddle +} // namespace paddle::drr diff --git a/paddle/fluid/platform/init.cc b/paddle/fluid/platform/init.cc index 8de851facd1cbc..ddd69ab1eb9e93 100644 --- a/paddle/fluid/platform/init.cc +++ b/paddle/fluid/platform/init.cc @@ -68,8 +68,7 @@ limitations under the License. */ COMMON_DECLARE_int32(paddle_num_threads); COMMON_DECLARE_int32(multiple_of_cupti_buffer_size); -namespace paddle { -namespace framework { +namespace paddle::framework { #ifdef _WIN32 #define strdup _strdup @@ -514,5 +513,4 @@ void InitMemoryMethod() { }); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/same_status_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/same_status_reshard_function.cc index 6452fe9d069296..bc60f3768f53cd 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/same_status_reshard_function.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/same_status_reshard_function.cc @@ -24,8 +24,7 @@ #include "paddle/phi/kernels/p_recv_kernel.h" #include "paddle/phi/kernels/p_send_kernel.h" -namespace phi { -namespace distributed { +namespace phi::distributed { bool SameStatusReshardFunction::IsSuitable( const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -113,5 +112,4 @@ void SameStatusReshardFunction::Eval(phi::DeviceContext* dev_ctx, SetDistProps(out, in.dims(), out_dist_attr); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/store/gloo_store.cc b/paddle/phi/core/distributed/store/gloo_store.cc index 4da028e55b4628..596838adbe57b8 100644 --- a/paddle/phi/core/distributed/store/gloo_store.cc +++ b/paddle/phi/core/distributed/store/gloo_store.cc @@ -14,8 +14,7 @@ #include "paddle/phi/core/distributed/store/gloo_store.h" -namespace phi { -namespace distributed { +namespace phi::distributed { GlooStore::GlooStore(const std::shared_ptr& store) : store_(store) {} @@ -43,5 +42,4 @@ void GlooStore::wait(const std::vector& keys, } } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/scatter.cc b/paddle/phi/infermeta/spmd_rules/scatter.cc index 53ea0fc345c38b..cb7173c5a1d74b 100644 --- a/paddle/phi/infermeta/spmd_rules/scatter.cc +++ b/paddle/phi/infermeta/spmd_rules/scatter.cc @@ -23,8 +23,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -233,5 +232,4 @@ SpmdInfo ScatterGradInferSpmd(const DistMetaTensor& index, {x_grad_dist_attr, updates_grad_dist_attr}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/jit/gen_base.cc b/paddle/phi/kernels/funcs/jit/gen_base.cc index ead08a6c29b53c..71701b96f3b640 100644 --- a/paddle/phi/kernels/funcs/jit/gen_base.cc +++ b/paddle/phi/kernels/funcs/jit/gen_base.cc @@ -27,8 +27,7 @@ PHI_DEFINE_bool(dump_jitcode, false, "Whether to dump the jitcode to file"); -namespace phi { -namespace jit { +namespace phi::jit { // refer do not need CanBeUsed, it would be the last one. void GenBase::dumpCode(const unsigned char* code) const { @@ -99,5 +98,4 @@ std::vector packed_groups(int n, int k, int* block_out, int* rest_out) { return groups; } -} // namespace jit -} // namespace phi +} // namespace phi::jit diff --git a/paddle/phi/kernels/funcs/jit/more/intrinsic/layer_norm.cc b/paddle/phi/kernels/funcs/jit/more/intrinsic/layer_norm.cc index 4b50de277a9c28..7211c0d0f110c9 100644 --- a/paddle/phi/kernels/funcs/jit/more/intrinsic/layer_norm.cc +++ b/paddle/phi/kernels/funcs/jit/more/intrinsic/layer_norm.cc @@ -19,10 +19,7 @@ #include "paddle/phi/backends/cpu/cpu_info.h" #include "paddle/phi/kernels/funcs/jit/registry.h" -namespace phi { -namespace jit { -namespace more { -namespace intrinsic { +namespace phi::jit::more::intrinsic { void LayerNorm(float* x, float* out, @@ -183,10 +180,7 @@ bool LayerNormKernel::CanBeUsed(const int& d) const { d >= YMM_FLOAT_BLOCK; } -} // namespace intrinsic -} // namespace more -} // namespace jit -} // namespace phi +} // namespace phi::jit::more::intrinsic namespace intrinsic = phi::jit::more::intrinsic; diff --git a/paddle/phi/kernels/funcs/lstm_compute.cc b/paddle/phi/kernels/funcs/lstm_compute.cc index e4b8a6961fd7e5..26e1bb9ac76c6a 100644 --- a/paddle/phi/kernels/funcs/lstm_compute.cc +++ b/paddle/phi/kernels/funcs/lstm_compute.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/phi/kernels/funcs/detail/lstm_cpu_kernel.h" #include "paddle/phi/kernels/funcs/detail/lstm_kernel.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct LstmUnitFunctor { @@ -101,5 +100,4 @@ template class LstmUnitFunctor; template class LstmUnitGradFunctor; template class LstmUnitGradFunctor; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/pooling.cc b/paddle/phi/kernels/funcs/pooling.cc index 47a1eaaab6577f..084bb54ebbba9f 100644 --- a/paddle/phi/kernels/funcs/pooling.cc +++ b/paddle/phi/kernels/funcs/pooling.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include #include "paddle/phi/backends/cpu/cpu_context.h" -namespace phi { -namespace funcs { +namespace phi::funcs { /* * Tensors are in NCHW or NHWC format. @@ -2196,5 +2195,4 @@ template class FractionalMaxPool3dGradFunctor; template class FractionalMaxPool3dFunctor; template class FractionalMaxPool3dGradFunctor; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/sparse/cpu/reshape_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/reshape_grad_kernel.cc index 8b3949badb77da..4b8206f7898117 100644 --- a/paddle/phi/kernels/sparse/cpu/reshape_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/reshape_grad_kernel.cc @@ -20,8 +20,7 @@ #include "paddle/phi/kernels/sparse/empty_kernel.h" #include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void ReshapeCooGradKernel(const Context& dev_ctx, @@ -43,8 +42,7 @@ void ReshapeCsrGradKernel(const Context& dev_ctx, ReshapeCsrKernel(dev_ctx, dout, x_shape, dx); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(reshape_coo_grad, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc index 9b4f8e4732af98..fb15a044973feb 100644 --- a/paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/softmax_grad_kernel.cc @@ -26,8 +26,7 @@ limitations under the License. */ #include "paddle/phi/kernels/softmax_grad_kernel.h" #include "paddle/phi/kernels/sparse/empty_kernel.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void SoftmaxCsrGradKernel(const Context& dev_ctx, @@ -203,8 +202,7 @@ void SoftmaxCooGradKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(softmax_csr_grad, CPU, diff --git a/paddle/phi/kernels/strings/strings_empty_kernel.cc b/paddle/phi/kernels/strings/strings_empty_kernel.cc index 10d958f354e2d3..c1f6619fbbeaa6 100644 --- a/paddle/phi/kernels/strings/strings_empty_kernel.cc +++ b/paddle/phi/kernels/strings/strings_empty_kernel.cc @@ -17,8 +17,7 @@ #include "paddle/phi/backends/all_context.h" #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace strings { +namespace phi::strings { template void EmptyKernel(const Context& dev_ctx, @@ -33,8 +32,7 @@ void EmptyLikeKernel(const Context& dev_ctx, StringTensor* out) { dev_ctx.template Alloc(out); } -} // namespace strings -} // namespace phi +} // namespace phi::strings using pstring = ::phi::dtype::pstring; From 402c9f35f28e11d8ce9a92ca8d0466f7218bfccd Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:01:14 +0800 Subject: [PATCH 137/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-15=20(#64770)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../distributed/test/sparse_sgd_rule_test.cc | 6 ++---- .../fluid/framework/data_device_transform.cc | 6 ++---- paddle/fluid/framework/data_feed_factory.cc | 6 ++---- paddle/fluid/framework/dense_tensor_array.h | 4 +--- paddle/fluid/framework/executor_gc_helper.cc | 6 ++---- .../ir/conv_elementwise_add_act_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/cost_model.cc | 6 ++---- .../ir/delete_elementwise_mul_op_pass.cc | 18 ++++++----------- .../framework/ir/delete_repeated_ops_pass.cc | 20 ++++++++----------- .../framework/ir/fuse_bn_add_act_pass.cc | 8 ++------ paddle/fluid/framework/ir/graph_test.cc | 12 ++++------- paddle/fluid/framework/ir/graph_traits.cc | 8 ++------ .../ir/identity_op_clean_pass_test.cc | 8 ++------ .../ir/onednn/multi_gru_fuse_pass.cc | 8 ++------ .../new_executor/program_interpreter.cc | 6 ++---- paddle/fluid/framework/phi_utils.cc | 6 ++---- paddle/fluid/imperative/flags.cc | 6 ++---- .../tensorrt/convert/anchor_generator_op.cc | 8 ++------ .../convert/cross_multihead_matmul_op.cc | 8 ++------ .../inference/tensorrt/convert/equal_op.cc | 8 ++------ .../inference/tensorrt/convert/reshape_op.cc | 8 ++------ .../inference/tensorrt/convert/squeeze2_op.cc | 8 ++------ .../operators/collective/c_broadcast_op.cc | 6 ++---- .../operators/collective/gen_nccl_id_op.cc | 12 ++++------- .../dialect/distributed/ir/dist_attribute.cc | 6 ++---- .../dialect/distributed/ir/dist_dialect.cc | 6 ++---- .../platform/profiler/cupti_data_process.cc | 8 ++------ .../backend/manual/manual_static_backend.cc | 8 ++------ paddle/fluid/pybind/io.cc | 6 ++---- paddle/fluid/pybind/reader_py.cc | 6 ++---- paddle/fluid/pybind/rpc.cc | 6 ++---- paddle/phi/backends/dynload/cudnn.cc | 6 ++---- paddle/phi/backends/dynload/hipblasLt.cc | 6 ++---- .../distributed/auto_parallel/process_mesh.cc | 6 ++---- .../global_and_sub_mesh_reshard_function.cc | 6 ++---- .../reshard/reshard_function_registry.cc | 6 ++---- .../phi/core/distributed/store/store_utils.cc | 6 ++---- .../spmd_rules/cross_entropy_with_softmax.cc | 6 ++---- .../spmd_rules/default_data_parallel.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/reduction.cc | 6 ++---- paddle/phi/kernels/funcs/matrix_bit_code.cc | 6 ++---- .../kernels/funcs/selected_rows_functor.cc | 10 ++++------ .../phi/kernels/sparse/batch_norm_kernel.cc | 6 ++---- .../kernels/sparse/cpu/addmm_grad_kernel.cc | 6 ++---- 44 files changed, 103 insertions(+), 225 deletions(-) diff --git a/paddle/fluid/distributed/test/sparse_sgd_rule_test.cc b/paddle/fluid/distributed/test/sparse_sgd_rule_test.cc index a7029d1e8b127a..6865f46d10b4cd 100644 --- a/paddle/fluid/distributed/test/sparse_sgd_rule_test.cc +++ b/paddle/fluid/distributed/test/sparse_sgd_rule_test.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "gtest/gtest.h" #include "paddle/fluid/distributed/the_one_ps.pb.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { TEST(sparse_value_naive_sgd_test, init_and_update) { SparseNaiveSGDRule rule; @@ -205,5 +204,4 @@ TEST(downpour_sparse_adam_test, test_init_and_update) { ASSERT_FLOAT_EQ(value[i], label[i]) << "i is " << i; } } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/data_device_transform.cc b/paddle/fluid/framework/data_device_transform.cc index b3ad9ea0b63d98..b92c41cefd4f5f 100644 --- a/paddle/fluid/framework/data_device_transform.cc +++ b/paddle/fluid/framework/data_device_transform.cc @@ -13,8 +13,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_device_transform.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void TransDataDevice(const phi::DenseTensor &in, const phi::Place &dst_place, @@ -52,5 +51,4 @@ void TransDataDevice(const phi::DenseTensor &in, TensorCopySync(in, dst_place, out); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/data_feed_factory.cc b/paddle/fluid/framework/data_feed_factory.cc index 88afa021b7c1b9..9924642e31308d 100644 --- a/paddle/fluid/framework/data_feed_factory.cc +++ b/paddle/fluid/framework/data_feed_factory.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "glog/logging.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class DataFeed; typedef std::shared_ptr (*Createdata_feedFunction)(); @@ -73,5 +72,4 @@ REGISTER_DATAFEED_CLASS(SlotRecordInMemoryDataFeed); #if (defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)) && !defined(_WIN32) REGISTER_DATAFEED_CLASS(MultiSlotFileInstantDataFeed); #endif -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/dense_tensor_array.h b/paddle/fluid/framework/dense_tensor_array.h index bc7a81d87400cf..48aacb6fbde631 100644 --- a/paddle/fluid/framework/dense_tensor_array.h +++ b/paddle/fluid/framework/dense_tensor_array.h @@ -17,6 +17,4 @@ limitations under the License. */ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/phi/core/tensor_array.h" -namespace paddle { -namespace framework {} // namespace framework -} // namespace paddle +namespace paddle::framework {} // namespace paddle::framework diff --git a/paddle/fluid/framework/executor_gc_helper.cc b/paddle/fluid/framework/executor_gc_helper.cc index 2fdc160a82bc01..f06bd9eb3854f8 100644 --- a/paddle/fluid/framework/executor_gc_helper.cc +++ b/paddle/fluid/framework/executor_gc_helper.cc @@ -28,8 +28,7 @@ #include "paddle/fluid/operators/controlflow/while_op_helper.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void OpInOutInfo::Build(const OperatorBase *op) { is_built_ = true; @@ -350,5 +349,4 @@ GetEagerDeletionCleanVarsForPartial(const ProgramDesc &origin_program, return result; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc b/paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc index e730a7e2d1d04e..bbc3722997d411 100644 --- a/paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc +++ b/paddle/fluid/framework/ir/conv_elementwise_add_act_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/phi/core/platform/device/gpu/gpu_info.h" #endif -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { #define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern); #define GET_NODES \ @@ -252,9 +250,7 @@ void ConvElementwiseAddActFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(conv_elementwise_add_act_fuse_pass, paddle::framework::ir::ConvElementwiseAddActFusePass); diff --git a/paddle/fluid/framework/ir/cost_model.cc b/paddle/fluid/framework/ir/cost_model.cc index 720e7a9ba7a6bc..34596d5f461e0c 100644 --- a/paddle/fluid/framework/ir/cost_model.cc +++ b/paddle/fluid/framework/ir/cost_model.cc @@ -21,8 +21,7 @@ #include "paddle/fluid/framework/scope.h" #include "paddle/phi/common/place.h" -namespace paddle { -namespace framework { +namespace paddle::framework { using ir::Graph; using phi::Event; @@ -263,5 +262,4 @@ CostData CostModel::ProfileMeasure( return cost_data; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/delete_elementwise_mul_op_pass.cc b/paddle/fluid/framework/ir/delete_elementwise_mul_op_pass.cc index a5aa6c65784b29..81c3ba4ad9c6cc 100644 --- a/paddle/fluid/framework/ir/delete_elementwise_mul_op_pass.cc +++ b/paddle/fluid/framework/ir/delete_elementwise_mul_op_pass.cc @@ -23,16 +23,11 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { struct FillMulPattern : public PatternBase { FillMulPattern(PDPattern* pattern, const std::string& name_scope); @@ -68,7 +63,8 @@ FillMulPattern::FillMulPattern(PDPattern* pattern, mul->LinksFrom({fill_out, mul_in}).LinksTo({mul_out}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { /* Delete "elementwise" if one of inputs is "1". @@ -114,9 +110,7 @@ void DeleteElementwiseMulOpPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(delete_elementwise_mul_op_pass, paddle::framework::ir::DeleteElementwiseMulOpPass); diff --git a/paddle/fluid/framework/ir/delete_repeated_ops_pass.cc b/paddle/fluid/framework/ir/delete_repeated_ops_pass.cc index 3cd97353819490..051f83b79442be 100644 --- a/paddle/fluid/framework/ir/delete_repeated_ops_pass.cc +++ b/paddle/fluid/framework/ir/delete_repeated_ops_pass.cc @@ -23,15 +23,11 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { bool HasOutVarName(Node* op_node, std::string name) { auto* op_desc = op_node->Op(); @@ -45,7 +41,8 @@ bool HasOutVarName(Node* op_node, std::string name) { return false; } -namespace patterns { +} // namespace paddle::framework::ir +namespace paddle::framework::ir::patterns { struct VarWithRepeatedOpsPattern : public PatternBase { VarWithRepeatedOpsPattern(PDPattern* pattern, @@ -78,7 +75,8 @@ VarWithRepeatedOpsPattern::VarWithRepeatedOpsPattern( }); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { /* Delete repeated ops, for example: @@ -300,9 +298,7 @@ void DeleteRepeatedOpsPass::ApplyImpl(ir::Graph* graph) const { LOG(INFO) << "Total delete op counts: " << total_delete_op_count; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(delete_repeated_ops_pass, paddle::framework::ir::DeleteRepeatedOpsPass); diff --git a/paddle/fluid/framework/ir/fuse_bn_add_act_pass.cc b/paddle/fluid/framework/ir/fuse_bn_add_act_pass.cc index 1fce7b1ed95a36..7db0578cab0174 100644 --- a/paddle/fluid/framework/ir/fuse_bn_add_act_pass.cc +++ b/paddle/fluid/framework/ir/fuse_bn_add_act_pass.cc @@ -20,9 +20,7 @@ #include "paddle/fluid/platform/enforce.h" #include "paddle/phi/core/platform/device/gpu/gpu_dnn.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void FuseBatchNormAddActPass::ApplyImpl(ir::Graph *graph) const { #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) @@ -385,9 +383,7 @@ std::vector FuseBatchNormAddActPass::ReplaceNode( return new_list; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fuse_bn_add_act_pass, paddle::framework::ir::FuseBatchNormAddActPass); diff --git a/paddle/fluid/framework/ir/graph_test.cc b/paddle/fluid/framework/ir/graph_test.cc index ad9ba9c7e76e2f..70349700e72d7e 100644 --- a/paddle/fluid/framework/ir/graph_test.cc +++ b/paddle/fluid/framework/ir/graph_test.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/program_desc.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class NOP : public OperatorBase { public: @@ -70,8 +69,7 @@ class DummyOpVarTypeInference : public VarTypeInference { public: void operator()(framework::InferVarTypeContext *ctx) const override {} }; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework REGISTER_OPERATOR(fake_sum, paddle::framework::NOP, @@ -85,8 +83,7 @@ REGISTER_OPERATOR(sum_without_infer_var_type, paddle::framework::NOP, paddle::framework::SumOpMaker); -namespace paddle { -namespace framework { +namespace paddle::framework { TEST(GraphTest, Basic) { ProgramDesc prog; @@ -337,5 +334,4 @@ TEST(GraphTest, TestMultiBlock) { FLAGS_convert_all_blocks = flag_temp; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/graph_traits.cc b/paddle/fluid/framework/ir/graph_traits.cc index 0bd62fcfc48611..c0d91028eadab1 100644 --- a/paddle/fluid/framework/ir/graph_traits.cc +++ b/paddle/fluid/framework/ir/graph_traits.cc @@ -17,9 +17,7 @@ #include #include -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { // // NodesDFSIterator @@ -204,6 +202,4 @@ Node *NodesTSIterator::operator->() { return sorted_[cursor_]; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/identity_op_clean_pass_test.cc b/paddle/fluid/framework/ir/identity_op_clean_pass_test.cc index b9d4e84b4d93d1..16b9496ce8e1e1 100644 --- a/paddle/fluid/framework/ir/identity_op_clean_pass_test.cc +++ b/paddle/fluid/framework/ir/identity_op_clean_pass_test.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/framework/ir/pass.h" #include "paddle/fluid/framework/ir/pass_tester_helper.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { TEST(identity_op_clean_pass, assign) { ProgramDesc program; @@ -113,8 +111,6 @@ TEST(identity_op_clean_pass, concat) { concat_num)); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(identity_op_clean_pass); diff --git a/paddle/fluid/framework/ir/onednn/multi_gru_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/multi_gru_fuse_pass.cc index 89f6c252953b53..9634ca0759c436 100644 --- a/paddle/fluid/framework/ir/onednn/multi_gru_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/multi_gru_fuse_pass.cc @@ -22,9 +22,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using EigenVectorArrayMap = Eigen::Map>; using string::PrettyLogDetail; @@ -208,9 +206,7 @@ MultiGRUFusePass::MultiGRUFusePass() { .End(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(multi_gru_fuse_pass, paddle::framework::ir::MultiGRUFusePass); diff --git a/paddle/fluid/framework/new_executor/program_interpreter.cc b/paddle/fluid/framework/new_executor/program_interpreter.cc index 45bdeed567eabf..c2714719e2a50a 100644 --- a/paddle/fluid/framework/new_executor/program_interpreter.cc +++ b/paddle/fluid/framework/new_executor/program_interpreter.cc @@ -46,8 +46,7 @@ PHI_DECLARE_bool(enable_host_event_recorder_hook); PD_DECLARE_bool(log_memory_stats); COMMON_DECLARE_string(static_runtime_data_save_path); COMMON_DECLARE_bool(save_static_runtime_data); -namespace paddle { -namespace framework { +namespace paddle::framework { ProgramInterpreter::ProgramInterpreter(const phi::Place& place, const BlockDesc& block, @@ -1762,5 +1761,4 @@ Variable* ProgramInterpreter::DebugVar(const std::string& name) const { PADDLE_THROW(common::errors::Unimplemented( "DebugVar is not implemented in ProgramInterpreter.")); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/phi_utils.cc b/paddle/fluid/framework/phi_utils.cc index 59538ddaf889ab..bbdde2dc9a3960 100644 --- a/paddle/fluid/framework/phi_utils.cc +++ b/paddle/fluid/framework/phi_utils.cc @@ -27,8 +27,7 @@ limitations under the License. */ #include "paddle/phi/core/type_defs.h" #include "paddle/utils/string/string_helper.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class KernelArgsNameMakerByOpProto : public KernelArgsNameMaker { public: @@ -360,5 +359,4 @@ phi::IntArray MakePhiIntArrayFromVarList( return result; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/imperative/flags.cc b/paddle/fluid/imperative/flags.cc index a1484468cb73bb..ed38e1b2b798f0 100644 --- a/paddle/fluid/imperative/flags.cc +++ b/paddle/fluid/imperative/flags.cc @@ -21,12 +21,10 @@ PHI_DEFINE_EXPORTED_uint64(dygraph_debug, "Debug level of dygraph. This flag is not " "open to users"); -namespace paddle { -namespace imperative { +namespace paddle::imperative { bool IsDebugEnabled() { return FLAGS_dygraph_debug != 0; } uint64_t GetDebugLevel() { return FLAGS_dygraph_debug; } -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative diff --git a/paddle/fluid/inference/tensorrt/convert/anchor_generator_op.cc b/paddle/fluid/inference/tensorrt/convert/anchor_generator_op.cc index 8ae7f89df70740..ba10a6458c9aea 100644 --- a/paddle/fluid/inference/tensorrt/convert/anchor_generator_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/anchor_generator_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/anchor_generator_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* Anchor Generator Op */ class AnchorGeneratorOpConverter : public OpConverter { @@ -85,8 +83,6 @@ class AnchorGeneratorOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(anchor_generator, AnchorGeneratorOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/cross_multihead_matmul_op.cc b/paddle/fluid/inference/tensorrt/convert/cross_multihead_matmul_op.cc index ede2f4d500d4ad..c5bc51403ac53d 100644 --- a/paddle/fluid/inference/tensorrt/convert/cross_multihead_matmul_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/cross_multihead_matmul_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/phi/common/data_type.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class CrossMultiheadMatMulOpConverter : public OpConverter { public: @@ -292,9 +290,7 @@ class CrossMultiheadMatMulOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(cross_multihead_matmul, CrossMultiheadMatMulOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/equal_op.cc b/paddle/fluid/inference/tensorrt/convert/equal_op.cc index c1e196725c64c0..0b7c713ed40186 100644 --- a/paddle/fluid/inference/tensorrt/convert/equal_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/equal_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class EqualOpConverter : public OpConverter { public: @@ -129,9 +127,7 @@ class NotEqualOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(equal, EqualOpConverter); REGISTER_TRT_OP_CONVERTER(not_equal, NotEqualOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/reshape_op.cc b/paddle/fluid/inference/tensorrt/convert/reshape_op.cc index 79289479faeade..295d6abbd95a41 100644 --- a/paddle/fluid/inference/tensorrt/convert/reshape_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/reshape_op.cc @@ -11,9 +11,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * ReshapeOp @@ -70,9 +68,7 @@ class ReshapeOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(reshape, ReshapeOpConverter); REGISTER_TRT_OP_CONVERTER(reshape2, ReshapeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/squeeze2_op.cc b/paddle/fluid/inference/tensorrt/convert/squeeze2_op.cc index 4f7b0b044398a4..4b63563da9e1f2 100644 --- a/paddle/fluid/inference/tensorrt/convert/squeeze2_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/squeeze2_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class Squeeze2OpConverter : public OpConverter { public: @@ -81,8 +79,6 @@ class Squeeze2OpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(squeeze2, Squeeze2OpConverter); diff --git a/paddle/fluid/operators/collective/c_broadcast_op.cc b/paddle/fluid/operators/collective/c_broadcast_op.cc index 08fb52fbfb0dd9..e53794444ecc8d 100644 --- a/paddle/fluid/operators/collective/c_broadcast_op.cc +++ b/paddle/fluid/operators/collective/c_broadcast_op.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_broadcast_op.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class CBroadcastOp : public framework::OperatorWithKernel { public: @@ -51,8 +50,7 @@ Reference: https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/us } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/gen_nccl_id_op.cc b/paddle/fluid/operators/collective/gen_nccl_id_op.cc index c57c0eee040bbe..b7a36b4f42d0de 100644 --- a/paddle/fluid/operators/collective/gen_nccl_id_op.cc +++ b/paddle/fluid/operators/collective/gen_nccl_id_op.cc @@ -25,14 +25,11 @@ limitations under the License. */ #include "paddle/phi/common/place.h" #include "paddle/phi/core/platform/gen_comm_id_helper.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace operators { +namespace paddle::operators { #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) static void GenNCCLID(std::vector* nccl_ids) { @@ -251,8 +248,7 @@ For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the ser } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc b/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc index 80e5390a5bc3b7..16db2c543e2c59 100644 --- a/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc +++ b/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc @@ -15,8 +15,7 @@ #include "paddle/fluid/pir/dialect/distributed/ir/dist_attribute.h" #include "paddle/fluid/pir/dialect/distributed/ir/attribute_storage.h" #include "paddle/phi/core/enforce.h" -namespace paddle { -namespace dialect { +namespace paddle::dialect { /// /// \brief ProcessMeshAttribute interface. /// @@ -163,8 +162,7 @@ OperationDistAttribute OperationDistAttribute::get( return Base::get(ctx, mesh, operands, results, chunk_id); } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::ProcessMeshAttribute) IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::TensorDistAttribute) IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::OperationDistAttribute) diff --git a/paddle/fluid/pir/dialect/distributed/ir/dist_dialect.cc b/paddle/fluid/pir/dialect/distributed/ir/dist_dialect.cc index 9b858b2fe2c844..73451c37983694 100644 --- a/paddle/fluid/pir/dialect/distributed/ir/dist_dialect.cc +++ b/paddle/fluid/pir/dialect/distributed/ir/dist_dialect.cc @@ -22,8 +22,7 @@ #include "paddle/phi/core/distributed/auto_parallel/utils.h" REGISTER_FILE_SYMBOLS(dist_dialect); -namespace paddle { -namespace dialect { +namespace paddle::dialect { DistDialect::DistDialect(pir::IrContext *context) : pir::Dialect(name(), context, pir::TypeId::get()) { @@ -124,7 +123,6 @@ pir::OpPrintFn DistDialect::PrintOperation(const pir::Operation &op) const { return nullptr; } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::DistDialect) diff --git a/paddle/fluid/platform/profiler/cupti_data_process.cc b/paddle/fluid/platform/profiler/cupti_data_process.cc index e86ad019fbba25..bff0f2bf70c637 100644 --- a/paddle/fluid/platform/profiler/cupti_data_process.cc +++ b/paddle/fluid/platform/profiler/cupti_data_process.cc @@ -21,9 +21,7 @@ #include "paddle/phi/core/platform/device/gpu/gpu_info.h" #include "paddle/phi/core/platform/profiler/utils.h" -namespace paddle { -namespace platform { -namespace details { +namespace paddle::platform::details { #ifdef PADDLE_WITH_CUPTI void AddKernelRecord(const CUpti_ActivityKernel4* kernel, uint64_t start_ns, @@ -375,6 +373,4 @@ void ProcessCuptiActivityRecord( } } #endif -} // namespace details -} // namespace platform -} // namespace paddle +} // namespace paddle::platform::details diff --git a/paddle/fluid/primitive/backend/manual/manual_static_backend.cc b/paddle/fluid/primitive/backend/manual/manual_static_backend.cc index 0b637debbe6b46..27504be79126d0 100644 --- a/paddle/fluid/primitive/backend/manual/manual_static_backend.cc +++ b/paddle/fluid/primitive/backend/manual/manual_static_backend.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/primitive/base/lazy_tensor.h" #include "paddle/fluid/primitive/primitive/primitive.h" -namespace paddle { -namespace primitive { -namespace backend { +namespace paddle::primitive::backend { using LazyTensor = paddle::primitive::LazyTensor; template <> @@ -98,6 +96,4 @@ std::tuple fused_gemm_epilogue_grad( return std::make_tuple(x_grad, y_grad, bias_grad); } -} // namespace backend -} // namespace primitive -} // namespace paddle +} // namespace paddle::primitive::backend diff --git a/paddle/fluid/pybind/io.cc b/paddle/fluid/pybind/io.cc index 834386d1fdf459..72bc9682c650ed 100644 --- a/paddle/fluid/pybind/io.cc +++ b/paddle/fluid/pybind/io.cc @@ -24,8 +24,7 @@ limitations under the License. */ #include "paddle/utils/pybind.h" namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { template void LoadCombine(const std::string &file_path, const std::vector &names, @@ -175,5 +174,4 @@ void BindIO(pybind11::module *m) { py::arg("program"), py::arg("pir_version") = -1); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/reader_py.cc b/paddle/fluid/pybind/reader_py.cc index 2e0bec5da6b420..8cf8613ef17dc4 100644 --- a/paddle/fluid/pybind/reader_py.cc +++ b/paddle/fluid/pybind/reader_py.cc @@ -39,8 +39,7 @@ COMMON_DECLARE_bool(reader_queue_speed_test_mode); // disable auto conversion to list in Python PYBIND11_MAKE_OPAQUE(phi::TensorArray); -namespace paddle { -namespace pybind { +namespace paddle::pybind { namespace py = pybind11; namespace reader = operators::reader; @@ -544,5 +543,4 @@ void BindReader(py::module *module) { py::return_value_policy::take_ownership); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/rpc.cc b/paddle/fluid/pybind/rpc.cc index bc947af36f9a1e..58eaf0d4c1fc33 100644 --- a/paddle/fluid/pybind/rpc.cc +++ b/paddle/fluid/pybind/rpc.cc @@ -21,8 +21,7 @@ namespace py = pybind11; using paddle::distributed::FutureWrapper; using paddle::distributed::RpcAgent; using paddle::distributed::WorkerInfo; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindWorkerInfo(py::module* m) { py::class_(*m, "WorkerInfo") @@ -130,5 +129,4 @@ void GetAllWorkerInfos(py::module* m) { }, py::call_guard()); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/dynload/cudnn.cc b/paddle/phi/backends/dynload/cudnn.cc index 069c09e35d299f..c474a1a4176883 100644 --- a/paddle/phi/backends/dynload/cudnn.cc +++ b/paddle/phi/backends/dynload/cudnn.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include "paddle/phi/core/enforce.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag cudnn_dso_flag; void* cudnn_dso_handle = nullptr; @@ -76,5 +75,4 @@ void EnforceCUDNNLoaded(const char* fn_name) { fn_name)); } -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/backends/dynload/hipblasLt.cc b/paddle/phi/backends/dynload/hipblasLt.cc index de13f05f1d00c5..5bd005c7f480f3 100644 --- a/paddle/phi/backends/dynload/hipblasLt.cc +++ b/paddle/phi/backends/dynload/hipblasLt.cc @@ -11,8 +11,7 @@ limitations under the License. */ #include "paddle/phi/backends/dynload/hipblasLt.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag hipblasLt_dso_flag; void *hipblasLt_dso_handle = nullptr; @@ -20,5 +19,4 @@ void *hipblasLt_dso_handle = nullptr; HIPBLASLT_BLAS_ROUTINE_EACH(DEFINE_WRAP); -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/core/distributed/auto_parallel/process_mesh.cc b/paddle/phi/core/distributed/auto_parallel/process_mesh.cc index f085a79b0f6e52..6142f6c0154125 100644 --- a/paddle/phi/core/distributed/auto_parallel/process_mesh.cc +++ b/paddle/phi/core/distributed/auto_parallel/process_mesh.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/proto_helper.h" #include "paddle/phi/core/distributed/auto_parallel/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::has_duplicates; using phi::distributed::auto_parallel::ProcessMeshProto; @@ -237,5 +236,4 @@ int SubMeshDim(const ProcessMesh &global_mesh, const ProcessMesh &sub_mesh) { return -1; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/global_and_sub_mesh_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/global_and_sub_mesh_reshard_function.cc index f2fb1dca1ed433..34dcc85527ff8b 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/global_and_sub_mesh_reshard_function.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/global_and_sub_mesh_reshard_function.cc @@ -22,8 +22,7 @@ #include "paddle/phi/kernels/p_recv_kernel.h" #include "paddle/phi/kernels/p_send_kernel.h" -namespace phi { -namespace distributed { +namespace phi::distributed { bool GlobalToSubMeshReshardFunction::IsSuitable( const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -134,5 +133,4 @@ void SubMeshToGlobalReshardFunction::Eval(phi::DeviceContext* dev_ctx, SetDistProps(out, in.dims(), out_dist_attr); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/reshard_function_registry.cc b/paddle/phi/core/distributed/auto_parallel/reshard/reshard_function_registry.cc index 9dcec8683d7375..32dcd90446c0a9 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/reshard_function_registry.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/reshard_function_registry.cc @@ -30,8 +30,7 @@ #include "paddle/phi/core/distributed/auto_parallel/reshard/same_status_reshard_function.h" #include "paddle/phi/core/distributed/auto_parallel/reshard/x_to_r_reshard_function.h" -namespace phi { -namespace distributed { +namespace phi::distributed { ReshardFunction* ChooseProperReshardFunction( const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -79,5 +78,4 @@ REGISTER_RESHARD_FUNC(CrossNdMeshReshardFunction); REGISTER_RESHARD_FUNC(GlobalToSubMeshReshardFunction); REGISTER_RESHARD_FUNC(SubMeshToGlobalReshardFunction); -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/store/store_utils.cc b/paddle/phi/core/distributed/store/store_utils.cc index e5bb7669c66a50..55f6544b154ac1 100644 --- a/paddle/phi/core/distributed/store/store_utils.cc +++ b/paddle/phi/core/distributed/store/store_utils.cc @@ -22,8 +22,7 @@ #include "paddle/phi/core/distributed/auto_parallel/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using auto_parallel::str_split; namespace { @@ -84,5 +83,4 @@ std::shared_ptr CreateOrGetGlobalTCPStore() { return store; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/cross_entropy_with_softmax.cc b/paddle/phi/infermeta/spmd_rules/cross_entropy_with_softmax.cc index 8c35f85b436d9f..920bfbe493e901 100644 --- a/paddle/phi/infermeta/spmd_rules/cross_entropy_with_softmax.cc +++ b/paddle/phi/infermeta/spmd_rules/cross_entropy_with_softmax.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -471,5 +470,4 @@ SpmdInfo CrossEntropyWithSoftmaxGradInferSpmd(const DistMetaTensor& label, {x_grad}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/default_data_parallel.cc b/paddle/phi/infermeta/spmd_rules/default_data_parallel.cc index 2716590bff4b57..e65fb33615ae69 100644 --- a/paddle/phi/infermeta/spmd_rules/default_data_parallel.cc +++ b/paddle/phi/infermeta/spmd_rules/default_data_parallel.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/inferspmd_utils.h" #include "paddle/phi/core/distributed/auto_parallel/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -162,5 +161,4 @@ SpmdInfo DefaultDataParallelInferSpmdReverse( ToArgDistAttr(output_dist_attrs)}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/reduction.cc b/paddle/phi/infermeta/spmd_rules/reduction.cc index 627395f9ad588f..40e826f5870e4a 100644 --- a/paddle/phi/infermeta/spmd_rules/reduction.cc +++ b/paddle/phi/infermeta/spmd_rules/reduction.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/utils.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -284,5 +283,4 @@ SpmdInfo ReductionGradInferSpmd(const DistMetaTensor& x, return spmd_info; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/matrix_bit_code.cc b/paddle/phi/kernels/funcs/matrix_bit_code.cc index 2fecb1c526c6e8..aa192cad3f0221 100644 --- a/paddle/phi/kernels/funcs/matrix_bit_code.cc +++ b/paddle/phi/kernels/funcs/matrix_bit_code.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/kernels/funcs/blas/blas.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct MatrixBitCodeFunctorAdd { @@ -359,5 +358,4 @@ void MatrixBitCodeFunctor::Sub(phi::DenseTensor *tmat) { template class MatrixBitCodeFunctor; template class MatrixBitCodeFunctor; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/selected_rows_functor.cc b/paddle/phi/kernels/funcs/selected_rows_functor.cc index 1626b54b10219c..88dbd6d1e09a95 100644 --- a/paddle/phi/kernels/funcs/selected_rows_functor.cc +++ b/paddle/phi/kernels/funcs/selected_rows_functor.cc @@ -32,8 +32,7 @@ limitations under the License. */ #include "glog/logging.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct SelectedRowsAdd { void operator()(const phi::CPUContext& context, @@ -413,7 +412,8 @@ template struct SelectedRowsAddToTensor; // Another group of functors is called "scatter updates", which means // use SelectedRows to update a dense tensor with different Ops, like // add or mul. -namespace scatter { +} // namespace phi::funcs +namespace phi::funcs::scatter { template typename std::enable_if::value>::type elementwise_add_to( @@ -1005,6 +1005,4 @@ struct UpdateToTensor { } }; -} // namespace scatter -} // namespace funcs -} // namespace phi +} // namespace phi::funcs::scatter diff --git a/paddle/phi/kernels/sparse/batch_norm_kernel.cc b/paddle/phi/kernels/sparse/batch_norm_kernel.cc index 857d815c5c4815..bef06371065197 100644 --- a/paddle/phi/kernels/sparse/batch_norm_kernel.cc +++ b/paddle/phi/kernels/sparse/batch_norm_kernel.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/phi/kernels/batch_norm_kernel.h" #include "paddle/phi/kernels/sparse/empty_kernel.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void BatchNormCooKernel(const Context& dev_ctx, @@ -62,8 +61,7 @@ void BatchNormCooKernel(const Context& dev_ctx, y->SetKmaps(x.GetKmaps()); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(batch_norm_coo, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/addmm_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/addmm_grad_kernel.cc index 41b5c15157ccc4..ecc2bdb6a3f743 100644 --- a/paddle/phi/kernels/sparse/cpu/addmm_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/addmm_grad_kernel.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void AddmmCooDenseGradKernel(const Context& dev_ctx UNUSED, @@ -50,8 +49,7 @@ void AddmmCsrDenseGradKernel(const Context& dev_ctx UNUSED, "Not support CPU backward kernel of 'sparse.addmm' now.")); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(addmm_coo_dense_grad, CPU, From 5e7c09c42e862f35688922999d6ba1cb545de38f Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:03:06 +0800 Subject: [PATCH 138/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-23=20(#64777)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 23 * format --- paddle/fluid/framework/block_desc.cc | 6 ++---- paddle/fluid/framework/io/crypto/cipher_utils.cc | 6 ++---- .../ir/multihead_matmul_fuse_pass_tester.cc | 8 ++------ .../ir/onednn/depthwise_conv_onednn_pass_tester.cc | 8 ++------ paddle/fluid/framework/ir/pass_test.cc | 8 ++------ paddle/fluid/framework/ir/quant_linear_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/relu6_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/relu6_fuse_pass_test.cc | 8 ++------ .../framework/ir/sigmoid_elementmul_fuse_pass.cc | 12 ++++-------- .../framework/new_executor/workqueue/workqueue.cc | 6 ++---- paddle/fluid/framework/pull_dense_worker.cc | 6 ++---- paddle/fluid/framework/unused_var_check.cc | 6 ++---- .../inference/tensorrt/convert/activation_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/assign_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/bmm_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/einsum_op.cc | 8 ++------ .../inference/tensorrt/convert/elementwise_op.cc | 8 ++------ .../inference/tensorrt/convert/hard_sigmoid_op.cc | 8 ++------ .../inference/tensorrt/convert/nearest_interp_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/square_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/tile_op.cc | 8 ++------ paddle/fluid/operators/batch_norm_op.cc | 6 ++---- paddle/fluid/operators/collective/recv_v2_op.cc | 6 ++---- paddle/fluid/operators/reader/read_op.cc | 6 ++---- paddle/fluid/platform/gloo_context.cc | 6 ++---- paddle/fluid/platform/profiler/profiler.cc | 6 ++---- paddle/fluid/pybind/tensor.cc | 6 ++---- paddle/phi/backends/dynload/nvrtc.cc | 6 ++---- paddle/phi/common/int_array.cc | 6 ++---- paddle/phi/common/memory_utils.cc | 8 ++------ .../distributed/auto_parallel/inferspmd_utils.cc | 6 ++---- paddle/phi/core/operators/reader/buffered_reader.cc | 8 ++------ paddle/phi/core/platform/device_event_gpu.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/argmax.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/optimizer.cc | 6 ++---- paddle/phi/kernels/funcs/eigen/l1_norm.cc | 6 ++---- paddle/phi/kernels/funcs/eigen/loss.cc | 6 ++---- paddle/phi/kernels/funcs/sequence_padding.cc | 6 ++---- paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc | 6 ++---- paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc | 6 ++---- paddle/phi/kernels/selected_rows/full_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/mv_grad_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/slice_grad_kernel.cc | 6 ++---- paddle/phi/kernels/strings/unicode.cc | 6 ++---- 44 files changed, 90 insertions(+), 214 deletions(-) diff --git a/paddle/fluid/framework/block_desc.cc b/paddle/fluid/framework/block_desc.cc index 61f33b724de60c..6a147394ff5873 100644 --- a/paddle/fluid/framework/block_desc.cc +++ b/paddle/fluid/framework/block_desc.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/framework/program_desc.h" -namespace paddle { -namespace framework { +namespace paddle::framework { VarDesc *BlockDesc::Var(const std::string &name) { auto it = vars_.find(name); @@ -385,5 +384,4 @@ bool BlockDesc::NeedUpdate(bool include_subs) { return need; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/io/crypto/cipher_utils.cc b/paddle/fluid/framework/io/crypto/cipher_utils.cc index bbf2284ff51003..58aad020a2e313 100644 --- a/paddle/fluid/framework/io/crypto/cipher_utils.cc +++ b/paddle/fluid/framework/io/crypto/cipher_utils.cc @@ -20,8 +20,7 @@ #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::string CipherUtils::GenKey(int length) { CryptoPP::AutoSeededRandomPool prng; @@ -115,5 +114,4 @@ bool CipherUtils::GetValue( const int CipherUtils::AES_DEFAULT_IV_SIZE = 128; const int CipherUtils::AES_DEFAULT_TAG_SIZE = 128; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/multihead_matmul_fuse_pass_tester.cc b/paddle/fluid/framework/ir/multihead_matmul_fuse_pass_tester.cc index 8fa90358d6a1c4..7471221c579953 100644 --- a/paddle/fluid/framework/ir/multihead_matmul_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/multihead_matmul_fuse_pass_tester.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void AddVarToScope(Scope* param_scope, const std::string& name, @@ -145,9 +143,7 @@ TEST(MultiHeadMatmulFusePass, pass_op_version_check) { .IsPassCompatible("multihead_matmul_fuse_pass_v2")); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(multihead_matmul_fuse_pass); USE_PASS(multihead_matmul_fuse_pass_v2); diff --git a/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass_tester.cc b/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass_tester.cc index 5fdb7ad959921d..ec5015cbfa78cc 100644 --- a/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass_tester.cc +++ b/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass_tester.cc @@ -17,9 +17,7 @@ #include "paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void SetOp(ProgramDesc* prog, const std::string& type, @@ -154,8 +152,6 @@ TEST(DepthwiseConvMKLDNNPass, basic) { EXPECT_EQ(after.onednn_conv_nodes, before.onednn_conv_nodes + 1); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(depthwise_conv_onednn_pass); diff --git a/paddle/fluid/framework/ir/pass_test.cc b/paddle/fluid/framework/ir/pass_test.cc index 48e6aa1c3a17b9..777466dbad2d1f 100644 --- a/paddle/fluid/framework/ir/pass_test.cc +++ b/paddle/fluid/framework/ir/pass_test.cc @@ -18,9 +18,7 @@ limitations under the License. */ #include "gtest/gtest.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; class Node; @@ -279,9 +277,7 @@ TEST(PassTest, TestPassRegistrarDeconstructor) { pass_registrary->~PassRegistrar(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(test_pass, paddle::framework::ir::TestPass) .RequirePassAttr("test_pass_attr") diff --git a/paddle/fluid/framework/ir/quant_linear_fuse_pass.cc b/paddle/fluid/framework/ir/quant_linear_fuse_pass.cc index 26d864c82c43d7..8d2e466d68b9b2 100644 --- a/paddle/fluid/framework/ir/quant_linear_fuse_pass.cc +++ b/paddle/fluid/framework/ir/quant_linear_fuse_pass.cc @@ -37,9 +37,7 @@ void ConvertTensorType(phi::DenseTensor* tensor) { } } // namespace -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { #define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern); #define GET_NODES \ @@ -314,9 +312,7 @@ int QuantLinearFusePass::ApplyQuantLinearFusePattern(Graph* graph, return found_count; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(quant_linear_fuse_pass, paddle::framework::ir::QuantLinearFusePass); diff --git a/paddle/fluid/framework/ir/relu6_fuse_pass.cc b/paddle/fluid/framework/ir/relu6_fuse_pass.cc index a7a52c23acd0b5..bab6bf717964ff 100644 --- a/paddle/fluid/framework/ir/relu6_fuse_pass.cc +++ b/paddle/fluid/framework/ir/relu6_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void Relu6FusePass::ApplyImpl(ir::Graph* graph) const { // This pass is now used for xpu, because xpu can fuse conv + bias + relu6 @@ -130,8 +128,6 @@ void Relu6FusePass::ApplyImpl(ir::Graph* graph) const { gpd(graph, handler); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(relu6_fuse_pass, paddle::framework::ir::Relu6FusePass); diff --git a/paddle/fluid/framework/ir/relu6_fuse_pass_test.cc b/paddle/fluid/framework/ir/relu6_fuse_pass_test.cc index 0eebea6a579077..d96b62a384b333 100644 --- a/paddle/fluid/framework/ir/relu6_fuse_pass_test.cc +++ b/paddle/fluid/framework/ir/relu6_fuse_pass_test.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { template void AddVarToScope(Scope* param_scope, @@ -63,8 +61,6 @@ TEST(Relu6FusePass, basic) { "clip should be mapped to relu6 after pass.")); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(relu6_fuse_pass); diff --git a/paddle/fluid/framework/ir/sigmoid_elementmul_fuse_pass.cc b/paddle/fluid/framework/ir/sigmoid_elementmul_fuse_pass.cc index b2bdc411a6b77f..72a9cf98a1773c 100644 --- a/paddle/fluid/framework/ir/sigmoid_elementmul_fuse_pass.cc +++ b/paddle/fluid/framework/ir/sigmoid_elementmul_fuse_pass.cc @@ -23,10 +23,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { struct SigmoidElementmulFusePattern : public PatternBase { SigmoidElementmulFusePattern(PDPattern* pattern, @@ -65,7 +62,8 @@ SigmoidElementmulFusePattern::SigmoidElementmulFusePattern( elemul_op->LinksFrom({sigmoid_x, sigmoid_out}).LinksTo({elemul_out}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { SigmoidElementmulFusePass::SigmoidElementmulFusePass() = default; @@ -114,9 +112,7 @@ void SigmoidElementmulFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(sigmoid_elementmul_fuse_pass, paddle::framework::ir::SigmoidElementmulFusePass); diff --git a/paddle/fluid/framework/new_executor/workqueue/workqueue.cc b/paddle/fluid/framework/new_executor/workqueue/workqueue.cc index c286e20844cb8a..0a9a2220676894 100644 --- a/paddle/fluid/framework/new_executor/workqueue/workqueue.cc +++ b/paddle/fluid/framework/new_executor/workqueue/workqueue.cc @@ -11,8 +11,7 @@ #include "paddle/fluid/platform/enforce.h" #include "paddle/phi/core/platform/profiler/event_tracing.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void WorkQueueOptions::Validate() const { PADDLE_ENFORCE_GT(name.size(), @@ -249,5 +248,4 @@ std::unique_ptr CreateWorkQueueGroup( return ptr; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/pull_dense_worker.cc b/paddle/fluid/framework/pull_dense_worker.cc index aab94a465537e4..a4b07e413f9743 100644 --- a/paddle/fluid/framework/pull_dense_worker.cc +++ b/paddle/fluid/framework/pull_dense_worker.cc @@ -19,8 +19,7 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; class Variable; @@ -269,5 +268,4 @@ void PullDenseWorker::MergeDenseParam() { } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/unused_var_check.cc b/paddle/fluid/framework/unused_var_check.cc index c95f2fe2516d9c..b1bc84260b1fc6 100644 --- a/paddle/fluid/framework/unused_var_check.cc +++ b/paddle/fluid/framework/unused_var_check.cc @@ -29,8 +29,7 @@ PHI_DEFINE_EXPORTED_bool( "Checking whether operator contains unused inputs, " "especially for grad operator. It should be in unittest."); -namespace paddle { -namespace framework { +namespace paddle::framework { std::unordered_set *GetThreadLocalUsedVarNameSet() { thread_local std::unordered_set used_var_name_set; @@ -133,5 +132,4 @@ void CheckUnusedVar(const OperatorBase &op, const Scope &scope) { } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/inference/tensorrt/convert/activation_op.cc b/paddle/fluid/inference/tensorrt/convert/activation_op.cc index f9057ab7b0a21a..9d3829c3e4b574 100644 --- a/paddle/fluid/inference/tensorrt/convert/activation_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/activation_op.cc @@ -23,9 +23,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/helper.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ActivationOpConverter : public OpConverter { public: @@ -187,9 +185,7 @@ class ThresholdedReluOpConverter : public ActivationOpConverter { }; #endif -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(relu, ReluOpConverter); REGISTER_TRT_OP_CONVERTER(sigmoid, SigmoidOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/assign_op.cc b/paddle/fluid/inference/tensorrt/convert/assign_op.cc index 06534a90a76d8d..a439ee71f54721 100644 --- a/paddle/fluid/inference/tensorrt/convert/assign_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/assign_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class AssignOpConverter : public OpConverter { public: @@ -32,8 +30,6 @@ class AssignOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(assign, AssignOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/bmm_op.cc b/paddle/fluid/inference/tensorrt/convert/bmm_op.cc index 861a4b05306c1f..7a67b11b0446dc 100644 --- a/paddle/fluid/inference/tensorrt/convert/bmm_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/bmm_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class BMMOpConverter : public OpConverter { public: @@ -42,8 +40,6 @@ class BMMOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(bmm, BMMOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/einsum_op.cc b/paddle/fluid/inference/tensorrt/convert/einsum_op.cc index df7854acc16820..6ae6f13222e469 100644 --- a/paddle/fluid/inference/tensorrt/convert/einsum_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/einsum_op.cc @@ -11,9 +11,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Einsum Op @@ -46,8 +44,6 @@ class EinsumOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(einsum, EinsumOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc index da6e063137a2bd..84c494d02c1e24 100644 --- a/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/elementwise_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/elementwise_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ElementwiseTensorOpConverter : public OpConverter { public: @@ -304,9 +302,7 @@ class PowOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(elementwise_add_weight, ElementwiseTensorAddOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/hard_sigmoid_op.cc b/paddle/fluid/inference/tensorrt/convert/hard_sigmoid_op.cc index 2a1576155a9127..875f6ba4d03a61 100644 --- a/paddle/fluid/inference/tensorrt/convert/hard_sigmoid_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/hard_sigmoid_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * HardSigmoidOp, IActivationLayer in TRT. This Layer doesn't has weights. @@ -49,8 +47,6 @@ class HardSigmoidOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(hard_sigmoid, HardSigmoidOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/nearest_interp_op.cc b/paddle/fluid/inference/tensorrt/convert/nearest_interp_op.cc index b613562da891b7..0fc929908e59f3 100644 --- a/paddle/fluid/inference/tensorrt/convert/nearest_interp_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/nearest_interp_op.cc @@ -12,9 +12,7 @@ limitations under the License. */ #include "paddle/fluid/framework/data_layout.h" #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class NearestInterpolateOpConverter : public OpConverter { public: @@ -100,8 +98,6 @@ class NearestInterpolateOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(nearest_interp, NearestInterpolateOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/square_op.cc b/paddle/fluid/inference/tensorrt/convert/square_op.cc index e06102d398cb85..259dc391709671 100644 --- a/paddle/fluid/inference/tensorrt/convert/square_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/square_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class SquareOpConverter : public OpConverter { public: @@ -40,8 +38,6 @@ class SquareOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(square, SquareOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/tile_op.cc b/paddle/fluid/inference/tensorrt/convert/tile_op.cc index 667386b11bd5b7..e373a2325d169b 100644 --- a/paddle/fluid/inference/tensorrt/convert/tile_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/tile_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class TileOpConverter : public OpConverter { public: @@ -109,8 +107,6 @@ class TileOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(tile, TileOpConverter); diff --git a/paddle/fluid/operators/batch_norm_op.cc b/paddle/fluid/operators/batch_norm_op.cc index a73b736f33553d..72c68446b60514 100644 --- a/paddle/fluid/operators/batch_norm_op.cc +++ b/paddle/fluid/operators/batch_norm_op.cc @@ -29,8 +29,7 @@ limitations under the License. */ #include "paddle/fluid/framework/infershape_utils.h" #include "paddle/phi/infermeta/multiary.h" -namespace paddle { -namespace operators { +namespace paddle::operators { void BatchNormOp::InferShape(framework::InferShapeContext *ctx) const { OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "BatchNorm"); @@ -548,8 +547,7 @@ phi::KernelKey BatchNormDoubleGradOp::GetExpectedKernelType( DECLARE_INPLACE_OP_INFERER(BatchNormDoubleGradOpInplaceInferer, {"DY", "DDY"}); -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/recv_v2_op.cc b/paddle/fluid/operators/collective/recv_v2_op.cc index 6422b9162ec768..3a9d84741c4105 100644 --- a/paddle/fluid/operators/collective/recv_v2_op.cc +++ b/paddle/fluid/operators/collective/recv_v2_op.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include -namespace paddle { -namespace operators { +namespace paddle::operators { class RecvOpV2 : public framework::OperatorWithKernel { public: @@ -106,8 +105,7 @@ Reference: https://docs.nvidia.com/deeplearning/nccl/user-guide/docs/usage/p2p.h } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/reader/read_op.cc b/paddle/fluid/operators/reader/read_op.cc index 00b1ee62258450..4f9c3bbd336ae5 100644 --- a/paddle/fluid/operators/reader/read_op.cc +++ b/paddle/fluid/operators/reader/read_op.cc @@ -17,8 +17,7 @@ #include "paddle/phi/core/framework/reader.h" #include "paddle/phi/core/platform/profiler/event_tracing.h" -namespace paddle { -namespace operators { +namespace paddle::operators { // Returns true if the two dimensions are compatible. // A dimension is compatible with the other if: @@ -193,8 +192,7 @@ class ReadOpMaker : public framework::OpProtoAndCheckerMaker { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; REGISTER_OPERATOR( diff --git a/paddle/fluid/platform/gloo_context.cc b/paddle/fluid/platform/gloo_context.cc index 092145ef2e1e64..d232116f40d3a4 100644 --- a/paddle/fluid/platform/gloo_context.cc +++ b/paddle/fluid/platform/gloo_context.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/platform/gloo_context.h" -namespace paddle { -namespace platform { +namespace paddle::platform { #if defined(PADDLE_WITH_GLOO) void GlooParallelContext::Init() { auto gloo_ptr = paddle::framework::GlooWrapper::GetInstance(); @@ -45,5 +44,4 @@ void GlooParallelContext::ReleaseContext() { } #endif -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/fluid/platform/profiler/profiler.cc b/paddle/fluid/platform/profiler/profiler.cc index 04089a6a462f62..a0736a0124a495 100644 --- a/paddle/fluid/platform/profiler/profiler.cc +++ b/paddle/fluid/platform/profiler/profiler.cc @@ -37,8 +37,7 @@ #include "paddle/phi/backends/device_manager.h" #endif -namespace paddle { -namespace platform { +namespace paddle::platform { void SynchronizeDevice() { #ifdef PADDLE_WITH_CUDA @@ -181,5 +180,4 @@ std::unique_ptr Profiler::Stop() { return std::unique_ptr(profiler_result_ptr); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index 319884d26cc858..3da5ce16cd894d 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -177,8 +177,7 @@ PYBIND11_MAKE_OPAQUE(paddle::framework::FetchUnmergedList); PYBIND11_MAKE_OPAQUE(paddle::framework::FetchList); PYBIND11_MAKE_OPAQUE(paddle::framework::FetchType); -namespace paddle { -namespace pybind { +namespace paddle::pybind { PyTypeObject *g_framework_tensor_pytype = nullptr; @@ -1105,5 +1104,4 @@ void BindTensor(pybind11::module &m) { // NOLINT }); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/dynload/nvrtc.cc b/paddle/phi/backends/dynload/nvrtc.cc index 0ed370801c6acd..119100845d9f36 100644 --- a/paddle/phi/backends/dynload/nvrtc.cc +++ b/paddle/phi/backends/dynload/nvrtc.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/backends/dynload/nvrtc.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag nvrtc_dso_flag; void* nvrtc_dso_handle = nullptr; @@ -30,5 +29,4 @@ bool HasNVRTC() { return nvrtc_dso_handle != nullptr; } -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/common/int_array.cc b/paddle/phi/common/int_array.cc index 75440bd2d5b818..50572f782a1bb8 100644 --- a/paddle/phi/common/int_array.cc +++ b/paddle/phi/common/int_array.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/common/place.h" #include "paddle/phi/core/tensor_utils.h" -namespace paddle { -namespace experimental { +namespace paddle::experimental { template IntArrayBase::IntArrayBase(const phi::DDim& dims) { @@ -125,5 +124,4 @@ IntArrayBase::IntArrayBase( } } -} // namespace experimental -} // namespace paddle +} // namespace paddle::experimental diff --git a/paddle/phi/common/memory_utils.cc b/paddle/phi/common/memory_utils.cc index 47b3ab3cc9107b..e9516b16f4cdcb 100644 --- a/paddle/phi/common/memory_utils.cc +++ b/paddle/phi/common/memory_utils.cc @@ -14,9 +14,7 @@ #include "paddle/phi/common/memory_utils.h" -namespace phi { - -namespace memory_utils { +namespace phi::memory_utils { Allocator::AllocationPtr Alloc(const phi::Place& place, size_t size, @@ -141,6 +139,4 @@ std::shared_ptr::type> GetXpuEvent( } #endif -} // namespace memory_utils - -} // namespace phi +} // namespace phi::memory_utils diff --git a/paddle/phi/core/distributed/auto_parallel/inferspmd_utils.cc b/paddle/phi/core/distributed/auto_parallel/inferspmd_utils.cc index 0cd44a8240bb1e..73ff5a34f1b3a6 100644 --- a/paddle/phi/core/distributed/auto_parallel/inferspmd_utils.cc +++ b/paddle/phi/core/distributed/auto_parallel/inferspmd_utils.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/inferspmd_utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { InferSpmdContext::InferSpmdContext( paddle::small_vector inputs, @@ -170,5 +169,4 @@ const SpmdRule& SpmdRuleFactory::GetSpmdRule( return it->second; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/operators/reader/buffered_reader.cc b/paddle/phi/core/operators/reader/buffered_reader.cc index dee2fc4e19e9bb..f9ec43714e6234 100644 --- a/paddle/phi/core/operators/reader/buffered_reader.cc +++ b/paddle/phi/core/operators/reader/buffered_reader.cc @@ -23,9 +23,7 @@ #include "paddle/phi/backends/device_manager.h" #include "paddle/phi/common/memory_utils.h" -namespace paddle { -namespace operators { -namespace reader { +namespace paddle::operators::reader { BufferedReader::~BufferedReader() { VLOG(1) << "~BufferedReader"; reader_->Shutdown(); @@ -412,6 +410,4 @@ void BufferedReader::ReadNextImpl(phi::TensorArray *out) { prev_pos_ = i; } -} // namespace reader -} // namespace operators -} // namespace paddle +} // namespace paddle::operators::reader diff --git a/paddle/phi/core/platform/device_event_gpu.cc b/paddle/phi/core/platform/device_event_gpu.cc index 5977e35bcd1d49..e803b38efb803b 100644 --- a/paddle/phi/core/platform/device_event_gpu.cc +++ b/paddle/phi/core/platform/device_event_gpu.cc @@ -16,8 +16,7 @@ #include "paddle/phi/core/platform/device_event_base.h" #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) -namespace paddle { -namespace platform { +namespace paddle::platform { struct CUDADeviceEventWrapper { CUDADeviceEventWrapper(const phi::Place& place, unsigned int flag) : inner_event_(flag) { @@ -99,8 +98,7 @@ void EventResetCUDA(const DeviceEvent* event) { // do nothing } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform using ::paddle::platform::kCPU; using ::paddle::platform::kCUDA; diff --git a/paddle/phi/infermeta/spmd_rules/argmax.cc b/paddle/phi/infermeta/spmd_rules/argmax.cc index baf8ec22762684..4152cbe1642246 100644 --- a/paddle/phi/infermeta/spmd_rules/argmax.cc +++ b/paddle/phi/infermeta/spmd_rules/argmax.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { SpmdInfo ArgMaxInferSpmdBase(const DistMetaTensor& x, int axis, @@ -115,5 +114,4 @@ SpmdInfo ArgMaxInferSpmdDynamic(const DistMetaTensor& x, return ArgMaxInferSpmdBase(x, axis.to(), keepdims, flatten); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/optimizer.cc b/paddle/phi/infermeta/spmd_rules/optimizer.cc index 72cd06fda3fbbc..d3114993589c26 100644 --- a/paddle/phi/infermeta/spmd_rules/optimizer.cc +++ b/paddle/phi/infermeta/spmd_rules/optimizer.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/elementwise.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, const DistMetaTensor& grad, @@ -271,5 +270,4 @@ SpmdInfo SgdInferSpmd(const DistMetaTensor& param, {param_dist_attr, master_param_dist_attr}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/eigen/l1_norm.cc b/paddle/phi/kernels/funcs/eigen/l1_norm.cc index dd42658ab28001..d0e700c8e44b96 100644 --- a/paddle/phi/kernels/funcs/eigen/l1_norm.cc +++ b/paddle/phi/kernels/funcs/eigen/l1_norm.cc @@ -13,8 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/phi/kernels/funcs/eigen/eigen_function.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct EigenL1Norm { @@ -50,5 +49,4 @@ struct EigenL1NormGrad { template struct EigenL1Norm; template struct EigenL1NormGrad; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/eigen/loss.cc b/paddle/phi/kernels/funcs/eigen/loss.cc index 48a18c73e5eeaf..f28864f519de10 100644 --- a/paddle/phi/kernels/funcs/eigen/loss.cc +++ b/paddle/phi/kernels/funcs/eigen/loss.cc @@ -13,8 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/phi/kernels/funcs/eigen/eigen_function.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct EigenRankLoss { @@ -137,5 +136,4 @@ struct EigenHingeLossGrad { template struct EigenHingeLoss; template struct EigenHingeLossGrad; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/sequence_padding.cc b/paddle/phi/kernels/funcs/sequence_padding.cc index d7b2edf88eb588..6402f266fcfbf8 100644 --- a/paddle/phi/kernels/funcs/sequence_padding.cc +++ b/paddle/phi/kernels/funcs/sequence_padding.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/backends/xpu/xpu_context.h" #endif -namespace phi { -namespace funcs { +namespace phi::funcs { template void CopyValidData(phi::DenseTensor* dst_tensor, @@ -249,5 +248,4 @@ template class UnpaddingLoDTensorFunctor; template class UnpaddingLoDTensorFunctor; #endif -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc index f5af7c644b0453..5c14e273fe20c1 100644 --- a/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc @@ -26,8 +26,7 @@ #include "paddle/phi/kernels/funcs/jit/kernels.h" #include "paddle/phi/kernels/funcs/sequence2batch.h" -namespace phi { -namespace fusion { +namespace phi::fusion { #define INIT_BASE_DEFINES \ auto x_lod = x.lod(); \ @@ -392,8 +391,7 @@ void FusionGRUKernel(const Context& dev_ctx, } } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL( fusion_gru, CPU, ALL_LAYOUT, phi::fusion::FusionGRUKernel, float, double) {} diff --git a/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc b/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc index f6b4db05abd3bf..51eb883fe89b78 100644 --- a/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc +++ b/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc @@ -26,8 +26,7 @@ PD_DECLARE_int32(inner_op_parallelism); -namespace phi { -namespace sr { +namespace phi::sr { template void AdamDenseParamSparseGradKernel( @@ -237,8 +236,7 @@ void AdamDenseParamSparseGradKernel( } } -} // namespace sr -} // namespace phi +} // namespace phi::sr PD_REGISTER_KERNEL(adam_dense_param_sparse_grad, CPU, diff --git a/paddle/phi/kernels/selected_rows/full_kernel.cc b/paddle/phi/kernels/selected_rows/full_kernel.cc index 66a507b95dd1ea..6212f8dd1de946 100644 --- a/paddle/phi/kernels/selected_rows/full_kernel.cc +++ b/paddle/phi/kernels/selected_rows/full_kernel.cc @@ -23,8 +23,7 @@ limitations under the License. */ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/full_kernel.h" -namespace phi { -namespace sr { +namespace phi::sr { template void FullKernel(const Context& dev_ctx, @@ -45,8 +44,7 @@ void FullWithTensorKernel(const Context& dev_ctx, dev_ctx, value, shape, dtype, out->mutable_value()); } -} // namespace sr -} // namespace phi +} // namespace phi::sr PD_REGISTER_KERNEL(full_sr, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/mv_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/mv_grad_kernel.cc index e6c1d8d865755c..a95fd36349651b 100644 --- a/paddle/phi/kernels/sparse/cpu/mv_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/mv_grad_kernel.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/phi/backends/cpu/cpu_context.h" #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void MvCooGradKernel(const Context& dev_ctx UNUSED, @@ -42,8 +41,7 @@ void MvCsrGradKernel(const Context& dev_ctx UNUSED, "Not support CPU backward kernel of 'sparse.mv' now.")); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL( mv_coo_grad, CPU, ALL_LAYOUT, phi::sparse::MvCooGradKernel, float, double) { diff --git a/paddle/phi/kernels/sparse/cpu/slice_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/slice_grad_kernel.cc index 6c6e92012eb760..24820b3981382e 100644 --- a/paddle/phi/kernels/sparse/cpu/slice_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/slice_grad_kernel.cc @@ -20,8 +20,7 @@ #include "paddle/phi/kernels/empty_kernel.h" #include "paddle/phi/kernels/funcs/slice_utils.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void SliceCooGradCompute(const Context& dev_ctx, @@ -246,8 +245,7 @@ void SliceCsrGradKernel(const Context& dev_ctx, SliceCsrGradCompute( dev_ctx, x, out_grad, axes_vec, starts_vec, ends_vec, x_grad); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(slice_coo_grad, CPU, diff --git a/paddle/phi/kernels/strings/unicode.cc b/paddle/phi/kernels/strings/unicode.cc index 71d9ef36cd16df..c8ab8205813529 100644 --- a/paddle/phi/kernels/strings/unicode.cc +++ b/paddle/phi/kernels/strings/unicode.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/backends/gpu/gpu_info.h" #include "paddle/phi/kernels/strings/unicode_flag.h" -namespace phi { -namespace strings { +namespace phi::strings { static const void* utils_map[4] = {nullptr}; // NOLINT static uint16_t CHAR_CASES_MAP[65536] = {0}; // NOLINT @@ -87,5 +86,4 @@ const uint16_t* GetGPUCharCasesMap() { } #endif -} // namespace strings -} // namespace phi +} // namespace phi::strings From 922ae73e86e77149993388cfeceb69b9446a7c87 Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:06:00 +0800 Subject: [PATCH 139/288] fix bug (#69869) --- .../infer_symbolic_shape/element_wise_binary.cc | 12 +++++++++++- 1 file changed, 11 insertions(+), 1 deletion(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc index 3c8b88af98c7cb..36585f74596533 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/element_wise_binary.cc @@ -147,6 +147,17 @@ bool FloorDivideOpInferSymbolicShape( }); } +bool MinimumOpInferSymbolicShape( + pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { + return InferSymbolicShapeElementWiseBinary( + op, + infer_context, + [](const symbol::DimExpr &x, const symbol::DimExpr &y) { + symbol::DimExprBuilder builder; + return builder.Min(x, y); + }); +} + OP_ELEMENT_WISE_BINARY(Add_) OP_ELEMENT_WISE_BINARY(BitwiseAnd) OP_ELEMENT_WISE_BINARY(BitwiseAnd_) @@ -186,7 +197,6 @@ OP_ELEMENT_WISE_BINARY(LogicalOr_) OP_ELEMENT_WISE_BINARY(LogicalXor) OP_ELEMENT_WISE_BINARY(LogicalXor_) OP_ELEMENT_WISE_BINARY(Maximum) -OP_ELEMENT_WISE_BINARY(Minimum) OP_ELEMENT_WISE_BINARY(MultiplySr) OP_ELEMENT_WISE_BINARY(MultiplySr_) OP_ELEMENT_WISE_BINARY(Multiply_) From ae67aa474d377139399a3bee1eb519c2452ff2b7 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:06:20 +0800 Subject: [PATCH 140/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-1=20(#64755)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../fluid/distributed/index_dataset/index_sampler.cc | 6 ++---- .../fluid/distributed/index_dataset/index_sampler.h | 4 ++-- .../fluid/distributed/index_dataset/index_wrapper.cc | 6 ++---- .../fluid/distributed/index_dataset/index_wrapper.h | 4 ++-- paddle/fluid/distributed/ps/service/heter_client.cc | 6 ++---- paddle/fluid/distributed/ps/service/heter_client.h | 4 ++-- paddle/fluid/distributed/ps/service/heter_server.cc | 6 ++---- paddle/fluid/distributed/ps/service/heter_server.h | 4 ++-- paddle/fluid/distributed/ps/wrapper/fleet.cc | 6 ++---- paddle/fluid/distributed/ps/wrapper/fleet.h | 4 ++-- paddle/fluid/distributed/ps/wrapper/ps_wrapper.h | 4 ++-- paddle/fluid/framework/data_set.cc | 6 ++---- paddle/fluid/framework/data_set.h | 4 ++-- paddle/fluid/framework/downpour_lite_worker.cc | 12 ++++-------- paddle/fluid/framework/downpour_worker.cc | 12 ++++-------- paddle/fluid/framework/downpour_worker_opt.cc | 6 ++---- paddle/fluid/framework/fleet/box_wrapper.cc | 6 ++---- paddle/fluid/framework/fleet/box_wrapper.cu | 4 ++-- paddle/fluid/framework/fleet/box_wrapper.h | 4 ++-- paddle/fluid/framework/fleet/fleet_wrapper.cc | 6 ++---- paddle/fluid/framework/fleet/fleet_wrapper.h | 4 ++-- paddle/fluid/framework/fleet/heter_context.h | 4 ++-- .../fleet/heter_ps/cudf/block_radix_topk.cuh | 4 ++-- .../fluid/framework/fleet/heter_ps/feature_value.h | 4 ++-- paddle/fluid/framework/fleet/heter_ps/hashtable.h | 4 ++-- .../framework/fleet/heter_ps/hashtable_kernel.cu | 4 ++-- .../framework/fleet/heter_ps/hashtable_kernel.kps | 4 ++-- paddle/fluid/framework/fleet/heter_ps/heter_comm.h | 4 ++-- .../fluid/framework/fleet/heter_ps/heter_comm_inl.h | 4 ++-- .../framework/fleet/heter_ps/heter_comm_kernel.h | 4 ++-- .../framework/fleet/heter_ps/heter_comm_kernel.kps | 4 ++-- paddle/fluid/framework/fleet/heter_ps/heter_ps.cc | 6 ++---- paddle/fluid/framework/fleet/heter_ps/heter_ps.cu | 4 ++-- paddle/fluid/framework/fleet/heter_ps/heter_ps.h | 4 ++-- .../fluid/framework/fleet/heter_ps/heter_ps_base.h | 4 ++-- .../fluid/framework/fleet/heter_ps/heter_resource.cc | 6 ++---- .../fluid/framework/fleet/heter_ps/heter_resource.h | 4 ++-- paddle/fluid/framework/fleet/heter_ps/mem_pool.h | 4 ++-- .../fluid/framework/fleet/heter_ps/optimizer.cuh.h | 4 ++-- paddle/fluid/framework/fleet/heter_wrapper.cc | 6 ++---- paddle/fluid/framework/fleet/heter_wrapper.h | 4 ++-- paddle/fluid/framework/fleet/nccl_wrapper.cc | 6 ++---- paddle/fluid/framework/fleet/nccl_wrapper.h | 4 ++-- paddle/fluid/framework/fleet/ps_gpu_wrapper.cc | 6 ++---- paddle/fluid/framework/fleet/ps_gpu_wrapper.cu | 4 ++-- paddle/fluid/framework/fleet/ps_gpu_wrapper.h | 4 ++-- paddle/fluid/framework/fleet/ps_gpu_wrapper.kps | 4 ++-- paddle/fluid/framework/heter_pipeline_trainer.cc | 6 ++---- paddle/fluid/framework/hetercpu_worker.cc | 6 ++---- paddle/fluid/framework/hogwild_worker.cc | 6 ++---- paddle/fluid/framework/io/fs.cc | 6 ++---- paddle/fluid/framework/io/shell.cc | 6 ++---- paddle/fluid/framework/multi_trainer.cc | 6 ++---- paddle/fluid/framework/pipeline_trainer.cc | 6 ++---- paddle/fluid/framework/ps_gpu_worker.cc | 6 ++---- paddle/fluid/framework/trainer.cc | 6 ++---- paddle/fluid/framework/variable_helper.h | 4 ++-- paddle/fluid/platform/densetensor_printer.cc | 12 ++++-------- paddle/fluid/platform/densetensor_printer.h | 4 ++-- paddle/fluid/pybind/box_helper_py.cc | 6 ++---- paddle/fluid/pybind/distributed_py.cc | 6 ++---- paddle/fluid/pybind/fleet_py.cc | 6 ++---- paddle/fluid/pybind/fleet_wrapper_py.cc | 6 ++---- paddle/fluid/pybind/gloo_wrapper_py.cc | 6 ++---- paddle/fluid/pybind/heter_wrapper_py.cc | 6 ++---- paddle/fluid/pybind/metrics_py.cc | 6 ++---- paddle/fluid/pybind/nccl_wrapper_py.cc | 6 ++---- paddle/fluid/pybind/ps_gpu_wrapper_py.cc | 6 ++---- paddle/phi/kernels/funcs/block_radix_topk.cuh | 4 ++-- paddle/phi/kernels/gpu/arg_min_max_kernel.cu | 2 +- paddle/utils/string/string_helper.cc | 6 ++---- paddle/utils/string/string_helper.h | 4 ++-- paddle/utils/tribool.h | 2 +- patches/eigen/Meta.h | 6 +++--- patches/eigen/TensorReductionGpu.h | 4 ++-- .../thrust/thrust/system/detail/generic/shuffle.h | 8 ++++---- .../thrust/thrust/system/detail/generic/shuffle.inl | 8 ++++---- 77 files changed, 163 insertions(+), 241 deletions(-) diff --git a/paddle/fluid/distributed/index_dataset/index_sampler.cc b/paddle/fluid/distributed/index_dataset/index_sampler.cc index ee9daeff802bbe..0fd7ed414a206c 100644 --- a/paddle/fluid/distributed/index_dataset/index_sampler.cc +++ b/paddle/fluid/distributed/index_dataset/index_sampler.cc @@ -16,8 +16,7 @@ #include "paddle/fluid/framework/data_feed.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { std::vector> LayerWiseSampler::sample( const std::vector>& user_inputs, @@ -134,5 +133,4 @@ std::vector float2int(std::vector tmp) { return tmp_int; } -} // end namespace distributed -} // end namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/index_dataset/index_sampler.h b/paddle/fluid/distributed/index_dataset/index_sampler.h index 67ad006b5598c5..7349327802f7f1 100644 --- a/paddle/fluid/distributed/index_dataset/index_sampler.h +++ b/paddle/fluid/distributed/index_dataset/index_sampler.h @@ -134,5 +134,5 @@ class LayerWiseSampler : public IndexSampler { std::vector> layer_ids_; }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/distributed/index_dataset/index_wrapper.cc b/paddle/fluid/distributed/index_dataset/index_wrapper.cc index 75e760d2288829..8661784da4bc3a 100644 --- a/paddle/fluid/distributed/index_dataset/index_wrapper.cc +++ b/paddle/fluid/distributed/index_dataset/index_wrapper.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/io/fs.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { std::shared_ptr IndexWrapper::s_instance_(nullptr); @@ -198,5 +197,4 @@ std::vector TreeIndex::GetAllLeafs() { return res; } -} // end namespace distributed -} // end namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/index_dataset/index_wrapper.h b/paddle/fluid/distributed/index_dataset/index_wrapper.h index 31f46b8e60b733..6786b54097f454 100644 --- a/paddle/fluid/distributed/index_dataset/index_wrapper.h +++ b/paddle/fluid/distributed/index_dataset/index_wrapper.h @@ -121,5 +121,5 @@ class IndexWrapper { std::unordered_map tree_map; }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/distributed/ps/service/heter_client.cc b/paddle/fluid/distributed/ps/service/heter_client.cc index c2726225a8f46a..d948ea03bc1b1f 100644 --- a/paddle/fluid/distributed/ps/service/heter_client.cc +++ b/paddle/fluid/distributed/ps/service/heter_client.cc @@ -17,8 +17,7 @@ #include "paddle/fluid/framework/convert_utils.h" #include "paddle/phi/core/platform/profiler.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { PD_DEFINE_int32(heter_world_size, 100, "group size"); // group max size PD_DEFINE_int32(switch_send_recv_timeout_s, 600, "switch_send_recv_timeout_s"); @@ -422,5 +421,4 @@ int HeterClient::Recv(int group_id, VLOG(4) << "Recv done"; return 0; } -} // namespace distributed -} // end namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/ps/service/heter_client.h b/paddle/fluid/distributed/ps/service/heter_client.h index 9de0682653441f..6811779bf74cc4 100755 --- a/paddle/fluid/distributed/ps/service/heter_client.h +++ b/paddle/fluid/distributed/ps/service/heter_client.h @@ -253,5 +253,5 @@ class HeterClient { int trainer_id_; }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/distributed/ps/service/heter_server.cc b/paddle/fluid/distributed/ps/service/heter_server.cc index c534c68d7220c7..525ea32128100f 100644 --- a/paddle/fluid/distributed/ps/service/heter_server.cc +++ b/paddle/fluid/distributed/ps/service/heter_server.cc @@ -16,8 +16,7 @@ #include "paddle/utils/string/split.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { // PD_DEFINE_string(cert_path, "./cert.pem", "cert.pem path"); // PD_DEFINE_string(key_path, "./key.pem", "key.pem path"); std::shared_ptr HeterServer::s_instance_ = nullptr; @@ -258,5 +257,4 @@ int SendAndRecvVariableHandler::QueryInSwitchWithScope( VLOG(4) << "heter server QueryInSwitchWithScope done"; return 0; } -} // end namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/ps/service/heter_server.h b/paddle/fluid/distributed/ps/service/heter_server.h index b40b0d6eb58f31..a6a9c60621ec09 100644 --- a/paddle/fluid/distributed/ps/service/heter_server.h +++ b/paddle/fluid/distributed/ps/service/heter_server.h @@ -682,5 +682,5 @@ class HeterServer { int ready_; }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/distributed/ps/wrapper/fleet.cc b/paddle/fluid/distributed/ps/wrapper/fleet.cc index 36468dcc51ff1a..089b538e75ed6a 100644 --- a/paddle/fluid/distributed/ps/wrapper/fleet.cc +++ b/paddle/fluid/distributed/ps/wrapper/fleet.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/fluid/distributed/ps/service/communicator/communicator.h" #include "paddle/fluid/distributed/ps/table/table.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { using framework::ProgramDesc; using framework::VarDesc; @@ -1055,5 +1054,4 @@ void FleetWrapper::SetDate(const uint64_t table_id, const std::string& date) { #endif } -} // end namespace distributed -} // end namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/ps/wrapper/fleet.h b/paddle/fluid/distributed/ps/wrapper/fleet.h index 4fa2dba2fdfc27..116b8cdf4c177c 100644 --- a/paddle/fluid/distributed/ps/wrapper/fleet.h +++ b/paddle/fluid/distributed/ps/wrapper/fleet.h @@ -348,5 +348,5 @@ class FleetWrapper { DISABLE_COPY_AND_ASSIGN(FleetWrapper); }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/distributed/ps/wrapper/ps_wrapper.h b/paddle/fluid/distributed/ps/wrapper/ps_wrapper.h index c472989ee84916..eba7984f3a4868 100644 --- a/paddle/fluid/distributed/ps/wrapper/ps_wrapper.h +++ b/paddle/fluid/distributed/ps/wrapper/ps_wrapper.h @@ -79,5 +79,5 @@ class PSWrapper { virtual void Save(WrapperContext& context) = 0; // NOLINT }; -} // end namespace distributed -} // end namespace paddle +} // namespace distributed +} // namespace paddle diff --git a/paddle/fluid/framework/data_set.cc b/paddle/fluid/framework/data_set.cc index 2c40a8396a00c3..aadb423f153d95 100644 --- a/paddle/fluid/framework/data_set.cc +++ b/paddle/fluid/framework/data_set.cc @@ -43,8 +43,7 @@ COMMON_DECLARE_int32(gpugraph_storage_mode); COMMON_DECLARE_string(graph_edges_split_mode); COMMON_DECLARE_bool(query_dest_rank_by_multi_node); -namespace paddle { -namespace framework { +namespace paddle::framework { // constructor template @@ -2196,5 +2195,4 @@ void SlotRecordDataset::DynamicAdjustReadersNum(int thread_num) { PrepareTrain(); } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/data_set.h b/paddle/fluid/framework/data_set.h index f455accfb244f4..52e1f5193ba224 100644 --- a/paddle/fluid/framework/data_set.h +++ b/paddle/fluid/framework/data_set.h @@ -445,5 +445,5 @@ class SlotRecordDataset : public DatasetImpl { bool enable_heterps_ = true; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle diff --git a/paddle/fluid/framework/downpour_lite_worker.cc b/paddle/fluid/framework/downpour_lite_worker.cc index ee3dfb4de52f3f..d84f17b6663af1 100644 --- a/paddle/fluid/framework/downpour_lite_worker.cc +++ b/paddle/fluid/framework/downpour_lite_worker.cc @@ -23,19 +23,16 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Variable; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework #if defined _WIN32 || defined __APPLE__ #else #define _LINUX #endif -namespace paddle { -namespace framework { +namespace paddle::framework { void DownpourLiteWorker::Initialize(const TrainerDesc& desc) { param_ = desc.downpour_param(); for (int i = 0; i < param_.sparse_table_size(); ++i) { @@ -597,6 +594,5 @@ void DownpourLiteWorker::TrainFiles() { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/downpour_worker.cc b/paddle/fluid/framework/downpour_worker.cc index a67bd42db9b07e..298654e08c1638 100644 --- a/paddle/fluid/framework/downpour_worker.cc +++ b/paddle/fluid/framework/downpour_worker.cc @@ -21,19 +21,16 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Variable; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework #if defined _WIN32 || defined __APPLE__ #else #define _LINUX #endif -namespace paddle { -namespace framework { +namespace paddle::framework { void DownpourWorker::Initialize(const TrainerDesc& desc) { param_ = desc.downpour_param(); for (int i = 0; i < param_.sparse_table_size(); ++i) { @@ -1108,5 +1105,4 @@ void DownpourWorker::TrainFiles() { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/downpour_worker_opt.cc b/paddle/fluid/framework/downpour_worker_opt.cc index 0d4bff15184cbe..2f36a851fcc74b 100644 --- a/paddle/fluid/framework/downpour_worker_opt.cc +++ b/paddle/fluid/framework/downpour_worker_opt.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include "paddle/fluid/operators/isfinite_op.h" #include "paddle/phi/core/platform/cpu_helper.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; class OperatorBase; @@ -559,5 +558,4 @@ void DownpourWorkerOpt::TrainFiles() { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/fleet/box_wrapper.cc b/paddle/fluid/framework/fleet/box_wrapper.cc index 8a1424f1c1d3a3..49f92d655006f0 100644 --- a/paddle/fluid/framework/fleet/box_wrapper.cc +++ b/paddle/fluid/framework/fleet/box_wrapper.cc @@ -23,8 +23,7 @@ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/phi/core/platform/device/gpu/gpu_info.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::shared_ptr BoxWrapper::s_instance_ = nullptr; gpuStream_t BoxWrapper::stream_list_[8]; @@ -341,6 +340,5 @@ void BoxWrapper::AddReplaceFeasign(boxps::PSAgentBase* p_agent, VLOG(0) << "End AddReplaceFeasign"; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/fleet/box_wrapper.cu b/paddle/fluid/framework/fleet/box_wrapper.cu index 67f563cdcd1807..39bbd9de6c99ef 100644 --- a/paddle/fluid/framework/fleet/box_wrapper.cu +++ b/paddle/fluid/framework/fleet/box_wrapper.cu @@ -365,6 +365,6 @@ void BoxWrapper::CopyForPush(const phi::Place& place, #undef EMBEDX_CASE } -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/box_wrapper.h b/paddle/fluid/framework/fleet/box_wrapper.h index aa3856a17904fe..934e0b5f30d7d6 100644 --- a/paddle/fluid/framework/fleet/box_wrapper.h +++ b/paddle/fluid/framework/fleet/box_wrapper.h @@ -1154,7 +1154,7 @@ class BoxHelper { bool get_random_replace_done_ = false; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #include "paddle/fluid/framework/fleet/box_wrapper_impl.h" diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.cc b/paddle/fluid/framework/fleet/fleet_wrapper.cc index 76f9ee993bef2a..e5386c9b44d5ae 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.cc +++ b/paddle/fluid/framework/fleet/fleet_wrapper.cc @@ -31,8 +31,7 @@ limitations under the License. */ #include "glog/logging.h" #include "paddle/fluid/framework/op_registry.h" -namespace paddle { -namespace framework { +namespace paddle::framework { const uint32_t MAX_FEASIGN_NUM = 1024 * 100 * 100; std::shared_ptr FleetWrapper::s_instance_ = NULL; @@ -1996,5 +1995,4 @@ size_t FleetWrapper::GetAbsoluteSum(size_t start, return ret; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/fleet/fleet_wrapper.h b/paddle/fluid/framework/fleet/fleet_wrapper.h index 53aa21c4c533b5..bd2a5a21447c15 100644 --- a/paddle/fluid/framework/fleet/fleet_wrapper.h +++ b/paddle/fluid/framework/fleet/fleet_wrapper.h @@ -436,5 +436,5 @@ class FleetWrapper { DISABLE_COPY_AND_ASSIGN(FleetWrapper); }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle diff --git a/paddle/fluid/framework/fleet/heter_context.h b/paddle/fluid/framework/fleet/heter_context.h index f7cce0ab44940a..7cad0a404cf07a 100644 --- a/paddle/fluid/framework/fleet/heter_context.h +++ b/paddle/fluid/framework/fleet/heter_context.h @@ -270,6 +270,6 @@ class HeterContext { } }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/cudf/block_radix_topk.cuh b/paddle/fluid/framework/fleet/heter_ps/cudf/block_radix_topk.cuh index 83ab82503361c3..b42579b0a720fe 100644 --- a/paddle/fluid/framework/fleet/heter_ps/cudf/block_radix_topk.cuh +++ b/paddle/fluid/framework/fleet/heter_ps/cudf/block_radix_topk.cuh @@ -342,5 +342,5 @@ class BlockRadixTopKRegister { unsigned int top_k_mask_; }; -}; // end namespace framework -}; // end namespace paddle +}; // namespace framework +}; // namespace paddle diff --git a/paddle/fluid/framework/fleet/heter_ps/feature_value.h b/paddle/fluid/framework/fleet/heter_ps/feature_value.h index a779da603b895c..dd989f289a5ad4 100644 --- a/paddle/fluid/framework/fleet/heter_ps/feature_value.h +++ b/paddle/fluid/framework/fleet/heter_ps/feature_value.h @@ -1192,7 +1192,7 @@ class GlobalAccessorFactory { VirtualAccessor* accessor_wrapper_ptr_ = nullptr; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/hashtable.h b/paddle/fluid/framework/fleet/heter_ps/hashtable.h index a23df8121176b1..5daeb9f34f77d4 100644 --- a/paddle/fluid/framework/fleet/heter_ps/hashtable.h +++ b/paddle/fluid/framework/fleet/heter_ps/hashtable.h @@ -257,6 +257,6 @@ class HashTable { size_t push_grad_value_size_; bool infer_mode_ = false; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu index 7addd12dac9f9a..f2e022b03c96a4 100644 --- a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu +++ b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.cu @@ -744,6 +744,6 @@ template void HashTable::update< // cudaStream_t stream); #endif -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.kps b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.kps index 7d581935008a5d..c9a370810aa647 100644 --- a/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.kps +++ b/paddle/fluid/framework/fleet/heter_ps/hashtable_kernel.kps @@ -369,6 +369,6 @@ template void HashTable::update< // size_t len, XPUStream stream); #endif -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm.h b/paddle/fluid/framework/fleet/heter_ps/heter_comm.h index 79cac5ca46a833..acb71f0e6a2b57 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm.h @@ -814,8 +814,8 @@ class HeterComm { bool is_infer_mode_ = false; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #include "paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h" diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h b/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h index 467ebcdd34f8d0..2524c86eb89a43 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_inl.h @@ -4223,6 +4223,6 @@ size_t HeterComm:: } return total_send_recv; } -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h index a38fc6a467c25b..42e545aad028c2 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.h @@ -339,6 +339,6 @@ class HeterCommKernel { int block_size_{256}; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.kps b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.kps index 7849816ce5fc9e..228440ff994735 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.kps +++ b/paddle/fluid/framework/fleet/heter_ps/heter_comm_kernel.kps @@ -427,6 +427,6 @@ template void HeterCommKernel::reduce_by_key< #endif -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc index cd2a3752ffe790..af75eb31f15779 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cc @@ -17,8 +17,7 @@ limitations under the License. */ #ifdef PADDLE_WITH_HETERPS -namespace paddle { -namespace framework { +namespace paddle::framework { HeterPsBase* HeterPsBase::get_instance( size_t capacity, @@ -109,6 +108,5 @@ void HeterPs::push_sparse(int num, comm_->push_sparse(num, d_keys, d_grads, len); } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu index 3fe05753e09a31..8c314afc98f67e 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.cu @@ -176,6 +176,6 @@ int HeterPs::dedup_keys_and_fillidx( filter_zero); } -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps.h b/paddle/fluid/framework/fleet/heter_ps/heter_ps.h index c472c2ed75a9d6..76c1258fb12ac5 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps.h @@ -95,6 +95,6 @@ class HeterPs : public HeterPsBase { #endif }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h b/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h index 8624425d8bfbd2..f3e19c79b22029 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_ps_base.h @@ -91,6 +91,6 @@ class HeterPsBase { virtual void set_mode(bool infer_mode) = 0; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_resource.cc b/paddle/fluid/framework/fleet/heter_ps/heter_resource.cc index 30dc6f5e83d4cb..3d6f30142a5f51 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_resource.cc +++ b/paddle/fluid/framework/fleet/heter_ps/heter_resource.cc @@ -29,8 +29,7 @@ limitations under the License. */ COMMON_DECLARE_bool(enable_auto_detect_gpu_topo); COMMON_DECLARE_bool(enable_auto_rdma_trans); -namespace paddle { -namespace framework { +namespace paddle::framework { #if defined(PADDLE_WITH_CUDA) GPUResource::GPUResource(std::vector &dev_ids, int index) { @@ -286,6 +285,5 @@ void HeterPsResource::set_multi_mf(int multi_mf_dim, int max_mf_dim) { << " max_mf_dim_: " << max_mf_dim_; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/heter_resource.h b/paddle/fluid/framework/fleet/heter_ps/heter_resource.h index 667b162cfe4a77..52a27d0d627d96 100644 --- a/paddle/fluid/framework/fleet/heter_ps/heter_resource.h +++ b/paddle/fluid/framework/fleet/heter_ps/heter_resource.h @@ -167,6 +167,6 @@ class HeterPsResource { std::vector>> keys2rank_vec_; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/mem_pool.h b/paddle/fluid/framework/fleet/heter_ps/mem_pool.h index 1574a2f98ebd1a..30c957f05ed68c 100644 --- a/paddle/fluid/framework/fleet/heter_ps/mem_pool.h +++ b/paddle/fluid/framework/fleet/heter_ps/mem_pool.h @@ -140,7 +140,7 @@ class HBMMemoryPoolFix : public managed { size_t max_byte_capacity_; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif #endif diff --git a/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h b/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h index b14baf1073854a..a8e7f858f70c79 100644 --- a/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h +++ b/paddle/fluid/framework/fleet/heter_ps/optimizer.cuh.h @@ -724,6 +724,6 @@ class SparseAdagradV2Optimizer { }; #endif -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/heter_wrapper.cc b/paddle/fluid/framework/fleet/heter_wrapper.cc index b6c0492b7079eb..9e6c71b33e63ad 100644 --- a/paddle/fluid/framework/fleet/heter_wrapper.cc +++ b/paddle/fluid/framework/fleet/heter_wrapper.cc @@ -31,8 +31,7 @@ limitations under the License. */ #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/device_worker.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::shared_ptr HeterWrapper::s_instance_ = NULL; bool HeterWrapper::is_initialized_ = false; @@ -358,6 +357,5 @@ void HeterWrapper::CallRemoteXpuSync( } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/fleet/heter_wrapper.h b/paddle/fluid/framework/fleet/heter_wrapper.h index 86938690f3e381..6da1dadc0a6ec0 100644 --- a/paddle/fluid/framework/fleet/heter_wrapper.h +++ b/paddle/fluid/framework/fleet/heter_wrapper.h @@ -125,6 +125,6 @@ class HeterWrapper { std::vector xpu_list_; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/nccl_wrapper.cc b/paddle/fluid/framework/fleet/nccl_wrapper.cc index e62aeb4cbd2a1f..70c85ff006d046 100644 --- a/paddle/fluid/framework/fleet/nccl_wrapper.cc +++ b/paddle/fluid/framework/fleet/nccl_wrapper.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/framework/fleet/nccl_wrapper.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::shared_ptr NCCLWrapper::s_instance_ = NULL; bool NCCLWrapper::is_initialized_ = false; @@ -88,5 +87,4 @@ void NCCLWrapper::SyncVar(const int root_rank, return; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/fleet/nccl_wrapper.h b/paddle/fluid/framework/fleet/nccl_wrapper.h index 274d1dff0a98b6..566269a182cede 100644 --- a/paddle/fluid/framework/fleet/nccl_wrapper.h +++ b/paddle/fluid/framework/fleet/nccl_wrapper.h @@ -91,5 +91,5 @@ class NCCLWrapper { DISABLE_COPY_AND_ASSIGN(NCCLWrapper); }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc index 2e6df061da17c6..e1469e1ead2dfe 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cc @@ -49,8 +49,7 @@ COMMON_DECLARE_int32(gpugraph_storage_mode); COMMON_DECLARE_bool(query_dest_rank_by_multi_node); COMMON_DECLARE_string(graph_edges_split_mode); -namespace paddle { -namespace framework { +namespace paddle::framework { #ifdef PADDLE_WITH_PSLIB void AfsWrapper::init(const std::string& fs_name, @@ -2915,6 +2914,5 @@ void PSGPUWrapper::PushSparseGrad(const phi::Place& place, VLOG(3) << "End PushSparseGrad"; } -} // namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu index 4e5a439c4048cd..cfee2aee7421cd 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.cu @@ -186,6 +186,6 @@ void PSGPUWrapper::SetEmbedxSGD(float mf_create_thresholds, feature_learning_rate); } -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.h b/paddle/fluid/framework/fleet/ps_gpu_wrapper.h index d0af4c3bdfcb59..b6db486fbfa781 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.h +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.h @@ -1196,6 +1196,6 @@ class PSGPUWrapper { static bool is_initialized_; }; -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps b/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps index ff4afb29e05bc1..e4d14bece4328c 100644 --- a/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps +++ b/paddle/fluid/framework/fleet/ps_gpu_wrapper.kps @@ -218,6 +218,6 @@ void PSGPUWrapper::CopyKeys(const phi::Place& place, xpu_wait(stream); } -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle #endif diff --git a/paddle/fluid/framework/heter_pipeline_trainer.cc b/paddle/fluid/framework/heter_pipeline_trainer.cc index 3aa3b5a3d1ad5a..9f77d926bdc7e1 100644 --- a/paddle/fluid/framework/heter_pipeline_trainer.cc +++ b/paddle/fluid/framework/heter_pipeline_trainer.cc @@ -19,8 +19,7 @@ #include "paddle/fluid/framework/trainer.h" #include "paddle/phi/core/framework/trainer_desc.pb.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Variable; @@ -335,6 +334,5 @@ Scope* HeterPipelineTrainer::GetWorkerScope(int thread_id) { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/hetercpu_worker.cc b/paddle/fluid/framework/hetercpu_worker.cc index f7eefad7dd3eed..71a4f606ee098d 100644 --- a/paddle/fluid/framework/hetercpu_worker.cc +++ b/paddle/fluid/framework/hetercpu_worker.cc @@ -27,8 +27,7 @@ limitations under the License. */ #define _LINUX #endif -namespace paddle { -namespace framework { +namespace paddle::framework { void HeterTask::PackTask(Scope* thread_scope, int taskid, @@ -1233,6 +1232,5 @@ void HeterCpuWorker::TrainFiles() { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/hogwild_worker.cc b/paddle/fluid/framework/hogwild_worker.cc index 066648176af37f..bba9a69e1c4804 100644 --- a/paddle/fluid/framework/hogwild_worker.cc +++ b/paddle/fluid/framework/hogwild_worker.cc @@ -73,8 +73,7 @@ PHI_DEFINE_EXPORTED_bool(gpugraph_enable_print_op_debug, false, "enable print op debug ,default false"); -namespace paddle { -namespace framework { +namespace paddle::framework { std::atomic HogwildWorker::quit_flag_(false); Barrier g_barrier; @@ -1776,5 +1775,4 @@ void HogwildWorker::PrintFetchVars() { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/io/fs.cc b/paddle/fluid/framework/io/fs.cc index ab648e3cd060cf..d12aa4e8114751 100644 --- a/paddle/fluid/framework/io/fs.cc +++ b/paddle/fluid/framework/io/fs.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "glog/logging.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { static void fs_add_read_converter_internal(std::string& path, // NOLINT bool& is_pipe, // NOLINT @@ -600,5 +599,4 @@ void fs_mv(const std::string& src, const std::string& dest) { } } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/io/shell.cc b/paddle/fluid/framework/io/shell.cc index abde3406cefcee..b6d2d5b51474e6 100644 --- a/paddle/fluid/framework/io/shell.cc +++ b/paddle/fluid/framework/io/shell.cc @@ -20,8 +20,7 @@ #include "paddle/fluid/platform/enforce.h" #include "paddle/phi/core/platform/timer.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::shared_ptr shell_fopen(const std::string& path, const std::string& mode) { @@ -455,5 +454,4 @@ std::vector shell_execute_cmd(const std::string& cmd, return std::vector({string::Sprintf("%d", ret), output}); } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/multi_trainer.cc b/paddle/fluid/framework/multi_trainer.cc index 78dfb8644c8260..a0de13b1129d6c 100644 --- a/paddle/fluid/framework/multi_trainer.cc +++ b/paddle/fluid/framework/multi_trainer.cc @@ -28,8 +28,7 @@ PHI_DEFINE_EXPORTED_bool(enable_dump_main_program, false, "enable dump main program, default false"); -namespace paddle { -namespace framework { +namespace paddle::framework { extern Barrier g_barrier; @@ -436,5 +435,4 @@ void MultiTrainer::ResetDataset(Dataset* dataset) { } #endif -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/pipeline_trainer.cc b/paddle/fluid/framework/pipeline_trainer.cc index ce4a38fe745ec9..fe6ac9b72716b6 100644 --- a/paddle/fluid/framework/pipeline_trainer.cc +++ b/paddle/fluid/framework/pipeline_trainer.cc @@ -18,8 +18,7 @@ #include "paddle/fluid/framework/trainer.h" #include "paddle/phi/core/framework/trainer_desc.pb.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void PipelineTrainer::Initialize(const TrainerDesc& trainer_desc, Dataset* dataset) { @@ -142,6 +141,5 @@ Scope* PipelineTrainer::GetWorkerScope(int thread_id) { return microbatch_scopes_[0]; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/ps_gpu_worker.cc b/paddle/fluid/framework/ps_gpu_worker.cc index 4ca4e3439d0010..4dac0c9ccdc3d1 100644 --- a/paddle/fluid/framework/ps_gpu_worker.cc +++ b/paddle/fluid/framework/ps_gpu_worker.cc @@ -31,8 +31,7 @@ limitations under the License. */ #define _LINUX #endif -namespace paddle { -namespace framework { +namespace paddle::framework { std::atomic PSGPUWorker::shape_check_count_(16); std::atomic PSGPUWorker::shape_check_flag_(true); @@ -580,6 +579,5 @@ void PSGPUWorker::ResetStat() { void PSGPUWorker::ProduceTasks() { return; } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/trainer.cc b/paddle/fluid/framework/trainer.cc index 49f10eabed1fac..f824c4cc0c0aac 100644 --- a/paddle/fluid/framework/trainer.cc +++ b/paddle/fluid/framework/trainer.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include "io/fs.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void TrainerBase::SetScope(Scope* root_scope) { root_scope_ = root_scope; } @@ -95,5 +94,4 @@ void TrainerBase::FinalizeDumpEnv() { queue_.reset(); } -} // end namespace framework -} // end namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/variable_helper.h b/paddle/fluid/framework/variable_helper.h index 8e50302f109872..645584f593c959 100644 --- a/paddle/fluid/framework/variable_helper.h +++ b/paddle/fluid/framework/variable_helper.h @@ -25,5 +25,5 @@ class Variable; TEST_API void InitializeVariable(Variable* var, proto::VarType::Type var_type); void CopyVariable(const Variable& src_var, Variable* dst_var); -} // end namespace framework -} // end namespace paddle +} // namespace framework +} // namespace paddle diff --git a/paddle/fluid/platform/densetensor_printer.cc b/paddle/fluid/platform/densetensor_printer.cc index 1e918050b2cf3e..f20135f9697cb7 100644 --- a/paddle/fluid/platform/densetensor_printer.cc +++ b/paddle/fluid/platform/densetensor_printer.cc @@ -21,14 +21,11 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Variable; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace platform { +namespace paddle::platform { void PrintVar(framework::Scope* scope, const std::string& var_name, @@ -81,5 +78,4 @@ void PrintVar(framework::Scope* scope, _ForEachDataType_(PrintTensorCallback); } -} // end namespace platform -} // end namespace paddle +} // namespace paddle::platform diff --git a/paddle/fluid/platform/densetensor_printer.h b/paddle/fluid/platform/densetensor_printer.h index da19b47b7c4a1f..99547a9855e0ca 100644 --- a/paddle/fluid/platform/densetensor_printer.h +++ b/paddle/fluid/platform/densetensor_printer.h @@ -29,5 +29,5 @@ void PrintVar(framework::Scope* scope, const std::string& var_name, const std::string& print_info, std::stringstream* out); -} // end namespace platform -} // end namespace paddle +} // namespace platform +} // namespace paddle diff --git a/paddle/fluid/pybind/box_helper_py.cc b/paddle/fluid/pybind/box_helper_py.cc index e49e0703ee6c6c..c1b1cd89815a45 100644 --- a/paddle/fluid/pybind/box_helper_py.cc +++ b/paddle/fluid/pybind/box_helper_py.cc @@ -35,8 +35,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindBoxHelper(py::module* m) { py::class_>( *m, "BoxPS") @@ -113,5 +112,4 @@ void BindBoxWrapper(py::module* m) { } // end BoxWrapper #endif -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/distributed_py.cc b/paddle/fluid/pybind/distributed_py.cc index 8b486f238f7d4f..d508df902f5956 100644 --- a/paddle/fluid/pybind/distributed_py.cc +++ b/paddle/fluid/pybind/distributed_py.cc @@ -55,8 +55,7 @@ limitations under the License. */ #include "paddle/phi/kernels/sync_batch_norm_kernel.h" -namespace paddle { -namespace pybind { +namespace paddle::pybind { using Tensor = paddle::Tensor; @@ -1434,5 +1433,4 @@ void BindDistributed(py::module *m) { py::call_guard()); } -} // end namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/fleet_py.cc b/paddle/fluid/pybind/fleet_py.cc index 650239c04346a8..b4fd77840d7fde 100644 --- a/paddle/fluid/pybind/fleet_py.cc +++ b/paddle/fluid/pybind/fleet_py.cc @@ -46,8 +46,7 @@ using paddle::distributed::GraphPyServer; using paddle::distributed::GraphPyService; using paddle::distributed::HeterClient; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindDistFleetWrapper(py::module* m) { py::class_>(*m, "DistFleetWrapper") @@ -454,5 +453,4 @@ void BindIndexSampler(py::module* m) { .def("init_beamsearch_conf", &IndexSampler::init_beamsearch_conf) .def("sample", &IndexSampler::sample); } -} // end namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/fleet_wrapper_py.cc b/paddle/fluid/pybind/fleet_wrapper_py.cc index 95f50705da41a7..20be7bac976c05 100644 --- a/paddle/fluid/pybind/fleet_wrapper_py.cc +++ b/paddle/fluid/pybind/fleet_wrapper_py.cc @@ -37,8 +37,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindFleetWrapper(py::module* m) { py::class_>( *m, "Fleet") @@ -99,5 +98,4 @@ void BindFleetWrapper(py::module* m) { .def("copy_table_by_feasign", &framework::FleetWrapper::CopyTableByFeasign); } // end FleetWrapper -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/gloo_wrapper_py.cc b/paddle/fluid/pybind/gloo_wrapper_py.cc index ba61d41f2041b8..739ccd55b54976 100644 --- a/paddle/fluid/pybind/gloo_wrapper_py.cc +++ b/paddle/fluid/pybind/gloo_wrapper_py.cc @@ -28,8 +28,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindGlooWrapper(py::module* m) { #if defined(PADDLE_WITH_HETERPS) && defined(PADDLE_WITH_PSCORE) py::class_>( @@ -59,5 +58,4 @@ void BindGlooWrapper(py::module* m) { .def("all_gather", &framework::GlooWrapper::AllGather) .def("all_gather", &framework::GlooWrapper::AllGather); } // end BindGlooWrapper -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/heter_wrapper_py.cc b/paddle/fluid/pybind/heter_wrapper_py.cc index 4edce885cc4741..2dba677e251137 100644 --- a/paddle/fluid/pybind/heter_wrapper_py.cc +++ b/paddle/fluid/pybind/heter_wrapper_py.cc @@ -29,8 +29,7 @@ limitations under the License. */ #include "paddle/fluid/framework/fleet/heter_wrapper.h" #include "paddle/fluid/pybind/heter_wrapper_py.h" -namespace paddle { -namespace pybind { +namespace paddle::pybind { #if defined(PADDLE_WITH_PSLIB) && !defined(PADDLE_WITH_HETERPS) void BindHeterWrapper(py::module* m) { py::class_>( @@ -44,5 +43,4 @@ void BindHeterWrapper(py::module* m) { .def("stop_xpu_service", &framework::HeterWrapper::StopXpuService); } // end HeterWrapper #endif -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/metrics_py.cc b/paddle/fluid/pybind/metrics_py.cc index 8bb06f5f7808c6..948ccbe2343224 100644 --- a/paddle/fluid/pybind/metrics_py.cc +++ b/paddle/fluid/pybind/metrics_py.cc @@ -31,8 +31,7 @@ limitations under the License. */ #include "paddle/fluid/pybind/metrics_py.h" #if defined(PADDLE_WITH_PSLIB) -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindMetrics(py::module* m) { py::class_>(*m, "Metric") @@ -53,6 +52,5 @@ void BindMetrics(py::module* m) { &framework::Metric::GetMetricNameList, py::call_guard()); } // end Metrics -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind #endif diff --git a/paddle/fluid/pybind/nccl_wrapper_py.cc b/paddle/fluid/pybind/nccl_wrapper_py.cc index efc9704aea1741..8c84a34c646a60 100644 --- a/paddle/fluid/pybind/nccl_wrapper_py.cc +++ b/paddle/fluid/pybind/nccl_wrapper_py.cc @@ -37,8 +37,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindNCCLWrapper(py::module* m) { py::class_(*m, "Nccl") .def(py::init()) @@ -47,5 +46,4 @@ void BindNCCLWrapper(py::module* m) { .def("set_rank_info", &framework::NCCLWrapper::SetRankInfo) .def("sync_var", &framework::NCCLWrapper::SyncVar); } // end NCCLWrapper -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/ps_gpu_wrapper_py.cc b/paddle/fluid/pybind/ps_gpu_wrapper_py.cc index 3ebdc82b1d121e..127b6390d3a8d4 100644 --- a/paddle/fluid/pybind/ps_gpu_wrapper_py.cc +++ b/paddle/fluid/pybind/ps_gpu_wrapper_py.cc @@ -29,8 +29,7 @@ limitations under the License. */ #include "paddle/fluid/framework/fleet/ps_gpu_wrapper.h" #include "paddle/fluid/pybind/ps_gpu_wrapper_py.h" -namespace paddle { -namespace pybind { +namespace paddle::pybind { #ifdef PADDLE_WITH_HETERPS void BindPSGPUWrapper(py::module* m) { py::class_>( @@ -158,5 +157,4 @@ void BindAfsWrapper(py::module* m) { } #endif #endif -} // end namespace pybind -} // end namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/kernels/funcs/block_radix_topk.cuh b/paddle/phi/kernels/funcs/block_radix_topk.cuh index 320d8ad8fc4f3e..78a5d391c16b9f 100644 --- a/paddle/phi/kernels/funcs/block_radix_topk.cuh +++ b/paddle/phi/kernels/funcs/block_radix_topk.cuh @@ -344,6 +344,6 @@ class BlockRadixTopKRegister { unsigned int top_k_mask_; }; -}; // end namespace framework -}; // end namespace paddle +}; // namespace framework +}; // namespace paddle #endif diff --git a/paddle/phi/kernels/gpu/arg_min_max_kernel.cu b/paddle/phi/kernels/gpu/arg_min_max_kernel.cu index c8109e4189e8e7..6795becc9d1308 100644 --- a/paddle/phi/kernels/gpu/arg_min_max_kernel.cu +++ b/paddle/phi/kernels/gpu/arg_min_max_kernel.cu @@ -37,7 +37,7 @@ namespace { // NOLINT template using KeyValuePair = cub::KeyValuePair; -} // end namespace +} // namespace #define FIXED_BLOCK_DIM_CASE_BASE(log2_block_dim, ...) \ case (1 << (log2_block_dim)): { \ diff --git a/paddle/utils/string/string_helper.cc b/paddle/utils/string/string_helper.cc index 685b00f23a6908..ed3095bd2d0648 100644 --- a/paddle/utils/string/string_helper.cc +++ b/paddle/utils/string/string_helper.cc @@ -20,8 +20,7 @@ #include #include -namespace paddle { -namespace string { +namespace paddle::string { // remove leading and tailing spaces std::string trim_spaces(const std::string& str) { @@ -85,5 +84,4 @@ char* LineFileReader::getdelim(FILE* f, char delim) { #endif } -} // end namespace string -} // end namespace paddle +} // namespace paddle::string diff --git a/paddle/utils/string/string_helper.h b/paddle/utils/string/string_helper.h index 01e0cb0b4eb858..2f1efb5cb5de38 100644 --- a/paddle/utils/string/string_helper.h +++ b/paddle/utils/string/string_helper.h @@ -388,5 +388,5 @@ class LineFileReader { size_t _buf_size = 0; size_t _length = 0; }; -} // end namespace string -} // end namespace paddle +} // namespace string +} // namespace paddle diff --git a/paddle/utils/tribool.h b/paddle/utils/tribool.h index 7f5de993752928..646a126e20199d 100644 --- a/paddle/utils/tribool.h +++ b/paddle/utils/tribool.h @@ -44,7 +44,7 @@ namespace detail { */ struct indeterminate_t {}; -} // end namespace detail +} // namespace detail class tribool; diff --git a/patches/eigen/Meta.h b/patches/eigen/Meta.h index b7b789a19c4e9a..ff0755aff7e8bc 100755 --- a/patches/eigen/Meta.h +++ b/patches/eigen/Meta.h @@ -750,7 +750,7 @@ struct aligned_storage { }; }; -} // end namespace internal +} // namespace internal namespace numext { @@ -799,8 +799,8 @@ template<> EIGEN_STRONG_INLINE EIGEN_DEVICE_FUNC bool not_equal_strict(const double& x,const double& y) { return std::not_equal_to()(x,y); } #endif -} // end namespace numext +} // namespace numext -} // end namespace Eigen +} // namespace Eigen #endif // EIGEN_META_H diff --git a/patches/eigen/TensorReductionGpu.h b/patches/eigen/TensorReductionGpu.h index 4807aaa2c1be75..5c976952c6fe80 100644 --- a/patches/eigen/TensorReductionGpu.h +++ b/patches/eigen/TensorReductionGpu.h @@ -989,8 +989,8 @@ struct OuterReducer { #endif // defined(EIGEN_USE_GPU) && defined(EIGEN_GPUCC) -} // end namespace internal -} // end namespace Eigen +} // namespace internal +} // namespace Eigen #endif // EIGEN_CXX11_TENSOR_TENSOR_REDUCTION_GPU_H // clang-format on diff --git a/patches/thrust/thrust/system/detail/generic/shuffle.h b/patches/thrust/thrust/system/detail/generic/shuffle.h index 87008aaa10c4af..3b5feb1c3def82 100644 --- a/patches/thrust/thrust/system/detail/generic/shuffle.h +++ b/patches/thrust/thrust/system/detail/generic/shuffle.h @@ -64,10 +64,10 @@ __host__ __device__ void shuffle_copy( OutputIterator result, URBG&& g); -} // end namespace generic -} // end namespace detail -} // end namespace system -} // end namespace thrust +} // namespace generic +} // namespace detail +} // namespace system +} // namespace thrust #include diff --git a/patches/thrust/thrust/system/detail/generic/shuffle.inl b/patches/thrust/thrust/system/detail/generic/shuffle.inl index a0a27833c62f76..6ea047f4dc80fa 100644 --- a/patches/thrust/thrust/system/detail/generic/shuffle.inl +++ b/patches/thrust/thrust/system/detail/generic/shuffle.inl @@ -214,7 +214,7 @@ __host__ __device__ void shuffle_copy( key_flag_scan_op()); } -} // end namespace generic -} // end namespace detail -} // end namespace system -} // end namespace thrust +} // namespace generic +} // namespace detail +} // namespace system +} // namespace thrust From 279fa697ae9c60389b305dc98cb109b175a2f05a Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E5=BC=A0=E6=98=A5=E4=B9=94?= <83450930+Liyulingyue@users.noreply.github.com> Date: Wed, 4 Dec 2024 11:37:28 +0800 Subject: [PATCH 141/288] [Docathon][Add API Legend No.2] (#69266) --- python/paddle/tensor/manipulation.py | 14 ++++++++++++++ 1 file changed, 14 insertions(+) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index eccbbe6fc26e1d..5ac5c345f6119a 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -1544,6 +1544,20 @@ def broadcast_tensors( .. _Introduction to Tensor: ../../guides/beginner/tensor_en.html#chapter5-broadcasting-of-tensor + The following figure illustrates the process of broadcasting three tensors to the same dimensions. + The dimensions of the three tensors are [4, 1, 3], [2, 3], and [4, 2, 1], respectively. During broadcasting, + alignment starts from the last dimension, and for each dimension, either the sizes of the two tensors in that dimension are equal, + or one of the tensors has a dimension of 1, or one of the tensors lacks that dimension. In the figure below, in the last dimension, + Tensor3 has a size of 1, while Tensor1 and Tensor2 have sizes of 3; thus, this dimension is expanded to 3 for all tensors. + In the second-to-last dimension, Tensor1 has a size of 2, and Tensor2 and Tensor3 both have sizes of 2; hence, this dimension is expanded to 2 for all tensors. + In the third-to-last dimension, Tensor2 lacks this dimension, while Tensor1 and Tensor3 have sizes of 4; consequently, + this dimension is expanded to 4 for all tensors. Ultimately, all tensors are expanded to [4, 2, 3]. + + .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/broadcast.png + :width: 800 + :alt: Illustration of BroadCast + :align: center + Args: input (list|tuple): ``input`` is a Tensor list or Tensor tuple which is with data type bool, float16, float32, float64, int32, int64, complex64, complex128. All the Tensors in ``input`` must have same data type. From d774b83073a5776e4efa458b29b9fe77b0ecf59b Mon Sep 17 00:00:00 2001 From: megemini Date: Wed, 4 Dec 2024 11:58:07 +0800 Subject: [PATCH 142/288] =?UTF-8?q?=E3=80=90Hackathon=207th=20PPSCI=20No.1?= =?UTF-8?q?2=E3=80=91Adam=E3=80=81AdamW=20=E4=BC=98=E5=8C=96=E5=99=A8?= =?UTF-8?q?=E6=94=AF=E6=8C=81=20amsgrad=20-part=20(#68079)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * [init] amsgrad * [update] refer.h * [Add] amsgrad gpu * [Add] amsgrad for adamw and fused * [Fix] adamw gpu kernel * [Update] fused adam kernel for gpu * [Update] xpu adam/adamw param list * [Update] tests for amsgrad * [Fix] moment2 max out settting values without amsgrad * [Update] unittest passed for adam and adamw * [Update] unittest passed for merged and fused amda * [Update] make moment2_max optional * [Update] test_adamw_op.py with new test cast * [Update] adam adamw with amsgrad formula * [Update] adam/adamw for test.cc * [Fix] xpu param name * [Fix] xpu param name & unittest * [Fix] xpu param type * [Fix] xpu unittest * [Fix] xpu unittest * [Fix] xpu unittest * [Fix] merged_adam_ op_compat.yaml * [Fix] remove UNUSED * [Fix] remove UNUSED * [Update] unittest adam op * [Fix] op_compat.yaml * [Update] assembly for adam adamw * [Fix] adamw.cc for assembly jit gen * [Update] adam with old ir test * [Update] codestyle * [Update] npu test rtol adamw * [Update] xpu amsgrad raise errors * [Fix] not test xpu amsgrad --- paddle/fluid/operators/fused/fused_adam_op.cc | 11 + .../fluid/operators/ops_signature/adam_sig.cc | 3 + .../operators/ops_signature/fused_adam_sig.cc | 5 +- paddle/fluid/pybind/eager_generator.cc | 12 + paddle/phi/infermeta/multiary.cc | 23 + paddle/phi/infermeta/multiary.h | 12 + paddle/phi/infermeta/spmd_rules/optimizer.cc | 112 ++-- paddle/phi/infermeta/spmd_rules/optimizer.h | 76 +-- paddle/phi/kernels/adam_kernel.h | 6 + paddle/phi/kernels/adamw_kernel.h | 3 + paddle/phi/kernels/cpu/adam_kernel.cc | 44 +- paddle/phi/kernels/cpu/adamw_kernel.cc | 28 +- paddle/phi/kernels/cpu/fused_adam_kernel.cc | 23 + paddle/phi/kernels/funcs/adam_functors.h | 148 +++++- paddle/phi/kernels/funcs/jit/gen/adam.cc | 52 +- paddle/phi/kernels/funcs/jit/gen/adam.h | 13 +- paddle/phi/kernels/funcs/jit/gen/adamw.cc | 61 ++- paddle/phi/kernels/funcs/jit/gen/adamw.h | 17 +- paddle/phi/kernels/funcs/jit/kernel_base.h | 36 +- paddle/phi/kernels/funcs/jit/kernel_key.cc | 11 +- paddle/phi/kernels/funcs/jit/refer/refer.h | 38 +- paddle/phi/kernels/funcs/jit/test.cc | 459 +++++++++------- paddle/phi/kernels/funcs/multi_tensor_apply.h | 2 +- paddle/phi/kernels/fused_adam_kernel.h | 3 + paddle/phi/kernels/gpu/adam_kernel.cu | 115 +++- paddle/phi/kernels/gpu/adamw_kernel.cu | 77 ++- paddle/phi/kernels/gpu/fused_adam_kernel.cu | 145 +++++- .../phi/kernels/selected_rows/adam_kernel.h | 3 + .../phi/kernels/selected_rows/adamw_kernel.h | 3 + .../kernels/selected_rows/cpu/adam_kernel.cc | 15 +- .../kernels/selected_rows/cpu/adamw_kernel.cc | 9 + .../kernels/selected_rows/gpu/adam_kernel.cu | 52 +- .../kernels/selected_rows/gpu/adamw_kernel.cu | 50 +- .../kernels/selected_rows/xpu/adam_kernel.cc | 14 +- paddle/phi/kernels/xpu/adam_kernel.cc | 74 ++- paddle/phi/kernels/xpu/adamw_kernel.cc | 88 ++-- .../ops/yaml/inconsistent/dygraph_ops.yaml | 8 +- .../phi/ops/yaml/inconsistent/static_ops.yaml | 8 +- paddle/phi/ops/yaml/op_compat.yaml | 16 +- paddle/phi/ops/yaml/ops.yaml | 28 +- python/paddle/optimizer/adam.py | 89 +++- python/paddle/optimizer/adamw.py | 58 ++- test/auto_parallel/test_api_dist_branch.py | 11 + .../fleet/hybrid_parallel_sharding_model.py | 4 + .../hybrid_parallel_sharding_state_dict.py | 2 +- .../cpp/phi/kernels/test_fused_adam_kernel.cc | 51 +- test/legacy_test/test_adam_op.py | 423 +++++++++++++-- .../test_adam_optimizer_fp32_fp64.py | 51 +- test/legacy_test/test_adamw_op.py | 491 ++++++++++++++---- test/legacy_test/test_fused_adam_op.py | 66 ++- test/legacy_test/test_merged_adam_op.py | 32 +- test/white_list/no_check_set_white_list.py | 3 + test/xpu/test_adam_op_xpu.py | 74 ++- test/xpu/test_adamw_op_xpu.py | 206 +++++--- test/xpu/test_merged_adam_op_xpu.py | 25 +- 55 files changed, 2669 insertions(+), 820 deletions(-) diff --git a/paddle/fluid/operators/fused/fused_adam_op.cc b/paddle/fluid/operators/fused/fused_adam_op.cc index d786dbd7c2728f..932bdbfd90a6c2 100644 --- a/paddle/fluid/operators/fused/fused_adam_op.cc +++ b/paddle/fluid/operators/fused/fused_adam_op.cc @@ -57,6 +57,9 @@ class FusedAdamOpMaker : public framework::OpProtoAndCheckerMaker { AddInput("LearningRate", "(Tensor, default Tensor) Learning rate"); AddInput("Moments1", "(Tensor) Input first moments").AsDuplicable(); AddInput("Moments2", "(Tensor) Input second moments").AsDuplicable(); + AddInput("Moments2Max", "(Tensor) Input second moments max for amsgrad") + .AsDispensable() + .AsDuplicable(); AddInput("Beta1Pows", "(Tensor, default Tensor) Input beta1 power accumulator") .AsDuplicable(); @@ -72,6 +75,10 @@ class FusedAdamOpMaker : public framework::OpProtoAndCheckerMaker { AddOutput("ParamsOut", "(Tensor) Output parameters").AsDuplicable(); AddOutput("Moments1Out", "(Tensor) Output first moments").AsDuplicable(); AddOutput("Moments2Out", "(Tensor) Output second moments").AsDuplicable(); + AddOutput("Moments2MaxOut", + "(Tensor) Output second moments max for amsgrad") + .AsDispensable() + .AsDuplicable(); AddOutput("Beta1PowsOut", "(Tensor) Output beta1 power accumulator") .AsDuplicable(); AddOutput("Beta2PowsOut", "(Tensor) Output beta2 power accumulator") @@ -122,6 +129,10 @@ class FusedAdamOpMaker : public framework::OpProtoAndCheckerMaker { "Whether to use global beta_pow for whole model instead of " "creating beta_pow for each parameter.") .SetDefault(false); + AddAttr("amsgrad", + "(bool, default false) " + "Whether to use the AMSGrad of this algorithm.") + .SetDefault(false); AddComment(R"DOC( Adam Optimizer. diff --git a/paddle/fluid/operators/ops_signature/adam_sig.cc b/paddle/fluid/operators/ops_signature/adam_sig.cc index f3e7eeb6b67629..7815a2a3166efd 100644 --- a/paddle/fluid/operators/ops_signature/adam_sig.cc +++ b/paddle/fluid/operators/ops_signature/adam_sig.cc @@ -24,6 +24,7 @@ KernelSignature AdamOpArgumentMapping(const ArgumentMappingContext& ctx) { "LearningRate", "Moment1", "Moment2", + "Moment2Max", "Beta1Pow", "Beta2Pow", "MasterParam", @@ -31,6 +32,7 @@ KernelSignature AdamOpArgumentMapping(const ArgumentMappingContext& ctx) { paddle::small_vector out_names = {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}; @@ -46,6 +48,7 @@ KernelSignature AdamOpArgumentMapping(const ArgumentMappingContext& ctx) { attr_names.emplace_back("min_row_size_to_use_multithread"); attr_names.emplace_back("multi_precision"); attr_names.emplace_back("use_global_beta_pow"); + attr_names.emplace_back("amsgrad"); if (ctx.IsSelectedRowsInput("Grad")) { return KernelSignature("adam_dense_param_sparse_grad", diff --git a/paddle/fluid/operators/ops_signature/fused_adam_sig.cc b/paddle/fluid/operators/ops_signature/fused_adam_sig.cc index dc787529a02a2f..f619beee9f718b 100644 --- a/paddle/fluid/operators/ops_signature/fused_adam_sig.cc +++ b/paddle/fluid/operators/ops_signature/fused_adam_sig.cc @@ -25,6 +25,7 @@ KernelSignature FusedAdamOpArgumentMapping( "LearningRate", "Moments1", "Moments2", + "Moments2Max", "Beta1Pows", "Beta2Pows", "MasterParams", @@ -32,6 +33,7 @@ KernelSignature FusedAdamOpArgumentMapping( paddle::small_vector out_names = {"ParamsOut", "Moments1Out", "Moments2Out", + "Moments2MaxOut", "Beta1PowsOut", "Beta2PowsOut", "MasterParamsOut"}; @@ -42,7 +44,8 @@ KernelSignature FusedAdamOpArgumentMapping( "weight_decay", "use_adamw", "multi_precision", - "use_global_beta_pow"}; + "use_global_beta_pow", + "amsgrad"}; return KernelSignature("fused_adam", std::move(in_names), diff --git a/paddle/fluid/pybind/eager_generator.cc b/paddle/fluid/pybind/eager_generator.cc index 3edec7b1f3b211..aaff35058ad8e1 100644 --- a/paddle/fluid/pybind/eager_generator.cc +++ b/paddle/fluid/pybind/eager_generator.cc @@ -3344,6 +3344,7 @@ std::map> op_passing_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, @@ -3351,6 +3352,7 @@ std::map> op_passing_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, @@ -3358,6 +3360,7 @@ std::map> op_passing_outs_map = { {"ParamsOut", "Moments1Out", "Moments2Out", + "Moments2MaxOut", "Beta1PowsOut", "Beta2PowsOut", "MasterParamsOut"}}, @@ -3365,6 +3368,7 @@ std::map> op_passing_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, @@ -3544,6 +3548,7 @@ std::map> op_ins_map = { "LearningRate", "Moment1", "Moment2", + "Moment2Max", "Beta1Pow", "Beta2Pow", "MasterParam"}}, @@ -3553,6 +3558,7 @@ std::map> op_ins_map = { "LearningRate", "Moment1", "Moment2", + "Moment2Max", "Beta1Pow", "Beta2Pow", "MasterParam"}}, @@ -3562,6 +3568,7 @@ std::map> op_ins_map = { "LearningRate", "Moments1", "Moments2", + "Moments2Max", "Beta1Pows", "Beta2Pows", "MasterParams", @@ -3572,6 +3579,7 @@ std::map> op_ins_map = { "LearningRate", "Moment1", "Moment2", + "Moment2Max", "Beta1Pow", "Beta2Pow", "MasterParam"}}, @@ -3723,6 +3731,7 @@ std::map> op_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, @@ -3730,6 +3739,7 @@ std::map> op_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, @@ -3737,6 +3747,7 @@ std::map> op_outs_map = { {"ParamsOut", "Moments1Out", "Moments2Out", + "Moments2MaxOut", "Beta1PowsOut", "Beta2PowsOut", "MasterParamsOut"}}, @@ -3744,6 +3755,7 @@ std::map> op_outs_map = { {"ParamOut", "Moment1Out", "Moment2Out", + "Moment2MaxOut", "Beta1PowOut", "Beta2PowOut", "MasterParamOut"}}, diff --git a/paddle/phi/infermeta/multiary.cc b/paddle/phi/infermeta/multiary.cc index ccac78de90bc0a..aeb676f4a7b169 100644 --- a/paddle/phi/infermeta/multiary.cc +++ b/paddle/phi/infermeta/multiary.cc @@ -152,6 +152,7 @@ void AdamInferMeta(const MetaTensor& param, const MetaTensor& learning_rate, const MetaTensor& moment1, const MetaTensor& moment2, + const MetaTensor& moment2_max, const MetaTensor& beta1_pow, const MetaTensor& beta2_pow, const MetaTensor& master_param, @@ -163,9 +164,11 @@ void AdamInferMeta(const MetaTensor& param, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, MetaTensor* param_out, MetaTensor* moment1_out, MetaTensor* moment2_out, + MetaTensor* moment2_max_out, MetaTensor* beta1_pow_out, MetaTensor* beta2_pow_out, MetaTensor* master_param_outs) { @@ -232,6 +235,10 @@ void AdamInferMeta(const MetaTensor& param, moment1_out->set_dtype(moment1.dtype()); moment2_out->set_dims(param_dims); moment2_out->set_dtype(moment2.dtype()); + if (amsgrad) { + moment2_max_out->set_dims(param_dims); + moment2_max_out->set_dtype(moment2.dtype()); + } beta1_pow_out->set_dims(beta1_pow_dims); beta1_pow_out->set_dtype(beta1_pow.dtype()); @@ -328,6 +335,7 @@ void AdamwInferMeta(const MetaTensor& param, const MetaTensor& learning_rate, const MetaTensor& moment1, const MetaTensor& moment2, + const MetaTensor& moment2_max, const MetaTensor& beta1_pow, const MetaTensor& beta2_pow, const MetaTensor& master_param, @@ -342,9 +350,11 @@ void AdamwInferMeta(const MetaTensor& param, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, MetaTensor* param_out, MetaTensor* moment1_out, MetaTensor* moment2_out, + MetaTensor* moment2_max_out, MetaTensor* beta1_pow_out, MetaTensor* beta2_pow_out, MetaTensor* master_param_outs) { @@ -353,6 +363,7 @@ void AdamwInferMeta(const MetaTensor& param, learning_rate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_param, @@ -364,9 +375,11 @@ void AdamwInferMeta(const MetaTensor& param, min_row_size_to_use_multithread, multi_precision, use_global_beta_pow, + amsgrad, param_out, moment1_out, moment2_out, + moment2_max_out, beta1_pow_out, beta2_pow_out, master_param_outs); @@ -3866,6 +3879,7 @@ void MergedAdamInferMeta( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& moment2_max, const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -3874,9 +3888,11 @@ void MergedAdamInferMeta( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out) {} @@ -5796,6 +5812,7 @@ void FusedAdamInferMeta( const MetaTensor& learning_rate, const std::vector& moments1, const std::vector& moments2, + const paddle::optional>& moments2_max, const std::vector& beta1_pows, const std::vector& beta2_pows, const paddle::optional>& master_params, @@ -5808,9 +5825,11 @@ void FusedAdamInferMeta( bool use_adamw, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector params_out, std::vector moments1_out, std::vector moments2_out, + std::vector moments2_max_out, std::vector beta1_pows_out, std::vector beta2_pows_out, std::vector master_params_out) { @@ -5822,6 +5841,10 @@ void FusedAdamInferMeta( moments1_out[i]->set_dtype(moments1[i]->dtype()); moments2_out[i]->set_dims(moments2[i]->dims()); moments2_out[i]->set_dtype(moments2[i]->dtype()); + if (amsgrad) { + moments2_max_out[i]->set_dims(moments2_max.get()[i]->dims()); + moments2_max_out[i]->set_dtype(moments2_max.get()[i]->dtype()); + } beta1_pows_out[i]->set_dims(beta1_pows[i]->dims()); beta1_pows_out[i]->set_dtype(beta1_pows[i]->dtype()); beta2_pows_out[i]->set_dims(beta2_pows[i]->dims()); diff --git a/paddle/phi/infermeta/multiary.h b/paddle/phi/infermeta/multiary.h index 7ae030e2ad64d0..757ec4aee65116 100644 --- a/paddle/phi/infermeta/multiary.h +++ b/paddle/phi/infermeta/multiary.h @@ -86,6 +86,7 @@ void AdamInferMeta(const MetaTensor& param, const MetaTensor& learning_rate, const MetaTensor& moment1, const MetaTensor& moment2, + const MetaTensor& moment2_max, const MetaTensor& beta1_pow, const MetaTensor& beta2_pow, const MetaTensor& master_param, @@ -97,9 +98,11 @@ void AdamInferMeta(const MetaTensor& param, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, MetaTensor* param_out, MetaTensor* moment1_out, MetaTensor* moment2_out, + MetaTensor* moment2_max_out, MetaTensor* beta1_pow_out, MetaTensor* beta2_pow_out, MetaTensor* master_param_outs); @@ -109,6 +112,7 @@ void AdamwInferMeta(const MetaTensor& param, const MetaTensor& learning_rate, const MetaTensor& moment1, const MetaTensor& moment2, + const MetaTensor& moment2_max, const MetaTensor& beta1_pow, const MetaTensor& beta2_pow, const MetaTensor& master_param, @@ -123,9 +127,11 @@ void AdamwInferMeta(const MetaTensor& param, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, MetaTensor* param_out, MetaTensor* moment1_out, MetaTensor* moment2_out, + MetaTensor* moment2_max_out, MetaTensor* beta1_pow_out, MetaTensor* beta2_pow_out, MetaTensor* master_param_outs); @@ -711,6 +717,7 @@ void MergedAdamInferMeta( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& moment2_max, const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -719,9 +726,11 @@ void MergedAdamInferMeta( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out); @@ -1117,6 +1126,7 @@ void FusedAdamInferMeta( const MetaTensor& learning_rate, const std::vector& moments1, const std::vector& moments2, + const paddle::optional>& moments2_max, const std::vector& beta1_pows, const std::vector& beta2_pows, const paddle::optional>& master_params, @@ -1129,9 +1139,11 @@ void FusedAdamInferMeta( bool use_adamw, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector params_out, std::vector moments1_out, std::vector moments2_out, + std::vector moments2_max_out, std::vector beta1_pows_out, std::vector beta2_pows_out, std::vector master_params_out); diff --git a/paddle/phi/infermeta/spmd_rules/optimizer.cc b/paddle/phi/infermeta/spmd_rules/optimizer.cc index d3114993589c26..81eeb80b58bbdb 100644 --- a/paddle/phi/infermeta/spmd_rules/optimizer.cc +++ b/paddle/phi/infermeta/spmd_rules/optimizer.cc @@ -24,22 +24,25 @@ limitations under the License. */ namespace phi::distributed { -SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, - const DistMetaTensor& grad, - const DistMetaTensor& learning_rate, - const DistMetaTensor& moment1, - const DistMetaTensor& moment2, - const DistMetaTensor& beta1_pow, - const DistMetaTensor& beta2_pow, - const DistMetaTensor& master_param, - const DistMetaTensor& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow) { +SpmdInfo AdamInferSpmdDynamic( + const DistMetaTensor& param, + const DistMetaTensor& grad, + const DistMetaTensor& learning_rate, + const DistMetaTensor& moment1, + const DistMetaTensor& moment2, + const paddle::optional& moment2_max, + const DistMetaTensor& beta1_pow, + const DistMetaTensor& beta2_pow, + const DistMetaTensor& master_param, + const DistMetaTensor& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad) { // shape check PADDLE_ENFORCE( param.dims().size() == grad.dims().size() && @@ -78,6 +81,9 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, CopyTensorDistAttrForOutput(moment1.dist_attr()); TensorDistAttr moment2_dist_attr = CopyTensorDistAttrForOutput(moment2.dist_attr()); + TensorDistAttr moment2_max_dist_attr = + amsgrad ? CopyTensorDistAttrForOutput(moment2_max.get().dist_attr()) + : TensorDistAttr(); TensorDistAttr beta1_pow_dist_attr = CopyTensorDistAttrForOutput(beta1_pow.dist_attr()); TensorDistAttr beta2_pow_dist_attr = @@ -115,6 +121,12 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, auto momentum1_src_dims_mapping = moment1.dist_attr().dims_mapping(); auto momentum2_src_dims_mapping = moment2.dist_attr().dims_mapping(); + std::vector momentum2_max_src_dims_mapping; + if (amsgrad) { + momentum2_max_src_dims_mapping = + moment2_max.get().dist_attr().dims_mapping(); + } + // Get the final dist attr for param, master_param, grad and momentum. // Whatever the input dist attrs are, the output dist attr should be same. // For a specific dim of the tensor: @@ -128,10 +140,20 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, // and the unshard tensors should keep unshard status. std::vector dst_dims_mapping; for (int64_t i = 0; i < param.dims().size(); ++i) { - std::vector shard_status{param_spmd_dims_mapping[i], - grad_spmd_dims_mapping[i], - momentum1_src_dims_mapping[i], - momentum2_src_dims_mapping[i]}; + std::vector shard_status; + if (amsgrad) { + shard_status.assign({param_spmd_dims_mapping[i], + grad_spmd_dims_mapping[i], + momentum1_src_dims_mapping[i], + momentum2_src_dims_mapping[i], + momentum2_max_src_dims_mapping[i]}); + + } else { + shard_status.assign({param_spmd_dims_mapping[i], + grad_spmd_dims_mapping[i], + momentum1_src_dims_mapping[i], + momentum2_src_dims_mapping[i]}); + } int64_t dst_shard_status = -1; for (auto status : shard_status) { if (status == -1) { @@ -171,12 +193,16 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, } moment1_dist_attr.set_dims_mapping(dst_dims_mapping); moment2_dist_attr.set_dims_mapping(dst_dims_mapping); + if (amsgrad) { + moment2_max_dist_attr.set_dims_mapping(dst_dims_mapping); + } return {{param_dist_attr, grad_dist_attr, lr_dist_attr, moment1_dist_attr, moment2_dist_attr, + moment2_max_dist_attr, beta1_pow_dist_attr, beta2_pow_dist_attr, master_param_dist_attr, @@ -184,35 +210,40 @@ SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, {param_dist_attr, moment1_dist_attr, moment2_dist_attr, + moment2_max_dist_attr, beta1_pow_dist_attr, beta2_pow_dist_attr, master_param_dist_attr}}; } -SpmdInfo AdamwInferSpmdDynamic(const DistMetaTensor& param, - const DistMetaTensor& grad, - const DistMetaTensor& learning_rate, - const DistMetaTensor& moment1, - const DistMetaTensor& moment2, - const DistMetaTensor& beta1_pow, - const DistMetaTensor& beta2_pow, - const DistMetaTensor& master_param, - const DistMetaTensor& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - float lr_ratio, - float coeff, - bool with_decay, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow) { +SpmdInfo AdamwInferSpmdDynamic( + const DistMetaTensor& param, + const DistMetaTensor& grad, + const DistMetaTensor& learning_rate, + const DistMetaTensor& moment1, + const DistMetaTensor& moment2, + const paddle::optional& moment2_max, + const DistMetaTensor& beta1_pow, + const DistMetaTensor& beta2_pow, + const DistMetaTensor& master_param, + const DistMetaTensor& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + float lr_ratio, + float coeff, + bool with_decay, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad) { return AdamInferSpmdDynamic(param, grad, learning_rate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_param, @@ -223,7 +254,8 @@ SpmdInfo AdamwInferSpmdDynamic(const DistMetaTensor& param, lazy_mode, min_row_size_to_use_multithread, multi_precision, - use_global_beta_pow); + use_global_beta_pow, + amsgrad); } SpmdInfo SgdInferSpmd(const DistMetaTensor& param, diff --git a/paddle/phi/infermeta/spmd_rules/optimizer.h b/paddle/phi/infermeta/spmd_rules/optimizer.h index c45ddcd0c97e11..3a372e8a7f7d94 100644 --- a/paddle/phi/infermeta/spmd_rules/optimizer.h +++ b/paddle/phi/infermeta/spmd_rules/optimizer.h @@ -23,42 +23,48 @@ limitations under the License. */ namespace phi { namespace distributed { -SpmdInfo AdamInferSpmdDynamic(const DistMetaTensor& param, - const DistMetaTensor& grad, - const DistMetaTensor& learning_rate, - const DistMetaTensor& moment1, - const DistMetaTensor& moment2, - const DistMetaTensor& beta1_pow, - const DistMetaTensor& beta2_pow, - const DistMetaTensor& master_param, - const DistMetaTensor& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow); +SpmdInfo AdamInferSpmdDynamic( + const DistMetaTensor& param, + const DistMetaTensor& grad, + const DistMetaTensor& learning_rate, + const DistMetaTensor& moment1, + const DistMetaTensor& moment2, + const paddle::optional& moment2_max, + const DistMetaTensor& beta1_pow, + const DistMetaTensor& beta2_pow, + const DistMetaTensor& master_param, + const DistMetaTensor& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad); -SpmdInfo AdamwInferSpmdDynamic(const DistMetaTensor& param, - const DistMetaTensor& grad, - const DistMetaTensor& learning_rate, - const DistMetaTensor& moment1, - const DistMetaTensor& moment2, - const DistMetaTensor& beta1_pow, - const DistMetaTensor& beta2_pow, - const DistMetaTensor& master_param, - const DistMetaTensor& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - float lr_ratio, - float coeff, - bool with_decay, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow); +SpmdInfo AdamwInferSpmdDynamic( + const DistMetaTensor& param, + const DistMetaTensor& grad, + const DistMetaTensor& learning_rate, + const DistMetaTensor& moment1, + const DistMetaTensor& moment2, + const paddle::optional& moment2_max, + const DistMetaTensor& beta1_pow, + const DistMetaTensor& beta2_pow, + const DistMetaTensor& master_param, + const DistMetaTensor& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + float lr_ratio, + float coeff, + bool with_decay, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad); SpmdInfo SgdInferSpmd(const DistMetaTensor& param, const DistMetaTensor& learning_rate, diff --git a/paddle/phi/kernels/adam_kernel.h b/paddle/phi/kernels/adam_kernel.h index b1a7f5a686530c..dd6ee99794e605 100644 --- a/paddle/phi/kernels/adam_kernel.h +++ b/paddle/phi/kernels/adam_kernel.h @@ -26,6 +26,7 @@ void AdamDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -37,9 +38,11 @@ void AdamDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs); @@ -52,6 +55,7 @@ void MergedAdamKernel( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& moment2_max, const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -60,9 +64,11 @@ void MergedAdamKernel( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out); diff --git a/paddle/phi/kernels/adamw_kernel.h b/paddle/phi/kernels/adamw_kernel.h index 5cbb38143ff6f7..3393c9a7027d41 100644 --- a/paddle/phi/kernels/adamw_kernel.h +++ b/paddle/phi/kernels/adamw_kernel.h @@ -26,6 +26,7 @@ void AdamwDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -40,9 +41,11 @@ void AdamwDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs); diff --git a/paddle/phi/kernels/cpu/adam_kernel.cc b/paddle/phi/kernels/cpu/adam_kernel.cc index 1a63b779b02a19..84b3d3c2257075 100644 --- a/paddle/phi/kernels/cpu/adam_kernel.cc +++ b/paddle/phi/kernels/cpu/adam_kernel.cc @@ -35,6 +35,7 @@ void AdamDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -46,9 +47,11 @@ void AdamDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -72,6 +75,13 @@ void AdamDenseKernel(const Context& dev_ctx, phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out); @@ -112,17 +122,20 @@ void AdamDenseKernel(const Context& dev_ctx, T* param_out_ptr = dev_ctx.template Alloc(param_out); T* mom1_out_ptr = dev_ctx.template Alloc(moment1_out); T* mom2_out_ptr = dev_ctx.template Alloc(moment2_out); + T* mom2_max_out_ptr = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; T learning_rate_ = learning_rate.data()[0] * (sqrt(1 - beta2_p) / (1 - beta1_p)); T eps = epsilon_ * sqrt(1 - beta2_p); - phi::jit::adam_attr_t attr(beta1_, beta2_); + phi::jit::adam_attr_t attr(beta1_, beta2_, amsgrad); int64_t numel = param.numel(); const T* param_ptr = param.data(); const T* mom1_ptr = moment1.data(); const T* mom2_ptr = moment2.data(); + const T* mom2_max_ptr = amsgrad ? moment2_max.get().data() : nullptr; const T* grad_ptr = grad.data(); auto adam = @@ -136,6 +149,9 @@ void AdamDenseKernel(const Context& dev_ctx, #endif for (int64_t i = 0; i < numel / chunk_size; ++i) { const int64_t offset = i * chunk_size; + const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr; + T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr; + adam(beta1_, beta2_, -learning_rate_, @@ -144,15 +160,21 @@ void AdamDenseKernel(const Context& dev_ctx, grad_ptr + offset, mom1_ptr + offset, mom2_ptr + offset, + mom2_max_in_data, param_ptr + offset, mom1_out_ptr + offset, mom2_out_ptr + offset, - param_out_ptr + offset); + mom2_max_out_data, + param_out_ptr + offset, + amsgrad); } if (numel % chunk_size != 0) { const int64_t offset = (numel / chunk_size) * chunk_size; const int64_t tail_numel = numel % chunk_size; + const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr; + T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr; + adam(beta1_, beta2_, -learning_rate_, @@ -161,10 +183,13 @@ void AdamDenseKernel(const Context& dev_ctx, grad_ptr + offset, mom1_ptr + offset, mom2_ptr + offset, + mom2_max_in_data, param_ptr + offset, mom1_out_ptr + offset, mom2_out_ptr + offset, - param_out_ptr + offset); + mom2_max_out_data, + param_out_ptr + offset, + amsgrad); } } @@ -176,6 +201,7 @@ void MergedAdamKernel( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& moment2_max, const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -184,9 +210,11 @@ void MergedAdamKernel( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out) { @@ -245,6 +273,11 @@ void MergedAdamKernel( T epsilon_ = epsilon.to(); for (size_t idx = 0; idx < param_num; idx++) { + const T* mom2_max_in_data = + amsgrad ? moment2_max.get()[idx]->data() : nullptr; + T* mom2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out[idx]) : nullptr; + phi::funcs::AdamFunctor functor( beta1_, beta2_, @@ -255,10 +288,13 @@ void MergedAdamKernel( dev_ctx.template Alloc(moment1_out[idx]), moment2[idx]->data(), dev_ctx.template Alloc(moment2_out[idx]), + mom2_max_in_data, + mom2_max_out_data, learning_rate[idx]->data(), grad[idx]->data(), param[idx]->data(), - dev_ctx.template Alloc(param_out[idx])); + dev_ctx.template Alloc(param_out[idx]), + amsgrad); functor(param[idx]->numel()); if (!use_global_beta_pow) { dev_ctx.template Alloc(beta1_pow_out[idx])[0] = diff --git a/paddle/phi/kernels/cpu/adamw_kernel.cc b/paddle/phi/kernels/cpu/adamw_kernel.cc index f8b8ea67e23bb6..868a0dd4cd7983 100644 --- a/paddle/phi/kernels/cpu/adamw_kernel.cc +++ b/paddle/phi/kernels/cpu/adamw_kernel.cc @@ -35,6 +35,7 @@ void AdamwDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -49,9 +50,11 @@ void AdamwDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -75,6 +78,7 @@ void AdamwDenseKernel(const Context& dev_ctx, learning_rate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_param, @@ -86,9 +90,11 @@ void AdamwDenseKernel(const Context& dev_ctx, min_row_size_to_use_multithread, multi_precision, use_global_beta_pow, + amsgrad, param_out, moment1_out, moment2_out, + moment2_max_out, beta1_pow_out, beta2_pow_out, master_param_outs); @@ -130,21 +136,25 @@ void AdamwDenseKernel(const Context& dev_ctx, T* param_out_ptr = dev_ctx.template Alloc(param_out); T* mom1_out_ptr = dev_ctx.template Alloc(moment1_out); T* mom2_out_ptr = dev_ctx.template Alloc(moment2_out); + T* mom2_max_out_ptr = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; T old_lr = learning_rate.data()[0]; T learning_rate_ = learning_rate.data()[0] * (sqrt(1 - beta2_p) / (1 - beta1_p)); T eps = epsilon_ * sqrt(1 - beta2_p); + phi::jit::adamw_attr_t attr(beta1_, beta2_, coeff_, amsgrad); int64_t numel = param.numel(); const T* param_ptr = param.data(); const T* mom1_ptr = moment1.data(); const T* mom2_ptr = moment2.data(); + const T* mom2_max_ptr = amsgrad ? moment2_max.get().data() : nullptr; const T* grad_ptr = grad.data(); auto adamw = phi::jit::KernelFuncs, phi::CPUPlace>::Cache().At( - 1); + attr); static constexpr int64_t chunk_size = 512; @@ -153,6 +163,9 @@ void AdamwDenseKernel(const Context& dev_ctx, #endif for (int64_t i = 0; i < numel / chunk_size; ++i) { const int64_t offset = i * chunk_size; + const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr; + T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr; + adamw(beta1_, beta2_, -learning_rate_, @@ -164,15 +177,21 @@ void AdamwDenseKernel(const Context& dev_ctx, grad_ptr + offset, mom1_ptr + offset, mom2_ptr + offset, + mom2_max_in_data, param_ptr + offset, mom1_out_ptr + offset, mom2_out_ptr + offset, - param_out_ptr + offset); + mom2_max_out_data, + param_out_ptr + offset, + amsgrad); } if (numel % chunk_size != 0) { const int64_t offset = (numel / chunk_size) * chunk_size; const int64_t tail_numel = numel % chunk_size; + const T* mom2_max_in_data = amsgrad ? mom2_max_ptr + offset : nullptr; + T* mom2_max_out_data = amsgrad ? mom2_max_out_ptr + offset : nullptr; + adamw(beta1_, beta2_, -learning_rate_, @@ -184,10 +203,13 @@ void AdamwDenseKernel(const Context& dev_ctx, grad_ptr + offset, mom1_ptr + offset, mom2_ptr + offset, + mom2_max_in_data, param_ptr + offset, mom1_out_ptr + offset, mom2_out_ptr + offset, - param_out_ptr + offset); + mom2_max_out_data, + param_out_ptr + offset, + amsgrad); } } diff --git a/paddle/phi/kernels/cpu/fused_adam_kernel.cc b/paddle/phi/kernels/cpu/fused_adam_kernel.cc index c6434be8077d9a..865188b37669ab 100644 --- a/paddle/phi/kernels/cpu/fused_adam_kernel.cc +++ b/paddle/phi/kernels/cpu/fused_adam_kernel.cc @@ -36,6 +36,7 @@ void FusedAdamKernel( const DenseTensor& learning_rate, const std::vector& moments1, const std::vector& moments2, + const paddle::optional>& moments2_max, const std::vector& beta1_pows, const std::vector& beta2_pows, const paddle::optional>& master_params, @@ -48,9 +49,11 @@ void FusedAdamKernel( bool use_adamw, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector params_out, std::vector moments1_out, std::vector moments2_out, + std::vector moments2_max_out, std::vector beta1_pows_out, std::vector beta2_pows_out, std::vector master_params_out) { @@ -79,6 +82,17 @@ void FusedAdamKernel( "is %d, the size of Input(params) is %d.", moments2.size(), params_num)); + if (amsgrad) { + PADDLE_ENFORCE_EQ( + params_num, + moments2_max.get().size(), + errors::InvalidArgument( + "The size of Input(moments2 max) must be equal to " + "Input(params), but got the size of Input(moments2 max) " + "is %d, the size of Input(params) is %d.", + moments2_max.get().size(), + params_num)); + } PADDLE_ENFORCE_EQ(params_num, beta1_pows.size(), errors::InvalidArgument( @@ -98,6 +112,8 @@ void FusedAdamKernel( for (size_t idx = 0; idx < params_num; idx++) { auto master_params_tmp = TensorPtrToOptionalTensor(master_params, idx); + auto moments2_max_tmp = TensorPtrToOptionalTensor(moments2_max, idx); + if (!use_adamw) { AdamDenseKernel( dev_ctx, @@ -106,6 +122,7 @@ void FusedAdamKernel( learning_rate, *moments1[idx], *moments2[idx], + moments2_max_tmp, *beta1_pows[idx], *beta2_pows[idx], master_params_tmp, @@ -117,9 +134,11 @@ void FusedAdamKernel( 1000, multi_precision, use_global_beta_pow, + amsgrad, params_out[idx], moments1_out[idx], moments2_out[idx], + amsgrad ? moments2_max_out[idx] : nullptr, beta1_pows_out[idx], beta2_pows_out[idx], master_params_out.empty() ? nullptr : master_params_out[idx]); @@ -131,6 +150,7 @@ void FusedAdamKernel( learning_rate, *moments1[idx], *moments2[idx], + moments2_max_tmp, *beta1_pows[idx], *beta2_pows[idx], master_params_tmp, @@ -145,9 +165,11 @@ void FusedAdamKernel( 1000, multi_precision, use_global_beta_pow, + amsgrad, params_out[idx], moments1_out[idx], moments2_out[idx], + amsgrad ? moments2_max_out[idx] : nullptr, beta1_pows_out[idx], beta2_pows_out[idx], master_params_out.empty() ? nullptr : master_params_out[idx]); @@ -164,4 +186,5 @@ PD_REGISTER_KERNEL( kernel->OutputAt(3).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(4).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(5).SetDataType(phi::DataType::UNDEFINED); + kernel->OutputAt(6).SetDataType(phi::DataType::UNDEFINED); } diff --git a/paddle/phi/kernels/funcs/adam_functors.h b/paddle/phi/kernels/funcs/adam_functors.h index 936b1d518fa95f..5d674f36fe836b 100644 --- a/paddle/phi/kernels/funcs/adam_functors.h +++ b/paddle/phi/kernels/funcs/adam_functors.h @@ -174,10 +174,13 @@ class AdamFunctor { T* moment1_out_; const T* moment2_; T* moment2_out_; + const T* moment2_max_; + T* moment2_max_out_; const T* lr_; const T* grad_; const T* param_; T* param_out_; + bool amsgrad_; public: AdamFunctor(T beta1, @@ -189,10 +192,13 @@ class AdamFunctor { T* mom1_out, const T* mom2, T* mom2_out, + const T* mom2_max, + T* mom2_max_out, const T* lr, const T* grad, const T* param, - T* param_out) + T* param_out, + bool amsgrad) : beta1_(beta1), beta2_(beta2), epsilon_(epsilon), @@ -202,16 +208,20 @@ class AdamFunctor { moment1_out_(mom1_out), moment2_(mom2), moment2_out_(mom2_out), + moment2_max_(mom2_max), + moment2_max_out_(mom2_max_out), lr_(lr), grad_(grad), param_(param), - param_out_(param_out) {} + param_out_(param_out), + amsgrad_(amsgrad) {} inline HOSTDEVICE void operator()(size_t i) const { // Merge all memory access together. T g = grad_[i]; T mom1 = moment1_[i]; T mom2 = moment2_[i]; + T lr = *lr_; T beta1_pow = *beta1_pow_; T beta2_pow = *beta2_pow_; @@ -222,7 +232,16 @@ class AdamFunctor { mom1 = beta1_ * mom1 + (1 - beta1_) * g; mom2 = beta2_ * mom2 + (1 - beta2_) * g * g; - p -= lr * (mom1 / (sqrt(mom2) + epsilon_ * sqrt(1 - beta2_pow))); + + if (amsgrad_) { + T mom2_max_ = std::max(mom2, moment2_max_[i]); + p -= lr * (mom1 / (sqrt(mom2_max_) + epsilon_ * sqrt(1 - beta2_pow))); + + // Write back to global memory + moment2_max_out_[i] = mom2_max_; + } else { + p -= lr * (mom1 / (sqrt(mom2) + epsilon_ * sqrt(1 - beta2_pow))); + } // Write back to global memory moment1_out_[i] = mom1; @@ -244,10 +263,13 @@ class AdamFunctor { T* moment1_out_; const T* moment2_; T* moment2_out_; + const T* moment2_max_; + T* moment2_max_out_; const T* lr_; const T* grad_; const T* param_; T* param_out_; + bool amsgrad_; public: AdamFunctor(T beta1, @@ -259,10 +281,13 @@ class AdamFunctor { T* mom1_out, const T* mom2, T* mom2_out, + const T* mom2_max, + T* mom2_max_out, const T* lr, const T* grad, const T* param, - T* param_out) + T* param_out, + bool amsgrad) : beta1_(beta1), beta2_(beta2), epsilon_(epsilon), @@ -272,10 +297,13 @@ class AdamFunctor { moment1_out_(mom1_out), moment2_(mom2), moment2_out_(mom2_out), + moment2_max_(mom2_max), + moment2_max_out_(mom2_max_out), lr_(lr), grad_(grad), param_(param), - param_out_(param_out) {} + param_out_(param_out), + amsgrad_(amsgrad) {} void operator()(size_t numel) const { Eigen::Map> g{ @@ -303,8 +331,20 @@ class AdamFunctor { moment1_out = beta1_ * mom1 + (1 - beta1_) * g; moment2_out = beta2_ * mom2 + (1 - beta2_) * g * g; - param_out = param - lr * (moment1_out / (moment2_out.sqrt() + - epsilon_ * sqrt(1 - beta2_pow))); + + if (amsgrad_) { + Eigen::Map> mom2_max{ + moment2_max_, static_cast(numel)}; + Eigen::Map> moment2_max_out{ + moment2_max_out_, static_cast(numel)}; + + moment2_max_out = moment2_out.cwiseMax(mom2_max); + param_out = param - lr * (moment1_out / (moment2_max_out.sqrt() + + epsilon_ * sqrt(1 - beta2_pow))); + } else { + param_out = param - lr * (moment1_out / (moment2_out.sqrt() + + epsilon_ * sqrt(1 - beta2_pow))); + } } }; @@ -324,6 +364,8 @@ class SparseAdamFunctor { MT* moment1_out_; const MT* moment2_; MT* moment2_out_; + const MT* moment2_max_; + MT* moment2_max_out_; const MT* lr_; const T* grad_; const T* param_; @@ -335,6 +377,7 @@ class SparseAdamFunctor { int64_t row_numel_; int64_t row_count_; bool lazy_mode_; + bool amsgrad_; public: SparseAdamFunctor(MT beta1, @@ -346,6 +389,8 @@ class SparseAdamFunctor { MT* mom1_out, const MT* mom2, MT* mom2_out, + const MT* mom2_max, + MT* mom2_max_out, const MT* lr, const T* grad, const T* param, @@ -355,7 +400,8 @@ class SparseAdamFunctor { const int64_t* rows, int64_t row_numel, int64_t row_count, - bool lazy_mode) + bool lazy_mode, + bool amsgrad) : beta1_(beta1), beta2_(beta2), epsilon_(epsilon), @@ -365,6 +411,8 @@ class SparseAdamFunctor { moment1_out_(mom1_out), moment2_(mom2), moment2_out_(mom2_out), + moment2_max_(mom2_max), + moment2_max_out_(mom2_max_out), lr_(lr), grad_(grad), param_(param), @@ -374,12 +422,14 @@ class SparseAdamFunctor { rows_(rows), row_numel_(row_numel), row_count_(row_count), - lazy_mode_(lazy_mode) {} + lazy_mode_(lazy_mode), + amsgrad_(amsgrad) {} inline HOSTDEVICE void adam_update(size_t i, MT g) const { // The following code is the same as dense MT mom1 = moment1_[i]; MT mom2 = moment2_[i]; + MT lr = *lr_; MT beta1_pow = *beta1_pow_; MT beta2_pow = *beta2_pow_; @@ -391,8 +441,18 @@ class SparseAdamFunctor { mom1 = beta1_ * mom1 + (static_cast(1.0) - beta1_) * g; mom2 = beta2_ * mom2 + (static_cast(1.0) - beta2_) * g * g; - p -= lr * (mom1 / (sqrt(mom2) + - epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + + if (amsgrad_) { + MT mom2_max_ = std::max(mom2, moment2_max_[i]); + p -= lr * (mom1 / (sqrt(mom2_max_) + + epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + + // Write back to global memory + moment2_max_out_[i] = mom2_max_; + } else { + p -= lr * (mom1 / (sqrt(mom2) + + epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + } // Write back to global memory moment1_out_[i] = mom1; @@ -430,6 +490,8 @@ class SparseAdamFunctor { T* moment1_out_; const T* moment2_; T* moment2_out_; + const T* moment2_max_; + T* moment2_max_out_; const T* lr_; const T* grad_; const T* param_; @@ -438,6 +500,7 @@ class SparseAdamFunctor { const int64_t* rows_; int64_t row_numel_; int64_t row_count_; + bool amsgrad_; public: SparseAdamFunctor(T beta1, @@ -449,6 +512,8 @@ class SparseAdamFunctor { T* mom1_out, const T* mom2, T* mom2_out, + const T* mom2_max, + T* mom2_max_out, const T* lr, const T* grad, const T* param, @@ -456,7 +521,8 @@ class SparseAdamFunctor { const int64_t* rows, int64_t row_numel, int64_t row_count, - bool lazy_mode UNUSED) + bool lazy_mode UNUSED, + bool amsgrad) : beta1_(beta1), beta2_(beta2), epsilon_(epsilon), @@ -466,18 +532,22 @@ class SparseAdamFunctor { moment1_out_(mom1_out), moment2_(mom2), moment2_out_(mom2_out), + moment2_max_(mom2_max), + moment2_max_out_(mom2_max_out), lr_(lr), grad_(grad), param_(param), param_out_(param_out), rows_(rows), row_numel_(row_numel), - row_count_(row_count) {} + row_count_(row_count), + amsgrad_(amsgrad) {} inline HOSTDEVICE void adam_update(size_t i, T g) const { // The following code is the same as dense T mom1 = moment1_[i]; T mom2 = moment2_[i]; + T lr = *lr_; T beta1_pow = *beta1_pow_; T beta2_pow = *beta2_pow_; @@ -488,7 +558,16 @@ class SparseAdamFunctor { mom1 = beta1_ * mom1 + (1 - beta1_) * g; mom2 = beta2_ * mom2 + (1 - beta2_) * g * g; - p -= lr * (mom1 / (sqrt(mom2) + epsilon_ * sqrt(1 - beta2_pow))); + + if (amsgrad_) { + T mom2_max_ = std::max(mom2, moment2_max_[i]); + p -= lr * (mom1 / (sqrt(mom2_max_) + epsilon_ * sqrt(1 - beta2_pow))); + + // Write back to global memory + moment2_max_out_[i] = mom2_max_; + } else { + p -= lr * (mom1 / (sqrt(mom2) + epsilon_ * sqrt(1 - beta2_pow))); + } // Write back to global memory moment1_out_[i] = mom1; @@ -520,7 +599,17 @@ class SparseAdamFunctor { mom1 = beta1_ * mom1; mom2 = beta2_ * mom2; - p -= lr * (mom1 / (sqrt(mom2) + epsilon_)); + if (amsgrad_) { + T mom2_max = moment2_max_[i * row_numel_ + k]; + T mom2_max_ = std::max(mom2, mom2_max); + p -= lr * (mom1 / (sqrt(mom2_max_) + epsilon_)); + + // Write back to global memory + moment2_max_out_[i * row_numel_ + k] = mom2_max_; + } else { + p -= lr * (mom1 / (sqrt(mom2) + epsilon_)); + } + // Write back to global memory moment1_out_[i * row_numel_ + k] = mom1; moment2_out_[i * row_numel_ + k] = mom2; @@ -578,6 +667,8 @@ class SparseAdamWFunctor { MT* moment1_out_; const MT* moment2_; MT* moment2_out_; + const MT* moment2_max_; + MT* moment2_max_out_; const MT* lr_; const T* grad_; const T* param_; @@ -589,6 +680,7 @@ class SparseAdamWFunctor { int64_t row_numel_; int64_t row_count_; bool lazy_mode_; + bool amsgrad_; public: SparseAdamWFunctor(MT beta1, @@ -602,6 +694,8 @@ class SparseAdamWFunctor { MT* mom1_out, const MT* mom2, MT* mom2_out, + const MT* mom2_max, + MT* mom2_max_out, const MT* lr, const T* grad, const T* param, @@ -611,7 +705,8 @@ class SparseAdamWFunctor { const int64_t* rows, int64_t row_numel, int64_t row_count, - bool lazy_mode) + bool lazy_mode, + bool amsgrad) : beta1_(beta1), beta2_(beta2), epsilon_(epsilon), @@ -623,6 +718,8 @@ class SparseAdamWFunctor { moment1_out_(mom1_out), moment2_(mom2), moment2_out_(mom2_out), + moment2_max_(mom2_max), + moment2_max_out_(mom2_max_out), lr_(lr), grad_(grad), param_(param), @@ -632,12 +729,14 @@ class SparseAdamWFunctor { rows_(rows), row_numel_(row_numel), row_count_(row_count), - lazy_mode_(lazy_mode) {} + lazy_mode_(lazy_mode), + amsgrad_(amsgrad) {} inline HOSTDEVICE void adamw_update(size_t i, MT g) const { // The following code is the same as dense MT mom1 = moment1_[i]; MT mom2 = moment2_[i]; + MT lr = *lr_ * lr_ratio_; MT lr_orig = lr; MT beta1_pow = *beta1_pow_; @@ -650,9 +749,20 @@ class SparseAdamWFunctor { mom1 = beta1_ * mom1 + (static_cast(1.0) - beta1_) * g; mom2 = beta2_ * mom2 + (static_cast(1.0) - beta2_) * g * g; + p -= lr_orig * coeff_ * p; - p -= lr * (mom1 / (sqrt(mom2) + - epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + + if (amsgrad_) { + MT mom2_max_ = std::max(mom2, moment2_max_[i]); + p -= lr * (mom1 / (sqrt(mom2_max_) + + epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + + // Write back to global memory + moment2_max_out_[i] = mom2_max_; + } else { + p -= lr * (mom1 / (sqrt(mom2) + + epsilon_ * sqrt(static_cast(1.0) - beta2_pow))); + } // Write back to global memory moment1_out_[i] = mom1; diff --git a/paddle/phi/kernels/funcs/jit/gen/adam.cc b/paddle/phi/kernels/funcs/jit/gen/adam.cc index fd151b75e8fbb8..af766f295381f5 100644 --- a/paddle/phi/kernels/funcs/jit/gen/adam.cc +++ b/paddle/phi/kernels/funcs/jit/gen/adam.cc @@ -28,8 +28,12 @@ void AdamJitCode::loadArgs() { static_cast(0xFFFFFFFFFFFFFFF8); static constexpr int64_t abi_pushes_offset = num_g_abi_regs * 8; - mov(reg_mom2_out_ptr, ptr[rsp + (abi_pushes_offset + 8)]); - mov(reg_param_out_ptr, ptr[rsp + (abi_pushes_offset + 16)]); + mov(reg_mom1_out_ptr, ptr[rsp + (abi_pushes_offset + 8)]); + mov(reg_mom2_out_ptr, ptr[rsp + (abi_pushes_offset + 16)]); + mov(reg_mom2_max_out_ptr, ptr[rsp + (abi_pushes_offset + 24)]); + mov(reg_param_out_ptr, ptr[rsp + (abi_pushes_offset + 32)]); + mov(reg_amsgrad, byte[rsp + (abi_pushes_offset + 40)]); + mov(eax, one_as_float); movd(xmm_one, eax); @@ -54,6 +58,9 @@ void AdamJitCode::loadArgs() { } void AdamJitCode::setTailOpmask() { + push(r13); + push(r14); + mov(r13, rcx); mov(rcx, reg_numel); @@ -65,6 +72,9 @@ void AdamJitCode::setTailOpmask() { kmovw(k1, r14d); mov(rcx, r13); + + pop(r14); + pop(r13); } void AdamJitCode::mainCode() { @@ -84,16 +94,32 @@ void AdamJitCode::mainCode() { vmovups(ptr[reg_mom1_out_ptr + reg_offset] | k1, ymm8); vmovups(ptr[reg_mom2_out_ptr + reg_offset] | k1, ymm7); - // sqrt(mom2) + eps - vsqrtps(ymm7 | k1, ymm7); - vaddps(ymm7 | k1, ymm7, ymm_eps); + // make a local label: `.without_amsgrad` + inLocalLabel(); + // if not amsgrad then update params + cmp(reg_amsgrad, 0); + je(".without_amsgrad", T_NEAR); + // load mom2_max + vmovups(ymm9 | k1, ptr[reg_mom2_max_ptr + reg_offset]); + // compare mom2 and mom2_max and save to mom2 + vmaxps(ymm7 | k1, ymm7, ymm9); + // store mom2_max + vmovups(ptr[reg_mom2_max_out_ptr + reg_offset] | k1, ymm7); + + L(".without_amsgrad"); + { + // sqrt(mom2) + eps + vsqrtps(ymm7 | k1, ymm7); + vaddps(ymm7 | k1, ymm7, ymm_eps); - // p + (-lr) * (mom1 / sqrt(mom2) + eps) - vdivps(ymm7 | k1, ymm8, ymm7); - vfmadd213ps(ymm7 | k1, ymm_lr, ptr[reg_param_ptr + reg_offset]); + // p + (-lr) * (mom1 / sqrt(mom2) + eps) + vdivps(ymm7 | k1, ymm8, ymm7); + vfmadd213ps(ymm7 | k1, ymm_lr, ptr[reg_param_ptr + reg_offset]); - // store p - vmovups(ptr[reg_param_out_ptr + reg_offset] | k1, ymm7); + // store p + vmovups(ptr[reg_param_out_ptr + reg_offset] | k1, ymm7); + } + outLocalLabel(); } void AdamJitCode::genCode() { @@ -104,18 +130,18 @@ void AdamJitCode::genCode() { loadArgs(); cmp(reg_numel, main_loop_elems_size); - jl("process_tail"); + jl("process_tail", T_NEAR); L("main_loop"); { mainCode(); add(reg_offset, offset_increment); cmp(reg_numel_without_tail, reg_offset); - jg("main_loop"); + jg("main_loop", T_NEAR); } cmp(reg_numel, reg_offset); - je("end"); + je("end", T_NEAR); L("process_tail"); { diff --git a/paddle/phi/kernels/funcs/jit/gen/adam.h b/paddle/phi/kernels/funcs/jit/gen/adam.h index 5c432e03ec9214..c4cbce01ccf16b 100644 --- a/paddle/phi/kernels/funcs/jit/gen/adam.h +++ b/paddle/phi/kernels/funcs/jit/gen/adam.h @@ -44,8 +44,8 @@ class AdamJitCode : public JitCode { reg64_t reg_grad_ptr{abi_param2}; reg64_t reg_mom1_ptr{abi_param3}; reg64_t reg_mom2_ptr{abi_param4}; - reg64_t reg_param_ptr{abi_param5}; - reg64_t reg_mom1_out_ptr{abi_param6}; + reg64_t reg_mom2_max_ptr{abi_param5}; + reg64_t reg_param_ptr{abi_param6}; xmm_t xmm_beta1 = xmm_t(0); xmm_t xmm_beta2 = xmm_t(1); @@ -63,9 +63,12 @@ class AdamJitCode : public JitCode { ymm_t ymm_one_sub_beta2 = ymm_t(5); ymm_t ymm_one = ymm_t(6); - reg64_t reg_mom2_out_ptr{r10}; - reg64_t reg_param_out_ptr{r11}; - reg64_t reg_numel_without_tail{r12}; + reg64_t reg_mom1_out_ptr{r10}; + reg64_t reg_mom2_out_ptr{r11}; + reg64_t reg_mom2_max_out_ptr{r12}; + reg64_t reg_param_out_ptr{r13}; + reg64_t reg_amsgrad{r14}; + reg64_t reg_numel_without_tail{r15}; reg64_t reg_offset{rax}; }; diff --git a/paddle/phi/kernels/funcs/jit/gen/adamw.cc b/paddle/phi/kernels/funcs/jit/gen/adamw.cc index 4a8545c24f9649..417e71f9658d8e 100644 --- a/paddle/phi/kernels/funcs/jit/gen/adamw.cc +++ b/paddle/phi/kernels/funcs/jit/gen/adamw.cc @@ -28,8 +28,12 @@ void AdamWJitCode::loadArgs() { static_cast(0xFFFFFFFFFFFFFFF8); static constexpr int64_t abi_pushes_offset = num_g_abi_regs * 8; - mov(reg_mom2_out_ptr, ptr[rsp + (abi_pushes_offset + 8)]); - mov(reg_param_out_ptr, ptr[rsp + (abi_pushes_offset + 16)]); + mov(reg_mom1_out_ptr, ptr[rsp + (abi_pushes_offset + 8)]); + mov(reg_mom2_out_ptr, ptr[rsp + (abi_pushes_offset + 16)]); + mov(reg_mom2_max_out_ptr, ptr[rsp + (abi_pushes_offset + 24)]); + mov(reg_param_out_ptr, ptr[rsp + (abi_pushes_offset + 32)]); + mov(reg_amsgrad, byte[rsp + (abi_pushes_offset + 40)]); + mov(eax, one_as_float); movd(xmm_one, eax); @@ -57,6 +61,9 @@ void AdamWJitCode::loadArgs() { } void AdamWJitCode::setTailOpmask() { + push(r13); + push(r14); + mov(r13, rcx); mov(rcx, reg_numel); @@ -68,6 +75,9 @@ void AdamWJitCode::setTailOpmask() { kmovw(k1, r14d); mov(rcx, r13); + + pop(r14); + pop(r13); } void AdamWJitCode::mainCode() { @@ -98,16 +108,32 @@ void AdamWJitCode::mainCode() { vmovups(ptr[reg_mom1_out_ptr + reg_offset] | k1, ymm12); vmovups(ptr[reg_mom2_out_ptr + reg_offset] | k1, ymm10); - // sqrt(mom2) + eps - vsqrtps(ymm10 | k1, ymm10); - vaddps(ymm10 | k1, ymm10, ymm_eps); + // // make a local label: `.without_amsgrad` + inLocalLabel(); + // if not amsgrad then update params + cmp(reg_amsgrad, 0); + je(".without_amsgrad", T_NEAR); + // load mom2_max + vmovups(ymm13 | k1, ptr[reg_mom2_max_ptr + reg_offset]); + // compare mom2 and mom2_max and save to mom2 + vmaxps(ymm10 | k1, ymm10, ymm13); + // store mom2_max + vmovups(ptr[reg_mom2_max_out_ptr + reg_offset] | k1, ymm10); + + L(".without_amsgrad"); + { + // sqrt(mom2) + eps + vsqrtps(ymm10 | k1, ymm10); + vaddps(ymm10 | k1, ymm10, ymm_eps); - // p + (-lr) * (mom1 / sqrt(mom2) + eps) - vdivps(ymm10 | k1, ymm12, ymm10); - vfmadd213ps(ymm10 | k1, ymm_lr, ymm11); + // p + (-lr) * (mom1 / sqrt(mom2) + eps) + vdivps(ymm10 | k1, ymm12, ymm10); + vfmadd213ps(ymm10 | k1, ymm_lr, ymm11); - // store p - vmovups(ptr[reg_param_out_ptr + reg_offset] | k1, ymm10); + // store p + vmovups(ptr[reg_param_out_ptr + reg_offset] | k1, ymm10); + } + outLocalLabel(); } void AdamWJitCode::genCode() { @@ -118,14 +144,14 @@ void AdamWJitCode::genCode() { loadArgs(); cmp(reg_numel, main_loop_elems_size); - jl("process_tail"); + jl("process_tail", T_NEAR); L("main_loop"); { mainCode(); add(reg_offset, offset_increment); cmp(reg_numel_without_tail, reg_offset); - jg("main_loop"); + jg("main_loop", T_NEAR); } cmp(reg_numel, reg_offset); @@ -142,13 +168,16 @@ void AdamWJitCode::genCode() { postCode(); } -class AdamWCreator : public JitCodeCreator { +class AdamWCreator : public JitCodeCreator { public: - bool CanBeUsed(const int& attr) const override { + bool CanBeUsed(const adamw_attr_t& attr) const override { return phi::backends::cpu::MayIUse(phi::backends::cpu::avx512f); } - size_t CodeSize(const int& attr) const override { return 96 + 32 * 8; } - std::unique_ptr CreateJitCode(const int& attr) const override { + size_t CodeSize(const adamw_attr_t& attr) const override { + return 96 + 32 * 8; + } + std::unique_ptr CreateJitCode( + const adamw_attr_t& attr) const override { return make_unique(attr, CodeSize(attr)); } }; diff --git a/paddle/phi/kernels/funcs/jit/gen/adamw.h b/paddle/phi/kernels/funcs/jit/gen/adamw.h index dab90e0e0f69e1..4147c5f0e383ea 100644 --- a/paddle/phi/kernels/funcs/jit/gen/adamw.h +++ b/paddle/phi/kernels/funcs/jit/gen/adamw.h @@ -26,14 +26,14 @@ namespace gen { class AdamWJitCode : public JitCode { public: - explicit AdamWJitCode(const int& attr, + explicit AdamWJitCode(const adamw_attr_t& attr, size_t code_size = 256 * 1024, void* code_ptr = nullptr) : JitCode(code_size, code_ptr) { this->genCode(); } - DECLARE_JIT_CODE(AdamJitCode); + DECLARE_JIT_CODE(AdamWJitCode); void genCode() override; void loadArgs(); void setTailOpmask(); @@ -44,8 +44,8 @@ class AdamWJitCode : public JitCode { reg64_t reg_grad_ptr{abi_param2}; reg64_t reg_mom1_ptr{abi_param3}; reg64_t reg_mom2_ptr{abi_param4}; - reg64_t reg_param_ptr{abi_param5}; - reg64_t reg_mom1_out_ptr{abi_param6}; + reg64_t reg_mom2_max_ptr{abi_param5}; + reg64_t reg_param_ptr{abi_param6}; xmm_t xmm_beta1 = xmm_t(0); xmm_t xmm_beta2 = xmm_t(1); @@ -69,9 +69,12 @@ class AdamWJitCode : public JitCode { ymm_t ymm_one_sub_beta2 = ymm_t(8); ymm_t ymm_one = ymm_t(9); - reg64_t reg_mom2_out_ptr{r10}; - reg64_t reg_param_out_ptr{r11}; - reg64_t reg_numel_without_tail{r12}; + reg64_t reg_mom1_out_ptr{r10}; + reg64_t reg_mom2_out_ptr{r11}; + reg64_t reg_mom2_max_out_ptr{r12}; + reg64_t reg_param_out_ptr{r13}; + reg64_t reg_amsgrad{r14}; + reg64_t reg_numel_without_tail{r15}; reg64_t reg_offset{rax}; }; diff --git a/paddle/phi/kernels/funcs/jit/kernel_base.h b/paddle/phi/kernels/funcs/jit/kernel_base.h index e08f7821793c02..a41c96a7562740 100644 --- a/paddle/phi/kernels/funcs/jit/kernel_base.h +++ b/paddle/phi/kernels/funcs/jit/kernel_base.h @@ -266,8 +266,10 @@ struct SgdTuple { typedef struct adam_attr_s { float beta1, beta2; + bool amsgrad; adam_attr_s() = default; - explicit adam_attr_s(float beta1, float beta2) : beta1(beta1), beta2(beta2) {} + explicit adam_attr_s(float beta1, float beta2, bool amsgrad) + : beta1(beta1), beta2(beta2), amsgrad(amsgrad) {} } adam_attr_t; template @@ -275,15 +277,36 @@ struct AdamTuple { static constexpr KernelType kernel_type = kAdam; typedef T data_type; typedef adam_attr_t attr_type; - typedef void (*func_type)( - T, T, T, T, int64_t, const T*, const T*, const T*, const T*, T*, T*, T*); + typedef void (*func_type)(T, + T, + T, + T, + int64_t, + const T*, + const T*, + const T*, + const T*, + const T*, + T*, + T*, + T*, + T*, + bool); }; +typedef struct adamw_attr_s { + float beta1, beta2, coeff; + bool amsgrad; + adamw_attr_s() = default; + explicit adamw_attr_s(float beta1, float beta2, float coeff, bool amsgrad) + : beta1(beta1), beta2(beta2), coeff(coeff), amsgrad(amsgrad) {} +} adamw_attr_t; + template struct AdamWTuple { static constexpr KernelType kernel_type = kAdamW; typedef T data_type; - typedef int attr_type; + typedef adamw_attr_t attr_type; typedef void (*func_type)(T, T, T, @@ -296,9 +319,12 @@ struct AdamWTuple { const T*, const T*, const T*, + const T*, + T*, + T*, T*, T*, - T*); + bool); }; typedef struct matmul_attr_s { diff --git a/paddle/phi/kernels/funcs/jit/kernel_key.cc b/paddle/phi/kernels/funcs/jit/kernel_key.cc index 818b3c0a9f1610..fddd5bd69ee025 100644 --- a/paddle/phi/kernels/funcs/jit/kernel_key.cc +++ b/paddle/phi/kernels/funcs/jit/kernel_key.cc @@ -67,7 +67,16 @@ int64_t JitCodeKey(const sgd_attr_t& attr) { template <> int64_t JitCodeKey(const adam_attr_t& attr) { - return static_cast(attr.beta1 + attr.beta2); + // if use amsgrad, we add `10` for hashcode + return static_cast(attr.beta1 + attr.beta2 + + (attr.amsgrad ? 10 : 0)); +} + +template <> +int64_t JitCodeKey(const adamw_attr_t& attr) { + // if use amsgrad, we add `10` for hashcode + return static_cast(attr.beta1 + attr.beta2 + attr.coeff + + (attr.amsgrad ? 10 : 0)); } } // namespace phi::jit diff --git a/paddle/phi/kernels/funcs/jit/refer/refer.h b/paddle/phi/kernels/funcs/jit/refer/refer.h index 23402bffcd9844..2629b0e531d723 100644 --- a/paddle/phi/kernels/funcs/jit/refer/refer.h +++ b/paddle/phi/kernels/funcs/jit/refer/refer.h @@ -523,16 +523,29 @@ void Adam(T beta1, const T* grad_ptr, const T* mom1_ptr, const T* mom2_ptr, + const T* mom2_max_ptr, const T* param_ptr, T* mom1_out_ptr, T* mom2_out_ptr, - T* param_out_ptr) { + T* mom2_max_out_ptr, + T* param_out_ptr, + bool amsgrad) { for (int i = 0; i < numel; ++i) { mom1_out_ptr[i] = beta1 * mom1_ptr[i] + (1 - beta1) * grad_ptr[i]; mom2_out_ptr[i] = beta2 * mom2_ptr[i] + (1 - beta2) * grad_ptr[i] * grad_ptr[i]; - param_out_ptr[i] = - param_ptr[i] + lr * (mom1_out_ptr[i] / (sqrt(mom2_out_ptr[i]) + eps)); + + if (amsgrad) { + T mom2_max_ = std::max(mom2_out_ptr[i], mom2_max_ptr[i]); + mom2_max_out_ptr[i] = mom2_max_; + + param_out_ptr[i] = + param_ptr[i] + lr * (mom1_out_ptr[i] / (sqrt(mom2_max_) + eps)); + } else { + T mom2_ = mom2_out_ptr[i]; + param_out_ptr[i] = + param_ptr[i] + lr * (mom1_out_ptr[i] / (sqrt(mom2_) + eps)); + } } } @@ -548,17 +561,30 @@ void AdamW(T beta1, const T* grad_ptr, const T* mom1_ptr, const T* mom2_ptr, + const T* mom2_max_ptr, const T* param_ptr, T* mom1_out_ptr, T* mom2_out_ptr, - T* param_out_ptr) { + T* mom2_max_out_ptr, + T* param_out_ptr, + bool amsgrad) { for (int i = 0; i < numel; ++i) { auto param_tmp = param_ptr[i] - old_lr * lr_ratio * coeff * param_ptr[i]; mom1_out_ptr[i] = beta1 * mom1_ptr[i] + (1 - beta1) * grad_ptr[i]; mom2_out_ptr[i] = beta2 * mom2_ptr[i] + (1 - beta2) * grad_ptr[i] * grad_ptr[i]; - param_out_ptr[i] = - param_tmp + lr * (mom1_out_ptr[i] / (sqrt(mom2_out_ptr[i]) + eps)); + + if (amsgrad) { + T mom2_max_ = std::max(mom2_out_ptr[i], mom2_max_ptr[i]); + mom2_max_out_ptr[i] = mom2_max_; + + param_out_ptr[i] = + param_tmp + lr * (mom1_out_ptr[i] / (sqrt(mom2_max_) + eps)); + } else { + T mom2_ = mom2_out_ptr[i]; + param_out_ptr[i] = + param_tmp + lr * (mom1_out_ptr[i] / (sqrt(mom2_) + eps)); + } } } diff --git a/paddle/phi/kernels/funcs/jit/test.cc b/paddle/phi/kernels/funcs/jit/test.cc index 6e1b7ee1536b4d..fa26bc87f079c3 100644 --- a/paddle/phi/kernels/funcs/jit/test.cc +++ b/paddle/phi/kernels/funcs/jit/test.cc @@ -695,184 +695,178 @@ void TestKernelMatMul() { template void TestKernelAdam() { - using T = typename KernelTuple::data_type; - VLOG(10) << "Test JITKernel: " << jit::to_string(KernelTuple::kernel_type); - const T lr = 0.1; - const T beta1 = 0.99; - const T beta2 = 0.95; - const T beta1_pow = beta1 * beta1; - const T beta2_pow = beta2 * beta2; - - const T epsilon = 0.000001; - const int64_t numel = 123; - - T learning_rate = lr * (sqrt(1 - beta2_pow) / (1 - beta1_pow)); - T eps = epsilon * sqrt(1 - beta2_pow); - - std::vector param(numel); - std::vector grad(numel); - std::vector mom1(numel); - std::vector mom2(numel); - - std::vector param_out(param.size()); - std::vector mom1_out(mom1.size()); - std::vector mom2_out(mom2.size()); - - RandomVec(numel, param.data(), 0.5f); - RandomVec(numel, grad.data(), 0.5f); - RandomVec(numel, mom1.data(), 0.5f); - RandomVec(numel, mom2.data(), 0.5f); - - auto ref = jit::GetReferFunc(); - EXPECT_TRUE(ref != nullptr); - jit::adam_attr_t attr(beta1, beta2); - ref(beta1, - beta2, - -learning_rate, - eps, - numel, - grad.data(), - mom1.data(), - mom2.data(), - param.data(), - mom1_out.data(), - mom2_out.data(), - param_out.data()); - - auto verifier = [](const typename KernelTuple::func_type tgt, - T beta1, - T beta2, - T lr, - T eps, - int64_t numel, - const std::vector& grad, - const std::vector& mom1, - const std::vector& mom2, - const std::vector& param, - const std::vector& ref_mom1_out, - const std::vector& ref_mom2_out, - const std::vector& ref_param_out) { - EXPECT_TRUE(tgt != nullptr); - EXPECT_EQ(param.size(), static_cast(numel)); - EXPECT_EQ(grad.size(), static_cast(numel)); - EXPECT_EQ(mom1.size(), static_cast(numel)); - EXPECT_EQ(mom2.size(), static_cast(numel)); - - std::vector jit_mom1_out(ref_mom1_out.size()); - std::vector jit_mom2_out(ref_mom2_out.size()); - std::vector jit_param_out(ref_param_out.size()); - - tgt(beta1, + for (bool amsgrad : {false, true}) { + using T = typename KernelTuple::data_type; + VLOG(10) << "Test JITKernel: " << jit::to_string(KernelTuple::kernel_type); + const T lr = 0.1; + const T beta1 = 0.99; + const T beta2 = 0.95; + const T beta1_pow = beta1 * beta1; + const T beta2_pow = beta2 * beta2; + + const T epsilon = 0.000001; + const int64_t numel = 123; + + T learning_rate = lr * (sqrt(1 - beta2_pow) / (1 - beta1_pow)); + T eps = epsilon * sqrt(1 - beta2_pow); + + std::vector param(numel); + std::vector grad(numel); + std::vector mom1(numel); + std::vector mom2(numel); + std::vector mom2_max(numel); + + std::vector param_out(param.size()); + std::vector mom1_out(mom1.size()); + std::vector mom2_out(mom2.size()); + std::vector mom2_max_out(mom2_max.size()); + + RandomVec(numel, param.data(), 0.5f); + RandomVec(numel, grad.data(), 0.5f); + RandomVec(numel, mom1.data(), 0.5f); + RandomVec(numel, mom2.data(), 0.5f); + if (amsgrad) { + RandomVec(numel, mom2_max.data(), 0.5f); + } + + auto ref = jit::GetReferFunc(); + EXPECT_TRUE(ref != nullptr); + jit::adam_attr_t attr(beta1, beta2, amsgrad); + + ref(beta1, beta2, - -lr, + -learning_rate, eps, numel, grad.data(), mom1.data(), mom2.data(), + mom2_max.data(), param.data(), - jit_mom1_out.data(), - jit_mom2_out.data(), - jit_param_out.data()); + mom1_out.data(), + mom2_out.data(), + mom2_max_out.data(), + param_out.data(), + amsgrad); - ExpectEQ(ref_mom1_out.data(), jit_mom1_out.data(), numel); - ExpectEQ(ref_mom2_out.data(), jit_mom2_out.data(), numel); - ExpectEQ(ref_param_out.data(), jit_param_out.data(), numel); - }; - TestAllImpls(attr, - verifier, - beta1, - beta2, - learning_rate, - eps, - numel, - grad, - mom1, - mom2, - param, - mom1_out, - mom2_out, - param_out); + auto verifier = [](const typename KernelTuple::func_type tgt, + T beta1, + T beta2, + T lr, + T eps, + int64_t numel, + const std::vector& grad, + const std::vector& mom1, + const std::vector& mom2, + const std::vector& mom2_max, + const std::vector& param, + const std::vector& ref_mom1_out, + const std::vector& ref_mom2_out, + const std::vector& ref_mom2_max_out, + const std::vector& ref_param_out, + bool amsgrad) { + EXPECT_TRUE(tgt != nullptr); + EXPECT_EQ(param.size(), static_cast(numel)); + EXPECT_EQ(grad.size(), static_cast(numel)); + EXPECT_EQ(mom1.size(), static_cast(numel)); + EXPECT_EQ(mom2.size(), static_cast(numel)); + if (amsgrad) { + EXPECT_EQ(mom2_max.size(), static_cast(numel)); + } + + std::vector jit_mom1_out(ref_mom1_out.size()); + std::vector jit_mom2_out(ref_mom2_out.size()); + std::vector jit_mom2_max_out(ref_mom2_max_out.size()); + std::vector jit_param_out(ref_param_out.size()); + + tgt(beta1, + beta2, + -lr, + eps, + numel, + grad.data(), + mom1.data(), + mom2.data(), + mom2_max.data(), + param.data(), + jit_mom1_out.data(), + jit_mom2_out.data(), + jit_mom2_max_out.data(), + jit_param_out.data(), + amsgrad); + + ExpectEQ(ref_mom1_out.data(), jit_mom1_out.data(), numel); + ExpectEQ(ref_mom2_out.data(), jit_mom2_out.data(), numel); + if (amsgrad) { + ExpectEQ(ref_mom2_max_out.data(), jit_mom2_max_out.data(), numel); + } + ExpectEQ(ref_param_out.data(), jit_param_out.data(), numel); + }; + TestAllImpls(attr, + verifier, + beta1, + beta2, + learning_rate, + eps, + numel, + grad, + mom1, + mom2, + mom2_max, + param, + mom1_out, + mom2_out, + mom2_max_out, + param_out, + amsgrad); + } } template void TestKernelAdamW() { - using T = typename KernelTuple::data_type; - VLOG(10) << "Test JITKernel: " << jit::to_string(KernelTuple::kernel_type); - const T old_lr = 0.1; - const T beta1 = 0.99; - const T beta2 = 0.95; - const T beta1_pow = beta1 * beta1; - const T beta2_pow = beta2 * beta2; - - const T epsilon = 0.000001; - const int64_t numel = 123; - const T lr_ratio = 0.2; - const T coeff = 0.3; - - T learning_rate = old_lr * (sqrt(1 - beta2_pow) / (1 - beta1_pow)); - T eps = epsilon * sqrt(1 - beta2_pow); - - std::vector param(numel); - std::vector grad(numel); - std::vector mom1(numel); - std::vector mom2(numel); - - std::vector param_out(param.size()); - std::vector mom1_out(mom1.size()); - std::vector mom2_out(mom2.size()); - - RandomVec(numel, param.data(), 0.5f); - RandomVec(numel, grad.data(), 0.5f); - RandomVec(numel, mom1.data(), 0.5f); - RandomVec(numel, mom2.data(), 0.5f); - auto ref = jit::GetReferFunc(); - EXPECT_TRUE(ref != nullptr); - ref(beta1, - beta2, - -learning_rate, - eps, - old_lr, - lr_ratio, - coeff, - numel, - grad.data(), - mom1.data(), - mom2.data(), - param.data(), - mom1_out.data(), - mom2_out.data(), - param_out.data()); - - auto verifier = [](const typename KernelTuple::func_type tgt, - T beta1, - T beta2, - T lr, - T eps, - T old_lr, - T lr_ratio, - T coeff, - int64_t numel, - const std::vector& grad, - const std::vector& mom1, - const std::vector& mom2, - const std::vector& param, - const std::vector& ref_mom1_out, - const std::vector& ref_mom2_out, - const std::vector& ref_param_out) { - EXPECT_TRUE(tgt != nullptr); - EXPECT_EQ(param.size(), static_cast(numel)); - EXPECT_EQ(grad.size(), static_cast(numel)); - EXPECT_EQ(mom1.size(), static_cast(numel)); - EXPECT_EQ(mom2.size(), static_cast(numel)); - - std::vector jit_mom1_out(ref_mom1_out.size()); - std::vector jit_mom2_out(ref_mom2_out.size()); - std::vector jit_param_out(ref_param_out.size()); - - tgt(beta1, + for (bool amsgrad : {false, true}) { + using T = typename KernelTuple::data_type; + VLOG(10) << "Test JITKernel: " << jit::to_string(KernelTuple::kernel_type); + const T old_lr = 0.1; + const T beta1 = 0.99; + const T beta2 = 0.95; + const T beta1_pow = beta1 * beta1; + const T beta2_pow = beta2 * beta2; + + const T epsilon = 0.000001; + const int64_t numel = 123; + const T lr_ratio = 0.2; + const T coeff = 0.3; + + T learning_rate = old_lr * (sqrt(1 - beta2_pow) / (1 - beta1_pow)); + T eps = epsilon * sqrt(1 - beta2_pow); + + std::vector param(numel); + std::vector grad(numel); + std::vector mom1(numel); + std::vector mom2(numel); + std::vector mom2_max(numel); + + std::vector param_out(param.size()); + std::vector mom1_out(mom1.size()); + std::vector mom2_out(mom2.size()); + std::vector mom2_max_out(mom2_max.size()); + + RandomVec(numel, param.data(), 0.5f); + RandomVec(numel, grad.data(), 0.5f); + RandomVec(numel, mom1.data(), 0.5f); + RandomVec(numel, mom2.data(), 0.5f); + if (amsgrad) { + RandomVec(numel, mom2_max.data()); + } + + auto ref = jit::GetReferFunc(); + EXPECT_TRUE(ref != nullptr); + jit::adamw_attr_t attr(beta1, beta2, coeff, amsgrad); + + ref(beta1, beta2, - -lr, + -learning_rate, eps, old_lr, lr_ratio, @@ -881,33 +875,95 @@ void TestKernelAdamW() { grad.data(), mom1.data(), mom2.data(), + mom2_max.data(), param.data(), - jit_mom1_out.data(), - jit_mom2_out.data(), - jit_param_out.data()); + mom1_out.data(), + mom2_out.data(), + mom2_max_out.data(), + param_out.data(), + amsgrad); - ExpectEQ(ref_mom1_out.data(), jit_mom1_out.data(), numel); - ExpectEQ(ref_mom2_out.data(), jit_mom2_out.data(), numel); - ExpectEQ(ref_param_out.data(), jit_param_out.data(), numel); - }; + auto verifier = [](const typename KernelTuple::func_type tgt, + T beta1, + T beta2, + T lr, + T eps, + T old_lr, + T lr_ratio, + T coeff, + int64_t numel, + const std::vector& grad, + const std::vector& mom1, + const std::vector& mom2, + const std::vector& mom2_max, + const std::vector& param, + const std::vector& ref_mom1_out, + const std::vector& ref_mom2_out, + const std::vector& ref_mom2_max_out, + const std::vector& ref_param_out, + bool amsgrad) { + EXPECT_TRUE(tgt != nullptr); + EXPECT_EQ(param.size(), static_cast(numel)); + EXPECT_EQ(grad.size(), static_cast(numel)); + EXPECT_EQ(mom1.size(), static_cast(numel)); + EXPECT_EQ(mom2.size(), static_cast(numel)); + if (amsgrad) { + EXPECT_EQ(mom2_max.size(), static_cast(numel)); + } + + std::vector jit_mom1_out(ref_mom1_out.size()); + std::vector jit_mom2_out(ref_mom2_out.size()); + std::vector jit_mom2_max_out(ref_mom2_max_out.size()); + std::vector jit_param_out(ref_param_out.size()); + + tgt(beta1, + beta2, + -lr, + eps, + old_lr, + lr_ratio, + coeff, + numel, + grad.data(), + mom1.data(), + mom2.data(), + mom2_max.data(), + param.data(), + jit_mom1_out.data(), + jit_mom2_out.data(), + jit_mom2_max_out.data(), + jit_param_out.data(), + amsgrad); + + ExpectEQ(ref_mom1_out.data(), jit_mom1_out.data(), numel); + ExpectEQ(ref_mom2_out.data(), jit_mom2_out.data(), numel); + if (amsgrad) { + ExpectEQ(ref_mom2_max_out.data(), jit_mom2_max_out.data(), numel); + } + ExpectEQ(ref_param_out.data(), jit_param_out.data(), numel); + }; - TestAllImpls(1, - verifier, - beta1, - beta2, - learning_rate, - eps, - old_lr, - lr_ratio, - coeff, - numel, - grad, - mom1, - mom2, - param, - mom1_out, - mom2_out, - param_out); + TestAllImpls(attr, + verifier, + beta1, + beta2, + learning_rate, + eps, + old_lr, + lr_ratio, + coeff, + numel, + grad, + mom1, + mom2, + mom2_max, + param, + mom1_out, + mom2_out, + mom2_max_out, + param_out, + amsgrad); + } } template @@ -1377,16 +1433,35 @@ TEST(JITKernel_key, emb_seq_pool) { } TEST(JITKernel_key, adam) { - jit::adam_attr_t attr1(0.4f, 0.9f); - jit::adam_attr_t attr2(0.4f, 0.9f); - jit::adam_attr_t attr3(0.1f, 0.3f); + jit::adam_attr_t attr1(0.4f, 0.9f, true); + jit::adam_attr_t attr2(0.4f, 0.9f, true); + jit::adam_attr_t attr3(0.1f, 0.3f, true); + jit::adam_attr_t attr4(0.1f, 0.3f, false); auto key1 = jit::JitCodeKey(attr1); auto key2 = jit::JitCodeKey(attr2); auto key3 = jit::JitCodeKey(attr3); + auto key4 = jit::JitCodeKey(attr4); EXPECT_TRUE(key1 == key2); EXPECT_TRUE(key2 != key3); + EXPECT_TRUE(key3 != key4); +} + +TEST(JITKernel_key, adamw) { + jit::adamw_attr_t attr1(0.4f, 0.9f, 0.7f, true); + jit::adamw_attr_t attr2(0.4f, 0.9f, 0.7f, true); + jit::adamw_attr_t attr3(0.1f, 0.3f, 0.2f, true); + jit::adamw_attr_t attr4(0.1f, 0.3f, 0.7f, false); + + auto key1 = jit::JitCodeKey(attr1); + auto key2 = jit::JitCodeKey(attr2); + auto key3 = jit::JitCodeKey(attr3); + auto key4 = jit::JitCodeKey(attr4); + + EXPECT_TRUE(key1 == key2); + EXPECT_TRUE(key2 != key3); + EXPECT_TRUE(key3 != key4); } TEST(JITKernel_key, sgd) { diff --git a/paddle/phi/kernels/funcs/multi_tensor_apply.h b/paddle/phi/kernels/funcs/multi_tensor_apply.h index 6811793c02dcb2..bf64752d9bdfbb 100644 --- a/paddle/phi/kernels/funcs/multi_tensor_apply.h +++ b/paddle/phi/kernels/funcs/multi_tensor_apply.h @@ -76,7 +76,7 @@ void LaunchMultiTensorApplyKernel( errors::InvalidArgument( "input_vector.size() != InputNum - 1, the input vector's size is " "unequal to InputNum - 1, please cheack grads, params, momemts1, " - "moments2, and, master_params.")); + "moments2, moments2_max(if use amsgrad), and, master_params.")); size_t length = input_vector[0].size(); PADDLE_ENFORCE_GT( length, diff --git a/paddle/phi/kernels/fused_adam_kernel.h b/paddle/phi/kernels/fused_adam_kernel.h index b44c7250d148ff..e908962251f065 100644 --- a/paddle/phi/kernels/fused_adam_kernel.h +++ b/paddle/phi/kernels/fused_adam_kernel.h @@ -27,6 +27,7 @@ void FusedAdamKernel( const DenseTensor &learning_rate, const std::vector &moments1, const std::vector &moments2, + const paddle::optional> &moments2_max, const std::vector &beta1_pows, const std::vector &beta2_pows, const paddle::optional> &master_params, @@ -39,9 +40,11 @@ void FusedAdamKernel( bool use_adamw, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector params_out, std::vector moments1_out, std::vector moments2_out, + std::vector moments2_max_out, std::vector beta1_pows_out, std::vector beta2_pows_out, std::vector master_params_out); diff --git a/paddle/phi/kernels/gpu/adam_kernel.cu b/paddle/phi/kernels/gpu/adam_kernel.cu index 56be43fecb0d17..e6528f92f530c3 100644 --- a/paddle/phi/kernels/gpu/adam_kernel.cu +++ b/paddle/phi/kernels/gpu/adam_kernel.cu @@ -40,13 +40,16 @@ __global__ void AdamKernelREG(MT beta1, MT* moment1_out, const MT* moment2, MT* moment2_out, + const MT* moment2_max, + MT* moment2_max_out, const MT* lr_, const TG* grad, const T* param, T* param_out, const MT* master_param, MT* master_param_out, - int64_t ndim) { + int64_t ndim, + bool amsgrad) { MT lr = *lr_; MT beta1_pow = beta1_pow_; MT beta2_pow = beta2_pow_; @@ -58,10 +61,22 @@ __global__ void AdamKernelREG(MT beta1, MT g = static_cast(grad[id]); MT mom1 = static_cast(moment1[id]); MT mom2 = static_cast(moment2[id]); + mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = static_cast(moment2_max[id]); + MT mom2_max_ = std::max(mom2, mom2_max); + moment2_max_out[id] = mom2_max_; + + denom = + (sqrt(mom2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } + p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); moment1_out[id] = mom1; @@ -83,13 +98,16 @@ __global__ void AdamKernelMEM(MT beta1, MT* moment1_out, const MT* moment2, MT* moment2_out, + const MT* moment2_max, + MT* moment2_max_out, const MT* lr_, const TG* grad, const T* param, T* param_out, const MT* master_param, MT* master_param_out, - int64_t ndim) { + int64_t ndim, + bool amsgrad) { MT lr = *lr_; MT beta1_pow = *beta1_pow_; MT beta2_pow = *beta2_pow_; @@ -101,10 +119,22 @@ __global__ void AdamKernelMEM(MT beta1, MT g = static_cast(grad[id]); MT mom1 = static_cast(moment1[id]); MT mom2 = static_cast(moment2[id]); + mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = static_cast(moment2_max[id]); + MT mom2_max_ = std::max(mom2, mom2_max); + moment2_max_out[id] = mom2_max_; + + denom = + (sqrt(mom2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } + p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); moment1_out[id] = mom1; @@ -134,6 +164,7 @@ void AdamDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -145,9 +176,11 @@ void AdamDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -155,6 +188,7 @@ void AdamDenseKernel(const Context& dev_ctx, const auto grad_type = grad.dtype(); VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow; + VLOG(4) << "amsgrad: " << amsgrad; bool skip_update_ = false; if (skip_update.is_initialized()) { @@ -174,6 +208,13 @@ void AdamDenseKernel(const Context& dev_ctx, phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out); @@ -207,6 +248,11 @@ void AdamDenseKernel(const Context& dev_ctx, multi_precision ? dev_ctx.template Alloc(master_param_outs) : nullptr; + const MPDType* moment2_max_in_data = + amsgrad ? moment2_max.get().data() : nullptr; + MPDType* moment2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; + // update param and moment int threads = 512; int blocks = (param.numel() + threads - 1) / threads; @@ -225,13 +271,16 @@ void AdamDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } else { AdamKernelREG<<>>( beta1_, @@ -243,13 +292,16 @@ void AdamDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } if (!use_global_beta_pow) { // Cpu update @@ -271,13 +323,16 @@ void AdamDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } else { AdamKernelMEM<<>>( beta1_, @@ -289,13 +344,16 @@ void AdamDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } if (!use_global_beta_pow) { // Update with gpu @@ -318,6 +376,7 @@ void MergedAdamKernel( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& moment2_max, const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -326,9 +385,11 @@ void MergedAdamKernel( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out) { @@ -347,6 +408,12 @@ void MergedAdamKernel( multi_precision ? dev_ctx.template Alloc(master_param_out[idx]) : nullptr; + const MPDType* moment2_max_in_data = + amsgrad ? moment2_max.get()[idx]->data() : nullptr; + MPDType* moment2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out[idx]) + : nullptr; + // update param and moment int threads = 512; int blocks = (param[idx]->numel() + threads - 1) / threads; @@ -367,13 +434,16 @@ void MergedAdamKernel( dev_ctx.template Alloc(moment1_out[idx]), moment2[idx]->data(), dev_ctx.template Alloc(moment2_out[idx]), + moment2_max_in_data, + moment2_max_out_data, learning_rate[idx]->data(), grad[idx]->data(), param[idx]->data(), dev_ctx.template Alloc(param_out[idx]), master_in_data, master_out_data, - param[idx]->numel()); + param[idx]->numel(), + amsgrad); } else { AdamKernelREG<<>>( beta1_, @@ -385,13 +455,16 @@ void MergedAdamKernel( dev_ctx.template Alloc(moment1_out[idx]), moment2[idx]->data(), dev_ctx.template Alloc(moment2_out[idx]), + moment2_max_in_data, + moment2_max_out_data, learning_rate[idx]->data(), grad[idx]->data(), param[idx]->data(), dev_ctx.template Alloc(param_out[idx]), master_in_data, master_out_data, - param[idx]->numel()); + param[idx]->numel(), + amsgrad); } if (!use_global_beta_pow) { // Cpu update @@ -413,13 +486,16 @@ void MergedAdamKernel( dev_ctx.template Alloc(moment1_out[idx]), moment2[idx]->data(), dev_ctx.template Alloc(moment2_out[idx]), + moment2_max_in_data, + moment2_max_out_data, learning_rate[idx]->data(), grad[idx]->data(), param[idx]->data(), dev_ctx.template Alloc(param_out[idx]), master_in_data, master_out_data, - param[idx]->numel()); + param[idx]->numel(), + amsgrad); } else { AdamKernelMEM<<>>( beta1_, @@ -431,13 +507,16 @@ void MergedAdamKernel( dev_ctx.template Alloc(moment1_out[idx]), moment2[idx]->data(), dev_ctx.template Alloc(moment2_out[idx]), + moment2_max_in_data, + moment2_max_out_data, learning_rate[idx]->data(), grad[idx]->data(), param[idx]->data(), dev_ctx.template Alloc(param_out[idx]), master_in_data, master_out_data, - param[idx]->numel()); + param[idx]->numel(), + amsgrad); } if (!use_global_beta_pow) { // Update with gpu @@ -464,9 +543,9 @@ PD_REGISTER_KERNEL(adam, phi::dtype::float16, phi::dtype::bfloat16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16 || kernel_key.dtype() == phi::DataType::BFLOAT16) { @@ -475,9 +554,10 @@ PD_REGISTER_KERNEL(adam, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } PD_REGISTER_KERNEL(merged_adam, @@ -489,8 +569,8 @@ PD_REGISTER_KERNEL(merged_adam, phi::dtype::float16, phi::dtype::bfloat16) { // Skip beta1_pow, beta2_pow data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16 || kernel_key.dtype() == phi::DataType::BFLOAT16) { @@ -499,7 +579,8 @@ PD_REGISTER_KERNEL(merged_adam, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/gpu/adamw_kernel.cu b/paddle/phi/kernels/gpu/adamw_kernel.cu index 3adeb258bc624f..df2715c269fdc0 100644 --- a/paddle/phi/kernels/gpu/adamw_kernel.cu +++ b/paddle/phi/kernels/gpu/adamw_kernel.cu @@ -43,13 +43,16 @@ __global__ void AdamWKernelREG(MT beta1, MT* moment1_out, const MT* moment2, MT* moment2_out, + const MT* moment2_max, + MT* moment2_max_out, const MT* lr_, const TG* grad, const T* param, T* param_out, const MT* master_param, MT* master_param_out, - int64_t ndim) { + int64_t ndim, + bool amsgrad) { MT lr = *lr_ * lr_ratio; MT beta1_pow = beta1_pow_; MT beta2_pow = beta2_pow_; @@ -67,7 +70,17 @@ __global__ void AdamWKernelREG(MT beta1, mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = static_cast(moment2_max[id]); + MT mom2_max_ = std::max(mom2, mom2_max); + moment2_max_out[id] = mom2_max_; + + denom = + (sqrt(mom2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); @@ -92,13 +105,16 @@ __global__ void AdamWKernelMEM(MT beta1, MT* moment1_out, const MT* moment2, MT* moment2_out, + const MT* moment2_max, + MT* moment2_max_out, const MT* lr_, const TG* grad, const T* param, T* param_out, const MT* master_param, MT* master_param_out, - int64_t ndim) { + int64_t ndim, + bool amsgrad) { MT lr = *lr_ * lr_ratio; MT beta1_pow = *beta1_pow_; MT beta2_pow = *beta2_pow_; @@ -116,7 +132,17 @@ __global__ void AdamWKernelMEM(MT beta1, mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = static_cast(moment2_max[id]); + MT mom2_max_ = std::max(mom2, mom2_max); + moment2_max_out[id] = mom2_max_; + + denom = + (sqrt(mom2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); @@ -147,6 +173,7 @@ void AdamwDenseKernel(const Context& dev_ctx, const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -161,9 +188,11 @@ void AdamwDenseKernel(const Context& dev_ctx, int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -173,6 +202,7 @@ void AdamwDenseKernel(const Context& dev_ctx, VLOG(4) << "multi_precision: " << multi_precision; VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow; + VLOG(4) << "amsgrad:" << amsgrad; MPDType coeff_ = static_cast(coeff); MPDType lr_ratio_ = static_cast(lr_ratio); @@ -196,6 +226,13 @@ void AdamwDenseKernel(const Context& dev_ctx, phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out); @@ -234,6 +271,11 @@ void AdamwDenseKernel(const Context& dev_ctx, multi_precision ? dev_ctx.template Alloc(master_param_outs) : nullptr; + const MPDType* moment2_max_in_data = + amsgrad ? moment2_max.get().data() : nullptr; + MPDType* moment2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; + // update param and moment int threads = 512; int blocks = (param.numel() + threads - 1) / threads; @@ -254,13 +296,16 @@ void AdamwDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } else { AdamWKernelREG<<>>( beta1_, @@ -274,13 +319,16 @@ void AdamwDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } if (!use_global_beta_pow) { // Cpu update @@ -304,13 +352,16 @@ void AdamwDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } else { AdamWKernelMEM<<>>( beta1_, @@ -324,13 +375,16 @@ void AdamwDenseKernel(const Context& dev_ctx, dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad.data(), param.data(), dev_ctx.template Alloc(param_out), master_in_data, master_out_data, - param.numel()); + param.numel(), + amsgrad); } if (!use_global_beta_pow) { // Update with gpu @@ -356,9 +410,9 @@ PD_REGISTER_KERNEL(adamw, phi::dtype::float16, phi::dtype::bfloat16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16 || kernel_key.dtype() == phi::DataType::BFLOAT16) { @@ -367,7 +421,8 @@ PD_REGISTER_KERNEL(adamw, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/gpu/fused_adam_kernel.cu b/paddle/phi/kernels/gpu/fused_adam_kernel.cu index a7b49ddea5d25c..4fd72aee0ddd4f 100644 --- a/paddle/phi/kernels/gpu/fused_adam_kernel.cu +++ b/paddle/phi/kernels/gpu/fused_adam_kernel.cu @@ -70,6 +70,7 @@ template @@ -88,7 +89,9 @@ struct FusedAdamFunctor { MT beta2_pow = beta_pow.GetBeta2PowValue(); T* __restrict__ p_ptr; const T* __restrict__ g_ptr; - MT* __restrict__ mom1_ptr, * __restrict__ mom2_ptr; + MT* __restrict__ mom1_ptr; + MT* __restrict__ mom2_ptr; + MT* __restrict__ mom2_max_ptr; MT* __restrict__ mp_ptr; int n; @@ -102,9 +105,14 @@ struct FusedAdamFunctor { p_ptr = static_cast(t_info.tensor_addrs[0][tensor_id]) + offset; mom1_ptr = static_cast(t_info.tensor_addrs[1][tensor_id]) + offset; mom2_ptr = static_cast(t_info.tensor_addrs[2][tensor_id]) + offset; + mom2_max_ptr = + AMSGrad ? static_cast(t_info.tensor_addrs[3][tensor_id]) + offset + : nullptr; mp_ptr = IsMultiPrecision - ? static_cast(t_info.tensor_addrs[3][tensor_id]) + offset + ? static_cast( + t_info.tensor_addrs[3 + (AMSGrad ? 1 : 0)][tensor_id]) + + offset : nullptr; n -= offset; @@ -122,6 +130,7 @@ struct FusedAdamFunctor { phi::AlignedVector mp_vec; phi::AlignedVector mom1_vec; phi::AlignedVector mom2_vec; + phi::AlignedVector mom2_max_vec; if (idx <= n - VecSize) { if (IsMultiPrecision) { phi::Load(mp_ptr + idx, &mp_vec); @@ -131,6 +140,9 @@ struct FusedAdamFunctor { phi::Load(g_ptr + idx, &g_vec); phi::Load(mom1_ptr + idx, &mom1_vec); phi::Load(mom2_ptr + idx, &mom2_vec); + if (AMSGrad) { + phi::Load(mom2_max_ptr + idx, &mom2_max_vec); + } } else { int size = n - idx; for (int j = 0; j < size; j++) { @@ -142,6 +154,9 @@ struct FusedAdamFunctor { g_vec[j] = g_ptr[idx + j]; mom1_vec[j] = static_cast(mom1_ptr[idx + j]); mom2_vec[j] = static_cast(mom2_ptr[idx + j]); + if (AMSGrad) { + mom2_max_vec[j] = static_cast(mom2_max_ptr[idx + j]); + } } #pragma unroll for (int j = size; j < VecSize; j++) { @@ -150,6 +165,9 @@ struct FusedAdamFunctor { mp_vec[j] = MT(0); mom1_vec[j] = MT(0); mom2_vec[j] = MT(0); + if (AMSGrad) { + mom2_max_vec[j] = MT(0); + } } } @@ -158,12 +176,14 @@ struct FusedAdamFunctor { MT p = IsMultiPrecision ? mp_vec[j] : static_cast(p_vec[j]); UpdateMoments(&mom1_vec[j], &mom2_vec[j], + AMSGrad ? &mom2_max_vec[j] : nullptr, static_cast(g_vec[j]), beta1, beta2); mp_vec[j] = UpdateParameter(p, mom1_vec[j], mom2_vec[j], + AMSGrad ? mom2_max_vec[j] : MT(0), beta1_pow, beta2_pow, lr, @@ -174,6 +194,9 @@ struct FusedAdamFunctor { if (idx <= n - VecSize) { phi::Store(mom1_vec, mom1_ptr + idx); phi::Store(mom2_vec, mom2_ptr + idx); + if (AMSGrad) { + phi::Store(mom2_max_vec, mom2_max_ptr + idx); + } if (IsMultiPrecision) { phi::Store(mp_vec, mp_ptr + idx); } @@ -189,6 +212,9 @@ struct FusedAdamFunctor { p_ptr[idx + j] = static_cast(mp_vec[j]); mom1_ptr[idx + j] = mom1_vec[j]; mom2_ptr[idx + j] = mom2_vec[j]; + if (AMSGrad) { + mom2_max_ptr[idx + j] = mom2_max_vec[j]; + } } } } @@ -198,21 +224,29 @@ struct FusedAdamFunctor { static __device__ __forceinline__ void UpdateMoments( MT* __restrict__ mom1_ptr, MT* __restrict__ mom2_ptr, + MT* __restrict__ mom2_max_ptr, MT g, MT beta1, MT beta2) { MT mom1 = static_cast(mom1_ptr[0]); MT mom2 = static_cast(mom2_ptr[0]); + mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; mom1_ptr[0] = mom1; mom2_ptr[0] = mom2; + + if (AMSGrad) { + MT mom2_max = static_cast(mom2_max_ptr[0]); + mom2_max_ptr[0] = std::max(mom2, mom2_max); + } } static __device__ __forceinline__ MT UpdateParameter(MT p, MT mom1, MT mom2, + MT mom2_max, MT beta1_pow, MT beta2_pow, MT lr, @@ -221,7 +255,15 @@ struct FusedAdamFunctor { if (UseAdamW) { p *= (static_cast(1.0) - lr * decay); } - MT denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + + MT denom; + if (AMSGrad) { + denom = + (sqrt(mom2_max) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } + p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); return p; } @@ -268,6 +310,7 @@ void FusedAdamKernel( const DenseTensor& learning_rate, const std::vector& moments1, const std::vector& moments2, + const paddle::optional>& moments2_max, const std::vector& beta1_pows, const std::vector& beta2_pows, const paddle::optional>& master_params, @@ -280,9 +323,11 @@ void FusedAdamKernel( bool use_adamw, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, std::vector params_out, std::vector moments1_out, std::vector moments2_out, + std::vector moments2_max_out, std::vector beta1_pows_out, std::vector beta2_pows_out, std::vector master_params_out) { @@ -316,6 +361,9 @@ void FusedAdamKernel( CopyTensorIfDifferent(dev_ctx, params, params_out); CopyTensorIfDifferent(dev_ctx, moments1, moments1_out); CopyTensorIfDifferent(dev_ctx, moments2, moments2_out); + if (amsgrad) { + CopyTensorIfDifferent(dev_ctx, moments2_max.get(), moments2_max_out); + } CopyTensorIfDifferent(dev_ctx, beta1_pows, beta1_pows_out, true); CopyTensorIfDifferent(dev_ctx, beta2_pows, beta2_pows_out, true); if (master_params) { @@ -346,11 +394,14 @@ void FusedAdamKernel( MPDType beta2_tmp = beta2.to(); std::vector> input_vector; - input_vector.reserve(4); + input_vector.reserve(5); input_vector.push_back(params_out); input_vector.push_back(moments1_out); input_vector.push_back(moments2_out); + if (amsgrad) { + input_vector.push_back(moments2_max_out); + } if (multi_precision) { input_vector.push_back(master_params_out); } @@ -359,9 +410,10 @@ void FusedAdamKernel( VLOG(4) << "multi_precision: " << multi_precision; #define PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - __multi_precision, __is_cpu_betapow, __use_adamw, __vec_size) \ + __multi_precision, __is_cpu_betapow, __use_adamw, __amsgrad, __vec_size) \ do { \ - constexpr int kInputNum = __multi_precision ? 5 : 4; \ + constexpr int kInputNum = \ + (__multi_precision ? 5 : 4) + (__amsgrad ? 1 : 0); \ constexpr int kMaxTensorSize = __multi_precision ? 48 : 60; \ constexpr int kMaxBlockSize = __multi_precision ? 320 : 320; \ constexpr int kBlockSize = 512; \ @@ -373,6 +425,7 @@ void FusedAdamKernel( __multi_precision, \ __is_cpu_betapow, \ __use_adamw, \ + __amsgrad, \ kInputNum, \ kMaxTensorSize, \ kMaxBlockSize> \ @@ -399,37 +452,77 @@ void FusedAdamKernel( if (multi_precision) { \ if (is_cpu_betapow) { \ if (use_adamw) { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - true, true, true, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, true, true, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, true, true, false, __vec_size); \ + } \ } else { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - true, true, false, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, true, false, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, true, false, false, __vec_size); \ + } \ } \ } else { \ if (use_adamw) { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - true, false, true, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, false, true, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, false, true, false, __vec_size); \ + } \ } else { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - true, false, false, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, false, false, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + true, false, false, false, __vec_size); \ + } \ } \ } \ } else { \ if (is_cpu_betapow) { \ if (use_adamw) { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - false, true, true, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, true, true, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, true, true, false, __vec_size); \ + } \ } else { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - false, true, false, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, true, false, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, true, false, false, __vec_size); \ + } \ } \ } else { \ if (use_adamw) { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - false, false, true, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, false, true, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, false, true, false, __vec_size); \ + } \ } else { \ - PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ - false, false, false, __vec_size); \ + if (amsgrad) { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, false, false, true, __vec_size); \ + } else { \ + PD_LAUNCH_MULTI_TENSOR_APPLY_ADAM_KERNEL_BASE( \ + false, false, false, false, __vec_size); \ + } \ } \ } \ } \ @@ -438,6 +531,9 @@ void FusedAdamKernel( int vec_size = GetVecSizeFromTensors(params_out); vec_size = GetVecSizeFromTensors(moments1_out, vec_size); vec_size = GetVecSizeFromTensors(moments2_out, vec_size); + if (amsgrad) { + vec_size = GetVecSizeFromTensors(moments2_max_out, vec_size); + } if (master_params) { vec_size = GetVecSizeFromTensors(master_params_out, vec_size); } @@ -496,12 +592,13 @@ PD_REGISTER_KERNEL(fused_adam, float, double) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); kernel->OutputAt(1).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(2).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(3).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(4).SetDataType(phi::DataType::UNDEFINED); kernel->OutputAt(5).SetDataType(phi::DataType::UNDEFINED); + kernel->OutputAt(6).SetDataType(phi::DataType::UNDEFINED); } diff --git a/paddle/phi/kernels/selected_rows/adam_kernel.h b/paddle/phi/kernels/selected_rows/adam_kernel.h index 79f87a8ed75c0c..3d7167fd69b4e8 100644 --- a/paddle/phi/kernels/selected_rows/adam_kernel.h +++ b/paddle/phi/kernels/selected_rows/adam_kernel.h @@ -29,6 +29,7 @@ void AdamDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -40,9 +41,11 @@ void AdamDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs); diff --git a/paddle/phi/kernels/selected_rows/adamw_kernel.h b/paddle/phi/kernels/selected_rows/adamw_kernel.h index 5dda8107d52e3e..5ca1dd62369029 100644 --- a/paddle/phi/kernels/selected_rows/adamw_kernel.h +++ b/paddle/phi/kernels/selected_rows/adamw_kernel.h @@ -29,6 +29,7 @@ void AdamwDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -43,9 +44,11 @@ void AdamwDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs); diff --git a/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc b/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc index 51eb883fe89b78..60bb0583bedad0 100644 --- a/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc +++ b/paddle/phi/kernels/selected_rows/cpu/adam_kernel.cc @@ -36,6 +36,7 @@ void AdamDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param UNUSED, @@ -47,9 +48,11 @@ void AdamDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision UNUSED, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs UNUSED) { @@ -73,6 +76,13 @@ void AdamDenseParamSparseGradKernel( phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, dev_ctx.GetPlace(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, dev_ctx.GetPlace(), false, beta2_pow_out); @@ -146,6 +156,8 @@ void AdamDenseParamSparseGradKernel( dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + amsgrad ? moment2_max.get().data() : nullptr, + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr, learning_rate.data(), grad_data, param.data(), @@ -153,7 +165,8 @@ void AdamDenseParamSparseGradKernel( rows, row_numel, grad_merge.rows().size(), - lazy_mode); + lazy_mode, + amsgrad); // update beta1 and beta2 if (!use_global_beta_pow) { dev_ctx.template Alloc(beta1_pow_out)[0] = diff --git a/paddle/phi/kernels/selected_rows/cpu/adamw_kernel.cc b/paddle/phi/kernels/selected_rows/cpu/adamw_kernel.cc index b7d8b18324de22..3b62d9520424d7 100644 --- a/paddle/phi/kernels/selected_rows/cpu/adamw_kernel.cc +++ b/paddle/phi/kernels/selected_rows/cpu/adamw_kernel.cc @@ -34,6 +34,7 @@ void AdamwDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -48,9 +49,11 @@ void AdamwDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -74,6 +77,7 @@ void AdamwDenseParamSparseGradKernel( learning_rate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_param, @@ -85,9 +89,11 @@ void AdamwDenseParamSparseGradKernel( min_row_size_to_use_multithread, multi_precision, use_global_beta_pow, + amsgrad, param_out, moment1_out, moment2_out, + moment2_max_out, beta1_pow_out, beta2_pow_out, master_param_outs); @@ -111,6 +117,7 @@ void AdamwDenseParamSparseGradKernel( learning_rate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_param, @@ -122,9 +129,11 @@ void AdamwDenseParamSparseGradKernel( min_row_size_to_use_multithread, multi_precision, use_global_beta_pow, + amsgrad, param_out, moment1_out, moment2_out, + moment2_max_out, beta1_pow_out, beta2_pow_out, master_param_outs); diff --git a/paddle/phi/kernels/selected_rows/gpu/adam_kernel.cu b/paddle/phi/kernels/selected_rows/gpu/adam_kernel.cu index 084721a721ee56..338d3dacb2138e 100644 --- a/paddle/phi/kernels/selected_rows/gpu/adam_kernel.cu +++ b/paddle/phi/kernels/selected_rows/gpu/adam_kernel.cu @@ -49,6 +49,8 @@ __global__ void SparseAdamCUDAKernelREG(MT beta1, MT* mom1_out_, const MT* mom2_, MT* mom2_out_, + const MT* mom2_max_, + MT* mom2_max_out_, const MT* lr_, const T* grad_, const T* param_, @@ -59,7 +61,8 @@ __global__ void SparseAdamCUDAKernelREG(MT beta1, int64_t row_numel, int64_t row_count, bool lazy_mode, - int ndim) { + int ndim, + bool amsgrad) { int id = blockIdx.x * blockDim.x + threadIdx.x; MT lr = *lr_; @@ -71,6 +74,7 @@ __global__ void SparseAdamCUDAKernelREG(MT beta1, } else { MT mom1 = mom1_[id]; MT mom2 = mom2_[id]; + MT p = master_param ? master_param[id] : static_cast(param_[id]); MT g = row_idx >= 0 ? static_cast(grad_[row_idx * row_numel + id % row_numel]) @@ -78,8 +82,18 @@ __global__ void SparseAdamCUDAKernelREG(MT beta1, mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = - (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = mom2_max_[id]; + MT moment2_max_ = std::max(mom2, mom2_max); + mom2_max_out_[id] = moment2_max_; + + denom = (sqrt(moment2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } + p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); // Write back to global memory @@ -101,6 +115,7 @@ void AdamDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -112,9 +127,11 @@ void AdamDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -140,6 +157,13 @@ void AdamDenseParamSparseGradKernel( phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out); @@ -173,6 +197,11 @@ void AdamDenseParamSparseGradKernel( multi_precision ? dev_ctx.template Alloc(master_param_outs) : nullptr; + const MPDType* moment2_max_in_data = + amsgrad ? moment2_max.get().data() : nullptr; + MPDType* moment2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; + if (grad.rows().size() == 0) { VLOG(3) << "grad row size is 0!!"; return; @@ -222,6 +251,8 @@ void AdamDenseParamSparseGradKernel( dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad_data, param.data(), @@ -232,7 +263,8 @@ void AdamDenseParamSparseGradKernel( row_numel, grad_merge.rows().size(), lazy_mode, - ndim); + ndim, + amsgrad); if (!use_global_beta_pow) { // Update with cpu dev_ctx.template HostAlloc(beta1_pow_out)[0] = @@ -251,6 +283,8 @@ void AdamDenseParamSparseGradKernel( dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad_data, param.data(), @@ -260,7 +294,8 @@ void AdamDenseParamSparseGradKernel( rows, row_numel, grad_merge.rows().size(), - lazy_mode); + lazy_mode, + amsgrad); // FIXME(minqiyang): remove BinarySearch in GPU later funcs::ForRange for_range(dev_ctx, param.numel()); @@ -289,9 +324,9 @@ PD_REGISTER_KERNEL(adam_dense_param_sparse_grad, double, phi::dtype::float16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16) { kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32); @@ -299,7 +334,8 @@ PD_REGISTER_KERNEL(adam_dense_param_sparse_grad, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/selected_rows/gpu/adamw_kernel.cu b/paddle/phi/kernels/selected_rows/gpu/adamw_kernel.cu index ee7eab855220aa..01a81c10b3e766 100644 --- a/paddle/phi/kernels/selected_rows/gpu/adamw_kernel.cu +++ b/paddle/phi/kernels/selected_rows/gpu/adamw_kernel.cu @@ -55,6 +55,8 @@ __global__ void SparseAdamWCUDAKernelREG(MT beta1, MT* mom1_out_, const MT* mom2_, MT* mom2_out_, + const MT* mom2_max_, + MT* mom2_max_out_, const MT* lr_, const T* grad_, const T* param_, @@ -65,7 +67,8 @@ __global__ void SparseAdamWCUDAKernelREG(MT beta1, int64_t row_numel, int64_t row_count, bool lazy_mode, - int ndim) { + int ndim, + bool amsgrad) { int id = blockIdx.x * blockDim.x + threadIdx.x; MT lr = *lr_ * lr_ratio; @@ -88,8 +91,17 @@ __global__ void SparseAdamWCUDAKernelREG(MT beta1, mom1 = beta1 * mom1 + (static_cast(1.0) - beta1) * g; mom2 = beta2 * mom2 + (static_cast(1.0) - beta2) * g * g; - MT denom = - (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + MT denom; + if (amsgrad) { + MT mom2_max = static_cast(mom2_max_[id]); + MT mom2_max_ = std::max(mom2, mom2_max); + mom2_max_out_[id] = mom2_max_; + + denom = (sqrt(mom2_max_) / sqrt(static_cast(1.0) - beta2_pow)) + + epsilon; + } else { + denom = (sqrt(mom2) / sqrt(static_cast(1.0) - beta2_pow)) + epsilon; + } p += (mom1 / denom) * (-(lr / (static_cast(1.0) - beta1_pow))); @@ -112,6 +124,7 @@ void AdamwDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -126,9 +139,11 @@ void AdamwDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { @@ -158,6 +173,13 @@ void AdamwDenseParamSparseGradKernel( phi::Copy(dev_ctx, param, dev_ctx.GetPlace(), false, param_out); phi::Copy(dev_ctx, moment1, dev_ctx.GetPlace(), false, moment1_out); phi::Copy(dev_ctx, moment2, dev_ctx.GetPlace(), false, moment2_out); + if (amsgrad) { + phi::Copy(dev_ctx, + moment2_max.get(), + dev_ctx.GetPlace(), + false, + moment2_max_out); + } if (!use_global_beta_pow) { phi::Copy(dev_ctx, beta1_pow, beta1_pow.place(), false, beta1_pow_out); phi::Copy(dev_ctx, beta2_pow, beta2_pow.place(), false, beta2_pow_out); @@ -196,6 +218,11 @@ void AdamwDenseParamSparseGradKernel( multi_precision ? dev_ctx.template Alloc(master_param_outs) : nullptr; + const MPDType* moment2_max_in_data = + amsgrad ? moment2_max.get().data() : nullptr; + MPDType* moment2_max_out_data = + amsgrad ? dev_ctx.template Alloc(moment2_max_out) : nullptr; + if (grad.rows().size() == 0) { VLOG(3) << "grad row size is 0!!"; return; @@ -247,6 +274,8 @@ void AdamwDenseParamSparseGradKernel( dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad_data, param.data(), @@ -257,7 +286,8 @@ void AdamwDenseParamSparseGradKernel( row_numel, grad_merge.rows().size(), lazy_mode, - ndim); + ndim, + amsgrad); if (!use_global_beta_pow) { // Update with cpu dev_ctx.template HostAlloc(beta1_pow_out)[0] = @@ -278,6 +308,8 @@ void AdamwDenseParamSparseGradKernel( dev_ctx.template Alloc(moment1_out), moment2.data(), dev_ctx.template Alloc(moment2_out), + moment2_max_in_data, + moment2_max_out_data, learning_rate.data(), grad_data, param.data(), @@ -287,7 +319,8 @@ void AdamwDenseParamSparseGradKernel( rows, row_numel, grad_merge.rows().size(), - lazy_mode); + lazy_mode, + amsgrad); // FIXME(minqiyang): remove BinarySearch in GPU later funcs::ForRange for_range(dev_ctx, param.numel()); @@ -316,9 +349,9 @@ PD_REGISTER_KERNEL(adamw_dense_param_sparse_grad, double, phi::dtype::float16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16) { kernel->OutputAt(1).SetDataType(phi::DataType::FLOAT32); @@ -326,7 +359,8 @@ PD_REGISTER_KERNEL(adamw_dense_param_sparse_grad, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/selected_rows/xpu/adam_kernel.cc b/paddle/phi/kernels/selected_rows/xpu/adam_kernel.cc index 47cd016506c004..8e53a9802c6875 100644 --- a/paddle/phi/kernels/selected_rows/xpu/adam_kernel.cc +++ b/paddle/phi/kernels/selected_rows/xpu/adam_kernel.cc @@ -34,6 +34,7 @@ void AdamDenseParamSparseGradKernel( const DenseTensor& learning_rate, const DenseTensor& moment1, const DenseTensor& moment2, + const paddle::optional& moment2_max, // UNUSED const DenseTensor& beta1_pow, const DenseTensor& beta2_pow, const paddle::optional& master_param, @@ -45,12 +46,19 @@ void AdamDenseParamSparseGradKernel( int64_t min_row_size_to_use_multithread, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, // UNUSED DenseTensor* param_out, DenseTensor* moment1_out, DenseTensor* moment2_out, + DenseTensor* moment2_max_out, // UNUSED DenseTensor* beta1_pow_out, DenseTensor* beta2_pow_out, DenseTensor* master_param_outs) { + PADDLE_ENFORCE_NE( + amsgrad, + true, + phi::errors::Unimplemented("Operation amsgrad is not supported yet.")); + using XPUType = typename XPUTypeTrait::Type; xpu::ctx_guard RAII_GUARD(dev_ctx.x_context()); float* param_ptr = nullptr; @@ -347,9 +355,9 @@ PD_REGISTER_KERNEL(adam_dense_param_sparse_grad, float, phi::dtype::float16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/xpu/adam_kernel.cc b/paddle/phi/kernels/xpu/adam_kernel.cc index a9c7e497567c1e..828b42654248ee 100644 --- a/paddle/phi/kernels/xpu/adam_kernel.cc +++ b/paddle/phi/kernels/xpu/adam_kernel.cc @@ -26,29 +26,38 @@ namespace phi { template -void AdamDenseKernel(const Context& dev_ctx, - const DenseTensor& param, - const DenseTensor& grad, - const DenseTensor& learning_rate, - const DenseTensor& moment1, - const DenseTensor& moment2, - const DenseTensor& beta1_pow, - const DenseTensor& beta2_pow, - const paddle::optional& master_param, - const paddle::optional& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow, - DenseTensor* param_out, - DenseTensor* moment1_out, - DenseTensor* moment2_out, - DenseTensor* beta1_pow_out, - DenseTensor* beta2_pow_out, - DenseTensor* master_param_outs) { +void AdamDenseKernel( + const Context& dev_ctx, + const DenseTensor& param, + const DenseTensor& grad, + const DenseTensor& learning_rate, + const DenseTensor& moment1, + const DenseTensor& moment2, + const paddle::optional& moment2_max, // UNUSED + const DenseTensor& beta1_pow, + const DenseTensor& beta2_pow, + const paddle::optional& master_param, + const paddle::optional& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad, // UNUSED + DenseTensor* param_out, + DenseTensor* moment1_out, + DenseTensor* moment2_out, + DenseTensor* moment2_max_out, // UNUSED + DenseTensor* beta1_pow_out, + DenseTensor* beta2_pow_out, + DenseTensor* master_param_outs) { + PADDLE_ENFORCE_NE( + amsgrad, + true, + phi::errors::Unimplemented("Operation amsgrad is not supported yet.")); + xpu::ctx_guard RAII_GUARD(dev_ctx.x_context()); float* param_ptr = nullptr; funcs::GetDataPointer( @@ -261,6 +270,8 @@ void MergedAdamKernel( const std::vector& learning_rate, const std::vector& moment1, const std::vector& moment2, + const paddle::optional>& + moment2_max, // UNUSED const std::vector& beta1_pow, const std::vector& beta2_pow, const paddle::optional>& master_param, @@ -269,12 +280,19 @@ void MergedAdamKernel( const Scalar& epsilon, bool multi_precision, bool use_global_beta_pow, + bool amsgrad, // UNUSED std::vector param_out, std::vector moment1_out, std::vector moment2_out, + std::vector moment2_max_out, // UNUSED std::vector beta1_pow_out, std::vector beta2_pow_out, std::vector master_param_out) { + PADDLE_ENFORCE_NE( + amsgrad, + true, + phi::errors::Unimplemented("Operation amsgrad is not supported yet.")); + VLOG(4) << "use_global_beta_pow:" << use_global_beta_pow; auto beta1_ = beta1.to(); @@ -480,18 +498,18 @@ void MergedAdamKernel( PD_REGISTER_KERNEL( adam, XPU, ALL_LAYOUT, phi::AdamDenseKernel, float, phi::dtype::float16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } PD_REGISTER_KERNEL(merged_adam, XPU, ALL_LAYOUT, phi::MergedAdamKernel, float) { // Skip beta1_pow, beta2_pow data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/kernels/xpu/adamw_kernel.cc b/paddle/phi/kernels/xpu/adamw_kernel.cc index 72c1c5d578eaf4..1d44be7eaef3dc 100644 --- a/paddle/phi/kernels/xpu/adamw_kernel.cc +++ b/paddle/phi/kernels/xpu/adamw_kernel.cc @@ -435,11 +435,11 @@ void AdamwDenseKernelKL3(const Context& dev_ctx, moment1_out->set_storage_properties(std::move(moment1_out_sp)); // for moment2 - float moment2_max = GetAbsMax(dev_ctx, - moment2_output_for_xdnn, - buffer_for_findmax, - moment2_out->numel()); - float moment2_scale_value = 65504.0f / moment2_max / 2.0f; + float moment2_max_ = GetAbsMax(dev_ctx, + moment2_output_for_xdnn, + buffer_for_findmax, + moment2_out->numel()); + float moment2_scale_value = 65504.0f / moment2_max_ / 2.0f; // int scale(Context* ctx, const T* x, T* y, int64_t len, bool // bias_after_scale, float _scale, float _bias); r = xpu::scale(dev_ctx.x_context(), @@ -477,32 +477,41 @@ void AdamwDenseKernelKL3(const Context& dev_ctx, } template -void AdamwDenseKernel(const Context& dev_ctx, - const DenseTensor& param, - const DenseTensor& grad, - const DenseTensor& learning_rate, - const DenseTensor& moment1, - const DenseTensor& moment2, - const DenseTensor& beta1_pow, - const DenseTensor& beta2_pow, - const paddle::optional& master_param, - const paddle::optional& skip_update, - const Scalar& beta1, - const Scalar& beta2, - const Scalar& epsilon, - float lr_ratio, - float coeff, - bool with_decay, - bool lazy_mode, - int64_t min_row_size_to_use_multithread, - bool multi_precision, - bool use_global_beta_pow, - DenseTensor* param_out, - DenseTensor* moment1_out, - DenseTensor* moment2_out, - DenseTensor* beta1_pow_out, - DenseTensor* beta2_pow_out, - DenseTensor* master_param_outs) { +void AdamwDenseKernel( + const Context& dev_ctx, + const DenseTensor& param, + const DenseTensor& grad, + const DenseTensor& learning_rate, + const DenseTensor& moment1, + const DenseTensor& moment2, + const paddle::optional& moment2_max, // UNUSED + const DenseTensor& beta1_pow, + const DenseTensor& beta2_pow, + const paddle::optional& master_param, + const paddle::optional& skip_update, + const Scalar& beta1, + const Scalar& beta2, + const Scalar& epsilon, + float lr_ratio, + float coeff, + bool with_decay, + bool lazy_mode, + int64_t min_row_size_to_use_multithread, + bool multi_precision, + bool use_global_beta_pow, + bool amsgrad, // UNUSED + DenseTensor* param_out, + DenseTensor* moment1_out, + DenseTensor* moment2_out, + DenseTensor* moment2_max_out, // UNUSED + DenseTensor* beta1_pow_out, + DenseTensor* beta2_pow_out, + DenseTensor* master_param_outs) { + PADDLE_ENFORCE_NE( + amsgrad, + true, + phi::errors::Unimplemented("Operation amsgrad is not supported yet.")); + auto dev_version = phi::backends::xpu::get_xpu_version(dev_ctx.GetPlace().GetDeviceId()); if (dev_version == phi::backends::xpu::XPUVersion::XPU3) { @@ -803,11 +812,11 @@ void AdamwDenseKernel(const Context& dev_ctx, moment1_out->set_storage_properties(std::move(moment1_out_sp)); // for moment2 - float moment2_max = GetAbsMax(dev_ctx, - moment2_output_for_xdnn, - buffer_for_findmax, - moment2_out->numel()); - float moment2_scale_value = 65504.0f / moment2_max / 2.0f; + float moment2_max_ = GetAbsMax(dev_ctx, + moment2_output_for_xdnn, + buffer_for_findmax, + moment2_out->numel()); + float moment2_scale_value = 65504.0f / moment2_max_ / 2.0f; // int scale(Context* ctx, const T* x, T* y, int64_t len, bool // bias_after_scale, float _scale, float _bias); r = xpu::scale(dev_ctx.x_context(), @@ -885,9 +894,9 @@ PD_REGISTER_KERNEL(adamw, phi::dtype::float16, phi::dtype::bfloat16) { // Skip beta1_pow, beta2_pow, skip_update data transform - kernel->InputAt(5).SetBackend(phi::Backend::ALL_BACKEND); kernel->InputAt(6).SetBackend(phi::Backend::ALL_BACKEND); - kernel->InputAt(8).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(7).SetBackend(phi::Backend::ALL_BACKEND); + kernel->InputAt(9).SetBackend(phi::Backend::ALL_BACKEND); if (kernel_key.dtype() == phi::DataType::FLOAT16 || kernel_key.dtype() == phi::DataType::BFLOAT16) { @@ -896,7 +905,8 @@ PD_REGISTER_KERNEL(adamw, kernel->OutputAt(3).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(4).SetDataType(phi::DataType::FLOAT32); kernel->OutputAt(5).SetDataType(phi::DataType::FLOAT32); + kernel->OutputAt(6).SetDataType(phi::DataType::FLOAT32); } - kernel->OutputAt(3).SetBackend(phi::Backend::UNDEFINED); kernel->OutputAt(4).SetBackend(phi::Backend::UNDEFINED); + kernel->OutputAt(5).SetBackend(phi::Backend::UNDEFINED); } diff --git a/paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml b/paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml index 3edf2cf8a75502..0f465e2aadf386 100755 --- a/paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml +++ b/paddle/phi/ops/yaml/inconsistent/dygraph_ops.yaml @@ -157,15 +157,15 @@ traits : paddle::dialect::ForwardOnlyTrait - op : fused_adam_ - args : (Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] beta1_pows, Tensor[] beta2_pows, Tensor[] master_params, Tensor skip_update, Scalar beta1, Scalar beta2, Scalar epsilon, int chunk_size, float weight_decay, bool use_adamw, bool multi_precision, bool use_global_beta_pow) - output : Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()} + args : (Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] moments2_max, Tensor[] beta1_pows, Tensor[] beta2_pows, Tensor[] master_params, Tensor skip_update, Scalar beta1, Scalar beta2, Scalar epsilon, int chunk_size, float weight_decay, bool use_adamw, bool multi_precision, bool use_global_beta_pow, bool amsgrad = false) + output : Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](moments2_max_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()} infer_meta : func : FusedAdamInferMeta kernel : func : fused_adam data_type : params - optional : skip_update, master_params - inplace : (params -> params_out), (moments1 -> moments1_out), (moments2 -> moments2_out), (beta1_pows -> beta1_pows_out), (beta2_pows -> beta2_pows_out), (master_params -> master_params_out) + optional : moments2_max, skip_update, master_params, moments2_max_out + inplace : (params -> params_out), (moments1 -> moments1_out), (moments2 -> moments2_out), (moments2_max -> moments2_max_out), (beta1_pows -> beta1_pows_out), (beta2_pows -> beta2_pows_out), (master_params -> master_params_out) traits : paddle::dialect::ForwardOnlyTrait - op : fused_gemm_epilogue diff --git a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml index bb6000f1d39353..a714a81f72379c 100644 --- a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml +++ b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml @@ -318,15 +318,15 @@ interfaces : paddle::dialect::InferSymbolicShapeInterface - op : fused_adam_ - args : (Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] beta1_pows, Tensor[] beta2_pows, Tensor[] master_params, Tensor skip_update, Scalar beta1, Scalar beta2, Scalar epsilon, int chunk_size, float weight_decay, bool use_adamw, bool multi_precision, bool use_global_beta_pow) - output : Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()} + args : (Tensor[] params, Tensor[] grads, Tensor learning_rate, Tensor[] moments1, Tensor[] moments2, Tensor[] moments2_max, Tensor[] beta1_pows, Tensor[] beta2_pows, Tensor[] master_params, Tensor skip_update, Scalar beta1, Scalar beta2, Scalar epsilon, int chunk_size, float weight_decay, bool use_adamw, bool multi_precision, bool use_global_beta_pow, bool amsgrad = false) + output : Tensor[](params_out){params.size()}, Tensor[](moments1_out){params.size()}, Tensor[](moments2_out){params.size()}, Tensor[](moments2_max_out){params.size()}, Tensor[](beta1_pows_out){params.size()}, Tensor[](beta2_pows_out){params.size()}, Tensor[](master_params_out){params.size()} infer_meta : func : FusedAdamInferMeta kernel : func : fused_adam data_type : params - optional : skip_update, master_params, master_params_out - inplace : (params -> params_out), (moments1 -> moments1_out), (moments2 -> moments2_out), (beta1_pows -> beta1_pows_out), (beta2_pows -> beta2_pows_out), (master_params -> master_params_out) + optional : moments2_max, skip_update, master_params, moments2_max_out, master_params_out + inplace : (params -> params_out), (moments1 -> moments1_out), (moments2 -> moments2_out), (moments2_max -> moments2_max_out), (beta1_pows -> beta1_pows_out), (beta2_pows -> beta2_pows_out), (master_params -> master_params_out) - op : fused_gate_attention args: (Tensor query, Tensor key, Tensor query_weight, Tensor key_weight, Tensor diff --git a/paddle/phi/ops/yaml/op_compat.yaml b/paddle/phi/ops/yaml/op_compat.yaml index e187c1eeee4fae..89a91aa264893a 100755 --- a/paddle/phi/ops/yaml/op_compat.yaml +++ b/paddle/phi/ops/yaml/op_compat.yaml @@ -58,9 +58,9 @@ - op : adam_ (adam) inputs : - {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam, skip_update: SkipUpdate} + {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, moment2_max: Moment2Max, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam, skip_update: SkipUpdate} outputs : - {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} + {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, moment2_max_out: Moment2MaxOut, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} scalar : beta1 : data_type : float @@ -81,9 +81,9 @@ - op : adamw_ (adamw) inputs : - {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam, skip_update: SkipUpdate} + {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, moment2_max: Moment2Max, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam, skip_update: SkipUpdate} outputs : - {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} + {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, moment2_max_out: Moment2MaxOut, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} scalar : beta1 : data_type : float @@ -1442,10 +1442,10 @@ - op : fused_adam_(fused_adam) inputs : {params : Params, grads : Grads, learning_rate : LearningRate, moments1 : Moments1, - moments2 : Moments2, beta1_pows : Beta1Pows, beta2_pows : Beta2Pows, master_params : MasterParams, + moments2 : Moments2, moments2_max : Moments2Max, beta1_pows : Beta1Pows, beta2_pows : Beta2Pows, master_params : MasterParams, skip_update : SkipUpdate} outputs : - {params_out : ParamsOut, moments1_out : Moments1Out, moments2_out : Moments2Out, + {params_out : ParamsOut, moments1_out : Moments1Out, moments2_out : Moments2Out, moments2_max_out : Moments2MaxOut, beta1_pows_out : Beta1PowsOut, beta2_pows_out : Beta2PowsOut, master_params_out : MasterParamsOut} - op : fused_attention @@ -2552,9 +2552,9 @@ - op : merged_adam_ inputs : - {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam} + {param: Param, grad: Grad, learning_rate: LearningRate, moment1: Moment1, moment2: Moment2, moment2_max: Moment2Max, beta1_pow: Beta1Pow, beta2_pow: Beta2Pow, master_param: MasterParam} outputs : - {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} + {param_out: ParamOut, moment1_out: Moment1Out, moment2_out: Moment2Out, moment2_max_out: Moment2MaxOut, beta1_pow_out: Beta1PowOut, beta2_pow_out: Beta2PowOut, master_param_out: MasterParamOut} scalar : beta1 : data_type : float diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index 3ebc02318276de..cc7a0152854d4c 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -90,17 +90,17 @@ traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : adam_ - args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false) - output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out) + args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor moment2_max, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false, bool amsgrad = false) + output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(moment2_max_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out) infer_meta : func : AdamInferMeta spmd_rule : AdamInferSpmdDynamic kernel : - func : adam {dense, dense, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense}, - adam_dense_param_sparse_grad {dense, selected_rows, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense} + func : adam {dense, dense, dense, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense, dense}, + adam_dense_param_sparse_grad {dense, selected_rows, dense, dense, dense, dense, dense, dense, dense, dense -> dense, dense, dense, dense, dense, dense, dense} data_type : param - optional : master_param, skip_update, master_param_out - inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) + optional : moment2_max, master_param, skip_update, moment2_max_out, master_param_out + inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (moment2_max -> moment2_max_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : adamax_ @@ -116,16 +116,16 @@ traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : adamw_ - args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, float lr_ratio = 1.0f, float coeff = 0.01f, bool with_decay = false, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false) - output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out) + args : (Tensor param, Tensor grad, Tensor learning_rate, Tensor moment1, Tensor moment2, Tensor moment2_max, Tensor beta1_pow, Tensor beta2_pow, Tensor master_param, Tensor skip_update, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, float lr_ratio = 1.0f, float coeff = 0.01f, bool with_decay = false, bool lazy_mode = false, int64_t min_row_size_to_use_multithread = 1000, bool multi_precision = false, bool use_global_beta_pow = false, bool amsgrad = false) + output : Tensor(param_out), Tensor(moment1_out), Tensor(moment2_out), Tensor(moment2_max_out), Tensor(beta1_pow_out), Tensor(beta2_pow_out), Tensor(master_param_out) infer_meta : func : AdamwInferMeta spmd_rule : AdamwInferSpmdDynamic kernel : func : adamw data_type : param - optional : master_param, skip_update, master_param_out - inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) + optional : moment2_max, master_param, skip_update, moment2_max_out, master_param_out + inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (moment2_max -> moment2_max_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : add_position_encoding @@ -3386,15 +3386,15 @@ traits : paddle::dialect::ForwardOnlyTrait - op : merged_adam_ - args : (Tensor[] param, Tensor[] grad, Tensor[] learning_rate, Tensor[] moment1, Tensor[] moment2, Tensor[] beta1_pow, Tensor[] beta2_pow, Tensor[] master_param, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool multi_precision = false, bool use_global_beta_pow = false) - output : Tensor[](param_out){param.size()}, Tensor[](moment1_out){param.size()}, Tensor[](moment2_out){param.size()}, Tensor[](beta1_pow_out){param.size()}, Tensor[](beta2_pow_out){param.size()}, Tensor[](master_param_out){param.size()} + args : (Tensor[] param, Tensor[] grad, Tensor[] learning_rate, Tensor[] moment1, Tensor[] moment2, Tensor[] moment2_max, Tensor[] beta1_pow, Tensor[] beta2_pow, Tensor[] master_param, Scalar beta1 = 0.9f, Scalar beta2 = 0.999f, Scalar epsilon = 1.0e-8f, bool multi_precision = false, bool use_global_beta_pow = false, bool amsgrad = false) + output : Tensor[](param_out){param.size()}, Tensor[](moment1_out){param.size()}, Tensor[](moment2_out){param.size()}, Tensor[](moment2_max_out){param.size()}, Tensor[](beta1_pow_out){param.size()}, Tensor[](beta2_pow_out){param.size()}, Tensor[](master_param_out){param.size()} infer_meta : func : MergedAdamInferMeta kernel : func : merged_adam data_type : param - optional: master_param, master_param_out - inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) + optional: moment2_max, master_param, moment2_max_out, master_param_out + inplace : (param -> param_out), (moment1 -> moment1_out), (moment2 -> moment2_out), (moment2_max -> moment2_max_out), (beta1_pow -> beta1_pow_out), (beta2_pow -> beta2_pow_out), (master_param -> master_param_out) traits : pir::SideEffectTrait, paddle::dialect::ForwardOnlyTrait - op : merged_momentum_ diff --git a/python/paddle/optimizer/adam.py b/python/paddle/optimizer/adam.py index eaf541e07cbfb1..32aefc70388dee 100644 --- a/python/paddle/optimizer/adam.py +++ b/python/paddle/optimizer/adam.py @@ -66,16 +66,17 @@ class Adam(Optimizer): .. math:: - t & = t + 1 - - moment\_1\_out & = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad - - moment\_2\_out & = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad - - learning\_rate & = learning\_rate * \ - \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} - - param\_out & = param - learning\_rate * \frac{moment\_1}{\sqrt{moment\_2} + \epsilon} + \begin{aligned} + &\hspace{5mm} t = t + 1 \\ + &\hspace{5mm} moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad \\ + &\hspace{5mm} moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad \\ + &\hspace{5mm} learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} \\ + &\hspace{5mm}\textbf{if} \: \textit{amsgrad}: \\ + &\hspace{15mm} moment\_2\_max\_out = max(moment\_2\_out, moment\_2\_max) \\ + &\hspace{15mm} param\_out = param - learning\_rate * \frac{moment\_1\_out}{\sqrt{moment\_2\_max\_out} + \epsilon} \\ + &\hspace{5mm}\textbf{else}: \: \\ + &\hspace{15mm} param\_out = param - learning\_rate * \frac{moment\_1\_out}{\sqrt{moment\_2\_out} + \epsilon} \\ + \end{aligned} Related paper: `Adam: A Method for Stochastic Optimization `_ @@ -117,6 +118,8 @@ class Adam(Optimizer): The default value is False. multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false. use_multi_tensor (bool, optional): Whether to use multi-tensor strategy to update all parameters at once . Default is false. + amsgrad (bool, optional): Whether to use the AMSGrad variant of this algorithm from the paper + `On the Convergence of Adam and Beyond `_. Default is false. name (str|None, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. The default value is None. @@ -191,6 +194,7 @@ class Adam(Optimizer): type: str _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" + _moment2_acc_max_str = "moment2_max" _beta1_pow_acc_str = "beta1_pow_acc" _beta2_pow_acc_str = "beta2_pow_acc" @@ -208,6 +212,7 @@ def __init__( lazy_mode: bool = False, multi_precision: bool = False, use_multi_tensor: bool = False, + amsgrad: bool = False, name: str | None = None, ) -> None: assert learning_rate is not None @@ -255,11 +260,17 @@ def __init__( self._param_dict = self._create_multi_tensor_dict() self._moment1_dict = self._create_multi_tensor_dict() self._moment2_dict = self._create_multi_tensor_dict() + self._moment2_max_dict = ( + self._create_multi_tensor_dict() if amsgrad else None + ) self._beta1_pow_acc_dict = self._create_multi_tensor_dict() self._beta2_pow_acc_dict = self._create_multi_tensor_dict() self._master_weight_dict = self._create_multi_tensor_dict() self._master_weight_dict['FP32_LODTensor'] = None + # whether to use AMSGrad + self._amsgrad = amsgrad + def _add_moments_pows(self, p): acc_dtype = p.dtype if self._is_dtype_fp16_or_bf16(acc_dtype): @@ -269,6 +280,8 @@ def _add_moments_pows(self, p): acc_dtype = core.VarDesc.VarType.FP32 self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) + if self._amsgrad: + self._add_accumulator(self._moment2_acc_max_str, p, dtype=acc_dtype) self._add_accumulator( name=self._beta1_pow_acc_str, param=p, @@ -332,6 +345,13 @@ def _append_optimize_op(self, block, param_and_grad): moment2 = self._get_accumulator_master( self._moment2_acc_str, param_and_grad[0] ) + moment2_max = ( + self._get_accumulator_master( + self._moment2_acc_max_str, param_and_grad[0] + ) + if self._amsgrad + else None + ) beta1_pow_acc = self._get_accumulator_master( self._beta1_pow_acc_str, param_and_grad[0] ) @@ -364,12 +384,13 @@ def _append_optimize_op(self, block, param_and_grad): self._get_auxiliary_var('found_inf') if in_pir_mode() else None ) - _, _, _, _, _, _ = _C_ops.adam_( + _, _, _, _, _, _, _ = _C_ops.adam_( param_and_grad[0], param_and_grad[1], lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -381,6 +402,7 @@ def _append_optimize_op(self, block, param_and_grad): 1000, find_master, False, + self._amsgrad, ) return None @@ -412,6 +434,7 @@ def _append_optimize_op(self, block, param_and_grad): "lazy_mode": self._lazy_mode, "min_row_size_to_use_multithread": 1000, "multi_precision": find_master, + "amsgrad": self._amsgrad, } if isinstance(self._beta1, Variable): @@ -427,6 +450,10 @@ def _append_optimize_op(self, block, param_and_grad): else: attrs['epsilon'] = self._epsilon + if self._amsgrad: + inputs['Moment2Max'] = [moment2_max] + outputs["Moment2MaxOut"] = [moment2_max] + if find_master: inputs["MasterParam"] = master_weight outputs["MasterParamOut"] = master_weight @@ -534,6 +561,11 @@ def _multi_tensor_init(self, target_block, parameters, param_group_idx): for param in parameters: moment1 = self._get_accumulator_master(self._moment1_acc_str, param) moment2 = self._get_accumulator_master(self._moment2_acc_str, param) + moment2_max = ( + self._get_accumulator_master(self._moment2_acc_max_str, param) + if self._amsgrad + else None + ) beta1_pow_acc = self._get_accumulator_master( self._beta1_pow_acc_str, param ) @@ -551,6 +583,10 @@ def _multi_tensor_init(self, target_block, parameters, param_group_idx): self._moment2_dict['FP32_LODTensor'][param_group_idx].append( moment2 ) + if self._amsgrad: + self._moment2_max_dict['FP32_LODTensor'][ + param_group_idx + ].append(moment2_max) self._beta1_pow_acc_dict['FP32_LODTensor'][ param_group_idx ].append(beta1_pow_acc) @@ -567,6 +603,10 @@ def _multi_tensor_init(self, target_block, parameters, param_group_idx): self._moment2_dict['FP16_LODTensor'][param_group_idx].append( moment2 ) + if self._amsgrad: + self._moment2_max_dict['FP16_LODTensor'][ + param_group_idx + ].append(moment2_max) self._beta1_pow_acc_dict['FP16_LODTensor'][ param_group_idx ].append(beta1_pow_acc) @@ -756,12 +796,17 @@ def _append_optimize_multi_tensor_op( found_inf, (core.eager.Tensor, pir.Value) ): self._set_auxiliary_var('found_inf', False) - _, _, _, _, _, _ = _C_ops.merged_adam_( + _, _, _, _, _, _, _ = _C_ops.merged_adam_( self._param_dict[key][param_group_idx], grad_dict[key], lr_dict[key], self._moment1_dict[key][param_group_idx], self._moment2_dict[key][param_group_idx], + ( + self._moment2_max_dict[key][param_group_idx] + if self._amsgrad + else None + ), self._beta1_pow_acc_dict[key][param_group_idx], self._beta2_pow_acc_dict[key][param_group_idx], master_weight, @@ -770,6 +815,7 @@ def _append_optimize_multi_tensor_op( self._epsilon, find_master, False, + self._amsgrad, ) elif in_pir_mode(): master_weight = self._master_weight_dict[key] @@ -778,12 +824,17 @@ def _append_optimize_multi_tensor_op( if master_weight is not None else None ) - _, _, _, _, _, _ = _C_ops.merged_adam_( + _, _, _, _, _, _, _ = _C_ops.merged_adam_( self._param_dict[key][param_group_idx], grad_dict[key], lr_dict[key], self._moment1_dict[key][param_group_idx], self._moment2_dict[key][param_group_idx], + ( + self._moment2_max_dict[key][param_group_idx] + if self._amsgrad + else None + ), self._beta1_pow_acc_dict[key][param_group_idx], self._beta2_pow_acc_dict[key][param_group_idx], master_weight, @@ -792,6 +843,7 @@ def _append_optimize_multi_tensor_op( self._epsilon, find_master, False, + self._amsgrad, ) else: inputs = { @@ -822,7 +874,17 @@ def _append_optimize_multi_tensor_op( "epsilon": self._epsilon, "beta1": _beta1, "beta2": _beta2, + "amsgrad": self._amsgrad, } + + if self._amsgrad: + inputs["Moment2Max"] = self._moment2_max_dict[key][ + param_group_idx + ] + outputs["Moment2MaxOut"] = self._moment2_max_dict[key][ + param_group_idx + ] + if find_master: inputs["MasterParam"] = self._master_weight_dict[key][ param_group_idx @@ -831,6 +893,7 @@ def _append_optimize_multi_tensor_op( key ][param_group_idx] attrs["multi_precision"] = find_master + target_block.append_op( type="merged_adam", inputs=inputs, diff --git a/python/paddle/optimizer/adamw.py b/python/paddle/optimizer/adamw.py index 595ab8b803ee96..17debe377a0d4f 100644 --- a/python/paddle/optimizer/adamw.py +++ b/python/paddle/optimizer/adamw.py @@ -54,17 +54,17 @@ class AdamW(Optimizer): .. math:: - t & = t + 1 - - moment\_1\_out & = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad - - moment\_2\_out & = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad - - learning\_rate & = learning\_rate * - \frac{\sqrt{1 - {\beta}_2^t}}{1 - {beta}_1^t} - - param\_out & = param - learning\_rate * (\frac{moment\_1}{\sqrt{moment\_2} + \epsilon} + \lambda * param) - + \begin{aligned} + &\hspace{5mm} t = t + 1 \\ + &\hspace{5mm} moment\_1\_out = {\beta}_1 * moment\_1 + (1 - {\beta}_1) * grad \\ + &\hspace{5mm} moment\_2\_out = {\beta}_2 * moment\_2 + (1 - {\beta}_2) * grad * grad \\ + &\hspace{5mm} learning\_rate = learning\_rate * \frac{\sqrt{1 - {\beta}_2^t}}{1 - {\beta}_1^t} \\ + &\hspace{5mm}\textbf{if} \: \textit{amsgrad}: \\ + &\hspace{15mm} moment\_2\_max\_out = max(moment\_2\_out, moment\_2\_max) \\ + &\hspace{15mm} param\_out = param - learning\_rate * (\frac{moment\_1\_out}{\sqrt{moment\_2\_max\_out} + \epsilon} + \lambda * param) \\ + &\hspace{5mm}\textbf{else}: \: \\ + &\hspace{15mm} param\_out = param - learning\_rate * (\frac{moment\_1\_out}{\sqrt{moment\_2\_out} + \epsilon} + \lambda * param) \\ + \end{aligned} Args: learning_rate (float|LRScheduler, optional): The learning rate used to update ``Parameter``. @@ -104,6 +104,8 @@ class AdamW(Optimizer): different semantics with the original Adam algorithm and may lead to different result. The default value is False. multi_precision (bool, optional): Whether to use multi-precision during weight updating. Default is false. + amsgrad (bool, optional): Whether to use the AMSGrad variant of this algorithm from the paper + `On the Convergence of Adam and Beyond `_. Default is false. name (str|None, optional): Normally there is no need for user to set this property. For more information, please refer to :ref:`api_guide_Name`. The default value is None. @@ -165,6 +167,7 @@ class AdamW(Optimizer): type: str _moment1_acc_str = "moment1" _moment2_acc_str = "moment2" + _moment2_acc_max_str = "moment2_max" _beta1_pow_acc_str = "beta1_pow_acc" _beta2_pow_acc_str = "beta2_pow_acc" @@ -183,6 +186,7 @@ def __init__( grad_clip: GradientClipBase | None = None, lazy_mode: bool = False, multi_precision: bool = False, + amsgrad: bool = False, name: str | None = None, ) -> None: assert learning_rate is not None @@ -284,6 +288,8 @@ def __init__( self._lazy_mode = lazy_mode self._multi_precision = multi_precision self._master_weights = {} + # whether to use AMSGrad + self._amsgrad = amsgrad self._default_dict = { 'weight_decay': float(weight_decay), @@ -375,12 +381,26 @@ def _add_moments_pows(self, p): self._add_accumulator( self._moment2_acc_str, p, dtype=core.VarDesc.VarType.FP16 ) + if self._amsgrad: + self._add_accumulator( + self._moment2_acc_max_str, + p, + dtype=core.VarDesc.VarType.FP16, + ) else: self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) + if self._amsgrad: + self._add_accumulator( + self._moment2_acc_max_str, p, dtype=acc_dtype + ) else: self._add_accumulator(self._moment1_acc_str, p, dtype=acc_dtype) self._add_accumulator(self._moment2_acc_str, p, dtype=acc_dtype) + if self._amsgrad: + self._add_accumulator( + self._moment2_acc_max_str, p, dtype=acc_dtype + ) self._add_accumulator( name=self._beta1_pow_acc_str, param=p, @@ -453,6 +473,13 @@ def _append_optimize_op(self, block, param_and_grad): moment2 = self._get_accumulator_master( self._moment2_acc_str, param_and_grad[0] ) + moment2_max = ( + self._get_accumulator_master( + self._moment2_acc_max_str, param_and_grad[0] + ) + if self._amsgrad + else None + ) beta1_pow_acc = self._get_accumulator_master( self._beta1_pow_acc_str, param_and_grad[0] ) @@ -492,12 +519,13 @@ def _append_optimize_op(self, block, param_and_grad): self._get_auxiliary_var('found_inf') if in_pir_mode() else None ) - _, _, _, _, _, _ = _C_ops.adamw_( + _, _, _, _, _, _, _ = _C_ops.adamw_( param_and_grad[0], param_and_grad[1], lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -512,6 +540,7 @@ def _append_optimize_op(self, block, param_and_grad): 1000, find_master, False, + self._amsgrad, ) return None else: @@ -549,6 +578,7 @@ def _append_optimize_op(self, block, param_and_grad): if self._lr_ratio is None else self._lr_ratio(param_and_grad[0]) ), + "amsgrad": self._amsgrad, } if isinstance(self._beta1, Variable): @@ -564,6 +594,10 @@ def _append_optimize_op(self, block, param_and_grad): else: attrs['epsilon'] = self._epsilon + if self._amsgrad: + inputs["Moment2Max"] = [moment2_max] + outputs["Moment2MaxOut"] = [moment2_max] + if find_master: inputs["MasterParam"] = master_weight outputs["MasterParamOut"] = master_weight diff --git a/test/auto_parallel/test_api_dist_branch.py b/test/auto_parallel/test_api_dist_branch.py index f01bf2171fc637..997699d956518a 100644 --- a/test/auto_parallel/test_api_dist_branch.py +++ b/test/auto_parallel/test_api_dist_branch.py @@ -307,6 +307,7 @@ def test_merged_adam_for_dist_tensor(self): lrs = [np.random.random(s).astype(mp_dtype) for s in lr_shape] moment1s = [np.random.random(s).astype(mp_dtype) for s in shapes] moment2s = [np.random.random(s).astype(mp_dtype) for s in shapes] + moment2s_max = [np.zeros(s).astype(mp_dtype) for s in shapes] beta1_pows = [np.random.random(s).astype(mp_dtype) for s in lr_shape] beta2_pows = [np.random.random(s).astype(mp_dtype) for s in lr_shape] master_params = [p.astype(mp_dtype) for p in params] @@ -326,6 +327,10 @@ def test_merged_adam_for_dist_tensor(self): local_moment2s, dist_moment2s, ) = self.create_local_and_dist_tensor_list_pair(moment2s) + ( + local_moment2s_max, + dist_moment2s_max, + ) = self.create_local_and_dist_tensor_list_pair(moment2s_max) ( local_beta1_pows, dist_beta1_pows, @@ -343,6 +348,7 @@ def test_merged_adam_for_dist_tensor(self): local_param_out, local_moment1s_out, local_moment2s_out, + local_moment2s_max_out, local_beta1_pow_out, local_beta2_pow_out, local_master_param_out, @@ -352,6 +358,7 @@ def test_merged_adam_for_dist_tensor(self): local_lrs, local_moment1s, local_moment2s, + local_moment2s_max, local_beta1_pows, local_beta2_pows, local_master_params, @@ -360,12 +367,14 @@ def test_merged_adam_for_dist_tensor(self): epsilon, True, False, + False, ) ( dist_param_out, dist_moment1s_out, dist_moment2s_out, + dist_moment2s_max_out, dist_beta1_pow_out, dist_beta2_pow_out, dist_master_param_out, @@ -375,6 +384,7 @@ def test_merged_adam_for_dist_tensor(self): dist_lrs, dist_moment1s, dist_moment2s, + dist_moment2s_max, dist_beta1_pows, dist_beta2_pows, dist_master_params, @@ -383,6 +393,7 @@ def test_merged_adam_for_dist_tensor(self): epsilon, True, False, + False, ) for i in range(len(local_param_out)): self.check_tensor_eq(local_param_out[i], dist_param_out[i]) diff --git a/test/collective/fleet/hybrid_parallel_sharding_model.py b/test/collective/fleet/hybrid_parallel_sharding_model.py index a16b7f708f7c3a..aecfe468e70704 100644 --- a/test/collective/fleet/hybrid_parallel_sharding_model.py +++ b/test/collective/fleet/hybrid_parallel_sharding_model.py @@ -334,19 +334,23 @@ def test_sharding_adam(self): sharded_accumulators = { 'embedding_0.w_0_beta2_pow_acc_0', 'linear_1.b_0_moment2_0', + 'linear_1.b_0_moment2_max_0', 'linear_0.b_0_beta1_pow_acc_0', 'linear_0.b_0_beta2_pow_acc_0', 'linear_1.b_0_moment1_0', 'linear_2.b_0_beta2_pow_acc_0', 'linear_2.b_0_moment2_0', + 'linear_2.b_0_moment2_max_0', 'embedding_0.w_0_moment1_0', 'embedding_0.w_0_beta1_pow_acc_0', 'linear_0.b_0_moment2_0', + 'linear_0.b_0_moment2_max_0', 'linear_2.b_0_moment1_0', 'linear_0.b_0_moment1_0', 'linear_1.b_0_beta2_pow_acc_0', 'linear_1.b_0_beta1_pow_acc_0', 'embedding_0.w_0_moment2_0', + 'embedding_0.w_0_moment2_max_0', 'linear_2.b_0_beta1_pow_acc_0', } self.sharding_model( diff --git a/test/collective/fleet/hybrid_parallel_sharding_state_dict.py b/test/collective/fleet/hybrid_parallel_sharding_state_dict.py index b9a5f55dc188ab..1a51407ddb5f39 100644 --- a/test/collective/fleet/hybrid_parallel_sharding_state_dict.py +++ b/test/collective/fleet/hybrid_parallel_sharding_state_dict.py @@ -146,7 +146,7 @@ def test_set_state_dict(self): # master_weights and accumulators state_dict["master_weights"] = {} all_param_names = [] - accumulator_names = ["moment1", "moment2"] + accumulator_names = ["moment1", "moment2", "moment2_max"] # local_params = dist_optimizer._rank2params[ dist_optimizer._sharding_rank diff --git a/test/cpp/phi/kernels/test_fused_adam_kernel.cc b/test/cpp/phi/kernels/test_fused_adam_kernel.cc index ec0926508c9e89..1b15c33481e22f 100644 --- a/test/cpp/phi/kernels/test_fused_adam_kernel.cc +++ b/test/cpp/phi/kernels/test_fused_adam_kernel.cc @@ -126,6 +126,7 @@ struct AdamInfo { std::vector master_params; std::vector moment1s; std::vector moment2s; + std::vector moment2s_max; std::vector beta1_pows; std::vector beta2_pows; DenseTensor learning_rate; @@ -136,6 +137,7 @@ struct AdamInfo { bool multi_precision; bool use_adamw; int chunk_size = 4096; + bool amsgrad; using MT = typename phi::dtype::MPTypeTrait::Type; @@ -145,14 +147,16 @@ struct AdamInfo { float beta2, float weight_decay, bool multi_precision, - bool use_adamw) + bool use_adamw, + bool amsgrad) : ctx(&ctx_ref), shapes(shapes), beta1(beta1), beta2(beta2), weight_decay(weight_decay), multi_precision(multi_precision), - use_adamw(use_adamw) { + use_adamw(use_adamw), + amsgrad(amsgrad) { std::vector> one_shapes(shapes.size(), std::vector(1, 1)); std::vector> learning_rate_shapes( @@ -163,6 +167,7 @@ struct AdamInfo { *ctx, learning_rate_shapes, 1e-3)[0]; moment1s = GenerateConstantTensorVectors(*ctx, shapes, 0); moment2s = GenerateConstantTensorVectors(*ctx, shapes, 0); + moment2s_max = GenerateConstantTensorVectors(*ctx, shapes, 0); if (multi_precision) { master_params.resize(shapes.size()); @@ -199,7 +204,8 @@ struct AdamInfo { other.beta2, other.weight_decay, other.multi_precision, - other.use_adamw); + other.use_adamw, + other.amsgrad); auto copy_tensor = [&other](const DenseTensor &x, DenseTensor *y) { Copy(*other.ctx, x, x.place(), false, y); }; @@ -215,6 +221,7 @@ struct AdamInfo { copy_tensors(other.master_params, &copied.master_params); copy_tensors(other.moment1s, &copied.moment1s); copy_tensors(other.moment2s, &copied.moment2s); + copy_tensors(other.moment2s_max, &copied.moment2s_max); copy_tensors(other.beta1_pows, &copied.beta1_pows); copy_tensors(other.beta2_pows, &copied.beta2_pows); copy_tensor(other.learning_rate, &copied.learning_rate); @@ -231,6 +238,7 @@ struct AdamInfo { auto master_param_metas = ToMetaTensorVector(master_params); auto moment1_metas = ToMetaTensorVector(moment1s); auto moment2_metas = ToMetaTensorVector(moment2s); + auto moment2_max_metas = ToMetaTensorVector(moment2s_max); auto beta1_pow_metas = ToMetaTensorVector(beta1_pows); auto beta2_pow_metas = ToMetaTensorVector(beta2_pows); @@ -239,6 +247,7 @@ struct AdamInfo { learning_rate, ToConstMetaTensorPtrVector(moment1_metas), ToConstMetaTensorPtrVector(moment2_metas), + ToConstMetaTensorPtrVector(moment2_max_metas), ToConstMetaTensorPtrVector(beta1_pow_metas), ToConstMetaTensorPtrVector(beta2_pow_metas), multi_precision @@ -254,9 +263,11 @@ struct AdamInfo { use_adamw, multi_precision, false, + amsgrad, ToMutableMetaTensorPtrVector(param_metas), ToMutableMetaTensorPtrVector(moment1_metas), ToMutableMetaTensorPtrVector(moment2_metas), + ToMutableMetaTensorPtrVector(moment2_max_metas), ToMutableMetaTensorPtrVector(beta1_pow_metas), ToMutableMetaTensorPtrVector(beta2_pow_metas), ToMutableMetaTensorPtrVector(master_param_metas)); @@ -268,6 +279,7 @@ struct AdamInfo { learning_rate, ToConstTensorPtrVector(moment1s), ToConstTensorPtrVector(moment2s), + ToConstTensorPtrVector(moment2s_max), ToConstTensorPtrVector(beta1_pows), ToConstTensorPtrVector(beta2_pows), multi_precision @@ -282,9 +294,11 @@ struct AdamInfo { use_adamw, multi_precision, false, + amsgrad, ToMutableTensorPtrVector(params), ToMutableTensorPtrVector(moment1s), ToMutableTensorPtrVector(moment2s), + ToMutableTensorPtrVector(moment2s_max), ToMutableTensorPtrVector(beta1_pows), ToMutableTensorPtrVector(beta2_pows), ToMutableTensorPtrVector(master_params)); @@ -299,6 +313,7 @@ struct AdamInfo { learning_rate, moment1s[idx], moment2s[idx], + moment2s_max[idx], beta1_pows[idx], beta2_pows[idx], multi_precision ? paddle::make_optional(master_params[idx]) @@ -314,9 +329,11 @@ struct AdamInfo { 1000, multi_precision, false, + amsgrad, ¶ms[idx], &moment1s[idx], &moment2s[idx], + &moment2s_max[idx], &beta1_pows[idx], &beta2_pows[idx], multi_precision ? &master_params[idx] : nullptr); @@ -331,6 +348,7 @@ struct AdamInfo { learning_rate, moment1s[idx], moment2s[idx], + moment2s_max[idx], beta1_pows[idx], beta2_pows[idx], multi_precision ? paddle::make_optional(master_params[idx]) @@ -343,9 +361,11 @@ struct AdamInfo { 1000, multi_precision, false, + amsgrad, ¶ms[idx], &moment1s[idx], &moment2s[idx], + &moment2s_max[idx], &beta1_pows[idx], &beta2_pows[idx], multi_precision ? &master_params[idx] : nullptr); @@ -401,6 +421,7 @@ template void TestFusedAdamBase(const std::vector> &shapes, float atol, bool use_adamw, + bool amsgrad, bool multi_precision = false, float beta1 = 0.9, float beta2 = 0.99, @@ -411,8 +432,14 @@ void TestFusedAdamBase(const std::vector> &shapes, using Context = typename std::remove_const< typename std::remove_pointer::type>::type; ctx.GetGenerator()->SetCurrentSeed(seed); - AdamInfo info1( - ctx, shapes, beta1, beta2, weight_decay, multi_precision, use_adamw); + AdamInfo info1(ctx, + shapes, + beta1, + beta2, + weight_decay, + multi_precision, + use_adamw, + amsgrad); auto info2 = AdamInfo::DeepCopy(info1); for (size_t i = 0; i < steps; ++i) { @@ -437,6 +464,7 @@ void TestFusedAdamBase(const std::vector> &shapes, PD_ADAM_TEST_COMP(master_params, MT); PD_ADAM_TEST_COMP(moment1s, MT); PD_ADAM_TEST_COMP(moment2s, MT); + PD_ADAM_TEST_COMP(moment2s_max, MT); } static auto GenerateRandomShapes(size_t n, uint64_t low, uint64_t high) { @@ -454,7 +482,9 @@ TEST(fused_adam, test_fp32_cpu) { auto shapes = GenerateRandomShapes(30, 10, 20); float atol = 0.0f; for (auto use_adamw : {false, true}) { - TestFusedAdamBase(shapes, atol, use_adamw); + for (auto amsgrad : {false, true}) { + TestFusedAdamBase(shapes, atol, use_adamw, amsgrad); + } } } @@ -463,7 +493,9 @@ TEST(fused_adam, test_fp32_gpu) { auto shapes = GenerateRandomShapes(40, 0, 2 << 18); float atol = 0.0f; for (auto use_adamw : {false, true}) { - TestFusedAdamBase(shapes, atol, use_adamw); + for (auto amsgrad : {false, true}) { + TestFusedAdamBase(shapes, atol, use_adamw, amsgrad); + } } } @@ -471,7 +503,10 @@ TEST(fused_adam, test_fp16_gpu) { auto shapes = GenerateRandomShapes(40, 0, 2 << 18); float atol = 5e-3f; for (auto use_adamw : {false, true}) { - TestFusedAdamBase(shapes, atol, use_adamw, true); + for (auto amsgrad : {false, true}) { + TestFusedAdamBase( + shapes, atol, use_adamw, amsgrad, true); + } } } #endif diff --git a/test/legacy_test/test_adam_op.py b/test/legacy_test/test_adam_op.py index 184f7fc04c87ba..9a9bf8e211f1c4 100644 --- a/test/legacy_test/test_adam_op.py +++ b/test/legacy_test/test_adam_op.py @@ -30,6 +30,7 @@ def adam_wrapper( LearningRate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_weight=None, @@ -38,13 +39,15 @@ def adam_wrapper( beta2=0.836, epsilon=1e-4, lazy_mode=False, + amsgrad=False, ): - _, _, _, _, _, _ = paddle._C_ops.adam_( + _, _, _, _, _, _, _ = paddle._C_ops.adam_( param, grad, LearningRate, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_weight, @@ -56,10 +59,16 @@ def adam_wrapper( 1000, False, False, + amsgrad, ) class TestAdamOp1(OpTest): + def set_amsgrad(self): + self.amsgrad = False + # no check `Moment2MaxOut` with amsgrad is False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with supplied attributes''' self.op_type = "adam" @@ -70,6 +79,7 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") learning_rate = 0.004 beta1 = 0.78 @@ -77,37 +87,62 @@ def setUp(self): epsilon = 1e-4 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), } - self.attrs = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} + self.attrs = { + 'epsilon': epsilon, + 'beta1': beta1, + 'beta2': beta2, + 'amsgrad': self.amsgrad, + } - param_out, moment1_out, moment2_out = adam_step(self.inputs, self.attrs) + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( + self.inputs, self.attrs + ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamOp1AMSGrad(TestAdamOp1): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None class TestAdamOp2(OpTest): def set_shape(self): self.shape = (102, 105) + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with supplied attributes''' self.op_type = "adam" @@ -119,6 +154,7 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, self.shape).astype("float32") # The second moment is positive moment2 = np.random.random(self.shape).astype("float32") + moment2_max = np.zeros(self.shape).astype("float32") learning_rate = 0.001 beta1 = 0.9 @@ -126,31 +162,41 @@ def setUp(self): epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), } - attributes = {'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2} + self.attrs = { + 'epsilon': epsilon, + 'beta1': beta1, + 'beta2': beta2, + 'amsgrad': self.amsgrad, + } - param_out, moment1_out, moment2_out = adam_step(self.inputs, attributes) + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( + self.inputs, self.attrs + ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) class TestAdamOnlyTailOp(TestAdamOp2): @@ -158,7 +204,22 @@ def set_shape(self): self.shape = 3 +class TestAdamOp2AMSGrad(TestAdamOp2): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamOpMultipleSteps(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Operator with supplied attributes''' self.op_type = "adam" @@ -171,6 +232,7 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") learning_rate = 0.001 self.beta1 = 0.9 @@ -178,12 +240,14 @@ def setUp(self): epsilon = 1e-8 self.beta1_pow = self.beta1**10 self.beta2_pow = self.beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([self.beta1_pow]).astype("float32"), 'Beta2Pow': np.array([self.beta2_pow]).astype("float32"), @@ -193,11 +257,12 @@ def setUp(self): 'epsilon': epsilon, 'beta1': self.beta1, 'beta2': self.beta2, + 'amsgrad': self.amsgrad, } def test_check_output(self): for _ in range(self.num_steps): - param_out, moment1_out, moment2_out = adam_step( + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( self.inputs, self.attrs ) @@ -206,18 +271,20 @@ def test_check_output(self): self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': beta1_pow_out, 'Beta2PowOut': beta2_pow_out, } # Verify output for this step - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) # Output of this step becomes input for next step self.inputs['Param'] = param_out self.inputs['Moment1'] = moment1_out self.inputs['Moment2'] = moment2_out + self.inputs['Moment2Max'] = moment2_max_out # Update powers of Beta1 and Beta2 for next time step self.inputs['Beta1Pow'] = beta1_pow_out @@ -229,18 +296,30 @@ def test_check_output(self): ) +class TestAdamOpMultipleStepsAMSGrad(TestAdamOpMultipleSteps): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + def adam_step(inputs, attributes): ''' Simulate one step of the adam optimizer :param inputs: dict of inputs :param attributes: dict of attributes - :return tuple: tuple of output param, moment1, moment2, + :return tuple: tuple of output param, moment1, moment2, moment2_max beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs['Moment2Max'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -256,11 +335,25 @@ def adam_step(inputs, attributes): else: beta2 = inputs['Beta2Tensor'][0] + amsgrad = attributes['amsgrad'] + moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) + lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) - param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon)) - return param_out, moment1_out, moment2_out + + if amsgrad: + moment2_max_out = np.maximum(moment2_out, moment2_max) + param_out = param - lr_t * ( + moment1_out / (np.sqrt(moment2_max_out) + epsilon) + ) + else: + moment2_max_out = np.empty_like(moment2_out) + param_out = param - lr_t * ( + moment1_out / (np.sqrt(moment2_out) + epsilon) + ) + + return param_out, moment1_out, moment2_out, moment2_max_out def adamw_step(inputs, attributes): @@ -268,13 +361,14 @@ def adamw_step(inputs, attributes): Simulate one step of the adam optimizer :param inputs: dict of inputs :param attributes: dict of attributes - :return tuple: tuple of output param, moment1, moment2, + :return tuple: tuple of output param, moment1, moment2, moment2_max, beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs['Moment2Max'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -294,12 +388,25 @@ def adamw_step(inputs, attributes): else: beta2 = inputs['Beta2Tensor'][0] + amsgrad = attributes["amsgrad"] + moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) + lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) - param_out = param - lr_t * (moment1_out / (np.sqrt(moment2_out) + epsilon)) - return param_out, moment1_out, moment2_out + if amsgrad: + moment2_max_out = np.maximum(moment2_out, moment2_max) + param_out = param - lr_t * ( + moment1_out / (np.sqrt(moment2_max_out) + epsilon) + ) + else: + moment2_max_out = np.empty_like(moment2_out) + param_out = param - lr_t * ( + moment1_out / (np.sqrt(moment2_out) + epsilon) + ) + + return param_out, moment1_out, moment2_out, moment2_max_out def adam_step_sparse( @@ -309,13 +416,14 @@ def adam_step_sparse( Simulate one step of the adam optimizer :param inputs: dict of inputs :param attributes: dict of attributes - :return tuple: tuple of output param, moment1, moment2, + :return tuple: tuple of output param, moment1, moment2, moment2_max, beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] # grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs['Moment2Max'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -323,9 +431,11 @@ def adam_step_sparse( beta1 = attributes['beta1'] beta2 = attributes['beta2'] epsilon = attributes['epsilon'] + amsgrad = attributes['amsgrad'] moment1_out = np.zeros(shape=[height, row_numel]) moment2_out = np.zeros(shape=[height, row_numel]) + moment2_max_out = np.zeros(shape=[height, row_numel]) param_out = np.zeros(shape=[height, row_numel]) def update_row(row_id, update_value): @@ -336,9 +446,20 @@ def update_row(row_id, update_value): update_value ) lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) - param_out[row_id] = param[row_id] - lr_t * ( - moment1_out[row_id] / (np.sqrt(moment2_out[row_id]) + epsilon) - ) + + if amsgrad: + moment2_max_out[row_id] = np.maximum( + moment2_out[row_id], moment2_max[row_id] + ) + param_out[row_id] = param[row_id] - lr_t * ( + moment1_out[row_id] + / (np.sqrt(moment2_max_out[row_id]) + epsilon) + ) + else: + moment2_max_out[row_id] = np.empty_like(moment2_out[row_id]) + param_out[row_id] = param[row_id] - lr_t * ( + moment1_out[row_id] / (np.sqrt(moment2_out[row_id]) + epsilon) + ) if lazy_mode: for idx, row_id in enumerate(rows): @@ -350,16 +471,21 @@ def update_row(row_id, update_value): update_value = np_grad[rows.index(row_id)] update_row(row_id, update_value) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max_out class TestSparseAdamOp(unittest.TestCase): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setup(self, scope, place, lazy_mode): beta1 = 0.78 beta2 = 0.836 epsilon = 1e-4 beta1_pow = np.array([beta1**10]).astype("float32") beta2_pow = np.array([beta2**10]).astype("float32") + self.set_amsgrad() height = 10 rows = [0, 4, 7] @@ -370,6 +496,7 @@ def setup(self, scope, place, lazy_mode): "Param": np.full((height, row_numel), 5.0).astype("float32"), "Moment1": np.full((height, row_numel), 5.0).astype("float32"), "Moment2": np.full((height, row_numel), 5.0).astype("float32"), + "Moment2Max": np.zeros((height, row_numel)).astype("float32"), 'Beta1Pow': beta1_pow, 'Beta2Pow': beta2_pow, "LearningRate": np.full((1), 2.0).astype("float32"), @@ -380,6 +507,7 @@ def setup(self, scope, place, lazy_mode): 'beta1': beta1, 'beta2': beta2, 'min_row_size_to_use_multithread': 2, + 'amsgrad': self.amsgrad, } grad_selected_rows = scope.var('Grad').get_selected_rows() @@ -394,7 +522,7 @@ def setup(self, scope, place, lazy_mode): self.sparse_inputs = ["Grad"] - param_out, mom1, mom2 = adam_step_sparse( + param_out, mom1, mom2, mom2_max = adam_step_sparse( self.dense_inputs, self.attrs, height, @@ -407,6 +535,7 @@ def setup(self, scope, place, lazy_mode): "ParamOut": param_out, "Moment1Out": mom1, "Moment2Out": mom2, + "Moment2MaxOut": mom2_max, 'Beta1PowOut': beta1_pow * beta1, 'Beta2PowOut': beta2_pow * beta2, } @@ -435,6 +564,10 @@ def check_with_place(self, place, lazy_mode): adam_op.run(scope, place) for key, np_array in self.outputs.items(): + # do not check keys in `no_check_set`` + if self.no_check_set is not None and key in self.no_check_set: + continue + out_var = scope.var(key).get_tensor() actual = np.array(out_var) actual = actual.reshape([actual.size]) @@ -458,7 +591,22 @@ def test_sparse_adam(self): self.check_with_place(place, lazy_mode) +class TestSparseAdamOpAMSGrad(TestSparseAdamOp): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamOpBetaVariable(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with beta as Variable''' self.op_type = "adam" @@ -469,6 +617,8 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") + beta1 = 0.85 beta2 = 0.95 @@ -476,12 +626,14 @@ def setUp(self): epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -489,23 +641,41 @@ def setUp(self): "Beta2Tensor": np.array([beta2]).astype("float32"), } - attributes = {'epsilon': epsilon} + self.attrs = {'epsilon': epsilon, 'amsgrad': self.amsgrad} - param_out, moment1_out, moment2_out = adam_step(self.inputs, attributes) + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( + self.inputs, self.attrs + ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamOpBetaVariableAMSGrad(TestAdamOpBetaVariable): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None class TestAdamOpBetaEpsilonVariable(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with beta/epsilon as Variable''' self.op_type = "adam" @@ -516,6 +686,8 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") + beta1 = 0.85 beta2 = 0.95 @@ -523,12 +695,14 @@ def setUp(self): epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -537,23 +711,41 @@ def setUp(self): "EpsilonTensor": np.array([epsilon]).astype("float32"), } - attributes = {'epsilon': epsilon} + self.attrs = {'epsilon': epsilon, 'amsgrad': self.amsgrad} - param_out, moment1_out, moment2_out = adam_step(self.inputs, attributes) + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( + self.inputs, self.attrs + ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamOpBetaEpsilonVariableAMSGrad(TestAdamOpBetaEpsilonVariable): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None class TestAdamOpWithGlobalBetaPow(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with global_beta_pow''' self.op_type = "adam" @@ -564,6 +756,8 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") + beta1 = 0.85 beta2 = 0.95 @@ -571,12 +765,14 @@ def setUp(self): epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -585,26 +781,46 @@ def setUp(self): "EpsilonTensor": np.array([epsilon]).astype("float32"), } - attributes = {'epsilon': epsilon} - - param_out, moment1_out, moment2_out = adam_step(self.inputs, attributes) + self.attrs = { + 'use_global_beta_pow': True, + 'epsilon': epsilon, + 'amsgrad': self.amsgrad, + } - self.attrs = {'use_global_beta_pow': True} + param_out, moment1_out, moment2_out, moment2_max_out = adam_step( + self.inputs, self.attrs + ) # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty. self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([]), 'Beta2PowOut': np.array([]), } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamOpWithGlobalBetaPowAMSGrad(TestAdamOpWithGlobalBetaPow): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None class TestAdamOpWithSkipUpdate(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test Adam Op with global_beta_pow''' self.op_type = "adam" @@ -615,6 +831,8 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") + beta1 = 0.85 beta2 = 0.95 @@ -622,12 +840,14 @@ def setUp(self): epsilon = 1e-8 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -637,24 +857,41 @@ def setUp(self): "SkipUpdate": np.array([True]).astype("bool"), } - attributes = {'epsilon': epsilon} - - self.attrs = {'use_global_beta_pow': True} + self.attrs = { + 'use_global_beta_pow': True, + 'epsilon': epsilon, + 'amsgrad': self.amsgrad, + } # use_global_beta_pow=True, Beta1PowOut and Beta2PowOut are empty. self.outputs = { 'Moment1Out': moment1, 'Moment2Out': moment2, + 'Moment2MaxOut': moment2_max, 'ParamOut': param, 'Beta1PowOut': np.array([]), 'Beta2PowOut': np.array([]), } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamOpWithSkipUpdateAMSGrad(TestAdamOpWithSkipUpdate): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None class TestAdamOpV2(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def test_pir_adam_op(self): with paddle.pir_utils.IrGuard(): place = base.CPUPlace() @@ -686,6 +923,7 @@ def test_pir_adam_op(self): beta2=beta2, weight_decay=0.01, epsilon=1e-8, + amsgrad=self.amsgrad, ) opt.minimize(loss) @@ -703,7 +941,9 @@ def test_adam_op_dygraph(self): linear = paddle.nn.Linear(13, 5) adam = paddle.optimizer.Adam( - learning_rate=0.01, parameters=linear.parameters() + learning_rate=0.01, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) out = linear(a) out.backward() @@ -715,7 +955,9 @@ def test_adam_op_with_state_dict(self): paddle.disable_static() emb = paddle.nn.Embedding(10, 10) - adam = paddle.optimizer.Adam(0.001, parameters=emb.parameters()) + adam = paddle.optimizer.Adam( + 0.001, parameters=emb.parameters(), amsgrad=self.amsgrad + ) state_dict = adam.state_dict() adam.set_state_dict(state_dict) @@ -727,6 +969,7 @@ def test_adam_op_with_state_dict(self): learning_rate=learning_rate, weight_decay=paddle.regularizer.L2Decay(0.001), parameters=emb.parameters(), + amsgrad=self.amsgrad, ) lr = adam.get_lr() state_dict = adam.state_dict() @@ -737,7 +980,9 @@ def test_adam_op_with_state_dict(self): learning_rate = np.array([0.01]).astype("float32") learning_rate = paddle.to_tensor(learning_rate) adam = paddle.optimizer.Adam( - learning_rate=learning_rate, parameters=emb.parameters() + learning_rate=learning_rate, + parameters=emb.parameters(), + amsgrad=self.amsgrad, ) params = adam.get_opti_var_name_list() @@ -751,7 +996,10 @@ def test_adam_with_grad_clip(self): linear = paddle.nn.Linear(13, 5) clip = paddle.nn.ClipGradByGlobalNorm(clip_norm=1.0) adam = paddle.optimizer.Adam( - 0.1, parameters=linear.parameters(), grad_clip=clip + 0.1, + parameters=linear.parameters(), + grad_clip=clip, + amsgrad=self.amsgrad, ) out = linear(a) out.backward() @@ -762,7 +1010,9 @@ def test_adam_with_grad_clip(self): def test_adam_op_with_set_lr(self): paddle.disable_static() linear = paddle.nn.Linear(10, 10) - adam = paddle.optimizer.Adam(0.1, parameters=linear.parameters()) + adam = paddle.optimizer.Adam( + 0.1, parameters=linear.parameters(), amsgrad=self.amsgrad + ) lr = 0.01 adam.set_lr(lr) @@ -780,15 +1030,24 @@ def test_adam_op_invalid_input(self): linear = paddle.nn.Linear(10, 10) with self.assertRaises(ValueError): adam = paddle.optimizer.Adam( - 0.1, beta1=-1, parameters=linear.parameters() + 0.1, + beta1=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) with self.assertRaises(ValueError): adam = paddle.optimizer.Adam( - 0.1, beta2=-1, parameters=linear.parameters() + 0.1, + beta2=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) with self.assertRaises(ValueError): adam = paddle.optimizer.Adam( - 0.1, epsilon=-1, parameters=linear.parameters() + 0.1, + epsilon=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) paddle.enable_static() @@ -798,7 +1057,10 @@ def test_adam_op_with_sparse_input_and_weight_decay(self): x = paddle.to_tensor(x_data, stop_gradient=False) emb = paddle.nn.Embedding(10, 10, sparse=True) adam = paddle.optimizer.Adam( - 0.001, parameters=emb.parameters(), weight_decay=0.01 + 0.001, + parameters=emb.parameters(), + weight_decay=0.01, + amsgrad=self.amsgrad, ) with self.assertRaises(RuntimeError): @@ -807,6 +1069,46 @@ def test_adam_op_with_sparse_input_and_weight_decay(self): adam.step() paddle.enable_static() + def test_adam_with_old_ir(self): + """TODO(megemini): old ir not used anymore""" + with paddle.pir_utils.OldIrGuard(): + paddle.enable_static() + paddle.seed(10) + np.random.seed(10) + exe = paddle.static.Executor() + train_program = paddle.static.Program() + startup_program = paddle.static.Program() + optimizer = paddle.optimizer.Adam(amsgrad=self.amsgrad) + + with paddle.static.program_guard(train_program, startup_program): + data = paddle.static.data( + shape=[2, 2], name='X', dtype='float32' + ) + hidden_layer = paddle.nn.Linear(2, 10) + hidden = hidden_layer(data) + loss = paddle.mean(hidden) + optimizer.minimize(loss) + exe.run(startup_program) + x = np.random.random(size=(2, 2)).astype('float32') + out = [] + for _ in range(5): + (loss_data,) = exe.run( + train_program, feed={"X": x}, fetch_list=[loss] + ) + out.append(loss_data) + return out + + +class TestAdamOpV2AMSGrad(TestAdamOpV2): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + class TestAdamOpV2WeightDecay(unittest.TestCase): def test_weight_decay_int(self): @@ -853,7 +1155,21 @@ def test_adam_op(self): adam.clear_gradients() +class TestAdamOpV2GroupAMSGrad(TestAdamOpV2Group): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestMultiTensorAdam(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def _adam_optimize_dygraph( self, place, @@ -883,6 +1199,7 @@ def _adam_optimize_dygraph( parameters=model.parameters(), use_multi_tensor=use_multi_tensor, multi_precision=use_amp, + amsgrad=self.amsgrad, ) else: parameters = list(model.parameters()) @@ -904,6 +1221,7 @@ def _adam_optimize_dygraph( ], use_multi_tensor=use_multi_tensor, multi_precision=use_amp, + amsgrad=self.amsgrad, ) for idx in range(2): @@ -940,7 +1258,9 @@ def _adam_optimize_static( train_program = paddle.static.Program() startup_program = paddle.static.Program() optimizer = paddle.optimizer.Adam( - multi_precision=use_amp, use_multi_tensor=use_multi_tensor + multi_precision=use_amp, + use_multi_tensor=use_multi_tensor, + amsgrad=self.amsgrad, ) with paddle.static.program_guard(train_program, startup_program): @@ -1073,6 +1393,17 @@ def test_pir_main(self): self._check_with_place_amp(place, use_amp) +class TestMultiTensorAdamAMSGrad(TestMultiTensorAdam): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + if __name__ == "__main__": paddle.enable_static() unittest.main() diff --git a/test/legacy_test/test_adam_optimizer_fp32_fp64.py b/test/legacy_test/test_adam_optimizer_fp32_fp64.py index d166dff5b3018f..36a54b9a701461 100644 --- a/test/legacy_test/test_adam_optimizer_fp32_fp64.py +++ b/test/legacy_test/test_adam_optimizer_fp32_fp64.py @@ -15,6 +15,8 @@ import os import unittest +from utils import static_guard + import paddle from paddle import base @@ -33,30 +35,31 @@ def get_places(): def main_test_func(place, dtype): - main = base.Program() - startup = base.Program() - with base.program_guard(main, startup): - with base.scope_guard(base.Scope()): - x = paddle.static.data(name='x', shape=[None, 13], dtype=dtype) - y = paddle.static.data(name='y', shape=[None, 1], dtype=dtype) - y_predict = paddle.static.nn.fc(x, size=1) - cost = paddle.nn.functional.square_error_cost( - input=y_predict, label=y - ) - avg_cost = paddle.mean(cost) - - adam_optimizer = paddle.optimizer.Adam(0.01) - adam_optimizer.minimize(avg_cost) - - fetch_list = [avg_cost] - train_reader = paddle.batch( - paddle.dataset.uci_housing.train(), batch_size=1 - ) - feeder = base.DataFeeder(place=place, feed_list=[x, y]) - exe = base.Executor(place) - exe.run(base.default_startup_program()) - for data in train_reader(): - exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) + with static_guard(): + main = base.Program() + startup = base.Program() + with base.program_guard(main, startup): + with base.scope_guard(base.Scope()): + x = paddle.static.data(name='x', shape=[None, 13], dtype=dtype) + y = paddle.static.data(name='y', shape=[None, 1], dtype=dtype) + y_predict = paddle.static.nn.fc(x, size=1) + cost = paddle.nn.functional.square_error_cost( + input=y_predict, label=y + ) + avg_cost = paddle.mean(cost) + + adam_optimizer = paddle.optimizer.Adam(0.01) + adam_optimizer.minimize(avg_cost) + + fetch_list = [avg_cost] + train_reader = paddle.batch( + paddle.dataset.uci_housing.train(), batch_size=1 + ) + feeder = base.DataFeeder(place=place, feed_list=[x, y]) + exe = base.Executor(place) + exe.run(base.default_startup_program()) + for data in train_reader(): + exe.run(main, feed=feeder.feed(data), fetch_list=fetch_list) class AdamFp32Test(unittest.TestCase): diff --git a/test/legacy_test/test_adamw_op.py b/test/legacy_test/test_adamw_op.py index 97d8e7f5eedd96..e953b9c795e335 100644 --- a/test/legacy_test/test_adamw_op.py +++ b/test/legacy_test/test_adamw_op.py @@ -30,6 +30,7 @@ def adamw_step(inputs, attributes): grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs['Moment2Max'] lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -54,11 +55,20 @@ def adamw_step(inputs, attributes): else: beta2 = inputs['Beta2Tensor'][0] + amsgrad = attributes['amsgrad'] + moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) - denom = (np.sqrt(moment2_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + + if amsgrad: + moment2_max_out = np.maximum(moment2_out, moment2_max) + denom = (np.sqrt(moment2_max_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + else: + moment2_max_out = np.empty_like(moment2_out) + denom = (np.sqrt(moment2_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + param_out = param + ((moment1_out / denom) * (-(lr / (1.0 - beta1_pow)))) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max_out def adamw_wrapper( @@ -67,6 +77,7 @@ def adamw_wrapper( lr, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_weight=None, @@ -78,13 +89,15 @@ def adamw_wrapper( weight_decay=0.01, with_decay=True, lazy_mode=False, + amsgrad=False, ): - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( param, grad, lr, moment1, moment2, + moment2_max, beta1_pow, beta2_pow, master_weight, @@ -99,10 +112,16 @@ def adamw_wrapper( 1000, False, False, + amsgrad, ) class TestAdamW(OpTest): + def set_amsgrad(self): + self.amsgrad = False + # no check `Moment2MaxOut` with amsgrad is False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test AdamW Op with supplied attributes''' self.op_type = "adamw" @@ -113,6 +132,7 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (102, 105)).astype("float32") # The second moment is positive moment2 = np.random.random((102, 105)).astype("float32") + moment2_max = np.zeros((102, 105)).astype("float32") learning_rate = 0.004 beta1 = 0.78 @@ -120,12 +140,14 @@ def setUp(self): epsilon = 1e-4 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -137,22 +159,35 @@ def setUp(self): 'beta2': beta2, "coeff": 0.5, "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max_out = adamw_step( self.inputs, self.attrs ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, } def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(no_check_set=self.no_check_set, check_pir=True) + + +class TestAdamWAMSGrad(TestAdamW): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None @unittest.skipIf( @@ -160,6 +195,10 @@ def test_check_output(self): "core is not compiled with CUDA nor XPU", ) class TestAdamW2(OpTest): + def set_amsgrad(self): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + def setUp(self): '''Test AdamW Op with supplied attributes''' self.op_type = "adamw" @@ -170,6 +209,7 @@ def setUp(self): moment1 = np.random.uniform(-1, 1, (2, 2)).astype("float32") # The second moment is positive moment2 = np.random.random((2, 2)).astype("float32") + moment2_max = np.zeros((2, 2)).astype("float32") learning_rate = 0.004 beta1 = 0.78 @@ -177,12 +217,14 @@ def setUp(self): epsilon = 1e-4 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1_pow]).astype("float32"), 'Beta2Pow': np.array([beta2_pow]).astype("float32"), @@ -195,15 +237,17 @@ def setUp(self): "lr_ratio": 0.1, "coeff": 0.5, "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max_out = adamw_step( self.inputs, self.attrs ) self.outputs = { 'Moment1Out': moment1_out, 'Moment2Out': moment2_out, + 'Moment2MaxOut': moment2_max_out, 'ParamOut': param_out, 'Beta1PowOut': np.array([beta1_pow]).astype("float32") * beta1, 'Beta2PowOut': np.array([beta2_pow]).astype("float32") * beta2, @@ -211,7 +255,8 @@ def setUp(self): def test_check_output(self): self.check_output_with_place( - ( + no_check_set=self.no_check_set, + place=( core.CUDAPlace(0) if not core.is_compiled_with_xpu() else core.XPUPlace(0) @@ -220,7 +265,21 @@ def test_check_output(self): ) +class TestAdamW2AMSGrad(TestAdamW2): + def set_amsgrad(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOp(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def test_adamw_op_dygraph(self): paddle.disable_static() value = np.arange(26).reshape(2, 13).astype("float32") @@ -231,6 +290,7 @@ def test_adamw_op_dygraph(self): parameters=linear.parameters(), apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) for _ in range(2): @@ -280,6 +340,7 @@ def test_adamw_op(self): beta2=beta2, weight_decay=0.01, epsilon=1e-8, + amsgrad=self.amsgrad, ) opt.minimize(loss) @@ -299,6 +360,7 @@ def test_adamw_op_dygraph_bypassing_step(self): parameters=linear.parameters(), apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) os.environ["FLAGS_shard_bypass_dygraph_optimizer"] = "1" for _ in range(2): @@ -317,6 +379,7 @@ def test_adamw_op_coverage(self): parameters=linear.parameters(), apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) assert adam.__str__() is not None @@ -351,6 +414,7 @@ def test_pir_adam_op(self): beta2=beta2, weight_decay=0.01, epsilon=1e-8, + amsgrad=self.amsgrad, ) opt.minimize(loss) @@ -366,18 +430,38 @@ def test_adamw_op_invalid_input(self): linear = paddle.nn.Linear(10, 10) with self.assertRaises(ValueError): adam = paddle.optimizer.AdamW( - 0.1, beta1=-1, parameters=linear.parameters() + 0.1, + beta1=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) with self.assertRaises(ValueError): adam = paddle.optimizer.AdamW( - 0.1, beta2=-1, parameters=linear.parameters() + 0.1, + beta2=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) with self.assertRaises(ValueError): adam = paddle.optimizer.AdamW( - 0.1, epsilon=-1, parameters=linear.parameters() + 0.1, + epsilon=-1, + parameters=linear.parameters(), + amsgrad=self.amsgrad, ) +class TestAdamWOpAMSGrad(TestAdamWOp): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOpGroup(TestAdamWOp): def test_adamw_op_dygraph(self): paddle.disable_static() @@ -393,6 +477,7 @@ def test_adamw_op_dygraph(self): ], apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) for _ in range(2): @@ -416,6 +501,7 @@ def test_adamw_op_dygraph_bypassing_step(self): ], apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) os.environ["FLAGS_shard_bypass_dygraph_optimizer"] = "1" @@ -427,7 +513,21 @@ def test_adamw_op_dygraph_bypassing_step(self): adam.clear_gradients() +class TestAdamWOpGroupAMSGrad(TestAdamWOpGroup): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOpMultiPrecisionWithMainGrad(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def _test_adamw_op_dygraph_place_amp_with_maingrad( self, place, shape, use_main_grad ): @@ -456,6 +556,7 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( main_grad = grad.astype(paddle.float32) moment1 = paddle.randn(shape).astype(paddle.float32) moment2 = paddle.randn(shape).astype(paddle.float32).abs() + moment2_max = paddle.zeros(shape).astype(paddle.float32) lr = paddle.zeros([1]).astype(paddle.float32) lr[0] = lr_rate beta1_pow_acc = paddle.ones([1]).astype(paddle.float32) @@ -472,14 +573,16 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( ) ref_moment_1 = moment1.astype(paddle.float32).clone().detach() ref_moment_2 = moment2.astype(paddle.float32).clone().detach() + ref_moment_2_max = moment2_max.astype(paddle.float32).clone().detach() # reference code - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( ref_param, main_grad, lr, ref_moment_1, ref_moment_2, + ref_moment_2_max, ref_beta1_pow_acc, ref_beta2_pow_acc, master_weight, @@ -494,15 +597,17 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, False, False, + self.amsgrad, ) if use_main_grad: - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( param, main_grad, lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -517,20 +622,29 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, find_master, False, + self.amsgrad, ) np.testing.assert_allclose( param.astype("float32").numpy(), ref_param.numpy(), rtol=1e-2 ) - np.testing.assert_allclose( - master_weight.numpy(), ref_param.numpy(), rtol=1e-6 - ) + + if self.amsgrad: + np.testing.assert_allclose( + master_weight.numpy(), ref_param.numpy(), rtol=1e-4 + ) + else: + np.testing.assert_allclose( + master_weight.numpy(), ref_param.numpy(), rtol=1e-6 + ) + else: - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( param, grad, lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -545,13 +659,20 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, find_master, False, + self.amsgrad, ) np.testing.assert_allclose( param.astype("float32").numpy(), ref_param.numpy(), rtol=1e-2 ) - np.testing.assert_allclose( - master_weight.numpy(), ref_param.numpy(), rtol=1e-6 - ) + + if self.amsgrad: + np.testing.assert_allclose( + master_weight.numpy(), ref_param.numpy(), rtol=1e-4 + ) + else: + np.testing.assert_allclose( + master_weight.numpy(), ref_param.numpy(), rtol=1e-6 + ) def _get_places(self): places = [] @@ -572,7 +693,23 @@ def test_main(self): ) +class TestAdamWOpMultiPrecisionWithMainGradAMSGrad( + TestAdamWOpMultiPrecisionWithMainGrad +): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOpMultiPrecision(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def _test_adamw_op_dygraph_place_amp(self, place, use_amp=False): paddle.disable_static() paddle.seed(10) @@ -592,6 +729,7 @@ def _test_adamw_op_dygraph_place_amp(self, place, use_amp=False): } ], multi_precision=use_amp, + amsgrad=self.amsgrad, ) for idx in range(2): @@ -635,13 +773,28 @@ def test_main(self): self._test_adamw_op_dygraph_place_amp(place, use_amp) +class TestAdamWOpMultiPrecisionAMSGrad(TestAdamWOpMultiPrecision): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOpError(unittest.TestCase): + def setUp(self): + self.amsgrad = False + def test_api_errors(self): def test_parameters_dtype1(): adam = paddle.optimizer.AdamW( learning_rate=0.01, parameters=paddle.randn((5, 5)), weight_decay=0.1, + amsgrad=self.amsgrad, ) def test_parameters_dtype2(): @@ -650,11 +803,15 @@ def test_parameters_dtype2(): learning_rate=0.01, parameters={'params': linear.parameters()}, weight_decay=0.1, + amsgrad=self.amsgrad, ) def test_parameters_dtype3(): adam = paddle.optimizer.AdamW( - learning_rate=0.01, parameters=None, weight_decay=0.1 + learning_rate=0.01, + parameters=None, + weight_decay=0.1, + amsgrad=self.amsgrad, ) def test_parameters_dtype4(): @@ -663,6 +820,7 @@ def test_parameters_dtype4(): learning_rate=0.01, parameters={'params': set(linear.parameters())}, weight_decay=0.1, + amsgrad=self.amsgrad, ) def test_learning_rate_dtype(): @@ -671,6 +829,7 @@ def test_learning_rate_dtype(): learning_rate=1, parameters=linear.parameters(), weight_decay=0.1, + amsgrad=self.amsgrad, ) def test_grad_clip_dtype(): @@ -680,6 +839,7 @@ def test_grad_clip_dtype(): parameters=linear.parameters(), weight_decay=0.1, grad_clip=0.1, + amsgrad=self.amsgrad, ) self.assertRaises(TypeError, test_parameters_dtype1) @@ -690,6 +850,17 @@ def test_grad_clip_dtype(): self.assertRaises(TypeError, test_grad_clip_dtype) +class TestAdamWOpErrorAMSGrad(TestAdamWOpError): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + class TestAdamWOpGroupWithLR(TestAdamWOp): def test_adamw_op_dygraph(self): paddle.disable_static() @@ -713,6 +884,7 @@ def test_adamw_op_dygraph(self): ], apply_decay_param_fun=lambda name: True, weight_decay=0.01, + amsgrad=self.amsgrad, ) for _ in range(2): @@ -723,6 +895,17 @@ def test_adamw_op_dygraph(self): adam.clear_gradients() +class TestAdamWOpGroupWithLRAMSGrad(TestAdamWOpGroupWithLR): + def setUp(self): + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + def simple_lr_setting(param, decay_rate, n_layers): if "fc_0" in param.name or "linear_1" in param.name: depth = int(param.name.split("_")[2]) + 1 @@ -743,6 +926,7 @@ def setUp(self): random.seed(2022) np.random.seed(2022) paddle.seed(2022) + self.amsgrad = False def test_adamw_op_dygraph(self): paddle.disable_static() @@ -762,16 +946,20 @@ def test_adamw_op_dygraph(self): fc1_w = np.array(linear1.weight) fc1_w_mon1 = np.zeros_like(fc1_w) fc1_w_mon2 = np.zeros_like(fc1_w) + fc1_w_mon2_max = np.zeros_like(fc1_w) fc1_b = np.array(linear1.bias) fc1_b_mon1 = np.zeros_like(fc1_b) fc1_b_mon2 = np.zeros_like(fc1_b) + fc1_b_mon2_max = np.zeros_like(fc1_b) fc2_w = np.array(linear2.weight) fc2_w_mon1 = np.zeros_like(fc2_w) fc2_w_mon2 = np.zeros_like(fc2_w) + fc2_w_mon2_max = np.zeros_like(fc2_w) fc2_b = np.array(linear2.bias) fc2_b_mon1 = np.zeros_like(fc2_b) fc2_b_mon2 = np.zeros_like(fc2_b) + fc2_b_mon2_max = np.zeros_like(fc2_b) simple_lr_fun = partial(simple_lr_setting, decay_rate=0.8, n_layers=2) learning_rate = 0.001 @@ -790,14 +978,18 @@ def test_adamw_op_dygraph(self): apply_decay_param_fun=lambda name: True, weight_decay=weight_decay, lr_ratio=simple_lr_fun, + amsgrad=self.amsgrad, ) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1**t]).astype("float32"), 'Beta2Pow': np.array([beta2**t]).astype("float32"), @@ -810,11 +1002,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": weight_decay, "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max_out = adamw_step( np_inputs, np_attrs ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max_out for i in range(5): a = paddle.to_tensor( @@ -825,35 +1018,39 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): out = paddle.mean(out) out.backward() - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = get_numpy_output( fc1_w, np.array(linear1.weight.grad), fc1_w_mon1, fc1_w_mon2, + fc1_w_mon2_max, simple_lr_fun(linear1.weight), i + 1, ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = get_numpy_output( fc1_b, np.array(linear1.bias.grad), fc1_b_mon1, fc1_b_mon2, + fc1_b_mon2_max, simple_lr_fun(linear1.bias), i + 1, ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = get_numpy_output( fc2_w, np.array(linear2.weight.grad), fc2_w_mon1, fc2_w_mon2, + fc2_w_mon2_max, simple_lr_fun(linear2.weight), i + 1, ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = get_numpy_output( fc2_b, np.array(linear2.bias.grad), fc2_b_mon1, fc2_b_mon2, + fc2_b_mon2_max, simple_lr_fun(linear2.bias), i + 1, ) @@ -927,16 +1124,28 @@ def test_adamw_op(self): fc1_w_mon2 = np.zeros(linear1.weight.shape).astype( "float32" ) + fc1_w_mon2_max = np.zeros(linear1.weight.shape).astype( + "float32" + ) fc1_b_mon1 = np.zeros(linear1.bias.shape).astype("float32") fc1_b_mon2 = np.zeros(linear1.bias.shape).astype("float32") + fc1_b_mon2_max = np.zeros(linear1.bias.shape).astype( + "float32" + ) fc2_w_mon1 = np.zeros(linear2.weight.shape).astype( "float32" ) fc2_w_mon2 = np.zeros(linear2.weight.shape).astype( "float32" ) + fc2_w_mon2_max = np.zeros(linear2.weight.shape).astype( + "float32" + ) fc2_b_mon1 = np.zeros(linear2.bias.shape).astype("float32") fc2_b_mon2 = np.zeros(linear2.bias.shape).astype("float32") + fc2_b_mon2_max = np.zeros(linear2.bias.shape).astype( + "float32" + ) cost = paddle.nn.functional.square_error_cost( input=out, label=y @@ -954,15 +1163,19 @@ def test_adamw_op(self): weight_decay=weight_decay, epsilon=epsilon, lr_ratio=simple_lr_fun, + amsgrad=self.amsgrad, ) opt.minimize(avg_cost) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1**t]).astype("float32"), 'Beta2Pow': np.array([beta2**t]).astype("float32"), @@ -975,11 +1188,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": weight_decay, "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( - np_inputs, np_attrs + param_out, moment1_out, moment2_out, moment2_max_out = ( + adamw_step(np_inputs, np_attrs) ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max_out fetch_list1 = [ "linear_0.w_0", @@ -1026,42 +1240,58 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): fc2_b = param[3] fc2_b_grad = params_and_gras[7] - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( - fc1_w, - fc1_w_grad, - fc1_w_mon1, - fc1_w_mon2, - simple_lr_fun(linear1.weight), - i + 1, + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = ( + get_numpy_output( + fc1_w, + fc1_w_grad, + fc1_w_mon1, + fc1_w_mon2, + fc1_w_mon2_max, + simple_lr_fun(linear1.weight), + i + 1, + ) ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( - fc1_b, - fc1_b_grad, - fc1_b_mon1, - fc1_b_mon2, - simple_lr_fun(linear1.bias), - i + 1, + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = ( + get_numpy_output( + fc1_b, + fc1_b_grad, + fc1_b_mon1, + fc1_b_mon2, + fc1_b_mon2_max, + simple_lr_fun(linear1.bias), + i + 1, + ) ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( - fc2_w, - fc2_w_grad, - fc2_w_mon1, - fc2_w_mon2, - simple_lr_fun(linear2.weight), - i + 1, + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = ( + get_numpy_output( + fc2_w, + fc2_w_grad, + fc2_w_mon1, + fc2_w_mon2, + fc2_w_mon2_max, + simple_lr_fun(linear2.weight), + i + 1, + ) ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( - fc2_b, - fc2_b_grad, - fc2_b_mon1, - fc2_b_mon2, - simple_lr_fun(linear2.bias), - i + 1, + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = ( + get_numpy_output( + fc2_b, + fc2_b_grad, + fc2_b_mon1, + fc2_b_mon2, + fc2_b_mon2_max, + simple_lr_fun(linear2.bias), + i + 1, + ) ) np.testing.assert_allclose(params_and_gras[0], fc1_w, rtol=1e-6) np.testing.assert_allclose(params_and_gras[2], fc1_b, rtol=1e-6) - np.testing.assert_allclose(params_and_gras[4], fc2_w, rtol=1e-6) + np.testing.assert_allclose( + params_and_gras[4], + fc2_w, + rtol=1e-6 if not core.is_compiled_with_xpu() else 1e-5, + ) np.testing.assert_allclose(params_and_gras[6], fc2_b, rtol=1e-6) paddle.disable_static() @@ -1122,16 +1352,28 @@ def test_adamw_op_with_pir(self): fc1_w_mon2 = np.zeros(linear1.weight.shape).astype( "float32" ) + fc1_w_mon2_max = np.zeros(linear1.weight.shape).astype( + "float32" + ) fc1_b_mon1 = np.zeros(linear1.bias.shape).astype("float32") fc1_b_mon2 = np.zeros(linear1.bias.shape).astype("float32") + fc1_b_mon2_max = np.zeros(linear1.bias.shape).astype( + "float32" + ) fc2_w_mon1 = np.zeros(linear2.weight.shape).astype( "float32" ) fc2_w_mon2 = np.zeros(linear2.weight.shape).astype( "float32" ) + fc2_w_mon2_max = np.zeros(linear2.weight.shape).astype( + "float32" + ) fc2_b_mon1 = np.zeros(linear2.bias.shape).astype("float32") fc2_b_mon2 = np.zeros(linear2.bias.shape).astype("float32") + fc2_b_mon2_max = np.zeros(linear2.bias.shape).astype( + "float32" + ) cost = paddle.nn.functional.square_error_cost( input=out, label=y @@ -1149,15 +1391,19 @@ def test_adamw_op_with_pir(self): weight_decay=weight_decay, epsilon=epsilon, lr_ratio=simple_lr_fun, + amsgrad=self.amsgrad, ) _, params_grads = opt.minimize(avg_cost) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1**t]).astype("float32"), 'Beta2Pow': np.array([beta2**t]).astype("float32"), @@ -1170,11 +1416,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": weight_decay, "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( - np_inputs, np_attrs + param_out, moment1_out, moment2_out, moment2_out_max = ( + adamw_step(np_inputs, np_attrs) ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_out_max exe = base.Executor(place) exe.run(train_startup) @@ -1263,42 +1510,58 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): fc2_b = param[3] fc2_b_grad = params_and_gras[1] - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( - fc1_w, - fc1_w_grad, - fc1_w_mon1, - fc1_w_mon2, - simple_lr_fun(linear1.weight), - i + 1, + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = ( + get_numpy_output( + fc1_w, + fc1_w_grad, + fc1_w_mon1, + fc1_w_mon2, + fc1_w_mon2_max, + simple_lr_fun(linear1.weight), + i + 1, + ) ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( - fc1_b, - fc1_b_grad, - fc1_b_mon1, - fc1_b_mon2, - simple_lr_fun(linear1.bias), - i + 1, + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = ( + get_numpy_output( + fc1_b, + fc1_b_grad, + fc1_b_mon1, + fc1_b_mon2, + fc1_b_mon2_max, + simple_lr_fun(linear1.bias), + i + 1, + ) ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( - fc2_w, - fc2_w_grad, - fc2_w_mon1, - fc2_w_mon2, - simple_lr_fun(linear2.weight), - i + 1, + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = ( + get_numpy_output( + fc2_w, + fc2_w_grad, + fc2_w_mon1, + fc2_w_mon2, + fc2_w_mon2_max, + simple_lr_fun(linear2.weight), + i + 1, + ) ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( - fc2_b, - fc2_b_grad, - fc2_b_mon1, - fc2_b_mon2, - simple_lr_fun(linear2.bias), - i + 1, + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = ( + get_numpy_output( + fc2_b, + fc2_b_grad, + fc2_b_mon1, + fc2_b_mon2, + fc2_b_mon2_max, + simple_lr_fun(linear2.bias), + i + 1, + ) ) np.testing.assert_allclose(params_and_gras[6], fc1_w, rtol=1e-6) np.testing.assert_allclose(params_and_gras[4], fc1_b, rtol=1e-6) - np.testing.assert_allclose(params_and_gras[2], fc2_w, rtol=1e-6) + np.testing.assert_allclose( + params_and_gras[2], + fc2_w, + rtol=1e-6 if not core.is_compiled_with_xpu() else 1e-5, + ) np.testing.assert_allclose(params_and_gras[0], fc2_b, rtol=1e-6) paddle.disable_static() @@ -1321,16 +1584,20 @@ def test_weight_decay_int(self): fc1_w = np.array(linear1.weight) fc1_w_mon1 = np.zeros_like(fc1_w) fc1_w_mon2 = np.zeros_like(fc1_w) + fc1_w_mon2_max = np.zeros_like(fc1_w) fc1_b = np.array(linear1.bias) fc1_b_mon1 = np.zeros_like(fc1_b) fc1_b_mon2 = np.zeros_like(fc1_b) + fc1_b_mon2_max = np.zeros_like(fc1_b) fc2_w = np.array(linear2.weight) fc2_w_mon1 = np.zeros_like(fc2_w) fc2_w_mon2 = np.zeros_like(fc2_w) + fc2_w_mon2_max = np.zeros_like(fc2_w) fc2_b = np.array(linear2.bias) fc2_b_mon1 = np.zeros_like(fc2_b) fc2_b_mon2 = np.zeros_like(fc2_b) + fc2_b_mon2_max = np.zeros_like(fc2_b) simple_lr_fun = partial(simple_lr_setting, decay_rate=0.8, n_layers=2) learning_rate = 0.001 @@ -1349,14 +1616,18 @@ def test_weight_decay_int(self): apply_decay_param_fun=lambda name: True, weight_decay=weight_decay, lr_ratio=simple_lr_fun, + amsgrad=self.amsgrad, ) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, 'Moment1': moment1, 'Moment2': moment2, + 'Moment2Max': moment2_max, 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pow': np.array([beta1**t]).astype("float32"), 'Beta2Pow': np.array([beta2**t]).astype("float32"), @@ -1369,11 +1640,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": float(weight_decay), "with_decay": True, + "amsgrad": self.amsgrad, } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_out_max = adamw_step( np_inputs, np_attrs ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_out_max for i in range(5): a = paddle.to_tensor( @@ -1384,35 +1656,39 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): out = paddle.mean(out) out.backward() - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = get_numpy_output( fc1_w, np.array(linear1.weight.grad), fc1_w_mon1, fc1_w_mon2, + fc1_w_mon2_max, simple_lr_fun(linear1.weight), i + 1, ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = get_numpy_output( fc1_b, np.array(linear1.bias.grad), fc1_b_mon1, fc1_b_mon2, + fc1_b_mon2_max, simple_lr_fun(linear1.bias), i + 1, ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = get_numpy_output( fc2_w, np.array(linear2.weight.grad), fc2_w_mon1, fc2_w_mon2, + fc2_w_mon2_max, simple_lr_fun(linear2.weight), i + 1, ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = get_numpy_output( fc2_b, np.array(linear2.bias.grad), fc2_b_mon1, fc2_b_mon2, + fc2_b_mon2_max, simple_lr_fun(linear2.bias), i + 1, ) @@ -1431,5 +1707,20 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): np.testing.assert_allclose(linear2.bias.numpy(), fc2_b, rtol=1e-6) +class TestAdamWOpLayerwiseLRAMSGrad(TestAdamWOpLayerwiseLR): + def setUp(self): + random.seed(2022) + np.random.seed(2022) + paddle.seed(2022) + + # xpu not support `amsgrad` + if core.is_compiled_with_xpu(): + self.amsgrad = False + self.no_check_set = ['Moment2MaxOut'] + else: + self.amsgrad = True + self.no_check_set = None + + if __name__ == "__main__": unittest.main() diff --git a/test/legacy_test/test_fused_adam_op.py b/test/legacy_test/test_fused_adam_op.py index 8bbc1fafef05b7..225d7c9ab68909 100644 --- a/test/legacy_test/test_fused_adam_op.py +++ b/test/legacy_test/test_fused_adam_op.py @@ -25,12 +25,13 @@ def fused_adam_step(inputs, attributes, num): Simulate one step of the fused_adam optimizer :param inputs: dict of inputs :param attributes: dict of attributes - :return tuple: tuple of output params, moments1, moments2, beta1_pows, beta2_pows + :return tuple: tuple of output params, moments1, moments2, moments2_max, beta1_pows, beta2_pows ''' params = inputs['Params'] grads = inputs['Grads'] moments1 = inputs['Moments1'] moments2 = inputs['Moments2'] + moments2_max = inputs['Moments2Max'] lr = inputs['LearningRate'] beta1_pows = inputs['Beta1Pows'] beta2_pows = inputs['Beta2Pows'] @@ -38,6 +39,7 @@ def fused_adam_step(inputs, attributes, num): params_out = [] moments1_out = [] moments2_out = [] + moments2_max_out = [] beta1_pows_out = [] beta2_pows_out = [] @@ -52,16 +54,37 @@ def fused_adam_step(inputs, attributes, num): else: beta2 = inputs['Beta2Tensor'][0][0] + amsgrad = attributes['amsgrad'] + for i in range(num): - moments1_out.append(beta1 * moments1[i][1] + (1 - beta1) * grads[i][1]) - moments2_out.append( - beta2 * moments2[i][1] + (1 - beta2) * np.square(grads[i][1]) + _moment1_out = beta1 * moments1[i][1] + (1 - beta1) * grads[i][1] + _moment2_out = beta2 * moments2[i][1] + (1 - beta2) * np.square( + grads[i][1] ) + + moments1_out.append(_moment1_out) + moments2_out.append(_moment2_out) + lr_t = lr * np.sqrt(1 - beta2_pows[i][1]) / (1 - beta1_pows[i][1]) - params_out.append( - params[i][1] - - lr_t * (moments1_out[i] / (np.sqrt(moments2_out[i]) + epsilon)) - ) + + if amsgrad: + _moment2_max = np.maximum(_moment2_out, moments2_max[i][1]) + moments2_max_out.append(_moment2_max) + + params_out.append( + params[i][1] + - lr_t + * (moments1_out[i] / (np.sqrt(moments2_max_out[i]) + epsilon)) + ) + else: + _moment2_max = np.empty_like(_moment2_out) + moments2_max_out.append(_moment2_max) + + params_out.append( + params[i][1] + - lr_t + * (moments1_out[i] / (np.sqrt(moments2_out[i]) + epsilon)) + ) for i in range(num): beta1_pows_out.append(beta1_pows[i][1] * beta1) @@ -71,12 +94,18 @@ def fused_adam_step(inputs, attributes, num): params_out, moments1_out, moments2_out, + moments2_max_out, beta1_pows_out, beta2_pows_out, ) class TestFusedAdamOp(OpTest): + def set_amsgrad(self): + self.amsgrad = False + # no check `Moment2MaxOut` with amsgrad is False + self.no_check_set = ['Moments2MaxOut'] + def setUp(self): paddle.enable_static() @@ -91,12 +120,14 @@ def setUp(self): epsilon = 1e-4 beta1_pow = beta1**10 beta2_pow = beta2**10 + self.set_amsgrad() self.attrs = { 'epsilon': epsilon, 'beta1': beta1, 'beta2': beta2, "chunk_size": 32 * 2048, + "amsgrad": self.amsgrad, } for i in range(num): @@ -126,6 +157,10 @@ def setUp(self): 'Moments2': [ ("moments2" + str(i), inputs_list[3][i]) for i in range(num) ], + 'Moments2Max': [ + ("moments2_max" + str(i), np.zeros_like(inputs_list[0][i])) + for i in range(num) + ], 'LearningRate': np.array([learning_rate]).astype("float32"), 'Beta1Pows': [ ("beta1_pows" + str(i), inputs_list[4][i]) for i in range(num) @@ -139,6 +174,7 @@ def setUp(self): params_out, moments1_out, moments2_out, + moments2_max_out, beta1_pows_out, beta2_pows_out, ) = fused_adam_step(self.inputs, self.attrs, num) @@ -150,6 +186,10 @@ def setUp(self): 'Moments2Out': [ ("moments2_out" + str(i), moments2_out[i]) for i in range(num) ], + 'Moments2MaxOut': [ + ("moments2_max_out" + str(i), moments2_max_out[i]) + for i in range(num) + ], 'ParamsOut': [ ("params_out" + str(i), params_out[i]) for i in range(num) ], @@ -166,7 +206,15 @@ def setUp(self): def test_check_output(self): paddle.enable_static() if paddle.is_compiled_with_cuda(): - self.check_output(check_dygraph=False) + self.check_output( + no_check_set=self.no_check_set, check_dygraph=False + ) + + +class TestFusedAdamOpAMSGrad(TestFusedAdamOp): + def set_amsgrad(self): + self.amsgrad = True + self.no_check_set = None if __name__ == "__main__": diff --git a/test/legacy_test/test_merged_adam_op.py b/test/legacy_test/test_merged_adam_op.py index 8d1295d6a33412..29c21f4561256a 100644 --- a/test/legacy_test/test_merged_adam_op.py +++ b/test/legacy_test/test_merged_adam_op.py @@ -27,6 +27,7 @@ def run_adam_op( lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -36,11 +37,13 @@ def run_adam_op( place, multi_precision=False, use_merged=False, + amsgrad=False, ): assert len(params) == len(grads) assert len(params) == len(lrs) assert len(params) == len(moment1s) assert len(params) == len(moment2s) + assert len(params) == len(moment2s_max) assert len(params) == len(beta1_pows) assert len(params) == len(beta1_pows) assert len(params) == len(master_params) @@ -52,24 +55,27 @@ def run_adam_op( lr_vars = [paddle.to_tensor(l) for l in lrs] moment1_vars = [paddle.to_tensor(m) for m in moment1s] moment2_vars = [paddle.to_tensor(m) for m in moment2s] + moment2_max_vars = [paddle.to_tensor(m) for m in moment2s_max] beta1_pow_vars = [paddle.to_tensor(b) for b in beta1_pows] beta2_pow_vars = [paddle.to_tensor(b) for b in beta2_pows] master_param_vars = [paddle.to_tensor(m_p) for m_p in master_params] if not use_merged: for i in range(len(param_vars)): - _, _, _, _, _, _ = _legacy_C_ops.adam( + _, _, _, _, _, _, _ = _legacy_C_ops.adam( param_vars[i], grad_vars[i], lr_vars[i], moment1_vars[i], moment2_vars[i], + moment2_max_vars[i], beta1_pow_vars[i], beta2_pow_vars[i], master_param_vars[i], param_vars[i], moment1_vars[i], moment2_vars[i], + moment2_max_vars[i], beta1_pow_vars[i], beta2_pow_vars[i], master_param_vars[i], @@ -81,14 +87,17 @@ def run_adam_op( beta2, 'multi_precision', multi_precision, + 'amsgrad', + amsgrad, ) else: - _, _, _, _, _, _ = _C_ops.merged_adam_( + _, _, _, _, _, _, _ = _C_ops.merged_adam_( param_vars, grad_vars, lr_vars, moment1_vars, moment2_vars, + moment2_max_vars, beta1_pow_vars, beta2_pow_vars, master_param_vars, @@ -97,12 +106,14 @@ def run_adam_op( epsilon, multi_precision, False, + amsgrad, ) outputs = { 'ParamOut': param_vars, 'Moment1Out': moment1_vars, 'Moment2Out': moment2_vars, + 'Moment2MaxOut': moment2_max_vars, 'Beta1PowOut': beta1_pow_vars, 'Beta2PowOut': beta2_pow_vars, 'MasterParamOut': master_param_vars, @@ -112,14 +123,21 @@ def run_adam_op( class TestMergedAdam(unittest.TestCase): + def set_amsgrad(self): + self.amsgrad = False + def setUp(self): paddle.disable_static() self.shapes = [[3, 4], [2, 7], [5, 6], [7, 8]] self.seed = 10 + self.set_amsgrad() def gen_rand_data(self, shapes, dtype): return [np.random.random(s).astype(dtype) for s in shapes] + def gen_zero_data(self, shapes, dtype): + return [np.zeros(s).astype(dtype) for s in shapes] + def prepare_data(self, shapes, multi_precision, seed, place): np.random.seed(seed) mp_dtype = np.float32 @@ -129,6 +147,7 @@ def prepare_data(self, shapes, multi_precision, seed, place): lrs = self.gen_rand_data([[1], [1], [1], [1]], mp_dtype) moment1s = self.gen_rand_data(shapes, mp_dtype) moment2s = self.gen_rand_data(shapes, mp_dtype) + moment2s_max = self.gen_zero_data(shapes, mp_dtype) beta1_pows = self.gen_rand_data([[1], [1], [1], [1]], mp_dtype) beta2_pows = self.gen_rand_data([[1], [1], [1], [1]], mp_dtype) master_params = [p.astype(mp_dtype) for p in params] @@ -138,6 +157,7 @@ def prepare_data(self, shapes, multi_precision, seed, place): lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -150,6 +170,7 @@ def check_with_place(self, place, multi_precision): lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -162,6 +183,7 @@ def run_op(use_merged): lrs=lrs, moment1s=moment1s, moment2s=moment2s, + moment2s_max=moment2s_max, beta1_pows=beta1_pows, beta2_pows=beta2_pows, master_params=master_params, @@ -171,6 +193,7 @@ def run_op(use_merged): place=place, multi_precision=multi_precision, use_merged=use_merged, + amsgrad=self.amsgrad, ) outs1 = run_op(True) @@ -206,5 +229,10 @@ def test_main(self): self.check_with_place(place, multi_precision) +class TestMergedAdamAMSGrad(TestMergedAdam): + def set_amsgrad(self): + self.amsgrad = True + + if __name__ == "__main__": unittest.main() diff --git a/test/white_list/no_check_set_white_list.py b/test/white_list/no_check_set_white_list.py index 16bf755eecf6ef..c244591490561e 100644 --- a/test/white_list/no_check_set_white_list.py +++ b/test/white_list/no_check_set_white_list.py @@ -40,4 +40,7 @@ 'rrelu', 'layer_norm', 'max_pool2d_v2', + 'adam', # AMSGrad variant no check moment2 max output + 'adamw', # AMSGrad variant no check moment2 max output + 'fused_adam', # AMSGrad variant no check moments2 max output ] diff --git a/test/xpu/test_adam_op_xpu.py b/test/xpu/test_adam_op_xpu.py index 54f8d36a187a4a..025881ed43be89 100644 --- a/test/xpu/test_adam_op_xpu.py +++ b/test/xpu/test_adam_op_xpu.py @@ -45,7 +45,7 @@ def setUp(self): self.set_shape() self.set_inputs() self.set_steps() - param_out, moment1_out, moment2_out = adam_step( + param_out, moment1_out, moment2_out, moment2_out_max = adam_step( self.inputs, self.attrs ) @@ -109,7 +109,11 @@ def set_inputs(self): } def test_check_output(self): - self.check_output_with_place(place=paddle.XPUPlace(0), atol=1e-2) + self.check_output_with_place( + no_check_set=['Moment2MaxOut'], + place=paddle.XPUPlace(0), + atol=1e-2, + ) class TestAdamOp2(TestAdamOp): '''Test Adam Op with supplied attributes''' @@ -163,7 +167,7 @@ def setUp(self): self.set_shape() self.set_inputs() self.set_steps() - param_out, moment1_out, moment2_out = adam_step( + param_out, moment1_out, moment2_out, moment2_out_max = adam_step( self.inputs, self.attrs ) @@ -207,8 +211,8 @@ def set_steps(self): def test_check_output(self): for _ in range(self.num_steps): - param_out, moment1_out, moment2_out = adam_step( - self.inputs, self.attrs + param_out, moment1_out, moment2_out, moment2_out_max = ( + adam_step(self.inputs, self.attrs) ) beta1_pow_out = self.inputs['Beta1Pow'] * self.beta1 @@ -223,7 +227,9 @@ def test_check_output(self): # Verify output for this step self.check_output_with_place( - place=paddle.XPUPlace(0), atol=1e-2 + no_check_set=['Moment2MaxOut'], + place=paddle.XPUPlace(0), + atol=1e-2, ) # Output of this step becomes input for next step @@ -246,13 +252,14 @@ def adam_step(inputs, attributes): Simulate one step of the adam optimizer :param inputs: dict of inputs :param attributes: dict of attributes - :return tuple: tuple of output param, moment1, moment2, + :return tuple: tuple of output param, moment1, moment2, moment2_max beta1 power accumulator and beta2 power accumulator ''' param = inputs['Param'] grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs.get('Moment2Max', None) lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -268,13 +275,27 @@ def adam_step(inputs, attributes): else: beta2 = inputs['Beta2Tensor'][0] + amsgrad = attributes.get('amsgrad', False) + moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) + lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) - param_out = param - lr_t * ( - moment1_out / (np.sqrt(moment2_out) + epsilon * np.sqrt(1 - beta2_pow)) - ) - return param_out, moment1_out, moment2_out + + if amsgrad: + moment2_max_out = np.maximum(moment2_out, moment2_max) + param_out = param - lr_t * ( + moment1_out + / (np.sqrt(moment2_max_out) + epsilon * np.sqrt(1 - beta2_pow)) + ) + else: + moment2_max_out = np.empty_like(moment2_out) + param_out = param - lr_t * ( + moment1_out + / (np.sqrt(moment2_out) + epsilon * np.sqrt(1 - beta2_pow)) + ) + + return param_out, moment1_out, moment2_out, moment2_max_out def adam_step_sparse( @@ -291,6 +312,7 @@ def adam_step_sparse( # grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs.get('Moment2Max', None) lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -298,9 +320,11 @@ def adam_step_sparse( beta1 = attributes['beta1'] beta2 = attributes['beta2'] epsilon = attributes['epsilon'] + amsgrad = attributes.get('amsgrad', False) moment1_out = np.zeros(shape=[height, row_numel]) moment2_out = np.zeros(shape=[height, row_numel]) + moment2_max_out = np.zeros(shape=[height, row_numel]) param_out = np.zeros(shape=[height, row_numel]) def update_row(row_id, update_value): @@ -311,9 +335,20 @@ def update_row(row_id, update_value): update_value ) lr_t = lr * np.sqrt(1 - beta2_pow) / (1 - beta1_pow) - param_out[row_id] = param[row_id] - lr_t * ( - moment1_out[row_id] / (np.sqrt(moment2_out[row_id]) + epsilon) - ) + + if amsgrad: + moment2_max_out[row_id] = np.maximum( + moment2_out[row_id], moment2_max[row_id] + ) + param_out[row_id] = param[row_id] - lr_t * ( + moment1_out[row_id] + / (np.sqrt(moment2_max_out[row_id]) + epsilon) + ) + else: + moment2_max_out[row_id] = np.empty_like(moment2_out[row_id]) + param_out[row_id] = param[row_id] - lr_t * ( + moment1_out[row_id] / (np.sqrt(moment2_out[row_id]) + epsilon) + ) if lazy_mode: for idx, row_id in enumerate(rows): @@ -325,7 +360,7 @@ def update_row(row_id, update_value): update_value = np_grad[rows.index(row_id)] update_row(row_id, update_value) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max_out class TestSparseAdamOp(unittest.TestCase): @@ -355,6 +390,7 @@ def setup(self, scope, place, lazy_mode): 'beta1': beta1, 'beta2': beta2, 'min_row_size_to_use_multithread': 2, + 'amsgrad': False, # Currently, xpu NOT support amsgrad. } grad_selected_rows = scope.var('Grad').get_selected_rows() @@ -369,7 +405,7 @@ def setup(self, scope, place, lazy_mode): self.sparse_inputs = ["Grad"] - param_out, mom1, mom2 = adam_step_sparse( + param_out, mom1, mom2, mom2_max = adam_step_sparse( self.dense_inputs, self.attrs, height, @@ -410,6 +446,9 @@ def check_with_place(self, place, lazy_mode): adam_op.run(scope, place) for key, np_array in self.outputs.items(): + if key in ['Moment2MaxOut']: # Currently, xpu NOT support amsgrad. + continue + out_var = scope.var(key).get_tensor() actual = np.array(out_var) actual = actual.reshape([actual.size]) @@ -452,6 +491,7 @@ def setup(self, scope, place, lazy_mode): 'beta1': beta1, 'beta2': beta2, 'min_row_size_to_use_multithread': 2, + 'amsgrad': False, # Currently, xpu NOT support amsgrad. } grad_selected_rows = scope.var('Grad').get_selected_rows() @@ -466,7 +506,7 @@ def setup(self, scope, place, lazy_mode): self.sparse_inputs = ["Grad"] - param_out, mom1, mom2 = adam_step_sparse( + param_out, mom1, mom2, mom2_max = adam_step_sparse( self.dense_inputs, self.attrs, height, diff --git a/test/xpu/test_adamw_op_xpu.py b/test/xpu/test_adamw_op_xpu.py index 3919272137e8d1..bb33e34051d3dd 100644 --- a/test/xpu/test_adamw_op_xpu.py +++ b/test/xpu/test_adamw_op_xpu.py @@ -35,6 +35,7 @@ def adamw_step(inputs, attributes): grad = inputs['Grad'] moment1 = inputs['Moment1'] moment2 = inputs['Moment2'] + moment2_max = inputs.get('Moment2Max', None) lr = inputs['LearningRate'] beta1_pow = inputs['Beta1Pow'] beta2_pow = inputs['Beta2Pow'] @@ -59,11 +60,21 @@ def adamw_step(inputs, attributes): else: beta2 = inputs['Beta2Tensor'][0] + amsgrad = attributes.get('amsgrad', False) + moment1_out = beta1 * moment1 + (1 - beta1) * grad moment2_out = beta2 * moment2 + (1 - beta2) * np.square(grad) - denom = (np.sqrt(moment2_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + + if amsgrad: + moment2_max_out = np.maximum(moment2_out, moment2_max) + denom = (np.sqrt(moment2_max_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + else: + moment2_max_out = np.empty_like(moment2_out) + denom = (np.sqrt(moment2_out) / np.sqrt(1.0 - beta2_pow)) + epsilon + param_out = param + ((moment1_out / denom) * (-(lr / (1.0 - beta1_pow)))) - return param_out, moment1_out, moment2_out + + return param_out, moment1_out, moment2_out, moment2_max_out def simple_lr_setting(param, decay_rate, n_layers): @@ -119,9 +130,10 @@ def setUp(self): 'beta2': beta2, "coeff": 0.5, "with_decay": True, + "amsgrad": False, # Currently, xpu NOT support amsgrad. } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max_out = adamw_step( self.inputs, self.attrs ) @@ -147,7 +159,9 @@ def init_shape(self): def test_check_output(self): paddle.enable_static() - self.check_output_with_place(place=paddle.XPUPlace(0)) + self.check_output_with_place( + no_check_set=['Moment2MaxOut'], place=paddle.XPUPlace(0) + ) # Currently, xpu NOT support amsgrad. def infer_dtype_from_inputs_outputs(self, inputs, outputs): self.__class__.dtype = self.dtype @@ -355,16 +369,20 @@ def test_adamw_op_dygraph(self): fc1_w = np.array(linear1.weight) fc1_w_mon1 = np.zeros_like(fc1_w) fc1_w_mon2 = np.zeros_like(fc1_w) + fc1_w_mon2_max = np.zeros_like(fc1_w) fc1_b = np.array(linear1.bias) fc1_b_mon1 = np.zeros_like(fc1_b) fc1_b_mon2 = np.zeros_like(fc1_b) + fc1_b_mon2_max = np.zeros_like(fc1_b) fc2_w = np.array(linear2.weight) fc2_w_mon1 = np.zeros_like(fc2_w) fc2_w_mon2 = np.zeros_like(fc2_w) + fc2_w_mon2_max = np.zeros_like(fc2_w) fc2_b = np.array(linear2.bias) fc2_b_mon1 = np.zeros_like(fc2_b) fc2_b_mon2 = np.zeros_like(fc2_b) + fc2_b_mon2_max = np.zeros_like(fc2_b) simple_lr_fun = partial( simple_lr_setting, decay_rate=0.8, n_layers=2 @@ -387,7 +405,9 @@ def test_adamw_op_dygraph(self): lr_ratio=simple_lr_fun, ) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, @@ -405,11 +425,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": weight_decay, "with_decay": True, + "amsgrad": False, # Currently, xpu NOT support amsgrad. } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max = adamw_step( np_inputs, np_attrs ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max for i in range(5): a = paddle.to_tensor( @@ -420,37 +441,49 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): out = paddle.mean(out) out.backward() - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( - fc1_w, - np.array(linear1.weight.grad), - fc1_w_mon1, - fc1_w_mon2, - simple_lr_fun(linear1.weight), - i + 1, + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = ( + get_numpy_output( + fc1_w, + np.array(linear1.weight.grad), + fc1_w_mon1, + fc1_w_mon2, + fc1_w_mon2_max, + simple_lr_fun(linear1.weight), + i + 1, + ) ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( - fc1_b, - np.array(linear1.bias.grad), - fc1_b_mon1, - fc1_b_mon2, - simple_lr_fun(linear1.bias), - i + 1, + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = ( + get_numpy_output( + fc1_b, + np.array(linear1.bias.grad), + fc1_b_mon1, + fc1_b_mon2, + fc1_b_mon2_max, + simple_lr_fun(linear1.bias), + i + 1, + ) ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( - fc2_w, - np.array(linear2.weight.grad), - fc2_w_mon1, - fc2_w_mon2, - simple_lr_fun(linear2.weight), - i + 1, + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = ( + get_numpy_output( + fc2_w, + np.array(linear2.weight.grad), + fc2_w_mon1, + fc2_w_mon2, + fc2_w_mon2_max, + simple_lr_fun(linear2.weight), + i + 1, + ) ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( - fc2_b, - np.array(linear2.bias.grad), - fc2_b_mon1, - fc2_b_mon2, - simple_lr_fun(linear2.bias), - i + 1, + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = ( + get_numpy_output( + fc2_b, + np.array(linear2.bias.grad), + fc2_b_mon1, + fc2_b_mon2, + fc2_b_mon2_max, + simple_lr_fun(linear2.bias), + i + 1, + ) ) opt.step() @@ -520,16 +553,28 @@ def test_adamw_op(self): fc1_w_mon2 = np.zeros(linear1.weight.shape).astype( "float32" ) + fc1_w_mon2_max = np.zeros(linear1.weight.shape).astype( + "float32" + ) fc1_b_mon1 = np.zeros(linear1.bias.shape).astype("float32") fc1_b_mon2 = np.zeros(linear1.bias.shape).astype("float32") + fc1_b_mon2_max = np.zeros(linear1.bias.shape).astype( + "float32" + ) fc2_w_mon1 = np.zeros(linear2.weight.shape).astype( "float32" ) fc2_w_mon2 = np.zeros(linear2.weight.shape).astype( "float32" ) + fc2_w_mon2_max = np.zeros(linear2.weight.shape).astype( + "float32" + ) fc2_b_mon1 = np.zeros(linear2.bias.shape).astype("float32") fc2_b_mon2 = np.zeros(linear2.bias.shape).astype("float32") + fc2_b_mon2_max = np.zeros(linear2.bias.shape).astype( + "float32" + ) cost = paddle.nn.functional.square_error_cost( input=out, label=y @@ -550,7 +595,9 @@ def test_adamw_op(self): ) _, params_grads = opt.minimize(avg_cost) - def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): + def get_numpy_output( + param, grad, moment1, moment2, moment2_max, lr_ratio, t + ): np_inputs = { 'Param': param, 'Grad': grad, @@ -568,11 +615,12 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): "lr_ratio": lr_ratio, "coeff": weight_decay, "with_decay": True, + "amsgrad": False, # Currently, xpu NOT support amsgrad. } - param_out, moment1_out, moment2_out = adamw_step( + param_out, moment1_out, moment2_out, moment2_max = adamw_step( np_inputs, np_attrs ) - return param_out, moment1_out, moment2_out + return param_out, moment1_out, moment2_out, moment2_max if paddle.framework.in_pir_mode(): fetch_list1 = [ @@ -637,37 +685,49 @@ def get_numpy_output(param, grad, moment1, moment2, lr_ratio, t): fc2_b = param[3] fc2_b_grad = params_and_gras[7] - fc1_w, fc1_w_mon1, fc1_w_mon2 = get_numpy_output( - fc1_w, - fc1_w_grad, - fc1_w_mon1, - fc1_w_mon2, - simple_lr_fun(linear1.weight), - i + 1, + fc1_w, fc1_w_mon1, fc1_w_mon2, fc1_w_mon2_max = ( + get_numpy_output( + fc1_w, + fc1_w_grad, + fc1_w_mon1, + fc1_w_mon2, + fc1_w_mon2_max, + simple_lr_fun(linear1.weight), + i + 1, + ) ) - fc1_b, fc1_b_mon1, fc1_b_mon2 = get_numpy_output( - fc1_b, - fc1_b_grad, - fc1_b_mon1, - fc1_b_mon2, - simple_lr_fun(linear1.bias), - i + 1, + fc1_b, fc1_b_mon1, fc1_b_mon2, fc1_b_mon2_max = ( + get_numpy_output( + fc1_b, + fc1_b_grad, + fc1_b_mon1, + fc1_b_mon2, + fc1_b_mon2_max, + simple_lr_fun(linear1.bias), + i + 1, + ) ) - fc2_w, fc2_w_mon1, fc2_w_mon2 = get_numpy_output( - fc2_w, - fc2_w_grad, - fc2_w_mon1, - fc2_w_mon2, - simple_lr_fun(linear2.weight), - i + 1, + fc2_w, fc2_w_mon1, fc2_w_mon2, fc2_w_mon2_max = ( + get_numpy_output( + fc2_w, + fc2_w_grad, + fc2_w_mon1, + fc2_w_mon2, + fc2_w_mon2_max, + simple_lr_fun(linear2.weight), + i + 1, + ) ) - fc2_b, fc2_b_mon1, fc2_b_mon2 = get_numpy_output( - fc2_b, - fc2_b_grad, - fc2_b_mon1, - fc2_b_mon2, - simple_lr_fun(linear2.bias), - i + 1, + fc2_b, fc2_b_mon1, fc2_b_mon2, fc2_b_mon2_max = ( + get_numpy_output( + fc2_b, + fc2_b_grad, + fc2_b_mon1, + fc2_b_mon2, + fc2_b_mon2_max, + simple_lr_fun(linear2.bias), + i + 1, + ) ) np.testing.assert_allclose( @@ -715,6 +775,7 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( main_grad = grad.astype(paddle.float32) moment1 = paddle.randn(shape).astype(paddle.float32) moment2 = paddle.randn(shape).astype(paddle.float32).abs() + moment2_max = paddle.zeros(shape).astype(paddle.float32) lr = paddle.zeros([1]).astype(paddle.float32) lr[0] = lr_rate beta1_pow_acc = paddle.ones([1]).astype(paddle.float32) @@ -731,14 +792,16 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( ) ref_moment_1 = moment1.astype(paddle.float32).clone().detach() ref_moment_2 = moment2.astype(paddle.float32).clone().detach() + ref_moment_2_max = moment2_max.astype(paddle.float32).clone().detach() # reference code - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( ref_param, main_grad, lr, ref_moment_1, ref_moment_2, + ref_moment_2_max, ref_beta1_pow_acc, ref_beta2_pow_acc, master_weight, @@ -753,15 +816,17 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, False, False, + False, # Currently, xpu NOT support amsgrad. ) if use_main_grad: - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( param, main_grad, lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -776,6 +841,7 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, find_master, False, + False, # Currently, xpu NOT support amsgrad. ) np.testing.assert_allclose( param.astype("float32").numpy(), ref_param.numpy(), rtol=1e-2 @@ -784,12 +850,13 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( master_weight.numpy(), ref_param.numpy(), rtol=1e-6 ) else: - _, _, _, _, _, _ = paddle._C_ops.adamw_( + _, _, _, _, _, _, _ = paddle._C_ops.adamw_( param, grad, lr, moment1, moment2, + moment2_max, beta1_pow_acc, beta2_pow_acc, master_weight, @@ -804,6 +871,7 @@ def _test_adamw_op_dygraph_place_amp_with_maingrad( 1000, find_master, False, + False, # Currently, xpu NOT support amsgrad. ) np.testing.assert_allclose( param.astype("float32").numpy(), ref_param.numpy(), rtol=1e-2 diff --git a/test/xpu/test_merged_adam_op_xpu.py b/test/xpu/test_merged_adam_op_xpu.py index 5848db0aabfe66..20cfdf1fe83332 100644 --- a/test/xpu/test_merged_adam_op_xpu.py +++ b/test/xpu/test_merged_adam_op_xpu.py @@ -34,6 +34,7 @@ def run_adam_op( lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -43,6 +44,7 @@ def run_adam_op( place, multi_precision=False, use_merged=False, + amsgrad=False, ): assert len(params) == len(grads) assert len(params) == len(lrs) @@ -59,24 +61,27 @@ def run_adam_op( lr_vars = [paddle.to_tensor(l) for l in lrs] moment1_vars = [paddle.to_tensor(m) for m in moment1s] moment2_vars = [paddle.to_tensor(m) for m in moment2s] + moment2_max_vars = [paddle.to_tensor(m) for m in moment2s_max] beta1_pow_vars = [paddle.to_tensor(b) for b in beta1_pows] beta2_pow_vars = [paddle.to_tensor(b) for b in beta2_pows] master_param_vars = [paddle.to_tensor(m_p) for m_p in master_params] if not use_merged: for i in range(len(param_vars)): - _, _, _, _, _, _ = _legacy_C_ops.adam( + _, _, _, _, _, _, _ = _legacy_C_ops.adam( param_vars[i], grad_vars[i], lr_vars[i], moment1_vars[i], moment2_vars[i], + moment2_max_vars[i], beta1_pow_vars[i], beta2_pow_vars[i], master_param_vars[i], param_vars[i], moment1_vars[i], moment2_vars[i], + moment2_max_vars[i], beta1_pow_vars[i], beta2_pow_vars[i], master_param_vars[i], @@ -88,14 +93,17 @@ def run_adam_op( beta2, 'multi_precision', False, + 'amsgrad', + amsgrad, ) else: - _, _, _, _, _, _ = _C_ops.merged_adam_( + _, _, _, _, _, _, _ = _C_ops.merged_adam_( param_vars, grad_vars, lr_vars, moment1_vars, moment2_vars, + moment2_max_vars, beta1_pow_vars, beta2_pow_vars, master_param_vars, @@ -104,12 +112,14 @@ def run_adam_op( epsilon, False, False, + amsgrad, ) outputs = { 'ParamOut': param_vars, 'Moment1Out': moment1_vars, 'Moment2Out': moment2_vars, + 'Moment2MaxOut': moment2_max_vars, 'Beta1PowOut': beta1_pow_vars, 'Beta2PowOut': beta2_pow_vars, 'MasterParamOut': master_param_vars, @@ -131,6 +141,9 @@ def setUp(self): def gen_rand_data(self, shapes, dtype): return [np.random.random(s).astype(dtype) for s in shapes] + def gen_zero_data(self, shapes, dtype): + return [np.zeros(s).astype(dtype) for s in shapes] + def prepare_data(self, shapes, seed): np.random.seed(seed) mp_dtype = np.float32 @@ -141,6 +154,7 @@ def prepare_data(self, shapes, seed): lrs = [learning_rate.copy() for _ in shapes] moment1s = self.gen_rand_data(shapes, mp_dtype) moment2s = self.gen_rand_data(shapes, mp_dtype) + moment2s_max = self.gen_zero_data(shapes, mp_dtype) beta1_pow = self.gen_rand_data([[1]], mp_dtype) beta2_pow = self.gen_rand_data([[1]], mp_dtype) beta1_pows = [beta1_pow.copy() for _ in shapes] @@ -152,6 +166,7 @@ def prepare_data(self, shapes, seed): lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -164,6 +179,7 @@ def check_with_place(self): lrs, moment1s, moment2s, + moment2s_max, beta1_pows, beta2_pows, master_params, @@ -176,6 +192,7 @@ def run_op(use_merged, place): lrs=lrs, moment1s=moment1s, moment2s=moment2s, + moment2s_max=moment2s_max, beta1_pows=beta1_pows, beta2_pows=beta2_pows, master_params=master_params, @@ -185,6 +202,7 @@ def run_op(use_merged, place): place=place, multi_precision=False, use_merged=use_merged, + amsgrad=False, # Currently, xpu NOT support amsgrad. ) outs1 = run_op(True, "xpu") @@ -197,6 +215,9 @@ def run_op(use_merged, place): self.assertEqual(len(outs1), len(outs4)) for key in outs1.keys(): + if key in ['Moment2MaxOut']: + continue + value1 = outs1[key] value2 = outs2[key] value3 = outs3[key] From 57aff1ffbe51640ad636de8ce48d5fc2a802bf82 Mon Sep 17 00:00:00 2001 From: Leo Chen Date: Wed, 4 Dec 2024 12:07:09 +0800 Subject: [PATCH 143/288] fix empty shape in einsum op (#69918) --- paddle/phi/api/lib/api_gen_utils.cc | 7 +++++++ paddle/phi/api/lib/data_transform.cc | 2 +- paddle/phi/kernels/impl/einsum_impl.h | 6 +++++- 3 files changed, 13 insertions(+), 2 deletions(-) diff --git a/paddle/phi/api/lib/api_gen_utils.cc b/paddle/phi/api/lib/api_gen_utils.cc index d7a12980ab1f34..1d4135a04764a4 100644 --- a/paddle/phi/api/lib/api_gen_utils.cc +++ b/paddle/phi/api/lib/api_gen_utils.cc @@ -805,6 +805,13 @@ void SetReplicatedDistAttrForOutput( phi::distributed::DistTensor* out, const phi::distributed::ProcessMesh& process_mesh) { if (out) { + if (out->dims().size() == -1 || out->dims().size() == 0) { + if (out->local_dims().size() != -1 && out->local_dims().size() != 0) { + out->unsafe_set_dims(out->local_dims()); + VLOG(3) + << "DistTensor out has empty shape, use its local value's shape"; + } + } // For inplace output, we also need to set replicated dist attr auto dist_attr = phi::distributed::TensorDistAttr(common::vectorize(out->dims())); diff --git a/paddle/phi/api/lib/data_transform.cc b/paddle/phi/api/lib/data_transform.cc index 4f7cd1fd2db75d..25e58abfdd0253 100644 --- a/paddle/phi/api/lib/data_transform.cc +++ b/paddle/phi/api/lib/data_transform.cc @@ -779,7 +779,7 @@ ReshardApiInputToKernelInput(phi::DeviceContext* dev_ctx, if (tensor_in) { phi::distributed::DistTensor* dist_tensor = static_cast(tensor_in.get()); - VLOG(4) << "ReshardIsNeededWithPartial" + VLOG(4) << "ReshardIsNeededWithPartial " << ReshardIsNeededWithPartial(dist_tensor->dist_attr(), dist_attr); if (ReshardIsNeededWithPartial(dist_tensor->dist_attr(), dist_attr)) { diff --git a/paddle/phi/kernels/impl/einsum_impl.h b/paddle/phi/kernels/impl/einsum_impl.h index 0d3f6a830b424d..9c73be86c05689 100644 --- a/paddle/phi/kernels/impl/einsum_impl.h +++ b/paddle/phi/kernels/impl/einsum_impl.h @@ -519,8 +519,12 @@ DenseTensor PerformContraction( label2type); trans_t = PerformTranspose( dev_ctx, reduct_t, perm, reordered_all_labels, label2type); - if (cache[operand_idx] != nullptr) + if (cache[operand_idx] != nullptr) { cache[operand_idx]->ShareBufferWith(trans_t); + cache[operand_idx]->Resize(trans_t.dims()); + VLOG(5) << "Set dims of cache[" << operand_idx + << "]: " << trans_t.dims(); + } } auto mul_dims = GetShapeByType( all_labels, label2type, perm, label2shape, {LabelType::Batch}); From 04fe843ab5aaa7214f6dd8d531d7ccaa566a1785 Mon Sep 17 00:00:00 2001 From: Function-Samuel <73766329+Function-Samuel@users.noreply.github.com> Date: Wed, 4 Dec 2024 12:15:31 +0800 Subject: [PATCH 144/288] =?UTF-8?q?=E3=80=90Comm=E3=80=91Fix=20csoftmax=20?= =?UTF-8?q?init=20bug=20(#69825)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../instruction/instruction_util.cc | 20 +++++++++++++++++-- 1 file changed, 18 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc index 40c82e4e1d7a1a..65beeb8dfeb27f 100644 --- a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc +++ b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc @@ -37,6 +37,8 @@ #include "paddle/pir/include/core/block_argument.h" #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) #include "paddle/common/flags.h" +#include "paddle/fluid/distributed/collective/process_group.h" +#include "paddle/fluid/distributed/collective/process_group_nccl.h" #include "paddle/phi/core/distributed/comm_context_manager.h" #include "paddle/phi/core/distributed/nccl_comm_context.h" #include "paddle/phi/core/platform/collective_helper.h" @@ -142,8 +144,20 @@ phi::DeviceContext* ParseDeviceContext(pir::Operation* op, op_attributes.at("ring_id").dyn_cast().data(); const auto& comm_context_manager = phi::distributed::CommContextManager::GetInstance(); + + phi::distributed::CommContext* comm_context = nullptr; if (comm_context_manager.Has(std::to_string(ring_id))) { - auto comm_context = comm_context_manager.Get(std::to_string(ring_id)); + comm_context = comm_context_manager.Get(std::to_string(ring_id)); + } else if (op_name.compare( + paddle::dialect::CSoftmaxWithCrossEntropyOp::name()) == + 0) { + auto map = distributed::ProcessGroupMapFromGid::getInstance(); + distributed::ProcessGroup* pg = map->get(ring_id); + comm_context = static_cast(pg) + ->GetOrCreateCommContext(place); + } + + if (comm_context) { dev_ctx = static_cast( static_cast(comm_context) ->GetDevContext()); @@ -153,7 +167,9 @@ phi::DeviceContext* ParseDeviceContext(pir::Operation* op, op_name.compare(paddle::dialect::AllReduce_Op::name()) == 0 || op_name.compare(paddle::dialect::Broadcast_Op::name()) == 0 || op_name.compare(paddle::dialect::BroadcastOp::name()) == 0 || - op_name.compare(paddle::dialect::AllGatherOp::name()) == 0) { + op_name.compare(paddle::dialect::AllGatherOp::name()) == 0 || + op_name.compare( + paddle::dialect::CSoftmaxWithCrossEntropyOp::name()) == 0) { if (phi::is_gpu_place(place) && execution_stream == kDefaultStream) { if (origin_dev_ctx != nullptr) { // set stream From eed933c2f127260564e9c8ef97b5656e9b5cff4f Mon Sep 17 00:00:00 2001 From: Terry <38135104+TR666@users.noreply.github.com> Date: Wed, 4 Dec 2024 13:21:16 +0800 Subject: [PATCH 145/288] [XPU] Optimization for rms_norm infer performance (#69899) * [XPU][PIR] add rms_norm_xpu_fuse_pass * [XPU] Optimization for rms_norm infer performance --- paddle/phi/kernels/xpu/rms_norm_kernel.cc | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/paddle/phi/kernels/xpu/rms_norm_kernel.cc b/paddle/phi/kernels/xpu/rms_norm_kernel.cc index c04c77532ee217..bcc00ed14b54f7 100644 --- a/paddle/phi/kernels/xpu/rms_norm_kernel.cc +++ b/paddle/phi/kernels/xpu/rms_norm_kernel.cc @@ -78,7 +78,7 @@ void RmsNormKernel(const Context& dev_ctx, dev_ctx.template Alloc(out); T* out_data = out->data(); float* inv_var_data = nullptr; - if (inv_var != nullptr) { + if (inv_var != nullptr && std::getenv("XPU_RMS_NORM_INFER_OPT") == nullptr) { dev_ctx.template Alloc(inv_var); inv_var_data = inv_var->data(); } From 5a87fe9cf653467e107c5198861cc21da471ece4 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 13:56:33 +0800 Subject: [PATCH 146/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-3=20(#64758)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 3 * part 3 --- .../fluid/distributed/fleet_executor/carrier.cc | 6 ++---- paddle/fluid/framework/io/save_runtime_graph.cc | 6 ++---- .../fusion_group/elementwise_group_detector.cc | 10 ++-------- .../framework/ir/lock_free_optimize_pass.cc | 8 ++------ .../ir/onednn/cpu_quantize_placement_pass.cc | 8 ++------ .../quant_transpose2_dequant_onednn_fuse_pass.cc | 8 ++------ .../ir/remove_padding_recover_padding_pass.cc | 12 ++++-------- .../onednn/onednn_legacy_instruction.cc | 6 ++---- .../onednn/onednn_mixed_instruction.cc | 6 ++---- paddle/fluid/framework/op_proto_maker.cc | 6 ++---- paddle/fluid/framework/operator.cc | 6 ++---- paddle/fluid/framework/prune.cc | 8 +++----- paddle/fluid/framework/tensor_util.cc | 6 ++---- paddle/fluid/framework/var_type_traits.cc | 12 ++++++------ .../inference/api/details/reset_tensor_array.cc | 12 ++++-------- .../fluid/inference/tensorrt/convert/pad_op.cc | 8 ++------ .../tensorrt/convert/preln_groupnorm_act_op.cc | 8 ++------ .../inference/tensorrt/convert/reduce_op.cc | 8 ++------ .../tensorrt/convert/reverse_roll_op.cc | 8 ++------ .../fluid/inference/tensorrt/convert/size_op.cc | 8 ++------ .../fluid/inference/tensorrt/convert/split_op.cc | 8 ++------ .../fluid/ir_adaptor/translator/op_translator.cc | 6 ++---- .../fluid/operators/collective/c_embedding_op.cc | 6 ++---- .../operators/collective/c_gen_nccl_id_op.cc | 6 ++---- .../operators/collective/c_reduce_avg_op.cc | 16 ++++++---------- .../operators/collective/c_reduce_min_op.cc | 16 ++++++---------- .../operators/collective/c_reduce_prod_op.cc | 16 ++++++---------- paddle/fluid/operators/set_value_op.cc | 16 ++++++---------- .../pir/dialect/kernel/ir/kernel_dialect.cc | 6 ++---- .../pir/dialect/operator/ir/manual_op_vjp.cc | 6 ++---- paddle/phi/api/lib/api_gen_utils.cc | 6 ++---- paddle/phi/backends/dynload/dynamic_loader.cc | 6 ++---- paddle/phi/core/distributed/comm_task_manager.cc | 6 ++---- paddle/phi/core/distributed/store/store.cc | 6 ++---- .../phi/core/memory/allocation/mmap_allocator.cc | 8 ++------ .../allocation/stream_safe_cuda_allocator.cc | 8 ++------ paddle/phi/infermeta/spmd_rules/squeeze.cc | 6 ++---- paddle/phi/kernels/funcs/eigen/constant.cc | 6 ++---- paddle/phi/kernels/funcs/matrix_reduce.cc | 6 ++---- .../fusion/cpu/fusion_repeated_fc_relu_kernel.cc | 6 ++---- paddle/phi/kernels/fusion/onednn/fc_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/softmax_kernel.cc | 6 ++---- paddle/utils/pybind.cc | 6 ++---- 43 files changed, 111 insertions(+), 233 deletions(-) diff --git a/paddle/fluid/distributed/fleet_executor/carrier.cc b/paddle/fluid/distributed/fleet_executor/carrier.cc index 4ad74377167e10..920aa3317e02cd 100644 --- a/paddle/fluid/distributed/fleet_executor/carrier.cc +++ b/paddle/fluid/distributed/fleet_executor/carrier.cc @@ -36,8 +36,7 @@ PHI_DEFINE_EXPORTED_bool( "executor."); COMMON_DECLARE_bool(cache_inference_while_scope); -namespace paddle { -namespace distributed { +namespace paddle::distributed { USE_INTERCEPTOR(Source); USE_INTERCEPTOR(Compute); @@ -422,5 +421,4 @@ void Carrier::CreateInterceptors( } } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/io/save_runtime_graph.cc b/paddle/fluid/framework/io/save_runtime_graph.cc index 2b22eaad1680d1..ecce19148d055f 100644 --- a/paddle/fluid/framework/io/save_runtime_graph.cc +++ b/paddle/fluid/framework/io/save_runtime_graph.cc @@ -17,8 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/node.h" #include "paddle/phi/common/port.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void save_string(std::string content, std::string type, @@ -113,5 +112,4 @@ void save_runtime_cinn_graph(const ir::Graph& graph, save_graph(graph, "graph", saved_path + "/subgraph.txt"); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/fusion_group/elementwise_group_detector.cc b/paddle/fluid/framework/ir/fusion_group/elementwise_group_detector.cc index 096f4b8d0ac427..0197de91e60f13 100644 --- a/paddle/fluid/framework/ir/fusion_group/elementwise_group_detector.cc +++ b/paddle/fluid/framework/ir/fusion_group/elementwise_group_detector.cc @@ -19,10 +19,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/fusion_group/operation.h" #include "paddle/fluid/framework/ir/subgraph_detector.h" -namespace paddle { -namespace framework { -namespace ir { -namespace fusion_group { +namespace paddle::framework::ir::fusion_group { static std::unordered_set elementwise_op_types; @@ -149,7 +146,4 @@ std::vector> ElementwiseGroupDetector::operator()( return SubgraphDetector(graph, teller)(); } -} // namespace fusion_group -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir::fusion_group diff --git a/paddle/fluid/framework/ir/lock_free_optimize_pass.cc b/paddle/fluid/framework/ir/lock_free_optimize_pass.cc index b6b05fbc28e5d3..c09b8d50d7dc09 100644 --- a/paddle/fluid/framework/ir/lock_free_optimize_pass.cc +++ b/paddle/fluid/framework/ir/lock_free_optimize_pass.cc @@ -21,9 +21,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { const char kSumGradOpName[] = "sum"; // NOLINT // TODO(minqiyang): only support sgd at current time, please add @@ -407,9 +405,7 @@ ir::Node* LockFreeOptimizePass::FindForwardOpViaBackwardOp( return nullptr; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(lock_free_optimize_pass, paddle::framework::ir::LockFreeOptimizePass); diff --git a/paddle/fluid/framework/ir/onednn/cpu_quantize_placement_pass.cc b/paddle/fluid/framework/ir/onednn/cpu_quantize_placement_pass.cc index a73280023ef842..cd80dc7f96d34a 100644 --- a/paddle/fluid/framework/ir/onednn/cpu_quantize_placement_pass.cc +++ b/paddle/fluid/framework/ir/onednn/cpu_quantize_placement_pass.cc @@ -17,9 +17,7 @@ limitations under the License. */ #include #include "paddle/fluid/framework/ir/onednn/onednn_pass_util.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; @@ -96,9 +94,7 @@ void CPUQuantizePlacementPass::ApplyImpl(ir::Graph* graph) const { gpd(graph, handler); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(cpu_quantize_placement_pass, paddle::framework::ir::CPUQuantizePlacementPass) diff --git a/paddle/fluid/framework/ir/onednn/quant_transpose2_dequant_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/quant_transpose2_dequant_onednn_fuse_pass.cc index 097d81af0a5177..18f781521b03e3 100644 --- a/paddle/fluid/framework/ir/onednn/quant_transpose2_dequant_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/quant_transpose2_dequant_onednn_fuse_pass.cc @@ -17,9 +17,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void FuseQuantTranspose2DequantOneDNNPass::FuseQuantizeTranspose2( Graph *graph, const std::string &transpose_type) const { @@ -203,9 +201,7 @@ FuseQuantTranspose2DequantOneDNNPass:: .End(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(quant_transpose2_dequant_onednn_fuse_pass, paddle::framework::ir::FuseQuantTranspose2DequantOneDNNPass); diff --git a/paddle/fluid/framework/ir/remove_padding_recover_padding_pass.cc b/paddle/fluid/framework/ir/remove_padding_recover_padding_pass.cc index 0432b3dd6cdd38..30791a3ecea6d0 100644 --- a/paddle/fluid/framework/ir/remove_padding_recover_padding_pass.cc +++ b/paddle/fluid/framework/ir/remove_padding_recover_padding_pass.cc @@ -18,10 +18,7 @@ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { void EmbEltwiseLayernorm::operator()() { // Create nodes for fused_embedding_eltwise_layernorm or // prompt_tuning_emb_eltwise_layernorm. @@ -169,7 +166,8 @@ void ElementWise::operator()() { elementwise_op->LinksFrom({elementwise_input, elementwise_weight}) .LinksTo({elementwise_out}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { void RemovePaddingRecoverPaddingPass::ApplyImpl(ir::Graph* graph) const { bool use_varseqlen = Get("use_varseqlen"); @@ -708,9 +706,7 @@ void RemovePaddingRecoverPaddingPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(remove_padding_recover_padding_pass, paddle::framework::ir::RemovePaddingRecoverPaddingPass); diff --git a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_legacy_instruction.cc b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_legacy_instruction.cc index 5ad7616846985e..068f915cfca1d2 100644 --- a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_legacy_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_legacy_instruction.cc @@ -37,8 +37,7 @@ #include "paddle/phi/backends/onednn/onednn_helper.h" #include "paddle/phi/kernels/funcs/data_layout_transform.h" -namespace paddle { -namespace framework { +namespace paddle::framework { static paddle::framework::Attribute ConvertPirAttribute2FrameworkAttribute( pir::Attribute attr, @@ -313,5 +312,4 @@ void OneDNNLegacyKernelInstruction::Run() { VLOG(6) << "Run op " << legacy_op_name_ << " kernel."; (*(phi_kernel_))((kernel_context_)); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_mixed_instruction.cc b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_mixed_instruction.cc index 6030367e7bd4a0..2d3b5e4cd1c76c 100644 --- a/paddle/fluid/framework/new_executor/instruction/onednn/onednn_mixed_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/onednn/onednn_mixed_instruction.cc @@ -40,8 +40,7 @@ #include "paddle/phi/backends/onednn/onednn_helper.h" #include "paddle/phi/kernels/funcs/data_layout_transform.h" -namespace paddle { -namespace framework { +namespace paddle::framework { OneDNNMixedPhiKernelInstruction::OneDNNMixedPhiKernelInstruction( size_t id, @@ -156,5 +155,4 @@ void OneDNNMixedPhiKernelInstruction::Run() { } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/op_proto_maker.cc b/paddle/fluid/framework/op_proto_maker.cc index a539f3684cfea6..a54abee42252aa 100644 --- a/paddle/fluid/framework/op_proto_maker.cc +++ b/paddle/fluid/framework/op_proto_maker.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void OpProtoAndCheckerMaker::Validate() { validated_ = true; @@ -118,5 +117,4 @@ void OpProtoAndCheckerMaker::operator()(proto::OpProto* proto, Validate(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index 0464b0f5267308..61a9fe5a33ef63 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -67,8 +67,7 @@ PD_DECLARE_bool(enable_unused_var_check); COMMON_DECLARE_bool(run_kp_kernel); PHI_DECLARE_bool(enable_host_event_recorder_hook); -namespace paddle { -namespace framework { +namespace paddle::framework { std::vector> kKernelPriority = { std::make_tuple(phi::GPUPlace(0), LibraryType::kCUDNN), @@ -3665,5 +3664,4 @@ void OperatorWithKernel::BuildPhiKernelContext( #endif } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/prune.cc b/paddle/fluid/framework/prune.cc index 17d54423ac579e..18ac96feff3bef 100644 --- a/paddle/fluid/framework/prune.cc +++ b/paddle/fluid/framework/prune.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_proto_maker.h" -namespace paddle { -namespace framework { +namespace paddle::framework { const char kFeedOpType[] = "feed"; // NOLINT const char kFetchOpType[] = "fetch"; // NOLINT @@ -686,7 +685,6 @@ std::tuple> PruneBackward( // Step 4. Return a tuple return std::make_tuple(framework::ProgramDesc(pruned_desc), pruned_progin_block_id_map); -} // namespace framework +} -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index 4a959bc96c721b..42f56be54472f5 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -33,8 +33,7 @@ limitations under the License. */ #include "dnnl_debug.h" // NOLINT #endif -namespace paddle { -namespace framework { +namespace paddle::framework { template void TensorCopyImpl(const TENSOR& src, @@ -1075,8 +1074,7 @@ std::ostream& operator<<(std::ostream& os, const LegacyLoD& lod) { return os; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework namespace phi { diff --git a/paddle/fluid/framework/var_type_traits.cc b/paddle/fluid/framework/var_type_traits.cc index 596974f622ec2a..f3f1f5753da362 100644 --- a/paddle/fluid/framework/var_type_traits.cc +++ b/paddle/fluid/framework/var_type_traits.cc @@ -42,12 +42,12 @@ #include "paddle/phi/core/raw_tensor.h" -namespace paddle { -namespace framework { +namespace paddle::framework { // Besides registering variable type id, it is helpful to register a // var_id -> std::type_index map (for example, get type names according to id) -namespace detail { +} // namespace paddle::framework +namespace paddle::framework::detail { template struct VarIdToTypeIndexMapInitializerImpl { @@ -125,7 +125,8 @@ struct VarIdToTypeIndexMapHolder { std::unordered_map type_to_id_map_; }; -} // namespace detail +} // namespace paddle::framework::detail +namespace paddle::framework { const std::type_index &VarTraitIdToTypeIndex(int var_id) { return detail::VarIdToTypeIndexMapHolder::ToTypeIndex(var_id); @@ -139,5 +140,4 @@ int TypeIndexToVarTraitId(const std::type_index &type) { return detail::VarIdToTypeIndexMapHolder::ToTypeId(type); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/inference/api/details/reset_tensor_array.cc b/paddle/fluid/inference/api/details/reset_tensor_array.cc index b393664d86aae9..e50016147c0e27 100644 --- a/paddle/fluid/inference/api/details/reset_tensor_array.cc +++ b/paddle/fluid/inference/api/details/reset_tensor_array.cc @@ -16,14 +16,11 @@ #include "glog/logging.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace details { +namespace paddle::details { // Should be called after the parameters are loaded. void TensorArrayBatchCleaner::CollectTensorArrays(framework::Scope *scope) { @@ -77,5 +74,4 @@ void TensorArrayBatchCleaner::ResetNoTensorVars() { } } -} // namespace details -} // namespace paddle +} // namespace paddle::details diff --git a/paddle/fluid/inference/tensorrt/convert/pad_op.cc b/paddle/fluid/inference/tensorrt/convert/pad_op.cc index c3e065ef0fc2de..6daf91fe37bfe3 100644 --- a/paddle/fluid/inference/tensorrt/convert/pad_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pad_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * PadOp. @@ -54,8 +52,6 @@ class PadOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(pad, PadOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/preln_groupnorm_act_op.cc b/paddle/fluid/inference/tensorrt/convert/preln_groupnorm_act_op.cc index 562ff08a7d8bb3..35f3f1aeffebc4 100644 --- a/paddle/fluid/inference/tensorrt/convert/preln_groupnorm_act_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/preln_groupnorm_act_op.cc @@ -16,9 +16,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/engine.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class PrelnGroupnormActOpConverter : public OpConverter { public: @@ -78,8 +76,6 @@ class PrelnGroupnormActOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(preln_groupnorm_act, PrelnGroupnormActOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/reduce_op.cc b/paddle/fluid/inference/tensorrt/convert/reduce_op.cc index 0a949e7a773f13..251010b2832e71 100644 --- a/paddle/fluid/inference/tensorrt/convert/reduce_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/reduce_op.cc @@ -21,9 +21,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ReduceOpConverter : public OpConverter { public: @@ -223,9 +221,7 @@ class ReduceAllOpConverter : public ReduceAnyOpConverter { ReduceAllOpConverter() { op_type = "reduce_all"; } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(reduce_sum, ReduceSumOpConverter); REGISTER_TRT_OP_CONVERTER(reduce_mean, ReduceMeanOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/reverse_roll_op.cc b/paddle/fluid/inference/tensorrt/convert/reverse_roll_op.cc index 952c41de1a859b..8ef789f23de615 100644 --- a/paddle/fluid/inference/tensorrt/convert/reverse_roll_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/reverse_roll_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/reverse_roll_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ReverseRollOpConverter : public OpConverter { public: void operator()(const framework::proto::OpDesc& op, @@ -73,8 +71,6 @@ class ReverseRollOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(reverse_roll, ReverseRollOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/size_op.cc b/paddle/fluid/inference/tensorrt/convert/size_op.cc index f214a958b8d6b4..a3c0d87a854e12 100644 --- a/paddle/fluid/inference/tensorrt/convert/size_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/size_op.cc @@ -16,9 +16,7 @@ #include #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class SizeOpConverter : public OpConverter { public: @@ -42,8 +40,6 @@ class SizeOpConverter : public OpConverter { ReplenishLayerAndOutput(layer, "size", {output_name}, test_mode); } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(size, SizeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/split_op.cc b/paddle/fluid/inference/tensorrt/convert/split_op.cc index 3d41616c65d68a..e97d9a879acf55 100644 --- a/paddle/fluid/inference/tensorrt/convert/split_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/split_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/split_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class SplitOpConverter : public OpConverter { public: @@ -130,8 +128,6 @@ class SplitOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(split, SplitOpConverter); diff --git a/paddle/fluid/ir_adaptor/translator/op_translator.cc b/paddle/fluid/ir_adaptor/translator/op_translator.cc index 314d0645a33a9f..cb9c0731ad948a 100644 --- a/paddle/fluid/ir_adaptor/translator/op_translator.cc +++ b/paddle/fluid/ir_adaptor/translator/op_translator.cc @@ -58,8 +58,7 @@ // paddle/fluid/pir/dialect/CMakeLists.txt. #include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" -namespace paddle { -namespace translator { +namespace paddle::translator { namespace { @@ -4024,5 +4023,4 @@ OpTranslator::OpTranslator() { special_handlers["c_sync_comm_stream"] = SyncCommStreamOpTranscriber(); } -} // namespace translator -} // namespace paddle +} // namespace paddle::translator diff --git a/paddle/fluid/operators/collective/c_embedding_op.cc b/paddle/fluid/operators/collective/c_embedding_op.cc index 5a3726a111463b..2c228e5a17775c 100644 --- a/paddle/fluid/operators/collective/c_embedding_op.cc +++ b/paddle/fluid/operators/collective/c_embedding_op.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_embedding_op.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class CEmbeddingOp : public framework::OperatorWithKernel { public: @@ -172,8 +171,7 @@ class CEmbeddingOpGradVarTypeInference : public framework::VarTypeInference { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_gen_nccl_id_op.cc b/paddle/fluid/operators/collective/c_gen_nccl_id_op.cc index bfe8ebd1aec752..5004439695097f 100644 --- a/paddle/fluid/operators/collective/c_gen_nccl_id_op.cc +++ b/paddle/fluid/operators/collective/c_gen_nccl_id_op.cc @@ -24,8 +24,7 @@ limitations under the License. */ #include "paddle/phi/core/platform/gen_comm_id_helper.h" COMMON_DECLARE_bool(dynamic_static_unified_comm); -namespace paddle { -namespace operators { +namespace paddle::operators { #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) static void GenNCCLID(std::vector* nccl_ids) { @@ -129,8 +128,7 @@ For trainer 1~n: start a gRPC server to get the UniqueId, once got, stop the ser } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_reduce_avg_op.cc b/paddle/fluid/operators/collective/c_reduce_avg_op.cc index 8c38d9efebf363..f8d827a708c004 100644 --- a/paddle/fluid/operators/collective/c_reduce_avg_op.cc +++ b/paddle/fluid/operators/collective/c_reduce_avg_op.cc @@ -14,27 +14,23 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_reduce_op.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class CReduceAvgOpMaker : public CReduceOpMaker { protected: std::string GetName() const override { return "Avg"; } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_reduce_min_op.cc b/paddle/fluid/operators/collective/c_reduce_min_op.cc index cacbc1a66e832a..7a8490f73e8fe0 100644 --- a/paddle/fluid/operators/collective/c_reduce_min_op.cc +++ b/paddle/fluid/operators/collective/c_reduce_min_op.cc @@ -14,19 +14,16 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_reduce_op.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class CReduceMinOpMaker : public CReduceOpMaker { protected: @@ -34,8 +31,7 @@ class CReduceMinOpMaker : public CReduceOpMaker { }; DEFINE_C_REDUCE_CPU_KERNEL(CReduceMin, kRedMin) -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_reduce_prod_op.cc b/paddle/fluid/operators/collective/c_reduce_prod_op.cc index 47f55bdaa5b19b..07649bd1f54c37 100644 --- a/paddle/fluid/operators/collective/c_reduce_prod_op.cc +++ b/paddle/fluid/operators/collective/c_reduce_prod_op.cc @@ -14,19 +14,16 @@ limitations under the License. */ #include "paddle/fluid/operators/collective/c_reduce_op.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class CReduceProdOpMaker : public CReduceOpMaker { protected: @@ -35,8 +32,7 @@ class CReduceProdOpMaker : public CReduceOpMaker { DEFINE_C_REDUCE_CPU_KERNEL(CReduceProd, kRedProd) -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/set_value_op.cc b/paddle/fluid/operators/set_value_op.cc index 2e92e7f8268208..f20f5bb50e4905 100644 --- a/paddle/fluid/operators/set_value_op.cc +++ b/paddle/fluid/operators/set_value_op.cc @@ -22,20 +22,17 @@ #include "paddle/phi/core/infermeta_utils.h" #include "paddle/phi/infermeta/unary.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class InferShapeContext; class OpDesc; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class SetValue : public framework::OperatorWithKernel { public: @@ -228,8 +225,7 @@ class SetValueGrad : public framework::OperatorWithKernel { DECLARE_INPLACE_OP_INFERER(SetValueOpInplaceInferer, {"Input", "Out"}); -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/pir/dialect/kernel/ir/kernel_dialect.cc b/paddle/fluid/pir/dialect/kernel/ir/kernel_dialect.cc index e9af3a035a4af4..4fc0f322bc9c76 100644 --- a/paddle/fluid/pir/dialect/kernel/ir/kernel_dialect.cc +++ b/paddle/fluid/pir/dialect/kernel/ir/kernel_dialect.cc @@ -24,8 +24,7 @@ REGISTER_FILE_SYMBOLS(kernel_dialect); -namespace paddle { -namespace dialect { +namespace paddle::dialect { void PrintKernelType(pir::Type type, std::ostream &os) { if (type.isa()) { @@ -255,8 +254,7 @@ pir::OpPrintFn OneDNNKernelDialect::PrintOperation( } #endif -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::KernelDialect) IR_DEFINE_EXPLICIT_TYPE_ID(paddle::dialect::CustomKernelDialect) diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_op_vjp.cc b/paddle/fluid/pir/dialect/operator/ir/manual_op_vjp.cc index af86f34db00136..6a9c00556bdfce 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_op_vjp.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_op_vjp.cc @@ -24,8 +24,7 @@ // TODO(wanghao107) // this file will be generated in pd_op.cc -namespace paddle { -namespace dialect { +namespace paddle::dialect { using IntArray = paddle::experimental::IntArray; std::vector> ExpandOp::Vjp( @@ -373,5 +372,4 @@ std::vector> FusedGemmEpilogueOp::Vjp( return res; } -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect diff --git a/paddle/phi/api/lib/api_gen_utils.cc b/paddle/phi/api/lib/api_gen_utils.cc index 1d4135a04764a4..5c9e1a2435e465 100644 --- a/paddle/phi/api/lib/api_gen_utils.cc +++ b/paddle/phi/api/lib/api_gen_utils.cc @@ -26,8 +26,7 @@ PHI_DECLARE_bool(use_stride_kernel); #include "paddle/phi/core/distributed/auto_parallel/dist_tensor.h" #include "paddle/phi/core/kernel_factory.h" -namespace paddle { -namespace experimental { +namespace paddle::experimental { /* ------------------ for input ----------------------- */ @@ -820,5 +819,4 @@ void SetReplicatedDistAttrForOutput( } } -} // namespace experimental -} // namespace paddle +} // namespace paddle::experimental diff --git a/paddle/phi/backends/dynload/dynamic_loader.cc b/paddle/phi/backends/dynload/dynamic_loader.cc index ffb2df4116a328..f046e439223d4a 100644 --- a/paddle/phi/backends/dynload/dynamic_loader.cc +++ b/paddle/phi/backends/dynload/dynamic_loader.cc @@ -72,8 +72,7 @@ PHI_DEFINE_string(rccl_dir, PD_DEFINE_string(xpti_dir, "", "Specify path for loading libxpti.so."); #endif -namespace phi { -namespace dynload { +namespace phi::dynload { struct PathNode { PathNode() = default; @@ -901,5 +900,4 @@ void* GetXPTIDsoHandle() { return nullptr; #endif } -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/core/distributed/comm_task_manager.cc b/paddle/phi/core/distributed/comm_task_manager.cc index 13bb2706a5db5a..97091391a5b8e9 100644 --- a/paddle/phi/core/distributed/comm_task_manager.cc +++ b/paddle/phi/core/distributed/comm_task_manager.cc @@ -37,8 +37,7 @@ #include "paddle/phi/core/distributed/nccl_comm_context.h" #endif -namespace phi { -namespace distributed { +namespace phi::distributed { std::thread CommTaskManager::comm_task_loop_thread_; std::thread CommTaskManager::comm_task_clear_loop_thread_; @@ -276,5 +275,4 @@ bool CommTaskManager::IsTimeout() { return std::chrono::duration_cast( current_timepoint - last_update_time_) >= timeout_; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/store/store.cc b/paddle/phi/core/distributed/store/store.cc index 5987b694b4e51e..ea07050b0ed908 100644 --- a/paddle/phi/core/distributed/store/store.cc +++ b/paddle/phi/core/distributed/store/store.cc @@ -15,8 +15,7 @@ #include "paddle/phi/core/distributed/store/store.h" #include "paddle/phi/core/enforce.h" -namespace phi { -namespace distributed { +namespace phi::distributed { int64_t Store::add(const std::string& key, int64_t value) { PADDLE_THROW( @@ -43,5 +42,4 @@ void Store::set(const std::string& key, const std::vector& value) { errors::InvalidArgument("Implement the set method in the subclass.")); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/memory/allocation/mmap_allocator.cc b/paddle/phi/core/memory/allocation/mmap_allocator.cc index 22574be4abf1a9..72318337a0f92d 100644 --- a/paddle/phi/core/memory/allocation/mmap_allocator.cc +++ b/paddle/phi/core/memory/allocation/mmap_allocator.cc @@ -30,9 +30,7 @@ COMMON_DECLARE_bool(use_shm_cache); -namespace paddle { -namespace memory { -namespace allocation { +namespace paddle::memory::allocation { std::string GetIPCName() { static std::random_device rd; @@ -421,8 +419,6 @@ void MemoryMapAllocationPool::Clear() { MemoryMapAllocationPool::~MemoryMapAllocationPool() { Clear(); } // NOLINT -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation #endif diff --git a/paddle/phi/core/memory/allocation/stream_safe_cuda_allocator.cc b/paddle/phi/core/memory/allocation/stream_safe_cuda_allocator.cc index b1cafc4d8889b3..96d68588e543bd 100644 --- a/paddle/phi/core/memory/allocation/stream_safe_cuda_allocator.cc +++ b/paddle/phi/core/memory/allocation/stream_safe_cuda_allocator.cc @@ -24,9 +24,7 @@ #include "paddle/phi/backends/gpu/rocm/hip_graph.h" #endif -namespace paddle { -namespace memory { -namespace allocation { +namespace paddle::memory::allocation { StreamSafeCUDAAllocation::StreamSafeCUDAAllocation( DecoratedAllocationPtr underlying_allocation, @@ -291,6 +289,4 @@ std::map> StreamSafeCUDAAllocator::allocator_map_; SpinLock StreamSafeCUDAAllocator::allocator_map_lock_; -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation diff --git a/paddle/phi/infermeta/spmd_rules/squeeze.cc b/paddle/phi/infermeta/spmd_rules/squeeze.cc index eb291d3a3adaa1..9c952345d22566 100644 --- a/paddle/phi/infermeta/spmd_rules/squeeze.cc +++ b/paddle/phi/infermeta/spmd_rules/squeeze.cc @@ -25,8 +25,7 @@ #include "paddle/phi/infermeta/spmd_rules/reshape.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -245,5 +244,4 @@ SpmdInfo SqueezeGradInferSpmd(const DistMetaTensor& x, return {{x.dist_attr(), spmd.first[0]}, {spmd.second[0]}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/eigen/constant.cc b/paddle/phi/kernels/funcs/eigen/constant.cc index 20bc05b3187221..1c795406d912cf 100644 --- a/paddle/phi/kernels/funcs/eigen/constant.cc +++ b/paddle/phi/kernels/funcs/eigen/constant.cc @@ -13,8 +13,7 @@ See the License for the specific language governing permissions and limitations under the License. */ #include "paddle/phi/kernels/funcs/eigen/eigen_function.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template struct EigenConstant { @@ -27,5 +26,4 @@ struct EigenConstant { template struct EigenConstant; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/matrix_reduce.cc b/paddle/phi/kernels/funcs/matrix_reduce.cc index 03bdc820abe07d..ca096cafc19274 100644 --- a/paddle/phi/kernels/funcs/matrix_reduce.cc +++ b/paddle/phi/kernels/funcs/matrix_reduce.cc @@ -17,8 +17,7 @@ #include "paddle/phi/kernels/funcs/reduce_function.h" #include "paddle/phi/kernels/funcs/reduce_functor.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template class MatrixReduceSumFunctor { @@ -58,5 +57,4 @@ template class MatrixReduceSumFunctor; template class MatrixReduceSumFunctor, CPUContext>; template class MatrixReduceSumFunctor, CPUContext>; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/fusion/cpu/fusion_repeated_fc_relu_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_repeated_fc_relu_kernel.cc index b52871620e30a2..b563d6561a73a5 100644 --- a/paddle/phi/kernels/fusion/cpu/fusion_repeated_fc_relu_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fusion_repeated_fc_relu_kernel.cc @@ -19,8 +19,7 @@ #include "paddle/phi/core/tensor_utils.h" #include "paddle/phi/kernels/funcs/jit/kernels.h" -namespace phi { -namespace fusion { +namespace phi::fusion { template static void fc_relu(const T* x, @@ -90,8 +89,7 @@ void FusionRepeatedFCReluKernel(const Context& dev_ctx, attr); } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fusion_repeated_fc_relu, CPU, diff --git a/paddle/phi/kernels/fusion/onednn/fc_kernel.cc b/paddle/phi/kernels/fusion/onednn/fc_kernel.cc index 20108138448ab2..d8de98201f899f 100644 --- a/paddle/phi/kernels/fusion/onednn/fc_kernel.cc +++ b/paddle/phi/kernels/fusion/onednn/fc_kernel.cc @@ -22,8 +22,7 @@ #include "paddle/phi/core/utils/data_type.h" #include "paddle/phi/kernels/funcs/common_shape.h" -namespace phi { -namespace fusion { +namespace phi::fusion { using phi::OneDNNContext; using phi::funcs::OneDNNGetDataType; @@ -696,8 +695,7 @@ void FCKernel(const Context& dev_ctx, })); } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fc, OneDNN, diff --git a/paddle/phi/kernels/sparse/cpu/softmax_kernel.cc b/paddle/phi/kernels/sparse/cpu/softmax_kernel.cc index 8d1bff66707220..efafe7300ae4ed 100644 --- a/paddle/phi/kernels/sparse/cpu/softmax_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/softmax_kernel.cc @@ -24,8 +24,7 @@ limitations under the License. */ #include "paddle/phi/kernels/softmax_kernel.h" #include "paddle/phi/kernels/sparse/empty_kernel.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void SoftmaxCsrKernel(const Context& dev_ctx, @@ -172,8 +171,7 @@ void SoftmaxCooKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(softmax_csr, CPU, diff --git a/paddle/utils/pybind.cc b/paddle/utils/pybind.cc index 93f33034c6c1fb..1e42ce55396780 100644 --- a/paddle/utils/pybind.cc +++ b/paddle/utils/pybind.cc @@ -18,8 +18,7 @@ #include "paddle/phi/core/enforce.h" COMMON_DECLARE_string(tensor_operants_mode); -namespace paddle { -namespace pybind { +namespace paddle::pybind { PyTypeObject* p_tensor_type = nullptr; PyTypeObject* p_string_tensor_type = nullptr; @@ -83,5 +82,4 @@ PyObject* ToPyObject(const paddle::Tensor& value, void EnableTensorOperantsToPhiMode() { FLAGS_tensor_operants_mode = "phi"; } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind From 477192e86a8063a1666bae9ac5277d9209e28ab6 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 13:57:56 +0800 Subject: [PATCH 147/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-12=20(#64767)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 12 * format * minor changes --- .../distributed/auto_parallel/dist_attr.cc | 8 ++------ .../distributed/fleet_executor/dist_model.cc | 6 ++---- .../distributed/ps/service/brpc_ps_client.cc | 12 ++++-------- paddle/fluid/framework/dist_multi_trainer.cc | 6 ++---- .../ir/conv_elementwise_add_fuse_pass.cc | 8 ++------ ...e_multihead_matmul_to_sparse_pass_tester.cc | 8 ++------ ...dding_eltwise_layernorm_fuse_pass_tester.cc | 8 ++------ paddle/fluid/framework/ir/fuse_bn_act_pass.cc | 16 ++++------------ .../ir/graph_pattern_detector_tester.cc | 8 ++------ .../ir/layernorm_shift_partition_fuse_pass.cc | 8 ++------ .../pylayer_op_eager_deletion_pass.cc | 8 ++------ paddle/fluid/framework/ir/node.cc | 8 ++------ .../ir/onednn/cpu_quantize_squash_pass.cc | 8 ++------ .../onednn/cpu_quantize_squash_pass_tester.cc | 8 ++------ ...atmul_transpose_reshape_onednn_fuse_pass.cc | 8 ++------ .../ir/onednn/quant_dequant_onednn_pass.cc | 8 ++------ paddle/fluid/framework/ir/pass.cc | 18 ++++++------------ .../framework/no_need_buffer_vars_inference.cc | 6 ++---- paddle/fluid/framework/op_version_registry.cc | 8 ++------ paddle/fluid/framework/program_converter.cc | 14 ++++++-------- paddle/fluid/imperative/all_reduce.cc | 6 ++---- paddle/fluid/imperative/var_helper.cc | 6 ++---- .../passes/adjust_cudnn_workspace_size_pass.cc | 8 ++------ .../tensorrt/convert/batch_norm_op.cc | 8 ++------ .../convert/layernorm_shift_partition_op.cc | 8 ++------ .../inference/tensorrt/convert/slice_op.cc | 8 ++------ .../profiler/custom_device/custom_tracer.cc | 6 ++---- paddle/fluid/pybind/compatible.cc | 6 ++---- paddle/phi/backends/cpu/cpu_info.cc | 8 ++------ paddle/phi/backends/dynload/mklml.cc | 6 ++---- .../reshard/s_to_s_reshard_function.cc | 6 ++---- paddle/phi/core/distributed/store/socket.cpp | 6 ++---- .../allocation/naive_best_fit_allocator.cc | 12 ++++-------- paddle/phi/core/memory/malloc.cc | 6 ++---- paddle/phi/core/platform/cpu_helper.cc | 6 ++---- paddle/phi/core/platform/profiler.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/fused_rope.cc | 6 ++---- paddle/phi/kernels/funcs/jit/gen/act.cc | 8 ++------ paddle/phi/kernels/funcs/jit/helper.cc | 6 ++---- .../kernels/selected_rows/uniform_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/conv_kernel.cc | 6 ++---- .../phi/kernels/sparse/cpu/sum_grad_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/unary_kernel.cc | 6 ++---- paddle/pir/src/core/utils.cc | 6 ++---- 44 files changed, 102 insertions(+), 242 deletions(-) diff --git a/paddle/fluid/distributed/auto_parallel/dist_attr.cc b/paddle/fluid/distributed/auto_parallel/dist_attr.cc index 7a0cecd60da1ea..c87ce1493b1a79 100644 --- a/paddle/fluid/distributed/auto_parallel/dist_attr.cc +++ b/paddle/fluid/distributed/auto_parallel/dist_attr.cc @@ -23,9 +23,7 @@ limitations under the License. */ #include "paddle/fluid/framework/var_desc.h" #include "paddle/phi/core/distributed/auto_parallel/proto_helper.h" -namespace paddle { -namespace distributed { -namespace auto_parallel { +namespace paddle::distributed::auto_parallel { using phi::distributed::auto_parallel::str_join; @@ -487,6 +485,4 @@ bool operator==(const OperatorDistAttr& lhs, const OperatorDistAttr& rhs) { return true; } -} // namespace auto_parallel -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed::auto_parallel diff --git a/paddle/fluid/distributed/fleet_executor/dist_model.cc b/paddle/fluid/distributed/fleet_executor/dist_model.cc index c073dcbd19a37c..e2b3385c1afd47 100644 --- a/paddle/fluid/distributed/fleet_executor/dist_model.cc +++ b/paddle/fluid/distributed/fleet_executor/dist_model.cc @@ -28,8 +28,7 @@ #include "paddle/fluid/framework/scope.h" #include "paddle/fluid/framework/tensor.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { namespace { bool IsPersistable(const framework::VarDesc *var) { @@ -704,5 +703,4 @@ bool DistModel::Run(const std::vector &input_data, return true; } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc index 232dbc944c7aa1..519d39484a7c55 100644 --- a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc +++ b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc @@ -24,15 +24,12 @@ static const int max_port = 65535; -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; class Variable; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace distributed { +namespace paddle::distributed { PD_DEFINE_int32(pserver_push_dense_merge_limit, 12, @@ -2074,5 +2071,4 @@ void BrpcPsClient::PushDenseRawGradient(std::shared_ptr &task, } } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/dist_multi_trainer.cc b/paddle/fluid/framework/dist_multi_trainer.cc index 9f5f178aff44a1..afca688c01fbcf 100644 --- a/paddle/fluid/framework/dist_multi_trainer.cc +++ b/paddle/fluid/framework/dist_multi_trainer.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/fluid/framework/device_worker_factory.h" #include "paddle/fluid/framework/trainer.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void DistMultiTrainer::Initialize(const TrainerDesc &trainer_desc, Dataset *dataset) { @@ -243,5 +242,4 @@ void DistMultiTrainer::MergeToRootScope(phi::DenseTensor *root_tensor, root_data[i] += data[i]; } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc b/paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc index 2f1f9bf7a71290..f6b96e8a34f9d4 100644 --- a/paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc +++ b/paddle/fluid/framework/ir/conv_elementwise_add_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/phi/core/platform/device/gpu/gpu_info.h" #endif -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { #define GET_IR_NODE(node__) GET_IR_NODE_FROM_SUBGRAPH(node__, node__, pattern); #define GET_NODES \ @@ -171,9 +169,7 @@ void ConvElementwiseAddFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_conv_eltwise_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(conv_elementwise_add_fuse_pass, paddle::framework::ir::ConvElementwiseAddFusePass); diff --git a/paddle/fluid/framework/ir/dense_multihead_matmul_to_sparse_pass_tester.cc b/paddle/fluid/framework/ir/dense_multihead_matmul_to_sparse_pass_tester.cc index b921ad759548c9..fff84e15e8f729 100644 --- a/paddle/fluid/framework/ir/dense_multihead_matmul_to_sparse_pass_tester.cc +++ b/paddle/fluid/framework/ir/dense_multihead_matmul_to_sparse_pass_tester.cc @@ -16,9 +16,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void AddVarToScope(Scope* param_scope, const std::string& name, @@ -147,9 +145,7 @@ TEST(DenseMultiHeadMatmulToSparsePass, basic) { num_fused_nodes_after)); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(multihead_matmul_fuse_pass); USE_PASS(multihead_matmul_fuse_pass_v2); diff --git a/paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass_tester.cc b/paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass_tester.cc index 2afd1a8fadadee..e4f302e7a4a73e 100644 --- a/paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/embedding_eltwise_layernorm_fuse_pass_tester.cc @@ -18,9 +18,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { TEST(EmbeddingElewiseLayernormFusePass, basic) { // inputs operator output @@ -100,8 +98,6 @@ TEST(EmbeddingElewiseLayernormFusePass, pass_op_version_check) { .IsPassCompatible("embedding_eltwise_layernorm_fuse_pass")); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(embedding_eltwise_layernorm_fuse_pass); diff --git a/paddle/fluid/framework/ir/fuse_bn_act_pass.cc b/paddle/fluid/framework/ir/fuse_bn_act_pass.cc index 590765f5389b1f..2a7f93fbf21fbb 100644 --- a/paddle/fluid/framework/ir/fuse_bn_act_pass.cc +++ b/paddle/fluid/framework/ir/fuse_bn_act_pass.cc @@ -19,19 +19,13 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir #include "paddle/phi/core/platform/device/gpu/gpu_dnn.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void FuseBatchNormActPass::ApplyImpl(ir::Graph *graph) const { #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) @@ -351,8 +345,6 @@ std::vector FuseBatchNormActPass::ReplaceNode( return new_list; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fuse_bn_act_pass, paddle::framework::ir::FuseBatchNormActPass); diff --git a/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc b/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc index c775677c51688b..640b2bb361afa6 100644 --- a/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc +++ b/paddle/fluid/framework/ir/graph_pattern_detector_tester.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; @@ -207,6 +205,4 @@ TEST(GraphPatternDetector, IntermediateCheck) { ASSERT_EQ(count, 1); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/layernorm_shift_partition_fuse_pass.cc b/paddle/fluid/framework/ir/layernorm_shift_partition_fuse_pass.cc index 8a53793fedd1dc..72103106ad5b23 100644 --- a/paddle/fluid/framework/ir/layernorm_shift_partition_fuse_pass.cc +++ b/paddle/fluid/framework/ir/layernorm_shift_partition_fuse_pass.cc @@ -23,9 +23,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; @@ -256,9 +254,7 @@ void LayerNormShiftPartitionFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(layernorm_shift_partition_fuse_pass, paddle::framework::ir::LayerNormShiftPartitionFusePass); diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/pylayer_op_eager_deletion_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/pylayer_op_eager_deletion_pass.cc index 9d7dfdb3569882..28add8a6ed32b0 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/pylayer_op_eager_deletion_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/pylayer_op_eager_deletion_pass.cc @@ -17,9 +17,7 @@ #include "paddle/fluid/framework/ir/pass.h" #include "paddle/fluid/operators/controlflow/op_variant.h" #include "paddle/fluid/operators/controlflow/pylayer_op_helper.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using OpVariant = operators::OpVariant; class PyLayerOpEagerDeletionPass : public Pass { protected: @@ -94,9 +92,7 @@ class PyLayerOpEagerDeletionPass : public Pass { } }; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(pylayer_op_eager_deletion_pass, paddle::framework::ir::PyLayerOpEagerDeletionPass); diff --git a/paddle/fluid/framework/ir/node.cc b/paddle/fluid/framework/ir/node.cc index 5516a2d799ad10..be1bb0865cba5f 100644 --- a/paddle/fluid/framework/ir/node.cc +++ b/paddle/fluid/framework/ir/node.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/node.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { // msvc15 don't support constexpr in correct way. // static constexpr member implies inline since CXX17 and may cause multiple // definition. @@ -39,6 +37,4 @@ std::unique_ptr CreateNodeForTest(OpDesc *op_desc) { return std::unique_ptr(new Node(op_desc)); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass.cc b/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass.cc index 20c84936bc8278..c543fcf22c0770 100644 --- a/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass.cc +++ b/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass.cc @@ -23,9 +23,7 @@ #include "paddle/phi/core/enforce.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -635,9 +633,7 @@ void CPUQuantizeSquashPass::ApplyImpl(ir::Graph* graph) const { QuantizeBf16Conv(graph); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(cpu_quantize_squash_pass, paddle::framework::ir::CPUQuantizeSquashPass); diff --git a/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass_tester.cc b/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass_tester.cc index e9a24fcac050f1..2eb8c2a8b2afb4 100644 --- a/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass_tester.cc +++ b/paddle/fluid/framework/ir/onednn/cpu_quantize_squash_pass_tester.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/naive_executor.h" #include "paddle/phi/common/place.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void SetOp(ProgramDesc* prog, const std::string& type, @@ -1178,8 +1176,6 @@ TEST(CpuQuantizeSquashPass, squash_all_u8_input_to_concat2) { BuildU8U8ConcatProgramDesc(1.2f, 1.2f), expected_operators, remove_nodes); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(cpu_quantize_squash_pass); diff --git a/paddle/fluid/framework/ir/onednn/matmul_transpose_reshape_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/matmul_transpose_reshape_onednn_fuse_pass.cc index bf5680bd14ea8c..91164de142fb6a 100644 --- a/paddle/fluid/framework/ir/onednn/matmul_transpose_reshape_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/matmul_transpose_reshape_onednn_fuse_pass.cc @@ -18,9 +18,7 @@ #include "paddle/phi/core/enforce.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -202,9 +200,7 @@ MatmulTransposeReshapeMKLDNNPass::MatmulTransposeReshapeMKLDNNPass() { .End(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(matmul_transpose_reshape_onednn_fuse_pass, paddle::framework::ir::MatmulTransposeReshapeMKLDNNPass); diff --git a/paddle/fluid/framework/ir/onednn/quant_dequant_onednn_pass.cc b/paddle/fluid/framework/ir/onednn/quant_dequant_onednn_pass.cc index 8678dbb8559ae0..279b12f41219bd 100644 --- a/paddle/fluid/framework/ir/onednn/quant_dequant_onednn_pass.cc +++ b/paddle/fluid/framework/ir/onednn/quant_dequant_onednn_pass.cc @@ -20,9 +20,7 @@ #include "paddle/fluid/framework/ir/onednn/onednn_pass_util.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void QuantDequantMkldnnPass::MarkSkipQuantizedOps( ir::Graph* graph, const std::unordered_set& skip_ops) const { @@ -758,9 +756,7 @@ void QuantDequantMkldnnPass::ApplyImpl(ir::Graph* graph) const { graph, "has_quant_info", "var_quant_scales", var_quant_scales); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(quant_dequant_onednn_pass, paddle::framework::ir::QuantDequantMkldnnPass); diff --git a/paddle/fluid/framework/ir/pass.cc b/paddle/fluid/framework/ir/pass.cc index 3b7731856440aa..53b3ee2de010d3 100644 --- a/paddle/fluid/framework/ir/pass.cc +++ b/paddle/fluid/framework/ir/pass.cc @@ -20,21 +20,17 @@ limitations under the License. */ #include "paddle/fluid/framework/op_proto_maker.h" #include "paddle/fluid/framework/program_utils.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -namespace ir { +} // namespace paddle::framework +namespace paddle::framework::ir { class Graph; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir #ifdef PADDLE_WITH_DNNL #include "paddle/fluid/platform/onednn_helper.h" #endif -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { static const char kParamScopeAttr[] = "__param_scope__"; // NOLINT @@ -300,6 +296,4 @@ PassRegistry &PassRegistry::Instance() { static PassRegistry g_pass_info_map; return g_pass_info_map; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir diff --git a/paddle/fluid/framework/no_need_buffer_vars_inference.cc b/paddle/fluid/framework/no_need_buffer_vars_inference.cc index 211348e5ece4eb..28abe615f2fa37 100644 --- a/paddle/fluid/framework/no_need_buffer_vars_inference.cc +++ b/paddle/fluid/framework/no_need_buffer_vars_inference.cc @@ -19,8 +19,7 @@ #include "paddle/fluid/framework/operator.h" #include "paddle/fluid/imperative/saved_variable_wrapper_list.h" -namespace paddle { -namespace framework { +namespace paddle::framework { const Attribute &InferNoNeedBufferVarsContext::GetAttr( const std::string &name) const { @@ -66,5 +65,4 @@ bool DyGraphInferNoNeedBufferVarsContext::HasOutput( return false; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/op_version_registry.cc b/paddle/fluid/framework/op_version_registry.cc index 88f999d358948b..71c166a27c5db1 100644 --- a/paddle/fluid/framework/op_version_registry.cc +++ b/paddle/fluid/framework/op_version_registry.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace compatible { +namespace paddle::framework::compatible { OpVersionDesc&& OpVersionDesc::NewInput(const std::string& name, const std::string& remark) { @@ -102,6 +100,4 @@ PassVersionCheckerRegistrar& PassVersionCheckerRegistrar::GetInstance() { // Provide a fake registration item for pybind testing. #include "paddle/fluid/framework/op_version_registry.inl" -} // namespace compatible -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::compatible diff --git a/paddle/fluid/framework/program_converter.cc b/paddle/fluid/framework/program_converter.cc index 19f5f9ab288ba4..f9727837c0e959 100644 --- a/paddle/fluid/framework/program_converter.cc +++ b/paddle/fluid/framework/program_converter.cc @@ -25,8 +25,7 @@ #include "paddle/fluid/framework/program_desc.h" #include "paddle/fluid/framework/version.h" -namespace paddle { -namespace framework { +namespace paddle::framework { using paddle::experimental::ExtractPlainVector; using paddle::experimental::WrapAsScalars; @@ -80,7 +79,8 @@ std::pair> DetectLegacyOps( return std::make_pair(is_legacy_program, legacy_op_map); } -namespace no_scalar { +} // namespace paddle::framework +namespace paddle::framework::no_scalar { void ConvertSetValueOp(OpDesc* op) { std::vector values = PADDLE_GET_CONST( std::vector, op->GetAttr("values", false)); @@ -188,9 +188,9 @@ void ConvertProgram(ProgramDesc* program) { } } } -} // namespace no_scalar +} // namespace paddle::framework::no_scalar -namespace scalar { +namespace paddle::framework::scalar { void ConvertSetValueOp(OpDesc* op) { std::vector values; @@ -317,6 +317,4 @@ void ConvertProgram(ProgramDesc* program) { } } } -} // namespace scalar -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::scalar diff --git a/paddle/fluid/imperative/all_reduce.cc b/paddle/fluid/imperative/all_reduce.cc index 25ba75f55e92b7..7f6ccc0d3549c7 100644 --- a/paddle/fluid/imperative/all_reduce.cc +++ b/paddle/fluid/imperative/all_reduce.cc @@ -34,8 +34,7 @@ #include "paddle/phi/core/platform/device_context.h" #include "paddle/utils/string/string_helper.h" -namespace paddle { -namespace imperative { +namespace paddle::imperative { static const phi::Place &GetVarPlace(const framework::Variable &src) { if (src.IsType()) { @@ -271,7 +270,6 @@ void AllReduce(const framework::Variable &src, AllReduce(src, dst, strategy, 0, true); } -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative #endif diff --git a/paddle/fluid/imperative/var_helper.cc b/paddle/fluid/imperative/var_helper.cc index 117b958168f88c..b8824973a20481 100644 --- a/paddle/fluid/imperative/var_helper.cc +++ b/paddle/fluid/imperative/var_helper.cc @@ -26,8 +26,7 @@ #include "paddle/phi/common/place.h" #include "paddle/phi/core/framework/reader.h" #include "paddle/phi/core/selected_rows.h" -namespace paddle { -namespace imperative { +namespace paddle::imperative { /* GetVariableWrapper */ template <> @@ -301,5 +300,4 @@ template void SetCachedValue( std::shared_ptr var, const phi::KernelKey &key, std::shared_ptr res); -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative diff --git a/paddle/fluid/inference/analysis/passes/adjust_cudnn_workspace_size_pass.cc b/paddle/fluid/inference/analysis/passes/adjust_cudnn_workspace_size_pass.cc index 217d52e0dad1c1..e77dcfee4e2c14 100644 --- a/paddle/fluid/inference/analysis/passes/adjust_cudnn_workspace_size_pass.cc +++ b/paddle/fluid/inference/analysis/passes/adjust_cudnn_workspace_size_pass.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/inference/analysis/argument.h" -namespace paddle { -namespace inference { -namespace analysis { +namespace paddle::inference::analysis { void AdjustCudnnWorkSpacePass::RunImpl(Argument* argument) { if (!argument->use_gpu()) return; @@ -40,6 +38,4 @@ std::string AdjustCudnnWorkSpacePass::repr() const { return "adjust-cudnn-work-space-pass"; } -} // namespace analysis -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::analysis diff --git a/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc b/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc index c819d5fed19a20..d7b447e0f5c38b 100644 --- a/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/batch_norm_op.cc @@ -18,9 +18,7 @@ namespace nvinfer1 { class IScaleLayer; // NOLINT } // namespace nvinfer1 -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class BatchNormOpConverter : public OpConverter { public: @@ -177,8 +175,6 @@ class BatchNormOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(batch_norm, BatchNormOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc b/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc index 335319eb8a2a25..25ef0f5d9e1575 100644 --- a/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/layernorm_shift_partition_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/plugin/layernorm_shift_partition_op.h" #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class LayerNormShiftPartitionOpConverter : public OpConverter { public: @@ -96,9 +94,7 @@ class LayerNormShiftPartitionOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(layernorm_shift_partition, LayerNormShiftPartitionOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/slice_op.cc b/paddle/fluid/inference/tensorrt/convert/slice_op.cc index 0d135085a4ec75..4a572f7d39871a 100644 --- a/paddle/fluid/inference/tensorrt/convert/slice_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/slice_op.cc @@ -11,9 +11,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class SliceOpConverter : public OpConverter { public: @@ -156,8 +154,6 @@ class SliceOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(slice, SliceOpConverter); diff --git a/paddle/fluid/platform/profiler/custom_device/custom_tracer.cc b/paddle/fluid/platform/profiler/custom_device/custom_tracer.cc index 5f907cd70e94f0..b2db390691eace 100644 --- a/paddle/fluid/platform/profiler/custom_device/custom_tracer.cc +++ b/paddle/fluid/platform/profiler/custom_device/custom_tracer.cc @@ -23,8 +23,7 @@ #include "paddle/phi/backends/device_manager.h" #endif -namespace paddle { -namespace platform { +namespace paddle::platform { CustomTracer::CustomTracer(const std::string& dev_type) : dev_type_(dev_type), context_(nullptr) { @@ -116,8 +115,7 @@ void CustomTracer::CollectTraceData(TraceEventCollector* collector) { collector_.ClearAll(); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform #ifdef PADDLE_WITH_CUSTOM_DEVICE void profiler_add_runtime_trace_event(C_Profiler prof, void* event) { diff --git a/paddle/fluid/pybind/compatible.cc b/paddle/fluid/pybind/compatible.cc index 25bf9c7bd05c49..f2bfce6a61829d 100644 --- a/paddle/fluid/pybind/compatible.cc +++ b/paddle/fluid/pybind/compatible.cc @@ -33,8 +33,7 @@ using paddle::framework::compatible::OpUpdateType; using paddle::framework::compatible::OpVersion; using paddle::framework::compatible::OpVersionDesc; -namespace paddle { -namespace pybind { +namespace paddle::pybind { namespace { using paddle::framework::compatible::PassVersionCheckerRegistrar; @@ -150,5 +149,4 @@ void BindCompatible(py::module *m) { BindOpVersion(m); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/cpu/cpu_info.cc b/paddle/phi/backends/cpu/cpu_info.cc index 2c78e9f706a3b8..3c492aa97cd066 100644 --- a/paddle/phi/backends/cpu/cpu_info.cc +++ b/paddle/phi/backends/cpu/cpu_info.cc @@ -48,9 +48,7 @@ PHI_DEFINE_EXPORTED_bool(use_pinned_memory, // NOLINT true, "If set, allocate cpu pinned memory."); -namespace phi { -namespace backends { -namespace cpu { +namespace phi::backends::cpu { size_t CpuTotalPhysicalMemory() { #ifdef __APPLE__ @@ -199,6 +197,4 @@ bool MayIUse(const cpu_isa_t cpu_isa) { } #endif -} // namespace cpu -} // namespace backends -} // namespace phi +} // namespace phi::backends::cpu diff --git a/paddle/phi/backends/dynload/mklml.cc b/paddle/phi/backends/dynload/mklml.cc index e5b490e519d124..79479a89046c32 100644 --- a/paddle/phi/backends/dynload/mklml.cc +++ b/paddle/phi/backends/dynload/mklml.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/backends/dynload/mklml.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag mklml_dso_flag; void* mklml_dso_handle = nullptr; @@ -29,5 +28,4 @@ DEFINE_WRAP(mkl_scsrmm); DEFINE_WRAP(mkl_dcsrmm); #endif -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/core/distributed/auto_parallel/reshard/s_to_s_reshard_function.cc b/paddle/phi/core/distributed/auto_parallel/reshard/s_to_s_reshard_function.cc index ee986abe4d84e9..74851f3df90ebe 100644 --- a/paddle/phi/core/distributed/auto_parallel/reshard/s_to_s_reshard_function.cc +++ b/paddle/phi/core/distributed/auto_parallel/reshard/s_to_s_reshard_function.cc @@ -25,8 +25,7 @@ #include "paddle/phi/kernels/reshape_kernel.h" #include "paddle/phi/kernels/transpose_kernel.h" -namespace phi { -namespace distributed { +namespace phi::distributed { bool SToSReshardFunction::IsSuitable(const DistTensor& in, const TensorDistAttr& out_dist_attr) { @@ -191,5 +190,4 @@ void SToSReshardFunctionCrossMesh::Eval(DeviceContext* dev_ctx, } } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/distributed/store/socket.cpp b/paddle/phi/core/distributed/store/socket.cpp index 8b260e9da202b8..2a8dd1da914ec4 100644 --- a/paddle/phi/core/distributed/store/socket.cpp +++ b/paddle/phi/core/distributed/store/socket.cpp @@ -24,8 +24,7 @@ #include #include -namespace phi { -namespace distributed { +namespace phi::distributed { #ifdef _WIN32 static int _get_sockname_of_win(int sock, char* out, int out_len) { @@ -76,5 +75,4 @@ std::string GetSockName(int fd) { return std::string(out.data()); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/memory/allocation/naive_best_fit_allocator.cc b/paddle/phi/core/memory/allocation/naive_best_fit_allocator.cc index 120f8a60df2e95..adf1d28d54be01 100644 --- a/paddle/phi/core/memory/allocation/naive_best_fit_allocator.cc +++ b/paddle/phi/core/memory/allocation/naive_best_fit_allocator.cc @@ -44,9 +44,7 @@ COMMON_DECLARE_uint64(initial_gpu_memory_in_mb); COMMON_DECLARE_uint64(reallocate_gpu_memory_in_mb); COMMON_DECLARE_bool(benchmark); -namespace paddle { -namespace memory { -namespace legacy { +namespace paddle::memory::legacy { template void *Alloc(const Place &place, size_t size); @@ -609,9 +607,9 @@ size_t Usage::operator()(const phi::GPUPinnedPlace &cuda_pinned) const { "'CUDAPinnedPlace' is not supported in CPU only device.")); #endif } -} // namespace legacy +} // namespace paddle::memory::legacy -namespace allocation { +namespace paddle::memory::allocation { phi::Allocation *NaiveBestFitAllocator::AllocateImpl(size_t size) { void *ptr = phi::VisitPlace(place_, legacy::AllocVisitor(size)); @@ -629,6 +627,4 @@ uint64_t NaiveBestFitAllocator::ReleaseImpl(const phi::Place &place) { return phi::VisitPlace(place, legacy::ReleaseVisitor()); } -} // namespace allocation -} // namespace memory -} // namespace paddle +} // namespace paddle::memory::allocation diff --git a/paddle/phi/core/memory/malloc.cc b/paddle/phi/core/memory/malloc.cc index a459bb544a2ea6..9af21c6b3453a7 100644 --- a/paddle/phi/core/memory/malloc.cc +++ b/paddle/phi/core/memory/malloc.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/phi/core/memory/allocation/allocator_facade.h" #include "paddle/phi/core/stream.h" -namespace paddle { -namespace memory { +namespace paddle::memory { std::shared_ptr AllocShared(const phi::Place& place, size_t size) { return allocation::AllocatorFacade::Instance().AllocShared(place, size); @@ -84,5 +83,4 @@ bool RecordStream(std::shared_ptr allocation, stream); } #endif -} // namespace memory -} // namespace paddle +} // namespace paddle::memory diff --git a/paddle/phi/core/platform/cpu_helper.cc b/paddle/phi/core/platform/cpu_helper.cc index acc6f7fb48495f..751c0a3bd0f934 100644 --- a/paddle/phi/core/platform/cpu_helper.cc +++ b/paddle/phi/core/platform/cpu_helper.cc @@ -26,8 +26,7 @@ limitations under the License. */ #include #endif -namespace paddle { -namespace platform { +namespace paddle::platform { void SetNumThreads(int num_threads) { #ifdef PADDLE_USE_OPENBLAS @@ -57,5 +56,4 @@ void SetNumThreads(int num_threads) { #endif } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/phi/core/platform/profiler.cc b/paddle/phi/core/platform/profiler.cc index 735a862d659229..b88980d966c6fb 100644 --- a/paddle/phi/core/platform/profiler.cc +++ b/paddle/phi/core/platform/profiler.cc @@ -53,8 +53,7 @@ thread_local std::shared_ptr> phi::ProfilerHelper::g_mem_event_list; std::mutex phi::ProfilerHelper::g_all_mem_event_lists_mutex; #endif -namespace paddle { -namespace platform { +namespace paddle::platform { MemEventRecorder MemEventRecorder::recorder; @@ -957,5 +956,4 @@ static void DockHostEventRecorderDevicePart( EmulateCorrelation(thr_events); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/phi/infermeta/spmd_rules/fused_rope.cc b/paddle/phi/infermeta/spmd_rules/fused_rope.cc index 134594236af10f..b3423a35d06a51 100644 --- a/paddle/phi/infermeta/spmd_rules/fused_rope.cc +++ b/paddle/phi/infermeta/spmd_rules/fused_rope.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/utils.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using auto_parallel::str_join; const int kNumHeadsDimIndex = 2; @@ -571,5 +570,4 @@ SpmdInfo FusedRopeGradInferSpmd(const DistMetaTensor& sin, return {dist_attrs, spmd_info.second}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/jit/gen/act.cc b/paddle/phi/kernels/funcs/jit/gen/act.cc index e1877c46c81b05..cd240028aec3c9 100644 --- a/paddle/phi/kernels/funcs/jit/gen/act.cc +++ b/paddle/phi/kernels/funcs/jit/gen/act.cc @@ -18,9 +18,7 @@ #include "paddle/phi/backends/cpu/cpu_info.h" #include "paddle/phi/kernels/funcs/jit/registry.h" -namespace phi { -namespace jit { -namespace gen { +namespace phi::jit::gen { const float ALIGN32_BEG exp_float_consts[] ALIGN32_END = { // NOLINT REPEAT_8TIMES(1.f), @@ -149,9 +147,7 @@ size_t VTanhCreator::CodeSize(const int& d) const { #undef DECLARE_ACT_CREATOR -} // namespace gen -} // namespace jit -} // namespace phi +} // namespace phi::jit::gen namespace gen = phi::jit::gen; diff --git a/paddle/phi/kernels/funcs/jit/helper.cc b/paddle/phi/kernels/funcs/jit/helper.cc index 7a92166ea00a9f..aa127f02787c69 100644 --- a/paddle/phi/kernels/funcs/jit/helper.cc +++ b/paddle/phi/kernels/funcs/jit/helper.cc @@ -18,8 +18,7 @@ #include "paddle/phi/core/enforce.h" -namespace phi { -namespace jit { +namespace phi::jit { std::map>& GetFuncCacheMap() { static thread_local std::map> g_func_cache_map; @@ -149,5 +148,4 @@ typename std::enable_if::value>::type pack_weights( "Only supports pack weights with float type.")); } -} // namespace jit -} // namespace phi +} // namespace phi::jit diff --git a/paddle/phi/kernels/selected_rows/uniform_kernel.cc b/paddle/phi/kernels/selected_rows/uniform_kernel.cc index 0af5d8788c71f7..4b6ea429782b26 100644 --- a/paddle/phi/kernels/selected_rows/uniform_kernel.cc +++ b/paddle/phi/kernels/selected_rows/uniform_kernel.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/uniform_kernel.h" -namespace phi { -namespace sr { +namespace phi::sr { template void UniformRawKernel(const Context& dev_ctx, @@ -58,8 +57,7 @@ void UniformKernel(const Context& dev_ctx, dev_ctx, shape, dtype, min, max, seed, out->mutable_value()); } -} // namespace sr -} // namespace phi +} // namespace phi::sr PD_REGISTER_KERNEL(uniform_raw_sr, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/conv_kernel.cc b/paddle/phi/kernels/sparse/cpu/conv_kernel.cc index 52695d8b4a3ffb..2534b04dba7438 100644 --- a/paddle/phi/kernels/sparse/cpu/conv_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/conv_kernel.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/kernels/funcs/blas/blas.h" #include "paddle/phi/kernels/sparse/cpu/conv.h" -namespace phi { -namespace sparse { +namespace phi::sparse { /** * x: (N, D, H, W, C) @@ -206,8 +205,7 @@ void Conv3dCooKernel(const Context& dev_ctx, counter); })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL( conv3d_coo, CPU, ALL_LAYOUT, phi::sparse::Conv3dCooKernel, float, double) { diff --git a/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc index a01662cbc48d1e..55912a9d5de343 100644 --- a/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/sum_grad_kernel.cc @@ -22,8 +22,7 @@ #include "paddle/phi/kernels/sparse/empty_kernel.h" #include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void SumCooGradCPUKernel(const Context& dev_ctx, @@ -194,8 +193,7 @@ void SumCooGradKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(sum_coo_grad, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/unary_kernel.cc b/paddle/phi/kernels/sparse/cpu/unary_kernel.cc index 3b8a74db21d417..c0ddb34f6f74f4 100644 --- a/paddle/phi/kernels/sparse/cpu/unary_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/unary_kernel.cc @@ -21,8 +21,7 @@ #include "paddle/phi/kernels/sparse/impl/unary_grad_kernel_impl.h" #include "paddle/phi/kernels/sparse/impl/unary_kernel_impl.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void DivScalarCooKernel(const Context& dev_ctx, @@ -56,8 +55,7 @@ void DivScalarCsrKernel(const Context& dev_ctx, dev, eigen_out, eigen_x, static_cast(scalar)); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse #define PD_REGISTER_SPARSE_UNARY_CPU_KERNEL(name, prefix) \ PD_REGISTER_KERNEL(name##_coo, \ diff --git a/paddle/pir/src/core/utils.cc b/paddle/pir/src/core/utils.cc index c95c7c814743d5..984e626172bf92 100644 --- a/paddle/pir/src/core/utils.cc +++ b/paddle/pir/src/core/utils.cc @@ -14,8 +14,7 @@ #include "paddle/pir/include/core/utils.h" -namespace pir { -namespace detail { +namespace pir::detail { std::size_t hash_combine(std::size_t lhs, std::size_t rhs) { lhs ^= rhs + 0x9e3779b9 + (lhs << 6) + (lhs >> 2); @@ -66,5 +65,4 @@ void PrintHeader(const std::string &header, std::ostream &os) { os << "===" << std::string(line_len, '-') << "===\n"; } -} // namespace detail -} // namespace pir +} // namespace pir::detail From 4a9c7a3dd6baac07702b268eaf6c3d584945720e Mon Sep 17 00:00:00 2001 From: tianshuo78520a <707759223@qq.com> Date: Wed, 4 Dec 2024 14:06:55 +0800 Subject: [PATCH 148/288] Fix clang build (#69924) --- paddle/fluid/framework/new_executor/pir_interpreter.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/framework/new_executor/pir_interpreter.cc b/paddle/fluid/framework/new_executor/pir_interpreter.cc index d49e41d274ef03..1338c5f8724362 100644 --- a/paddle/fluid/framework/new_executor/pir_interpreter.cc +++ b/paddle/fluid/framework/new_executor/pir_interpreter.cc @@ -1194,8 +1194,8 @@ void PirInterpreter::RecordStreamForGC(InstructionBase* instr) { } } #endif - auto TensorRecordStream = [&stream, &instr, &skip_record_stream]( - phi::DenseTensor& tensor) { + auto TensorRecordStream = [&stream, + &skip_record_stream](phi::DenseTensor& tensor) { auto allocation = tensor.Holder(); if (allocation == nullptr) { return; From 36b88c856c90672a6aa39ffdc50a5698f62de078 Mon Sep 17 00:00:00 2001 From: tianshuo78520a <707759223@qq.com> Date: Wed, 4 Dec 2024 14:11:36 +0800 Subject: [PATCH 149/288] Add DCU K100 test (#69903) * Add DCU K100 test * Fix --- tools/get_quick_disable_lt.py | 10 +++++++++- 1 file changed, 9 insertions(+), 1 deletion(-) diff --git a/tools/get_quick_disable_lt.py b/tools/get_quick_disable_lt.py index 6db1916edb9075..a75b8884cf7551 100644 --- a/tools/get_quick_disable_lt.py +++ b/tools/get_quick_disable_lt.py @@ -19,6 +19,7 @@ import paddle from paddle.base import core +from paddle.device import cuda def get_disable_ut_by_url(url): @@ -43,7 +44,14 @@ def download_file(): url = "https://sys-p0.bj.bcebos.com/prec/{}".format('disable_ut') if paddle.is_compiled_with_rocm(): - url = "https://sys-p0.bj.bcebos.com/prec/{}".format('disable_ut_rocm') + if cuda.get_device_name() == 'K100_AI': + url = "https://sys-p0.bj.bcebos.com/prec/{}".format( + 'disable_ut_rocm_k100' + ) + else: + url = "https://sys-p0.bj.bcebos.com/prec/{}".format( + 'disable_ut_rocm' + ) disabled_ut_list = get_disable_ut_by_url(url) From 5313be94418232e948179a10896a43f7a2057c87 Mon Sep 17 00:00:00 2001 From: waliwali777 Date: Wed, 4 Dec 2024 14:14:42 +0800 Subject: [PATCH 150/288] [CI] Remind re-run when auto_parallel CI exit -6 (#69212) --- tools/auto_parallel/ci_auto_parallel.sh | 128 ++++++++++++++++++------ 1 file changed, 100 insertions(+), 28 deletions(-) diff --git a/tools/auto_parallel/ci_auto_parallel.sh b/tools/auto_parallel/ci_auto_parallel.sh index ebe9f22ef368da..d05102b4391856 100644 --- a/tools/auto_parallel/ci_auto_parallel.sh +++ b/tools/auto_parallel/ci_auto_parallel.sh @@ -21,6 +21,12 @@ mkdir -p /workspace/case_logs export log_path=/workspace/case_logs export case_list=() +global_total_count=0 +global_success_count=0 +global_exit_250_arr=() +global_runtime_fail_arr=() +global_verification_fail_arr=() + install_paddle(){ echo -e "\033[31m ---- Install paddlepaddle-gpu \033" if [ -n "$paddle" ];then @@ -70,17 +76,68 @@ if [[ $1 -ne 0 ]] && [[ $1 -ne 250 ]];then EXCODE=2 if [ ! -f ${log_path}/$2 ];then echo -e "\033[31m run $2 CI FAIL \033" - else - mv ${log_path}/$2 ${log_path}/$2_FAIL.log - echo -e "\033[31m ${log_path}/$2_FAIL \033" - tail -70 ${log_path}/$2_FAIL.log +else + mv ${log_path}/$2 ${log_path}/$2_FAIL.log + echo -e "\033[31m ${log_path}/$2_FAIL \033" + tail -70 ${log_path}/$2_FAIL.log fi exit $EXCODE else - echo -e "\033[32m run $3 CI SUCCESS \033" + echo -e "\033[32m The $3 CI has completed \033" fi } +function execute_func_list(){ + cd ${log_path} || { echo "Failed to enter log_path: $log_path"; return 1; } + total_count=0 + success_count=0 + runtime_fail_count=0 + verification_fail_count=0 + exit_250_count=0 + while IFS= read -r func_name; do + let total_count++ + let global_total_count++ + execute_num=1 + while true; do + bash $1 exec_case $func_name $FLAGS_install_deps $FLAGS_download_data + result=$? + if [ $result -eq 0 ]; then + echo -e "\033[32m test success!" + let success_count++ + let global_success_count++ + elif [ $result -eq 2 ]; then + echo -e "\033[31m verification failed!" + let verification_fail_count++ + global_verification_fail_arr+=("$func_name") + elif [ $result -eq 250 ]; then + if [ $execute_num -eq 1 ]; then + echo -e "\033[31m fist time execute failed, try again!" + let execute_num++ + continue + else + echo -e "\033[31m second time execute failed, exit!" + let exit_250_count++ + global_exit_250_arr+=("$func_name") + fi + else + echo "test failed!" + mv ${log_path}/$func_name ${log_path}/${func_name}_FAIL.log + echo -e "\033[31m ${log_path}/$func_name_FAIL \033" + tail -15 ${log_path}/${func_name}_FAIL.log + let runtime_fail_count++ + global_runtime_fail_arr+=("$func_name") + fi + break + done + done < functions.txt + echo -e "\033[31m $2 test case has complicated \033" + echo -e "\033[31m $(printf '\t') total tests : $total_count \033" + echo -e "\033[31m $(printf '\t') success tests : $success_count \033" + echo -e "\033[31m $(printf '\t') runtime fail tests : $runtime_fail_count \033" + echo -e "\033[31m $(printf '\t') verification fail tests : $verification_fail_count \033" + echo -e "\033[31m $(printf '\t') exit 250 tests(intermittent issue) : $exit_250_count \033" +} + # Get the list of pending cases get_diff_TO_case # Remove duplicates and store the results back to the original list @@ -101,24 +158,10 @@ if [[ ${#case_list[*]} -ne 0 ]];then export FLAGS_install_deps=0 for case in ${case_list[*]};do echo -e "\033[31m ---- running case $case_num/${#case_list[*]}: ${case} \033" - if [[ ${case} == "llama_auto" ]];then - bash /workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh llama_case_list_auto $FLAGS_install_deps $FLAGS_download_data - print_info $? `ls -lt ${log_path} | grep "llama" | head -n 1 | awk '{print $9}'` ${case} - export FLAGS_install_deps=1 - export FLAGS_download_data="llama ""$FLAGS_download_data" - let case_num++ - elif [[ ${case} == "auto_unit_test" ]];then + if [[ ${case} == "auto_unit_test" ]];then bash /workspace/Paddle/tools/auto_parallel/ci_case_unit.sh auto_unit_test print_info $? `ls -lt ${log_path} | grep "test" | head -n 1 | awk '{print $9}'` ${case} let case_num++ - elif [[ ${case} == "gpt-3_auto" ]];then - bash /workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh llm_gpt_case_list_auto $FLAGS_install_deps $FLAGS_download_data - print_info $? `ls -lt ${log_path} | grep "llm_gpt_dygraph_auto_" | head -n 1 | awk '{print $9}'` ${case} - let case_num++ - elif [[ ${case} == "gpt-3_dygraph" ]];then - bash /workspace/PaddleNLP/scripts/distribute/ci_case_dy.sh llm_gpt_case_list_dygraph $FLAGS_install_deps $FLAGS_download_data - print_info $? `ls -lt ${log_path} | grep "llm_gpt" | head -n 1 | awk '{print $9}'` ${case} - let case_num++ elif [[ ${case} == "dygraph_unit_test" ]];then bash /workspace/Paddle/tools/auto_parallel/ci_case_unit.sh dygraph_unit_test print_info $? `ls -lt ${log_path} | grep "test" | head -n 1 | awk '{print $9}'` ${case} @@ -127,22 +170,51 @@ if [[ ${#case_list[*]} -ne 0 ]];then bash /workspace/Paddle/tools/auto_parallel/ci_case_unit.sh llama_auto_unit_test print_info $? `ls -lt ${log_path} | grep "test" | head -n 1 | awk '{print $9}'` ${case} let case_num++ + elif [[ ${case} == "llama_auto" ]];then + cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh + bash $cmd prepare_case llama_case_list_auto $FLAGS_install_deps $FLAGS_download_data + execute_func_list $cmd llama_auto + export FLAGS_install_deps=1 + export FLAGS_download_data="llama ""$FLAGS_download_data" + let case_num++ + elif [[ ${case} == "gpt-3_auto" ]];then + cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh + bash $cmd prepare_case llm_gpt_case_list_auto $FLAGS_install_deps $FLAGS_download_data + execute_func_list $cmd gpt-3_auto + let case_num++ + elif [[ ${case} == "gpt-3_dygraph" ]];then + cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_dy.sh + bash $cmd prepare_case llm_gpt_case_list_dygraph $FLAGS_install_deps $FLAGS_download_data + execute_func_list $cmd gpt-3_dygraph + let case_num++ else echo -e "\033[31m ---- no ${case} \033" let case_num++ fi done echo -e "\033[31m ---- end run case \033" - cd ${log_path} - if [ ! -f *FAIL* ];then - FF=0 + + echo -e "\033[31m ---- total tests : $global_total_count \033" + if [ ${#global_exit_250_arr[@]} -ne 0 ]; then + echo -e "\033[32m ---- exit 250 test : ${#global_exit_250_arr[@]} \033" + for case in "${global_exit_250_arr[@]}"; do + echo -e "\t$case(exit 250)" + done + fi + + if [ ${#global_runtime_fail_arr[@]} -eq 0 ] && [ ${#global_verification_fail_arr[@]} -eq 0 ]; then + echo -e "\033[32m ---- all cases Success \033" EXCODE=0 - echo -e "\033[32m ---- all case Success \033" else - FF=`ls *FAIL*|wc -l` - EXCODE=2 - echo -e "\033[31m ---- case Failed number: ${FF} \033" - ls *_FAIL* + echo -e "\033[32m ---- runtime failed test : ${#global_runtime_fail_arr[@]} \033" + for case in "${global_runtime_fail_arr[@]}"; do + echo -e "\t$case(failed)" + done + echo -e "\033[32m ---- verification failed test : ${#global_verification_fail_arr[@]} \033" + for case in "${global_verification_fail_arr[@]}"; do + echo -e "\t$case(failed)" + done + EXCODE=1 fi else echo -e "\033[32m Changed Not CI case, Skips \033" From c77940894bed844c7cfaf5087f86011397ee8551 Mon Sep 17 00:00:00 2001 From: tianshuo78520a <707759223@qq.com> Date: Wed, 4 Dec 2024 14:41:13 +0800 Subject: [PATCH 151/288] Update approval;test=document_fix (#69933) --- tools/check_file_diff_approvals.sh | 84 +++++++++++++++--------------- 1 file changed, 42 insertions(+), 42 deletions(-) diff --git a/tools/check_file_diff_approvals.sh b/tools/check_file_diff_approvals.sh index 3c0a22a78efc14..5933c864c6f808 100644 --- a/tools/check_file_diff_approvals.sh +++ b/tools/check_file_diff_approvals.sh @@ -58,8 +58,8 @@ function run_tools_test() { changed_env_var_count=`git diff -U0 upstream/$BRANCH ${PADDLE_ROOT}/paddle | grep 'DEFINE_EXPORTED' | grep -v '@@' | wc -l` if [[ $changed_env_var_count -gt 0 ]]; then - echo_line="You must have one RD (phlrain or luotao1 or Aurelius84) approval for changing the FLAGS, which manages the environment variables.\n" - check_approval 1 phlrain luotao1 Aurelius84 + echo_line="You must have one RD (phlrain or luotao1) approval for changing the FLAGS, which manages the environment variables.\n" + check_approval 1 phlrain luotao1 fi changed_deprecated_tests_count=$(expr $(git ls-tree -r --name-only HEAD ${PADDLE_ROOT}/test/deprecated | grep '^test' | wc -l) - $(git ls-tree -r --name-only upstream/$BRANCH ${PADDLE_ROOT}/test/deprecated | grep '^tes' | wc -l)) @@ -96,20 +96,20 @@ done DEPS_PHI_IN_IR=`git diff --name-only upstream/$BRANCH | grep -E "paddle/pir/" | grep "CMakeList" |xargs -r git diff -U0 upstream/$BRANCH --| grep "^\+" | grep "phi" || true` echo "DEPS_PHI_IN_IR:${DEPS_PHI_IN_IR}" if [ "${DEPS_PHI_IN_IR}" ] && [ "${DEPS_PHI_IN_IR}" != "" ]; then - echo_line="You must have one RD (Aurelius84, phlrain, zhangbo9674, winter-wang) approval for the CMakeLists.txt with DEPS phi* in paddle/pir directory.\n" - check_approval 1 Aurelius84 phlrain zhangbo9674 winter-wang + echo_line="You must have one RD (phlrain, zhangbo9674, winter-wang) approval for the CMakeLists.txt with DEPS phi* in paddle/pir directory.\n" + check_approval 1 phlrain zhangbo9674 winter-wang fi FILTER=`git diff --name-only upstream/develop | grep -v "tools/"` HAS_CONST_CAST=`git diff -U0 upstream/$BRANCH $FILTER | grep '^\+' | grep -o -m 1 "const_cast" || true` if [ ${HAS_CONST_CAST} ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="You must have one RD (XiaoguangHu01,zhiqiu,Xreki,luotao1,qili93,Aurelius84) approval for the usage of const_cast.\n" - check_approval 1 XiaoguangHu01 zhiqiu Xreki luotao1 qili93 Aurelius84 + echo_line="You must have one RD (XiaoguangHu01, zhiqiu, Xreki, zhangbo9674, zyfncg, phlrain) approval for the usage of const_cast.\n" + check_approval 1 XiaoguangHu01 zhiqiu Xreki zhangbo9674 zyfncg phlrain fi HAS_PADDLE_GET=`git diff -U0 upstream/$BRANCH $FILTER |grep "^+" |grep -o -m 1 "paddle::get" || true` if [ ${HAS_PADDLE_GET} ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="paddle::get is not recommended for direct use, because it may throw an bad_variant_access exception without any stack information, so please use PADDLE_GET(_**)(dtype, value) series macros here. If these macros cannot meet your needs, please use try-catch to handle paddle::get and request luotao1 or Aurelius84 review and approve.\n" - check_approval 1 luotao1 Aurelius84 + echo_line="paddle::get is not recommended for direct use, because it may throw an bad_variant_access exception without any stack information, so please use PADDLE_GET(_**)(dtype, value) series macros here. If these macros cannot meet your needs, please use try-catch to handle paddle::get and request luotao1 or zhangbo9674 or phlrain review and approve.\n" + check_approval 1 luotao1 zhangbo9674 phlrain fi FILTER=`git diff --name-only upstream/develop | grep -v "tools/"` @@ -133,39 +133,39 @@ fi HAS_DEFINE_FLAG=`git diff -U0 upstream/$BRANCH |grep -o -m 1 "DEFINE_int32" |grep -o -m 1 "DEFINE_bool" | grep -o -m 1 "DEFINE_string" || true` if [ ${HAS_DEFINE_FLAG} ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="You must have one RD zyfncg or Aurelius84 approval for the usage (either add or delete) of DEFINE_int32/DEFINE_bool/DEFINE_string flag.\n" - check_approval 1 zyfncg Aurelius84 + echo_line="You must have one RD zyfncg or zhangbo9674 or phlrain approval for the usage (either add or delete) of DEFINE_int32/DEFINE_bool/DEFINE_string flag.\n" + check_approval 1 zyfncg zhangbo9674 phlrain fi NO_NPU_FILE=`git diff --name-only upstream/$BRANCH | grep -v "_npu.py"` HAS_UNITTEST_SKIP=`git diff -U0 upstream/$BRANCH ${NO_NPU_FILE} | grep "^+[[:space:]]\{0,\}@unittest.skip" || true` if [ "${HAS_UNITTEST_SKIP}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="Unittest is not allowed to be disabled.\nYou must have one RD (kolinwei(Recommend), wanghuancoder, luotao1, QingshuChen, qili93 or ZzSean or Aurelius84) approval for the usage of @unittest.skip or @unittest.skipIf.\n${HAS_UNITTEST_SKIP}\n" - check_approval 1 kolinwei wanghuancoder luotao1 QingshuChen qili93 ZzSean Aurelius84 + echo_line="Unittest is not allowed to be disabled.\nYou must have one RD (kolinwei(Recommend), wanghuancoder, luotao1, QingshuChen) approval for the usage of @unittest.skip or @unittest.skipIf.\n${HAS_UNITTEST_SKIP}\n" + check_approval 1 kolinwei wanghuancoder luotao1 QingshuChen fi HAS_MODIFIED_DEMO_CMAKE=`git diff --name-only upstream/$BRANCH | grep "paddle/fluid/inference/api/demo_ci/CMakeLists.txt" || true` if [ "${HAS_MODIFIED_DEMO_CMAKE}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="You must have one RD (yuanlehome (Recommend), vivienfanghuagood or Aurelius84) approval for paddle/fluid/inference/api/demo_ci/CMakeLists.txt.\nwhich manages the compilation parameter of inference demo\n" - check_approval 1 yuanlehome vivienfanghuagood Aurelius84 + echo_line="You must have one RD (yuanlehome (Recommend), vivienfanghuagood) approval for paddle/fluid/inference/api/demo_ci/CMakeLists.txt.\nwhich manages the compilation parameter of inference demo\n" + check_approval 1 yuanlehome vivienfanghuagood fi HAS_MODIFIED_DECLARATIONS=`git diff -U0 upstream/$BRANCH |grep "^+" |grep "paddle/phi/kernels/declarations.h" || true` if [ "${HAS_MODIFIED_DECLARATIONS}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="You must be approved by zyfncg or Aurelius84 for paddle/phi/kernels/declarations.h using. Thanks!\n" - check_approval 1 zyfncg Aurelius84 + echo_line="You must be approved by zyfncg or zhangbo9674 or phlrain for paddle/phi/kernels/declarations.h using. Thanks!\n" + check_approval 1 zyfncg zhangbo9674 phlrain fi HAS_USED_CCTESTOLD=`git diff -U0 upstream/$BRANCH |grep "cc_test_old" || true` if [ "${HAS_USED_CCTESTOLD}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="You must be approved by phlrain or risemeup1 or zhangbo9674 or Galaxy1458 for using cc_test_old. Thanks!\n" - check_approval 1 phlrain risemeup1 zhangbo9674 Galaxy1458 + echo_line="You must be approved by phlrain or risemeup1 or zhangbo9674 for using cc_test_old. Thanks!\n" + check_approval 1 phlrain risemeup1 zhangbo9674 fi HAS_USED_CCTEST=`git diff -U0 upstream/$BRANCH |grep "cc_test" || true` if [ "${HAS_USED_CCTEST}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="Paddle utest will gradually discard cc_test\n instead, the paddle_test is recommended,\n if you must use cc_test, you must be approved by risemeup1 or zhangbo9674 or Galaxy1458 for using cc_test. Thanks!\n" - check_approval 1 risemeup1 zhangbo9674 Galaxy1458 + echo_line="Paddle utest will gradually discard cc_test\n instead, the paddle_test is recommended,\n if you must use cc_test, you must be approved by risemeup1 or zhangbo9674 for using cc_test. Thanks!\n" + check_approval 1 risemeup1 zhangbo9674 fi HAS_CREATE_NEW_PASS=`git diff -U0 upstream/$BRANCH |grep "paddle/pir/include/pass/pass.h" || true` @@ -281,8 +281,8 @@ fi ALL_PADDLE_ENFORCE=`git diff -U0 upstream/$BRANCH |grep "^+" |grep -zoE "PADDLE_ENFORCE\(.[^,\);]+.[^;]*\);\s" || true` if [ "${ALL_PADDLE_ENFORCE}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="PADDLE_ENFORCE is not recommended. Please use PADDLE_ENFORCE_EQ/NE/GT/GE/LT/LE or PADDLE_ENFORCE_NOT_NULL or PADDLE_ENFORCE_GPU_SUCCESS instead, see [ https://github.com/PaddlePaddle/Paddle/wiki/PADDLE_ENFORCE-Rewriting-Specification ] for details.\nYou must have one RD (luotao1 (Recommend) or Aurelius84) approval for the usage (either add or delete) of PADDLE_ENFORCE.\n${ALL_PADDLE_ENFORCE}\n" - check_approval 1 luotao1 Aurelius84 + echo_line="PADDLE_ENFORCE is not recommended. Please use PADDLE_ENFORCE_EQ/NE/GT/GE/LT/LE or PADDLE_ENFORCE_NOT_NULL or PADDLE_ENFORCE_GPU_SUCCESS instead, see [ https://github.com/PaddlePaddle/Paddle/wiki/PADDLE_ENFORCE-Rewriting-Specification ] for details.\nYou must have one RD (luotao1 (Recommend) or zhangbo9674 or phlrain) approval for the usage (either add or delete) of PADDLE_ENFORCE.\n${ALL_PADDLE_ENFORCE}\n" + check_approval 1 luotao1 zhangbo9674 phlrain fi ALL_ADDED_LINES=$(git diff -U0 upstream/$BRANCH |grep "^+" || true) @@ -290,8 +290,8 @@ ALL_PADDLE_CHECK=$(echo $ALL_ADDED_LINES |grep -zoE "(PADDLE_ENFORCE[A-Z_]{0,9}| VALID_PADDLE_CHECK=$(echo "$ALL_PADDLE_CHECK" | grep -zoE '(PADDLE_ENFORCE[A-Z_]{0,9}|PADDLE_THROW)\(([^,;]+,)*[^";]*errors::.[^"]*".[^";]{20,}.[^;]*\);\s' || true) INVALID_PADDLE_CHECK=$(echo "$ALL_PADDLE_CHECK" |grep -vxF "$VALID_PADDLE_CHECK" || true) if [ "${INVALID_PADDLE_CHECK}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="The error message you wrote in PADDLE_ENFORCE{_**} or PADDLE_THROW does not meet our error message writing specification. Possible errors include 1. the error message is empty / 2. the error message is too short / 3. the error type is not specified. Please read the specification [ https://github.com/PaddlePaddle/Paddle/wiki/Paddle-Error-Message-Writing-Specification ], then refine the error message. If it is a mismatch, please request luotao1 or Aurelius84 review and approve.\nThe PADDLE_ENFORCE{_**} or PADDLE_THROW entries that do not meet the specification are as follows:\n${INVALID_PADDLE_CHECK}\n" - check_approval 1 luotao1 Aurelius84 + echo_line="The error message you wrote in PADDLE_ENFORCE{_**} or PADDLE_THROW does not meet our error message writing specification. Possible errors include 1. the error message is empty / 2. the error message is too short / 3. the error type is not specified. Please read the specification [ https://github.com/PaddlePaddle/Paddle/wiki/Paddle-Error-Message-Writing-Specification ], then refine the error message. If it is a mismatch, please request zhangbo9674 or phlrain or luotao1 review and approve.\nThe PADDLE_ENFORCE{_**} or PADDLE_THROW entries that do not meet the specification are as follows:\n${INVALID_PADDLE_CHECK}\n" + check_approval 1 luotao1 zhangbo9674 phlrain fi EMPTY_GRAD_OP_REGISTERED=`echo $ALL_ADDED_LINES |grep -zoE "REGISTER_OP_WITHOUT_GRADIENT\([^;.]*\)[;\s]" || echo $ALL_ADDED_LINES |grep -zoE "[[:graph:]]*EmptyGradOpMaker<[[:graph:]]*>" || true` @@ -302,8 +302,8 @@ fi INVALID_UNITTEST_ASSERT_CHECK=`echo "$ALL_ADDED_LINES" | grep -zoE '\+\s+((assert\s+)|(self\.assert(True|Equal)\())(\s*\+\s*)?(np|numpy)\.(allclose|array_equal)[^+]*' || true` if [ "${INVALID_UNITTEST_ASSERT_CHECK}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="It is recommended to use 'np.testing.assert_allclose' and 'np.testing.assert_array_equal' instead of 'self.assertTrue(np.allclose(...))' and 'self.assertTrue(np.array_equal(...))'.\nPlease modify the code below. If anything is unclear, please read the specification [ https://github.com/PaddlePaddle/community/blob/master/rfcs/CodeStyle/20220805_code_style_improvement_for_unittest.md#background ]. If it is a mismatch, please request SigureMo (Recommend) or luotao1 or Aurelius84 review and approve.\nThe code that do not meet the specification are as follows:\n${INVALID_UNITTEST_ASSERT_CHECK}\n" - check_approval 1 SigureMo luotao1 Aurelius84 + echo_line="It is recommended to use 'np.testing.assert_allclose' and 'np.testing.assert_array_equal' instead of 'self.assertTrue(np.allclose(...))' and 'self.assertTrue(np.array_equal(...))'.\nPlease modify the code below. If anything is unclear, please read the specification [ https://github.com/PaddlePaddle/community/blob/master/rfcs/CodeStyle/20220805_code_style_improvement_for_unittest.md#background ]. If it is a mismatch, please request SigureMo (Recommend) or luotao1 review and approve.\nThe code that do not meet the specification are as follows:\n${INVALID_UNITTEST_ASSERT_CHECK}\n" + check_approval 1 SigureMo luotao1 fi TEST_FILE_ADDED_LINES=$(git diff -U0 upstream/$BRANCH -- test |grep "^+") @@ -422,21 +422,21 @@ if [ "${NEW_OP_ADDED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then GET_KERNEL_TYPE_FUNC_CNT=`git diff -U0 --diff-filter=A upstream/$BRANCH |grep "+" |grep -czoE "GetExpectedKernelType[(][^(){}]+[)][^{]+[{][^}]+[}]" || true` INDICATE_VAR_DTYPE_CNT=`git diff -U0 --diff-filter=A upstream/$BRANCH |grep "+" |grep -co "IndicateVarDataType" || true` if [ ${GET_KERNEL_TYPE_FUNC_CNT} -gt ${INDICATE_VAR_DTYPE_CNT} ]; then - echo_line="If you override GetExpectedKernelType method of OperatorWithKernel, please use OperatorWithKernel::IndicateVarDataType() method to get specific input variable's dtype, which checked whether the input variable is initialized (The details in https://github.com/PaddlePaddle/FluidDoc/pull/1527). If you don't use this method to check, you must have one RD (luotao1 or Aurelius84) approval for the usage of other methods.\n" - check_approval 1 luotao1 Aurelius84 + echo_line="If you override GetExpectedKernelType method of OperatorWithKernel, please use OperatorWithKernel::IndicateVarDataType() method to get specific input variable's dtype, which checked whether the input variable is initialized (The details in https://github.com/PaddlePaddle/FluidDoc/pull/1527). If you don't use this method to check, you must have one RD (zhangbo9674 or phlrain or luotao1) approval for the usage of other methods.\n" + check_approval 1 luotao1 zhangbo9674 phlrain fi fi HAS_OPERATORBASE_FLAG=`git diff -U0 --diff-filter=A upstream/$BRANCH | grep -E "public[[:space:]]+.*OperatorBase" || true` if [ "${HAS_OPERATORBASE_FLAG}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="In order to support dynamic graph, all ops are not recommended to inherit OperatorBase. Please use OperatorWithKernel instead.\nYou must have one RD (phlrain (Recommend), luotao1, XiaoguangHu01, or qili93 or Aurelius84) approval for the inherit of OperatorBase.\nYou inherit the OperatorBase class. The corresponding lines are as follows:\n${HAS_OPERATORBASE_FLAG}" - check_approval 1 phlrain luotao1 XiaoguangHu01 qili93 Aurelius84 + echo_line="In order to support dynamic graph, all ops are not recommended to inherit OperatorBase. Please use OperatorWithKernel instead.\nYou must have one RD (phlrain (Recommend), luotao1, XiaoguangHu01) approval for the inherit of OperatorBase.\nYou inherit the OperatorBase class. The corresponding lines are as follows:\n${HAS_OPERATORBASE_FLAG}" + check_approval 1 phlrain luotao1 XiaoguangHu01 fi HAS_INPLACE_TESTS=`git diff -U0 upstream/$BRANCH |grep "+" |grep -E "inplace_atol[[:space:]]*=.*" || true` if [ "${HAS_INPLACE_TESTS}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="The calculation results of setting inplace enabled and disabled must be equal, that is, it's not recommended to set inplace_atol.\n If you do need to use inplace_atol, you must have one RD (XiaoguangHu01, phlrain, luotao1, QingshuChen, Aurelius84) approval for the usage of inplace_atol.\nThe corresponding lines are as follows:\n${HAS_INPLACE_TESTS}\n" - check_approval 1 XiaoguangHu01 phlrain luotao1 QingshuChen Aurelius84 + echo_line="The calculation results of setting inplace enabled and disabled must be equal, that is, it's not recommended to set inplace_atol.\n If you do need to use inplace_atol, you must have one RD (XiaoguangHu01, phlrain, luotao1, QingshuChen) approval for the usage of inplace_atol.\nThe corresponding lines are as follows:\n${HAS_INPLACE_TESTS}\n" + check_approval 1 XiaoguangHu01 phlrain luotao1 QingshuChen fi OP_FILE_CHANGED=`git diff --name-only --diff-filter=AMR upstream/$BRANCH |grep -oE ".+_op..*" || true` @@ -451,8 +451,8 @@ if [ "${OP_FILE_CHANGED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then done if [ "${ERROR_LINES}" != "" ]; then ERROR_LINES=${ERROR_LINES//+/'\n+\t'} - echo_line="Using ShareDataWith or ShareBufferWith is not recommended. You must have one RD's (zhhsplendid (Recommend), zhiqiu or luotao1 or qili93 or Aurelius84) approval to use these methods. For more information, please refer to https://github.com/PaddlePaddle/Paddle/wiki/ShareDataWith-is-prohibited-in-OP. The error lines are as follows:${ERROR_LINES}" - check_approval 1 zhhsplendid zhiqiu luotao1 qili93 Aurelius84 + echo_line="Using ShareDataWith or ShareBufferWith is not recommended. You must have one RD's (zhhsplendid (Recommend), zhiqiu or luotao1) approval to use these methods. For more information, please refer to https://github.com/PaddlePaddle/Paddle/wiki/ShareDataWith-is-prohibited-in-OP. The error lines are as follows:${ERROR_LINES}" + check_approval 1 zhhsplendid zhiqiu luotao1 fi fi @@ -468,8 +468,8 @@ if [ "${CMAKE_FILE_CHANGED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then done if [ "${ERROR_LINES}" != "" ]; then ERROR_LINES=${ERROR_LINES//+/'\n+\t'} - echo_line="Change compilation flag of warnings is not recommended. You must have one RD's (zhiqiu (Recommend), luotao1 or phlrain or Aurelius84) approval to use these methods. " - check_approval 1 zhiqiu luotao1 phlrain Aurelius84 + echo_line="Change compilation flag of warnings is not recommended. You must have one RD's (zhiqiu (Recommend), luotao1 or phlrain) approval to use these methods. " + check_approval 1 zhiqiu luotao1 phlrain fi fi @@ -482,8 +482,8 @@ if [ "${NEW_OP_TEST_ADDED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then CHECK_WHOLE=$CHECK_OUTPUT$CHECK_OUTPUT_WITH_PLACE$CHECK_GRAD$CHECK_GRAD_CHECK if [ "${CHECK_WHOLE}" != "" ] ; then CHECK_OP=${CHECK_WHOLE//+/'\n+'} - echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), fuyinno4, QingshuChen(Recommend for kunlun), zhiqiu or qili93 (Recommend for NPU) , luotao1, phlrain or ZzSean or Aurelius84) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n" - check_approval 1 Xreki fuyinno4 QingshuChen zhiqiu qili93 luotao1 phlrain ZzSean Aurelius84 + echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), fuyinno4, QingshuChen(Recommend for kunlun), zhiqiu, luotao1, phlrain or ZzSean) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n" + check_approval 1 Xreki fuyinno4 QingshuChen zhiqiu luotao1 phlrain fi fi @@ -499,8 +499,8 @@ if [ "${UNITTEST_FILE_CHANGED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then done if [ "${ERROR_LINES}" != "" ]; then ERROR_LINES=${ERROR_LINES//+/'\n+\t'} - echo_line="It is an Op accuracy problem, please take care of it. You must have one RD (zhangting2020 (Recommend), luotao1 or phlrain, qili93, QingshuChen or Aurelius84) approval for the usage (either add or delete) of @skip_check_grad_ci. For more information, please refer to: https://github.com/PaddlePaddle/Paddle/wiki/Gradient-Check-Is-Required-for-Op-Test. The corresponding lines are as follows:\n${ERROR_LINES}\n" - check_approval 1 zhangting2020 luotao1 phlrain qili93 QingshuChen Aurelius84 + echo_line="It is an Op accuracy problem, please take care of it. You must have one RD (zhangting2020 (Recommend), luotao1 or phlrain, QingshuChen) approval for the usage (either add or delete) of @skip_check_grad_ci. For more information, please refer to: https://github.com/PaddlePaddle/Paddle/wiki/Gradient-Check-Is-Required-for-Op-Test. The corresponding lines are as follows:\n${ERROR_LINES}\n" + check_approval 1 zhangting2020 luotao1 phlrain QingshuChen fi fi @@ -550,12 +550,12 @@ UNITYBUILD_RULE_CHANGED=$(git diff --name-only upstream/$BRANCH | grep "unity_build_rule.cmake" || true) if [ -n "${UNITYBUILD_RULE_CHANGED}" -a -n "${GIT_PR_ID}" ]; then echo_line="You must have one RD (Avin0323(Recommend) or zhwesky2010 or - wanghuancoder or luotao1 or Aurelius84) approval for modifying + wanghuancoder or luotao1) approval for modifying unity_build_rule.cmake which the rules of Unity Build." echo_line=$(echo ${echo_line}) # Avin0323(23427135) zhwesky2010(52485244) # wanghuancoder(26922892) luotao1(6836917) - check_approval 1 Avin0323 zhwesky2010 wanghuancoder luotao1 Aurelius84 + check_approval 1 Avin0323 zhwesky2010 wanghuancoder luotao1 fi if [ -n "${echo_list}" ];then From 181a484b6af911c423bafaf1205cd5d7365ea856 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Wed, 4 Dec 2024 14:57:47 +0800 Subject: [PATCH 152/288] [CINN] fix add_n_array sym infer bugs (#69919) * fix add_n_array sym infer bugs * fix compile --- paddle/pir/src/dialect/shape/utils/shape_analysis.cc | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index add8193428af1a..694a10eb8d81c4 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -559,6 +559,11 @@ void ShapeConstraintIRAnalysis::InferShapeOrDataForValue(Value val) { op->dyn_cast(); if (infer_symbolic_shape_interface) { infer_symbolic_shape_interface.InferSymbolicShape(&context_); + // Note(ooooo): Temporarily skip check for CombineOp because TensorArray + // inputs. + if (op->isa()) { + return; + } int index = -1; for (auto& result_value : op->results()) { index++; From d186ce4c1a5131f5563d31c60fc7a6473bc13817 Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:20:00 +0800 Subject: [PATCH 153/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-10=20(#64765)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 10 * format --- .../collective/processgroup_comm_utils.cc | 6 ++---- .../distributed/fleet_executor/sink_interceptor.cc | 6 ++---- paddle/fluid/distributed/ps/service/ps_client.cc | 6 ++---- paddle/fluid/eager/api/utils/hook_utils.cc | 6 ++---- paddle/fluid/framework/data_layout_transform.cc | 6 ++---- paddle/fluid/framework/device_worker.cc | 6 ++---- paddle/fluid/framework/heter_section_worker.cc | 6 ++---- paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.cc | 11 ++--------- .../ir/memory_optimize_pass/eager_deletion_pass.cc | 8 ++------ .../while_op_eager_deletion_pass.cc | 8 ++------ .../ir/onednn/depthwise_conv_onednn_pass.cc | 8 ++------ .../ir/onednn/matmul_activation_onednn_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/reverse_roll_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/silu_fuse_pass.cc | 8 ++------ paddle/fluid/framework/ir/transfer_layout_pass.cc | 8 ++------ .../framework/ir/trt_remove_amp_strategy_op_pass.cc | 8 ++------ .../instruction/builtin_combine_instruction.cc | 6 ++---- paddle/fluid/framework/version.cc | 6 ++---- .../tensorrt/convert/elementwiseadd_transpose_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/gather_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/gelu_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/pool2d_op.cc | 8 ++------ .../inference/tensorrt/convert/remove_padding_op.cc | 8 ++------ .../inference/tensorrt/convert/temporal_shift_op.cc | 8 ++------ paddle/fluid/inference/tensorrt/convert/unbind_op.cc | 8 ++------ .../operators/pscore/heter_listen_and_serv_op.cc | 6 ++---- .../fluid/operators/tensorrt/tensorrt_engine_op.cc | 7 ++----- paddle/fluid/pybind/auto_parallel_py.cc | 6 ++---- paddle/fluid/pybind/bind_fleet_executor.cc | 12 ++++-------- paddle/fluid/pybind/eager_py_layer.cc | 6 ++---- paddle/fluid/pybind/jit.cc | 6 ++---- paddle/phi/backends/dynload/cublasLt.cc | 6 ++---- paddle/phi/backends/dynload/warprnnt.cc | 6 ++---- paddle/phi/core/enforce.cc | 12 ++++++------ paddle/phi/infermeta/spmd_rules/rms_norm.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/rules.cc | 6 ++---- paddle/phi/kernels/autotune/switch_autotune.cc | 6 ++---- paddle/phi/kernels/funcs/jit/gen/embseqpool.cc | 8 ++------ .../fusion/cpu/fused_softmax_mask_grad_kernel.cc | 6 ++---- paddle/phi/kernels/selected_rows/assign_kernel.cc | 6 ++---- paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc | 6 ++---- test/cpp/fluid/platform/bfloat16_test.cc | 6 ++---- 43 files changed, 92 insertions(+), 218 deletions(-) diff --git a/paddle/fluid/distributed/collective/processgroup_comm_utils.cc b/paddle/fluid/distributed/collective/processgroup_comm_utils.cc index eec697f5239450..36b33d58eb4a06 100644 --- a/paddle/fluid/distributed/collective/processgroup_comm_utils.cc +++ b/paddle/fluid/distributed/collective/processgroup_comm_utils.cc @@ -21,8 +21,7 @@ #include "paddle/fluid/distributed/collective/process_group_custom.h" #endif -namespace phi { -namespace detail { +namespace phi::detail { // FIXME(paddle-dev): Since the singleton of ProcessGroup in fluid is used in // SyncBN, the fluid symbol will be dependent on external hardware access. @@ -63,5 +62,4 @@ ccl::CCLComm GetCCLComm(const Place& place, int global_gid) { } } -} // namespace detail -} // namespace phi +} // namespace phi::detail diff --git a/paddle/fluid/distributed/fleet_executor/sink_interceptor.cc b/paddle/fluid/distributed/fleet_executor/sink_interceptor.cc index de21c77b8fbb29..f7e76f95ceb1a5 100644 --- a/paddle/fluid/distributed/fleet_executor/sink_interceptor.cc +++ b/paddle/fluid/distributed/fleet_executor/sink_interceptor.cc @@ -16,8 +16,7 @@ #include "paddle/fluid/distributed/fleet_executor/task_node.h" -namespace paddle { -namespace distributed { +namespace paddle::distributed { SinkInterceptor::SinkInterceptor(int64_t interceptor_id, TaskNode* node) : Interceptor(interceptor_id, node), @@ -64,5 +63,4 @@ void SinkInterceptor::Run(const InterceptorMessage& msg) { } REGISTER_INTERCEPTOR(Sink, SinkInterceptor); -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/distributed/ps/service/ps_client.cc b/paddle/fluid/distributed/ps/service/ps_client.cc index b1743bef45790d..877df53e9ba8f8 100644 --- a/paddle/fluid/distributed/ps/service/ps_client.cc +++ b/paddle/fluid/distributed/ps/service/ps_client.cc @@ -26,8 +26,7 @@ #include "paddle/fluid/framework/fleet/gloo_wrapper.h" #endif -namespace paddle { -namespace distributed { +namespace paddle::distributed { REGISTER_PSCORE_CLASS(PSClient, BrpcPsClient); REGISTER_PSCORE_CLASS(PSClient, PsLocalClient); REGISTER_PSCORE_CLASS(PSClient, GraphBrpcClient); @@ -117,5 +116,4 @@ PSClient *PSClientFactory::Create(const PSParameter &ps_config) { VLOG(3) << "Create PSClient[" << service_param.client_class() << "] success"; return client; } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/eager/api/utils/hook_utils.cc b/paddle/fluid/eager/api/utils/hook_utils.cc index 20207b86c58493..edfb94b52023f7 100644 --- a/paddle/fluid/eager/api/utils/hook_utils.cc +++ b/paddle/fluid/eager/api/utils/hook_utils.cc @@ -19,8 +19,7 @@ #include "paddle/fluid/eager/utils.h" #include "paddle/phi/core/dense_tensor.h" -namespace egr { -namespace egr_utils_api { +namespace egr::egr_utils_api { int64_t RegisterGradientHookForTensor( const paddle::Tensor& tensor, @@ -96,5 +95,4 @@ void RetainGradForTensor(const paddle::Tensor& tensor) { void RegisterBackwardFinalHook(const std::function& hook) { Controller::Instance().RegisterBackwardFinalHook(hook); } -} // namespace egr_utils_api -} // namespace egr +} // namespace egr::egr_utils_api diff --git a/paddle/fluid/framework/data_layout_transform.cc b/paddle/fluid/framework/data_layout_transform.cc index f20de0284e7805..9c546aadd4738c 100644 --- a/paddle/fluid/framework/data_layout_transform.cc +++ b/paddle/fluid/framework/data_layout_transform.cc @@ -18,8 +18,7 @@ #include "paddle/phi/core/utils/data_type.h" #include "paddle/phi/kernels/funcs/math_function.h" -namespace paddle { -namespace framework { +namespace paddle::framework { std::vector GetAxis(const DataLayout& from, const DataLayout& to) { PADDLE_ENFORCE_NE( @@ -102,5 +101,4 @@ void TransDataLayout(DataLayout from_layout, out->set_layout(to_layout); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/device_worker.cc b/paddle/fluid/framework/device_worker.cc index 5b00710a56d655..2917354eab00ad 100644 --- a/paddle/fluid/framework/device_worker.cc +++ b/paddle/fluid/framework/device_worker.cc @@ -21,8 +21,7 @@ namespace phi { class DenseTensor; } // namespace phi -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; @@ -496,5 +495,4 @@ void DeviceWorker::DumpField(const Scope& scope, writer_.Flush(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/heter_section_worker.cc b/paddle/fluid/framework/heter_section_worker.cc index b847af72e3be7c..48cf68dfffece8 100644 --- a/paddle/fluid/framework/heter_section_worker.cc +++ b/paddle/fluid/framework/heter_section_worker.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "paddle/phi/core/platform/cpu_helper.h" #include "paddle/phi/core/platform/device_context.h" -namespace paddle { -namespace framework { +namespace paddle::framework { void SetMicroId(paddle::framework::Scope* scope, phi::DeviceContext* dev_ctx, @@ -554,6 +553,5 @@ void HeterSectionWorker::TrainFilesWithProfiler() { } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework #endif diff --git a/paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.cc b/paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.cc index 9091a8f0039201..7570e52cf379eb 100644 --- a/paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.cc @@ -14,11 +14,7 @@ #include "paddle/fluid/framework/ir/fc_gru_fuse_pass_tester.h" -namespace paddle { -namespace framework { -namespace ir { - -namespace fc_gru_test { +namespace paddle::framework::ir::fc_gru_test { TEST(FcGruFusePass, basic) { std::unique_ptr graph = PrepareGraph(); auto pass = PassRegistry::Instance().Get("fc_gru_fuse_pass"); @@ -50,9 +46,6 @@ TEST(FcGruFusePass, basic) { "expectations after fuse")); } -} // namespace fc_gru_test -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir::fc_gru_test USE_PASS(fc_gru_fuse_pass); diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc index 3935ff455f1554..6981e16663e3b4 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc @@ -26,9 +26,7 @@ #include "paddle/fluid/framework/ir/graph_helper.h" #include "paddle/fluid/framework/ir/memory_optimize_pass/memory_optimization_var_info.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { // op -> variables which can be deleted after op runs using OpToVarNameSetMap = std::unordered_mapApply(graph); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(eager_deletion_pass, paddle::framework::ir::EagerDeletionPass) .RequirePassAttr(paddle::framework::ir::kMemOptVarInfoMapList) diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/while_op_eager_deletion_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/while_op_eager_deletion_pass.cc index aee02fbb16c7f0..75375eb8ce2aa4 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/while_op_eager_deletion_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/while_op_eager_deletion_pass.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/operators/controlflow/op_variant.h" #include "paddle/fluid/operators/controlflow/while_op_helper.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using OpVariant = operators::OpVariant; class WhileOpEagerDeletionPass : public ir::Pass { @@ -105,9 +103,7 @@ class WhileOpEagerDeletionPass : public ir::Pass { } }; -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(while_op_eager_deletion_pass, paddle::framework::ir::WhileOpEagerDeletionPass); diff --git a/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass.cc b/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass.cc index bcbac368e7dba3..62b398463d91e7 100644 --- a/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass.cc +++ b/paddle/fluid/framework/ir/onednn/depthwise_conv_onednn_pass.cc @@ -17,9 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; @@ -101,9 +99,7 @@ void DepthwiseConvMKLDNNPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_depthwise_conv_onednn_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(depthwise_conv_onednn_pass, paddle::framework::ir::DepthwiseConvMKLDNNPass); diff --git a/paddle/fluid/framework/ir/onednn/matmul_activation_onednn_fuse_pass.cc b/paddle/fluid/framework/ir/onednn/matmul_activation_onednn_fuse_pass.cc index bfd513983b8d85..3ec9dbd7e0b35c 100644 --- a/paddle/fluid/framework/ir/onednn/matmul_activation_onednn_fuse_pass.cc +++ b/paddle/fluid/framework/ir/onednn/matmul_activation_onednn_fuse_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using string::PrettyLogDetail; @@ -284,9 +282,7 @@ MatmulActivationMkldnnFusePass::MatmulActivationMkldnnFusePass() { .End(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(matmul_activation_onednn_fuse_pass, paddle::framework::ir::MatmulActivationMkldnnFusePass); diff --git a/paddle/fluid/framework/ir/reverse_roll_fuse_pass.cc b/paddle/fluid/framework/ir/reverse_roll_fuse_pass.cc index f132c4232dad42..f4910e28469e01 100644 --- a/paddle/fluid/framework/ir/reverse_roll_fuse_pass.cc +++ b/paddle/fluid/framework/ir/reverse_roll_fuse_pass.cc @@ -33,9 +33,7 @@ GET_IR_NODE(reshape2_50_op); \ GET_IR_NODE(reshape2_50_out); -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; ReverseRollFusePass::ReverseRollFusePass() { // NOLINT AddOpCompat(OpCompat("reshape2")) @@ -189,9 +187,7 @@ void ReverseRollFusePass::ApplyImpl(ir::Graph* graph) const { fuse_count += ApplyPattern(graph, false); AddStatis(fuse_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(reverse_roll_fuse_pass, paddle::framework::ir::ReverseRollFusePass); diff --git a/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc b/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc index 26db2906eb8188..581f29c3cc60a9 100644 --- a/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc +++ b/paddle/fluid/framework/ir/seq_concat_fc_fuse_pass.cc @@ -17,9 +17,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { struct FuseExpr {}; @@ -340,9 +338,7 @@ void SeqConcatFcFusePass::ApplyImpl(ir::Graph* graph) const { AddStatis(fuse_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(seq_concat_fc_fuse_pass, paddle::framework::ir::SeqConcatFcFusePass); diff --git a/paddle/fluid/framework/ir/silu_fuse_pass.cc b/paddle/fluid/framework/ir/silu_fuse_pass.cc index 07def58f95dbb3..c1b9a17868e2ca 100644 --- a/paddle/fluid/framework/ir/silu_fuse_pass.cc +++ b/paddle/fluid/framework/ir/silu_fuse_pass.cc @@ -16,9 +16,7 @@ #include #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void SiluFusePass::ApplyImpl(ir::Graph* graph) const { // This pass is used for cutlass, because cutlass can fuse conv + bias + silu @@ -79,8 +77,6 @@ void SiluFusePass::ApplyImpl(ir::Graph* graph) const { gpd(graph, handler); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(silu_fuse_pass, paddle::framework::ir::SiluFusePass); diff --git a/paddle/fluid/framework/ir/transfer_layout_pass.cc b/paddle/fluid/framework/ir/transfer_layout_pass.cc index fb1d2677423c18..868635c06ee500 100644 --- a/paddle/fluid/framework/ir/transfer_layout_pass.cc +++ b/paddle/fluid/framework/ir/transfer_layout_pass.cc @@ -25,9 +25,7 @@ #include "paddle/phi/common/place.h" #include "paddle/phi/core/dense_tensor.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { namespace { void InsertLayoutTransOp(ir::Graph *graph, @@ -348,8 +346,6 @@ void TransferLayoutPass::ApplyImpl(ir::Graph *graph) const { AddStatis(static_cast(valid_ops.size())); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(transfer_layout_pass, paddle::framework::ir::TransferLayoutPass); diff --git a/paddle/fluid/framework/ir/trt_remove_amp_strategy_op_pass.cc b/paddle/fluid/framework/ir/trt_remove_amp_strategy_op_pass.cc index 2b491f9064ac3f..aadd9c86125cc1 100644 --- a/paddle/fluid/framework/ir/trt_remove_amp_strategy_op_pass.cc +++ b/paddle/fluid/framework/ir/trt_remove_amp_strategy_op_pass.cc @@ -23,9 +23,7 @@ #include "paddle/fluid/framework/ir/node.h" #include "paddle/phi/common/data_type.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { namespace { template @@ -154,9 +152,7 @@ void TrtRemoveAMPStrategyOpPass::ApplyImpl(Graph *graph) const { } } } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(trt_remove_amp_strategy_op_pass, paddle::framework::ir::TrtRemoveAMPStrategyOpPass); diff --git a/paddle/fluid/framework/new_executor/instruction/builtin_combine_instruction.cc b/paddle/fluid/framework/new_executor/instruction/builtin_combine_instruction.cc index 0119c34da659ab..a1e5f1fb14f548 100644 --- a/paddle/fluid/framework/new_executor/instruction/builtin_combine_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/builtin_combine_instruction.cc @@ -16,8 +16,7 @@ #include "paddle/fluid/framework/new_executor/instruction/instruction_util.h" #include "paddle/fluid/framework/new_executor/new_executor_defs.h" -namespace paddle { -namespace framework { +namespace paddle::framework { BuiltinCombineInstruction::BuiltinCombineInstruction( size_t id, @@ -36,5 +35,4 @@ BuiltinCombineInstruction::BuiltinCombineInstruction( void BuiltinCombineInstruction::Run() {} -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/version.cc b/paddle/fluid/framework/version.cc index 834b843fdf45da..2272f26ca1251a 100644 --- a/paddle/fluid/framework/version.cc +++ b/paddle/fluid/framework/version.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include -namespace paddle { -namespace framework { +namespace paddle::framework { bool IsProgramVersionSupported(int64_t version) { /* So far, all old versions of phi::DenseTensor are supported in the @@ -48,5 +47,4 @@ std::string DumpVersion(const int64_t version) { return buffer.str(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/inference/tensorrt/convert/elementwiseadd_transpose_op.cc b/paddle/fluid/inference/tensorrt/convert/elementwiseadd_transpose_op.cc index 2303b57a1bac5c..683a271c028510 100644 --- a/paddle/fluid/inference/tensorrt/convert/elementwiseadd_transpose_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/elementwiseadd_transpose_op.cc @@ -16,9 +16,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/engine.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ElementwiseaddTransposeOpConverter : public OpConverter { public: void operator()(const framework::proto::OpDesc& op, @@ -46,8 +44,6 @@ class ElementwiseaddTransposeOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(fuse_eleadd_transpose, ElementwiseaddTransposeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/gather_op.cc b/paddle/fluid/inference/tensorrt/convert/gather_op.cc index 06c43c663daeec..fdd0ff12c2f852 100644 --- a/paddle/fluid/inference/tensorrt/convert/gather_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/gather_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Gather Op @@ -58,8 +56,6 @@ class GatherOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(gather, GatherOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/gelu_op.cc b/paddle/fluid/inference/tensorrt/convert/gelu_op.cc index 201dddb000e57f..07a7521a45d756 100644 --- a/paddle/fluid/inference/tensorrt/convert/gelu_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/gelu_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/gelu_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Gelu converter from fluid to tensorRT. @@ -233,8 +231,6 @@ class GeluOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(gelu, GeluOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc index cc75bc6d7a7c6d..1531b10072d5c7 100644 --- a/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/pool2d_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/pool_op_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { inline void DealCeilMode(const nvinfer1::Dims &input_shape, std::vector ksize, @@ -231,8 +229,6 @@ class Pool2dOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/remove_padding_op.cc b/paddle/fluid/inference/tensorrt/convert/remove_padding_op.cc index 3b05d7b10065eb..3f12ad35be4def 100644 --- a/paddle/fluid/inference/tensorrt/convert/remove_padding_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/remove_padding_op.cc @@ -15,9 +15,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" #include "paddle/fluid/inference/tensorrt/plugin/remove_padding_plugin.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Remove padding of transformer'input. @@ -71,8 +69,6 @@ class RemovePadding : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(remove_padding, RemovePadding); diff --git a/paddle/fluid/inference/tensorrt/convert/temporal_shift_op.cc b/paddle/fluid/inference/tensorrt/convert/temporal_shift_op.cc index 6395e3edc5b283..eda6dcafb16d66 100644 --- a/paddle/fluid/inference/tensorrt/convert/temporal_shift_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/temporal_shift_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * TemporalShiftOp. @@ -218,8 +216,6 @@ class TemporalShiftOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(temporal_shift, TemporalShiftOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/unbind_op.cc b/paddle/fluid/inference/tensorrt/convert/unbind_op.cc index ca26d09afb5b34..19033c39d414e2 100644 --- a/paddle/fluid/inference/tensorrt/convert/unbind_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/unbind_op.cc @@ -11,9 +11,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { /* * Unbind Op @@ -80,7 +78,5 @@ class UnbindOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(unbind, UnbindOpConverter); diff --git a/paddle/fluid/operators/pscore/heter_listen_and_serv_op.cc b/paddle/fluid/operators/pscore/heter_listen_and_serv_op.cc index 61e3218bb939cc..dbd3f8d9b3fa0b 100644 --- a/paddle/fluid/operators/pscore/heter_listen_and_serv_op.cc +++ b/paddle/fluid/operators/pscore/heter_listen_and_serv_op.cc @@ -20,8 +20,7 @@ PHI_DEFINE_EXPORTED_int32(rpc_send_thread_num, 12, "number of threads for rpc send"); -namespace paddle { -namespace operators { +namespace paddle::operators { static void split(const std::string &str, char sep, @@ -209,8 +208,7 @@ class HeterListenAndServOpMaker : public framework::OpProtoAndCheckerMaker { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc b/paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc index 6008cd642828d9..665b188ad3c18c 100644 --- a/paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc +++ b/paddle/fluid/operators/tensorrt/tensorrt_engine_op.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/operators/tensorrt/tensorrt_engine_op.h" -namespace paddle { - -namespace operators { +namespace paddle::operators { class TensorRTEngineOpMaker : public framework::OpProtoAndCheckerMaker { public: @@ -46,8 +44,7 @@ class TensorRTEngineInferVarType : public framework::VarTypeInference { void operator()(framework::InferVarTypeContext *ctx) const override {} }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/pybind/auto_parallel_py.cc b/paddle/fluid/pybind/auto_parallel_py.cc index 5c170a55694085..de1fd45ef0cdd4 100644 --- a/paddle/fluid/pybind/auto_parallel_py.cc +++ b/paddle/fluid/pybind/auto_parallel_py.cc @@ -58,8 +58,7 @@ namespace py = pybind11; // NOLINT -namespace paddle { -namespace pybind { +namespace paddle::pybind { static bool PyCheckInteger(PyObject *obj) { #if PY_VERSION_HEX < 0x03000000 @@ -945,5 +944,4 @@ infer_backward(const phi::distributed::SpmdRule &self, const py::args &args) { return self.InferBackward(ctx); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/bind_fleet_executor.cc b/paddle/fluid/pybind/bind_fleet_executor.cc index 531b645f7a7de7..b7772f778d2a5a 100644 --- a/paddle/fluid/pybind/bind_fleet_executor.cc +++ b/paddle/fluid/pybind/bind_fleet_executor.cc @@ -33,8 +33,7 @@ namespace py = pybind11; -namespace pybind11 { -namespace detail { +namespace pybind11::detail { // Note: use same enum number of float16 in numpy. // import numpy as np @@ -59,11 +58,9 @@ struct npy_format_descriptor { static constexpr auto name = _("float16"); }; -} // namespace detail -} // namespace pybind11 +} // namespace pybind11::detail -namespace paddle { -namespace pybind { +namespace paddle::pybind { using paddle::distributed::DependType; using paddle::distributed::DistModel; @@ -299,5 +296,4 @@ void BindFleetExecutor(py::module* m) { .value("INT32", DistModelDataType::INT32) .value("FLOAT16", DistModelDataType::FLOAT16); } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/eager_py_layer.cc b/paddle/fluid/pybind/eager_py_layer.cc index a997edd73e21b3..c695c5357e0bdc 100644 --- a/paddle/fluid/pybind/eager_py_layer.cc +++ b/paddle/fluid/pybind/eager_py_layer.cc @@ -42,8 +42,7 @@ limitations under the License. */ using egr::ConvertToDistTensor; -namespace paddle { -namespace pybind { +namespace paddle::pybind { PyTypeObject* p_pylayer_type; extern PyTypeObject* p_tensor_type; @@ -818,5 +817,4 @@ void BindEagerPyLayer(PyObject* module) { } } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/jit.cc b/paddle/fluid/pybind/jit.cc index 3dde422b554be4..f10dda26d5329e 100644 --- a/paddle/fluid/pybind/jit.cc +++ b/paddle/fluid/pybind/jit.cc @@ -31,8 +31,7 @@ limitations under the License. */ namespace py = pybind11; -namespace paddle { -namespace pybind { +namespace paddle::pybind { PyTypeObject *g_jit_function_pytype = nullptr; using Variable = paddle::framework::Variable; @@ -172,5 +171,4 @@ void BindSot(pybind11::module *m) { #endif } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/backends/dynload/cublasLt.cc b/paddle/phi/backends/dynload/cublasLt.cc index 55caab905270b8..91eca0bbbcec02 100644 --- a/paddle/phi/backends/dynload/cublasLt.cc +++ b/paddle/phi/backends/dynload/cublasLt.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/backends/dynload/cublasLt.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag cublasLt_dso_flag; void *cublasLt_dso_handle = nullptr; @@ -23,5 +22,4 @@ void *cublasLt_dso_handle = nullptr; CUBLASLT_BLAS_ROUTINE_EACH(DEFINE_WRAP); -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/backends/dynload/warprnnt.cc b/paddle/phi/backends/dynload/warprnnt.cc index 115ee16bffd720..6ea049bd9b1c70 100644 --- a/paddle/phi/backends/dynload/warprnnt.cc +++ b/paddle/phi/backends/dynload/warprnnt.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/backends/dynload/warprnnt.h" -namespace phi { -namespace dynload { +namespace phi::dynload { std::once_flag warprnnt_dso_flag; void* warprnnt_dso_handle = nullptr; @@ -24,5 +23,4 @@ void* warprnnt_dso_handle = nullptr; WARPRNNT_ROUTINE_EACH(DEFINE_WRAP); -} // namespace dynload -} // namespace phi +} // namespace phi::dynload diff --git a/paddle/phi/core/enforce.cc b/paddle/phi/core/enforce.cc index 47db8f8851fc55..809f78b1cb21bd 100644 --- a/paddle/phi/core/enforce.cc +++ b/paddle/phi/core/enforce.cc @@ -31,8 +31,7 @@ limitations under the License. */ #endif // PADDLE_WITH_CUDA COMMON_DECLARE_int32(call_stack_level); -namespace phi { -namespace enforce { +namespace phi::enforce { void ThrowWarnInternal(const std::string& msg) { LOG(WARNING) << "WARNING :" << msg; @@ -42,7 +41,8 @@ void ThrowWarnInternal(const std::string& msg) { /**************************** NVIDIA ERROR ********************************/ #ifdef PADDLE_WITH_CUDA -namespace details { +} // namespace phi::enforce +namespace phi::enforce::details { template struct ExternalApiProtoType {}; @@ -71,7 +71,8 @@ DEFINE_EXTERNAL_API_PROTO_TYPE(ncclResult_t, NCCL); #undef DEFINE_EXTERNAL_API_PROTO_TYPE -} // namespace details +} // namespace phi::enforce::details +namespace phi::enforce { template inline const char* GetErrorMsgUrl(T status) { @@ -224,5 +225,4 @@ template std::string GetExternalErrorMsg(ncclResult_t); #endif // PADDLE_WITH_CUDA -} // namespace enforce -} // namespace phi +} // namespace phi::enforce diff --git a/paddle/phi/infermeta/spmd_rules/rms_norm.cc b/paddle/phi/infermeta/spmd_rules/rms_norm.cc index 442354be715b90..a21a31e4940f7e 100644 --- a/paddle/phi/infermeta/spmd_rules/rms_norm.cc +++ b/paddle/phi/infermeta/spmd_rules/rms_norm.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/infermeta/spmd_rules/spmd_rule_macro_define.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -203,5 +202,4 @@ SpmdInfo RmsNormGradInferSpmd(const DistMetaTensor& x, {x_grad, scale_grad}); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/infermeta/spmd_rules/rules.cc b/paddle/phi/infermeta/spmd_rules/rules.cc index 3def2b3f0fd356..ad6c1129f9fb7b 100644 --- a/paddle/phi/infermeta/spmd_rules/rules.cc +++ b/paddle/phi/infermeta/spmd_rules/rules.cc @@ -31,8 +31,7 @@ limitations under the License. */ * directly in the header file */ -namespace phi { -namespace distributed { +namespace phi::distributed { // matmul rule PD_REGISTER_SPMD_RULE(matmul, @@ -729,5 +728,4 @@ PD_REGISTER_SPMD_RULE(nonzero, // add_n PD_REGISTER_SPMD_RULE(add_n, PD_INFER_SPMD(phi::distributed::AddNInferSpmd)); -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/autotune/switch_autotune.cc b/paddle/phi/kernels/autotune/switch_autotune.cc index 2c87caee5c9467..f2ada2be1459f3 100644 --- a/paddle/phi/kernels/autotune/switch_autotune.cc +++ b/paddle/phi/kernels/autotune/switch_autotune.cc @@ -19,8 +19,7 @@ COMMON_DECLARE_bool(use_autotune); -namespace phi { -namespace autotune { +namespace phi::autotune { void AutoTuneStatus::EnableAutoTune() { FLAGS_use_autotune = true; @@ -71,5 +70,4 @@ void AutoTuneStatus::Update() { } } -} // namespace autotune -} // namespace phi +} // namespace phi::autotune diff --git a/paddle/phi/kernels/funcs/jit/gen/embseqpool.cc b/paddle/phi/kernels/funcs/jit/gen/embseqpool.cc index d6abe04e25da45..7c3508daf22e80 100644 --- a/paddle/phi/kernels/funcs/jit/gen/embseqpool.cc +++ b/paddle/phi/kernels/funcs/jit/gen/embseqpool.cc @@ -20,9 +20,7 @@ #include "paddle/phi/kernels/funcs/jit/macro.h" #include "paddle/phi/kernels/funcs/jit/registry.h" -namespace phi { -namespace jit { -namespace gen { +namespace phi::jit::gen { void EmbSeqPoolJitCode::genCode() { preCode(); @@ -164,9 +162,7 @@ class EmbSeqPoolCreator : public JitCodeCreator { } }; -} // namespace gen -} // namespace jit -} // namespace phi +} // namespace phi::jit::gen namespace gen = phi::jit::gen; diff --git a/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_grad_kernel.cc b/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_grad_kernel.cc index eb94d71b956a04..3f2ca3d72dd3a3 100644 --- a/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_grad_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_grad_kernel.cc @@ -15,8 +15,7 @@ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/softmax_grad_kernel.h" -namespace phi { -namespace fusion { +namespace phi::fusion { template void FusedSoftmaxMaskGradKernel(const Context& dev_ctx, @@ -28,8 +27,7 @@ void FusedSoftmaxMaskGradKernel(const Context& dev_ctx, dev_ctx, out, out_grad, 3, x_grad); // axis for softmax } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fused_softmax_mask_grad, CPU, diff --git a/paddle/phi/kernels/selected_rows/assign_kernel.cc b/paddle/phi/kernels/selected_rows/assign_kernel.cc index 081d85e68c959f..43b8154055c72e 100644 --- a/paddle/phi/kernels/selected_rows/assign_kernel.cc +++ b/paddle/phi/kernels/selected_rows/assign_kernel.cc @@ -17,8 +17,7 @@ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/assign_kernel.h" -namespace phi { -namespace sr { +namespace phi::sr { // Note: use `const paddle::optional& x` // as input if needed @@ -31,8 +30,7 @@ void AssignKernel(const Context& dev_ctx, phi::AssignKernel(dev_ctx, x.value(), out->mutable_value()); } -} // namespace sr -} // namespace phi +} // namespace phi::sr PD_REGISTER_KERNEL_FOR_ALL_DTYPE(assign_sr, CPU, diff --git a/paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc b/paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc index d9692acb649aa9..4341e774ac0e8d 100644 --- a/paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc +++ b/paddle/phi/kernels/sparse/cpu/pool_grad_kernel.cc @@ -21,8 +21,7 @@ limitations under the License. */ #include "paddle/phi/kernels/funcs/pooling.h" #include "paddle/phi/kernels/funcs/sparse/convolution.h" -namespace phi { -namespace sparse { +namespace phi::sparse { template void MaxPoolCooGradCPUKernel(const CPUContext& dev_ctx, @@ -86,8 +85,7 @@ void MaxPoolCooGradKernel(const Context& dev_ctx, })); } -} // namespace sparse -} // namespace phi +} // namespace phi::sparse PD_REGISTER_KERNEL(maxpool_coo_grad, CPU, diff --git a/test/cpp/fluid/platform/bfloat16_test.cc b/test/cpp/fluid/platform/bfloat16_test.cc index 38e4da8d3d67e0..bca3de8470093b 100644 --- a/test/cpp/fluid/platform/bfloat16_test.cc +++ b/test/cpp/fluid/platform/bfloat16_test.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace platform { +namespace paddle::platform { using bfloat16 = phi::dtype::bfloat16; using namespace phi::dtype; // NOLINT @@ -164,5 +163,4 @@ TEST(bfloat16, isnan) { EXPECT_EQ(std::isnan(c), true); } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform From 511b142ac80a5953a5e5202851c77ef7b8a7e69e Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:26:33 +0800 Subject: [PATCH 154/288] [CINN]Slice_op is not supported to enter CINN when there is no data in the parameters (#69868) * Determine the type of supplementary TensorListExprs * restore modifications lost during merge * refine code * refine code --- .../lower_cinn_fusion_op_pass.cc | 2 - paddle/cinn/hlir/framework/pir/utils.cc | 40 +++++++++++- .../infer_sym_slice_utils.h | 28 ++++---- .../infer_symbolic_shape/unary_infer_sym.cc | 64 +++++++++++++++---- 4 files changed, 108 insertions(+), 26 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc index 32640cd1ab899d..87223efd62aa3b 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/lower_cinn_fusion_op_pass.cc @@ -39,8 +39,6 @@ class FusionOpPattern : public pir::OpRewritePattern { ::pir::IrContext* ctx = ::pir::IrContext::Instance(); auto* program = fusion_op->GetParentProgram(); auto& shape_analysis = pir::ShapeAnalysisManager::Instance().Get(program); - VLOG(4) << "Program before lowering: \n" - << pir::CustomPrintHelper(*program, shape_analysis.PrintHook()); // TODO(zhangyuqin1998): Replace pir::Group with a new structure OpLoweringGroupPtr group = GetGroup(fusion_op); diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 82d6e1ffad86fc..12bc3fe115f60c 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -349,11 +349,47 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { if (FLAGS_disable_dyshape_in_train) { return false; } + + auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( + const_cast<::pir::Operation&>(op).GetParentProgram()); + + const auto& HasData = + [&](const symbol::ShapeOrDataDimExprs& shape_or_data) -> bool { + if (shape_or_data.isa()) { + bool has_data = true; + const symbol::TensorListShapeOrDataDimExprs& list = + shape_or_data.dyn_cast(); + for (const auto& item : list) { + has_data = has_data && item.data().has_value(); + } + return has_data; + } else if (shape_or_data.isa()) { + return shape_or_data.data().has_value(); + } + PADDLE_THROW(::common::errors::InvalidArgument( + "The starts and ends parameters of pd_op.slice currently only support " + "two types: TensorListShapeOrDataDimExprs and " + "TensorShapeOrDataDimExprs")); + }; + + const auto& IsProcessableSlice = [&]() -> bool { + const ::pir::Value& starts_value = op.operand_source(1); + const ::pir::Value& ends_value = op.operand_source(2); + const symbol::ShapeOrDataDimExprs& starts_shape_data = + shape_analysis.GetShapeOrDataForValue(starts_value); + const symbol::ShapeOrDataDimExprs& ends_shape_data = + shape_analysis.GetShapeOrDataForValue(ends_value); + return HasData(starts_shape_data) && HasData(ends_shape_data); + }; + + if (op.isa() && !IsProcessableSlice()) { + return true; + } + if (!HaveUnkDim(op)) { return false; } - auto& shape_analysis = ::pir::ShapeAnalysisManager::Instance().Get( - const_cast<::pir::Operation&>(op).GetParentProgram()); + std::unordered_set input_exprs = [&]() { std::unordered_set res; for (const auto& input_value : op.operands_source()) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h index b1302c1c4ec062..7d84a1829a9d4a 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h @@ -18,27 +18,33 @@ namespace paddle::dialect::slice_utils { -inline ExprVec GetExprVecFromData(const ShapeOrData &shapeordata) { - if (shapeordata.isa()) { - ExprVec result; +inline bool GetExprVecOfStartEnd( + const symbol::ShapeOrDataDimExprs &shape_or_data, + std::vector *expr_vec) { + if (shape_or_data.isa()) { TensorListExprs list = - shapeordata.dyn_cast(); + shape_or_data.dyn_cast(); for (size_t i = 0; i < list.size(); i++) { PADDLE_ENFORCE_EQ(list.at(i).data().has_value(), true, common::errors::InvalidArgument( "i-th element of list has no value, please check")); for (auto expr : list.at(i).data().value()) { - result.emplace_back(expr); + expr_vec->emplace_back(expr); } } - return result; + return true; + } else if (shape_or_data.isa()) { + if (shape_or_data.data().has_value()) { + *expr_vec = shape_or_data.data().value(); + return true; + } + return false; } else { - PADDLE_ENFORCE_EQ(shapeordata.data().has_value(), - true, - common::errors::InvalidArgument( - "Input `shapeordata.data` is empty, please check")); - return shapeordata.data().value(); + PADDLE_THROW(::common::errors::InvalidArgument( + "The starts and ends parameters of pd_op.slice currently only support " + "two types: TensorListShapeOrDataDimExprs and " + "TensorShapeOrDataDimExprs")); } } diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 2bb863f9a46f3e..5891da7e808acb 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3227,24 +3227,66 @@ bool ShuffleChannelOpInferSymbolicShape( bool SliceOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { pir::Value operand_source = op->operand_source(0); - pir::Value operand_starts = op->operand_source(1); - pir::Value operand_ends = op->operand_source(2); pir::Value res = op->result(0); - const symbol::ShapeOrDataDimExprs &starts_shape_data = - infer_context->GetShapeOrDataForValue(operand_starts); - const symbol::ShapeOrDataDimExprs &ends_shape_data = - infer_context->GetShapeOrDataForValue(operand_ends); - std::vector axes_vec = details::GetVectorAttr(op, "axes"); - - ExprVec starts = slice_utils::GetExprVecFromData(starts_shape_data); - ExprVec ends = slice_utils::GetExprVecFromData(ends_shape_data); - std::vector infer_flags = details::GetVectorAttr(op, "infer_flags"); const std::vector decrease_axis = details::GetVectorAttr(op, "decrease_axis"); + auto GetExprVec = [&](std::vector *expr_vec, + const int &operand_idx, + const std::string &attr_name) -> bool { + if (op->operand_source(operand_idx)) { + const symbol::ShapeOrDataDimExprs &se_shape_data = + infer_context->GetShapeOrDataForValue( + op->operand_source(operand_idx)); + if (slice_utils::GetExprVecOfStartEnd(se_shape_data, expr_vec)) { + return true; + } + PADDLE_ENFORCE_EQ( + se_shape_data.shape().at(0).isa() && + (static_cast(axes_vec.size()) == + se_shape_data.shape().at(0).dyn_cast()), + true, + common::errors::InvalidArgument( + "The size of axes must equal size of starts and ends.")); + return false; + } else { + if (op->attributes().find(attr_name) != op->attributes().end()) { + const std::vector se_raw = + paddle::dialect::details::GetVectorAttr(op, attr_name); + for (const int64_t &se : se_raw) { + expr_vec->push_back(symbol::DimExpr{se}); + } + return true; + } + return false; + } + }; + + std::vector starts; + std::vector ends; + if (!GetExprVec(&starts, 1, "starts") || !GetExprVec(&ends, 2, "ends")) { + const auto &in_shapeordata = + infer_context->GetShapeOrDataForValue(op->operand_source(0)); + // NOTE(gongshaotian): When there is no data value in the starts and ends + // parameters, only the shape value is processed regardless of whether the + // input has a data value, and the data value is no longer processed. + std::vector out_shape = in_shapeordata.shape(); + for (size_t i = 0; i < axes_vec.size(); i++) { + int64_t axis = axes_vec[i]; + out_shape[axis] = infer_context->GetNextSymName(); + } + ExprVec out_dims = paddle::dialect::slice_utils::GetDecreasedDims( + out_shape, decrease_axis); + infer_context->SetShapeOrDataForValue( + res, + symbol::ShapeOrDataDimExprs{ + symbol::TensorShapeOrDataDimExprs(out_dims)}); + return true; + } + infer_context->SetShapeOrDataForValue( res, slice_utils::SliceRawInferSymbolicShape(operand_source, From 1f610477cb41b49b0bc9ae65da0992a8a2470243 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:27:56 +0800 Subject: [PATCH 155/288] Fix cpu kernel bug (#69878) * fix fall back cpu kernel bug * update * fix compile bug * fix bug * merge code * fix x86 kernel * polish code * polish code --- .../hlir/framework/pir/compilation_cache.cc | 7 ++- .../hlir/framework/pir/compilation_cache.h | 8 ++- .../hlir/framework/pir/compilation_task.cc | 16 +++-- .../hlir/framework/pir/compilation_task.h | 6 +- .../hlir/framework/pir/op_lowering_impl.cc | 56 +++++++---------- paddle/cinn/hlir/framework/pir/utils.h | 2 +- .../fluid/pir/dialect/operator/utils/utils.cc | 60 ++++++++++++++++++ .../fluid/pir/dialect/operator/utils/utils.h | 2 + .../pir/transforms/pd_op_to_kernel_pass.cc | 63 ++++++------------- 9 files changed, 131 insertions(+), 89 deletions(-) diff --git a/paddle/cinn/hlir/framework/pir/compilation_cache.cc b/paddle/cinn/hlir/framework/pir/compilation_cache.cc index 1843f0f3f57d0f..5af808b9a5135e 100644 --- a/paddle/cinn/hlir/framework/pir/compilation_cache.cc +++ b/paddle/cinn/hlir/framework/pir/compilation_cache.cc @@ -47,12 +47,15 @@ void* BackendResource::GetCX86HostFuncPtr() const { return ptr; } -pir::CINNKernelInfo BackendResource::GenerateKernelInfo() const { +pir::CINNKernelInfo BackendResource::GenerateKernelInfo( + bool need_x86_kernel) const { pir::CINNKernelInfo kernel_info; kernel_info.fn_name = host_fn_name_; kernel_info.fn_ptr = GetHostFuncPtr(); kernel_info.infer_shape_fn_ptr = GetInferFuncPtr(); - kernel_info.CX86_fn_ptr = GetCX86HostFuncPtr(); + if (need_x86_kernel) { + kernel_info.CX86_fn_ptr = GetCX86HostFuncPtr(); + } kernel_info.symbol_args_map = GetSymbolArgsMap(); kernel_info.temp_space_sizes = GetTempSpaceSizes(); return kernel_info; diff --git a/paddle/cinn/hlir/framework/pir/compilation_cache.h b/paddle/cinn/hlir/framework/pir/compilation_cache.h index bea6631cbbd9ab..eb54bd3389a113 100644 --- a/paddle/cinn/hlir/framework/pir/compilation_cache.h +++ b/paddle/cinn/hlir/framework/pir/compilation_cache.h @@ -55,7 +55,7 @@ class BackendResource final { const std::shared_ptr& GetBackendCompiler() const { return backend_compiler_; } - pir::CINNKernelInfo GenerateKernelInfo() const; + pir::CINNKernelInfo GenerateKernelInfo(bool need_x86_kernel = false) const; const std::string& GetHostFuncName() const { return host_fn_name_; } private: @@ -69,7 +69,8 @@ class BackendResource final { class CompilationResult final { public: - explicit CompilationResult(const Target& target) : target_(target) {} + explicit CompilationResult(const Target& target, bool need_x86_kernel = false) + : target_(target), have_cx86_kernel_(need_x86_kernel) {} const std::shared_ptr& GetBackendResource() const { return backend_resource_; } @@ -91,12 +92,13 @@ class CompilationResult final { ::common::errors::PreconditionNotMet( "Found backend_resource_ is nullptr, please " "call SetBackendResource first.")); - return backend_resource_->GenerateKernelInfo(); + return backend_resource_->GenerateKernelInfo(have_cx86_kernel_); } private: Target target_; std::shared_ptr backend_resource_{nullptr}; + bool have_cx86_kernel_{false}; }; } // namespace pir diff --git a/paddle/cinn/hlir/framework/pir/compilation_task.cc b/paddle/cinn/hlir/framework/pir/compilation_task.cc index 29b46fd4dabbde..978fe8571277a9 100644 --- a/paddle/cinn/hlir/framework/pir/compilation_task.cc +++ b/paddle/cinn/hlir/framework/pir/compilation_task.cc @@ -41,6 +41,8 @@ void GroupCompilationContext::SetLoweredFuncs( CX86_lowered_funcs_.push_back(std::move(predicate2func.second)); } infer_shape_lowered_func_ = std::move(funcs.infer_shape_func); + + need_x86_kernel_ = (CX86_predicates_.size() > 0); } std::string GroupCompilationContext::PrintPredicate2Funcs() const { @@ -79,6 +81,7 @@ void GroupCompilationContext::PrepareModuleBuilder() { ::common::errors::InvalidArgument( "The size of predicates and lowered_funcs should be " "the same.")); + for (const ir::Expr& predicate : CX86_predicates_) { CX86_module_builder_.AddPredicate(predicate); } @@ -203,15 +206,19 @@ void CompilationTask::Lowering() { std::shared_ptr CompilationTask::CodegenAndJit() { context_->PrepareModuleBuilder(); + ir::Module ir_module = context_->module_builder_.Build(); ir::Module ir_moduleCX86 = context_->CX86_module_builder_.Build(); - return BuildPirCINNKernelInfo(ir_module, ir_moduleCX86); + return BuildPirCINNKernelInfo( + ir_module, ir_moduleCX86, context_->NeedCompileCX86Kernel()); } std::shared_ptr CompilationTask::BuildPirCINNKernelInfo( - const ir::Module& module, const ir::Module& CX86module) { - auto compilation_result = - std::make_shared(context_->target_); + const ir::Module& module, + const ir::Module& CX86module, + bool need_x86_kernel) { + auto compilation_result = std::make_shared( + context_->target_, need_x86_kernel); auto backend_resource = std::make_shared( context_->target_, context_->group_->FuncName(), @@ -223,6 +230,7 @@ std::shared_ptr CompilationTask::BuildPirCINNKernelInfo( backend_resource->GetBackendCompiler()->AppendCX86(CX86module); backend_resource->GetBackendCompiler()->EndCompile(); compilation_result->SetBackendResource(backend_resource); + VLOG(5) << "End to compile module into cuda kernel."; return compilation_result; } diff --git a/paddle/cinn/hlir/framework/pir/compilation_task.h b/paddle/cinn/hlir/framework/pir/compilation_task.h index 0e89886f07d605..433f5c2472532e 100644 --- a/paddle/cinn/hlir/framework/pir/compilation_task.h +++ b/paddle/cinn/hlir/framework/pir/compilation_task.h @@ -40,6 +40,7 @@ class GroupCompilationContext { void SetLoweredFuncs(BucketLoweredFuncsWrapper&& funcs); void PrepareModuleBuilder(); std::string PrintPredicate2Funcs() const; + bool NeedCompileCX86Kernel() const { return need_x86_kernel_; } private: friend class CompilationTask; @@ -58,6 +59,7 @@ class GroupCompilationContext { ir::LoweredFunc infer_shape_lowered_func_; ir::Module::Builder module_builder_; ir::Module::Builder CX86_module_builder_; + bool need_x86_kernel_{false}; }; class CompilationTask { @@ -74,7 +76,9 @@ class CompilationTask { private: std::shared_ptr CodegenAndJit(); std::shared_ptr BuildPirCINNKernelInfo( - const ir::Module& module, const ir::Module& CX86module); + const ir::Module& module, + const ir::Module& CX86module, + bool need_x86_kernel = false); GroupCompilationContext* context_; }; diff --git a/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc b/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc index 75b177877b612f..4124071fafb7e1 100644 --- a/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc +++ b/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc @@ -218,6 +218,9 @@ BucketLoweredFuncsWrapper OpLowererImpl::BucketLower( } // The last func is x86 kernel. for (size_t i = funcs.size() - 1; i < funcs.size(); ++i) { + if (funcs[i]->body == ir::Expr(-1)) { + continue; + } funcs[i]->name = funcs[i]->name + "_CX86"; funcs_wrapper.predicate2funcsCX86.emplace_back(cond2func_bodies[i].first, funcs[i]); @@ -750,44 +753,29 @@ ir::Expr OpLowererImpl::LowerX86(const OpLoweringGroupPtr& group, // for some op, it will output more tmp value and regard as // XX_0, XX_1, so we log them in tmp_tensor_info; - auto need_lower_x86 = [&]() -> bool { - for (auto* op : ops) { - for (size_t i = 0; i < op->num_operands(); ++i) { - auto in = op->operand_source(i); - if (!in || !in.type()) { - continue; - } - auto type_info = in.type().dyn_cast(); - auto dtype = type_info.dtype(); - const auto& dims = type_info.dims(); - std::vector sym_shape; - // 1. dynamic shape not need lower x86 - if (::common::contain_unknown_dim(dims)) { - return false; - } - // 2. size < 4 not need lower x86 - int64_t sym_shape_size = 1; - for (int i = 0; i < dims.size(); ++i) { - sym_shape_size *= dims[i]; - if (sym_shape_size > 4) { - return false; - } - } + std::vector<::pir::Value> vec_inputs; + std::vector<::pir::Value> vec_outputs; + for (auto* op : ops) { + for (size_t i = 0; i < op->num_operands(); ++i) { + auto in = op->operand_source(i); + if (!in || !in.type()) { + continue; } - std::vector out_types; - std::vector> out_shapes; - CollectOutputInfo(op, &out_types, &out_shapes, group); - for (const auto& tt : out_types) { - // 3. float16 not need lower x86 - if (tt.is_float16()) { - return false; - } + vec_inputs.push_back(in); + } + + for (size_t i = 0; i < op->num_results(); ++i) { + auto out = op->result(i); + if (!out || !out.type()) { + continue; } + + vec_outputs.push_back(out); } - return true; - }; - if (!need_lower_x86()) { + } + + if (!paddle::dialect::CanGroupOpRunCpuKernel(vec_inputs, vec_outputs)) { return ir::Expr(-1); } diff --git a/paddle/cinn/hlir/framework/pir/utils.h b/paddle/cinn/hlir/framework/pir/utils.h index 532ebf812c8be0..04ad3d01d6c6aa 100644 --- a/paddle/cinn/hlir/framework/pir/utils.h +++ b/paddle/cinn/hlir/framework/pir/utils.h @@ -33,7 +33,7 @@ struct CINNKernelInfo { std::string fn_name; void* fn_ptr; void* infer_shape_fn_ptr; - void* CX86_fn_ptr; + void* CX86_fn_ptr{nullptr}; struct ArgDimIdx { int arg_idx; diff --git a/paddle/fluid/pir/dialect/operator/utils/utils.cc b/paddle/fluid/pir/dialect/operator/utils/utils.cc index 91f7bf7c261e0b..7bedc450b4725c 100644 --- a/paddle/fluid/pir/dialect/operator/utils/utils.cc +++ b/paddle/fluid/pir/dialect/operator/utils/utils.cc @@ -595,5 +595,65 @@ std::vector> ConstructStopGradient(pir::Operation* op) { return stop_gradients; } +bool CanGroupOpRunCpuKernel(const std::vector<::pir::Value>& vec_inputs, + const std::vector<::pir::Value>& vec_output) { + for (size_t i = 0; i < vec_inputs.size(); ++i) { + auto tmp_in = vec_inputs[i]; + if (!tmp_in || !tmp_in.type()) { + continue; + } + + phi::DDim in_dims; + + if (auto type_info = + tmp_in.type() + .dyn_cast()) { + auto type = tmp_in.type().dyn_cast(); + in_dims = type.dims(); + if (type.place().GetType() != phi::AllocationType::CPU) { + return false; + } + } else if (auto type_info = + tmp_in.type().dyn_cast()) { + in_dims = type_info.dims(); + } + + // 1. dynamic shape not need lower x86 + if (::common::contain_unknown_dim(in_dims)) { + return false; + } + // 2. size < 4 not need lower x86 + if (phi::product(in_dims) > 4) { + return false; + } + } + + for (size_t i = 0; i < vec_output.size(); ++i) { + const auto& out = vec_output[i]; + + if (!out || !out.type()) { + continue; + } + + if (out.type().isa()) { + auto type = out.type().dyn_cast(); + + if (type.dtype().isa<::pir::BFloat16Type>()) { + return false; + } + + if (::common::contain_unknown_dim(type.dims())) { + return false; + } + + if (phi::product(type.dims()) > 4) { + return false; + } + } + } + + return true; +} + } // namespace dialect } // namespace paddle diff --git a/paddle/fluid/pir/dialect/operator/utils/utils.h b/paddle/fluid/pir/dialect/operator/utils/utils.h index a7c29e6ea9b244..3d6c4c46103d7c 100644 --- a/paddle/fluid/pir/dialect/operator/utils/utils.h +++ b/paddle/fluid/pir/dialect/operator/utils/utils.h @@ -204,6 +204,8 @@ void SetStopGradient(T value, Args... args) { } std::vector> ConstructStopGradient(pir::Operation* op); +bool CanGroupOpRunCpuKernel(const std::vector<::pir::Value>& vec_inputs, + const std::vector<::pir::Value>& vec_output); } // namespace dialect } // namespace paddle diff --git a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc index 0b0326e1b31b5e..88aa69f1d32312 100644 --- a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc +++ b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc @@ -52,6 +52,12 @@ #include "paddle/phi/core/kernel_factory.h" #include "paddle/pir/include/core/builtin_op.h" #include "paddle/pir/include/dialect/control_flow/ir/cf_op.h" + +#ifdef PADDLE_WITH_CINN +#include "paddle/cinn/hlir/dialect/runtime/ir/jit_kernel_op.h" +#include "paddle/cinn/hlir/framework/pir/utils.h" +#endif + #ifdef PADDLE_WITH_DNNL #include "paddle/fluid/pir/dialect/operator/ir/onednn_op.h" #include "paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.h" @@ -296,49 +302,6 @@ static bool NeedFallBackFromGPUDNN2GPU(pir::Operation* op, } #endif -bool CanRunOnCpuKernel(const std::vector<::pir::Value>& vec_inputs, - ::pir::Operation* op) { - bool can_run_cpu = true; - for (size_t i = 0; i < vec_inputs.size(); ++i) { - auto tmp_in = vec_inputs[i]; - if (!tmp_in) { - continue; - } - - if (tmp_in.type().isa()) { - auto type = tmp_in.type().dyn_cast(); - if (type.place().GetType() != phi::AllocationType::CPU) { - can_run_cpu = false; - break; - } - - if (phi::product(type.dims()) > 4) { - can_run_cpu = false; - break; - } - } - } - - for (size_t i = 0; i < op->num_results(); ++i) { - auto out = op->result(i); - - if (!out || !out.type()) { - continue; - } - - if (out.type().isa()) { - auto type = out.type().dyn_cast(); - if (::common::contain_unknown_dim(type.dims()) || - phi::product(type.dims()) > 4) { - can_run_cpu = false; - break; - } - } - } - - return can_run_cpu; -} - static phi::Backend DeriveBackend(const std::string& op, const phi::Place& place, const OpYamlInfoParser* op_info_parser, @@ -2145,8 +2108,20 @@ void HandleForSpecialOp( auto dst_backend = phi::TransToPhiBackend(place); auto exec_backend = paddle::dialect::PlaceAttribute::get(ctx, place); - if (CanRunOnCpuKernel(in_temps, op_item)) { + + bool run_cpu_kernel = CanGroupOpRunCpuKernel(in_temps, op_item->results()); +#ifdef PADDLE_WITH_CINN + + cinn::dialect::JitKernelOp jit_kernel_op = + op_item->dyn_cast(); + const cinn::hlir::framework::pir::CINNKernelInfo& kernel_info = + jit_kernel_op.cinn_kernel_info(); + if (run_cpu_kernel && kernel_info.CX86_fn_ptr == nullptr) { // change dst_backend to cpu + run_cpu_kernel = false; + } +#endif + if (run_cpu_kernel) { dst_backend = phi::Backend::CPU; exec_backend = paddle::dialect::PlaceAttribute::get( From 1b28d33c1665124e1fb0b2840f2b5713ced7095c Mon Sep 17 00:00:00 2001 From: zhanghonggeng <43205915+zhanghonggeng@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:40:02 +0800 Subject: [PATCH 156/288] Add white list for auto_layout_pass (#68946) * Add white list for auto_layout_pass * test_1 * test_2 * remove autolayout_enabled * remove enable_auto_layout_pass flag in analysis config * add preferlayout for conv2d and fusedconv2daddact * add mixed_precision_mode * fix CI PR-CI-Mac-Python3 * Replace transfer_layout_pass with auto_layout_pass in inference * fix * update analysis predictor * Code changes based on comments * Run AutoMixedPrecisionPass before cinn. * Modify conflicts * fix PR-CI-Codestyle-Check * fix conflict * fix conflicts --- .../fluid/inference/api/analysis_predictor.cc | 42 +++-- .../interface/layout_transformation.cc | 53 ++++++ .../interface/layout_transformation.hpp | 6 + .../transforms/general/auto_layout_pass.cc | 174 +++++++++++++++--- .../general/auto_layout_simplify_pass.cc | 4 + paddle/phi/ops/yaml/ops.yaml | 4 +- 6 files changed, 249 insertions(+), 34 deletions(-) diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 9d18e1bb1d9acb..0af49227a86804 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -131,7 +131,7 @@ #include "paddle/pir/include/pass/pass_registry.h" COMMON_DECLARE_bool(pir_apply_inplace_pass); - +COMMON_DECLARE_bool(enable_auto_layout_pass); namespace paddle { namespace { #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) @@ -804,6 +804,20 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { pass->name()) != this->config_.ir_debug_passes_.end(); }; + auto AddAutoLayoutPasses = [&](pir::PassManager &pass_manager) { + auto &pass_registry = pir::PassRegistry::Instance(); + std::vector passes = {"auto_layout_pass", + "auto_layout_simplify_pass"}; + + for (const auto &pass_name : passes) { + if (std::find(config_.deleted_passes_.begin(), + config_.deleted_passes_.end(), + pass_name) == config_.deleted_passes_.end()) { + pass_manager.AddPass(pass_registry.Get(pass_name)); + } + } + }; + auto AddAutoMixedPrecisionPass = [&](pir::PassManager &pass_manager) { auto auto_mixed_precision_pass = ::pir::CreateAutoMixedPrecisionPass(); if (std::find(config_.deleted_passes_.begin(), @@ -867,10 +881,13 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (config_.enable_gpu_mixed_) { AddAutoMixedPrecisionPass(fused_op_pm); - fused_op_pm.AddPass( - pir::PassRegistry::Instance().Get("transfer_layout_pass")); + if (FLAGS_enable_auto_layout_pass) { + AddAutoLayoutPasses(fused_op_pm); + } else { + fused_op_pm.AddPass( + pir::PassRegistry::Instance().Get("transfer_layout_pass")); + } } - fused_op_pm.Run(pir_program_.get()); } } @@ -909,7 +926,6 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { } } } - #ifdef PADDLE_WITH_XPU } else if (config_.use_xpu()) { // xpu @@ -999,12 +1015,16 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (!config_.cinn_enabled()) { AddAutoMixedPrecisionPass(basic_pass_pm); - auto transfer_layout_pass = ::pir::CreateTransferLayoutPass(); - if (std::find(config_.deleted_passes_.begin(), - config_.deleted_passes_.end(), - transfer_layout_pass->name()) == - config_.deleted_passes_.end()) { - basic_pass_pm.AddPass(std::move(transfer_layout_pass)); + if (FLAGS_enable_auto_layout_pass) { + AddAutoLayoutPasses(basic_pass_pm); + } else { + auto transfer_layout_pass = ::pir::CreateTransferLayoutPass(); + if (std::find(config_.deleted_passes_.begin(), + config_.deleted_passes_.end(), + transfer_layout_pass->name()) == + config_.deleted_passes_.end()) { + basic_pass_pm.AddPass(std::move(transfer_layout_pass)); + } } } } diff --git a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc index 88de5c193e00ae..6b55f5905e6deb 100644 --- a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc +++ b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc @@ -102,6 +102,32 @@ common::DataLayout PreferLayoutImpl(pir::Operation* op) { return common::StringToDataLayout(data_format_attr.AsString()); } +template <> +common::DataLayout PreferLayoutImpl(pir::Operation* op) { + auto data_format_attr = op->attribute("data_format"); + if (!data_format_attr) { + PADDLE_THROW(common::errors::InvalidArgument( + "op (%s) should have attribute `data_format`, but got %s", + op, + data_format_attr)); + } + + auto concrete_op = op->dyn_cast(); + if (auto in = concrete_op.x()) { + if (auto in_type = in.type()) { + if (in_type.isa()) { + if (auto tensor_type = in_type.dyn_cast()) { + if (tensor_type.dtype().isa()) { + return common::DataLayout::NHWC; + } + } + } + } + } + + return common::StringToDataLayout(data_format_attr.AsString()); +} + template <> bool CanBeModifiedImpl(pir::Operation* op) { return false; @@ -352,6 +378,33 @@ void RewriteByLayoutImpl(pir::Operation* op, RewriteByInfermeta(op, new_layout); } +template <> +void RewriteByLayoutImpl(pir::Operation* op, + common::DataLayout new_layout) { + auto concrete_op = op->dyn_cast(); + auto axis = concrete_op.axis(); + if (!axis || !(axis.defining_op()->isa())) { + PADDLE_THROW(common::errors::InvalidArgument( + "Argmax's axis must be processed when rewrite by layout.")); + } + + auto axis_op = axis.defining_op()->dyn_cast(); + int axis_value = + axis_op.attribute("value").dyn_cast().data().to(); + + PADDLE_ENFORCE_EQ( + axis_value, + 1, + common::errors::InvalidArgument( + "Argmax's axis was expected as 1, but got %d", axis_value)); + axis.defining_op()->set_attribute( + "value", + ScalarAttribute::get(pir::IrContext::Instance(), phi::Scalar(3))); + + // infer new meta for argmax + RewriteByInfermeta(op, new_layout); +} + template <> void RewriteByLayoutImpl(pir::Operation* op, common::DataLayout new_layout) { diff --git a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.hpp b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.hpp index cf9b98edf29de1..21952a09e39cf3 100644 --- a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.hpp +++ b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.hpp @@ -118,6 +118,9 @@ OVERLOAD_PREFER_LAYOUT(Conv2dOp); OVERLOAD_CAN_BE_MODIFIED(Conv2dOp); OVERLOAD_REWRITE_BY_LAYOUT(Conv2dOp); +class Conv2dTransposeOp; +OVERLOAD_PREFER_LAYOUT(Conv2dTransposeOp); + class GroupNormOp; OVERLOAD_REWRITE_BY_LAYOUT(GroupNormOp); OVERLOAD_RELEVANT_INPUTS(GroupNormOp); @@ -154,6 +157,9 @@ class ConcatOp; OVERLOAD_REWRITE_BY_LAYOUT(ConcatOp); OVERLOAD_RELEVANT_INPUTS(ConcatOp); +class ArgmaxOp; +OVERLOAD_REWRITE_BY_LAYOUT(ArgmaxOp); + class Pool2dOp; OVERLOAD_RELEVANT_INPUTS(Pool2dOp); OVERLOAD_REWRITE_BY_LAYOUT(Pool2dOp); diff --git a/paddle/fluid/pir/transforms/general/auto_layout_pass.cc b/paddle/fluid/pir/transforms/general/auto_layout_pass.cc index e53679f8e2bbc2..41584cb8fd537c 100644 --- a/paddle/fluid/pir/transforms/general/auto_layout_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_layout_pass.cc @@ -24,9 +24,11 @@ #include "paddle/fluid/inference/api/paddle_pass_builder.h" #include "paddle/fluid/pir/dialect/operator/interface/layout_transformation.h" #include "paddle/fluid/pir/dialect/operator/ir/control_flow_op.h" +#include "paddle/fluid/pir/dialect/operator/ir/op_attribute.h" #include "paddle/fluid/pir/dialect/operator/ir/op_dialect.h" #include "paddle/fluid/pir/dialect/operator/ir/op_type.h" #include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/phi/common/data_type.h" #include "paddle/pir/include/core/builtin_dialect.h" #include "paddle/pir/include/core/ir_context.h" #include "paddle/pir/include/core/op_trait.h" @@ -37,9 +39,14 @@ namespace { +extern const std::set op_in_NHWC; +extern const std::set op_in_NCHW; +extern const std::set op_with_axis; + class AutoLayoutPass : public pir::Pass { public: AutoLayoutPass() : pir::Pass("auto_layout_pass", 2) {} + void Run(pir::Operation* op) override { for (size_t i = 0; i < op->num_regions(); ++i) { auto& region = op->region(i); @@ -58,6 +65,14 @@ class AutoLayoutPass : public pir::Pass { private: void RewriteLayout(pir::Operation* op, const std::vector& input_values) { // NOLINT + if (op->isa() || + op->isa()) { + auto layout_interface = + op->dyn_cast(); + layout_interface.RewriteByLayout(op, common::DataLayout::NHWC); + return; + } + auto InferMetaSpecificOp = [&]() { // Op not implement InferMetaInterface interface, so we need to rewrite // manually @@ -96,15 +111,26 @@ class AutoLayoutPass : public pir::Pass { } } - bool IsInsertTransposeOpBefore(pir::Operation* op) { - bool is_insert_transpose = false; - - auto JudgeOperand = [&](const pir::Value& operand, - std::vector layout) { + bool JudgeOperand(const pir::Value& operand, + const std::vector& layout) { + if (operand.type().isa()) { + auto defined_op = operand.defining_op(); + for (auto inner_operand : defined_op->operands_source()) { + if (JudgeOperand(inner_operand, NCHW2NHWC_)) { + return true; + } + } + return false; + } else { if (!JudgeValue(operand)) return false; auto transposeInputOp = operand.defining_op(); if (!transposeInputOp) return false; + pir::Operation* op = transposeInputOp.operation(); + if (!op->HasAttribute("source")) return false; + auto source = + transposeInputOp.attribute("source").AsString(); + if (source != "auto_layout_pass") return false; const auto perm_attr = transposeInputOp.attribute("perm"); std::vector perm; @@ -113,18 +139,15 @@ class AutoLayoutPass : public pir::Pass { perm.push_back(attr.dyn_cast().data()); } return perm == layout; - }; + } + } + + bool IsInsertTransposeOpBefore(pir::Operation* op) { + bool is_insert_transpose = false; + for (pir::Value operand : op->operands_source()) { - if (operand.type().isa()) { - auto defined_op = operand.defining_op(); - for (auto inner_operand : defined_op->operands_source()) { - is_insert_transpose = JudgeOperand(inner_operand, NHWC2NCHW_); - if (is_insert_transpose) break; - } - } else { - is_insert_transpose = JudgeOperand(operand, NHWC2NCHW_); - } if (is_insert_transpose) break; + is_insert_transpose = JudgeOperand(operand, NHWC2NCHW_); } return is_insert_transpose; } @@ -138,10 +161,17 @@ class AutoLayoutPass : public pir::Pass { if (op->HasTrait()) continue; if (op->operands().size() == 0) continue; - // NHWC ops branch, Only support conv2d and fused_conv2d_add_act now, it - // will add white list later. - if (op->isa() || - op->isa()) { + // NHWC ops branch, Only support + // conv2d、fused_conv2d_add_act、conv2d_transpose now, it will add white + // list later. + if (op_in_NHWC.find(op_name) != op_in_NHWC.end()) { + auto layout_interface = + op->dyn_cast(); + common::DataLayout new_layout = layout_interface.PreferLayout(op); + if (new_layout != common::DataLayout::NHWC) { + continue; + } + if (op->HasAttribute("data_format") && op->attribute("data_format").AsString() == "NCHW") { @@ -150,7 +180,9 @@ class AutoLayoutPass : public pir::Pass { RewriteLayout(op, op->operands_source()); DoTransposeOpResult(op, builder); } - } else if (IsInsertTransposeOpBefore(op)) { + } else if (op_in_NCHW.find(op_name) == op_in_NCHW.end() && + op_with_axis.find(op_name) == op_with_axis.end() && + IsInsertTransposeOpBefore(op)) { VLOG(4) << "enter NCHW op: " << op_name; DoTransposeOpOperand(op, builder); RewriteLayout(op, op->operands_source()); @@ -175,11 +207,16 @@ class AutoLayoutPass : public pir::Pass { builder.set_insertion_point(op); // For conv2d, only transpose the input. - if (op->isa()) { + if (op->isa() || + op->isa()) { auto inp = op->operand(0); if (!JudgeValue(inp.source())) return; auto transpose_op = builder.Build(inp.source(), NCHW2NHWC_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); pir::SetNewLayoutForValue(transpose_op->result(0), common::DataLayout::NHWC); inp.set_source(transpose_op->result(0)); @@ -191,6 +228,10 @@ class AutoLayoutPass : public pir::Pass { // Canbe optimize with cache when not eliminate the transpose op. auto transpose_op = builder.Build( operand.source(), NCHW2NHWC_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); pir::SetNewLayoutForValue(transpose_op->result(0), common::DataLayout::NHWC); operand.set_source(transpose_op->result(0)); @@ -203,16 +244,107 @@ class AutoLayoutPass : public pir::Pass { if (!JudgeValue(result)) continue; auto transpose_op = builder.Build(result, NHWC2NCHW_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); pir::SetNewLayoutForValue(transpose_op->result(0), common::DataLayout::NCHW); result.ReplaceAllUsesWith(transpose_op->result(0)); transpose_op->operand(0).set_source(result); } } + pir::IrContext* ctx_ = pir::IrContext::Instance(); const std::vector NCHW2NHWC_ = {0, 2, 3, 1}; const std::vector NHWC2NCHW_ = {0, 3, 1, 2}; }; +const std::set op_in_NHWC = { + "pd_op.fused_conv2d_add_act", "pd_op.conv2d", "pd_op.conv2d_transpose"}; +const std::set op_in_NCHW = {"pd_op.max_pool2d_with_index", + "pd_op.fractional_max_pool2d", + "pd_op.unpool3d", + "pd_op.unpool", + "pd_op.correlation", + "pd_op.depthwise_conv2d", + "pd_op.grid_sample", + "pd_op.shuffle_channel", + "cf.yield", + "pd_op.reshape", + "pd_op.instance_norm", + "pd_op.batch_norm_", + "pd_op.bilinear_interp", + "pd_op.shape", + "pd_op.deformable_conv", + "pd_op.set_value_with_tensor_", + "pd_op.set_value_with_tensor"}; +const std::set op_with_axis = { + "pd_op.all", + "pd_op.amax", + "pd_op.amin", + "pd_op.any", + "pd_op.argmin", + "pd_op.argsort", + "pd_op.box_coder", + "pd_op.cross", + "pd_op.cross_entropy_with_softmax", + "pd_op.cummax", + "pd_op.cummin", + "pd_op.cumsum", + "pd_op.diagonal", + "pd_op.fake_channel_wise_dequantize_max_abs", + "pd_op.fake_channel_wise_quantize_abs_max", + "pd_op.fake_channel_wise_quantize_dequantize_abs_max", + "pd_op.flatten", + "pd_op.flip", + "pd_op.frame", + "pd_op.frobenius_norm", + "pd_op.gather", + "pd_op.gumbel_softmax", + "pd_op.index_add", + "pd_op.index_select", + "pd_op.index_select_strided", + "pd_op.kthvalue", + "pd_op.layer_norm", + "pd_op.log_softmax", + "pd_op.logcumsumexp", + "pd_op.logsumexp", + "pd_op.max", + "pd_op.maxout", + "pd_op.mean", + "pd_op.mode", + "pd_op.nanmedian", + "pd_op.norm", + "pd_op.overlap_add", + "pd_op.p_norm", + "pd_op.prod", + "pd_op.put_along_axis", + "pd_op.renorm", + "pd_op.repeat_interleave", + "pd_op.repeat_interleave_with_tensor_index", + "pd_op.reverse", + "pd_op.roll", + "pd_op.slice", + "pd_op.split", + "pd_op.split_with_num", + "pd_op.squeeze", + "pd_op.stack", + "pd_op.sum", + "pd_op.take_along_axis", + "pd_op.tensor_unfold", + "pd_op.topk", + "pd_op.trace", + "pd_op.unbind", + "pd_op.unique_consecutive", + "pd_op.dequantize_linear", + "pd_op.min", + "pd_op.quantize_linear", + "pd_op.softmax", + "pd_op.sparse_momentum", + "pd_op.unique", + "pd_op.unsqueeze", + "pd_op.unstack"}; + } // namespace namespace pir { diff --git a/paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.cc b/paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.cc index 402df61d8a72a2..31c05c279f2d41 100644 --- a/paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.cc @@ -47,6 +47,10 @@ class RedundantTransposePattern if (!before_transpose->isa()) { return false; } + if (!(before_transpose->HasAttribute("source"))) return false; + auto source = + before_transpose->attribute("source").AsString(); + if (source != "auto_layout_pass") return false; const auto before_perm_attr = before_transpose->attribute("perm"); diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index cc7a0152854d4c..5d85e1a78993e9 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -289,7 +289,7 @@ kernel : func : argmax data_type : x - interfaces : paddle::dialect::InferSymbolicShapeInterface + interfaces : paddle::dialect::InferSymbolicShapeInterface, paddle::dialect::LayoutTransformationInterface traits : paddle::dialect::ForwardOnlyTrait - op : argmin @@ -1054,7 +1054,7 @@ func : conv2d_transpose data_type : x backward : conv2d_transpose_grad - interfaces : paddle::dialect::InferSymbolicShapeInterface + interfaces : paddle::dialect::InferSymbolicShapeInterface, paddle::dialect::LayoutTransformationInterface - op : conv2d_transpose_bias args : (Tensor x, Tensor filter, Tensor bias, int[] strides={1, 1}, int[] paddings={0, 0}, int[] output_padding={}, IntArray output_size={}, str padding_algorithm="EXPLICIT", int groups=1, int[] dilations={1, 1}, str data_format="NCHW") From 0fbfabe75a578007dbdb3eb92fc7300d7ec91530 Mon Sep 17 00:00:00 2001 From: Lei Ding <69283446+Dmovic@users.noreply.github.com> Date: Wed, 4 Dec 2024 15:47:01 +0800 Subject: [PATCH 157/288] [CINN] Update loop fusion relax control flow (#69794) * [CINN] Update loop fusion relax control flow * update condition check * refine code --- .../tactic/compute_at_reduction_tactic.cc | 232 +++++++++++++----- 1 file changed, 174 insertions(+), 58 deletions(-) diff --git a/paddle/cinn/ir/group_schedule/tactic/compute_at_reduction_tactic.cc b/paddle/cinn/ir/group_schedule/tactic/compute_at_reduction_tactic.cc index 74dae85c91565d..98e2c0ee77b80c 100644 --- a/paddle/cinn/ir/group_schedule/tactic/compute_at_reduction_tactic.cc +++ b/paddle/cinn/ir/group_schedule/tactic/compute_at_reduction_tactic.cc @@ -44,31 +44,68 @@ void ComputeAtReductionTactic::Init(ScheduleContext* context) { context_ = context; } -bool ForExtentsEqual(const std::vector& first, - const std::vector& second) { - if (first.size() != second.size()) { +bool ControlFlowAllEqual(const std::vector& first, + const std::vector& second) { + // Check if without false case and condition is an index expression, only + // ir::LT, ir::LE, now + const auto IsIndexCondWithoutFalseCase = + [&](const ir::IfThenElse* if_op) -> bool { + if (if_op->false_case.defined()) return false; + auto cond = if_op->condition; + if (cond.As()) { + auto lt = cond.As(); + return lt->a().is_index() && lt->b().is_index(); + } + if (cond.As()) { + auto le = cond.As(); + return le->a().is_index() && le->b().is_index(); + } return false; - } - for (size_t i = 0; i < first.size(); ++i) { - const ir::For* first_for = first[i].As(); - const ir::For* second_for = second[i].As(); - PADDLE_ENFORCE_NOT_NULL( - first_for, - ::common::errors::InvalidArgument("The input node should be a For!")); - PADDLE_ENFORCE_NOT_NULL( - second_for, - ::common::errors::InvalidArgument("The input node should be a For!")); + }; - if (!ir::ir_utils::IRCompare(first_for->extent, second_for->extent)) { - return false; - } - if (first_for->for_type() != second_for->for_type()) { + const auto ControlFlowEqual = [&](const ir::Expr& first, + const ir::Expr& second) -> bool { + if (first.As() && second.As()) { + auto first_for = first.As(); + auto second_for = second.As(); + if (first_for->for_type() != second_for->for_type()) return false; + return ir::ir_utils::IRCompare(first_for->extent, second_for->extent); + } else if (first.As() && second.As()) { + auto first_if = first.As(); + auto second_if = second.As(); + if (!IsIndexCondWithoutFalseCase(first_if)) return false; + if (!IsIndexCondWithoutFalseCase(second_if)) return false; + return ir::ir_utils::IRCompare(first_if->condition, second_if->condition); + } else { + VLOG(8) << "Is not for or if_then_else node, first: " << first + << " second: " << second; return false; } + return false; + }; + + if (first.size() != second.size()) return false; + for (size_t i = 0; i < first.size(); ++i) { + if (!ControlFlowEqual(first[i], second[i])) return false; } return true; } +bool IsForEqual(const std::vector& first, + const std::vector& second) { + return ControlFlowAllEqual(first, second); +} + +void CheckAndComputeAt(ir::IRSchedule* sch, + const std::string& src_id, + const std::string& dst_id) { + auto block = sch->GetBlock(src_id); + auto loop = sch->GetLoops(dst_id).back(); + auto root = sch->GetRootBlock(block); + CheckComputeAtValidation(block, loop, root); + sch->SimpleComputeAt(block, loop); +} + bool BlockWithSameLoop(const std::vector& first, const std::vector& second) { VLOG(8) << "First inner loop: " << first.back(); @@ -87,20 +124,62 @@ std::string BlockToName(const ir::Expr& block) { ->name; } +struct GetControlFlowFunctor { + explicit GetControlFlowFunctor(const Expr& block) : block_(block) {} + + std::vector operator()(const Expr& expr) { + PADDLE_ENFORCE_NOT_NULL( + block_.As(), + ::common::errors::NotFound("The expr should be ScheduleBlockRealize.")); + end_ = false; + GetControlFlow(expr); + return result_; + } + + private: + void GetControlFlow(const Expr& expr) { + if (end_) return; + if (expr.As()) { + control_flow_.emplace_back(expr); + GetControlFlow(expr.As()->body); + control_flow_.pop_back(); + } else if (expr.As()) { + if (BlockToName(expr) == BlockToName(block_)) { + result_ = control_flow_; + end_ = true; + return; + } else { + GetControlFlow(expr.As()->schedule_block); + } + } else if (expr.As()) { + GetControlFlow(expr.As()->body); + } else if (expr.As()) { + for (auto& stmt : expr.As()->stmts) GetControlFlow(stmt); + } else if (expr.As()) { + control_flow_.emplace_back(expr); + GetControlFlow(expr.As()->true_case); + if (expr.As()->false_case.defined()) + GetControlFlow(expr.As()->false_case); + control_flow_.pop_back(); + } + } + + std::vector control_flow_{}; + std::vector result_{}; + bool end_{false}; + const Expr& block_; +}; + void ComputeAtReductionTactic::Apply(ir::IRSchedule* sch, const std::string& block_id) { - const auto ContainsConditionOrLet = [&](const ir::Expr& expr) -> bool { - const auto condition_or_let = ir::ir_utils::CollectIRNodesWithoutTensor( - expr, [&](const Expr* x) -> bool { - if (x->As()) return true; - if (x->As()) return true; - if (x->As()) return true; - return false; - }); - return !condition_or_let.empty(); + const auto ContainsLet = [&](const ir::Expr& expr) -> bool { + const auto let_set = ir::ir_utils::CollectIRNodesWithoutTensor( + expr, [&](const Expr* x) -> bool { return x->As(); }); + return !let_set.empty(); }; - // Should analyze condition when dependency tools are done. - if (ContainsConditionOrLet(sch->GetModule().GetExprs().front())) return; + + // Should analyze let when dependency tools are done. + if (ContainsLet(sch->GetModule().GetExprs().front())) return; if (!compute_at_reduce_init_done_) { for (const auto& block : sch->GetAllBlocks()) { @@ -124,10 +203,15 @@ void ComputeAtReductionTactic::ComputeAtReduceInit( const auto GetRootInitBlockId = [&](const std::vector& blocks) -> std::optional { + const std::vector cur_loops = sch->GetLoops(block_id); for (const auto& block : blocks) { const std::string root_block_name = BlockToName(block); - if (ir::IsReduceInitTensorName(root_block_name)) - return std::optional{root_block_name}; + if (ir::IsReduceInitTensorName(root_block_name)) { + const std::vector root_loops = sch->GetLoops(root_block_name); + if (IsForEqual(root_loops, cur_loops)) { + return std::optional{root_block_name}; + } + } } return std::nullopt; }; @@ -141,12 +225,7 @@ void ComputeAtReductionTactic::ComputeAtReduceInit( sch->GetLoops(block_id))) return; - const std::vector root_loops = sch->GetLoops(root_init_block_id); - const std::vector cur_loops = sch->GetLoops(block_id); - if (!ForExtentsEqual(root_loops, cur_loops)) return; - - sch->SimpleComputeAt(sch->GetBlock(block_id), - sch->GetLoops(root_init_block_id).back()); + CheckAndComputeAt(sch, block_id, root_init_block_id); } std::optional FindCandidateBlockId( @@ -168,6 +247,58 @@ std::optional FindCandidateBlockId( return ret; }; + const auto ReplaceIterWithMap = + [&](const ir::Expr& expr, + const std::unordered_map& for_var_map) -> ir::Expr { + ir::Expr map_expr = ir::ir_utils::IRCopy(expr); + for (const auto& [lhs_var, rhs_var] : for_var_map) { + auto tmp_var = + ir::_Var_::Make(rhs_var->lower_bound, + rhs_var->upper_bound, + lhs_var->name + "_compare_var_" + rhs_var->name, + rhs_var->is_reduce_axis, + rhs_var->is_symbolic_constant, + rhs_var->is_keepdim); + map_expr = + ir::analyzer::ReplaceVarWithExpr(map_expr, {lhs_var}, {tmp_var}); + map_expr = + ir::analyzer::ReplaceVarWithExpr(map_expr, {rhs_var}, {tmp_var}); + } + return map_expr; + }; + + const auto ConditionWithIter = + [&](const ir::Expr& block, + const std::unordered_map& for_var_map) + -> std::vector { + std::vector control_flows; + for (auto& cf : GetControlFlowFunctor(block)(sch->GetRootBlock(block))) { + auto tmp_cf = ir::ir_utils::IRCopy(cf); + if (tmp_cf.As()) { + auto if_then_else = tmp_cf.As(); + if_then_else->condition = + ReplaceIterWithMap(if_then_else->condition, for_var_map); + } + control_flows.push_back(tmp_cf); + } + return control_flows; + }; + + const auto ControlFlowWithIterEqual = + [&](const ir::Expr& first_block, + const ir::Expr& second_block, + const std::unordered_map& for_var_map) -> bool { + // Handle index expression. + if (ir::ir_utils::CollectIRNodesWithoutTensor( + sch->GetLoops(second_block).back(), + [](const Expr* x) { return x->As(); }) + .size() > 1) { + return false; + } + return ControlFlowAllEqual(ConditionWithIter(first_block, for_var_map), + ConditionWithIter(second_block, for_var_map)); + }; + const auto IndicesWithIterValues = [&](const std::vector& indices, const ir::ScheduleBlockRealize* sbr, @@ -176,23 +307,7 @@ std::optional FindCandidateBlockId( std::vector tensor_indices; std::vector map_iter_values; for (const auto& iter_value : sbr->iter_values) { - ir::Expr map_iter_value = ir::ir_utils::IRCopy(iter_value); - for (const auto& [lhs_var, rhs_var] : for_var_map) { - // cinn::optim::ReplaceVarWithExpr( - // &map_iter_value, lhs_var, ir::ir_utils::IRCopy(rhs_var)); - auto tmp_var = - ir::_Var_::Make(rhs_var->lower_bound, - rhs_var->upper_bound, - lhs_var->name + "_compare_var_" + rhs_var->name, - rhs_var->is_reduce_axis, - rhs_var->is_symbolic_constant, - rhs_var->is_keepdim); - map_iter_value = ir::analyzer::ReplaceVarWithExpr( - map_iter_value, {lhs_var}, {tmp_var}); - map_iter_value = ir::analyzer::ReplaceVarWithExpr( - map_iter_value, {rhs_var}, {tmp_var}); - } - map_iter_values.push_back(map_iter_value); + map_iter_values.push_back(ReplaceIterWithMap(iter_value, for_var_map)); } for (ir::Expr index : indices) { ir::Expr index_value = ir::analyzer::ReplaceVarWithExpr( @@ -243,9 +358,11 @@ std::optional FindCandidateBlockId( if (IndicesContainLoad(target_load)) return false; const std::vector first_loops = sch->GetLoops(first_block); const std::vector second_loops = sch->GetLoops(second_block); - if (!ForExtentsEqual(first_loops, second_loops)) return false; + if (first_loops.size() != second_loops.size()) return false; std::unordered_map for_var_map = ConstructForVarMap(first_loops, second_loops); + if (!ControlFlowWithIterEqual(first_block, second_block, for_var_map)) + return false; for (const auto& load_node : load_nodes) { const auto node = load_node.As(); @@ -384,6 +501,7 @@ bool IsSafeComputeAt(ir::IRSchedule* sch, void ComputeAtReductionTactic::ComputeAtReduceLoad( ir::IRSchedule* sch, const std::string& block_id) { + if (!ir::analyzer::IsReductionSBlock(sch->GetBlock(block_id))) return; // 1. Find candidate block, load buffer with same indices. std::optional candidate_block_id_value = FindCandidateBlockId(sch, sch->GetAllBlocks(), sch->GetBlock(block_id)); @@ -398,10 +516,8 @@ void ComputeAtReductionTactic::ComputeAtReduceLoad( if (!IsSafeComputeAt(sch, candidate_block_id, block_id)) return; VLOG(8) << "Compate at is safe: " << block_id; - // 3. Compute at schedule. - const std::vector candidate_block_loops = - sch->GetLoops(candidate_block_id); - sch->SimpleComputeAt(sch->GetBlock(block_id), candidate_block_loops.back()); + // 3. Check and compute at schedule. + CheckAndComputeAt(sch, block_id, candidate_block_id); } std::unique_ptr CreateComputeAtReductionTactic() { From 15b6f00b4340dcb78bfd7b671b4dcac200fb609d Mon Sep 17 00:00:00 2001 From: co63oc Date: Wed, 4 Dec 2024 16:43:38 +0800 Subject: [PATCH 158/288] [Lod][fluid_ops]lod_rank_table.cc (#69923) --- paddle/fluid/framework/CMakeLists.txt | 13 +--- paddle/fluid/framework/lod_rank_table.cc | 65 ------------------- paddle/fluid/framework/lod_rank_table.h | 62 ------------------ paddle/fluid/operators/CMakeLists.txt | 2 +- paddle/fluid/operators/unity_build_rule.cmake | 1 - 5 files changed, 2 insertions(+), 141 deletions(-) delete mode 100644 paddle/fluid/framework/lod_rank_table.cc delete mode 100644 paddle/fluid/framework/lod_rank_table.h diff --git a/paddle/fluid/framework/CMakeLists.txt b/paddle/fluid/framework/CMakeLists.txt index 7bef3485feb84c..63f8a8bb2284d7 100755 --- a/paddle/fluid/framework/CMakeLists.txt +++ b/paddle/fluid/framework/CMakeLists.txt @@ -464,11 +464,6 @@ if(WITH_PSCORE) ) endif() -cc_library( - lod_rank_table - SRCS lod_rank_table.cc - DEPS lod_tensor) - cc_library( feed_fetch_method SRCS feed_fetch_method.cc @@ -489,7 +484,6 @@ set(NAIVE_EXECUTOR_DEPS scope phi glog - lod_rank_table feed_fetch_method feed_hook graph_to_program_pass @@ -552,7 +546,6 @@ if(WITH_DISTRIBUTE) box_wrapper metrics densetensor_printer - lod_rank_table feed_fetch_method feed_hook ${GLOB_DISTRIBUTE_DEPS} @@ -580,7 +573,7 @@ if(WITH_DISTRIBUTE) # pull_dense_worker.cc section_worker.cc heter_section_worker.cc device_worker_factory.cc data_set.cc DEPS op_registry # device_context scope framework_proto data_feed_proto heter_service_proto trainer_desc_proto glog # index_sampler index_wrapper sampler index_dataset_proto - # lod_rank_table framework_io fleet_wrapper heter_wrapper box_wrapper metrics densetensor_printer feed_fetch_method feed_hook + # framework_io fleet_wrapper heter_wrapper box_wrapper metrics densetensor_printer feed_fetch_method feed_hook # graph_to_program_pass variable_helper # heter_service_proto fleet heter_server brpc fleet_executor # graph_gpu_wrapper) @@ -616,7 +609,6 @@ if(WITH_DISTRIBUTE) index_sampler index_wrapper index_dataset_proto - lod_rank_table framework_io fleet_wrapper heter_wrapper @@ -681,7 +673,6 @@ if(WITH_DISTRIBUTE) DEPS op_registry scope glog - lod_rank_table framework_io fleet_wrapper heter_wrapper @@ -734,7 +725,6 @@ elseif(WITH_PSLIB) DEPS op_registry scope glog - lod_rank_table framework_io fleet_wrapper heter_wrapper @@ -774,7 +764,6 @@ else() DEPS op_registry scope glog - lod_rank_table framework_io fleet_wrapper heter_wrapper diff --git a/paddle/fluid/framework/lod_rank_table.cc b/paddle/fluid/framework/lod_rank_table.cc deleted file mode 100644 index 6e04ea4582b045..00000000000000 --- a/paddle/fluid/framework/lod_rank_table.cc +++ /dev/null @@ -1,65 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#include "paddle/fluid/framework/lod_rank_table.h" - -#include "glog/logging.h" -#include "paddle/fluid/platform/enforce.h" - -namespace paddle { -namespace framework { -void LoDRankTable::Reset(const LegacyLoD& lod, size_t level) { - this->coarse_lod_.clear(); - this->items_.clear(); - PADDLE_ENFORCE_LT( - level, - lod.size(), - common::errors::InvalidArgument( - "Cannot reset LoD since the level %d is less than lod size %d.", - level, - lod.size())); - coarse_lod_.reserve(level); - for (size_t i = 0; i < level; ++i) { - coarse_lod_.push_back(lod[i]); - } - auto& vec = lod[level]; - for (size_t i = 0; i < vec.size() - 1; ++i) { - TableItem item = {0, 0}; - item.index = i; - item.length = vec[i + 1] - vec[i]; - VLOG(10) << "Add item to rank table " << item.index << " " << item.length; - items_.emplace_back(item); - } - // NOTE(yuyang18): - // - // The time complexity of stable_sort is O(N*log(N)) if additional memory is - // available. It is easy to debug and unit test when using `stable_sort` - // instead of `sort`. Also, the items of a rank table will not be too large. - std::stable_sort( - items_.begin(), items_.end(), [](const TableItem& a, const TableItem& b) { - return a.length > b.length; - }); -} - -} // namespace framework - -std::ostream& operator<<(std::ostream& out, - const framework::LoDRankTable& table) { - out << "NumOfSequence " << table.items().size() << "\n"; - for (auto& each_item : table.items()) { - out << "\tSeq #" << each_item.index << ", Len=" << each_item.length << "\n"; - } - return out; -} -} // namespace paddle diff --git a/paddle/fluid/framework/lod_rank_table.h b/paddle/fluid/framework/lod_rank_table.h deleted file mode 100644 index e69f217aa49562..00000000000000 --- a/paddle/fluid/framework/lod_rank_table.h +++ /dev/null @@ -1,62 +0,0 @@ -/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserved. - -Licensed under the Apache License, Version 2.0 (the "License"); -you may not use this file except in compliance with the License. -You may obtain a copy of the License at - - http://www.apache.org/licenses/LICENSE-2.0 - -Unless required by applicable law or agreed to in writing, software -distributed under the License is distributed on an "AS IS" BASIS, -WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -See the License for the specific language governing permissions and -limitations under the License. */ - -#pragma once -#include -#include - -#include "paddle/fluid/framework/lod_tensor.h" - -namespace paddle { -namespace framework { - -// LoD Rank Table stores the `level` of `lod` which is ordered by sequence -// length in descending order. It is useful when implement dynamic RNN and is -// shared by dynamic RNN memory, dynamic RNN slice input and dynamic RNN slice -// output operators. -// -// The table item contains two element. The length of sequence and the index of -// sequence in that level. -// -// LoDRankTable also stores the coarse_lod, which is the lod information whose -// level is less than input level, in order to restore the output LoD -// information. -class LoDRankTable { - public: - struct TableItem { - size_t index; - size_t length; - }; - - LoDRankTable() {} - - void Reset(const LegacyLoD& lod, size_t level); - - const std::vector& items() const { return this->items_; } - - const LegacyLoD& coarse_lod() const { return this->coarse_lod_; } - - size_t level() const { return coarse_lod_.size(); } - - private: - LegacyLoD coarse_lod_; - std::vector items_; -}; - -} // namespace framework - -std::ostream& operator<<(std::ostream& out, - const framework::LoDRankTable& table); - -} // namespace paddle diff --git a/paddle/fluid/operators/CMakeLists.txt b/paddle/fluid/operators/CMakeLists.txt index 0944bd7c5773f5..06ad7be3691003 100644 --- a/paddle/fluid/operators/CMakeLists.txt +++ b/paddle/fluid/operators/CMakeLists.txt @@ -100,7 +100,7 @@ cc_library(ops_extra_info SRCS ops_extra_info.cc DEPS attribute phi common) set(COMMON_OP_DEPS ${COMMON_OP_DEPS} phi common) set(COMMON_OP_DEPS ${COMMON_OP_DEPS} selected_rows_utils -lod_tensor lod_rank_table executor static_prim_api) +lod_tensor executor static_prim_api) set(COMMON_OP_DEPS ${COMMON_OP_DEPS} static_prim_api static_utils static_global_utils prim_utils) set(COMMON_OP_DEPS ${COMMON_OP_DEPS} box_wrapper ps_gpu_wrapper) set(COMMON_OP_DEPS ${COMMON_OP_DEPS} processgroup_comm_utils) diff --git a/paddle/fluid/operators/unity_build_rule.cmake b/paddle/fluid/operators/unity_build_rule.cmake index 5fa44e2a566dd0..fca285a9a552c7 100644 --- a/paddle/fluid/operators/unity_build_rule.cmake +++ b/paddle/fluid/operators/unity_build_rule.cmake @@ -136,7 +136,6 @@ register_unity_group( register_unity_group( cc lod_array_length_op.cc - lod_rank_table_op.cc lod_reset_op.cc lod_tensor_to_array_op.cc log_softmax_op.cc From 74f83fdba267750ef6cd0af7869e5db6743ca73f Mon Sep 17 00:00:00 2001 From: mori0umi <121707718+mori0umi@users.noreply.github.com> Date: Wed, 4 Dec 2024 16:51:01 +0800 Subject: [PATCH 159/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91[Paddle=20TensorRT?= =?UTF-8?q?=20No.63]=20Add=20pd=5Fop.mish=20converter=20(#69705)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add mish * fix codestyle * add more test * fix typo * empty * add test --- .../transforms/tensorrt/trt_op_marker_pass.cc | 2 + python/paddle/tensorrt/impls/activation.py | 14 +++++ test/tensorrt/test_converter_activation.py | 56 +++++++++++++++++++ 3 files changed, 72 insertions(+) diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index 8e2889f3e865de..ae2d09a827c7f6 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -259,6 +259,7 @@ class ActOpPattern : public pir::OpRewritePattern { }; using TanhOpPattern = ActOpPattern; using CeluOpPattern = ActOpPattern; +using MishOpPattern = ActOpPattern; class Pool2dOpPattern : public pir::OpRewritePattern { @@ -2260,6 +2261,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); + ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); diff --git a/python/paddle/tensorrt/impls/activation.py b/python/paddle/tensorrt/impls/activation.py index cb278a1bfdc633..20e8cfe6fb9611 100644 --- a/python/paddle/tensorrt/impls/activation.py +++ b/python/paddle/tensorrt/impls/activation.py @@ -135,6 +135,20 @@ def swish_silu_converter(network, paddle_op, inputs): return trt_prod(network, inputs[0], layer_output) +@converter_registry.register("pd_op.mish", trt_version="8.x") +def mish_converter(network, paddle_op, inputs): + x = inputs[0] + softplus_layer = network.add_activation(x, trt.ActivationType.SOFTPLUS) + softplus_output = softplus_layer.get_output(0) + + tanh_layer = network.add_activation( + softplus_output, trt.ActivationType.TANH + ) + tanh_output = tanh_layer.get_output(0) + + return trt_prod(network, x, tanh_output) + + @converter_registry.register("pd_op.celu", trt_version="8.x") def celu_converter(network, paddle_op, inputs): input_tensor = inputs[0] diff --git a/test/tensorrt/test_converter_activation.py b/test/tensorrt/test_converter_activation.py index fa14d69e8721b4..c3f077364c14b2 100644 --- a/test/tensorrt/test_converter_activation.py +++ b/test/tensorrt/test_converter_activation.py @@ -158,5 +158,61 @@ def test_trt_result(self): self.check_trt_result() +class TestMishCase1TRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.functional.mish + self.api_args = { + "x": np.random.randn(2).astype("float32"), + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1]} + self.max_shape = {"x": [5]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestMishCase2TRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.functional.mish + self.api_args = { + "x": np.random.randn(2, 3).astype("float32"), + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3]} + self.max_shape = {"x": [5, 3]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestMishCase3TRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.functional.mish + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("float32"), + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestMishCase4TRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.nn.functional.mish + self.api_args = { + "x": np.random.randn(2, 3, 4, 2).astype("float32"), + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 4, 2]} + self.max_shape = {"x": [5, 3, 4, 2]} + + def test_trt_result(self): + self.check_trt_result() + + if __name__ == '__main__': unittest.main() From 99a6045b40b1f1a661a9792aba7823c1e1c93bad Mon Sep 17 00:00:00 2001 From: yangrongxinuser <109195068+yangrongxinuser@users.noreply.github.com> Date: Wed, 4 Dec 2024 16:53:25 +0800 Subject: [PATCH 160/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?TensorRT=20No.25=E3=80=91Add=20pd=5Fop.maximum=20converter=20(#?= =?UTF-8?q?69835)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- python/paddle/tensorrt/impls/math.py | 8 ++++ test/tensorrt/test_converter_math.py | 57 ++++++++++++++++++++++++++++ 2 files changed, 65 insertions(+) diff --git a/python/paddle/tensorrt/impls/math.py b/python/paddle/tensorrt/impls/math.py index 22f4d7344b43ff..20e3c767ae4c8d 100644 --- a/python/paddle/tensorrt/impls/math.py +++ b/python/paddle/tensorrt/impls/math.py @@ -234,3 +234,11 @@ def sqrt_converter(network, paddle_op, inputs): input_tensor = trt_cast(network, inputs[0], trt.float32) layer = network.add_unary(input_tensor, trt.UnaryOperation.LOG) return layer.get_output(0) + + +@converter_registry.register("pd_op.maximum", trt_version="8.x") +def maximum_converter(network, paddle_op, inputs): + max_layer = add_elementwise_layer( + network, paddle_op, inputs, trt.ElementWiseOperation.MAX + ) + return max_layer diff --git a/test/tensorrt/test_converter_math.py b/test/tensorrt/test_converter_math.py index d3af54e3922985..b6bb62f2f2a66c 100644 --- a/test/tensorrt/test_converter_math.py +++ b/test/tensorrt/test_converter_math.py @@ -393,5 +393,62 @@ def test_trt_result(self): self.check_trt_result() +class TestMaximumTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.maximum + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("float32"), + "y": np.random.randn(2, 3, 4).astype("float32"), + } + self.program_config = {"feed_list": ["x", "y"]} + self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestMinimumBroadcastTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.maximum + self.api_args = { + "x": np.random.randn(2, 3, 4).astype("float32"), + "y": np.random.randn(4).astype("float32"), + } + self.program_config = {"feed_list": ["x", "y"]} + self.min_shape = {"x": [1, 3, 4], "y": [4]} + self.max_shape = {"x": [5, 3, 4], "y": [4]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestMinimumIntTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.maximum + self.api_args = { + "x": np.random.randint( + low=1, high=100, size=(2, 3, 4), dtype="int64" + ), + "y": np.random.randint( + low=1, high=100, size=(2, 3, 4), dtype="int64" + ), + } + self.dynamic_shape_data = { + "x": lambda shape: np.random.randint( + 1, 100, size=shape, dtype="int64" + ), + "y": lambda shape: np.random.randint( + 1, 100, size=shape, dtype="int64" + ), + } + self.program_config = {"feed_list": ["x", "y"]} + self.min_shape = {"x": [1, 3, 4], "y": [1, 3, 4]} + self.max_shape = {"x": [5, 3, 4], "y": [5, 3, 4]} + + def test_trt_result(self): + self.check_trt_result() + + if __name__ == '__main__': unittest.main() From c1c326156c9e47a8cfc744c4fb55d31daee32e89 Mon Sep 17 00:00:00 2001 From: ShenLiang <1422485404@qq.com> Date: Wed, 4 Dec 2024 17:32:06 +0800 Subject: [PATCH 161/288] [FleetY]fix bug of alltoall send/recv when numel=0 (#69026) (#69877) * fix bug of alltoall base --- .../collective/process_group_nccl.cc | 33 +++++++++---------- 1 file changed, 15 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/distributed/collective/process_group_nccl.cc b/paddle/fluid/distributed/collective/process_group_nccl.cc index 6257adb3a24b8f..7abd54d2cdc098 100644 --- a/paddle/fluid/distributed/collective/process_group_nccl.cc +++ b/paddle/fluid/distributed/collective/process_group_nccl.cc @@ -312,17 +312,6 @@ std::shared_ptr ProcessGroupNCCL::AllToAll( CheckSizeOnEachRank(out_dim, out_size_each_rank, size_); CheckSizeOnEachRank(in_dim, in_size_each_rank, size_); - // NOTE: Since `all_to_all` needs other processes' participation, it cannot - // simply be covered by static checks. Factors are set to 0 here to skip the - // shape check. Its shape check will be done by dynamic checks with - // FLAGS_enable_nccl_dynamic_check. - phi::distributed::CommStaticCheck::CheckShape(*out_tensor, - in_tensor, - /*dst_rank*/ rank_, - /*cur_rank*/ rank_, - size_, - /*out_size_factor*/ 0, - /*in_size_factor*/ 0); return Collective( [&](phi::distributed::NCCLCommContext* comm_context, gpuStream_t stream) { if (FLAGS_enable_nccl_dynamic_check) { @@ -334,8 +323,11 @@ std::shared_ptr ProcessGroupNCCL::AllToAll( size_, comm_context->GetNcclComm()); } - int64_t in_row_size = in_tensor.numel() / in_dim[0], - out_row_size = out_tensor->numel() / out_dim[0]; + + int64_t in_row_size = + in_dim[0] == 0 ? 0 : in_tensor.numel() / in_dim[0]; + int64_t out_row_size = + out_dim[0] == 0 ? 0 : out_tensor->numel() / out_dim[0]; int64_t in_offset = 0, in_numel = 0, out_offset = 0, out_numel = 0; phi::DenseTensor input_partial, output_partial; @@ -357,13 +349,18 @@ std::shared_ptr ProcessGroupNCCL::AllToAll( GroupStart(); for (auto i = 0; i < size_; i++) { in_numel = in_size_each_rank[i] * in_row_size; - input_partial = GetPartialTensor(in_tensor, in_offset, in_numel); - comm_context->Send(input_partial, in_numel, i, stream); - in_offset += in_numel; + if (in_numel > 0) { + input_partial = GetPartialTensor(in_tensor, in_offset, in_numel); + comm_context->Send(input_partial, in_numel, i, stream); + } + in_offset += in_numel; out_numel = out_size_each_rank[i] * out_row_size; - output_partial = GetPartialTensor(*out_tensor, out_offset, out_numel); - comm_context->Recv(&output_partial, out_numel, i, stream); + if (out_numel > 0) { + output_partial = + GetPartialTensor(*out_tensor, out_offset, out_numel); + comm_context->Recv(&output_partial, out_numel, i, stream); + } out_offset += out_numel; } GroupEnd(); From 39dccfef5b5163086d2920169142be05053f1697 Mon Sep 17 00:00:00 2001 From: huangjiyi <43315610+huangjiyi@users.noreply.github.com> Date: Wed, 4 Dec 2024 20:10:08 +0800 Subject: [PATCH 162/288] [CINN] Fix horizontal fusion with reduce dim equals one (#69937) --- paddle/cinn/operator_fusion/pattern_fuser.h | 21 ++++++++++++++------- test/ir/pir/cinn/test_reduce_fusion.py | 14 ++++++++++++++ 2 files changed, 28 insertions(+), 7 deletions(-) diff --git a/paddle/cinn/operator_fusion/pattern_fuser.h b/paddle/cinn/operator_fusion/pattern_fuser.h index bf4e6269a985e8..ea3031bbc2fb93 100644 --- a/paddle/cinn/operator_fusion/pattern_fuser.h +++ b/paddle/cinn/operator_fusion/pattern_fuser.h @@ -373,14 +373,21 @@ static bool IsLoopFrameworkEqual(const StmtPattern& lhs, VLOG(4) << "rhs " << rhs_loops.DebugStr(); // TODO(huangjiyi): support horizontal fusion without reduce dims euqal. - auto has_reduce_dim = [](const MaybeLoopFramework& loops) -> bool { - return std::any_of(loops.is_reduce.begin(), - loops.is_reduce.end(), - [](bool b) { return b; }); + const auto get_reduce_loop = [](const MaybeLoopFramework& loop) { + LoopExprs reduce_loop; + for (int i = 0; i < loop.is_reduce.size(); ++i) { + if (loop.is_reduce[i]) { + reduce_loop.push_back(loop.loop[i]); + } + } + return reduce_loop; }; - bool reduce_euqal = has_reduce_dim(lhs_loops) && has_reduce_dim(rhs_loops) - ? lhs_loops.is_reduce == rhs_loops.is_reduce - : true; + const auto lhs_reduce_loop = get_reduce_loop(lhs_loops); + const auto rhs_reduce_loop = get_reduce_loop(rhs_loops); + + bool reduce_euqal = lhs_reduce_loop.empty() || rhs_reduce_loop.empty() + ? true + : lhs_reduce_loop == rhs_reduce_loop; const auto& squeezed_lhs_loops = SqueezeLoopFramework(lhs_loops); const auto& squeezed_rhs_loops = SqueezeLoopFramework(rhs_loops); diff --git a/test/ir/pir/cinn/test_reduce_fusion.py b/test/ir/pir/cinn/test_reduce_fusion.py index 10953fb7e16b97..bcdcc697766c3d 100644 --- a/test/ir/pir/cinn/test_reduce_fusion.py +++ b/test/ir/pir/cinn/test_reduce_fusion.py @@ -185,6 +185,20 @@ def init(): self.check_accuracy_and_kernel_num(init, func, kernel_num=1) + def test_horizontal_fusion_with_reduce_dim_equals_one(self): + def func(x): + a = x + 1 + a = paddle.max(a, axis=[0]) + b = x * 2 + b = paddle.max(b, axis=[2]) + return a, b + + def init(): + x = paddle.rand((1, 32, 8), dtype='float32') + return (x,) + + self.check_accuracy_and_kernel_num(init, func) + if __name__ == "__main__": unittest.main() From d72d6ad1e22897a1681acc2338fad8e9a6edd9cb Mon Sep 17 00:00:00 2001 From: PuQing Date: Wed, 4 Dec 2024 20:38:09 +0800 Subject: [PATCH 163/288] =?UTF-8?q?=E3=80=90Infer=20Symbolic=20Shape=20No.?= =?UTF-8?q?232=E3=80=91Add=20infer=5Fsymbol=5Fshape=20for=20StridedSlice?= =?UTF-8?q?=20=20(#69911)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- .../infer_sym_slice_utils.h | 216 ++++++++++++++++++ .../infer_symbolic_shape/unary_infer_sym.cc | 42 +++- .../infer_symbolic_shape/unary_infer_sym.h | 2 +- paddle/phi/ops/yaml/ops.yaml | 2 +- python/paddle/base/variable_index.py | 11 +- python/paddle/tensor/manipulation.py | 17 +- test/legacy_test/test_strided_slice_op.py | 15 +- 7 files changed, 287 insertions(+), 18 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h index 7d84a1829a9d4a..5ad82c9d38a242 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h @@ -115,6 +115,7 @@ inline ExprVec GetSliceDims(const ExprVec &in_dims, for (size_t i = 0; i < axes.size(); ++i) { auto out_dim = ends[i] - starts[i]; int64_t axis = axes[i]; + // If in_dims[axis] or ends[i] have symbol, nedd get Min(in_dims[axis] - // start[i], ends[i] - start[i] ) if (!out_dim.isa() && @@ -266,4 +267,219 @@ inline ShapeOrData SliceRawInferSymbolicShape( return out_shape; } + +inline ExprVec GetStridesSliceDims( + const ExprVec &in_dims, + const std::vector &axes, + const ExprVec &starts_base, + const ExprVec &ends_base, + const ExprVec &strides_base, + std::vector *infer_flags = nullptr) { + ExprVec starts = starts_base; + ExprVec ends = ends_base; + ExprVec strides = strides_base; + auto IsMaxInt = [](const symbol::DimExpr &expr) { + return expr.isa() && + expr.Get() == + static_cast(std::numeric_limits::max()); + }; + + for (size_t i = 0; i < axes.size(); ++i) { + int64_t axis = axes.at(i); + int64_t start_i = 0; + + if (starts.at(i).isa()) { + if (in_dims.at(axis).isa()) { + starts.at(i) = + (starts.at(i).Get() > in_dims.at(axis).Get()) + ? in_dims.at(axis) + : starts.at(i); + starts.at(i) = + (starts.at(i).Get() < -in_dims.at(axis).Get()) + ? symbol::DimExpr({-1}) * in_dims.at(axis) + : starts.at(i); + } + start_i = starts.at(i).Get(); + } + + int64_t end_i = 0; + if (ends.at(i).isa()) { + if (in_dims.at(axis).isa()) { + ends[i] = std::min(ends.at(i).Get(), + in_dims.at(axis).Get()); + } + if (ends.at(i).Get() < 0) { + ends[i] = ends.at(i) + in_dims.at(axis); + } + if (ends.at(i).isa()) { + end_i = ends.at(i).Get(); + } + } + + ends.at(i) = IsMaxInt(ends.at(i)) ? in_dims.at(axis) : ends.at(i); + bool both_negative_or_positive = + (start_i >= 0 && end_i >= 0) || (start_i <= 0 && end_i <= 0); + bool start_negative_end_positive = start_i <= 0 && end_i >= 0; + bool start_positive_end_negative = start_i >= 0 && end_i <= 0; + + if (both_negative_or_positive) { + continue; + } else if (start_negative_end_positive) { + starts.at(i) = starts.at(i) + in_dims.at(axis); + } else if (start_positive_end_negative) { + starts.at(i) = starts.at(i) - in_dims.at(axis); + } else { + PADDLE_THROW(common::errors::Fatal("Dead code")); + } + } + + ExprVec slice_dims(in_dims); + PADDLE_ENFORCE_EQ( + (axes.size() == starts.size() && axes.size() == ends.size() && + axes.size() == strides.size()), + true, + common::errors::InvalidArgument( + "The size of axes must equal size of starts, ends, and strides.")); + + for (size_t i = 0; i < axes.size(); ++i) { + auto out_dim = symbol::DimExpr({-1}) * ((starts[i] - ends[i]) / strides[i]); + int64_t axis = axes[i]; + + if (!out_dim.isa() && + (!in_dims[axis].isa() || !ends[i].isa())) { + symbol::List min_lists{ + symbol::DimExpr({-1}) * ((starts[i] - in_dims[axis]) / strides[i]), + out_dim}; + + slice_dims[axis] = + symbol::DimExpr({symbol::Min({min_lists})}); + } else { + slice_dims[axis] = out_dim; + } + } + + return slice_dims; +} + +inline ShapeOrData StridedSliceRawInferSymbolicShape( + const pir::Value x, + const pir::Value out, + const ExprVec &starts_expr, + const ExprVec &ends_expr, + const ExprVec &strides_expr, + const std::vector &axes_raw, + const std::vector &infer_flags_raw, + const std::vector &decrease_axis, + pir::InferSymbolicShapeContext *infer_context) { + const auto &in_shapeordata = infer_context->GetShapeOrDataForValue(x); + ExprVec starts = starts_expr; + ExprVec ends = ends_expr; + ExprVec strides = strides_expr; + std::vector infer_flags = [&infer_flags_raw, &axes_raw] { + return infer_flags_raw.empty() ? std::vector(axes_raw.size(), 1) + : infer_flags_raw; + }(); + + const auto &GetShapeDimExprs = [&]() -> symbol::ShapeOrDataDimExprs { + const ExprVec &in_dims = in_shapeordata.shape(); + std::vector axes = FormatSliceAxes(axes_raw, in_dims.size()); + ExprVec slice_dims = + GetStridesSliceDims(in_dims, axes, starts, ends, strides, &infer_flags); + ExprVec out_dims = GetDecreasedDims(slice_dims, decrease_axis); + + auto IsOne = [](const symbol::DimExpr &expr) { + return expr.isa() && expr.dyn_cast() == 1; + }; + auto IsIntType = [](pir::Value value) { + const auto &dtype = value.type().dyn_cast().dtype(); + return dtype.isa() || dtype.isa(); + }; + if (IsIntType(x) && + (out_dims.empty() || (out_dims.size() == 1 && IsOne(out_dims[0])))) { + return symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs( + out_dims, + std::vector{infer_context->GetNextSymName()})}; + } + + return symbol::ShapeOrDataDimExprs{ + symbol::TensorShapeOrDataDimExprs(out_dims)}; + }; + + // When `pd.slice` is operating on a tensor which is produced by a `pd.shape` + // op, the result should be written into data. + const auto &GetDataDimExprs = [&]() -> symbol::ShapeOrDataDimExprs { + std::vector out_data; + + // Currently, we DO NOT support the case that any element in `axes` `starts` + // or `ends` is a Symbol. + auto vec_int64 = details::VecExpr2Int64(starts); + PADDLE_ENFORCE_EQ( + vec_int64.has_value(), + true, + common::errors::InvalidArgument( + "for slice op, all the elements in `starts` must be int64_t")); + std::vector starts_int = vec_int64.value(); + + vec_int64 = details::VecExpr2Int64(ends); + PADDLE_ENFORCE_EQ( + vec_int64.has_value(), + true, + common::errors::InvalidArgument( + "for slice op, all the elements in `ends` must be int64_t")); + std::vector ends_int = vec_int64.value(); + + vec_int64 = details::VecExpr2Int64(strides); + PADDLE_ENFORCE_EQ( + vec_int64.has_value(), + true, + common::errors::InvalidArgument( + "for slice op, all the elements in `strides` must be int64_t")); + + const int64_t start = + starts_int[0] < 0 ? starts_int[0] + in_shapeordata.data().value().size() + : starts_int[0]; + const int64_t end = [&]() -> int64_t { + if (ends_int[0] < 0) { + return ends_int[0] + in_shapeordata.data().value().size(); + } + if (ends_int[0] == + static_cast(std::numeric_limits::max())) { + return in_shapeordata.data().value().size(); + } + return ends_int[0]; + }(); + + const int64_t stride = [&]() -> int64_t { + if (strides[0].isa()) { + return strides[0].Get(); + } + return 1; + }(); + + for (int64_t i = start; i < end; i += stride) { + out_data.push_back(in_shapeordata.data().value().at(i)); + } + + const ExprVec shape = GetDecreasedDims( + ExprVec{static_cast(out_data.size())}, decrease_axis); + return symbol::ShapeOrDataDimExprs{ + symbol::TensorShapeOrDataDimExprs(shape, out_data)}; + }; + + const auto &out_shape = in_shapeordata.data().has_value() + ? GetDataDimExprs() + : GetShapeDimExprs(); + if (out_shape.data().has_value() && out_shape.shape().empty()) { // 0D tensor + const paddle::dialect::DenseTensorType &tensor_type = + out.type().dyn_cast(); + const auto &out_ddim = tensor_type.dims(); + if (out_ddim.size() == 1 && out_ddim[0] == 1) { // value is 1D + return symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs( + std::vector{1}, out_shape.data().value())}; + } + } + + return out_shape; +} + } // namespace paddle::dialect::slice_utils diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 5891da7e808acb..93881316b3b82f 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3515,12 +3515,42 @@ bool SplitWithNumOpInferSymbolicShape( return true; } -// bool StridedSliceOpInferSymbolicShape(pir::Operation *op, -// pir::InferSymbolicShapeContext -// *infer_context) { -// // pass -// return true; -// } +bool StridedSliceOpInferSymbolicShape( + pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { + pir::Value operand_source = op->operand_source(0); + pir::Value operand_starts = op->operand_source(1); + pir::Value operand_ends = op->operand_source(2); + pir::Value operand_strides = op->operand_source(3); + pir::Value res = op->result(0); + + const symbol::ShapeOrDataDimExprs &starts_shape_data = + infer_context->GetShapeOrDataForValue(operand_starts); + const symbol::ShapeOrDataDimExprs &ends_shape_data = + infer_context->GetShapeOrDataForValue(operand_ends); + const symbol::ShapeOrDataDimExprs &strides_shape_data = + infer_context->GetShapeOrDataForValue(operand_strides); + + ExprVec starts = slice_utils::GetExprVecFromData(starts_shape_data); + ExprVec ends = slice_utils::GetExprVecFromData(ends_shape_data); + ExprVec strides = slice_utils::GetExprVecFromData(strides_shape_data); + + std::vector axes_vec = details::GetVectorAttr(op, "axes"); + std::vector axes_vec_64(axes_vec.begin(), axes_vec.end()); + + infer_context->SetShapeOrDataForValue( + res, + slice_utils::StridedSliceRawInferSymbolicShape(operand_source, + res, + starts, + ends, + strides, + axes_vec_64, + std::vector{}, + std::vector{}, + infer_context)); + + return true; +} bool SumOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h index 4004f4afd48b0d..7789c9718669f3 100755 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h @@ -139,7 +139,7 @@ OP_DECLARE_INFER_SYMBOLIC_SHAPE(SplitWithNum) OP_DECLARE_INFER_SYMBOLIC_SHAPE(SquaredL2Norm) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Squeeze) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Squeeze_) -// OP_DECLARE_INFER_SYMBOLIC_SHAPE(StridedSlice) +OP_DECLARE_INFER_SYMBOLIC_SHAPE(StridedSlice) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Sum) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Svd) OP_DECLARE_INFER_SYMBOLIC_SHAPE(SetValue) diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index 5d85e1a78993e9..4275e5f72153dd 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -4800,7 +4800,7 @@ kernel : func : strided_slice backward : strided_slice_grad - # interfaces : paddle::dialect::InferSymbolicShapeInterface + interfaces : paddle::dialect::InferSymbolicShapeInterface - op : sum args : (Tensor x, IntArray axis={}, DataType dtype=DataType::UNDEFINED, bool keepdim=false) diff --git a/python/paddle/base/variable_index.py b/python/paddle/base/variable_index.py index cd06712477aa4f..b2b74bd524b1f0 100644 --- a/python/paddle/base/variable_index.py +++ b/python/paddle/base/variable_index.py @@ -765,7 +765,16 @@ def get_tensor_with_basic_indexing( stride = attrs['strides'] if use_strided_slice: # TODO(zoooo0820): support strided_slice_array until PIR API is ready - + if in_pir_mode(): + if isinstance(st, (list, tuple)): + if paddle.utils._contain_var(st): + st = paddle.utils.get_int_tensor_list(st) + if isinstance(end, (list, tuple)): + if paddle.utils._contain_var(end): + end = paddle.utils.get_int_tensor_list(end) + if isinstance(stride, (list, tuple)): + if paddle.utils._contain_var(stride): + stride = paddle.utils.get_int_tensor_list(stride) out = paddle._C_ops.strided_slice(x, axes, st, end, stride) if len(decrease_axes) > 0: out = paddle._C_ops.squeeze(out, decrease_axes) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 5ac5c345f6119a..1b8414ac520837 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -5638,7 +5638,22 @@ def strided_slice( >>> sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2) >>> # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2]. """ - if in_dynamic_or_pir_mode(): + if in_dynamic_mode(): + return _C_ops.strided_slice(x, axes, starts, ends, strides) + elif in_pir_mode(): + + def _convert_to_tensor_list(input): + if isinstance(input, paddle.pir.Value): + input.stop_gradient = True + elif isinstance(input, (list, tuple)): + if paddle.utils._contain_var(input): + input = paddle.utils.get_int_tensor_list(input) + return input + + starts = _convert_to_tensor_list(starts) + ends = _convert_to_tensor_list(ends) + strides = _convert_to_tensor_list(strides) + return _C_ops.strided_slice(x, axes, starts, ends, strides) else: helper = LayerHelper('strided_slice', **locals()) diff --git a/test/legacy_test/test_strided_slice_op.py b/test/legacy_test/test_strided_slice_op.py index eec7c3ae019d58..03664f7768d448 100644 --- a/test/legacy_test/test_strided_slice_op.py +++ b/test/legacy_test/test_strided_slice_op.py @@ -326,7 +326,6 @@ def setUp(self): starts_tensor.append( ("x" + str(index), np.ones(1).astype('int32') * ele) ) - self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor} self.outputs = {'Out': self.output} self.attrs = { @@ -351,7 +350,7 @@ def config(self): self.starts_infer = [1, 10, 2] def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -395,7 +394,7 @@ def config(self): self.ends_infer = [3, 1, 4] def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -433,7 +432,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -471,7 +470,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -516,7 +515,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -554,7 +553,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_symbol_infer=False) def test_check_grad_normal(self): self.check_grad( @@ -644,7 +643,7 @@ def test_dygraph_op(self): sliced_1 = paddle.strided_slice( x, axes=axes, starts=starts, ends=ends, strides=strides_1 ) - assert sliced_1.shape == (3, 2, 2, 2) + assert sliced_1.shape == [3, 2, 2, 2] @unittest.skipIf( not paddle.is_compiled_with_cuda(), From f0bbc5aed24a275430c19dd8f3ff6bc1d4bf64be Mon Sep 17 00:00:00 2001 From: walkalone20 <73780235+walkalone20@users.noreply.github.com> Date: Wed, 4 Dec 2024 20:46:40 +0800 Subject: [PATCH 164/288] =?UTF-8?q?=E3=80=90Hackathon=206th=20Fundable=20P?= =?UTF-8?q?rojects=202=20No.29=E3=80=91=20Fix=20modernize-concat-nested-na?= =?UTF-8?q?mespaces-part-17=20(#64776)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * part 17 * nested-namespace-part-17 * Update dist_meta_tensor.cc --- paddle/common/enforce.cc | 6 ++---- .../distributed/ps/table/ssd_sparse_table.cc | 6 ++---- paddle/fluid/framework/custom_operator.cc | 6 ++---- .../framework/ir/cudnn_placement_pass_tester.cc | 8 ++------ ...delete_remove_padding_recover_padding_pass.cc | 12 ++++-------- .../framework/ir/dense_fc_to_sparse_pass.cc | 12 ++++-------- paddle/fluid/framework/ir/fc_fuse_pass_tester.cc | 8 ++------ paddle/fluid/framework/ir/fc_gru_fuse_pass.cc | 14 ++++---------- .../ir/fusion_group/fusion_group_pass.cc | 8 ++------ paddle/fluid/framework/ir/graph_viz_pass.cc | 8 ++------ paddle/fluid/framework/ir/inplace_op_var_pass.cc | 8 ++------ .../fluid/framework/ir/map_op_to_another_pass.cc | 8 ++------ .../ir/simplify_with_basic_ops_pass_tester.cc | 8 ++------ .../no_event_garbage_collector.cc | 6 ++---- .../control_flow/assert_instruction.cc | 6 ++---- .../control_flow/pylayer_instruction.cc | 6 ++---- .../control_flow/select_input_instruction.cc | 6 ++---- .../framework/new_executor/interpreter/plan.cc | 8 ++------ .../new_executor/workqueue/events_waiter.cc | 6 ++---- paddle/fluid/framework/op_version_proto.cc | 10 ++-------- paddle/fluid/framework/scope_pool.cc | 6 ++---- .../analysis/ir_passes/tensorrt_subgraph_pass.cc | 8 ++------ .../fluid/inference/tensorrt/convert/range_op.cc | 8 ++------ .../fluid/inference/tensorrt/convert/shape_op.cc | 8 ++------ .../tensorrt/convert/strided_slice_op.cc | 8 ++------ .../fluid/inference/tensorrt/convert/top_k_op.cc | 8 ++------ paddle/fluid/ir_adaptor/translator/utils.cc | 12 ++++-------- paddle/fluid/jit/engine/interpreter_engine.cc | 6 ++---- .../fluid/operators/collective/c_allgather_op.cc | 6 ++---- .../collective/c_comm_init_multitrainer_op.cc | 6 ++---- paddle/fluid/operators/controlflow/op_variant.cc | 6 ++---- .../fluid/operators/fused/fused_transpose_op.cc | 6 ++---- paddle/fluid/operators/memcpy_d2h_op.cc | 16 ++++++---------- .../primitive/decomp_utils/decomp_eager_utils.cc | 6 ++---- paddle/fluid/pybind/const_value.cc | 6 ++---- paddle/fluid/pybind/pybind.cc | 6 ++---- paddle/phi/api/lib/int_array.cc | 6 ++---- .../auto_parallel/dist_meta_tensor.cc | 6 ++---- paddle/phi/core/platform/profiler/utils.cc | 6 ++---- paddle/phi/infermeta/spmd_rules/transpose.cc | 6 ++---- .../phi/kernels/funcs/fake_quantize_functor.cc | 6 ++---- paddle/phi/kernels/funcs/im2col.cc | 6 ++---- .../cpu/distributed_fused_lamb_init_kernel.cc | 6 ++---- .../fused_softmax_mask_upper_triangle_kernel.cc | 6 ++---- .../kernels/fusion/onednn/fused_conv_kernel.cc | 6 ++---- .../kernels/fusion/onednn/fusion_gru_kernel.cc | 6 ++---- .../selected_rows/elementwise_multiply_kernel.cc | 6 ++---- 47 files changed, 106 insertions(+), 242 deletions(-) diff --git a/paddle/common/enforce.cc b/paddle/common/enforce.cc index 66942f887bc442..1e54ac7d66c20a 100644 --- a/paddle/common/enforce.cc +++ b/paddle/common/enforce.cc @@ -54,8 +54,7 @@ std::atomic_bool paddle_fatal_skip{false}; } // namespace -namespace common { -namespace enforce { +namespace common::enforce { void SkipPaddleFatal(bool skip) { paddle_fatal_skip.store(skip); } bool IsPaddleFatalSkip() { return paddle_fatal_skip.load(); } @@ -123,5 +122,4 @@ std::string GetCurrentTraceBackString(bool for_signal) { return sout.str(); } -} // namespace enforce -} // namespace common +} // namespace common::enforce diff --git a/paddle/fluid/distributed/ps/table/ssd_sparse_table.cc b/paddle/fluid/distributed/ps/table/ssd_sparse_table.cc index 241f78c4f7c6ef..ab64acf14798d7 100644 --- a/paddle/fluid/distributed/ps/table/ssd_sparse_table.cc +++ b/paddle/fluid/distributed/ps/table/ssd_sparse_table.cc @@ -29,8 +29,7 @@ PHI_DEFINE_EXPORTED_string(rocksdb_path, "database", "path of sparse table rocksdb file"); -namespace paddle { -namespace distributed { +namespace paddle::distributed { int32_t SSDSparseTable::Initialize() { MemorySparseTable::Initialize(); @@ -3204,5 +3203,4 @@ int32_t SSDSparseTable::CacheTable(uint16_t pass_id) { return 0; } -} // namespace distributed -} // namespace paddle +} // namespace paddle::distributed diff --git a/paddle/fluid/framework/custom_operator.cc b/paddle/fluid/framework/custom_operator.cc index 60fcd11c3f7b91..d2e1a5bf162a40 100644 --- a/paddle/fluid/framework/custom_operator.cc +++ b/paddle/fluid/framework/custom_operator.cc @@ -54,8 +54,7 @@ limitations under the License. */ COMMON_DECLARE_string(tensor_operants_mode); COMMON_DECLARE_bool(enable_pir_in_executor); -namespace paddle { -namespace framework { +namespace paddle::framework { // custom op kernel call function define static void RunKernelFunc( @@ -1315,8 +1314,7 @@ LoadOpMetaInfoAndRegisterOp(const std::string& dso_name) { return op_meta_info_map.GetMap(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework #ifdef PADDLE_WITH_CUSTOM_DEVICE void PD_RegisterOperator(const char* kernel_name_cstr, diff --git a/paddle/fluid/framework/ir/cudnn_placement_pass_tester.cc b/paddle/fluid/framework/ir/cudnn_placement_pass_tester.cc index 534b1266ffe812..989524e4c247f8 100644 --- a/paddle/fluid/framework/ir/cudnn_placement_pass_tester.cc +++ b/paddle/fluid/framework/ir/cudnn_placement_pass_tester.cc @@ -18,9 +18,7 @@ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/operator.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class PlacementPassTest { private: @@ -128,8 +126,6 @@ TEST(CUDNNPlacementPass, placement_name) { PlacementPassTest().PlacementNameTest(); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(cudnn_placement_pass); diff --git a/paddle/fluid/framework/ir/delete_remove_padding_recover_padding_pass.cc b/paddle/fluid/framework/ir/delete_remove_padding_recover_padding_pass.cc index 6800eafb6a6d14..4ffd61077c2ee5 100644 --- a/paddle/fluid/framework/ir/delete_remove_padding_recover_padding_pass.cc +++ b/paddle/fluid/framework/ir/delete_remove_padding_recover_padding_pass.cc @@ -18,10 +18,7 @@ #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { void RecoverPadding::operator()() { // Create nodes for recover_padding. @@ -38,7 +35,8 @@ void RecoverPadding::operator()() { recover_padding_op->LinksFrom({recover_padding_input}) .LinksTo({recover_padding_out}); } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { void DeleteRemovePaddingRecoverPaddingPass::ApplyImpl(ir::Graph *graph) const { PADDLE_ENFORCE_NOT_NULL( @@ -94,9 +92,7 @@ void DeleteRemovePaddingRecoverPaddingPass::ApplyImpl(ir::Graph *graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(delete_remove_padding_recover_padding_pass, paddle::framework::ir::DeleteRemovePaddingRecoverPaddingPass); diff --git a/paddle/fluid/framework/ir/dense_fc_to_sparse_pass.cc b/paddle/fluid/framework/ir/dense_fc_to_sparse_pass.cc index 83c27e83491c09..8fbfe625a636d3 100644 --- a/paddle/fluid/framework/ir/dense_fc_to_sparse_pass.cc +++ b/paddle/fluid/framework/ir/dense_fc_to_sparse_pass.cc @@ -18,10 +18,7 @@ #include "paddle/fluid/framework/ir/graph_pattern_detector.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { -namespace patterns { +namespace paddle::framework::ir::patterns { PDNode *patterns::DenseFC::operator()() { auto *fc = pattern->NewNode(fc_repr())->assert_is_op("fc"); @@ -47,7 +44,8 @@ PDNode *patterns::DenseFC::operator()() { return fc_out; } -} // namespace patterns +} // namespace paddle::framework::ir::patterns +namespace paddle::framework::ir { DenseFCToSparsePass::DenseFCToSparsePass() { AddOpCompat(OpCompat("fc")) @@ -140,9 +138,7 @@ void DenseFCToSparsePass::ApplyImpl(Graph *graph) const { AddStatis(found_dense_fc_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(dense_fc_to_sparse_pass, paddle::framework::ir::DenseFCToSparsePass); diff --git a/paddle/fluid/framework/ir/fc_fuse_pass_tester.cc b/paddle/fluid/framework/ir/fc_fuse_pass_tester.cc index 3e26fd55cc9abb..51ccdce2303722 100644 --- a/paddle/fluid/framework/ir/fc_fuse_pass_tester.cc +++ b/paddle/fluid/framework/ir/fc_fuse_pass_tester.cc @@ -17,9 +17,7 @@ #include "paddle/fluid/framework/ir/fc_fuse_pass.h" #include "paddle/fluid/framework/ir/pass_tester_helper.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void AddVarToScope(Scope* param_scope, const std::string& name, @@ -98,8 +96,6 @@ TEST(FCFusePass, basic) { num_fc_nodes_after)); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(fc_fuse_pass); diff --git a/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc b/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc index ef465b8d664b79..5d640572012869 100644 --- a/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc +++ b/paddle/fluid/framework/ir/fc_gru_fuse_pass.cc @@ -18,15 +18,11 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/utils/string/pretty_log.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class Scope; -} // namespace framework -} // namespace paddle +} // namespace paddle::framework -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; @@ -356,9 +352,7 @@ void FCGRUFusePass::ApplyImpl(ir::Graph* graph) const { fusion_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(mul_gru_fuse_pass, paddle::framework::ir::MulGRUFusePass); REGISTER_PASS(fc_gru_fuse_pass, paddle::framework::ir::FCGRUFusePass); diff --git a/paddle/fluid/framework/ir/fusion_group/fusion_group_pass.cc b/paddle/fluid/framework/ir/fusion_group/fusion_group_pass.cc index 8dace0aeb386a9..ad5222c364e4a4 100644 --- a/paddle/fluid/framework/ir/fusion_group/fusion_group_pass.cc +++ b/paddle/fluid/framework/ir/fusion_group/fusion_group_pass.cc @@ -24,9 +24,7 @@ namespace phi { class DeviceCodePool; } // namespace phi -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Node; @@ -173,9 +171,7 @@ void FusionGroupPass::InsertFusionGroupOp( GraphSafeRemoveNodes(graph, internal_nodes); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(fusion_group_pass, paddle::framework::ir::FusionGroupPass) .RequirePassAttr("use_gpu"); diff --git a/paddle/fluid/framework/ir/graph_viz_pass.cc b/paddle/fluid/framework/ir/graph_viz_pass.cc index 4d53cd45b9b3c5..2128c9d3fc2566 100644 --- a/paddle/fluid/framework/ir/graph_viz_pass.cc +++ b/paddle/fluid/framework/ir/graph_viz_pass.cc @@ -25,9 +25,7 @@ limitations under the License. */ #include "paddle/fluid/inference/analysis/dot.h" #include "paddle/fluid/inference/analysis/helper.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { using inference::analysis::Dot; namespace { std::string FormatName(const Node* node) { @@ -186,9 +184,7 @@ GraphVizPass::marked_nodes_t GraphVizPass::ConsumeMarkedNodes( return res; } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(graph_viz_pass, paddle::framework::ir::GraphVizPass) .RequirePassAttr(paddle::framework::ir::kGraphvizPath); diff --git a/paddle/fluid/framework/ir/inplace_op_var_pass.cc b/paddle/fluid/framework/ir/inplace_op_var_pass.cc index 7648fd0c89a26c..80cee71aee2904 100644 --- a/paddle/fluid/framework/ir/inplace_op_var_pass.cc +++ b/paddle/fluid/framework/ir/inplace_op_var_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/ir/node.h" #include "paddle/fluid/framework/op_version_registry.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { class Graph; @@ -120,9 +118,7 @@ void InplaceOpVarPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_subgraph_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(inplace_op_var_pass, paddle::framework::ir::InplaceOpVarPass); REGISTER_PASS_CAPABILITY(inplace_op_var_pass) diff --git a/paddle/fluid/framework/ir/map_op_to_another_pass.cc b/paddle/fluid/framework/ir/map_op_to_another_pass.cc index 6fbc3e81ea6f27..05de8417af613c 100755 --- a/paddle/fluid/framework/ir/map_op_to_another_pass.cc +++ b/paddle/fluid/framework/ir/map_op_to_another_pass.cc @@ -19,9 +19,7 @@ #include "paddle/fluid/framework/op_version_registry.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { void MapOp2AnotherPass::ApplyImpl(ir::Graph* graph) const { PADDLE_ENFORCE_NOT_NULL( @@ -78,9 +76,7 @@ void MapOp2AnotherPass::ApplyImpl(ir::Graph* graph) const { AddStatis(found_count); } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir REGISTER_PASS(map_op_to_another_pass, paddle::framework::ir::MapOp2AnotherPass); REGISTER_PASS_CAPABILITY(map_op_to_another_pass) diff --git a/paddle/fluid/framework/ir/simplify_with_basic_ops_pass_tester.cc b/paddle/fluid/framework/ir/simplify_with_basic_ops_pass_tester.cc index 78f76d3a01a1fe..8991d25ea8a24a 100644 --- a/paddle/fluid/framework/ir/simplify_with_basic_ops_pass_tester.cc +++ b/paddle/fluid/framework/ir/simplify_with_basic_ops_pass_tester.cc @@ -17,9 +17,7 @@ limitations under the License. */ #include "paddle/fluid/framework/ir/pass_tester_helper.h" #include "paddle/fluid/framework/ir/simplify_with_basic_ops_pass.h" -namespace paddle { -namespace framework { -namespace ir { +namespace paddle::framework::ir { TEST(SimplifyWithBasicOpsPass, dropout) { for (std::string dropout_implementation : @@ -87,8 +85,6 @@ TEST(SimplifyWithBasicOpsPass, dropout) { } } -} // namespace ir -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::ir USE_PASS(simplify_with_basic_ops_pass); diff --git a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc index dbf89671b55c37..1b2afe533dc220 100644 --- a/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc +++ b/paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/framework/new_executor/garbage_collector/no_event_garbage_collector.h" -namespace paddle { -namespace framework { +namespace paddle::framework { InterpreterCoreNoEventGarbageCollector::InterpreterCoreNoEventGarbageCollector() : queue_(nullptr), ctxs_() { @@ -132,5 +131,4 @@ void InterpreterCoreNoEventGarbageCollector::Add( } } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/assert_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/assert_instruction.cc index b3448ec8f8574f..26e58d2b6ad901 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/assert_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/assert_instruction.cc @@ -18,8 +18,7 @@ #include "paddle/fluid/pir/dialect/operator/ir/control_flow_op.h" #include "paddle/phi/kernels/funcs/tensor_formatter.h" -namespace paddle { -namespace framework { +namespace paddle::framework { AssertInstruction::AssertInstruction(size_t id, const phi::Place& place, ::pir::Operation* op, @@ -101,5 +100,4 @@ void AssertInstruction::Run() { error_msg)); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc index b5fdbf17da90c6..4e605f8da55aba 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/pylayer_instruction.cc @@ -40,8 +40,7 @@ #include "paddle/fluid/platform/onednn_helper.h" #endif -namespace paddle { -namespace framework { +namespace paddle::framework { PyLayerInstruction::PyLayerInstruction( size_t id, @@ -160,5 +159,4 @@ void PyLayerInstruction::Run() { fwd_inter_->Run({}, false); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc index 10db08c920ba42..c9b27bb4a414b5 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/select_input_instruction.cc @@ -17,8 +17,7 @@ #include "paddle/fluid/framework/new_executor/new_executor_defs.h" #include "paddle/fluid/framework/new_executor/pir_adaptor/pir_adaptor_util.h" -namespace paddle { -namespace framework { +namespace paddle::framework { SelectInputInstruction::SelectInputInstruction( size_t id, @@ -139,5 +138,4 @@ void SelectInputInstruction::Run() { VisitVarType(*selected, AssignFunctor(out_)); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/new_executor/interpreter/plan.cc b/paddle/fluid/framework/new_executor/interpreter/plan.cc index 66e824599fc6d6..3d7b01398d211b 100644 --- a/paddle/fluid/framework/new_executor/interpreter/plan.cc +++ b/paddle/fluid/framework/new_executor/interpreter/plan.cc @@ -16,9 +16,7 @@ #include "paddle/fluid/framework/program_desc.h" -namespace paddle { -namespace framework { -namespace interpreter { +namespace paddle::framework::interpreter { Plan::Plan(const std::vector>& job_list, const std::unordered_map>& @@ -92,6 +90,4 @@ void Plan::SetIrProgram(const std::string& job_type, int64_t Plan::MicroBatchNum() const { return micro_batch_num_; } -} // namespace interpreter -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::interpreter diff --git a/paddle/fluid/framework/new_executor/workqueue/events_waiter.cc b/paddle/fluid/framework/new_executor/workqueue/events_waiter.cc index 55d8d966b81f1c..c0a27eda438f6e 100644 --- a/paddle/fluid/framework/new_executor/workqueue/events_waiter.cc +++ b/paddle/fluid/framework/new_executor/workqueue/events_waiter.cc @@ -18,8 +18,7 @@ #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace framework { +namespace paddle::framework { constexpr EventsWaiter::EventId kEmptyEventId = 0; @@ -215,5 +214,4 @@ std::string EventsWaiter::GetEventName(const EventId& id) { return iter->second.name; } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/framework/op_version_proto.cc b/paddle/fluid/framework/op_version_proto.cc index 8be9323098c971..ac302ffaad0b6b 100644 --- a/paddle/fluid/framework/op_version_proto.cc +++ b/paddle/fluid/framework/op_version_proto.cc @@ -14,10 +14,7 @@ limitations under the License. */ #include "paddle/fluid/framework/op_version_proto.h" -namespace paddle { -namespace framework { -namespace compatible { -namespace pb { +namespace paddle::framework::compatible::pb { const std::unordered_map& GetLegacyOpVersions() { static std::unordered_map op_versions = { {"not_equal", 1}, @@ -103,7 +100,4 @@ const std::unordered_map& GetLegacyOpVersions() { {"equal", 1}}; return op_versions; } -} // namespace pb -} // namespace compatible -} // namespace framework -} // namespace paddle +} // namespace paddle::framework::compatible::pb diff --git a/paddle/fluid/framework/scope_pool.cc b/paddle/fluid/framework/scope_pool.cc index 61bf41e6a64547..63861ca1178f3e 100644 --- a/paddle/fluid/framework/scope_pool.cc +++ b/paddle/fluid/framework/scope_pool.cc @@ -13,8 +13,7 @@ // limitations under the License. #include "paddle/fluid/framework/scope_pool.h" -namespace paddle { -namespace framework { +namespace paddle::framework { ScopePool &ScopePool::Instance() { // NOLINT static ScopePool pool; @@ -53,5 +52,4 @@ void ScopePool::Clear() { scopes_.clear(); } -} // namespace framework -} // namespace paddle +} // namespace paddle::framework diff --git a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc index 18a5ffc815a6d1..590f0aaceb7dbb 100644 --- a/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc +++ b/paddle/fluid/inference/analysis/ir_passes/tensorrt_subgraph_pass.cc @@ -38,9 +38,7 @@ #include "paddle/phi/common/backend.h" #include "paddle/phi/common/data_type.h" -namespace paddle { -namespace inference { -namespace analysis { +namespace paddle::inference::analysis { namespace { // if in mixed model precision, we should make all tensorrt_engine's output @@ -934,9 +932,7 @@ std::string TensorRtSubgraphPass::CreateTensorRTOp( return engine_key + std::to_string(predictor_id); } -} // namespace analysis -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::analysis REGISTER_PASS(tensorrt_subgraph_pass, paddle::inference::analysis::TensorRtSubgraphPass) diff --git a/paddle/fluid/inference/tensorrt/convert/range_op.cc b/paddle/fluid/inference/tensorrt/convert/range_op.cc index 073b51b8c0734f..4e6847f6c4a656 100644 --- a/paddle/fluid/inference/tensorrt/convert/range_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/range_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class RangeOpConverter : public OpConverter { public: @@ -63,8 +61,6 @@ class RangeOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(range, RangeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/shape_op.cc b/paddle/fluid/inference/tensorrt/convert/shape_op.cc index 9fa4237a3dd676..62998cedda82d6 100644 --- a/paddle/fluid/inference/tensorrt/convert/shape_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/shape_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class ShapeOpConverter : public OpConverter { public: @@ -41,8 +39,6 @@ class ShapeOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(shape, ShapeOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc b/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc index a0a9ad2b981f92..beb872aa3144af 100644 --- a/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/strided_slice_op.cc @@ -14,9 +14,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/convert/op_converter.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class StridedSliceOpConverter : public OpConverter { public: @@ -113,8 +111,6 @@ class StridedSliceOpConverter : public OpConverter { } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(strided_slice, StridedSliceOpConverter); diff --git a/paddle/fluid/inference/tensorrt/convert/top_k_op.cc b/paddle/fluid/inference/tensorrt/convert/top_k_op.cc index c22d2bd8115646..84017e76bbbe64 100644 --- a/paddle/fluid/inference/tensorrt/convert/top_k_op.cc +++ b/paddle/fluid/inference/tensorrt/convert/top_k_op.cc @@ -23,9 +23,7 @@ limitations under the License. */ #include "paddle/fluid/inference/tensorrt/helper.h" #include "paddle/fluid/platform/enforce.h" -namespace paddle { -namespace inference { -namespace tensorrt { +namespace paddle::inference::tensorrt { class TopKOpConverter : public OpConverter { public: @@ -97,9 +95,7 @@ class TopKOpConverter : public OpConverter { ("top_k (Output: " + out_name + "," + indices_name + ")").c_str()); } }; -} // namespace tensorrt -} // namespace inference -} // namespace paddle +} // namespace paddle::inference::tensorrt REGISTER_TRT_OP_CONVERTER(top_k, TopKOpConverter); REGISTER_TRT_OP_CONVERTER(top_k_v2, TopKOpConverter); diff --git a/paddle/fluid/ir_adaptor/translator/utils.cc b/paddle/fluid/ir_adaptor/translator/utils.cc index cec465c9d18aa2..19f6ca87079c72 100644 --- a/paddle/fluid/ir_adaptor/translator/utils.cc +++ b/paddle/fluid/ir_adaptor/translator/utils.cc @@ -28,8 +28,7 @@ #include "paddle/fluid/pir/dialect/operator/ir/op_onednn_dialect.h" #endif -namespace paddle { -namespace dialect { +namespace paddle::dialect { bool HaveOpToMultiKernelsMap(std::string op_name) { for (const auto& map : {&op_to_multi_kernels_map, &sp_op_to_multi_kernels_map}) { @@ -54,11 +53,9 @@ bool IsOneDNNOnlyOp(std::string op_name) { } #endif -} // namespace dialect -} // namespace paddle +} // namespace paddle::dialect -namespace paddle { -namespace translator { +namespace paddle::translator { pir::Operation* InsertSliceOperationForTarget( pir::IrContext* ctx, @@ -129,5 +126,4 @@ std::vector CheckUnregisteredOperation( return unregistered_ops; } -} // namespace translator -} // namespace paddle +} // namespace paddle::translator diff --git a/paddle/fluid/jit/engine/interpreter_engine.cc b/paddle/fluid/jit/engine/interpreter_engine.cc index a068325dc3e287..0bba3ebd2e554b 100644 --- a/paddle/fluid/jit/engine/interpreter_engine.cc +++ b/paddle/fluid/jit/engine/interpreter_engine.cc @@ -24,8 +24,7 @@ #include "paddle/pir/include/core/program.h" #include "paddle/pir/include/core/value.h" -namespace paddle { -namespace jit { +namespace paddle::jit { InterpreterEngine::InterpreterEngine( const std::shared_ptr &info, @@ -104,5 +103,4 @@ std::unique_ptr InterpreterEngine::Clone(void *stream) { return std::unique_ptr(x); } -} // namespace jit -} // namespace paddle +} // namespace paddle::jit diff --git a/paddle/fluid/operators/collective/c_allgather_op.cc b/paddle/fluid/operators/collective/c_allgather_op.cc index 35af8aa5f4c8df..1742d25318e042 100644 --- a/paddle/fluid/operators/collective/c_allgather_op.cc +++ b/paddle/fluid/operators/collective/c_allgather_op.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include -namespace paddle { -namespace operators { +namespace paddle::operators { class CAllGatherOp : public framework::OperatorWithKernel { public: @@ -70,8 +69,7 @@ reference: https://docs.nvidia.com/deeplearning/sdk/nccl-developer-guide/docs/us } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/collective/c_comm_init_multitrainer_op.cc b/paddle/fluid/operators/collective/c_comm_init_multitrainer_op.cc index ae8cfcc25db0fc..6acadd0497127a 100644 --- a/paddle/fluid/operators/collective/c_comm_init_multitrainer_op.cc +++ b/paddle/fluid/operators/collective/c_comm_init_multitrainer_op.cc @@ -33,8 +33,7 @@ limitations under the License. */ #include "paddle/phi/core/platform/collective_helper.h" #endif -namespace paddle { -namespace operators { +namespace paddle::operators { class CCommInitMultiTrainerInferShape : public framework::InferShapeBase { public: @@ -98,8 +97,7 @@ Initialize collective communication context within this trainer } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/controlflow/op_variant.cc b/paddle/fluid/operators/controlflow/op_variant.cc index b69a9917a3eed2..b210071562ac62 100644 --- a/paddle/fluid/operators/controlflow/op_variant.cc +++ b/paddle/fluid/operators/controlflow/op_variant.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/operators/controlflow/op_variant.h" -namespace paddle { -namespace operators { +namespace paddle::operators { struct InputsVisitor { template @@ -98,5 +97,4 @@ void AppendOpVariantByOpName( } } -} // namespace operators -} // namespace paddle +} // namespace paddle::operators diff --git a/paddle/fluid/operators/fused/fused_transpose_op.cc b/paddle/fluid/operators/fused/fused_transpose_op.cc index 02091138e137d8..44217e74072194 100644 --- a/paddle/fluid/operators/fused/fused_transpose_op.cc +++ b/paddle/fluid/operators/fused/fused_transpose_op.cc @@ -17,8 +17,7 @@ #include "paddle/fluid/operators/transpose_op.h" -namespace paddle { -namespace operators { +namespace paddle::operators { class FusedTransposeOpMaker : public Transpose2OpMaker { protected: @@ -48,8 +47,7 @@ class FusedTransposeOpMaker : public Transpose2OpMaker { } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/operators/memcpy_d2h_op.cc b/paddle/fluid/operators/memcpy_d2h_op.cc index 0c3bd42d470e2f..8033cdb6489016 100644 --- a/paddle/fluid/operators/memcpy_d2h_op.cc +++ b/paddle/fluid/operators/memcpy_d2h_op.cc @@ -15,20 +15,17 @@ limitations under the License. */ #include "paddle/phi/core/infermeta_utils.h" #include "paddle/phi/infermeta/unary.h" -namespace paddle { -namespace framework { +namespace paddle::framework { class OpDesc; class InferShapeContext; template class EmptyGradOpMaker; -} // namespace framework -namespace imperative { +} // namespace paddle::framework +namespace paddle::imperative { class OpBase; -} // namespace imperative -} // namespace paddle +} // namespace paddle::imperative -namespace paddle { -namespace operators { +namespace paddle::operators { class MemcpyD2HOp : public framework::OperatorWithKernel { public: @@ -81,8 +78,7 @@ raise error if the type is not listed above. } }; -} // namespace operators -} // namespace paddle +} // namespace paddle::operators namespace ops = paddle::operators; diff --git a/paddle/fluid/primitive/decomp_utils/decomp_eager_utils.cc b/paddle/fluid/primitive/decomp_utils/decomp_eager_utils.cc index cd0a4779ecf82b..b43971aeddfb2b 100644 --- a/paddle/fluid/primitive/decomp_utils/decomp_eager_utils.cc +++ b/paddle/fluid/primitive/decomp_utils/decomp_eager_utils.cc @@ -14,8 +14,7 @@ #include "paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.h" #include "paddle/fluid/primitive/decomp_utils/decomp_utils.h" -namespace paddle { -namespace primitive { +namespace paddle::primitive { template <> void set_output(const paddle::Tensor& x_tmp, paddle::Tensor* x) { x->set_impl(x_tmp.impl()); @@ -27,5 +26,4 @@ void by_pass(const paddle::Tensor& x, Tensor* out) { set_output(x, out); } -} // namespace primitive -} // namespace paddle +} // namespace paddle::primitive diff --git a/paddle/fluid/pybind/const_value.cc b/paddle/fluid/pybind/const_value.cc index 484a0537a1ce7c..4d44662a7923bf 100644 --- a/paddle/fluid/pybind/const_value.cc +++ b/paddle/fluid/pybind/const_value.cc @@ -23,8 +23,7 @@ limitations under the License. */ #include "paddle/fluid/framework/details/dgc_const_values.h" #endif -namespace paddle { -namespace pybind { +namespace paddle::pybind { void BindConstValue(pybind11::module* m) { m->def("kEmptyVarName", [] { return framework::kEmptyVarName; }); @@ -79,5 +78,4 @@ void BindConstValue(pybind11::module* m) { #endif } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 5b4fbc7041c014..9a27f20bc1483f 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -251,8 +251,7 @@ DECLARE_FILE_SYMBOLS(aligned_allocator); DECLARE_FILE_SYMBOLS(pass_timing); DECLARE_FILE_SYMBOLS(op_compatible_info); -namespace paddle { -namespace pybind { +namespace paddle::pybind { PyTypeObject *g_framework_scope_pytype = nullptr; PyTypeObject *g_framework_densetensorarray_pytype = nullptr; @@ -3352,5 +3351,4 @@ All parameter, weight, gradient are variables in Paddle. BindDistApi(&m); #endif } -} // namespace pybind -} // namespace paddle +} // namespace paddle::pybind diff --git a/paddle/phi/api/lib/int_array.cc b/paddle/phi/api/lib/int_array.cc index 8d1dba506a6bf7..a924bad0fa345b 100644 --- a/paddle/phi/api/lib/int_array.cc +++ b/paddle/phi/api/lib/int_array.cc @@ -18,8 +18,7 @@ limitations under the License. */ #include "paddle/phi/api/lib/tensor_copy.h" #include "paddle/phi/common/place.h" -namespace paddle { -namespace experimental { +namespace paddle::experimental { template <> IntArrayBase::IntArrayBase(const Tensor& tensor) { // NOLINT @@ -69,5 +68,4 @@ IntArrayBase::IntArrayBase(const std::vector& tensor_list) { } } -} // namespace experimental -} // namespace paddle +} // namespace paddle::experimental diff --git a/paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.cc b/paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.cc index 09b798ab3a4319..bdbbe933996695 100644 --- a/paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.cc +++ b/paddle/phi/core/distributed/auto_parallel/dist_meta_tensor.cc @@ -16,8 +16,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/dist_tensor.h" -namespace phi { -namespace distributed { +namespace phi::distributed { phi::DDim DistMetaTensor::dims() const { // member values in tensor_ have higher priority than those in DistMetaTensor @@ -51,5 +50,4 @@ bool DistMetaTensor::initialized() const { return tensor_ != nullptr || dist_attr_ != TensorDistAttr(); } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/core/platform/profiler/utils.cc b/paddle/phi/core/platform/profiler/utils.cc index bd33feb061e2c4..f740c0e31d7b01 100644 --- a/paddle/phi/core/platform/profiler/utils.cc +++ b/paddle/phi/core/platform/profiler/utils.cc @@ -20,8 +20,7 @@ limitations under the License. */ #include "glog/logging.h" #include "paddle/phi/core/platform/device/gpu/gpu_info.h" -namespace paddle { -namespace platform { +namespace paddle::platform { template <> std::string json_vector( @@ -171,5 +170,4 @@ const char* StringTracerEventType(phi::TracerEventType type) { } } -} // namespace platform -} // namespace paddle +} // namespace paddle::platform diff --git a/paddle/phi/infermeta/spmd_rules/transpose.cc b/paddle/phi/infermeta/spmd_rules/transpose.cc index cbafade6ea9ca6..0ed7cccd7e40fc 100644 --- a/paddle/phi/infermeta/spmd_rules/transpose.cc +++ b/paddle/phi/infermeta/spmd_rules/transpose.cc @@ -19,8 +19,7 @@ limitations under the License. */ #include "paddle/phi/core/distributed/auto_parallel/utils.h" #include "paddle/phi/infermeta/spmd_rules/utils.h" -namespace phi { -namespace distributed { +namespace phi::distributed { using phi::distributed::auto_parallel::str_join; @@ -211,5 +210,4 @@ SpmdInfo TransposeGradInferSpmd(const DistMetaTensor& out_grad, return {{out_grad_dist_attr}, {x_grad_dist_attr}}; } -} // namespace distributed -} // namespace phi +} // namespace phi::distributed diff --git a/paddle/phi/kernels/funcs/fake_quantize_functor.cc b/paddle/phi/kernels/funcs/fake_quantize_functor.cc index 70771c6742d5a8..6d450f6edc4a47 100644 --- a/paddle/phi/kernels/funcs/fake_quantize_functor.cc +++ b/paddle/phi/kernels/funcs/fake_quantize_functor.cc @@ -14,8 +14,7 @@ limitations under the License. */ #include "paddle/phi/kernels/funcs/fake_quantize_functor.h" -namespace phi { -namespace funcs { +namespace phi::funcs { template void FindAbsMaxFunctor::operator()(const Context &ctx, @@ -356,5 +355,4 @@ template class ChannelClipFakeQuantDequantFunctor; template class FindRangeAbsMaxFunctor; template class ClipAndFakeQuantDequantFunctor; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/im2col.cc b/paddle/phi/kernels/funcs/im2col.cc index 7d8d035c7068fb..6647d2c64a1b89 100644 --- a/paddle/phi/kernels/funcs/im2col.cc +++ b/paddle/phi/kernels/funcs/im2col.cc @@ -19,8 +19,7 @@ namespace phi { class CPUContext; } // namespace phi -namespace phi { -namespace funcs { +namespace phi::funcs { /* * im = [input_channels, input_height, input_width] @@ -361,5 +360,4 @@ template class Col2ImFunctor>; -} // namespace funcs -} // namespace phi +} // namespace phi::funcs diff --git a/paddle/phi/kernels/fusion/cpu/distributed_fused_lamb_init_kernel.cc b/paddle/phi/kernels/fusion/cpu/distributed_fused_lamb_init_kernel.cc index 40519926f1dbc2..8899cf35057a15 100644 --- a/paddle/phi/kernels/fusion/cpu/distributed_fused_lamb_init_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/distributed_fused_lamb_init_kernel.cc @@ -16,8 +16,7 @@ #include "paddle/common/errors.h" #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace fusion { +namespace phi::fusion { template void DistributedFusedLambInitOpKernel( @@ -51,8 +50,7 @@ void DistributedFusedLambInitOpKernel( PADDLE_THROW(common::errors::Unavailable( "Do not support expert count op for cpu kernel now.")); } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(distributed_fused_lamb_init, CPU, diff --git a/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_upper_triangle_kernel.cc b/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_upper_triangle_kernel.cc index 8bbfda8d22d813..bf8f52c7487a59 100644 --- a/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_upper_triangle_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fused_softmax_mask_upper_triangle_kernel.cc @@ -16,8 +16,7 @@ #include "paddle/common/errors.h" #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace fusion { +namespace phi::fusion { template void FusedSoftmaxMaskFuseUpperTriangleKernel(const Context& dev_ctx, @@ -30,8 +29,7 @@ void FusedSoftmaxMaskFuseUpperTriangleKernel(const Context& dev_ctx, "Softmax mask fuse op only supports GPU now.")); } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fused_softmax_mask_upper_triangle, CPU, diff --git a/paddle/phi/kernels/fusion/onednn/fused_conv_kernel.cc b/paddle/phi/kernels/fusion/onednn/fused_conv_kernel.cc index 9c19c9a202c161..edb78bb94d4ac1 100644 --- a/paddle/phi/kernels/fusion/onednn/fused_conv_kernel.cc +++ b/paddle/phi/kernels/fusion/onednn/fused_conv_kernel.cc @@ -15,8 +15,7 @@ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/onednn/conv_function.h" -namespace phi { -namespace fusion { +namespace phi::fusion { template void FusedConv2DKernel(const Context& dev_ctx, @@ -156,8 +155,7 @@ KernelKey ConvGetKernelTypeForVar(const GetKernelTypeForVarContext* ctx) { tensor.place(), tensor.layout(), expected_kernel_type.dtype()); } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fused_conv2d, OneDNN, diff --git a/paddle/phi/kernels/fusion/onednn/fusion_gru_kernel.cc b/paddle/phi/kernels/fusion/onednn/fusion_gru_kernel.cc index 0a5ce10722b05e..57c70df919a5f1 100644 --- a/paddle/phi/kernels/fusion/onednn/fusion_gru_kernel.cc +++ b/paddle/phi/kernels/fusion/onednn/fusion_gru_kernel.cc @@ -21,8 +21,7 @@ #include "paddle/phi/core/kernel_registry.h" -namespace phi { -namespace fusion { +namespace phi::fusion { using phi::OneDNNContext; using phi::funcs::CreateKey; @@ -642,8 +641,7 @@ void FusionGRUKernel(const Context& dev_ctx, } } -} // namespace fusion -} // namespace phi +} // namespace phi::fusion PD_REGISTER_KERNEL(fusion_gru, OneDNN, diff --git a/paddle/phi/kernels/selected_rows/elementwise_multiply_kernel.cc b/paddle/phi/kernels/selected_rows/elementwise_multiply_kernel.cc index bfdc4087fd0362..4f37a7c34a3169 100644 --- a/paddle/phi/kernels/selected_rows/elementwise_multiply_kernel.cc +++ b/paddle/phi/kernels/selected_rows/elementwise_multiply_kernel.cc @@ -22,8 +22,7 @@ limitations under the License. */ #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/elementwise_multiply_kernel.h" -namespace phi { -namespace sr { +namespace phi::sr { template void MultiplyRawKernel(const Context& dev_ctx, @@ -53,8 +52,7 @@ void MultiplyKernel(const Context& dev_ctx, MultiplyRawKernel(dev_ctx, x, y, axis, out); } -} // namespace sr -} // namespace phi +} // namespace phi::sr using complex64 = ::phi::dtype::complex; using complex128 = ::phi::dtype::complex; From a53da524cd103e037dfc74c239b4d3eb5ae8ae10 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Wed, 4 Dec 2024 21:05:27 +0800 Subject: [PATCH 165/288] [CINN] fix masked select grad decomp bug (#69927) * try to fix masked decomp bug * revert code --- paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h | 6 +++--- 1 file changed, 3 insertions(+), 3 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index 97272c05c35dcb..bf859e5af0243f 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1253,9 +1253,9 @@ void masked_select_grad(const Tensor& x, grad_num *= promoted_out_grad.shape()[i]; } - auto end = full({1}, x_num, x.dtype(), x.place()); - auto start = full({1}, 0, x.dtype(), x.place()); - auto step = full({1}, 1, x.dtype(), x.place()); + auto end = full({1}, x_num, promoted_x.dtype(), x.place()); + auto start = full({1}, 0, promoted_x.dtype(), x.place()); + auto step = full({1}, 1, promoted_x.dtype(), x.place()); auto x_arange = backend::arange( start, end, step, promoted_x.dtype(), promoted_x.place()); From 173d018f8a2e2d9cf7e8e76b420869be4a393b74 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Wed, 4 Dec 2024 21:55:13 +0800 Subject: [PATCH 166/288] [Dy2St] Record patched name to avoid rollback failures (#69940) --- python/paddle/jit/api.py | 1 + .../jit/dy2static/program_translator.py | 20 +++++++----- python/paddle/nn/layer/layers.py | 6 ++++ test/dygraph_to_static/test_rollback.py | 32 +++++++++++++++++++ 4 files changed, 51 insertions(+), 8 deletions(-) diff --git a/python/paddle/jit/api.py b/python/paddle/jit/api.py index a2a7886621be5e..5941e1c4ac6475 100644 --- a/python/paddle/jit/api.py +++ b/python/paddle/jit/api.py @@ -335,6 +335,7 @@ def decorated(python_func): logging_utils.warn( f"`{class_name}.forward` has already been decorated somewhere. It will be redecorated to replace previous one." ) + function._original_funcs["forward"] = function.forward function.forward = decorated(function.forward) return function else: diff --git a/python/paddle/jit/dy2static/program_translator.py b/python/paddle/jit/dy2static/program_translator.py index 272893331c4c43..435150629a9701 100644 --- a/python/paddle/jit/dy2static/program_translator.py +++ b/python/paddle/jit/dy2static/program_translator.py @@ -433,6 +433,8 @@ def __init__(self, function, input_spec=None, **kwargs): self._cuda_graph_capture_mode = "" self._cuda_graph_pool_id = 0 self._property = kwargs.get("property", False) + # Note: Record the patched method name for rollback. + self._patched_name = None self._get_debug_name() def _get_debug_name(self) -> str: @@ -679,19 +681,21 @@ def rollback_impl(class_instance): return self._dygraph_function # only rollback sub-functions on path of top _dygraph_function - func_name = self._dygraph_function.__name__ - assert ( - func_name in self.class_instance._original_funcs - ), f"Not Found function '{func_name}' in class '{self.class_instance.__class__}'." - func = self.class_instance._original_funcs[func_name] - setattr( - self.class_instance, func_name, func.__get__(self.class_instance) + fn_name = ( + self._patched_name + if self._patched_name is not None + else self._dygraph_function.__name__ ) + assert ( + fn_name in self.class_instance._original_funcs + ), f"Not Found function '{fn_name}' in class '{self.class_instance.__class__}'." + func = self.class_instance._original_funcs[fn_name] + setattr(self.class_instance, fn_name, func.__get__(self.class_instance)) for sublayer in self.class_instance.sublayers(include_self=False): rollback_impl(sublayer) - return getattr(self.class_instance, func_name) + return getattr(self.class_instance, fn_name) def __deepcopy__(self, memo): """ diff --git a/python/paddle/nn/layer/layers.py b/python/paddle/nn/layer/layers.py index 8b3d9e4769da5b..2ac94dd9aa7c0d 100644 --- a/python/paddle/nn/layer/layers.py +++ b/python/paddle/nn/layer/layers.py @@ -1758,6 +1758,12 @@ def _remove_if_exist(*dicts): if name in d: del d[name] + if isinstance( + value, paddle.jit.dy2static.program_translator.StaticFunction + ): + object.__setattr__(self, name, value) + value._patched_name = name + return if isinstance(getattr(type(self), name, None), property): object.__setattr__(self, name, value) params = self.__dict__.get('_parameters', None) diff --git a/test/dygraph_to_static/test_rollback.py b/test/dygraph_to_static/test_rollback.py index 563eb6357cde5c..e946ebeec4d5fe 100644 --- a/test/dygraph_to_static/test_rollback.py +++ b/test/dygraph_to_static/test_rollback.py @@ -144,5 +144,37 @@ def test_rollback(self): self.assertTrue(not isinstance(net.func, StaticFunction)) +class FuncRollbackWithPatchedFunction(paddle.nn.Layer): + def __init__(self) -> None: + super().__init__() + + def forward(self, x): + return x + 1 + + +def patched_fn(self, x): + return x + 2 + + +FuncRollbackWithPatchedFunction.forward = patched_fn + + +class TestRollBackWithPatchedFunction(Dy2StTestBase): + @test_ast_only + def test_rollback(self): + x = paddle.zeros([2, 2]) + net = FuncRollbackWithPatchedFunction() + dy_out = net(x) + static_net = paddle.jit.to_static(net, full_graph=True) + st_out = static_net(x) + static_net.forward.rollback() + dy_out_rollback = net(x) + + self.assertTrue(not isinstance(net.forward, StaticFunction)) + + np.testing.assert_array_equal(dy_out.numpy(), st_out.numpy()) + np.testing.assert_array_equal(dy_out.numpy(), dy_out_rollback.numpy()) + + if __name__ == "__main__": unittest.main() From 4c9bc9e3cd7680200be9f244f9a5d374345a6741 Mon Sep 17 00:00:00 2001 From: PuQing Date: Wed, 4 Dec 2024 23:17:13 +0800 Subject: [PATCH 167/288] [CINN] Revert GetExprVecFromData (#69963) * Enhance strided_slice operation with infer_flags and decrease_axis parameters * fix * fix * fix * fix * fix * fix * Revert "fix" This reverts commit 6374d3ca78d8851c81d7bf5c54a0441eb9937f77. * fix * fix * revert GetExprVecFromData --- .../infer_sym_slice_utils.h | 24 +++++++++++++++++++ 1 file changed, 24 insertions(+) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h index 5ad82c9d38a242..b97a34e9489147 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h @@ -48,6 +48,30 @@ inline bool GetExprVecOfStartEnd( } } +inline ExprVec GetExprVecFromData(const ShapeOrData &shapeordata) { + if (shapeordata.isa()) { + ExprVec result; + TensorListExprs list = + shapeordata.dyn_cast(); + for (size_t i = 0; i < list.size(); i++) { + PADDLE_ENFORCE_EQ(list.at(i).data().has_value(), + true, + common::errors::InvalidArgument( + "i-th element of list has no value, please check")); + for (auto expr : list.at(i).data().value()) { + result.emplace_back(expr); + } + } + return result; + } else { + PADDLE_ENFORCE_EQ(shapeordata.data().has_value(), + true, + common::errors::InvalidArgument( + "Input `shapeordata.data` is empty, please check")); + return shapeordata.data().value(); + } +} + inline ExprVec GetSliceDims(const ExprVec &in_dims, const std::vector &axes, const ExprVec &starts_base, From e8369ad0b78aea32502607f52d79ac102eda1388 Mon Sep 17 00:00:00 2001 From: cubehan3 Date: Thu, 5 Dec 2024 09:46:30 +0800 Subject: [PATCH 168/288] Improve speed by replacing range with slice (#69938) --- python/paddle/autograd/ir_backward.py | 11 +++-------- 1 file changed, 3 insertions(+), 8 deletions(-) diff --git a/python/paddle/autograd/ir_backward.py b/python/paddle/autograd/ir_backward.py index 810abfdac4be25..33c4c8cfcf0507 100644 --- a/python/paddle/autograd/ir_backward.py +++ b/python/paddle/autograd/ir_backward.py @@ -884,10 +884,8 @@ def append_yield( after_ops_num = len(bwd_block.ops) # update grad_op structure - bwd_ops = [ - bwd_block.ops[i] - for i in range(before_ops_num, after_ops_num) - ] + bwd_ops = bwd_block.ops[before_ops_num:after_ops_num] + # update input_grad map update_input_grad_map( op, input_grads, get_real_op_inputs(op) @@ -907,10 +905,7 @@ def append_yield( after_ops_num = len(bwd_block.ops) # update grad_op structure - bwd_ops = [ - bwd_block.ops[i] - for i in range(before_ops_num, after_ops_num) - ] + bwd_ops = bwd_block.ops[before_ops_num:after_ops_num] # update input_grad map update_input_grad_map( From dc3144963fa00ee4f009ce65ed1aa5867048c35f Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Thu, 5 Dec 2024 09:58:36 +0800 Subject: [PATCH 169/288] [Auto Parallel] Inter api publication and add docs. (#69896) --- python/paddle/distributed/__init__.py | 22 ++ .../auto_parallel/intermediate/parallelize.py | 296 ++++++++++++++++- .../intermediate/pipeline_parallel.py | 27 ++ .../intermediate/tensor_parallel.py | 299 ++++++++++++++++-- .../hybrid_strategy/parallel_api.py | 107 +++---- 5 files changed, 655 insertions(+), 96 deletions(-) diff --git a/python/paddle/distributed/__init__.py b/python/paddle/distributed/__init__.py index c194aaadc9586e..4747925e2b47b8 100644 --- a/python/paddle/distributed/__init__.py +++ b/python/paddle/distributed/__init__.py @@ -45,6 +45,18 @@ to_static, unshard_dtensor, ) +from .auto_parallel.intermediate.parallelize import parallelize +from .auto_parallel.intermediate.pipeline_parallel import SplitPoint +from .auto_parallel.intermediate.tensor_parallel import ( + ColWiseParallel, + PrepareLayerInput, + PrepareLayerOutput, + RowWiseParallel, + SequenceParallelBegin, + SequenceParallelDisable, + SequenceParallelEnable, + SequenceParallelEnd, +) from .auto_parallel.placement_type import ( Partial, Replicate, @@ -177,4 +189,14 @@ "Strategy", "DistModel", "unshard_dtensor", + "parallelize", + "SequenceParallelEnd", + "SequenceParallelBegin", + "SequenceParallelEnable", + "SequenceParallelDisable", + "ColWiseParallel", + "RowWiseParallel", + "PrepareLayerOutput", + "PrepareLayerInput", + "SplitPoint", ] diff --git a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py index 5d1510e01a946e..31f15f44a77858 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/parallelize.py +++ b/python/paddle/distributed/auto_parallel/intermediate/parallelize.py @@ -11,18 +11,262 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import warnings +from typing import TYPE_CHECKING, TypedDict + +from typing_extensions import NotRequired + +from paddle.distributed import fleet +from paddle.framework import core from .parallel_base import ParallelOptimizer, parallelize_model_and_optimizer from .pipeline_parallel import pipeline_parallel from .sharded_data_parallel import sharded_data_parallel from .tensor_parallel import tensor_parallel +if TYPE_CHECKING: + import paddle + + from .pipeline_parallel import SplitPoint + from .tensor_parallel import PlanBase + + class _DPConfig(TypedDict): + sharding_level: str | int + + class _MPConfig(TypedDict): + parallelize_plan: dict[str, PlanBase | list[PlanBase]] + + class _PPConfig(TypedDict): + split_spec: str | dict[str, SplitPoint] + global_spec: NotRequired[str] + + class _ParallelizeConfig(TypedDict): + dp_config: NotRequired[_DPConfig] + mp_config: NotRequired[_MPConfig] + pp_config: NotRequired[_PPConfig] + def parallelize( - model, optimizer, mesh=None, dp_config=None, mp_config=None, pp_config=None -): - # TODO(yaliu): global mesh and split axis support + model: paddle.nn.Layer, + optimizer: paddle.optimizer.Optimizer | None = None, + mesh: paddle.distributed.ProcessMesh | None = None, + config: _ParallelizeConfig | None = None, +) -> tuple[paddle.nn.Layer, paddle.optimizer.Optimizer]: + """ + + Parallelize the model and optimizer from a single card version to a distributed version. + + Args: + model (paddle.nn.Layer): the model to be parallelized. + optimizer (paddle.optimizer.Optimizer, optional): the optimizer to be parallelized. + Could be `None` if no optimizer to be parallelized. + mesh (paddle.distributed.ProcessMesh, optional): the process mesh for parallelize the model and the optimizer. + Best practice: calling `dist.auto_parallel.set_mesh` to set the global mesh ahead of calling `parallelize` + and keep the `mesh` parameter as `None. + If the `mesh` is not None, the mesh passed to `parallelize` will overwrite the mesh set by `set_mesh`. + config (dict, optional): a dict contains the parallel config. + The keys of the dict can be chosen from `dp_config`, `mp_config` and `pp_config` which will be used to + determine the parallel method for data parallel, tensor parallel and pipeline parallel separately. + A valid config can be like this: {"dp_config": for more information refer the `dp_config` section of + this doc, "mp_config": for more information refer the `mp_config` section of this doc, "pp_config": + for more information refer the `pp_config` section of this doc}. + + dp_config (dict): a dict specifying the data parallel config. The keys of `dp_config` is `sharding_level`. + The value of `sharding_level` can be chosen from 0/1/2/3, which means pure data parallel, sharding + parallel stage 1, sharding parallel stage 2 and sharding parallel stage 3 separately. A valid + dp_config can be like this: {"sharding_level": 2}. + + mp_config (dict): a dict specifying the tensor parallel config. The keys of `mp_config` is + `parallelize_plan`. The value of `parallelize_plan` is another dict, mapping a layer name or a param + name to a specific parallel plan. Note that the layer name could be written in regular format. If + mapping a param name to a specific plan, the name of the param must be ended with `weight` or `bias`. + And all valid parallel plan is `ColWiseParallel`, `RowWiseParallel`, `SequenceParallelBegin, + `SequenceParallelDisable`, `SequenceParallelEnable`, `SequenceParallelEnd`, `PrepareLayerInput` and + `PrepareLayerOutput`. A valid mp_config can be like this: {"llama.embed_tokens": dist.ColWiseParallel(), + "llama.norm": dist.SequenceParallelEnable(), "lm_head.weight": dist.ColWiseParallel()}. + + pp_config (dict): a dict specifying the pipeline parallel config. The keys of `pp_config` is `split_spec` + and `global_spec`. The `split_spec` can be a dict or a string. If the `split_spec` is a dict, it maps + a layer name to a `SplitPoint`, note that the layer name could be written in regular format. The + pipeline parallel will exactly split the model at the point indicated by the map. If the `split_spec` + is a string, it contains the prefix of a set of layers. The pipeline parallel will automatically split + the model evenly at target layer. The `global_spec` is a string indicating a layer that contains global + tensors, which will be duplicated through all stages of the pipeline parallel. Some valid pp_config + can be list these: {"split_spec": "llama.layers", "global_spec": "llama.global_layer"} + or {"split_spec": {"llama.layers.1": SplitPoint.END}}. + + Note: + If the mesh is `None` or neither of `dp_config`, `mp_config` and `pp_config` is in the config, this + api will do nothing but return the model and optimizer passed in. + + Returns: + model, optimizer: the model and the optimizer after parallelize + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class ModelConfig: + ... def __init__(self): + ... self.vocab_size = 10 + ... self.hidden_size = 20 + ... self.intermediate_size = 20 + ... self.num_layers = 2 + + >>> model_config = ModelConfig() + + >>> class LlamaRMSNorm(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.weight = paddle.create_parameter( + ... shape=[model_config.hidden_size], + ... dtype=paddle.get_default_dtype(), + ... ) + ... + ... def forward(self, input): + ... pass + + >>> class LlamaAttention(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... + ... self.qkv_proj = paddle.nn.Linear( + ... model_config.hidden_size, + ... model_config.hidden_size * 3, + ... bias_attr=False, + ... ) + ... + ... self.o_proj = paddle.nn.Linear( + ... model_config.hidden_size, + ... model_config.hidden_size, + ... bias_attr=False, + ... ) + ... + ... def forward(self, input): + ... pass + + >>> class LlamaMLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.gate_up_proj = paddle.nn.Linear( + ... model_config.hidden_size, + ... model_config.intermediate_size * 2, + ... bias_attr=False + ... ) + ... + ... self.down_proj = paddle.nn.Linear( + ... model_config.intermediate_size, model_config.hidden_size, bias_attr=False + ... ) + ... + ... def forward(self, input): + ... pass + + >>> class LlamaDecoderLayer(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.self_attn = LlamaAttention() + ... self.mlp = LlamaMLP() + ... self.input_layernorm = LlamaRMSNorm() + ... self.post_attention_layernorm = LlamaRMSNorm() + ... + ... def forward(self, input): + ... pass + + >>> class LlamaModel(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.embedding = paddle.nn.Embedding(model_config.vocab_size, model_config.hidden_size) + ... decoder_layers = [] + ... for _ in range(model_config.num_layers): + ... decoder_layers.append(LlamaDecoderLayer()) + ... + ... self.layers = paddle.nn.LayerList(decoder_layers) + ... self.norm = LlamaRMSNorm() + ... + ... def forward(self, input): + ... pass + + >>> class LlamaLMHead(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.weight = self.create_parameter( + ... shape=[model_config.hidden_size, model_config.vocab_size], + ... dtype=paddle.get_default_dtype(), + ... ) + ... + ... def forward(self, input): + ... pass + + >>> class LlamaForCausalLM(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.llama = LlamaModel() + ... self.lm_head = LlamaLMHead() + ... + ... def forward(self, input): + ... pass + + >>> mesh = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["dp", "mp", "pp"]) + >>> dist.auto_parallel.set_mesh(mesh) + >>> parallel_config = { + ... "dp_config": {'sharding_level': 1}, + ... "mp_config": { + ... "parallelize_plan": { + ... "llama.embed_tokens": [ + ... dist.ColWiseParallel(), + ... dist.SequenceParallelBegin(), + ... ], + ... "llama.position_embedding": [ + ... dist.ColWiseParallel(), + ... dist.SequenceParallelBegin(), + ... ], + ... "llama.layers.*.self_attn.qkv_proj": dist.ColWiseParallel(), + ... "llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(), + ... "llama.layers.*.self_attn": dist.SequenceParallelDisable(), + ... "llama.layers.*.mlp.gate_up_proj": dist.ColWiseParallel(), + ... "llama.layers.*.mlp.down_proj": dist.RowWiseParallel(), + ... "llama.layers.*.mlp": dist.SequenceParallelDisable( + ... need_transpose=False + ... ), + ... "lm_head.weight": dist.ColWiseParallel(), + ... "lm_head": dist.SequenceParallelEnd(), + ... } + ... }, + ... "pp_config": {'split_spec': "llama.layers"} + ... } + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> model = LlamaForCausalLM() + >>> optimizer = paddle.optimizer.AdamW(parameters=model.parameters()) + >>> dist_model, dist_optimizer = dist.parallelize(model, optimizer, config=parallel_config) # type: ignore[arg-type] + >>> # This case need to be executed in multi-card environment + >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py + + """ + if config is None: + warnings.warn( + "The `parallelize will do nothing since the config is `None`." + ) + return model, optimizer + assert isinstance(config, dict) + if mesh is not None: + assert isinstance( + mesh, core.ProcessMesh + ), "The mesh must be an instance of paddle.distributed.ProcessMesh." + g_mesh = fleet.auto.get_mesh() + if g_mesh is not None and g_mesh != mesh: + warnings.warn( + "The mesh set by `fleet.auto.set_mesh` is different with the mesh pass to " + "`parallelize`. Will overwrite the previous mesh" + ) + fleet.auto.set_mesh(mesh) + pp_config = config.get('pp_config') + mp_config = config.get('mp_config') + dp_config = config.get('dp_config') if pp_config is not None: assert isinstance(pp_config, dict) model, optimizer = pipeline_parallel( @@ -51,18 +295,49 @@ def parallelize( has_parallelized_model = False -def parallelize_model( - model, mesh=None, dp_config=None, mp_config=None, pp_config=None -): +def parallelize_model(model, mesh=None, config=None): + if config is None: + warnings.warn( + "The `parallelize_model will do nothing since the config is `None`." + ) + return model + assert isinstance(config, dict) + if mesh is not None: + assert isinstance( + mesh, core.ProcessMesh + ), "The mesh must be an instance of paddle.distributed.ProcessMesh." + g_mesh = fleet.auto.get_mesh() + if g_mesh is not None and g_mesh != mesh: + warnings.warn( + "The mesh set by `fleet.auto.set_mesh` is different with the mesh pass to " + "`parallelize_model`. Will overwrite the previous mesh" + ) + fleet.auto.set_mesh(mesh) global has_parallelized_model has_parallelized_model = True - model, _ = parallelize(model, None, mesh, dp_config, mp_config, pp_config) + model, _ = parallelize(model, None, mesh, config) return model -def parallelize_optimizer( - optimizer, mesh=None, dp_config=None, mp_config=None, pp_config=None -): +def parallelize_optimizer(optimizer, mesh=None, config=None): + if config is None: + warnings.warn( + "The `parallelize_optimizer will do nothing since the config is `None`." + ) + return optimizer + assert isinstance(config, dict) + if mesh is not None: + assert isinstance( + mesh, core.ProcessMesh + ), "The mesh must be an instance of paddle.distributed.ProcessMesh." + g_mesh = fleet.auto.get_mesh() + if g_mesh is not None and g_mesh != mesh: + warnings.warn( + "The mesh set by `fleet.auto.set_mesh` is different with the mesh pass to " + "`parallelize_optimizer`. Will overwrite the previous mesh" + ) + fleet.auto.set_mesh(mesh) + global has_parallelized_model assert ( has_parallelized_model @@ -80,6 +355,7 @@ def parallelize_optimizer( param.is_dist() ), "Please use model after parallelize to create optimizer." + dp_config = config.get('dp_config') level = None sharding_mesh_dim = None if dp_config is not None: diff --git a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py index 5604866044e317..ac38684fc68c8e 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/pipeline_parallel.py @@ -28,6 +28,33 @@ class SplitPoint(Enum): + """ + Marking the position of the split. + BEGINNING: will split the model before the specified layer. + END: will split the model after the specified layer. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> pp_config = { + ... 'fc1': dist.SplitPoint.END + ... } + """ + BEGINNING = 0 END = 1 diff --git a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py index 67b83a5de63617..e8d7e550516524 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py @@ -11,14 +11,24 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations + import logging import re +from typing import TYPE_CHECKING import paddle import paddle.distributed as dist from .parallel_base import ParallelModel, ParallelOptimizer, is_tensor +if TYPE_CHECKING: + from collections.abc import Callable + + from paddle import Tensor + from paddle.distributed import ProcessMesh + from paddle.nn import Layer + def c_split(x, process_mesh, need_transpose): index = process_mesh.dim_names.index('mp') # get the axis for the split @@ -80,17 +90,45 @@ def apply(self, layer, process_mesh, shard_weight, shard_bias): class ColWiseParallel(PlanBase): """ - Col wise parallel plan. + Col wise parallel plan for mp config. Will try to split weight on the second dim and the bias on the first dim. This api is designed for paddle.nn.Linear or paddle.nn.Embedding. If any other instance of paddle.nn.Layer is passed, this plan will try to split `layer.weight` and `layer.bias` if it has. - Note: `layer.weight` should have two dims. - Note: `layer.bias` should have one dim. + Note: + 1. `layer.weight` should have two dims. + 2. `layer.bias` should have one dim. + + Args: + gather_output (bool): Whether gather the output to change it from a local tensor to a global tensor. + If gather the local tensor to global, an extra communication will be called. + The default value is `False`, which means keeping the output as a local tensor. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.ColWiseParallel() + ... } + """ - def __init__(self, gather_output=False): + def __init__(self, gather_output: bool = False) -> None: super().__init__() self.gather_output = gather_output @@ -145,15 +183,42 @@ def apply(self, layer, process_mesh, shard_weight=True, shard_bias=True): class RowWiseParallel(PlanBase): """ - Row wise parallel plan. + Row wise parallel plan for mp config. Will try to split weight on the first dim. This api is designed for paddle.nn.Linear or paddle.nn.Embedding. If any other instance of paddle.nn.Layer is passed, this plan will try to split `layer.weight` if it has. - Note: `layer.weight` should have two dims. + Note: + `layer.weight` should have two dims. + + Args: + is_input_parallel (bool): Whether the input is a local tensor or a global tensor. If the input is a + global tensor, an extra split will be called. The default value is `True`， + which means the input is a local tensor. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.RowWiseParallel() + ... } """ - def __init__(self, is_input_parallel=True): + def __init__(self, is_input_parallel: bool = True) -> None: super().__init__() self.is_input_parallel = is_input_parallel @@ -201,10 +266,50 @@ def apply(self, layer, process_mesh, shard_weight=True, shard_bias=False): class PrepareLayerInput(PlanBase): """ Prepare the input of specific layer. User should provide one callable function. - The function should take exactly one parameter named `process_mesh` and return the pre hook. + + Args: + fn (callable): A function that prepare the layer input. The function should take exactly + one parameter named `process_mesh` and return the pre hook. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> def layer_input_hook(process_mesh): + ... def hook(layer, input, output): + ... return input + ... return hook + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.PrepareLayerOutput(layer_input_hook) + ... } """ - def __init__(self, fn=None): + def __init__( + self, + fn: ( + Callable[ + [ProcessMesh], + Callable[ + [Layer, tuple[Tensor], tuple[Tensor]], [tuple[Tensor]] + ], + ] + | None + ) = None, + ) -> None: super().__init__() assert callable(fn) self.fn = fn @@ -216,10 +321,50 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): class PrepareLayerOutput(PlanBase): """ Prepare the output of specific layer. User should provide one callable function. - The function should take exactly one parameter named `process_mesh` and return the post hook. + + Args: + fn (callable): A function that prepare the layer input. The function should take exactly + one parameter named `process_mesh` and return the post hook. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> def layer_output_hook(process_mesh): + ... def hook(layer, input, output): + ... return output + ... return hook + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.PrepareLayerOutput(layer_output_hook) + ... } """ - def __init__(self, fn=None): + def __init__( + self, + fn: ( + Callable[ + [ProcessMesh], + Callable[ + [Layer, tuple[Tensor], tuple[Tensor]], [tuple[Tensor]] + ], + ] + | None + ) = None, + ) -> None: super().__init__() assert callable(fn) self.fn = fn @@ -230,14 +375,40 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): class SequenceParallelBegin(PlanBase): """ - With need_transpose=True, this plan will transpose and reshard the output from [b, s, h] to [s/mp, b, h]. - With need_transpose=False, this plan will reshard the output from [s, b, h] to [s/mp, b, h]. - + Sequence parallel plan for mp config. This plan marks the beginning of the sp and should be added to the LAST layer before the sp range. - DON'T mark any layer in the sp range. + + Note: + DON'T mark any layer in the sp range. + + Args: + need_transpose (bool): the default value is `True`. With `need_transpose=True`, this plan will transfer + the output from [b, s, h] to [s/mp, b, h]. With `need_transpose=False`, this plan will transfer + the output from [s, b, h] to [s/mp, b, h]. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.SequenceParallelBegin() + ... } """ - def __init__(self, need_transpose=True): + def __init__(self, need_transpose: bool = True) -> None: super().__init__() self.need_transpose = need_transpose @@ -256,14 +427,40 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): class SequenceParallelEnd(PlanBase): """ - With need_transpose=True, this plan will reshard and transpose the input from [s/mp, b, h] to [b, s, h]. - With need_transpose=False, this plan will reshard the input from [s/mp, b, h] to [s, b, h]. - + Sequence parallel plan for mp config. This plan marks the ending of the sp and should be added to the FIRST layer after the sp range. - DON'T mark any layer in the sp range. + + Note: + DON'T mark any layer in the sp range. + + Args: + need_transpose (bool): the default value is `True`. With `need_transpose=True`, this plan will transfer + the input from [s/mp, b, h] to [b, s, h]. With `need_transpose=False`, this plan will transfer the + input from [s/mp, b, h] to [s, b, h]. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.SequenceParallelEnd() + ... } """ - def __init__(self, need_transpose=True): + def __init__(self, need_transpose: bool = True) -> None: super().__init__() self.need_transpose = need_transpose @@ -282,10 +479,32 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): class SequenceParallelEnable(PlanBase): """ + Sequence parallel plan for mp config. Do sequence parallel on the layer. Note the input should be in [b, s, h] format. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.SequenceParallelEnable() + ... } """ - def __init__(self): + def __init__(self) -> None: super().__init__() def sequence_parallel_begin(self, process_mesh): @@ -317,16 +536,38 @@ def apply(self, layer, process_mesh, shard_weight=None, shard_bias=None): class SequenceParallelDisable(PlanBase): """ + Sequence parallel plan for mp config. Disable sequence parallel on the layer. - If the need_transpose is true: - - change the input from [s/mp, b, h] to [b, s, h] - - change the output from [b, s, h] to [s/mp, b, h] - If the need_transpose is False: - - change the input from [s/mp, b, h] to [s, b, h] - - change the output from [s, b, h] to [s/mp, b, h] + + Args: + need_transpose (bool): the default value is `True`. If the need_transpose is `True`: this plan will transfer + the input from [s/mp, b, h] to [b, s, h] and then transfer the output from [b, s, h] to [s/mp, b, h]. + If the need_transpose is `False`: this plan will transfer the input from [s/mp, b, h] to [s, b, h] and + then transfer the output from [s, b, h] to [s/mp, b, h]. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + + >>> class MLP(paddle.nn.Layer): + ... def __init__(self): + ... super().__init__() + ... self.fc1 = paddle.nn.Linear(8, 8) + ... self.fc2 = paddle.nn.Linear(8, 8) + ... + ... def forward(self, input): + ... return self.fc2(self.fc1(input)) + + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> layer = MLP() + >>> mp_config = { + ... 'fc1': dist.SequenceParallelDisable() + ... } """ - def __init__(self, need_transpose=True): + def __init__(self, need_transpose: bool = True) -> None: super().__init__() self.need_transpose = need_transpose diff --git a/test/auto_parallel/hybrid_strategy/parallel_api.py b/test/auto_parallel/hybrid_strategy/parallel_api.py index cdacf26bcd9387..76b22f449c7a4b 100644 --- a/test/auto_parallel/hybrid_strategy/parallel_api.py +++ b/test/auto_parallel/hybrid_strategy/parallel_api.py @@ -23,18 +23,9 @@ import paddle.distributed as dist from paddle import LazyGuard from paddle.distributed.auto_parallel.intermediate.parallelize import ( - parallelize, parallelize_model, parallelize_optimizer, ) -from paddle.distributed.auto_parallel.intermediate.tensor_parallel import ( - ColWiseParallel, - RowWiseParallel, - SequenceParallelBegin, - SequenceParallelDisable, - SequenceParallelEnable, - SequenceParallelEnd, -) from paddle.io import BatchSampler, DataLoader, Dataset @@ -224,65 +215,67 @@ def parallel_model(self, layer): if self.mp > 1: if not self.sequence_parallel: plan = { - "llama.embed_tokens": ColWiseParallel(gather_output=True), - "llama.position_embedding": ColWiseParallel(), - "llama.layers.*.self_attn.q_proj": ColWiseParallel( + "llama.embed_tokens": dist.ColWiseParallel( + gather_output=True + ), + "llama.position_embedding": dist.ColWiseParallel(), + "llama.layers.*.self_attn.q_proj": dist.ColWiseParallel( gather_output=True ), - "llama.layers.*.self_attn.k_proj": ColWiseParallel( + "llama.layers.*.self_attn.k_proj": dist.ColWiseParallel( gather_output=True ), - "llama.layers.*.self_attn.v_proj": ColWiseParallel( + "llama.layers.*.self_attn.v_proj": dist.ColWiseParallel( gather_output=True ), - "llama.layers.*.self_attn.o_proj": RowWiseParallel( + "llama.layers.*.self_attn.o_proj": dist.RowWiseParallel( is_input_parallel=False ), - "llama.layers.*.mlp.gate_proj": ColWiseParallel(), - "llama.layers.*.mlp.up_proj": ColWiseParallel(), - "llama.layers.*.mlp.down_proj": RowWiseParallel(), - "lm_head.weight": ColWiseParallel(), + "llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.up_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.down_proj": dist.RowWiseParallel(), + "lm_head.weight": dist.ColWiseParallel(), } else: if self.prepare_input_output: plan = { - "llama.embed_tokens": ColWiseParallel(), - "llama.position_embedding": ColWiseParallel(), - "llama.layers.*.self_attn.q_proj": ColWiseParallel(), - "llama.layers.*.self_attn.k_proj": ColWiseParallel(), - "llama.layers.*.self_attn.v_proj": ColWiseParallel(), - "llama.layers.*.self_attn.o_proj": RowWiseParallel(), - "llama.layers.*.mlp.gate_proj": ColWiseParallel(), - "llama.layers.*.mlp.up_proj": ColWiseParallel(), - "llama.layers.*.mlp.down_proj": RowWiseParallel(), - "lm_head.weight": ColWiseParallel(), - "llama.layers.*.input_layernorm": SequenceParallelEnable(), - "llama.layers.*.post_attention_layernorm": SequenceParallelEnable(), - "llama.norm": SequenceParallelEnable(), + "llama.embed_tokens": dist.ColWiseParallel(), + "llama.position_embedding": dist.ColWiseParallel(), + "llama.layers.*.self_attn.q_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.k_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.v_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(), + "llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.up_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.down_proj": dist.RowWiseParallel(), + "lm_head.weight": dist.ColWiseParallel(), + "llama.layers.*.input_layernorm": dist.SequenceParallelEnable(), + "llama.layers.*.post_attention_layernorm": dist.SequenceParallelEnable(), + "llama.norm": dist.SequenceParallelEnable(), } else: plan = { "llama.embed_tokens": [ - ColWiseParallel(), - SequenceParallelBegin(), + dist.ColWiseParallel(), + dist.SequenceParallelBegin(), ], "llama.position_embedding": [ - ColWiseParallel(), - SequenceParallelBegin(), + dist.ColWiseParallel(), + dist.SequenceParallelBegin(), ], - "llama.layers.*.self_attn.q_proj": ColWiseParallel(), - "llama.layers.*.self_attn.k_proj": ColWiseParallel(), - "llama.layers.*.self_attn.v_proj": ColWiseParallel(), - "llama.layers.*.self_attn.o_proj": RowWiseParallel(), - "llama.layers.*.self_attn": SequenceParallelDisable(), - "llama.layers.*.mlp.gate_proj": ColWiseParallel(), - "llama.layers.*.mlp.up_proj": ColWiseParallel(), - "llama.layers.*.mlp.down_proj": RowWiseParallel(), - "llama.layers.*.mlp": SequenceParallelDisable( + "llama.layers.*.self_attn.q_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.k_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.v_proj": dist.ColWiseParallel(), + "llama.layers.*.self_attn.o_proj": dist.RowWiseParallel(), + "llama.layers.*.self_attn": dist.SequenceParallelDisable(), + "llama.layers.*.mlp.gate_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.up_proj": dist.ColWiseParallel(), + "llama.layers.*.mlp.down_proj": dist.RowWiseParallel(), + "llama.layers.*.mlp": dist.SequenceParallelDisable( need_transpose=False ), - "lm_head.weight": ColWiseParallel(), - "lm_head": SequenceParallelEnd(), + "lm_head.weight": dist.ColWiseParallel(), + "lm_head": dist.SequenceParallelEnd(), } mp_config = {'parallelize_plan': plan} @@ -290,28 +283,28 @@ def parallel_model(self, layer): learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001 ) + config = { + 'dp_config': dp_config, + 'mp_config': mp_config, + 'pp_config': pp_config, + } + if self.one_api: optimizer = create_optimizer(layer, lr_scheduler) - model, optimizer = parallelize( + model, optimizer = dist.parallelize( layer, optimizer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, + config=config, ) else: layer = parallelize_model( layer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, + config=config, ) optimizer = create_optimizer(layer, lr_scheduler) optimizer = parallelize_optimizer( optimizer, - dp_config=dp_config, - mp_config=mp_config, - pp_config=pp_config, + config=config, ) self.check_mp(layer) return layer, optimizer, lr_scheduler From 50980c0e26e6ba994c07db0e7c43a3ba02f62d90 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Thu, 5 Dec 2024 10:09:58 +0800 Subject: [PATCH 170/288] [SOT][3.13] Enable SOT Python 3.13 unittest in CI (#69950) --- paddle/scripts/paddle_build.sh | 2 +- test/sot/skip_files_py313 | 4 ++++ 2 files changed, 5 insertions(+), 1 deletion(-) create mode 100644 test/sot/skip_files_py313 diff --git a/paddle/scripts/paddle_build.sh b/paddle/scripts/paddle_build.sh index 4da26620934d8e..eb0dc4fe780e7b 100644 --- a/paddle/scripts/paddle_build.sh +++ b/paddle/scripts/paddle_build.sh @@ -4755,7 +4755,7 @@ function main() { cicheck_sot) check_run_sot_ci export WITH_SHARED_PHI=ON - PYTHON_VERSIONS=(3.8 3.9 3.10 3.11 3.12) + PYTHON_VERSIONS=(3.13 3.8 3.9 3.10 3.11 3.12) for PY_VERSION in ${PYTHON_VERSIONS[@]}; do ln -sf $(which python${PY_VERSION}) /usr/local/bin/python ln -sf $(which pip${PY_VERSION}) /usr/local/bin/pip diff --git a/test/sot/skip_files_py313 b/test/sot/skip_files_py313 new file mode 100644 index 00000000000000..503edee8d074e8 --- /dev/null +++ b/test/sot/skip_files_py313 @@ -0,0 +1,4 @@ +./test_model_switch_training.py +./test_sot_cost_model.py +./test_sot_dynamic_shape.py +./test_sot_export.py From 2cf75398d219a2f7a62f6ccfec16295ab51cae7f Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Thu, 5 Dec 2024 10:24:03 +0800 Subject: [PATCH 171/288] [CINN] Force full_op to be recomputed (#69897) * Optimize recompute algorithm analyze runtime * rename function name * Force full_op to be recomputed * Update recompute.py --- paddle/fluid/pybind/pir.cc | 2 +- python/paddle/decomposition/recompute.py | 16 +++++++++++++--- 2 files changed, 14 insertions(+), 4 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 88118f00231fb4..162f4ede734284 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -687,7 +687,7 @@ void BindBlock(py::module *m) { .def("add_arg", &Block::AddArg) .def("add_kwarg", &Block::AddKwarg) .def("erase_kwarg", &Block::EraseKwarg) - .def("get_value_from_op_idxs", + .def("get_values_by_op_idx", [](Block &self, const py::list &op_idxs) -> py::list { py::list value_list; auto it = self.begin(); diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index f06122f40d206a..2caf85769f7d5a 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -132,6 +132,12 @@ "pd_op.sigmoid", ] +# define the ops that are tending to recompute.These ops are more likely to save memory and get fused. +TENDING_TO_RECOMPUTE_OPS: list[str] = [ + "pd_op.full_int_array", + "pd_op.full", +] + VIEW_OPS: list[str] = [] RANDOM_OPS: list[str] = ["pd_op.randint", "pd_op.uniform", "pd_op.dropout"] @@ -415,6 +421,7 @@ def auto_recompute( random_ops = RANDOM_OPS compute_intensive_ops = COMPUTE_INTENSIVE_OPS + tending_to_recompute_ops = TENDING_TO_RECOMPUTE_OPS unrecomputable_ops = random_ops + compute_intensive_ops @@ -467,6 +474,9 @@ def _ban_recomputation(value_node): if AGGRESSIVE_RECOMPUTATION: return value_node.get_defining_op().name() in unrecomputable_ops else: + if value_node.get_defining_op().name() in tending_to_recompute_ops: + return False + if value_node.get_defining_op().name() not in recomputable_ops: return True @@ -843,7 +853,7 @@ def classify_value_node(program, grad_outputs, fwd_op_end_idx): required_fw_op_idxs = list(range(0, fwd_op_end_idx + 1)) required_fw_value_nodes = backward_utils.ValueSet( - program.global_block().get_value_from_op_idxs(required_fw_op_idxs) + program.global_block().get_values_by_op_idx(required_fw_op_idxs) ) required_bw_ops = set() @@ -856,7 +866,7 @@ def classify_value_node(program, grad_outputs, fwd_op_end_idx): if op in required_bw_ops: required_bw_op_idxs.append(idx) required_bw_value_nodes = backward_utils.ValueSet( - program.global_block().get_value_from_op_idxs(required_bw_op_idxs) + program.global_block().get_values_by_op_idx(required_bw_op_idxs) ) unclaimed_ops = { @@ -870,7 +880,7 @@ def classify_value_node(program, grad_outputs, fwd_op_end_idx): if op in unclaimed_ops: unclaimed_op_idxs.append(idx) unclaimed_value_nodes = backward_utils.ValueSet( - program.global_block().get_value_from_op_idxs(unclaimed_op_idxs) + program.global_block().get_values_by_op_idx(unclaimed_op_idxs) ) return ( From ecec6f1a53662ac6fd6ca788d2766bd18c63e378 Mon Sep 17 00:00:00 2001 From: Zhan Rongrui <46243324+zrr1999@users.noreply.github.com> Date: Thu, 5 Dec 2024 10:38:42 +0800 Subject: [PATCH 172/288] [SOT][Faster Guard] add `ENV_SOT_ENABLE_GUARD_TREE` (#69836) --- python/paddle/jit/sot/utils/__init__.py | 2 ++ python/paddle/jit/sot/utils/envs.py | 10 ++++++++++ 2 files changed, 12 insertions(+) diff --git a/python/paddle/jit/sot/utils/__init__.py b/python/paddle/jit/sot/utils/__init__.py index ee30343229bf43..c5b4f99d58ee26 100644 --- a/python/paddle/jit/sot/utils/__init__.py +++ b/python/paddle/jit/sot/utils/__init__.py @@ -19,6 +19,7 @@ ENV_MIN_GRAPH_SIZE, ENV_SOT_ALLOW_DYNAMIC_SHAPE, ENV_SOT_ENABLE_FASTER_GUARD, + ENV_SOT_ENABLE_GUARD_TREE, ENV_SOT_EXPORT, ENV_SOT_LOG_LEVEL, ENV_SOT_WITH_CONTROL_FLOW, @@ -27,6 +28,7 @@ cost_model_guard, export_guard, faster_guard_guard, + guard_tree_guard, min_graph_size_guard, sot_step_profiler_guard, strict_mode_guard, diff --git a/python/paddle/jit/sot/utils/envs.py b/python/paddle/jit/sot/utils/envs.py index 7068dc067efda3..5822c05bc789ca 100644 --- a/python/paddle/jit/sot/utils/envs.py +++ b/python/paddle/jit/sot/utils/envs.py @@ -42,6 +42,10 @@ "SOT_ENABLE_FASTER_GUARD", False, ) +ENV_SOT_ENABLE_GUARD_TREE = BooleanEnvironmentVariable( + "SOT_ENABLE_GUARD_TREE", + False, +) ENV_SOT_EVENT_LEVEL = IntegerEnvironmentVariable("SOT_EVENT_LEVEL", 0) ENV_ENABLE_SOT_STEP_PROFILER = BooleanEnvironmentVariable( "ENABLE_SOT_STEP_PROFILER", False @@ -93,6 +97,12 @@ def faster_guard_guard(value: bool): yield +@contextmanager +def guard_tree_guard(value: bool): + with EnvironmentVariableGuard(ENV_SOT_ENABLE_GUARD_TREE, value): + yield + + @contextmanager def sot_step_profiler_guard(value: bool): with EnvironmentVariableGuard(ENV_ENABLE_SOT_STEP_PROFILER, value): From 39cab51ba6e2a36d38ee681fa1b4913ab5159f48 Mon Sep 17 00:00:00 2001 From: risemeup1 <62429225+risemeup1@users.noreply.github.com> Date: Thu, 5 Dec 2024 10:52:42 +0800 Subject: [PATCH 173/288] [PIR]get value from op id (#69909) * get value from op id * fix * fix * fix * fix * fix * fix * fix * fix --- .../pir/transforms/pd_op_to_kernel_pass.cc | 4 ++- paddle/fluid/pybind/pir.cc | 34 ++++++++++++++++++ paddle/pir/include/core/block.h | 6 ++++ .../src/dialect/shape/utils/shape_analysis.cc | 1 - test/ir/pir/test_build_op.py | 35 +++++++++++++++++++ 5 files changed, 78 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc index 88aa69f1d32312..5cb241ceb82d6f 100644 --- a/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc +++ b/paddle/fluid/pir/transforms/pd_op_to_kernel_pass.cc @@ -3520,7 +3520,9 @@ void ProcessBlock( std::unique_ptr PdOpLowerToKernelPass(pir::Program* prog, phi::Place place) { auto program = std::make_unique(pir::IrContext::Instance()); - + if (FLAGS_print_ir) { + std::cout << "IR before lowering = " << *prog << std::endl; + } auto block = prog->block(); pir::IrContext* ctx = pir::IrContext::Instance(); diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 162f4ede734284..93cb26b0419848 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -513,6 +513,40 @@ void BindProgram(py::module *m) { return op_list; }, return_value_policy::reference) + .def( + "get_value_by_op_id", + [](Program &self, py::object op_ids) { + std::vector op_ids_list; + if (py::isinstance(op_ids)) { + op_ids_list.push_back(op_ids.cast()); + } else if (py::isinstance(op_ids)) { + for (auto item : op_ids) { + op_ids_list.push_back(item.cast()); + } + } else { + PADDLE_THROW( + "Invalid op_ids format. Please provide either a single " + "integer or a list of integers."); + } + + std::list all_ops = self.block()->get_recursive_ops(); + std::vector value_list; + + for (auto op : all_ops) { + if (std::find(op_ids_list.begin(), op_ids_list.end(), op->id()) != + op_ids_list.end()) { + for (auto value : op->results()) { + value_list.push_back(value); + } + } + } + + if (value_list.empty()) { + PADDLE_THROW( + "Can't find the corresponding opresult from the op ids"); + } + return value_list; + }) .def("get_output_value_by_name", [](Program &self, const std::string &name) { return name_analysis::GetOutputValueByName(self, name); diff --git a/paddle/pir/include/core/block.h b/paddle/pir/include/core/block.h index c4479686c69e78..c5e55151c152c8 100644 --- a/paddle/pir/include/core/block.h +++ b/paddle/pir/include/core/block.h @@ -176,6 +176,12 @@ class IR_API Block { return num; } + OpListType get_recursive_ops() { + OpListType ops; + Walk([&ops](Operation *op) { ops.push_back(op); }); + return ops; + } + private: Block(Block &) = delete; Block &operator=(const Block &) = delete; diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 694a10eb8d81c4..92568eaba9a990 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -804,7 +804,6 @@ pir::PrintHooks ShapeConstraintIRAnalysis::PrintHook() { } } printer.os << " }"; - printer.os << "\t(op_" << op.id() << ")"; }; return print_hook; } diff --git a/test/ir/pir/test_build_op.py b/test/ir/pir/test_build_op.py index e39b193bb091b2..fa190ef92a6fc0 100644 --- a/test/ir/pir/test_build_op.py +++ b/test/ir/pir/test_build_op.py @@ -175,5 +175,40 @@ def test_build_tensorrt_engine_op(self): ) +class TestGetValueByOpId(unittest.TestCase): + def test_get_value_by_op_id(self): + def true_func(): + return paddle.tensor.fill_constant( + shape=[2, 3], dtype='int32', value=2 + ) + + def false_func(): + return paddle.tensor.fill_constant( + shape=[3, 2], dtype='int32', value=-1 + ) + + main_program = paddle.static.Program() + startup_program = paddle.static.Program() + with paddle.static.program_guard(main_program, startup_program): + x = paddle.tensor.fill_constant( + shape=[1], dtype='float32', value=0.1 + ) + y = paddle.tensor.fill_constant( + shape=[1], dtype='float32', value=0.23 + ) + pred = paddle.less_than(y, x) + out = paddle.static.nn.cond(pred, true_func, false_func) + value1 = main_program.get_value_by_op_id(65) + self.assertEqual( + out.get_defining_op().id(), + value1[0].get_defining_op().id(), + ) + value2 = main_program.get_value_by_op_id([58, 65]) + self.assertEqual( + 58, + value2[0].get_defining_op().id(), + ) + + if __name__ == "__main__": unittest.main() From 973dadd7a80abe3b13c07857a875a631197c0645 Mon Sep 17 00:00:00 2001 From: Li Dinghao <103986681+SCUcookie@users.noreply.github.com> Date: Thu, 5 Dec 2024 10:55:53 +0800 Subject: [PATCH 174/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20No.11=E3=80=91=E6=96=B0=E5=A2=9E=20`Tensor.=5F=5Fdlpac?= =?UTF-8?q?k=5F=5F()`=20(#69781)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * resubmit * modify * add test file * finish * add test * modify * precommit * modify * modify * modify --- .../base/dygraph/tensor_patch_methods.py | 35 +- .../test_tensor_attr_consistency.py | 1 + test/legacy_test/test_dlpack_basic.py | 299 ++++++++++++++++++ 3 files changed, 334 insertions(+), 1 deletion(-) create mode 100644 test/legacy_test/test_dlpack_basic.py diff --git a/python/paddle/base/dygraph/tensor_patch_methods.py b/python/paddle/base/dygraph/tensor_patch_methods.py index ebb123df3cc593..15de98a154e072 100644 --- a/python/paddle/base/dygraph/tensor_patch_methods.py +++ b/python/paddle/base/dygraph/tensor_patch_methods.py @@ -129,7 +129,6 @@ def _to_static_var(self, to_parameter=False, **kwargs): 'strides', 'offset', '__cuda_array_interface__', - '__dlpack_device__', ] param_keys = ['stop_gradient', 'trainable'] if isinstance(self, EagerParamBase): @@ -1366,6 +1365,39 @@ def __cuda_array_interface__(self): "version": 2, } + def __dlpack__(self, stream=None): + """ + Creates a DLPack capsule of the current tensor to be exported to other libraries. + Args: + stream (int | None): An optional Python integer representing a pointer + to a CUDA stream. Synchronizes the tensor with this + stream before exporting. + If None or -1, no synchronization is performed. + If 0, the default stream is used. + """ + + if self.is_sparse(): + raise AttributeError( + "Can't get __dlpack__ from a Tensor that requires gradients, " + "use tensor.detach() if gradients are not required." + ) + + if not self.stop_gradient: + raise RuntimeError( + "Can't get __dlpack__ from Tensor that requires gradients. " + "If gradients aren't required, use tensor.detach() to get a tensor without gradient." + ) + + if stream is not None: + if self.place.is_gpu_place(): + current_stream = paddle.device.cuda.current_stream() + if stream != current_stream: + event = paddle.device.cuda.Event() + event.record(current_stream) + current_stream.synchronize() + + return paddle.to_dlpack(self) + if not hasattr(core, "eager"): return @@ -1410,6 +1442,7 @@ def __cuda_array_interface__(self): ("_use_gpudnn", _use_gpudnn), ("_md5sum", _md5sum), ("__cuda_array_interface__", __cuda_array_interface__), + ("__dlpack__", __dlpack__), ("__dlpack_device__", __dlpack_device__), ): setattr(core.eager.Tensor, method_name, method) diff --git a/test/dygraph_to_static/test_tensor_attr_consistency.py b/test/dygraph_to_static/test_tensor_attr_consistency.py index 48518f9927cc08..b59456c0fd174f 100644 --- a/test/dygraph_to_static/test_tensor_attr_consistency.py +++ b/test/dygraph_to_static/test_tensor_attr_consistency.py @@ -78,6 +78,7 @@ 'value', 'zero_', "__cuda_array_interface__", + '__dlpack__', "__dlpack_device__", ] ) diff --git a/test/legacy_test/test_dlpack_basic.py b/test/legacy_test/test_dlpack_basic.py new file mode 100644 index 00000000000000..6c50fde94fdb1b --- /dev/null +++ b/test/legacy_test/test_dlpack_basic.py @@ -0,0 +1,299 @@ +# Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from utils import dygraph_guard + +import paddle +from paddle import base + + +@unittest.skipIf( + paddle.core.is_compiled_with_xpu(), + "xpu does not support dlpack", +) +class TestDLPack(unittest.TestCase): + def test_dlpack_dygraph(self): + with dygraph_guard(): + tensor = paddle.to_tensor(np.array([1, 2, 3, 4]).astype("int")) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(tensor) + out_from_dlpack_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + dlpack_v2 = tensor.__dlpack__() + out_from_dlpack_v2 = paddle.from_dlpack(dlpack_v2) + self.assertTrue( + isinstance(out_from_dlpack_v1, paddle.base.core.eager.Tensor) + ) + self.assertTrue( + isinstance(out_from_dlpack_v2, paddle.base.core.eager.Tensor) + ) + self.assertEqual(str(tensor.place), str(out_from_dlpack_v1.place)) + self.assertEqual(str(tensor.place), str(out_from_dlpack_v2.place)) + np.testing.assert_array_equal( + out_from_dlpack_v1.numpy(), np.array([1, 2, 3, 4]).astype("int") + ) + np.testing.assert_array_equal( + out_from_dlpack_v2.numpy(), np.array([1, 2, 3, 4]).astype("int") + ) + + def test_dlpack_tensor_larger_than_2dim(self): + with dygraph_guard(): + numpy_data = np.random.randn(4, 5, 6) + t = paddle.to_tensor(numpy_data) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(t) + dlpack_v2 = t.__dlpack__() + out_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + out_v2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(str(t.place), str(out_v1.place)) + self.assertEqual(str(t.place), str(out_v2.place)) + np.testing.assert_allclose(numpy_data, out_v1.numpy(), rtol=1e-05) + np.testing.assert_allclose(numpy_data, out_v2.numpy(), rtol=1e-05) + + def test_dlpack_dtype_and_place_consistency(self): + with dygraph_guard(): + dtypes = [ + "float16", + "float32", + "float64", + "int8", + "int16", + "int32", + "int64", + "uint8", + "bool", + ] + places = [paddle.CPUPlace()] + if paddle.device.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + dtypes.append("bfloat16") + + data = np.ones((2, 3, 4)) + for place in places: + for dtype in dtypes: + x = paddle.to_tensor(data, dtype=dtype, place=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + o_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + dlpack_v2 = x.__dlpack__() + o_v2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(x.dtype, o_v1.dtype) + self.assertEqual(x.dtype, o_v2.dtype) + np.testing.assert_allclose( + x.numpy(), o_v1.numpy(), rtol=1e-05 + ) + np.testing.assert_allclose( + x.numpy(), o_v2.numpy(), rtol=1e-05 + ) + self.assertEqual(str(x.place), str(o_v1.place)) + self.assertEqual(str(x.place), str(o_v2.place)) + + complex_dtypes = ["complex64", "complex128"] + for place in places: + for dtype in complex_dtypes: + x = paddle.to_tensor( + [[1 + 6j, 2 + 5j, 3 + 4j], [4 + 3j, 5 + 2j, 6 + 1j]], + dtype=dtype, + place=place, + ) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + o_v1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + dlpack_v2 = x.__dlpack__() + o_v2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(x.dtype, o_v1.dtype) + self.assertEqual(x.dtype, o_v2.dtype) + np.testing.assert_allclose( + x.numpy(), o_v1.numpy(), rtol=1e-05 + ) + np.testing.assert_allclose( + x.numpy(), o_v2.numpy(), rtol=1e-05 + ) + self.assertEqual(str(x.place), str(o_v1.place)) + self.assertEqual(str(x.place), str(o_v2.place)) + + def test_dlpack_deletion(self): + # See Paddle issue 47171 + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + a = paddle.rand(shape=[3, 5], dtype="float32").to( + device=place + ) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(a) + dlpack_v2 = a.__dlpack__() + b1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + b2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(str(a.place), str(b1.place)) + self.assertEqual(str(a.place), str(b2.place)) + + def test_to_dlpack_for_loop(self): + # See Paddle issue 50120 + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.rand([3, 5]).to(device=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + dlpack_v2 = x.__dlpack__() + + def test_to_dlpack_modification(self): + # See Paddle issue 50120 + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.rand([3, 5]).to(device=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + dlpack_v2 = x.__dlpack__() + y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + y2 = paddle.from_dlpack(dlpack_v2) + y1[1:2, 2:5] = 2.0 + y2[1:2, 2:5] = 2.0 + np.testing.assert_allclose(x.numpy(), y1.numpy()) + np.testing.assert_allclose(x.numpy(), y2.numpy()) + self.assertEqual(str(x.place), str(y1.place)) + self.assertEqual(str(x.place), str(y2.place)) + + def test_to_dlpack_data_ptr_consistency(self): + # See Paddle issue 50120 + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.rand([3, 5]).to(device=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + dlpack_v2 = x.__dlpack__() + y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + y2 = paddle.from_dlpack(dlpack_v2) + + self.assertEqual(x.data_ptr(), y1.data_ptr()) + self.assertEqual(x.data_ptr(), y2.data_ptr()) + self.assertEqual(str(x.place), str(y1.place)) + self.assertEqual(str(x.place), str(y2.place)) + + def test_to_dlpack_strides_consistency(self): + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.rand([10, 10]).to(device=place) + x_strided = x[::2, ::2] + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x_strided) + dlpack_v2 = x_strided.__dlpack__() + y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + y2 = paddle.from_dlpack(dlpack_v2) + + self.assertEqual(x_strided.strides, y1.strides) + self.assertEqual(x_strided.strides, y2.strides) + self.assertEqual(str(x_strided.place), str(y1.place)) + self.assertEqual(str(x_strided.place), str(y2.place)) + np.testing.assert_equal(x_strided.numpy(), y1.numpy()) + np.testing.assert_equal(x_strided.numpy(), y2.numpy()) + + def test_to_dlpack_from_zero_dim(self): + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.to_tensor(1.0, place=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + dlpack_v2 = x.__dlpack__() + y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + y2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(x.data_ptr(), y1.data_ptr()) + self.assertEqual(x.data_ptr(), y2.data_ptr()) + self.assertEqual(str(x.place), str(y1.place)) + self.assertEqual(str(x.place), str(y2.place)) + self.assertEqual(y1.shape, []) + self.assertEqual(y2.shape, []) + self.assertEqual(y1.numel().item(), 1) + self.assertEqual(y2.numel().item(), 1) + np.testing.assert_array_equal(x.numpy(), y1.numpy()) + np.testing.assert_array_equal(x.numpy(), y2.numpy()) + + def test_to_dlpack_from_zero_size(self): + with dygraph_guard(): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + for place in places: + for _ in range(4): + x = paddle.zeros([0, 10]).to(device=place) + dlpack_v1 = paddle.utils.dlpack.to_dlpack(x) + dlpack_v2 = x.__dlpack__() + y1 = paddle.utils.dlpack.from_dlpack(dlpack_v1) + y2 = paddle.from_dlpack(dlpack_v2) + self.assertEqual(x.data_ptr(), y1.data_ptr()) + self.assertEqual(x.data_ptr(), y2.data_ptr()) + self.assertEqual(str(x.place), str(y1.place)) + self.assertEqual(str(x.place), str(y2.place)) + self.assertEqual(y1.shape, [0, 10]) + self.assertEqual(y2.shape, [0, 10]) + self.assertEqual(y1.numel().item(), 0) + self.assertEqual(y2.numel().item(), 0) + np.testing.assert_array_equal(x.numpy(), y1.numpy()) + np.testing.assert_array_equal(x.numpy(), y2.numpy()) + + def test_dlpack_with_custom_stream(self): + if not paddle.is_compiled_with_cuda(): + self.skipTest("Test requires CUDA support.") + with dygraph_guard(): + paddle.set_device('gpu:0') + s1 = paddle.device.Stream() + s2 = paddle.device.Stream() + e = paddle.device.Event() + s2.wait_event(e) + x = paddle.to_tensor([1, 2, 3], dtype='float32') + s1.synchronize() + dlpack_capsule = x.__dlpack__(s1) + y = paddle.from_dlpack(dlpack_capsule) + np.testing.assert_array_equal(x.numpy(), y.numpy()) + self.assertTrue(s1.query(), "Stream s1 did not complete all tasks.") + self.assertTrue(s2.query(), "Stream s2 did not complete all tasks.") + + +@unittest.skipIf( + paddle.core.is_compiled_with_xpu(), + "xpu does not support dlpack", +) +class TestRaiseError(unittest.TestCase): + def test_dlpack_invalid_sparse(self): + sparse_tensor = paddle.sparse.sparse_coo_tensor( + indices=[[0]], values=[1], shape=[3] + ) + with self.assertRaises(AttributeError): + sparse_tensor.__dlpack__() + + def test_dlpack_requires_grad(self): + tensor_with_grad = paddle.to_tensor( + [1.0, 2.0, 3.0], stop_gradient=False + ) + with self.assertRaises(RuntimeError): + tensor_with_grad.__dlpack__() + + +if __name__ == "__main__": + unittest.main() From d3668a7259c3bdb8a23e400d541618eb239cf19e Mon Sep 17 00:00:00 2001 From: Terry <38135104+TR666@users.noreply.github.com> Date: Thu, 5 Dec 2024 11:14:36 +0800 Subject: [PATCH 175/288] [XPU] support xblas::fc_fusion for fc_xpu(bfp16) (#69942) --- .../phi/kernels/fusion/xpu/fc_xpu_kernel.cc | 134 ++++++++++++++++++ 1 file changed, 134 insertions(+) diff --git a/paddle/phi/kernels/fusion/xpu/fc_xpu_kernel.cc b/paddle/phi/kernels/fusion/xpu/fc_xpu_kernel.cc index e9571ff32882d6..b2047d6ec99c7e 100644 --- a/paddle/phi/kernels/fusion/xpu/fc_xpu_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/fc_xpu_kernel.cc @@ -16,9 +16,17 @@ #include "paddle/phi/backends/xpu/enforce_xpu.h" #include "paddle/phi/core/kernel_registry.h" +#ifdef PADDLE_WITH_XPU_XRE5 +#include "xblas/cublasLt.h" +namespace xblas = baidu::xpu::xblas; +#endif + namespace phi { namespace fusion { +using XPUTypeFP16 = typename XPUTypeTrait::Type; +using XPUTypeBF16 = typename XPUTypeTrait::Type; + template ::value && + std::is_same::value && + std::is_same::value) { + // use xte to speedup bfloat16 calc + // whether to enable this feature requires a trade-off between performance + // precision + if (std::getenv("XPU_PADDLE_FC_BFLOAT16_XTE") != nullptr) { + xpu::ctx_guard RAII_GUARD(ctx.x_context()); + const int MAXPTR_N = ctx.x_context()->max_ptr_size(); + int x_len = m * k; + XPUTypeFP16* x_data_fp16 = nullptr; + x_data_fp16 = RAII_GUARD.alloc_l3_or_gm(x_len); + PADDLE_ENFORCE_XDNN_NOT_NULL(x_data_fp16); + int w_len = k * n; + XPUTypeFP16* w_data_fp16 = nullptr; + w_data_fp16 = RAII_GUARD.alloc_l3_or_gm(w_len); + PADDLE_ENFORCE_XDNN_NOT_NULL(w_data_fp16); + + float* xte_scale_x = nullptr; + float* xte_scale_w = nullptr; + xte_scale_x = RAII_GUARD.alloc_l3_or_gm(1); + PADDLE_ENFORCE_XDNN_NOT_NULL(xte_scale_x); + xte_scale_w = RAII_GUARD.alloc_l3_or_gm(1); + PADDLE_ENFORCE_XDNN_NOT_NULL(xte_scale_w); + + float* xte_x_maxptr = nullptr; + float* xte_w_maxptr = nullptr; + if (x_max_data == nullptr) { + xte_x_maxptr = RAII_GUARD.alloc_l3_or_gm(MAXPTR_N); + PADDLE_ENFORCE_XDNN_NOT_NULL(xte_x_maxptr); + int r = xpu::findmax(ctx.x_context(), x_data, xte_x_maxptr, x_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_findmax"); + r = xpu::cast_te(ctx.x_context(), + x_data, + xte_x_maxptr, + x_data_fp16, + xte_scale_x, + x_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_cast_te"); + } else { + int r = xpu::cast_te(ctx.x_context(), + x_data, + x_max_data, + x_data_fp16, + xte_scale_x, + x_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_cast_te"); + } + if (w_max_data == nullptr) { + xte_w_maxptr = RAII_GUARD.alloc_l3_or_gm(MAXPTR_N); + PADDLE_ENFORCE_XDNN_NOT_NULL(xte_w_maxptr); + int r = xpu::findmax(ctx.x_context(), w_data, xte_w_maxptr, w_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_findmax"); + r = xpu::cast_te(ctx.x_context(), + w_data, + xte_w_maxptr, + w_data_fp16, + xte_scale_w, + w_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_cast_te"); + } else { + int r = xpu::cast_te(ctx.x_context(), + w_data, + w_max_data, + w_data_fp16, + xte_scale_w, + w_len); + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xpu_cast_te"); + } + int r = + xblas::fc_fusion( + ctx.x_context(), + x_data_fp16, + w_data_fp16, + out_data, + m, + n, + k, + transpose_x, + true, + x_max_data ? x_max_data : xte_x_maxptr, + w_max_data ? w_max_data : xte_w_maxptr, + out_max_data, + transpose_x ? m : k, + k, + n, + alpha, + beta, + bias_data, + act, + xte_scale_x, + xte_scale_w); + + PADDLE_ENFORCE_XDNN_SUCCESS(r, "xblas_fc_fusion"); + } + } + if (std::getenv("XPU_PADDLE_FC_BFLOAT16_XTE") == nullptr) { + int r = xpu:: + fc_fusion( // TX/TW/TY/TGEMM + ctx.x_context(), // ctx + x_data, // x + w_data, // w + out_data, // y + m, // m + n, // n + k, // k + transpose_x, // x_trans + true, // w_trans + x_max_data, // x_maxptr + w_max_data, // w_maxptr + out_max_data, // y_maxptr + transpose_x ? m : k, // ldx + k, // ldw + n, // ldy + alpha, // alpha + beta, // beta + bias_data, // bias + act, // act + scale_max_data); // scale + + PADDLE_ENFORCE_XDNN_SUCCESS(r, "fc_xpu"); + } +#else int r = xpu::fc_fusion( // TX/TW/TY/TGEMM ctx.x_context(), // ctx @@ -101,6 +234,7 @@ void FcXPUKernelImpl(const Context& ctx, scale_max_data); // scale PADDLE_ENFORCE_XDNN_SUCCESS(r, "fc_xpu"); +#endif } #define FC_XPU_KERNEL_IMPL(x_dtype_, w_dtype_, out_dtype_, gemm_dtype_) \ From 0d84381d3cd7d1340c068dae4e3fb713b53219a3 Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Thu, 5 Dec 2024 11:36:26 +0800 Subject: [PATCH 176/288] [Operation] Refine op print funtion (#69916) * refind op print funtion * refine op print funtion * fix bug --- .../hlir/dialect/operator/ir/manual_op.cc | 8 ++++++-- .../dialect/operator/ir/control_flow_op.cc | 8 +++----- .../dialect/operator/ir/manual_pylayer_op.cc | 8 +++----- paddle/pir/include/core/ir_printer.h | 4 ++++ paddle/pir/src/core/builtin_op.cc | 4 +++- paddle/pir/src/core/ir_printer.cc | 20 +++++++++++++------ 6 files changed, 33 insertions(+), 19 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc b/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc index fc02cf954aa5ca..014c9d1fd2f8ed 100644 --- a/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc +++ b/paddle/cinn/hlir/dialect/operator/ir/manual_op.cc @@ -104,7 +104,9 @@ void GroupOp::Print(pir::IrPrinter& printer) { auto& os = printer.os; auto op = operation(); printer.PrintOpResult(*op); - os << " = \"" << name() << "\" [id:" << op->id() << "]"; + os << " = "; + printer.PrintOpName(*op); + printer.PrintOpId(*op); printer.PrintOpOperands(*op); os << " -> "; printer.PrintOpReturnType(*op); @@ -188,7 +190,9 @@ void FusionOp::Print(pir::IrPrinter& printer) { auto& os = printer.os; auto op = operation(); printer.PrintOpResult(*op); - os << " = \"" << name() << "\" [id:" << op->id() << "]"; + os << " = "; + printer.PrintOpName(*op); + printer.PrintOpId(*op); printer.PrintOpOperands(*op); os << " -> "; printer.PrintOpReturnType(*op); diff --git a/paddle/fluid/pir/dialect/operator/ir/control_flow_op.cc b/paddle/fluid/pir/dialect/operator/ir/control_flow_op.cc index de216d5f9c57a8..4cc24e74df34d3 100644 --- a/paddle/fluid/pir/dialect/operator/ir/control_flow_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/control_flow_op.cc @@ -188,11 +188,9 @@ void IfOp::Print(pir::IrPrinter &printer) { auto &os = printer.os; auto op = operation(); printer.PrintOpResult(*op); - os << " = \"" << name() << "\""; - - if (VLOG_IS_ON(1)) { - os << " [id:" << op->id() << "]"; - } + os << " = "; + printer.PrintOpName(*op); + printer.PrintOpId(*op); printer.PrintOpOperands(*op); printer.PrintAttributeMap(*op); diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc index 3a85b535a4e0e1..aa4b5baca0ec5d 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc @@ -120,11 +120,9 @@ void PyLayerOp::Print(pir::IrPrinter &printer) { auto &os = printer.os; auto op = operation(); printer.PrintOpResult(*op); - os << " = pd_op.pylayer"; - - if (VLOG_IS_ON(1)) { - os << " [id:" << op->id() << "]"; - } + os << " = "; + printer.PrintOpName(*op); + printer.PrintOpId(*op); printer.PrintOpOperands(*op); printer.PrintAttributeMap(*op); diff --git a/paddle/pir/include/core/ir_printer.h b/paddle/pir/include/core/ir_printer.h index 4bb08c5d25df50..c27af48b22c289 100644 --- a/paddle/pir/include/core/ir_printer.h +++ b/paddle/pir/include/core/ir_printer.h @@ -64,6 +64,10 @@ class IR_API IrPrinter : public BasicIrPrinter { void PrintAttributeMap(const Operation& op); + void PrintOpName(const Operation& op); + + void PrintOpId(const Operation& op); + void PrintOpOperands(const Operation& op); void PrintOperandsType(const Operation& op); diff --git a/paddle/pir/src/core/builtin_op.cc b/paddle/pir/src/core/builtin_op.cc index 1666d7da479be9..2e4ca74f4dd49e 100644 --- a/paddle/pir/src/core/builtin_op.cc +++ b/paddle/pir/src/core/builtin_op.cc @@ -204,7 +204,9 @@ void GroupOp::Print(IrPrinter &printer) { auto &os = printer.os; auto op = operation(); printer.PrintOpResult(*op); - os << " = \"" << name() << "\" [id:" << op->id() << "]"; + os << " = "; + printer.PrintOpName(*op); + printer.PrintOpId(*op); printer.PrintOpOperands(*op); os << " -> "; printer.PrintOpReturnType(*op); diff --git a/paddle/pir/src/core/ir_printer.cc b/paddle/pir/src/core/ir_printer.cc index ea4bb0e28c5c1f..d7c04faa8575af 100644 --- a/paddle/pir/src/core/ir_printer.cc +++ b/paddle/pir/src/core/ir_printer.cc @@ -29,6 +29,8 @@ #include "paddle/pir/include/core/utils.h" #include "paddle/pir/include/core/value.h" +COMMON_DECLARE_bool(print_ir); + namespace pir { namespace { @@ -186,13 +188,10 @@ void IrPrinter::PrintOperation(const Operation& op) { void IrPrinter::PrintOperationWithNoRegion(const Operation& op) { // TODO(lyk): add API to get opresults directly PrintOpResult(op); - os << " ="; + os << " = "; + PrintOpName(op); - os << " \"" << op.name() << "\""; - - if (VLOG_IS_ON(1)) { - os << " [id:" << op.id() << "]"; - } + PrintOpId(op); // TODO(lyk): add API to get operands directly PrintOpOperands(op); @@ -323,6 +322,15 @@ void IrPrinter::PrintAttributeMap(const Operation& op) { os << "}"; } +void IrPrinter::PrintOpName(const Operation& op) { + os << "\"" << op.name() << "\""; +} +void IrPrinter::PrintOpId(const Operation& op) { + if (VLOG_IS_ON(1) || FLAGS_print_ir) { + os << " [id:" << op.id() << "]"; + } +} + void IrPrinter::PrintOpOperands(const Operation& op) { os << " ("; auto num_op_operands = op.num_operands(); From 9a5ceb5b7a864ec6a5bbccdac417120a04eb73a7 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Thu, 5 Dec 2024 11:55:25 +0800 Subject: [PATCH 177/288] [Kernel] Optimize cudaMemcpy with CudaIntArray (#69930) * optimize cudaMemcpy with CudaIntArray * use pass by value instead of by pointer * update for other framework * add matrix_transpose in __all__ * refine error message --- paddle/phi/kernels/funcs/scatter.cu.h | 9 +- .../impl/elementwise_grad_kernel_impl.h | 156 ++++++++++++------ python/paddle/tensor/__init__.py | 1 + 3 files changed, 107 insertions(+), 59 deletions(-) diff --git a/paddle/phi/kernels/funcs/scatter.cu.h b/paddle/phi/kernels/funcs/scatter.cu.h index d159c8dbf8558c..eb6a112eba6e57 100644 --- a/paddle/phi/kernels/funcs/scatter.cu.h +++ b/paddle/phi/kernels/funcs/scatter.cu.h @@ -109,10 +109,11 @@ __global__ void ScatterNdCUDAKernel(const T* update, "The index is out of bounds, " "please check whether the dimensions of index and " "input meet the requirements. It should " - "be less than [%d] and greater or equal to [%d], but received [%d]", - output_dims[j], - -output_dims[j], - index_value); + "be less than [%ld] and greater or equal to [%ld], but received " + "[%ld]", + static_cast(output_dims[j]), + -static_cast(output_dims[j]), + static_cast(index_value)); if (index_value < 0) { index_value += output_dims[j]; } diff --git a/paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h b/paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h index 900d9db979414c..62c398758c5f54 100644 --- a/paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h +++ b/paddle/phi/kernels/impl/elementwise_grad_kernel_impl.h @@ -237,7 +237,7 @@ struct DivDoubleDDOut { template struct DivDoubleDDOut_Only_DDY { - HOSTDEVICE T operator()(const T& ddx, + HOSTDEVICE T operator()(const T& ddx UNUSED, const T& ddy, const T& y, const T& out) const { @@ -297,6 +297,84 @@ void ComputeDDoutWithBroadcast(const CPUContext& dev_ctx UNUSED, #if defined(__NVCC__) || defined(__HIPCC__) +/* +Since __global__ does not allow std::vector as a type parameter, +a custom CudaIntArray is used to pass an array containing a small number(<=8) of +integers, e.g. pass an shape array(rank<=8) to a kernel function. +*/ +#define MAX_SIZE 8 +#define STR(x) #x +#define XSTR(x) STR(x) + +struct CudaIntArray { + int a0, a1, a2, a3, a4, a5, a6, a7; + + CudaIntArray(const int& a0_, + const int& a1_, + const int& a2_, + const int& a3_, + const int& a4_, + const int& a5_, + const int& a6_, + const int& a7_) + : a0(a0_), + a1(a1_), + a2(a2_), + a3(a3_), + a4(a4_), + a5(a5_), + a6(a6_), + a7(a7_) {} + + __device__ __host__ int operator[](const int64_t& idx) const { +#if defined(__CUDA_ARCH__) || defined(__HIPCC__) + assert(0 <= idx && idx < MAX_SIZE); +#endif + + switch (idx) { + case 0: + return a0; + case 1: + return a1; + case 2: + return a2; + case 3: + return a3; + case 4: + return a4; + case 5: + return a5; + case 6: + return a6; + case 7: + return a7; + default: + return 0; + } + } +}; + +CudaIntArray initCudaIntArray(const int* vec, const int& size) { + PADDLE_ENFORCE_LE( + size, + MAX_SIZE, + common::errors::OutOfRange( + "Given size to init CudaIntArray must be less than" XSTR(MAX_SIZE))); + PADDLE_ENFORCE_GT( + size, + 0, + common::errors::OutOfRange( + "Given size to init CudaIntArray must be greater than 0")); + return CudaIntArray(size > 0 ? vec[0] : 0, + size > 1 ? vec[1] : 0, + size > 2 ? vec[2] : 0, + size > 3 ? vec[3] : 0, + size > 4 ? vec[4] : 0, + size > 5 ? vec[5] : 0, + size > 6 ? vec[6] : 0, + size > 7 ? vec[7] : 0); +} + template __global__ void ComputeDDoutWithoutBroadcastGPUKernel(const T* ddx_data, const T* ddy_data, @@ -310,6 +388,7 @@ __global__ void ComputeDDoutWithoutBroadcastGPUKernel(const T* ddx_data, ddout_data[tid] = dout_op(ddx_data[tid], ddy_data[tid], y_data[tid], out_data[tid]); } + template void ComputeDDoutWithoutBroadcast(const GPUContext& dev_ctx UNUSED, const phi::DenseTensor& ddx, @@ -333,17 +412,18 @@ void ComputeDDoutWithoutBroadcast(const GPUContext& dev_ctx UNUSED, } template -__global__ void ComputeDDoutWithBroadcastGPUKernel(const T* ddx_data, - const T* ddy_data, - const T* y_data, - const T* out_data, - T* ddout_data, - int numel, - const int* x_dims_array, - const int* y_dims_array, - const int* out_dims_array, - const int max_dim, - DDout_OP dout_op) { +__global__ void ComputeDDoutWithBroadcastGPUKernel( + const T* ddx_data, + const T* ddy_data, + const T* y_data, + const T* out_data, + T* ddout_data, + int numel, + const CudaIntArray x_dims_array, + const CudaIntArray y_dims_array, + const CudaIntArray out_dims_array, + const int max_dim, + DDout_OP dout_op) { int tid = threadIdx.x + blockIdx.x * blockDim.x; if (tid >= numel) return; int x_index = 0, y_index = 0, x_index_prod = 1, y_index_prod = 1, @@ -383,49 +463,14 @@ void ComputeDDoutWithBroadcast(const GPUContext& dev_ctx UNUSED, auto* y_data = y.data(); auto* out_data = out.data(); auto* ddout_data = ddout->data(); - DenseTensor x_dims_array_gpu; - x_dims_array_gpu.Resize({max_dim}); - int* x_dims_array_gpu_data = dev_ctx.template Alloc(&x_dims_array_gpu); -#if defined(__NVCC__) - cudaMemcpy(x_dims_array_gpu_data, - x_dims_array, - sizeof(int) * max_dim, - cudaMemcpyHostToDevice); -#else - hipMemcpy(x_dims_array_gpu_data, - x_dims_array, - sizeof(int) * max_dim, - hipMemcpyHostToDevice); -#endif - DenseTensor y_dims_array_gpu; - y_dims_array_gpu.Resize({max_dim}); - int* y_dims_array_gpu_data = dev_ctx.template Alloc(&y_dims_array_gpu); -#if defined(__NVCC__) - cudaMemcpy(y_dims_array_gpu_data, - y_dims_array, - sizeof(int) * max_dim, - cudaMemcpyHostToDevice); -#else - hipMemcpy(y_dims_array_gpu_data, - y_dims_array, - sizeof(int) * max_dim, - hipMemcpyHostToDevice); -#endif - DenseTensor out_dims_array_gpu; - out_dims_array_gpu.Resize({max_dim}); - int* out_dims_array_gpu_data = - dev_ctx.template Alloc(&out_dims_array_gpu); -#if defined(__NVCC__) - cudaMemcpy(out_dims_array_gpu_data, - out_dims_array, - sizeof(int) * max_dim, - cudaMemcpyHostToDevice); -#else - hipMemcpy(out_dims_array_gpu_data, - out_dims_array, - sizeof(int) * max_dim, - hipMemcpyHostToDevice); -#endif + + // Use the lightweight `CudaIntArray` structure to avoid unnecessary copy time + // caused by `cudaMemcpy` or `cudaMemcpyAsync`. + CudaIntArray x_dims_array_gpu_data = initCudaIntArray(x_dims_array, max_dim); + CudaIntArray y_dims_array_gpu_data = initCudaIntArray(y_dims_array, max_dim); + CudaIntArray out_dims_array_gpu_data = + initCudaIntArray(out_dims_array, max_dim); + int block = 512; int64_t grid = (out_numel + block - 1) / block; auto stream = reinterpret_cast(dev_ctx).stream(); @@ -687,6 +732,7 @@ void DivideDoubleGradKernel(const Context& dev_ctx, } } } + template void ElementwiseFMaxGradKernel(const Context& dev_ctx, const DenseTensor& x, diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 55daaae0873ede..815f0b498fdb0b 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -88,6 +88,7 @@ lu_unpack, matmul, matrix_power, + matrix_transpose, multi_dot, mv, norm, From 0243019fd44fbef4c0512543cf6af29c52e1d797 Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Thu, 5 Dec 2024 12:40:36 +0800 Subject: [PATCH 178/288] remove useless doc, test=document_fix (#69969) --- .../intermediate/tensor_parallel.py | 16 ---------------- 1 file changed, 16 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py index e8d7e550516524..7cdae1a0e38259 100644 --- a/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py +++ b/python/paddle/distributed/auto_parallel/intermediate/tensor_parallel.py @@ -140,14 +140,6 @@ def gather_hook(layer, input, output): return gather_hook def apply(self, layer, process_mesh, shard_weight=True, shard_bias=True): - """ - With calling of this function, parameters will be marked as split and turn in to shard_tensor. - :param layer: paddle.nn.Layer, layer to be split - :param process_mesh: dist.ProcessMesh, process_mesh where the split will work on - :param shard_weight: BOOL, whether shard the weight or not - :param shard_bias: BOOL, whether shard the weight or not - :return: no return, the shard will happen on the origin layer - """ index = process_mesh.dim_names.index('mp') # get the axis for the split size = len(process_mesh.shape) placement = [dist.Replicate() for _ in range(size)] @@ -229,14 +221,6 @@ def split_hook(layer, input, output): return split_hook def apply(self, layer, process_mesh, shard_weight=True, shard_bias=False): - """ - With calling of this function, parameters will be marked as split and turn in to shard_tensor. - :param layer: paddle.nn.Layer, layer to be split - :param process_mesh: dist.ProcessMesh, process_mesh where the split will work on - :param shard_weight: BOOL, whether shard the weight or not - :param shard_bias: BOOL, whether shard the weight or not - :return: no return, the shard will happen on the origin layer - """ index = process_mesh.dim_names.index('mp') # get the axis for the split size = len(process_mesh.shape) placement = [dist.Replicate() for _ in range(size)] From 6362cce51c4401edcb6e779c42244b21174c00b9 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Thu, 5 Dec 2024 13:18:48 +0800 Subject: [PATCH 179/288] [CINN]fix DimExpr Simplify (#69932) --- .../src/dialect/shape/utils/dim_expr_util.cc | 2 ++ .../shape_dialect/simplify_dim_expr_test.cc | 32 +++++++++++++++++++ 2 files changed, 34 insertions(+) diff --git a/paddle/pir/src/dialect/shape/utils/dim_expr_util.cc b/paddle/pir/src/dialect/shape/utils/dim_expr_util.cc index 14b41adcff58ed..48d4f55b9e443c 100644 --- a/paddle/pir/src/dialect/shape/utils/dim_expr_util.cc +++ b/paddle/pir/src/dialect/shape/utils/dim_expr_util.cc @@ -1044,6 +1044,7 @@ DimExpr Simplify(const DimExpr& expr) { DimExpr ret = expr; for (bool keep_rewrite = true; keep_rewrite;) { keep_rewrite = false; + const DimExpr& expr_before_run_pipeline = ret; DoPass>(&keep_rewrite, &ret); DoPass>(&keep_rewrite, &ret); DoPass>(&keep_rewrite, &ret); @@ -1071,6 +1072,7 @@ DimExpr Simplify(const DimExpr& expr) { DoPass(&keep_rewrite, &ret); DoPass(&keep_rewrite, &ret); DoPass(&keep_rewrite, &ret); + if (expr_before_run_pipeline == ret) break; } return ret; } diff --git a/test/cpp/pir/shape_dialect/simplify_dim_expr_test.cc b/test/cpp/pir/shape_dialect/simplify_dim_expr_test.cc index d54785521c655f..25f23cd73de44b 100644 --- a/test/cpp/pir/shape_dialect/simplify_dim_expr_test.cc +++ b/test/cpp/pir/shape_dialect/simplify_dim_expr_test.cc @@ -182,4 +182,36 @@ TEST(Simplify, FoldBroadcast) { ASSERT_TRUE(simplify_broadcast3 == add); } +TEST(Simplify, Case1) { + // Mul(Broadcast(S11, S8), Broadcast(S10, S13, S4, S7), Broadcast(S12, S3, S6, + // S9), 1 / (S0), 16, 1 / (49)) + DimExpr S11{"S11"}; + DimExpr S8{"S8"}; + DimExpr mul_op1 = Broadcast{{S11, S8}}; + + DimExpr S10{"S10"}; + DimExpr S13{"S13"}; + DimExpr S4{"S4"}; + DimExpr S7{"S7"}; + DimExpr mul_op2 = Broadcast{{S10, S13, S4, S7}}; + + DimExpr S12{"S12"}; + DimExpr S3{"S3"}; + DimExpr S6{"S6"}; + DimExpr S9{"S9"}; + DimExpr mul_op3 = Broadcast{{S12, S3, S6, S9}}; + + DimExpr S0{"S0"}; + DimExpr mul_op4 = Reciprocal(S0); + + DimExpr mul_op5 = DimExpr(16); + + DimExpr mul_op6 = Reciprocal(DimExpr(49)); + + List mul_list{mul_op1, mul_op2, mul_op3, mul_op4, mul_op5, mul_op6}; + DimExpr dim_expr{Mul{mul_list}}; + + ASSERT_TRUE((SimplifyDimExpr(dim_expr)) == dim_expr); +} + } // namespace symbol::test From c6ac9738b1baf2ed38b790e09e04fc255ad59864 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Thu, 5 Dec 2024 13:51:08 +0800 Subject: [PATCH 180/288] [CINN]add zero process pass (#69550) * add zero process pass * update * update * update code * revert code * revert code --- .../operator/transforms/add_cinn_pass.cc | 5 +- .../replace_zero_scale_to_full_pass.cc | 176 ++++++++++++++++++ .../replace_zero_scale_to_full_pass.h | 27 +++ 3 files changed, 206 insertions(+), 2 deletions(-) create mode 100644 paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.cc create mode 100644 paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc index 7ee13878a29f3b..1e18161bf16165 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc @@ -52,10 +52,12 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/remove_assign_out_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/replace_dynamic_expand_pass.h" +#include "paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/shape_ops_fallback_to_phi_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/specify_input_dynamic_dim_util.h" #include "paddle/cinn/hlir/dialect/operator/transforms/split_generate_shape_into_shape_ops_pass.h" #include "paddle/fluid/pir/transforms/build_cinn_pass.h" +#include "paddle/fluid/pir/transforms/general/common_subexpression_elimination_pass.h" #include "paddle/fluid/pir/transforms/general/dead_code_elimination_pass.h" #include "paddle/fluid/pir/transforms/gpu/fused_gemm_epilogue_pass.h" @@ -122,6 +124,7 @@ void ApplyPdToCinnPass( CreatePassManager) { std::shared_ptr pass_manager = CreatePassManager(); pass_manager->AddPass(cinn::dialect::ir::CreateReduceAsToSumPass()); + pass_manager->AddPass(cinn::dialect::ir::CreateReplaceZeroScaleToFullPass()); pass_manager->AddPass(pir::CreateFusedGemmEpiloguePass()); if (FLAGS_enable_fuse_parallel_matmul_pass) { pass_manager->AddPass(cinn::dialect::ir::CreateFuseParallelMatmulPass()); @@ -134,7 +137,6 @@ void ApplyPdToCinnPass( pass_manager->AddPass(pir::CreateDeadCodeEliminationPass()); - // pass_manager->EnableIRPrinting(); pass_manager->Run(program); } @@ -150,7 +152,6 @@ void ApplyCinnPreprocessPass( cinn::dialect::ir::CreateFuseShapeOpsIntoGenerateShapeOpPass()); pass_manager->AddPass(pir::CreateDeadCodeEliminationPass()); } - pass_manager->Run(program); } diff --git a/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.cc new file mode 100644 index 00000000000000..2606b6ae2ca7a2 --- /dev/null +++ b/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.cc @@ -0,0 +1,176 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h" + +#include "paddle/cinn/hlir/dialect/operator/ir/cinn_op.h" +#include "paddle/cinn/hlir/dialect/operator/ir/manual_op.h" +#include "paddle/fluid/pir/dialect/operator/ir/op_attribute.h" +#include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pass/pass_registry.h" +#include "paddle/pir/include/pattern_rewrite/pattern_applicator.h" +#include "paddle/pir/include/pattern_rewrite/pattern_match.h" +#include "paddle/pir/include/pattern_rewrite/pattern_rewrite_driver.h" + +namespace cinn { +namespace dialect { +namespace ir { + +bool IsGeneByFullOp(pir::Operation* op, int32_t input_idx) { + return input_idx < op->num_operands() && op->operand_source(input_idx) && + op->operand_source(input_idx).defining_op() && + op->operand_source(input_idx) + .defining_op() + ->isa(); +} + +float GetFullValue(paddle::dialect::FullOp full_op) { + return full_op.attribute("value") + .dyn_cast() + .data() + .to(); +} + +bool ReplaceWithFullOp(pir::Operation* op, + pir::PatternRewriter* rewriter, + int32_t align_input_idx) { + auto out_type = op->result(0).type(); + if (!out_type.isa()) { + return false; + } + auto tensor_type = out_type.dyn_cast(); + if (!(out_type.dyn_cast().IsDynamicShape())) { + auto phi_dtype = paddle::dialect::TransToPhiDataType(tensor_type.dtype()); + auto full_op = rewriter->Build( + phi::vectorize(tensor_type.dims()), 0.0, phi_dtype); + + rewriter->ReplaceAllUsesWith(op->result(0), full_op.result(0)); + + return true; + } + + return false; +} + +class ReplaceZeroScaleToFullPattern + : public pir::OpRewritePattern { + public: + using pir::OpRewritePattern::OpRewritePattern; + + bool MatchAndRewrite(paddle::dialect::ScaleOp op, + pir::PatternRewriter& rewriter) const override { + if (IsGeneByFullOp(op, 1)) { + auto full_op = op.operand_source(1) + .defining_op() + ->dyn_cast(); + auto scale_value = GetFullValue(full_op); + auto bias_value = + op->attributes().at("bias").dyn_cast().data(); + + if (scale_value == 0.0f && bias_value == 0.0f) { + // repalce to full(0) + return ReplaceWithFullOp(op, &rewriter, 0); + } + } + + return false; + } +}; + +class ReplaceMultiplyToFullPattern + : public pir::OpRewritePattern { + public: + using pir::OpRewritePattern::OpRewritePattern; + + bool MatchAndRewrite(paddle::dialect::MultiplyOp op, + pir::PatternRewriter& rewriter) const override { + if (IsGeneByFullOp(op, 0)) { + auto full_op = op.operand_source(0) + .defining_op() + ->dyn_cast(); + auto full_value = GetFullValue(full_op); + + if (full_value == 0.0f) { + return ReplaceWithFullOp(op, &rewriter, 1); + } + } + + if (IsGeneByFullOp(op, 1)) { + auto full_op = op.operand_source(1) + .defining_op() + ->dyn_cast(); + auto full_value = GetFullValue(full_op); + + if (full_value == 0.0f) { + return ReplaceWithFullOp(op, &rewriter, 0); + } + } + + return false; + } +}; + +class ReplaceAddToFullPattern + : public pir::OpRewritePattern { + public: + using pir::OpRewritePattern::OpRewritePattern; + + bool MatchAndRewrite(paddle::dialect::AddOp op, + pir::PatternRewriter& rewriter) const override { + if (IsGeneByFullOp(op, 0) && IsGeneByFullOp(op, 1)) { + auto x_full_op = op.operand_source(0) + .defining_op() + ->dyn_cast(); + auto x_full_value = GetFullValue(x_full_op); + + auto y_full_op = op.operand_source(1) + .defining_op() + ->dyn_cast(); + auto y_full_value = GetFullValue(y_full_op); + + if (x_full_value == 0.0f && y_full_value == 0.0f) { + return ReplaceWithFullOp(op, &rewriter, 0); + } + } + return false; + } +}; + +class ReplaceZeroScaleToFullPass : public pir::PatternRewritePass { + public: + ReplaceZeroScaleToFullPass() + : pir::PatternRewritePass("replace_zero_scale_to_full_pass", 1) {} + + pir::RewritePatternSet InitializePatterns(pir::IrContext* context) override { + pir::RewritePatternSet ps(context); + + // replace x * 0 to full(0) + ps.Add(context); + ps.Add(context); + ps.Add(context); + return ps; + } + + bool CanApplyOn(pir::Operation* op) const override { + return op->num_regions() > 0; + } +}; + +std::unique_ptr CreateReplaceZeroScaleToFullPass() { + return std::make_unique(); +} +} // namespace ir +} // namespace dialect +} // namespace cinn diff --git a/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h b/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h new file mode 100644 index 00000000000000..a1b69ab2ee28bd --- /dev/null +++ b/paddle/cinn/hlir/dialect/operator/transforms/replace_zero_scale_to_full_pass.h @@ -0,0 +1,27 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pattern_rewrite/frozen_rewrite_pattern_set.h" + +namespace cinn { +namespace dialect { +namespace ir { + +std::unique_ptr CreateReplaceZeroScaleToFullPass(); +} // namespace ir +} // namespace dialect +} // namespace cinn From 066cbad24a97278c8d5c90007021001db5074799 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:14:54 +0800 Subject: [PATCH 181/288] [CINN]move reduce as pass before split fw bw (#69643) * move reduce as pass before split fw bw * update * fix bug * fix bug * fix bug * fix bug --- paddle/fluid/pybind/pir.cc | 23 +++++++++++++++++++ .../jit/dy2static/pir_partial_program.py | 13 +++++++---- 2 files changed, 32 insertions(+), 4 deletions(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 93cb26b0419848..5c68b8283647a6 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -50,6 +50,7 @@ #include "paddle/fluid/pir/dialect/operator/utils/op_yaml_info_parser.h" #include "paddle/fluid/pir/dialect/operator/utils/utils.h" #include "paddle/fluid/pir/transforms/general/common_subexpression_elimination_pass.h" +#include "paddle/fluid/pir/transforms/general/dead_code_elimination_pass.h" #include "paddle/fluid/pir/transforms/gpu/fused_bn_add_act_pass.h" #include "paddle/fluid/pir/transforms/passes.h" #include "paddle/fluid/pir/utils/general_functions.h" @@ -84,6 +85,7 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_util.h" #include "paddle/cinn/hlir/dialect/operator/transforms/pir_to_py_code_converter.h" +#include "paddle/cinn/hlir/dialect/operator/transforms/reduce_as_to_sum_pass.h" #include "paddle/cinn/hlir/framework/pir_compiler.h" #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #endif @@ -2511,6 +2513,26 @@ std::shared_ptr ApplyCommonSubexpressionEliminationPass( return program; } +std::shared_ptr ApplyReduceAsToSumPass( + std::shared_ptr program) { +#ifdef PADDLE_WITH_CINN + pir::PassManager pm(pir::IrContext::Instance(), 2); + pm.AddPass(cinn::dialect::ir::CreateReduceAsToSumPass()); + pm.AddPass(pir::CreateDeadCodeEliminationPass()); + pm.Run(program.get()); + if (FLAGS_print_ir) { + std::cout << "IR After ReduceAsToSumPass -------------------" << std::endl; + std::cout << *program << std::endl; + } + return program; +#else + PADDLE_THROW(common::errors::Unimplemented( + "Currently we only support ReduceAsToSumPass Pass for Pir under " + "@to_static, please " + "compile PaddlePaddle with CINN")); +#endif +} + std::shared_ptr ApplyFusedBnAddActPass( std::shared_ptr program) { pir::PassManager pm(pir::IrContext::Instance(), 3); @@ -2529,6 +2551,7 @@ void BindIrPass(pybind11::module *m) { m->def("infer_symbolic_shape_pass", InferSymbolicShapePass); m->def("apply_cse_pass", ApplyCommonSubexpressionEliminationPass); m->def("apply_bn_add_act_pass", ApplyFusedBnAddActPass); + m->def("reduce_as_sum_pass", ApplyReduceAsToSumPass); py::class_> pass(*m, "Pass", diff --git a/python/paddle/jit/dy2static/pir_partial_program.py b/python/paddle/jit/dy2static/pir_partial_program.py index 9b228c348516f1..d6956f4089ed84 100644 --- a/python/paddle/jit/dy2static/pir_partial_program.py +++ b/python/paddle/jit/dy2static/pir_partial_program.py @@ -457,8 +457,9 @@ def __call__(self, program): class ValuePreservePass: OP_NAME_PREFIX = "preserved_value_" - def __init__(self, values): + def __init__(self, values, use_cinn_pass): self.values = values + self.use_cinn_pass = use_cinn_pass def apply(self, program): raise RuntimeError("Not implemented.") @@ -523,9 +524,11 @@ def __call__(self, program): return program -class FusedBnAddActPass(ValuePreservePass): +class FullGraphPreProcessPass(ValuePreservePass): def apply(self, program): program = paddle.base.libpaddle.pir.apply_bn_add_act_pass(program) + if self.use_cinn_pass: + program = paddle.base.libpaddle.pir.reduce_as_sum_pass(program) return program @@ -1043,13 +1046,15 @@ def _append_backward_desc(self, train_runnable_program: RunnableProgram): ) # construct a runnable program. - fused_bn_add_act_pass = FusedBnAddActPass( - [inputs, params, targets, x_grad_value, p_grad_value, o_grad_value] + fused_bn_add_act_pass = FullGraphPreProcessPass( + [inputs, params, targets, x_grad_value, p_grad_value, o_grad_value], + cinn_is_enabled(self._build_strategy, self._backend), ) forward_index_pass = IndicesPreservePass( [forward_end_idx, backward_start_op_index, backward_end_op_index], fused_bn_add_act_pass, ) + program = forward_index_pass(program) ( inputs, From 6f262ba949227d30e410133ab434bb2ecdfd00f6 Mon Sep 17 00:00:00 2001 From: Chen Zhiyang <1792266893@qq.com> Date: Thu, 5 Dec 2024 14:22:59 +0800 Subject: [PATCH 182/288] refine switch ir (#69956) --- python/paddle/pir_utils.py | 16 ++-------------- 1 file changed, 2 insertions(+), 14 deletions(-) diff --git a/python/paddle/pir_utils.py b/python/paddle/pir_utils.py index 5fb294a6ab27a5..74502d1086ad68 100644 --- a/python/paddle/pir_utils.py +++ b/python/paddle/pir_utils.py @@ -18,6 +18,7 @@ def _switch_to_pir_(): + bind_datatype() paddle.base.framework.global_var._use_pir_api_ = True paddle.framework.set_flags({"FLAGS_enable_pir_in_executor": True}) paddle.pir.register_paddle_dialect() @@ -39,6 +40,7 @@ def _switch_to_pir_(): def _switch_to_old_ir_(): + bind_vartype() paddle.base.framework.global_var._use_pir_api_ = False paddle.framework.set_flags({"FLAGS_enable_pir_in_executor": False}) @@ -71,7 +73,6 @@ def __enter__(self): if not self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": True}) paddle.base.framework.global_var._use_pir_api_ = True - bind_datatype() self._switch_to_pir() def __exit__(self, exc_type, exc_val, exc_tb): @@ -80,7 +81,6 @@ def __exit__(self, exc_type, exc_val, exc_tb): if not self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": False}) paddle.base.framework.global_var._use_pir_api_ = False - bind_vartype() self._switch_to_old_ir() def _switch_to_pir(self): @@ -111,8 +111,6 @@ def __enter__(self): paddle.enable_static() if self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": False}) - paddle.base.framework.global_var._use_pir_api_ = False - bind_vartype() _switch_to_old_ir_() def __exit__(self, exc_type, exc_val, exc_tb): @@ -120,8 +118,6 @@ def __exit__(self, exc_type, exc_val, exc_tb): paddle.disable_static() if self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": True}) - paddle.base.framework.global_var._use_pir_api_ = True - bind_datatype() _switch_to_pir_() @@ -132,15 +128,11 @@ def __enter__(self): ] if not self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": True}) - paddle.base.framework.global_var._use_pir_api_ = True - bind_datatype() self._switch_to_pir() def __exit__(self, exc_type, exc_val, exc_tb): if not self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": False}) - paddle.base.framework.global_var._use_pir_api_ = False - bind_vartype() self._switch_to_old_ir() def _switch_to_pir(self): @@ -168,15 +160,11 @@ def __enter__(self): ] if self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": False}) - paddle.base.framework.global_var._use_pir_api_ = False - bind_vartype() _switch_to_old_ir_() def __exit__(self, exc_type, exc_val, exc_tb): if self.old_flag: paddle.framework.set_flags({"FLAGS_enable_pir_api": True}) - paddle.base.framework.global_var._use_pir_api_ = True - bind_datatype() _switch_to_pir_() From d4e1784b8caa2473247b1583b539873a7dfa971b Mon Sep 17 00:00:00 2001 From: lizexu123 <39205361+lizexu123@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:29:55 +0800 Subject: [PATCH 183/288] [Paddle TensorRT] Paddle-TensorRT support fp16 and dynamic graph APIs into static graph support predictor (#69597) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fp16 * support fp16 * fix * fix * fix * fix * merge develop * fix * fix * fix * fix * fix * fox * fix * fix * fix * fix * fix * 暴露PrecisionMode * fix * fix * fix --- python/paddle/tensorrt/__init__.py | 4 +- python/paddle/tensorrt/converter.py | 124 +++++++----- python/paddle/tensorrt/converter_utils.py | 6 + python/paddle/tensorrt/export.py | 177 +++++++++--------- python/paddle/tensorrt/impls/math.py | 39 ++-- python/paddle/tensorrt/util.py | 84 +++++++++ test/tensorrt/tensorrt_test_base.py | 18 +- test/tensorrt/test_converter_conv.py | 1 + test/tensorrt/test_converter_model_dummy.py | 3 + .../tensorrt/test_converter_model_resnet50.py | 3 +- test/tensorrt/test_export.py | 113 +++++++++-- 11 files changed, 397 insertions(+), 175 deletions(-) diff --git a/python/paddle/tensorrt/__init__.py b/python/paddle/tensorrt/__init__.py index c7b15d2b7b4d58..3e79effc9a640b 100644 --- a/python/paddle/tensorrt/__init__.py +++ b/python/paddle/tensorrt/__init__.py @@ -12,11 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. -from .export import Input, TensorRTConfig, convert, convert_loaded_model +from .export import Input, PrecisionMode, TensorRTConfig, convert __all__ = [ 'Input', 'TensorRTConfig', 'convert', - 'convert_loaded_model', + 'PrecisionMode', ] diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index 907b8365cf7464..1ce3eeef17e0a3 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -15,7 +15,6 @@ import ctypes import hashlib import logging -import os import numpy as np import tensorrt as trt @@ -48,65 +47,29 @@ from .impls.vision import * # noqa: F403 from .register import converter_registry from .util import ( + TensorRTConfigManager, + get_cache_path, + get_trt_version, get_trt_version_list, + is_shape_tensor, map_dtype, + remove_duplicate_value, weight_to_tensor, zero_dims_to_one_dims, ) version_list = get_trt_version_list() - -def get_cache_path(): - home_path = os.path.expanduser("~") - cache_path = os.path.join(home_path, ".pp_trt_cache") - - if not os.path.exists(cache_path): - os.makedirs(cache_path) - return cache_path - - _logger = get_logger( __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s' ) -def get_trt_version(): - return trt.__version__ - - -def remove_duplicate_value(value_list): - ret_list = [] - ret_list_id = [] - for value in value_list: - if value.id not in ret_list_id: - ret_list.append(value) - ret_list_id.append(value.id) - return ret_list - - -# We use a special rule to judge whether a paddle value is a shape tensor. -# The rule is consistent with the rule in C++ source code(collect_shape_manager.cc). -# We use the rule for getting min/max/opt value shape from collect_shape_manager. -# We don't use trt_tensor.is_shape_tensor, because sometimes, the trt_tensor that corresponding to paddle value is not a shape tensor -# when it is a output in this trt graph, but it is a shape tensor when it is a input in next trt graph. -def is_shape_tensor(value): - dims = value.shape - total_elements = 1 - if ( - dims.count(-1) > 1 - ): # we can only deal with the situation that is has one dynamic dims - return False - for dim in dims: - total_elements *= abs(dim) # add abs for dynamic shape -1 - is_int_dtype = value.dtype == paddle.int32 or value.dtype == paddle.int64 - return total_elements <= 8 and total_elements >= 1 and is_int_dtype - - class PaddleToTensorRTConverter: - def __init__(self, paddle_program, scope): + def __init__(self, paddle_program, scope, trt_config=None): self.scope = scope self.program = paddle_program + self.trt_config = trt_config params = paddle_program.global_block().all_parameters() param_dict = {} # save parameters @@ -116,6 +79,17 @@ def __init__(self, paddle_program, scope): # weights = trt.Weights(weight_array) param_dict.update({name: weight_array}) self.param_dict = param_dict + + trt_manager = TensorRTConfigManager() + if ( + self.trt_config is not None + and self.trt_config.tensorrt_ops_run_float + ): + trt_manager.set_force_fp32_ops( + self.trt_config.tensorrt_ops_run_float + ) + _logger.info(f"force_fp32_ops: {trt_manager.get_force_fp32_ops()}") + self.input_info = {} self.trt_output_value_map = {} @@ -157,6 +131,8 @@ def __is_output_value(value): return input_values, graph_output_values def convert_subgraph_to_trt(self, program, group_op): + from .export import PrecisionMode + _logger.info(f"start process {group_op}") operations = next(iter(group_op.blocks())).ops @@ -282,7 +258,7 @@ def convert_subgraph_to_trt(self, program, group_op): value_to_trt_tensor[result.id] = None # Set TRT min/opt/max input shape and the value of shape tensor - for value in origin_input_value: + for i, value in enumerate(origin_input_value): trt_input = value_to_trt_tensor[value.id] if isinstance(trt_input, trt.Weights): continue @@ -324,6 +300,7 @@ def convert_subgraph_to_trt(self, program, group_op): max_shape = get_value_shape_range_info( value, False, paddle.base.core.ShapeMode.kMAX ) + if trt_input.is_shape_tensor: min_value = get_value_shape_range_info( value, True, paddle.base.core.ShapeMode.kMIN @@ -419,7 +396,58 @@ def convert_subgraph_to_trt(self, program, group_op): ): # trt version >= 8.6 config.builder_optimization_level = 5 config.set_memory_pool_limit(trt.MemoryPoolType.WORKSPACE, 1 << 30) - trt_engine = builder.build_engine(network, config) + + if self.trt_config is not None: + precision_mode = self.trt_config.precision_mode + if self.trt_config is not None and precision_mode == PrecisionMode.FP16: + if builder.platform_has_fast_fp16: + config.set_flag(trt.BuilderFlag.FP16) + _logger.info("Run Paddle-TRT FP16 mode") + else: + _logger.warning( + "Hardware does not support FP16. Continuing in FP32 mode." + ) + elif ( + self.trt_config is not None and precision_mode == PrecisionMode.BF16 + ): + if version_list[0] >= 9: + if builder.platform_has_fast_bfp16 and hasattr( + builder, 'plateform_has_fast_bf16' + ): + config.set_flag(trt.BuilderFlag.BF16) + _logger.info("Run Paddle-TRT BF16 mode") + else: + _logger.warning( + "Hardware does not support BF16. Continuing in FP32 mode." + ) + else: + if builder.platform_has_fast_fp16: + config.set_flag(trt.BuilderFlag.FP16) + _logger.warning( + "Because the version of TensorRT is less than 9.0, run Paddle-TRT FP16 mode" + ) + else: + _logger.warning( + "Hardware does not support FP16. Continuing in FP32 mode." + ) + elif self.trt_config is not None: + _logger.info( + f"Default precision mode {self.trt_config.precision_mode}" + ) + + if ( + version_list[0] > 8 + or version_list[0] == 8 + and version_list[1] >= 2 + and version_list[2] >= 1 + ): + if ( + self.trt_config is not None + and self.trt_config.tensorrt_ops_run_float + ): + config.set_flag(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS) + + trt_engine = builder.build_serialized_network(network, config) trt_params = paddle.base.libpaddle.TRTEngineParams() trt_params.min_input_shape = min_shape_map trt_params.max_input_shape = max_shape_map @@ -435,7 +463,7 @@ def convert_subgraph_to_trt(self, program, group_op): CACHE_ROOT = get_cache_path() CACHE_FILE = f"{CACHE_ROOT}/engine_{engine_name}.trt" with open(CACHE_FILE, "wb") as f: - f.write(trt_engine.serialize()) + f.write(trt_engine) PIR_DUMP_FILE = f"{CACHE_ROOT}/engine_{engine_name}.pir" with open(PIR_DUMP_FILE, "w") as f: f.write(group_str) @@ -503,5 +531,5 @@ def convert_program_to_trt(self): orin_out_values[o_i].replace_all_uses_with(new_out[o_i]) self.program.global_block().remove_op(op) - # # Call clear_shape_info to clear the previous shape information + # Call clear_shape_info to clear the previous shape information clear_shape_info() diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index 8a9de7fc7bc721..49a0346e236bfe 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -136,6 +136,8 @@ def get_positive_dim(dim, dim_size): def add_elementwise_layer(network, paddle_op, inputs, op_type): + from paddle.tensorrt.util import support_fp32_mix_precision + weight_shape = paddle_op.operands()[1].source().shape input_shape = paddle_op.operands()[0].source().shape @@ -157,6 +159,7 @@ def add_elementwise_layer(network, paddle_op, inputs, op_type): weight_tensor.name, ) layer = network.add_elementwise(lhs_val, rhs_val, op_type) + support_fp32_mix_precision(paddle_op.name(), layer) return layer.get_output(0) @@ -414,6 +417,8 @@ def trt_reduce_to_scalar(network, tensor): def convert_conv2d(network, paddle_op, inputs): + from paddle.tensorrt.util import support_fp32_mix_precision + if ( paddle_op.name() == "pd_op.conv2d" or paddle_op.name() == "pd_op.depthwise_conv2d" @@ -520,6 +525,7 @@ def convert_conv2d(network, paddle_op, inputs): nv_dilations = trt.DimsHW(1, 1) layer.dilation_nd = nv_dilations + support_fp32_mix_precision(paddle_op.name(), layer) return layer.get_output(0) diff --git a/python/paddle/tensorrt/export.py b/python/paddle/tensorrt/export.py index 53a2522031f7e4..502d4b9dd3af77 100644 --- a/python/paddle/tensorrt/export.py +++ b/python/paddle/tensorrt/export.py @@ -15,11 +15,13 @@ from __future__ import annotations import os +from enum import Enum import numpy as np import paddle from paddle.base import core, dygraph +from paddle.base.executor import scope_guard from paddle.base.framework import ( Variable, ) @@ -143,6 +145,23 @@ def generate_input_data(self): return self.input_min_data, self.input_optim_data, self.input_max_data +class PrecisionMode(Enum): + FP32 = "FP32" + FP16 = "FP16" + BF16 = "BF16" + INT8 = "INT8" + + """ + This class defines different precision modes that can be used to configure + TensorRT optimization. The modes include FP32, FP16, BF16, and INT8. + Specifies the precision mode for TensorRT optimization. The options are: + - PrecisionMode.FP32: 32-bit floating point precision (default). + - PrecisionMode.FP16: 16-bit floating point precision. + - PrecisionMode.INT8: 8-bit integer precision. + - PrecisionMode.BFP16: 16-bit Brain Floating Point precision. Only supported in TensorRT versions greater than 9.0. + """ + + class TensorRTConfig: def __init__( self, @@ -150,6 +169,8 @@ def __init__( min_subgraph_size: int | None = 3, save_model_dir: str | None = None, disable_ops: str | list | None = None, + precision_mode: PrecisionMode = PrecisionMode.FP32, + tensorrt_ops_run_float: str | list | None = None, ) -> None: """ A class for configuring TensorRT optimizations. @@ -160,9 +181,18 @@ def __init__( min_subgraph_size (int, optional): The minimum number of operations in a subgraph for TensorRT to optimize (default is 3). save_model_dir (str, optional): - The directory where the optimized model will be saved (default is None). + The directory where the optimized model will be saved (default is not to save). disable_ops : (str|list, optional): A string representing the names of operations that should not be entering by TensorRT (default is None). + precision_mode (PrecisionMode, optional): + Specifies the precision mode for TensorRT optimization. The options are: + - PrecisionMode.FP32: 32-bit floating point precision (default). + - PrecisionMode.FP16: 16-bit floating point precision. + - PrecisionMode.INT8: 8-bit integer precision. + - PrecisionMode.BFP16: 16-bit Brain Floating Point precision. Only supported in TensorRT versions greater than 9.0. + tensorrt_ops_run_float (str|list, optional): + A set of operation names that should be executed using FP32 precision regardless of the `tensorrt_precision_mode` setting. + The directory where the optimized model will be saved (default is None). Returns: None @@ -172,6 +202,7 @@ def __init__( >>> from paddle.tensorrt.export import ( ... Input, ... TensorRTConfig, + ... PrecisionMode, ... ) >>> input = Input( ... min_input_shape=(1,100), @@ -183,10 +214,14 @@ def __init__( >>> trt_config = TensorRTConfig(inputs=[input]) >>> trt_config.disable_ops = "pd_op.dropout" + >>> trt_config.precision_mode = PrecisionMode.FP16 + >>> trt_config.tensorrt_ops_run_float = "pd_op.conv2d" """ self.inputs = inputs self.min_subgraph_size = min_subgraph_size self.save_model_dir = save_model_dir + self.precision_mode = precision_mode + self.tensorrt_ops_run_float = tensorrt_ops_run_float self.disable_ops = disable_ops paddle.framework.set_flags( {'FLAGS_trt_min_group_size': min_subgraph_size} @@ -238,7 +273,9 @@ def convert_to_trt(program, trt_config, scope): program_with_pir = run_pir_pass(program, partition_mode=True) # Step4: run TRTConverter (would lower group_op into tensorrt_engine_op) - converter = PaddleToTensorRTConverter(program_with_pir, scope) + converter = PaddleToTensorRTConverter( + program_with_pir, scope, trt_config=trt_config + ) converter.convert_program_to_trt() trt_output_var = [] @@ -260,18 +297,19 @@ def convert_to_trt(program, trt_config, scope): place = paddle.CUDAPlace(0) exe = paddle.static.Executor(place) - paddle.static.save_inference_model( - trt_config.save_model_dir, - input_values, - trt_output_var, - exe, - program=program_with_pir, - ) + with scope_guard(scope): + paddle.static.save_inference_model( + trt_config.save_model_dir, + input_values, + trt_output_var, + exe, + program=program_with_pir, + ) return program_with_pir # Obtain a program with tensorrt_op for dynamic-to-static scenarios. -def convert(function=None, input_spec=None, config=None, **kwargs): +def _convert_(function=None, input_spec=None, config=None, **kwargs): """ Convert a dynamic graph API to a static graph and apply TensorRT optimizations if relevant parameters are configured. @@ -287,61 +325,6 @@ def convert(function=None, input_spec=None, config=None, **kwargs): Returns: tuple: A tuple containing two elements. The first element is the TensorRT optimized program., optionally optimized with TensorRT if configured. The second element is the scope containing the parameters. - Examples: - .. code-block:: python - >>> # example - >>> from paddle import nn - >>> from paddle.static import InputSpec - >>> import paddle - >>> from paddle.tensorrt.export import ( - ... Input, - ... TensorRTConfig, - ... convert, - ... ) - >>> import paddle.nn.functional as F - - >>> class CumsumModel(nn.Layer): - ... def __init__(self, input_dim): - ... super().__init__() - ... self.linear = nn.Linear(input_dim, input_dim) - - >>> def forward(self, x): - ... linear_out = self.linear(x) - ... relu_out = F.relu(linear_out) - ... axis = paddle.full([1], 2, dtype='int64') - ... out = paddle.cumsum(relu_out, axis=axis) - ... return out - - >>> def test_run(): - ... with paddle.pir_utils.IrGuard(): - ... input_config = Input( - ... min_input_shape=(9, 10, 11), - ... optim_input_shape=(9, 10, 11), - ... max_input_shape=(9, 10, 11), - ... ) - ... trt_config = TensorRTConfig(inputs=[input_config]) - ... for i, input_instrance in enumerate(trt_config.inputs): - ... min_data, _, max_data = input_instrance.generate_input_data() - ... paddle.disable_static() - ... x = paddle.to_tensor(min_data) - ... net = CumsumModel(input_dim=min_data.shape[-1]) - ... out=net(x) - ... input_spec = [InputSpec(shape=min_data.shape, dtype='float32')] - ... program_with_trt ,scope= convert( - ... net, - ... input_spec=input_spec, - ... config=trt_config, - ... full_graph=True, - ... ) - ... output_var = program_with_trt.list_vars()[-1] - ... with paddle.pir_utils.IrGuard(): - ... with paddle.static.scope_guard(scope): - ... place=paddle.CUDAPlace(0) - ... executor=paddle.static.Executor(place) - ... output=executor.run(program_with_trt, feed={"x": min_data}, fetch_list=[output_var],scope=scope) - - >>> test_run() - """ # Converts dynamic graph APIs into static graph static_net = paddle.jit.to_static( @@ -508,17 +491,23 @@ def convert(function=None, input_spec=None, config=None, **kwargs): param_or_buffer_tensor._share_data_with(src_tensor) with paddle.pir_utils.IrGuard(): main_program = concrete_program.main_program + output_vars = concrete_program.outputs + paddle.base.executor._add_pir_fetch_ops( + program=main_program, fetch_list=output_vars, fetch_var_name="fetch" + ) program_with_trt = convert_to_trt(main_program, config, scope) return program_with_trt, scope # Obtain a program with tensorrt_op by directly loading the model. -def convert_loaded_model(model_dir, config): +def convert(model_path, config): """ Loading a PaddlePaddle Model and Exporting the TensorRT-Optimized Program. Args: - model_dir(str):The directory path where the PaddlePaddle model is located. + model_path(str):The directory path where the PaddlePaddle model is located. + The model path can either include the model directory and prefix (e.g., 'model_dir/inference'), + or it can be the full path to the model (e.g., 'model_dir/inference.json'). config(TensorRTConfig):The configuration of TensorRTConfig. Returns: @@ -534,7 +523,6 @@ def convert_loaded_model(model_dir, config): ... Input, ... TensorRTConfig, ... export, - ... convert_loaded_model, ... ) >>> import os >>> from paddle import nn @@ -598,7 +586,7 @@ def convert_loaded_model(model_dir, config): ... trt_save_path = os.path.join(temp_dir.name, 'trt') ... trt_config.save_model_dir = trt_save_path - ... program_with_trt = convert_loaded_model(save_path, trt_config) + ... program_with_trt = paddle.tensorrt.convert(save_path, trt_config) ... # Create a config for inference. ... config = paddle_infer.Config( @@ -620,9 +608,9 @@ def convert_loaded_model(model_dir, config): ... output_converted = predictor.run([model_inputs]) """ - if os.path.abspath(config.save_model_dir) == os.path.abspath(model_dir): + if os.path.abspath(config.save_model_dir) == os.path.abspath(model_path): raise ValueError( - "The `config.save_model_dir` and `model_dir` cannot be the same. Please specify a different directory for saving the model." + "The `config.save_model_dir` and `model_path` cannot be the same. Please specify a different directory for saving the model." ) scope = paddle.static.global_scope() @@ -630,31 +618,46 @@ def convert_loaded_model(model_dir, config): exe = paddle.static.Executor(place) is_json = True - if os.path.exists(model_dir + '.json'): - is_json = True - elif os.path.exists(model_dir + '.pdmodel'): - is_json = False + + if os.path.isfile(model_path): + model_path = model_path + model_dir, model_file = os.path.split(model_path) + model_prefix, ext = os.path.splitext(model_file) + if ext == '.json': + is_json = True + elif ext == '.pdmodel': + is_json = False + else: + raise ValueError( + f"Unsupported extension {ext}. Only support json/pdmodel" + ) else: - raise ValueError( - f"No valid model file found in the directory '{model_dir}'. Expected either 'json' or 'pdmodel'. Please ensure that the directory contains one of these files." - ) + model_prefix = model_path + if os.path.exists(model_prefix + '.json'): + is_json = True + elif os.path.exists(model_prefix + '.pdmodel'): + is_json = False + else: + raise ValueError( + f"No valid model file found in the directory '{model_path}'. Expected either 'json' or 'pdmodel'. Please ensure that the directory contains one of these files." + ) if is_json: with paddle.pir_utils.IrGuard(): [program, feed_target_names, fetch_targets] = ( paddle.static.io.load_inference_model( - model_dir, + model_path, executor=exe, ) ) else: - paddle.framework.set_flags({"FLAGS_enable_pir_in_executor": True}) - [program, feed_target_names, fetch_targets] = ( - paddle.static.io.load_inference_model( - model_dir, - executor=exe, + with paddle.pir_utils.OldIrGuard(): + os.environ['FLAGS_enable_pir_in_executor'] = '1' + [program, feed_target_names, fetch_targets] = ( + paddle.static.io.load_inference_model( + model_path, + executor=exe, + ) ) - ) - paddle.framework.set_flags({"FLAGS_enable_pir_in_executor": False}) - + os.environ['FLAGS_enable_pir_in_executor'] = '0' return convert_to_trt(program, config, scope) diff --git a/python/paddle/tensorrt/impls/math.py b/python/paddle/tensorrt/impls/math.py index 20e3c767ae4c8d..40a9a16291d23d 100644 --- a/python/paddle/tensorrt/impls/math.py +++ b/python/paddle/tensorrt/impls/math.py @@ -24,12 +24,8 @@ fill_constant_layer, get_axes_for_reduce_op, trt_cast, - trt_div, trt_expand, - trt_floor_div, trt_max, - trt_prod, - trt_sub, ) from paddle.tensorrt.register import converter_registry @@ -157,6 +153,8 @@ def _get_constant_or_expand_tensor( @converter_registry.register("pd_op.remainder", trt_version="8.x") @converter_registry.register("pd_op.remainder_", trt_version="8.x") def remainder_converter(network, paddle_op, inputs): + from paddle.tensorrt.util import support_fp32_mix_precision + weight_shape = paddle_op.operands()[1].source().shape input_shape = inputs[0].shape @@ -178,22 +176,29 @@ def remainder_converter(network, paddle_op, inputs): input_tensor.name, weight_tensor.name, ) - - # Check if floor division is needed is_floor_div = input_tensor.dtype != trt.DataType.INT32 - - # Floor division - quotient = ( - trt_floor_div(network, lhs_val, rhs_val) - if is_floor_div - else trt_div(network, lhs_val, rhs_val) - ) + if is_floor_div: + quotient_layer = network.add_elementwise( + lhs_val, rhs_val, trt.ElementWiseOperation.FLOOR_DIV + ) + else: + quotient_layer = network.add_elementwise( + lhs_val, rhs_val, trt.ElementWiseOperation.DIV + ) + quotient = quotient_layer.get_output(0) + support_fp32_mix_precision(paddle_op.name(), quotient_layer) # Multiply rhs by the quotient - product = trt_prod(network, rhs_val, quotient) - - # Subtract the product from lhs to get the remainder - remainder = trt_sub(network, lhs_val, product) + product_layer = network.add_elementwise( + rhs_val, quotient, trt.ElementWiseOperation.PROD + ) + product = product_layer.get_output(0) + support_fp32_mix_precision(paddle_op.name(), product_layer) + remainder_layer = network.add_elementwise( + lhs_val, product, trt.ElementWiseOperation.SUB + ) + remainder = remainder_layer.get_output(0) + support_fp32_mix_precision(paddle_op.name(), remainder_layer) return remainder diff --git a/python/paddle/tensorrt/util.py b/python/paddle/tensorrt/util.py index 72f917a84bfd3b..b402adbd2290ea 100644 --- a/python/paddle/tensorrt/util.py +++ b/python/paddle/tensorrt/util.py @@ -12,6 +12,8 @@ # See the License for the specific language governing permissions and # limitations under the License. +import logging +import os import paddle @@ -20,6 +22,11 @@ except Exception as e: pass from paddle import pir +from paddle.base.log_helper import get_logger + +_logger = get_logger( + __name__, logging.INFO, fmt='%(asctime)s-%(levelname)s: %(message)s' +) def map_dtype(pd_dtype): @@ -142,6 +149,42 @@ def mark_buitlin_op(program): op.set_bool_attr("__l_trt__", True) +class TensorRTConfigManager: + _instance = None + + def __new__(cls, *args, **kwargs): + if not cls._instance: + cls._instance = super().__new__(cls, *args, **kwargs) + cls._instance._init() + return cls._instance + + def _init(self): + self.force_fp32_ops = [] + + def set_force_fp32_ops(self, ops): + if ops is None: + self.force_fp32_ops = [] + elif isinstance(ops, str): + self.force_fp32_ops = [ops] + elif isinstance(ops, list): + self.force_fp32_ops = ops + else: + raise ValueError("Ops should be a string, list, or None.") + + def get_force_fp32_ops(self): + return self.force_fp32_ops + + +# In TensorRT FP16 inference, this function sets the precision of specific +# operators to FP32, ensuring numerical accuracy for these operations. +def support_fp32_mix_precision(op_type, layer): + trt_manager = TensorRTConfigManager() + force_fp32_ops = trt_manager.get_force_fp32_ops() + if op_type in force_fp32_ops: + layer.reset_precision() + layer.precision = trt.DataType.FLOAT + + def weight_to_tensor(network, paddle_value, trt_tensor, use_op_name): # the following op needn't cast trt.Weight to ITensor, because the layer need weight as input forbid_cast_op = [ @@ -171,3 +214,44 @@ def zero_dims_to_one_dims(network, trt_tensor): shuffle_layer = network.add_shuffle(trt_tensor) shuffle_layer.reshape_dims = (1,) return shuffle_layer.get_output(0) + + +# We use a special rule to judge whether a paddle value is a shape tensor. +# The rule is consistent with the rule in C++ source code(collect_shape_manager.cc). +# We use the rule for getting min/max/opt value shape from collect_shape_manager. +# We don't use trt_tensor.is_shape_tensor, because sometimes, the trt_tensor that corresponding to paddle value is not a shape tensor +# when it is a output in this trt graph, but it is a shape tensor when it is a input in next trt graph. +def is_shape_tensor(value): + dims = value.shape + total_elements = 1 + if ( + dims.count(-1) > 1 + ): # we can only deal with the situation that is has one dynamic dims + return False + for dim in dims: + total_elements *= abs(dim) # add abs for dynamic shape -1 + is_int_dtype = value.dtype == paddle.int32 or value.dtype == paddle.int64 + return total_elements <= 8 and total_elements >= 1 and is_int_dtype + + +def get_cache_path(): + home_path = os.path.expanduser("~") + cache_path = os.path.join(home_path, ".pp_trt_cache") + + if not os.path.exists(cache_path): + os.makedirs(cache_path) + return cache_path + + +def remove_duplicate_value(value_list): + ret_list = [] + ret_list_id = [] + for value in value_list: + if value.id not in ret_list_id: + ret_list.append(value) + ret_list_id.append(value.id) + return ret_list + + +def get_trt_version(): + return trt.__version__ diff --git a/test/tensorrt/tensorrt_test_base.py b/test/tensorrt/tensorrt_test_base.py index c4995adafdf478..bef4f87968b09a 100755 --- a/test/tensorrt/tensorrt_test_base.py +++ b/test/tensorrt/tensorrt_test_base.py @@ -20,6 +20,10 @@ import paddle from paddle.base import core from paddle.tensorrt.converter import PaddleToTensorRTConverter +from paddle.tensorrt.export import ( + Input, + TensorRTConfig, +) from paddle.tensorrt.util import ( mark_buitlin_op, run_pir_pass, @@ -37,6 +41,7 @@ def __init__(self, methodName='runTest'): self.max_shape = None self.target_marker_op = "" self.dynamic_shape_data = {} + self.enable_fp16 = None def create_fake_program(self): if self.python_api is None: @@ -248,8 +253,19 @@ def check_trt_result(self, rtol=1e-5, atol=1e-5): program_with_trt = run_pir_pass(main_program, partition_mode=True) # run TRTConverter(would lower group_op into tensorrt_engine_op) + trt_config = None + if self.enable_fp16: + input = Input( + min_input_shape=self.min_shape, + optim_input_shape=self.min_shape, + max_input_shape=self.max_shape, + ) + trt_config = TensorRTConfig(inputs=[input]) + trt_config.tensorrt_precision_mode = "FP16" - converter = PaddleToTensorRTConverter(program_with_trt, scope) + converter = PaddleToTensorRTConverter( + program_with_trt, scope, trt_config + ) converter.convert_program_to_trt() # check whether has trt op diff --git a/test/tensorrt/test_converter_conv.py b/test/tensorrt/test_converter_conv.py index e9d14cd5edbaaf..21281a70cf4d4e 100644 --- a/test/tensorrt/test_converter_conv.py +++ b/test/tensorrt/test_converter_conv.py @@ -40,6 +40,7 @@ def setUp(self): self.program_config = {"feed_list": ["x"]} self.min_shape = {"x": [1, 3, 8, 8]} self.max_shape = {"x": [10, 3, 8, 8]} + self.enable_fp16 = True def test_trt_result(self): self.check_trt_result() diff --git a/test/tensorrt/test_converter_model_dummy.py b/test/tensorrt/test_converter_model_dummy.py index 765e6b97a80711..f9e4808e6058e7 100644 --- a/test/tensorrt/test_converter_model_dummy.py +++ b/test/tensorrt/test_converter_model_dummy.py @@ -21,6 +21,7 @@ from paddle.tensorrt.export import ( Input, + PrecisionMode, TensorRTConfig, convert_to_trt, ) @@ -43,6 +44,8 @@ def test_paddle_to_tensorrt_conversion_dummy(self): _, input_optim_data, _ = input_config.generate_input_data() # Create a TensorRTConfig with inputs as a required field. trt_config = TensorRTConfig(inputs=[input_config]) + trt_config.precision_mode = PrecisionMode.FP16 + trt_config.tensorrt_ops_run_float = "pd_op.add" output_var = program.list_vars()[-1] diff --git a/test/tensorrt/test_converter_model_resnet50.py b/test/tensorrt/test_converter_model_resnet50.py index 4d17a623edc266..08d82220abd686 100644 --- a/test/tensorrt/test_converter_model_resnet50.py +++ b/test/tensorrt/test_converter_model_resnet50.py @@ -44,7 +44,7 @@ def test_paddle_to_tensorrt_conversion_r50(self): # Set input input_config = Input( min_input_shape=(1, 3, 224, 224), - optim_input_shape=(2, 3, 224, 224), + optim_input_shape=(1, 3, 224, 224), max_input_shape=(4, 3, 224, 224), input_data_type='float32', ) @@ -71,6 +71,7 @@ def test_paddle_to_tensorrt_conversion_r50(self): output_expected = standardize(output_expected[0]) output_trt = standardize(output_converted[0]) + # Check that the results are close to each other within a tolerance of 1e-3 np.testing.assert_allclose( output_expected, diff --git a/test/tensorrt/test_export.py b/test/tensorrt/test_export.py index 4cea28f27579ae..611143fe747cf1 100644 --- a/test/tensorrt/test_export.py +++ b/test/tensorrt/test_export.py @@ -25,8 +25,7 @@ from paddle.tensorrt.export import ( Input, TensorRTConfig, - convert, - convert_loaded_model, + _convert_, ) from paddle.tensorrt.util import ( predict_program, @@ -99,7 +98,7 @@ def forward(self, x): return out -class TestConvertLoadedModel(unittest.TestCase): +class TestConvert(unittest.TestCase): def setUp(self): paddle.seed(2024) self.temp_dir = tempfile.TemporaryDirectory() @@ -157,7 +156,7 @@ def test_paddle_to_tensorrt_conversion_cumsum(self): model_dir = self.save_path # Obtain tensorrt_engine_op by passing the model path and trt_config.(converted_program) - program_with_trt = convert_loaded_model(model_dir, trt_config) + program_with_trt = paddle.tensorrt.convert(model_dir, trt_config) # Create a config for inference. config = paddle_infer.Config( @@ -170,7 +169,6 @@ def test_paddle_to_tensorrt_conversion_cumsum(self): else: config.disable_gpu() predictor = paddle_infer.create_predictor(config) - input_names = predictor.get_input_names() paddle.disable_static() for i, input_instrance in enumerate(trt_config.inputs): @@ -179,13 +177,13 @@ def test_paddle_to_tensorrt_conversion_cumsum(self): output_converted = predictor.run([model_inputs]) -class TestConvert(unittest.TestCase): +class TestConvert_(unittest.TestCase): def test_run(self): with paddle.pir_utils.IrGuard(): input_config = Input( min_input_shape=(9, 10, 11), optim_input_shape=(9, 10, 11), - max_input_shape=(9, 10, 11), + max_input_shape=(10, 10, 11), ) trt_config = TensorRTConfig(inputs=[input_config]) for i, input_instrance in enumerate(trt_config.inputs): @@ -195,20 +193,24 @@ def test_run(self): net = CumsumModel(input_dim=min_data.shape[-1]) out = net(x) - input_spec = [InputSpec(shape=min_data.shape, dtype='float32')] - program_with_trt, scope = convert( + input_spec = [ + InputSpec(shape=[None, 10, 11], dtype='float32', name='x') + ] + program_with_trt, scope = _convert_( net, input_spec=input_spec, config=trt_config, - full_graph=True, ) + output_var = program_with_trt.list_vars()[-1] + output_converted = predict_program( program_with_trt, {"x": min_data}, [output_var], scope=scope, ) + output_expected = out.numpy() output_converted_np = output_converted[0] @@ -223,6 +225,17 @@ def test_run(self): class TestConvertMultipleInputs(unittest.TestCase): + def setUp(self): + self.temp_dir = tempfile.TemporaryDirectory() + self.save_path = os.path.join( + self.temp_dir.name, 'tensor_axis_cumsum_multiple' + ) + self.place = ( + paddle.CUDAPlace(0) + if paddle.is_compiled_with_cuda() + else paddle.CPUPlace() + ) + def test_run(self): with paddle.pir_utils.IrGuard(): input_config = Input( @@ -236,6 +249,7 @@ def test_run(self): max_input_shape=(1, 1, 28, 28), ) trt_config = TensorRTConfig(inputs=[input_config, input_config2]) + trt_config.save_model_dir = os.path.join(self.temp_dir.name, 'trt') min_data_list = [] max_data_list = [] @@ -259,24 +273,28 @@ def test_run(self): ), ] - program_with_trt, scope = convert( + program_with_trt, scope = _convert_( net, input_spec=input_spec, config=trt_config, full_graph=True, ) - output_var = program_with_trt.list_vars()[-1] - output_converted = predict_program( - program_with_trt, - {"input1": min_data_list[0], "input2": min_data_list[1]}, - [output_var], - scope=scope, + config = paddle_infer.Config( + trt_config.save_model_dir + '.json', + trt_config.save_model_dir + '.pdiparams', ) - output_expected = out.numpy() + + if paddle.is_compiled_with_cuda(): + config.enable_use_gpu(100, 0) + else: + config.disable_gpu() + + predictor = paddle_infer.create_predictor(config) + output_converted = predictor.run(x) output_converted_np = output_converted[0] + output_expected = out.numpy() - # Check that the results are close to each other within a tolerance of 1e-2 np.testing.assert_allclose( output_expected, output_converted_np, @@ -286,5 +304,62 @@ def test_run(self): ) +class TestConvertPredictor(unittest.TestCase): + def setUp(self): + self.temp_dir = tempfile.TemporaryDirectory() + self.save_path = os.path.join( + self.temp_dir.name, 'tensor_axis_cumsum_predictor' + ) + self.place = ( + paddle.CUDAPlace(0) + if paddle.is_compiled_with_cuda() + else paddle.CPUPlace() + ) + + def test_run(self): + input_config = Input( + min_input_shape=(9, 10, 11), + optim_input_shape=(9, 10, 11), + max_input_shape=(10, 10, 11), + ) + trt_config = TensorRTConfig(inputs=[input_config]) + trt_config.save_model_dir = os.path.join(self.temp_dir.name, 'trt') + + min_data, _, max_data = input_config.generate_input_data() + net = CumsumModel(input_dim=min_data.shape[-1]) + x = paddle.to_tensor(min_data) + out = net(x).numpy() + + input_spec = [ + InputSpec(shape=[None, 10, 11], dtype='float32', name='x') + ] + program_with_trt, scope = _convert_( + net, + input_spec=input_spec, + config=trt_config, + ) + + config = paddle_infer.Config( + trt_config.save_model_dir + '.json', + trt_config.save_model_dir + '.pdiparams', + ) + + if paddle.is_compiled_with_cuda(): + config.enable_use_gpu(100, 0) + else: + config.disable_gpu() + predictor = paddle_infer.create_predictor(config) + + output_converted = predictor.run([x]) + output_converted_np = output_converted[0] + np.testing.assert_allclose( + out, + output_converted_np, + rtol=1e-2, + atol=1e-2, + err_msg="Outputs are not within the 1e-2 tolerance", + ) + + if __name__ == "__main__": unittest.main() From 855291ae5bd1db45dfaeaeaebc023c5209760e30 Mon Sep 17 00:00:00 2001 From: co63oc Date: Thu, 5 Dec 2024 14:36:55 +0800 Subject: [PATCH 184/288] Fix (#69922) --- python/paddle/incubate/distributed/fleet/fleet_util.py | 4 ++-- .../distributed/fleet/parameter_server/ir/pserver_pass.py | 1 - python/paddle/incubate/optimizer/pipeline.py | 4 ++-- python/paddle/jit/dy2static/utils.py | 5 +---- python/paddle/static/nn/metric.py | 6 ++++-- 5 files changed, 9 insertions(+), 11 deletions(-) diff --git a/python/paddle/incubate/distributed/fleet/fleet_util.py b/python/paddle/incubate/distributed/fleet/fleet_util.py index b290bb0d6e8bcf..a96172c2f38107 100644 --- a/python/paddle/incubate/distributed/fleet/fleet_util.py +++ b/python/paddle/incubate/distributed/fleet/fleet_util.py @@ -1427,7 +1427,7 @@ def get_global_metrics( >>> # below is part of example model >>> label = paddle.static.data(name="click", shape=[-1, 1],\ - ... dtype="int64", lod_level=0) + ... dtype="int64") >>> emb = my_slot_net(slots, label) # emb can be fc layer of size 1 >>> similarity_norm = paddle.nn.functional.sigmoid(paddle.clip(\ ... emb, min=-15.0, max=15.0), name="similarity_norm")\ @@ -1632,7 +1632,7 @@ def print_global_metrics( >>> # below is part of model >>> label = paddle.static.data(name="click", shape=[-1, 1],\ - ... dtype="int64", lod_level=0) + ... dtype="int64") >>> emb = my_slot_net(slots, label) # emb can be fc layer of size 1 >>> similarity_norm = paddle.nn.functional.sigmoid(paddle.clip(\ ... emb, min=-15.0, max=15.0), name="similarity_norm")\ diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py index 9f63499db54840..c240c1af089b5b 100644 --- a/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py +++ b/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py @@ -787,7 +787,6 @@ def add_large_scale_op( persistable=False, dtype="int64", shape=[1, 1], - lod_level=0, ) # insert grad split to ids and tensor op diff --git a/python/paddle/incubate/optimizer/pipeline.py b/python/paddle/incubate/optimizer/pipeline.py index 6d0666c242558a..219f96c472099d 100644 --- a/python/paddle/incubate/optimizer/pipeline.py +++ b/python/paddle/incubate/optimizer/pipeline.py @@ -55,8 +55,8 @@ class PipelineOptimizer: >>> paddle.enable_static() >>> with base.device_guard("gpu:0"): - ... x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64', lod_level=0) - ... y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64', lod_level=0) + ... x = paddle.static.data(name='x', shape=[-1, 1], dtype='int64') + ... y = paddle.static.data(name='y', shape=[-1, 1], dtype='int64') ... data_loader = base.io.DataLoader.from_generator( ... feed_list=[x, y], ... capacity=64, diff --git a/python/paddle/jit/dy2static/utils.py b/python/paddle/jit/dy2static/utils.py index c939a5e3d0d511..e5e04bf12609b7 100644 --- a/python/paddle/jit/dy2static/utils.py +++ b/python/paddle/jit/dy2static/utils.py @@ -71,7 +71,7 @@ ] -def data_layer_not_check(name, shape, dtype='float32', lod_level=0): +def data_layer_not_check(name, shape, dtype='float32'): """ This function creates a Tensor on the global block. The created Tensor doesn't check the dtype and the shape of feed data because dygraph input @@ -93,8 +93,6 @@ def data_layer_not_check(name, shape, dtype='float32', lod_level=0): dtype (np.dtype|VarType|str, optional): The type of the data. Supported dtype: bool, float16, float32, float64, int8, int16, int32, int64, uint8. Default: float32 - lod_level (int, optional): The LoD level of the DenseTensor. Usually users - don't have to set this value. Default: 0 Returns: Tensor: The global Tensor that gives access to the data. @@ -111,7 +109,6 @@ def data_layer_not_check(name, shape, dtype='float32', lod_level=0): dtype=dtype, type=core.VarDesc.VarType.DENSE_TENSOR, stop_gradient=True, - lod_level=lod_level, is_data=True, need_check_feed=False, ) diff --git a/python/paddle/static/nn/metric.py b/python/paddle/static/nn/metric.py index c0d82b5a0b4a28..94f91ef923f48f 100644 --- a/python/paddle/static/nn/metric.py +++ b/python/paddle/static/nn/metric.py @@ -190,6 +190,7 @@ def auc( .. code-block:: python :name: example-1 + >>> # doctest: +SKIP("This has diff in xdoctest env") >>> import paddle >>> import numpy as np >>> paddle.enable_static() @@ -219,6 +220,7 @@ def auc( # you can learn the usage of ins_tag_weight by the following code. + >>> # doctest: +SKIP("This has diff in xdoctest env") >>> import paddle >>> import numpy as np >>> paddle.enable_static() @@ -226,7 +228,7 @@ def auc( >>> paddle.seed(2023) >>> data = paddle.static.data(name="input", shape=[-1, 32,32], dtype="float32") >>> label = paddle.static.data(name="label", shape=[-1], dtype="int64") - >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1,16], lod_level=0, dtype='float64') + >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1,16], dtype='float64') >>> fc_out = paddle.static.nn.fc(x=data, size=2) >>> predict = paddle.nn.functional.softmax(x=fc_out) >>> result=paddle.static.auc(input=predict, label=label, ins_tag_weight=ins_tag_weight) @@ -421,7 +423,7 @@ def ctr_metric_bundle(input, label, ins_tag_weight=None): >>> data = paddle.static.data(name="data", shape=[-1, 32], dtype="float32") >>> label = paddle.static.data(name="label", shape=[-1, 1], dtype="int32") >>> predict = paddle.nn.functional.sigmoid(paddle.static.nn.fc(x=data, size=1)) - >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1, 1], lod_level=0, dtype='int64') + >>> ins_tag_weight = paddle.static.data(name='ins_tag_weight', shape=[-1, 1], dtype='int64') >>> auc_out = paddle.static.ctr_metric_bundle(input=predict, label=label, ins_tag_weight=ins_tag_weight) """ if ins_tag_weight is None: From b9b5911511ef1fb91e2d4d7d2b5649270ebf2667 Mon Sep 17 00:00:00 2001 From: co63oc Date: Thu, 5 Dec 2024 14:37:50 +0800 Subject: [PATCH 185/288] Fix (#69967) --- .../custom_device_common_op_registry.cc | 23 ------ .../phi/kernels/custom/assign_pos_kernel.cc | 72 +++++++++++++++++++ .../custom/limit_by_capacity_kernel.cc | 69 ++++++++++++++++++ .../phi/kernels/custom/number_count_kernel.cc | 49 +++++++++++++ .../custom/prune_gate_by_capacity_kernel.cc | 57 +++++++++++++++ .../kernels/custom/random_routing_kernel.cc | 62 ++++++++++++++++ 6 files changed, 309 insertions(+), 23 deletions(-) create mode 100644 paddle/phi/kernels/custom/assign_pos_kernel.cc create mode 100644 paddle/phi/kernels/custom/limit_by_capacity_kernel.cc create mode 100644 paddle/phi/kernels/custom/number_count_kernel.cc create mode 100644 paddle/phi/kernels/custom/prune_gate_by_capacity_kernel.cc create mode 100644 paddle/phi/kernels/custom/random_routing_kernel.cc diff --git a/paddle/fluid/operators/custom_device_common_op_registry.cc b/paddle/fluid/operators/custom_device_common_op_registry.cc index e880817d1ab1ba..38b1d461eab991 100644 --- a/paddle/fluid/operators/custom_device_common_op_registry.cc +++ b/paddle/fluid/operators/custom_device_common_op_registry.cc @@ -1548,29 +1548,6 @@ void RegisterCustomDeviceCommonKernel(const std::string& dev_type) { barrier, device_type, paddle::operators::BarrierOpCustomDeviceKernel) {} - REGISTER_OP_CUSTOM_DEVICE_KERNEL( - number_count, - device_type, - paddle::operators::NumberCountOpCustomDeviceKernel) {} - REGISTER_OP_CUSTOM_DEVICE_KERNEL( - limit_by_capacity, - device_type, - paddle::operators::LimitByCapacityOpCustomDeviceKernel) {} - REGISTER_OP_CUSTOM_DEVICE_KERNEL( - prune_gate_by_capacity, - device_type, - paddle::operators::PruneGateByCapacityCustomDeviceKernel) {} - REGISTER_OP_CUSTOM_DEVICE_KERNEL( - random_routing, - device_type, - paddle::operators::RandomRoutingOpCustomDeviceKernel, - paddle::operators::RandomRoutingOpCustomDeviceKernel, - paddle::operators::RandomRoutingOpCustomDeviceKernel< - phi::dtype::float16>) {} - REGISTER_OP_CUSTOM_DEVICE_KERNEL( - assign_pos, - device_type, - paddle::operators::AssignPosCustomDeviceKernel) {} REGISTER_OP_CUSTOM_DEVICE_KERNEL( global_scatter, diff --git a/paddle/phi/kernels/custom/assign_pos_kernel.cc b/paddle/phi/kernels/custom/assign_pos_kernel.cc new file mode 100644 index 00000000000000..760cbe0feac8d2 --- /dev/null +++ b/paddle/phi/kernels/custom/assign_pos_kernel.cc @@ -0,0 +1,72 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/kernels/assign_pos_kernel.h" +#include "paddle/phi/backends/all_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/core/tensor_utils.h" + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +namespace phi { + +template +void AssignPosKernel(const Context& dev_ctx, + const DenseTensor& x, + const DenseTensor& cum_count_in, + const DenseTensor& eff_num_len_in, + DenseTensor* out) { + // assign pos decides which tokens should be fetched belong to specially + // counter orderly. + auto cum_count = &cum_count_in; // (counter number) int32 | int64 + auto numbers = &x; // (batch_size * seq_len, topk) int32 + auto eff_num_len = &eff_num_len_in; // (sum(cum_count)) + // out: (cum_count) value ranges + // from 0 to batch_size * + // seq_len * topk + + phi::DenseTensor cpu_eff_num_len; + int64_t cpu_eff_num_len_data = 0; + if (eff_num_len->place().GetType() == phi::AllocationType::CPU) { + cpu_eff_num_len_data = eff_num_len->data()[0]; + } else { + phi::Copy(dev_ctx, *eff_num_len, phi::CPUPlace(), true, &cpu_eff_num_len); + cpu_eff_num_len_data = cpu_eff_num_len.data()[0]; + } + + out->Resize({cpu_eff_num_len_data}); + dev_ctx.template Alloc(out); + + phi::DenseTensor numbers_cpu, cum_count_cpu; + phi::Copy(dev_ctx, *numbers, phi::CPUPlace(), true, &numbers_cpu); + phi::Copy(dev_ctx, *cum_count, phi::CPUPlace(), true, &cum_count_cpu); + auto* numbers_data = numbers_cpu.data(); + auto* cum_count_data = cum_count_cpu.data(); + + std::vector out_data(cpu_eff_num_len_data); + for (int64_t i = 0; i < numbers->numel(); ++i) { + int number_idx = numbers_data[i]; + if (number_idx > -1) { + cum_count_data[number_idx] -= 1; + int p = cum_count_data[number_idx]; + out_data[p] = i; + } + } + phi::TensorFromVector(out_data, dev_ctx, out); +} + +} // namespace phi + +PD_REGISTER_KERNEL( + assign_pos, Custom, ALL_LAYOUT, phi::AssignPosKernel, int64_t) {} +#endif diff --git a/paddle/phi/kernels/custom/limit_by_capacity_kernel.cc b/paddle/phi/kernels/custom/limit_by_capacity_kernel.cc new file mode 100644 index 00000000000000..c00db99a981227 --- /dev/null +++ b/paddle/phi/kernels/custom/limit_by_capacity_kernel.cc @@ -0,0 +1,69 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/backends/all_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/core/tensor_utils.h" + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +namespace phi { + +template +void LimitByCapacityKernel(const Context& dev_ctx, + const DenseTensor& expert_count_in, + const DenseTensor& capacity_in, + int n_worker, + DenseTensor* out) { + auto expert_count = &expert_count_in; + auto capacity = &capacity_in; + + auto n_expert = expert_count->numel() / n_worker; + + dev_ctx.template Alloc(out); + std::vector out_data(out->numel()); + phi::DenseTensor expert_count_cpu, capacity_cpu; + phi::Copy(dev_ctx, *expert_count, phi::CPUPlace(), true, &expert_count_cpu); + phi::Copy(dev_ctx, *capacity, phi::CPUPlace(), true, &capacity_cpu); + + auto* ec_data = expert_count_cpu.data(); + auto* capacity_data = capacity_cpu.data(); + int eid, wid; + for (int64_t i = 0; i < expert_count->numel(); ++i) { + wid = i / n_expert; + eid = i % n_expert; + auto proposal = ec_data[i]; + auto cap_left = capacity_data[eid]; + capacity_data[eid] -= proposal; + if (cap_left >= proposal) { + out_data[wid * n_expert + eid] = proposal; + } else if (cap_left >= 0) { + out_data[wid * n_expert + eid] = cap_left; + } else { + out_data[wid * n_expert + eid] = 0; + } + } + + auto out_dims = out->dims(); + phi::TensorFromVector(out_data, dev_ctx, out); + out->Resize(out_dims); +} + +} // namespace phi + +PD_REGISTER_KERNEL(limit_by_capacity, + Custom, + ALL_LAYOUT, + phi::LimitByCapacityKernel, + int64_t) {} +#endif diff --git a/paddle/phi/kernels/custom/number_count_kernel.cc b/paddle/phi/kernels/custom/number_count_kernel.cc new file mode 100644 index 00000000000000..4afb51861348b0 --- /dev/null +++ b/paddle/phi/kernels/custom/number_count_kernel.cc @@ -0,0 +1,49 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/kernels/number_count_kernel.h" + +#include "paddle/phi/backends/all_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/core/tensor_utils.h" + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +namespace phi { +template +void NumberCountKernel(const Context& dev_ctx, + const DenseTensor& numbers_in, + int upper_range, + DenseTensor* out) { + auto numbers = &numbers_in; + auto number_count = out; + number_count->Resize({upper_range}); + dev_ctx.template Alloc(number_count); + phi::DenseTensor cpu_tensor; + phi::Copy(dev_ctx, *numbers, phi::CPUPlace(), true, &cpu_tensor); + std::vector count(upper_range); + for (auto i = 0; i < cpu_tensor.numel(); ++i) { + auto idx = static_cast(cpu_tensor.data()[i]); + if (idx >= 0 && idx < upper_range) { + count[idx] += 1; + } + } + phi::TensorFromVector(count, dev_ctx, number_count); + number_count->Resize({upper_range}); +} +} // namespace phi + +PD_REGISTER_KERNEL( + number_count, Custom, ALL_LAYOUT, phi::NumberCountKernel, int64_t) {} + +#endif diff --git a/paddle/phi/kernels/custom/prune_gate_by_capacity_kernel.cc b/paddle/phi/kernels/custom/prune_gate_by_capacity_kernel.cc new file mode 100644 index 00000000000000..87384032e2dffa --- /dev/null +++ b/paddle/phi/kernels/custom/prune_gate_by_capacity_kernel.cc @@ -0,0 +1,57 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/backends/all_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/core/tensor_utils.h" + +#ifdef PADDLE_WITH_CUSTOM_DEVICE +namespace phi { + +template +void PruneGateByCapacityKernel(const Context& dev_ctx, + const DenseTensor& gate_idx_in, + const DenseTensor& expert_count_in, + int64_t n_expert, + int64_t n_worker, + DenseTensor* new_gate_idx) { + auto* gate_idx = &gate_idx_in; + auto* expert_count = &expert_count_in; + + dev_ctx.template Alloc(new_gate_idx); + + phi::DenseTensor expert_count_cpu, gate_idx_cpu; + phi::Copy(dev_ctx, *expert_count, phi::CPUPlace(), true, &expert_count_cpu); + phi::Copy(dev_ctx, *gate_idx, phi::CPUPlace(), true, &gate_idx_cpu); + auto expert_count_data = expert_count_cpu.data(); + auto gate_idx_data = gate_idx_cpu.data(); + std::vector new_gate_idx_data(gate_idx->numel()); + for (auto i = 0; i < gate_idx->numel(); ++i) { + auto orig_cap = expert_count_data[gate_idx_data[i]]--; + if (orig_cap <= 0) { + new_gate_idx_data[i] = -1; + } else { + new_gate_idx_data[i] = gate_idx_data[i]; + } + } +} + +} // namespace phi + +PD_REGISTER_KERNEL(prune_gate_by_capacity, + Custom, + ALL_LAYOUT, + phi::PruneGateByCapacityKernel, + int64_t) {} +#endif diff --git a/paddle/phi/kernels/custom/random_routing_kernel.cc b/paddle/phi/kernels/custom/random_routing_kernel.cc new file mode 100644 index 00000000000000..62ccc8409d3118 --- /dev/null +++ b/paddle/phi/kernels/custom/random_routing_kernel.cc @@ -0,0 +1,62 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/kernels/random_routing_kernel.h" +#include "paddle/phi/backends/all_context.h" +#include "paddle/phi/core/kernel_registry.h" +#include "paddle/phi/core/tensor_utils.h" +#ifdef PADDLE_WITH_CUSTOM_DEVICE +namespace phi { + +template +void RandomRoutingKernel(const Context& dev_ctx, + const DenseTensor& prob_in, + const DenseTensor& topk_value_in, + const DenseTensor& topk_idx_in, + DenseTensor* out) { + auto topk_idx = &topk_idx_in; + auto topk_value = &topk_value_in; + auto prob = &prob_in; + + size_t D = topk_idx->dims()[1]; + + phi::DenseTensor topk_value_cpu, prob_cpu; + phi::Copy(dev_ctx, *topk_value, phi::CPUPlace(), true, &topk_value_cpu); + phi::Copy(dev_ctx, *prob, phi::CPUPlace(), true, &prob_cpu); + auto* topk_value_data = topk_value_cpu.data(); + auto* prob_data = prob_cpu.data(); + std::vector out_data(topk_idx->numel()); + + for (int64_t idx = 0; idx < topk_idx->numel(); ++idx) { + size_t row = idx / D; + size_t col = idx % D; + if (col == 1 && static_cast(2) * topk_value_data[idx] < prob_data[row]) { + out_data[idx] = static_cast(-1); + } + } + auto out_dims = out->dims(); + phi::TensorFromVector(out_data, dev_ctx, out); + out->Resize(out_dims); +} + +} // namespace phi + +PD_REGISTER_KERNEL(random_routing, + Custom, + ALL_LAYOUT, + phi::RandomRoutingKernel, + float, + double, + phi::dtype::float16) {} +#endif From 12fbad19f45f67361008c25460b2cee5d0cdf306 Mon Sep 17 00:00:00 2001 From: winter-wang <78149749+winter-wang@users.noreply.github.com> Date: Thu, 5 Dec 2024 14:38:26 +0800 Subject: [PATCH 186/288] fix the bug for create_shaped_type fucntion. (#69947) --- paddle/fluid/pybind/pir.cc | 2 +- test/ir/pir/test_build_op.py | 2 ++ 2 files changed, 3 insertions(+), 1 deletion(-) diff --git a/paddle/fluid/pybind/pir.cc b/paddle/fluid/pybind/pir.cc index 5c68b8283647a6..775863fe9ba81b 100644 --- a/paddle/fluid/pybind/pir.cc +++ b/paddle/fluid/pybind/pir.cc @@ -1631,7 +1631,7 @@ void BindType(py::module *m) { }); m->def("create_shaped_type", - [](Type &type, const std::vector &shape) -> Type { + [](Type &type, const std::vector &shape) -> Type { if (type.isa()) { DenseTensorType src_type = type.dyn_cast(); DenseTensorType dst_type = diff --git a/test/ir/pir/test_build_op.py b/test/ir/pir/test_build_op.py index fa190ef92a6fc0..3af891eaff0495 100644 --- a/test/ir/pir/test_build_op.py +++ b/test/ir/pir/test_build_op.py @@ -55,6 +55,8 @@ def test_build_mean_op(self): .name(), "pd_op.tanh", ) + paddle.pir.create_shaped_type(tanh_out.type(), [3148873728]) + paddle.pir.create_shaped_type(tanh_out.type(), [1]) class TestBuildOp2(unittest.TestCase): From d353102d6901be422984654b10c10742715d907b Mon Sep 17 00:00:00 2001 From: Siming Dai <908660116@qq.com> Date: Thu, 5 Dec 2024 14:42:11 +0800 Subject: [PATCH 187/288] Update broadcast_input_data for mp (#69882) * update broadcast data * fix single element --- .../fleet/utils/hybrid_parallel_util.py | 59 ++++++++++++------- 1 file changed, 37 insertions(+), 22 deletions(-) diff --git a/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py b/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py index 79e532e2bbb224..317dec05c57387 100644 --- a/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py +++ b/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py @@ -165,6 +165,39 @@ def _broadcast_object_list_help(object_list, hcg): ) +def _process_element(hcg, place, element): + cur_device = paddle.get_device() + dev = cur_device.split(":")[0] + if isinstance(element, core.eager.Tensor): + with framework.no_grad(): + if ( + in_dynamic_mode() + and not eval(f"element.place.is_{dev}_place")() + ): + element_gpu = element._copy_to(place, True) + element._clear_data() + element_gpu._share_buffer_to(element) + _broadcast_data_help(element, element.shape, element.dtype, hcg) + elif isinstance(element, (dict, list, tuple)): + return _broadcast_nested_data(hcg, place, element) + else: + _broadcast_object_list_help([element], hcg) + + +def _broadcast_nested_data(hcg, place, data): + if isinstance(data, dict): + return { + key: _process_element(hcg, place, value) + for key, value in data.items() + } + elif isinstance(data, list): + return [_process_element(hcg, place, item) for item in data] + elif isinstance(data, tuple): + return tuple(_process_element(hcg, place, item) for item in data) + else: + raise TypeError(f"Unsupported data type: {type(data)}") + + def broadcast_input_data(hcg, *inputs, **kwargs): cur_device = paddle.get_device() dev = cur_device.split(":")[0] @@ -185,28 +218,10 @@ def broadcast_input_data(hcg, *inputs, **kwargs): else: place = eval(f"paddle.{dev.upper()}Place")(dev_idx) - for v in inputs: - if isinstance(v, core.eager.Tensor): - with framework.no_grad(): - if in_dynamic_mode() and not eval(f"v.place.is_{dev}_place")(): - v_gpu = v._copy_to(place, True) - v._clear_data() - v_gpu._share_buffer_to(v) - _broadcast_data_help(v, v.shape, v.dtype, hcg) - else: - _broadcast_object_list_help(v, hcg) - - for k, v in kwargs.items(): - if isinstance(v, core.eager.Tensor): - with framework.no_grad(): - if in_dynamic_mode() and not eval(f"v.place.is_{dev}_place")(): - v_gpu = v._copy_to(place, True) - v._clear_data() - v_gpu._share_buffer_to(v) - _broadcast_data_help(v, v.shape, v.dtype, hcg) - kwargs[k] = v - else: - kwargs[k] = _broadcast_object_list_help(v, hcg) + if len(inputs) > 0: + inputs = _broadcast_nested_data(hcg, place, inputs) + if len(kwargs) > 0: + kwargs = _broadcast_nested_data(hcg, place, kwargs) return inputs, kwargs From cc5a52abdb2f011c5a6296c2a80eabee85c7448b Mon Sep 17 00:00:00 2001 From: co63oc Date: Thu, 5 Dec 2024 14:46:37 +0800 Subject: [PATCH 188/288] [Lod][fluid_ops]test_variable.py (#69904) --- test/book/notest_understand_sentiment.py | 4 +-- .../test_trt_multiclass_nms3_op_deprecated.py | 4 +-- test/deprecated/legacy_test/dist_fleet_ctr.py | 2 -- test/deprecated/legacy_test/test_dataset.py | 28 +++++++++---------- ..._a_sync_optimizer_auto_async_deprecated.py | 1 - ...est_dist_fleet_heter_program_deprecated.py | 4 +-- .../test_dist_fleet_ps13_deprecated.py | 8 ++---- .../test_dist_fleet_ps2_deprecated.py | 8 ++---- .../test_dist_fleet_ps3_deprecated.py | 8 ++---- .../test_dist_fleet_ps4_deprecated.py | 8 ++---- .../test_dist_fleet_ps5_deprecated.py | 8 ++---- .../test_dist_fleet_ps6_deprecated.py | 8 ++---- .../test_dist_fleet_ps_deprecated.py | 8 ++---- .../test_entry_attr2_deprecated.py | 2 +- .../legacy_test/test_entry_attr_deprecated.py | 2 +- .../legacy_test/test_fleet_deprecated.py | 2 -- .../test_fleet_nocvm_1_deprecated.py | 2 -- .../test_fleet_unitaccessor_deprecated.py | 6 ++-- .../legacy_test/test_layers_deprecated.py | 16 ++++------- test/legacy_test/dist_ctr.py | 2 -- test/legacy_test/dist_fleet_ctr.py | 2 -- .../dist_fleet_heter_pipeline_ctr.py | 2 -- test/legacy_test/dist_fleet_simnet_bow.py | 12 ++------ .../dist_fleet_sparse_embedding_ctr.py | 2 -- test/legacy_test/dist_text_classification.py | 8 ++---- test/legacy_test/fleet_heter_ps_training.py | 2 -- .../ir_memory_optimize_net_base.py | 4 +-- test/legacy_test/nets.py | 2 +- test/legacy_test/simple_nets.py | 4 +-- test/legacy_test/test_data_feeder.py | 2 +- .../test_dataset_consistency_inspection.py | 3 -- test/legacy_test/test_detection.py | 5 +--- test/legacy_test/test_dist_fleet_minimize.py | 12 ++------ test/legacy_test/test_dist_fleet_ps11.py | 16 +++-------- test/legacy_test/test_dist_fleet_ps12.py | 12 ++------ .../test_dist_fleet_sparse_embedding_ctr.py | 2 -- test/legacy_test/test_dist_fleet_spmt.py | 12 ++------ test/legacy_test/test_dist_transpiler.py | 12 ++++---- test/legacy_test/test_dist_tree_index.py | 14 +++------- .../test_eager_deletion_dynamic_rnn_base.py | 4 +-- test/legacy_test/test_fleet_pyramid_hash.py | 4 +-- test/legacy_test/test_fleet_rolemaker.py | 4 +-- test/legacy_test/test_fleet_rolemaker_2.py | 4 +-- test/legacy_test/test_fleet_rolemaker_3.py | 4 +-- test/legacy_test/test_monitor.py | 4 +-- test/legacy_test/test_print_op.py | 8 ++---- test/legacy_test/test_psroi_pool_op.py | 4 +-- test/legacy_test/test_pull_gpups_sparse_op.py | 2 +- test/legacy_test/test_pyramid_hash_op.py | 4 +-- test/legacy_test/test_regularizer.py | 4 +-- test/legacy_test/test_regularizer_api.py | 4 +-- test/legacy_test/test_tdm_child_op.py | 4 +-- test/legacy_test/test_tdm_sampler_op.py | 4 +-- test/legacy_test/test_uniform_random_op.py | 4 +-- 54 files changed, 102 insertions(+), 219 deletions(-) diff --git a/test/book/notest_understand_sentiment.py b/test/book/notest_understand_sentiment.py index 24a9fa1c1cf24f..df48dec6f900e4 100644 --- a/test/book/notest_understand_sentiment.py +++ b/test/book/notest_understand_sentiment.py @@ -72,9 +72,7 @@ def train( dict_dim = len(word_dict) class_dim = 2 - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") if not parallel: diff --git a/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py b/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py index 218050f7813e5d..00e89ce908cf7c 100644 --- a/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py +++ b/test/deprecated/ir/inference/test_trt_multiclass_nms3_op_deprecated.py @@ -118,9 +118,9 @@ class number import paddle from ppdet.modeling import ops boxes = paddle.static.data(name='bboxes', shape=[81, 4], - dtype='float32', lod_level=1) + dtype='float32') scores = paddle.static.data(name='scores', shape=[81], - dtype='float32', lod_level=1) + dtype='float32') out, index = ops.multiclass_nms(bboxes=boxes, scores=scores, background_label=0, diff --git a/test/deprecated/legacy_test/dist_fleet_ctr.py b/test/deprecated/legacy_test/dist_fleet_ctr.py index 8e8eab9fe909fb..d5b550993c96b9 100644 --- a/test/deprecated/legacy_test/dist_fleet_ctr.py +++ b/test/deprecated/legacy_test/dist_fleet_ctr.py @@ -67,13 +67,11 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/deprecated/legacy_test/test_dataset.py b/test/deprecated/legacy_test/test_dataset.py index 80813f75685ecf..49b93634f9904e 100644 --- a/test/deprecated/legacy_test/test_dataset.py +++ b/test/deprecated/legacy_test/test_dataset.py @@ -105,7 +105,7 @@ def test_run_with_dump(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -201,7 +201,7 @@ def test_set_download_cmd(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -267,7 +267,7 @@ def test_in_memory_dataset_run(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -365,12 +365,12 @@ def test_in_memory_dataset_masterpatch(self): with base.program_guard(train_program, startup_program): for slot in slots[:2]: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) for slot in slots[2:]: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) @@ -521,7 +521,7 @@ def test_in_memory_dataset_run_2(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) @@ -645,7 +645,7 @@ def test_queue_dataset_run(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -724,7 +724,7 @@ def test_queue_dataset_run_2(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) @@ -793,7 +793,7 @@ def test_queue_dataset_run_3(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[None, 1], dtype="int64", lod_level=1 + name=slot, shape=[None, 1], dtype="int64" ) slots_vars.append(var) @@ -861,7 +861,7 @@ def test_run_with_inmemory_dataset_train_debug_mode(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -927,7 +927,7 @@ def test_cuda_in_memory_dataset_run(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 + name=slot, shape=[-1, 1], dtype="int64" ) slots_vars.append(var) @@ -1021,7 +1021,7 @@ def test_dataset_fleet(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1]) @@ -1093,7 +1093,7 @@ def test_dataset_fleet2(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1]) @@ -1226,7 +1226,7 @@ def test_bosps_dataset_fleet2(self): slots_vars = [] for slot in slots: var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="float32", lod_level=1 + name=slot, shape=[-1, 1], dtype="float32" ) slots_vars.append(var) fake_cost = paddle.subtract(slots_vars[0], slots_vars[-1]) diff --git a/test/deprecated/legacy_test/test_dist_fleet_a_sync_optimizer_auto_async_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_a_sync_optimizer_auto_async_deprecated.py index 98850be660d8a0..8d2fe0e1b0eb3e 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_a_sync_optimizer_auto_async_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_a_sync_optimizer_auto_async_deprecated.py @@ -50,7 +50,6 @@ def test_a_sync_optimizer3(self): name="x", shape=[-1, 1], dtype="int64", - lod_level=1, ) x_embedding = paddle.static.nn.embedding( is_distributed=False, diff --git a/test/deprecated/legacy_test/test_dist_fleet_heter_program_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_heter_program_deprecated.py index ca39dee4c3099e..9690be3a704c61 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_heter_program_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_heter_program_deprecated.py @@ -70,9 +70,7 @@ def build_input(self): ) sparse_input_ids = [ - paddle.static.data( - name="C" + str(i), shape=[-1, 1], lod_level=1, dtype="int64" - ) + paddle.static.data(name="C" + str(i), shape=[-1, 1], dtype="int64") for i in range(1, 27) ] diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps13_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps13_deprecated.py index 6c5da347c041a0..6019cdbdd4b712 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps13_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps13_deprecated.py @@ -75,9 +75,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -108,7 +106,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.sparse_embedding( @@ -139,7 +137,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.sparse_embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps2_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps2_deprecated.py index ce3d2c1b5cb62d..b90282fad0e283 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps2_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps2_deprecated.py @@ -74,9 +74,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -108,7 +106,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.sparse_embedding( @@ -139,7 +137,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.sparse_embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps3_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps3_deprecated.py index d76af1a93059d6..e8882539ad1ed7 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps3_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps3_deprecated.py @@ -71,9 +71,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = False # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.embedding( input=q, @@ -106,7 +104,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.embedding( @@ -139,7 +137,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps4_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps4_deprecated.py index 2585ab99c75215..20036eb767bf23 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps4_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps4_deprecated.py @@ -71,9 +71,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -104,7 +102,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.sparse_embedding( @@ -135,7 +133,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.sparse_embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps5_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps5_deprecated.py index 914c31134542e4..0df0638fd7b5d0 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps5_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps5_deprecated.py @@ -71,9 +71,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.embedding( input=q, @@ -106,7 +104,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.embedding( @@ -139,7 +137,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps6_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps6_deprecated.py index bbda76ae3f32c0..5d4c180dfa7753 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps6_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps6_deprecated.py @@ -71,9 +71,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -104,7 +102,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.sparse_embedding( @@ -135,7 +133,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.sparse_embedding( diff --git a/test/deprecated/legacy_test/test_dist_fleet_ps_deprecated.py b/test/deprecated/legacy_test/test_dist_fleet_ps_deprecated.py index 66612fa224aa7a..d605de062960aa 100644 --- a/test/deprecated/legacy_test/test_dist_fleet_ps_deprecated.py +++ b/test/deprecated/legacy_test/test_dist_fleet_ps_deprecated.py @@ -71,9 +71,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.embedding( input=q, @@ -106,7 +104,7 @@ def get_loss(cos_q_pt, cos_q_nt): label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="pos_title_ids", shape=[-1, 1], dtype="int64" ) # embedding pt_emb = paddle.static.nn.embedding( @@ -139,7 +137,7 @@ def get_loss(cos_q_pt, cos_q_nt): ) # nt nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 + name="neg_title_ids", shape=[-1, 1], dtype="int64" ) # embedding nt_emb = paddle.static.nn.embedding( diff --git a/test/deprecated/legacy_test/test_entry_attr2_deprecated.py b/test/deprecated/legacy_test/test_entry_attr2_deprecated.py index c00013bb746e82..7b10ffe92c5914 100644 --- a/test/deprecated/legacy_test/test_entry_attr2_deprecated.py +++ b/test/deprecated/legacy_test/test_entry_attr2_deprecated.py @@ -29,7 +29,7 @@ def embedding_layer(self): with base.scope_guard(scope): with base.program_guard(prog): input = paddle.static.data( - name="dnn_data", shape=[-1, 1], dtype="int64", lod_level=1 + name="dnn_data", shape=[-1, 1], dtype="int64" ) emb = paddle.static.nn.embedding( input=input, diff --git a/test/deprecated/legacy_test/test_entry_attr_deprecated.py b/test/deprecated/legacy_test/test_entry_attr_deprecated.py index fc73bdf9c399a5..521dbd55b29c55 100644 --- a/test/deprecated/legacy_test/test_entry_attr_deprecated.py +++ b/test/deprecated/legacy_test/test_entry_attr_deprecated.py @@ -68,7 +68,7 @@ def spaese_layer(self): with base.scope_guard(scope): with base.program_guard(prog): input = paddle.static.data( - name="dnn_data", shape=[-1, 1], dtype="int64", lod_level=1 + name="dnn_data", shape=[-1, 1], dtype="int64" ) prob = ProbabilityEntry(0.5) emb = paddle.static.nn.sparse_embedding( diff --git a/test/deprecated/legacy_test/test_fleet_deprecated.py b/test/deprecated/legacy_test/test_fleet_deprecated.py index f5b57cd0fa1d32..250cd702cb338b 100644 --- a/test/deprecated/legacy_test/test_fleet_deprecated.py +++ b/test/deprecated/legacy_test/test_fleet_deprecated.py @@ -64,7 +64,6 @@ def test_pslib_1(self): name="show", shape=[-1, 1], dtype="int64", - lod_level=1, ) emb = paddle.static.nn.embedding( input=show, @@ -84,7 +83,6 @@ def test_pslib_1(self): name="click", shape=[-1, 1], dtype="int64", - lod_level=1, ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/deprecated/legacy_test/test_fleet_nocvm_1_deprecated.py b/test/deprecated/legacy_test/test_fleet_nocvm_1_deprecated.py index fcb533d660a9b0..91b54ddadcfb1f 100644 --- a/test/deprecated/legacy_test/test_fleet_nocvm_1_deprecated.py +++ b/test/deprecated/legacy_test/test_fleet_nocvm_1_deprecated.py @@ -62,7 +62,6 @@ def test_pslib_1(self): name="show", shape=[-1, 1], dtype="int64", - lod_level=1, ) emb = paddle.static.nn.embedding( input=show, @@ -76,7 +75,6 @@ def test_pslib_1(self): name="click", shape=[-1, 1], dtype="int64", - lod_level=1, ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/deprecated/legacy_test/test_fleet_unitaccessor_deprecated.py b/test/deprecated/legacy_test/test_fleet_unitaccessor_deprecated.py index 7cfc94bc58468c..fbb322c960317f 100644 --- a/test/deprecated/legacy_test/test_fleet_unitaccessor_deprecated.py +++ b/test/deprecated/legacy_test/test_fleet_unitaccessor_deprecated.py @@ -58,9 +58,7 @@ def test_pslib_1(self): startup_program = base.Program() scope = base.Scope() with base.program_guard(train_program, startup_program): - show = paddle.static.data( - name="show", shape=[-1, 1], dtype="int64", lod_level=1 - ) + show = paddle.static.data(name="show", shape=[-1, 1], dtype="int64") emb = paddle.static.nn.embedding( input=show, size=[1, 1], @@ -70,7 +68,7 @@ def test_pslib_1(self): ) fc = paddle.static.nn.fc(x=emb, size=1, activation=None) label = paddle.static.data( - name="click", shape=[-1, 1], dtype="int64", lod_level=1 + name="click", shape=[-1, 1], dtype="int64" ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/deprecated/legacy_test/test_layers_deprecated.py b/test/deprecated/legacy_test/test_layers_deprecated.py index e3b5a1b2a167f3..20a2d67b96594f 100644 --- a/test/deprecated/legacy_test/test_layers_deprecated.py +++ b/test/deprecated/legacy_test/test_layers_deprecated.py @@ -526,9 +526,7 @@ def test_group_norm(self): def _test_static_specific(input): with self.static_graph(): - X = paddle.static.data( - name='X', shape=shape, dtype='float32', lod_level=1 - ) + X = paddle.static.data(name='X', shape=shape, dtype='float32') ret = paddle.static.nn.group_norm( input=X, groups=2, @@ -551,9 +549,7 @@ def _test_static_specific(input): def _test_static(input): with self.static_graph(): - X = paddle.static.data( - name='X', shape=shape, dtype='float32', lod_level=1 - ) + X = paddle.static.data(name='X', shape=shape, dtype='float32') groupNorm = paddle.nn.GroupNorm( num_channels=shape[1], num_groups=2, @@ -695,7 +691,7 @@ def test_spectral_norm(self): with self.static_graph(): Weight = paddle.static.data( - name='Weight', shape=shape, dtype='float32', lod_level=1 + name='Weight', shape=shape, dtype='float32' ) ret = paddle.static.nn.spectral_norm( weight=Weight, dim=1, power_iters=2 @@ -712,7 +708,7 @@ def test_spectral_norm(self): with self.static_graph(): Weight = paddle.static.data( - name='Weight', shape=shape, dtype='float32', lod_level=1 + name='Weight', shape=shape, dtype='float32' ) spectralNorm = paddle.nn.SpectralNorm(shape, dim=1, power_iters=2) ret = spectralNorm(Weight) @@ -1381,9 +1377,7 @@ def make_uniform_random_batch_size_like(self): def test_row_conv(self): # TODO(minqiyang): dygraph do not support lod now with self.static_graph(): - x = paddle.static.data( - name='x', shape=[-1, 16], dtype='float32', lod_level=1 - ) + x = paddle.static.data(name='x', shape=[-1, 16], dtype='float32') out = paddle.static.nn.row_conv(input=x, future_context_size=2) return out diff --git a/test/legacy_test/dist_ctr.py b/test/legacy_test/dist_ctr.py index dc296ef19c7db6..4811c802fae376 100644 --- a/test/legacy_test/dist_ctr.py +++ b/test/legacy_test/dist_ctr.py @@ -35,13 +35,11 @@ def get_model(self, batch_size=2): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/dist_fleet_ctr.py b/test/legacy_test/dist_fleet_ctr.py index e944c024e41946..2aa4790c9427da 100644 --- a/test/legacy_test/dist_fleet_ctr.py +++ b/test/legacy_test/dist_fleet_ctr.py @@ -65,13 +65,11 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py index fd812745825c31..98208a82cba726 100644 --- a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py +++ b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py @@ -52,13 +52,11 @@ def net(self, args, batch_size=4, lr=0.01): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/dist_fleet_simnet_bow.py b/test/legacy_test/dist_fleet_simnet_bow.py index d11fcc65a0789b..8ab2e2c08f076d 100644 --- a/test/legacy_test/dist_fleet_simnet_bow.py +++ b/test/legacy_test/dist_fleet_simnet_bow.py @@ -92,19 +92,13 @@ def train_network( is_pyreader=False, ): # query - q = paddle.static.data( - name="query_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="query_ids", shape=[-1, 1], dtype="int64") # label data label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt - pt = paddle.static.data( - name="pos_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + pt = paddle.static.data(name="pos_title_ids", shape=[-1, 1], dtype="int64") # nt - nt = paddle.static.data( - name="neg_title_ids", shape=[-1, 1], dtype="int64", lod_level=1 - ) + nt = paddle.static.data(name="neg_title_ids", shape=[-1, 1], dtype="int64") datas = [q, label, pt, nt] diff --git a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py index 77a5375901a7b2..136b8298ec486a 100644 --- a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py @@ -56,13 +56,11 @@ def net(self, args, batch_size=4, lr=0.01): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/dist_text_classification.py b/test/legacy_test/dist_text_classification.py index 0e3c79d758c803..f94601ec59c0c6 100644 --- a/test/legacy_test/dist_text_classification.py +++ b/test/legacy_test/dist_text_classification.py @@ -96,9 +96,7 @@ def conv_net( def inference_network(dict_dim): - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") out = conv_net(data, dict_dim) return out @@ -126,9 +124,7 @@ def get_model(self, batch_size=2): word_dict, dict_dim = get_worddict(vocab) # Input data - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") label = paddle.static.data(name='label', shape=[-1, 1], dtype='int64') # Train program diff --git a/test/legacy_test/fleet_heter_ps_training.py b/test/legacy_test/fleet_heter_ps_training.py index 74ced0bd2b0df2..425aabc74eed96 100644 --- a/test/legacy_test/fleet_heter_ps_training.py +++ b/test/legacy_test/fleet_heter_ps_training.py @@ -45,13 +45,11 @@ def net(batch_size=4, lr=0.01): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/ir_memory_optimize_net_base.py b/test/legacy_test/ir_memory_optimize_net_base.py index 3cad6a23b6315d..1527497eb5a9c2 100644 --- a/test/legacy_test/ir_memory_optimize_net_base.py +++ b/test/legacy_test/ir_memory_optimize_net_base.py @@ -58,9 +58,7 @@ def check_network_convergence( return paddle.seed(100) - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") diff --git a/test/legacy_test/nets.py b/test/legacy_test/nets.py index d8a4236591c2b4..58c15f9d96d399 100644 --- a/test/legacy_test/nets.py +++ b/test/legacy_test/nets.py @@ -326,7 +326,7 @@ def sequence_conv_pool( input_dim = 100 #len(word_dict) emb_dim = 128 hid_dim = 512 - data = paddle.static.data(name="words", shape=[None, 1], dtype="int64", lod_level=1) + data = paddle.static.data(name="words", shape=[None, 1], dtype="int64") emb = paddle.static.nn.embedding(input=data, size=[input_dim, emb_dim], is_sparse=True) seq_conv = base.nets.sequence_conv_pool(input=emb, num_filters=hid_dim, diff --git a/test/legacy_test/simple_nets.py b/test/legacy_test/simple_nets.py index 945532f1eacc84..643efcf67ed74b 100644 --- a/test/legacy_test/simple_nets.py +++ b/test/legacy_test/simple_nets.py @@ -190,9 +190,7 @@ def bow_net( This model is from https://github.com/PaddlePaddle/models: base/PaddleNLP/text_classification/nets.py """ - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") emb = paddle.static.nn.embedding( input=data, is_sparse=is_sparse, size=[dict_dim, emb_dim] diff --git a/test/legacy_test/test_data_feeder.py b/test/legacy_test/test_data_feeder.py index 22e56176eeaa9c..dfc94a6fe420fd 100644 --- a/test/legacy_test/test_data_feeder.py +++ b/test/legacy_test/test_data_feeder.py @@ -48,7 +48,7 @@ def test_lod_level_1_converter(self): # lod_level = 1 # each sentence has a different number of words sentences = paddle.static.data( - name='sentences', shape=[-1, 1], dtype='int64', lod_level=1 + name='sentences', shape=[-1, 1], dtype='int64' ) label = paddle.static.data( name='label', shape=[-1, 1], dtype='int64' diff --git a/test/legacy_test/test_dataset_consistency_inspection.py b/test/legacy_test/test_dataset_consistency_inspection.py index f33a1610b91571..192b6ef7a611d9 100644 --- a/test/legacy_test/test_dataset_consistency_inspection.py +++ b/test/legacy_test/test_dataset_consistency_inspection.py @@ -424,7 +424,6 @@ def test_var_consistency_insepection(self): name=str(feat_name), shape=[-1, 1], dtype='int64', - lod_level=1, ) ) @@ -435,7 +434,6 @@ def test_var_consistency_insepection(self): name=str(feat_name), shape=[-1, 1], dtype='int64', - lod_level=1, ) ) @@ -462,7 +460,6 @@ def test_var_consistency_insepection(self): name=str(feat_name), shape=[-1, 1], dtype='int64', - lod_level=1, ) ) diff --git a/test/legacy_test/test_detection.py b/test/legacy_test/test_detection.py index c59789d3852c39..494be1e428fd51 100644 --- a/test/legacy_test/test_detection.py +++ b/test/legacy_test/test_detection.py @@ -235,7 +235,7 @@ def test_distribute_fpn_proposals_error(self): program = Program() with program_guard(program): fpn_rois = paddle.static.data( - name='data_error', shape=[10, 4], dtype='int32', lod_level=1 + name='data_error', shape=[10, 4], dtype='int32' ) rois_num = paddle.static.data( name='rois_num', shape=[None], dtype='int32' @@ -258,7 +258,6 @@ def test_distribute_fpn_proposals_error2(self): name='min_max_level_error1', shape=[10, 4], dtype='float32', - lod_level=1, ) self.assertRaises( AssertionError, @@ -277,7 +276,6 @@ def test_distribute_fpn_proposals_error3(self): name='min_max_level_error2', shape=[10, 4], dtype='float32', - lod_level=1, ) self.assertRaises( AssertionError, @@ -296,7 +294,6 @@ def test_distribute_fpn_proposals_error4(self): name='min_max_level_error3', shape=[10, 4], dtype='float32', - lod_level=1, ) self.assertRaises( AssertionError, diff --git a/test/legacy_test/test_dist_fleet_minimize.py b/test/legacy_test/test_dist_fleet_minimize.py index 5cc2f37259dd65..e6ae1d9dd1799f 100644 --- a/test/legacy_test/test_dist_fleet_minimize.py +++ b/test/legacy_test/test_dist_fleet_minimize.py @@ -74,9 +74,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="1", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="1", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -106,9 +104,7 @@ def get_loss(cos_q_pt, cos_q_nt): # label data label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt - pt = paddle.static.data( - name="2", shape=[-1, 1], dtype="int64", lod_level=1 - ) + pt = paddle.static.data(name="2", shape=[-1, 1], dtype="int64") # embedding pt_emb = paddle.static.nn.sparse_embedding( input=pt, @@ -137,9 +133,7 @@ def get_loss(cos_q_pt, cos_q_nt): bias_attr=base.ParamAttr(name="__fc_b__"), ) # nt - nt = paddle.static.data( - name="3", shape=[-1, 1], dtype="int64", lod_level=1 - ) + nt = paddle.static.data(name="3", shape=[-1, 1], dtype="int64") # embedding nt_emb = paddle.static.nn.sparse_embedding( input=nt, diff --git a/test/legacy_test/test_dist_fleet_ps11.py b/test/legacy_test/test_dist_fleet_ps11.py index fedfae90d5fcbe..f2a2cadb75f6ff 100755 --- a/test/legacy_test/test_dist_fleet_ps11.py +++ b/test/legacy_test/test_dist_fleet_ps11.py @@ -74,9 +74,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="1", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="1", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -106,9 +104,7 @@ def get_loss(cos_q_pt, cos_q_nt): # label data label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt - pt = paddle.static.data( - name="2", shape=[-1, 1], dtype="int64", lod_level=1 - ) + pt = paddle.static.data(name="2", shape=[-1, 1], dtype="int64") # embedding pt_emb = paddle.static.nn.sparse_embedding( input=pt, @@ -137,9 +133,7 @@ def get_loss(cos_q_pt, cos_q_nt): bias_attr=base.ParamAttr(name="__fc_b__"), ) # nt - nt = paddle.static.data( - name="3", shape=[-1, 1], dtype="int64", lod_level=1 - ) + nt = paddle.static.data(name="3", shape=[-1, 1], dtype="int64") # embedding nt_emb = paddle.static.nn.sparse_embedding( input=nt, @@ -221,9 +215,7 @@ def test_gpups_dataset(self): slots = ["slot1", "slot2", "slot3", "slot4"] slots_vars = [] for slot in slots: - var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 - ) + var = paddle.static.data(name=slot, shape=[-1, 1], dtype="int64") slots_vars.append(var) dataset = paddle.distributed.InMemoryDataset() diff --git a/test/legacy_test/test_dist_fleet_ps12.py b/test/legacy_test/test_dist_fleet_ps12.py index 84a0d6e9222c69..e0b751e7fe2868 100644 --- a/test/legacy_test/test_dist_fleet_ps12.py +++ b/test/legacy_test/test_dist_fleet_ps12.py @@ -75,9 +75,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="1", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="1", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -107,9 +105,7 @@ def get_loss(cos_q_pt, cos_q_nt): # label data label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt - pt = paddle.static.data( - name="2", shape=[-1, 1], dtype="int64", lod_level=1 - ) + pt = paddle.static.data(name="2", shape=[-1, 1], dtype="int64") # embedding pt_emb = paddle.static.nn.sparse_embedding( input=pt, @@ -138,9 +134,7 @@ def get_loss(cos_q_pt, cos_q_nt): bias_attr=base.ParamAttr(name="__fc_b__"), ) # nt - nt = paddle.static.data( - name="3", shape=[-1, 1], dtype="int64", lod_level=1 - ) + nt = paddle.static.data(name="3", shape=[-1, 1], dtype="int64") # embedding nt_emb = paddle.static.nn.sparse_embedding( input=nt, diff --git a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py index 6795ff96858a0c..0aa1b689a069ee 100644 --- a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py @@ -193,13 +193,11 @@ def net(): name="dnn_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) lr_data = paddle.static.data( name="lr_data", shape=[-1, 1], dtype="int64", - lod_level=1, ) label = paddle.static.data( name="click", diff --git a/test/legacy_test/test_dist_fleet_spmt.py b/test/legacy_test/test_dist_fleet_spmt.py index 2bb7f60dac50e0..e05c58f7e4e0c2 100644 --- a/test/legacy_test/test_dist_fleet_spmt.py +++ b/test/legacy_test/test_dist_fleet_spmt.py @@ -72,9 +72,7 @@ def get_loss(cos_q_pt, cos_q_nt): is_sparse = True # query - q = paddle.static.data( - name="1", shape=[-1, 1], dtype="int64", lod_level=1 - ) + q = paddle.static.data(name="1", shape=[-1, 1], dtype="int64") # embedding q_emb = paddle.static.nn.sparse_embedding( input=q, @@ -104,9 +102,7 @@ def get_loss(cos_q_pt, cos_q_nt): # label data label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") # pt - pt = paddle.static.data( - name="2", shape=[-1, 1], dtype="int64", lod_level=1 - ) + pt = paddle.static.data(name="2", shape=[-1, 1], dtype="int64") # embedding pt_emb = paddle.static.nn.sparse_embedding( input=pt, @@ -135,9 +131,7 @@ def get_loss(cos_q_pt, cos_q_nt): bias_attr=base.ParamAttr(name="__fc_b__"), ) # nt - nt = paddle.static.data( - name="3", shape=[-1, 1], dtype="int64", lod_level=1 - ) + nt = paddle.static.data(name="3", shape=[-1, 1], dtype="int64") # embedding nt_emb = paddle.static.nn.sparse_embedding( input=nt, diff --git a/test/legacy_test/test_dist_transpiler.py b/test/legacy_test/test_dist_transpiler.py index ce5cc047a3fd65..81ce791af84d28 100644 --- a/test/legacy_test/test_dist_transpiler.py +++ b/test/legacy_test/test_dist_transpiler.py @@ -341,13 +341,13 @@ def net_conf(self): dict_size, embedding_size, neg_num = 10000, 8, 5 input_word = paddle.static.data( - name="input_word", shape=[-1, 1], dtype='int64', lod_level=1 + name="input_word", shape=[-1, 1], dtype='int64' ) true_word = paddle.static.data( - name='true_label', shape=[-1, 1], dtype='int64', lod_level=1 + name='true_label', shape=[-1, 1], dtype='int64' ) neg_word = paddle.static.data( - name="neg_label", shape=[-1, 1], dtype='int64', lod_level=1 + name="neg_label", shape=[-1, 1], dtype='int64' ) inputs = [input_word, true_word, neg_word] @@ -686,13 +686,13 @@ def emb_pool(ids, table_name, is_distributed): return pool title_ids = paddle.static.data( - name='title_ids', shape=[-1, 1], dtype='int64', lod_level=1 + name='title_ids', shape=[-1, 1], dtype='int64' ) brand_ids = paddle.static.data( - name='brand_ids', shape=[-1, 1], dtype='int64', lod_level=1 + name='brand_ids', shape=[-1, 1], dtype='int64' ) profile_ids = paddle.static.data( - name='brand_ids', shape=[-1, 1], dtype='int64', lod_level=1 + name='brand_ids', shape=[-1, 1], dtype='int64' ) title_emb = emb_pool(title_ids, self.lookup_table_name, is_distributed) brand_emb = emb_pool(brand_ids, self.lookup_table_name, is_distributed) diff --git a/test/legacy_test/test_dist_tree_index.py b/test/legacy_test/test_dist_tree_index.py index 1e7e2cff143c44..8a0be93960831f 100644 --- a/test/legacy_test/test_dist_tree_index.py +++ b/test/legacy_test/test_dist_tree_index.py @@ -28,19 +28,13 @@ def create_feeds(): user_input = paddle.static.data( - name="item_id", shape=[-1, 1], dtype="int64", lod_level=1 + name="item_id", shape=[-1, 1], dtype="int64" ) - item = paddle.static.data( - name="unit_id", shape=[-1, 1], dtype="int64", lod_level=1 - ) + item = paddle.static.data(name="unit_id", shape=[-1, 1], dtype="int64") - label = paddle.static.data( - name="label", shape=[-1, 1], dtype="int64", lod_level=1 - ) - labels = paddle.static.data( - name="labels", shape=[-1, 1], dtype="int64", lod_level=1 - ) + label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") + labels = paddle.static.data(name="labels", shape=[-1, 1], dtype="int64") feed_list = [user_input, item, label, labels] return feed_list diff --git a/test/legacy_test/test_eager_deletion_dynamic_rnn_base.py b/test/legacy_test/test_eager_deletion_dynamic_rnn_base.py index f852a0a918cfa5..a9973319f0fa26 100644 --- a/test/legacy_test/test_eager_deletion_dynamic_rnn_base.py +++ b/test/legacy_test/test_eager_deletion_dynamic_rnn_base.py @@ -34,9 +34,7 @@ def train(network, use_cuda, batch_size=32, pass_num=2): reader = fake_imdb_reader(word_dict_size, batch_size * 40) train_reader = paddle.batch(reader, batch_size=batch_size) - data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 - ) + data = paddle.static.data(name="words", shape=[-1, 1], dtype="int64") label = paddle.static.data(name="label", shape=[-1, 1], dtype="int64") diff --git a/test/legacy_test/test_fleet_pyramid_hash.py b/test/legacy_test/test_fleet_pyramid_hash.py index cfd02ee72ced9b..6d72326fe121f2 100644 --- a/test/legacy_test/test_fleet_pyramid_hash.py +++ b/test/legacy_test/test_fleet_pyramid_hash.py @@ -31,9 +31,7 @@ def test_dist_geo_server_transpiler(self): num_voc = 128 embed_dim = 64 x_shape, x_lod = [16, 10], [[3, 5, 2, 6]] - x = paddle.static.data( - name='x', shape=x_shape, dtype='int32', lod_level=1 - ) + x = paddle.static.data(name='x', shape=x_shape, dtype='int32') hash_embd = search_pyramid_hash( input=x, num_emb=embed_dim, diff --git a/test/legacy_test/test_fleet_rolemaker.py b/test/legacy_test/test_fleet_rolemaker.py index 1dc7595ea89715..1aee080af78d11 100644 --- a/test/legacy_test/test_fleet_rolemaker.py +++ b/test/legacy_test/test_fleet_rolemaker.py @@ -87,11 +87,11 @@ def test_pslib_1(self): scope = base.Scope() with base.program_guard(train_program, startup_program): show = paddle.static.data( - name="show", shape=[-1, 1], dtype="float32", lod_level=1 + name="show", shape=[-1, 1], dtype="float32" ) fc = paddle.static.nn.fc(x=show, size=1, activation=None) label = paddle.static.data( - name="click", shape=[-1, 1], dtype="int64", lod_level=1 + name="click", shape=[-1, 1], dtype="int64" ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/legacy_test/test_fleet_rolemaker_2.py b/test/legacy_test/test_fleet_rolemaker_2.py index 364cfb17e04535..26f99db7491a40 100644 --- a/test/legacy_test/test_fleet_rolemaker_2.py +++ b/test/legacy_test/test_fleet_rolemaker_2.py @@ -64,11 +64,11 @@ def test_pslib_2(self): scope = base.Scope() with base.program_guard(train_program, startup_program): show = paddle.static.data( - name="show", shape=[-1, 1], dtype="float32", lod_level=1 + name="show", shape=[-1, 1], dtype="float32" ) fc = paddle.static.nn.fc(x=show, size=1, activation=None) label = paddle.static.data( - name="click", shape=[-1, 1], dtype="int64", lod_level=1 + name="click", shape=[-1, 1], dtype="int64" ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/legacy_test/test_fleet_rolemaker_3.py b/test/legacy_test/test_fleet_rolemaker_3.py index 16c908db66ebd1..96e2d6488185f8 100644 --- a/test/legacy_test/test_fleet_rolemaker_3.py +++ b/test/legacy_test/test_fleet_rolemaker_3.py @@ -61,11 +61,11 @@ def test_pslib_1(self): scope = base.Scope() with base.program_guard(train_program, startup_program): show = paddle.static.data( - name="show", shape=[-1, 1], dtype="float32", lod_level=1 + name="show", shape=[-1, 1], dtype="float32" ) fc = paddle.static.nn.fc(x=show, size=1, activation=None) label = paddle.static.data( - name="click", shape=[-1, 1], dtype="int64", lod_level=1 + name="click", shape=[-1, 1], dtype="int64" ) label_cast = paddle.cast(label, dtype='float32') cost = paddle.nn.functional.log_loss(fc, label_cast) diff --git a/test/legacy_test/test_monitor.py b/test/legacy_test/test_monitor.py index 32ac831dc7e090..942ad9cc77d719 100644 --- a/test/legacy_test/test_monitor.py +++ b/test/legacy_test/test_monitor.py @@ -54,9 +54,7 @@ def test_dataset_run_with_stat(self): slots = ["slot1", "slot2", "slot3", "slot4"] slots_vars = [] for slot in slots: - var = paddle.static.data( - name=slot, shape=[-1, 1], dtype="int64", lod_level=1 - ) + var = paddle.static.data(name=slot, shape=[-1, 1], dtype="int64") slots_vars.append(var) embs = [] diff --git a/test/legacy_test/test_print_op.py b/test/legacy_test/test_print_op.py index 9b4b9c83336194..a28cf1fd0af4f3 100755 --- a/test/legacy_test/test_print_op.py +++ b/test/legacy_test/test_print_op.py @@ -37,9 +37,7 @@ def setUp(self): self.x_tensor.set(tensor_np, self.place) def build_network(self, only_forward, **kargs): - x = paddle.static.data( - 'x', shape=[-1, 3], dtype=self.dtype, lod_level=1 - ) + x = paddle.static.data('x', shape=[-1, 3], dtype=self.dtype) x.stop_gradient = False paddle.static.Print(input=x, **kargs) loss = paddle.mean(x) @@ -75,9 +73,7 @@ def test_backward(self): def test_all_parameters(self): prog = paddle.static.Program() with paddle.static.program_guard(prog, paddle.static.Program()): - x = paddle.static.data( - 'x', shape=[-1, 3], dtype=self.dtype, lod_level=1 - ) + x = paddle.static.data('x', shape=[-1, 3], dtype=self.dtype) x.stop_gradient = False for print_tensor_name in [True, False]: diff --git a/test/legacy_test/test_psroi_pool_op.py b/test/legacy_test/test_psroi_pool_op.py index a28dfa8947dfdd..c7fd8a4212a09c 100644 --- a/test/legacy_test/test_psroi_pool_op.py +++ b/test/legacy_test/test_psroi_pool_op.py @@ -375,9 +375,7 @@ def setUp(self): name='x', shape=[2, 490, 28, 28] ) self.x = np.random.random([2, 490, 28, 28]).astype(np.float32) - self.boxes_placeholder = paddle.static.data( - name='boxes', shape=[3, 4], lod_level=1 - ) + self.boxes_placeholder = paddle.static.data(name='boxes', shape=[3, 4]) self.boxes = np.array( [[1, 5, 8, 10], [4, 2, 6, 7], [12, 12, 19, 21]] ).astype(np.float32) diff --git a/test/legacy_test/test_pull_gpups_sparse_op.py b/test/legacy_test/test_pull_gpups_sparse_op.py index 9ed6173a602e85..c2e327662275a2 100644 --- a/test/legacy_test/test_pull_gpups_sparse_op.py +++ b/test/legacy_test/test_pull_gpups_sparse_op.py @@ -33,7 +33,7 @@ def test_static_graph(self): slots = [] with base.program_guard(train_program, startup_program): l = paddle.static.data( - name='input', shape=[-1, 1], dtype="int64", lod_level=1 + name='input', shape=[-1, 1], dtype="int64" ) slots.append(l) output = _pull_gpups_sparse( diff --git a/test/legacy_test/test_pyramid_hash_op.py b/test/legacy_test/test_pyramid_hash_op.py index b7173774921644..6bad9d08357c13 100644 --- a/test/legacy_test/test_pyramid_hash_op.py +++ b/test/legacy_test/test_pyramid_hash_op.py @@ -26,9 +26,7 @@ def test_api(self): num_voc = 128 embed_dim = 64 x_shape, x_lod = [16, 10], [[3, 5, 2, 6]] - x = paddle.static.data( - name='x', shape=x_shape, dtype='int32', lod_level=1 - ) + x = paddle.static.data(name='x', shape=x_shape, dtype='int32') hash_embed = search_pyramid_hash( input=x, num_emb=embed_dim, diff --git a/test/legacy_test/test_regularizer.py b/test/legacy_test/test_regularizer.py index 1fac4cae10d0c7..6ba3cb0c41a352 100644 --- a/test/legacy_test/test_regularizer.py +++ b/test/legacy_test/test_regularizer.py @@ -115,7 +115,7 @@ def check_l2decay_regularizer(self, place, model): main_prog=main_prog, startup_prog=startup_prog ): data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 + name="words", shape=[-1, 1], dtype="int64" ) label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" @@ -141,7 +141,7 @@ def check_l2decay(self, place, model): main_prog=main_prog, startup_prog=startup_prog ): data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 + name="words", shape=[-1, 1], dtype="int64" ) label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" diff --git a/test/legacy_test/test_regularizer_api.py b/test/legacy_test/test_regularizer_api.py index c29cecb08cb0ad..8707228b3c7549 100644 --- a/test/legacy_test/test_regularizer_api.py +++ b/test/legacy_test/test_regularizer_api.py @@ -79,7 +79,7 @@ def check_l2decay_regularizer(self, place, model): main_prog=main_prog, startup_prog=startup_prog ): data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 + name="words", shape=[-1, 1], dtype="int64" ) label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" @@ -105,7 +105,7 @@ def check_l2decay(self, place, model): main_prog=main_prog, startup_prog=startup_prog ): data = paddle.static.data( - name="words", shape=[-1, 1], dtype="int64", lod_level=1 + name="words", shape=[-1, 1], dtype="int64" ) label = paddle.static.data( name="label", shape=[-1, 1], dtype="int64" diff --git a/test/legacy_test/test_tdm_child_op.py b/test/legacy_test/test_tdm_child_op.py index 274795f01d5e1b..cd10a2e20d05a6 100644 --- a/test/legacy_test/test_tdm_child_op.py +++ b/test/legacy_test/test_tdm_child_op.py @@ -149,9 +149,7 @@ def config(self): class TestTDMChildShape(unittest.TestCase): def test_shape(self): with paddle_static_guard(): - x = paddle.static.data( - name='x', shape=[-1, 1], dtype='int32', lod_level=1 - ) + x = paddle.static.data(name='x', shape=[-1, 1], dtype='int32') tdm_tree_info = create_tdm_tree() tree_info_np = np.array(tdm_tree_info).astype('int32') diff --git a/test/legacy_test/test_tdm_sampler_op.py b/test/legacy_test/test_tdm_sampler_op.py index 5e341bb8268b0b..64334431486d9c 100644 --- a/test/legacy_test/test_tdm_sampler_op.py +++ b/test/legacy_test/test_tdm_sampler_op.py @@ -254,9 +254,7 @@ def config(self): class TestTDMSamplerShape(unittest.TestCase): def test_shape(self): with paddle_static_guard(): - x = paddle.static.data( - name='x', shape=[-1, 1], dtype='int32', lod_level=1 - ) + x = paddle.static.data(name='x', shape=[-1, 1], dtype='int32') tdm_tree_travel = create_tdm_travel() tdm_tree_layer = create_tdm_layer() layer_node_num_list = [len(i) for i in tdm_tree_layer] diff --git a/test/legacy_test/test_uniform_random_op.py b/test/legacy_test/test_uniform_random_op.py index 3c1faf4996a67b..2587b034deb7bb 100644 --- a/test/legacy_test/test_uniform_random_op.py +++ b/test/legacy_test/test_uniform_random_op.py @@ -320,9 +320,7 @@ def test_api(self): with paddle.static.program_guard( paddle.static.Program(), paddle.static.Program() ): - x = paddle.static.data( - 'x', shape=[-1, 16], dtype='float32', lod_level=1 - ) + x = paddle.static.data('x', shape=[-1, 16], dtype='float32') linear = paddle.nn.Linear( in_features=x.shape[-1], From 80e8ea0e6e8eb7d2c502505f2ff7a1481e7f9fd5 Mon Sep 17 00:00:00 2001 From: anotherme Date: Thu, 5 Dec 2024 16:48:41 +0800 Subject: [PATCH 189/288] [CustomDevice] quant api support custom device arch (#69971) --- paddle/phi/infermeta/unary.cc | 2 +- python/paddle/nn/quant/quantized_linear.py | 63 ++++++++++++---------- 2 files changed, 37 insertions(+), 28 deletions(-) diff --git a/paddle/phi/infermeta/unary.cc b/paddle/phi/infermeta/unary.cc index 704afc8a769537..065ab2357a97bb 100644 --- a/paddle/phi/infermeta/unary.cc +++ b/paddle/phi/infermeta/unary.cc @@ -5950,7 +5950,7 @@ void WeightQuantizeInferMeta(const MetaTensor& x, const int32_t group_size, MetaTensor* out, MetaTensor* scale) { -#ifndef PADDLE_WITH_HIP +#ifdef PADDLE_WITH_CUDA PADDLE_ENFORCE_EQ( ((arch == 70) || (arch == 75) || (arch == 80) || (arch == 86) || (arch == 89) || (arch == 90)), diff --git a/python/paddle/nn/quant/quantized_linear.py b/python/paddle/nn/quant/quantized_linear.py index 532f868772323b..1bd7655f66781a 100644 --- a/python/paddle/nn/quant/quantized_linear.py +++ b/python/paddle/nn/quant/quantized_linear.py @@ -20,6 +20,10 @@ from paddle import _C_ops from paddle.base.data_feeder import check_dtype from paddle.base.framework import convert_np_dtype_to_dtype_ +from paddle.device import ( + is_compiled_with_cuda, + is_compiled_with_rocm, +) from paddle.device.cuda import get_device_capability from paddle.framework import ( LayerHelper, @@ -40,17 +44,21 @@ def _get_arch_info(): # Get SMVersion from device. - cuda_version = paddle.version.cuda() - if ( - cuda_version is not None and cuda_version != 'False' - ) or paddle.is_compiled_with_rocm(): - major, minor = get_device_capability() - arch = int(major * 10 + minor) - return arch + if is_compiled_with_cuda() or is_compiled_with_rocm(): + cuda_version = paddle.version.cuda() + if ( + cuda_version is not None and cuda_version != 'False' + ) or paddle.is_compiled_with_rocm(): + major, minor = get_device_capability() + arch = int(major * 10 + minor) + return arch + else: + raise ValueError( + "Paddle is not compiled with CUDA, we cannot get SMVersion from device, please try to compile Paddle with CUDA" + ) else: - raise ValueError( - "Paddle is not compiled with CUDA, we cannot get SMVersion from device, please try to compile Paddle with CUDA" - ) + # Defaut arch value for type checking. + return 0 def weight_quantize( @@ -90,15 +98,15 @@ def weight_quantize( if arch is None: arch = _get_arch_info() - assert ( - arch == 70 - or arch == 75 - or arch == 80 - or arch == 86 - or arch == 89 - or arch == 90 - or paddle.is_compiled_with_rocm() - ), f"Currently weight_quantize only support SM70/75/80/86/89/90. but got {arch} " + if is_compiled_with_cuda(): + assert ( + arch == 70 + or arch == 75 + or arch == 80 + or arch == 86 + or arch == 89 + or arch == 90 + ), f"Currently weight_quantize only support SM70/75/80/86/89/90. but got {arch} " assert ( group_size == -1 or group_size == 64 or group_size == 128 @@ -224,14 +232,15 @@ def weight_only_linear( if arch is None: arch = _get_arch_info() - assert ( - arch == 70 - or arch == 75 - or arch == 80 - or arch == 86 - or arch == 89 - or arch == 90 - ), f"Currently weight_quantize only support SM70/75/80/86/89/90. but got {arch} " + if is_compiled_with_cuda(): + assert ( + arch == 70 + or arch == 75 + or arch == 80 + or arch == 86 + or arch == 89 + or arch == 90 + ), f"Currently weight_quantize only support SM70/75/80/86/89/90. but got {arch} " assert ( group_size == -1 or group_size == 64 or group_size == 128 ), f"Currently weight_quantize only support group size of -1, 64 or 128. but got {group_size} " From 778bc80ee65808095ed7568d68cd7adbb60c1096 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Thu, 5 Dec 2024 17:03:03 +0800 Subject: [PATCH 190/288] [CINN]Add OriginalAttributesFilter for grad op shape cache (#69881) * [CINN]Add ValidAttrFilterForShapeCache * refine * change name --- .../operator/transforms/add_cinn_pass.cc | 4 ++ .../cache_grad_op_symbol_shape_gen.py | 58 ++++++++++++++++--- .../operator/utils/shape_analysis_utils.h | 3 + .../shape/utils/original_attributes_filter.h | 47 +++++++++++++++ .../dialect/shape/utils/shape_analysis.h | 10 ++-- .../transforms/shape_optimization_pass.cc | 6 +- .../shape/utils/original_attributes_filter.cc | 50 ++++++++++++++++ .../src/dialect/shape/utils/shape_analysis.cc | 41 +------------ 8 files changed, 166 insertions(+), 53 deletions(-) create mode 100644 paddle/pir/include/dialect/shape/utils/original_attributes_filter.h create mode 100644 paddle/pir/src/dialect/shape/utils/original_attributes_filter.cc diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc index 1e18161bf16165..5b0493ea06f0cf 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc @@ -18,11 +18,13 @@ #include "paddle/common/errors.h" #include "paddle/common/flags.h" #include "paddle/fluid/pir/dialect/operator/ir/op_dialect.h" +#include "paddle/fluid/pir/dialect/operator/utils/shape_analysis_utils.h" #include "paddle/phi/core/enforce.h" #include "paddle/pir/include/core/ir_context.h" #include "paddle/pir/include/core/program.h" #include "paddle/pir/include/dialect/shape/ir/shape_dialect.h" #include "paddle/pir/include/dialect/shape/transforms/shape_optimization_pass.h" +#include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #include "paddle/pir/include/pass/pass_manager.h" #include "paddle/cinn/hlir/dialect/operator/ir/manual_op.h" @@ -101,6 +103,8 @@ void ApplyShapeOptimizationPass( const std::function()>& CreatePassManager) { std::shared_ptr pass_manager = CreatePassManager(); + pir::OriginalAttributesFilter::Instance().SetOriginalAttributesMap( + paddle::dialect::GetAllOpOriginalAttributes()); bool has_dynamic_shape = HasDynamicShape(*program); if (has_dynamic_shape) { if (FLAGS_cinn_specify_input_dynamic_dim) { diff --git a/paddle/fluid/pir/dialect/op_generator/cache_grad_op_symbol_shape_gen.py b/paddle/fluid/pir/dialect/op_generator/cache_grad_op_symbol_shape_gen.py index 10b00ac27dbf65..0bd69f2e0a9aed 100644 --- a/paddle/fluid/pir/dialect/op_generator/cache_grad_op_symbol_shape_gen.py +++ b/paddle/fluid/pir/dialect/op_generator/cache_grad_op_symbol_shape_gen.py @@ -37,11 +37,23 @@ }} // namespace paddle """ +GET_ORIGINAL_ATTR_MAP_FUNC_CODE_TEMPLATE = """ +std::unordered_map> GetAllOpOriginalAttributes() {{ + return {{{original_attr_map_items} + }}; +}} +""" + +ORIGINAL_ATTR_MAP_ITEM_CODE_TEMPLATE = """ + {{"{op_name}", {{{original_attr_name_list}}}}},""" + CACHE_GRAD_OP_SYMBOL_SHAPE_FUNC_CODE_TEMPLATE = """ void {op_name}Op::CacheGradOpSymbolicShape(pir::InferSymbolicShapeContext* infer_context) {{ {create_grad_op_shape_info_code} pir::InferSymbolicShapeCacheKey op_shape_info( - "{grad_op_name}", {{{input_shape_list}}}, this->operation()->attributes()); + "{grad_op_name}", + {{{input_shape_list}}}, + pir::GetOrderedOriginalAttributes("{grad_op_name}", this->operation()->attributes())); {create_grad_op_output_shape_code} std::vector output_shape_or_data{{{output_shape_list}}}; @@ -105,7 +117,11 @@ def parse_yaml(self, op_yaml_files, op_compat_yaml_file): return op_info_maps def gen_cpp_file_code(self, cpp_file_path): - body_code = "" + cache_func_code = "" + original_attr_map_items_code = "" + get_op_name_with_dialect = ( + lambda op_name: self.dialect_name + "." + op_name + ) for op_info_item in self.op_info_maps.values(): if op_info_item.backward_name is None: continue @@ -119,7 +135,26 @@ def gen_cpp_file_code(self, cpp_file_path): ): continue + original_attr_name = set(op_info_item.attribute_name_list) & set( + grad_op_item.attribute_name_list + ) + convert_to_cpp_str = lambda str: '"' + str + '"' + original_attr_name_list_str = f"{', '.join(convert_to_cpp_str(item) for item in original_attr_name)}" + original_attr_map_items_code += ( + ORIGINAL_ATTR_MAP_ITEM_CODE_TEMPLATE.format( + op_name=get_op_name_with_dialect( + op_info_item.backward_name + ), + original_attr_name_list=original_attr_name_list_str, + ) + ) for op_phi_name in op_info_item.op_phi_name: + original_attr_map_items_code += ( + ORIGINAL_ATTR_MAP_ITEM_CODE_TEMPLATE.format( + op_name=get_op_name_with_dialect(op_phi_name), + original_attr_name_list=original_attr_name_list_str, + ) + ) create_grad_op_shape_info_code = "" for input_name in grad_op_item.input_name_list: if input_name in grad_op_item.forward_input_name_list: @@ -213,17 +248,17 @@ def gen_cpp_file_code(self, cpp_file_path): logging.warning( f"{op_phi_name}'s grad op has some exception, please check it in yaml file." ) - body_code += UNIMPLEMENTED_CODE_TEMPLATE.format( + cache_func_code += UNIMPLEMENTED_CODE_TEMPLATE.format( op_name=to_pascal_case(op_phi_name), ) continue - body_code += CACHE_GRAD_OP_SYMBOL_SHAPE_FUNC_CODE_TEMPLATE.format( + cache_func_code += CACHE_GRAD_OP_SYMBOL_SHAPE_FUNC_CODE_TEMPLATE.format( op_name=to_pascal_case(op_phi_name), create_grad_op_shape_info_code=create_grad_op_shape_info_code, - grad_op_name=self.dialect_name - + "." - + grad_op_item.op_phi_name[0], + grad_op_name=get_op_name_with_dialect( + grad_op_item.op_phi_name[0] + ), input_shape_list=", ".join( [ input_name + SHAPE_VAR_NAME_SUFFIX @@ -252,10 +287,15 @@ def gen_cpp_file_code(self, cpp_file_path): if kernel_func_name == op_origin_name: continue inplace_suffix = '_' if is_inplace_version else '' - body_code += UNIMPLEMENTED_CODE_TEMPLATE.format( + cache_func_code += UNIMPLEMENTED_CODE_TEMPLATE.format( op_name=to_pascal_case(kernel_func_name) + inplace_suffix ) + get_original_attr_map_func_code = ( + GET_ORIGINAL_ATTR_MAP_FUNC_CODE_TEMPLATE.format( + original_attr_map_items=original_attr_map_items_code + ) + ) directory_path = os.path.dirname(cpp_file_path) if not os.path.exists(directory_path): @@ -264,7 +304,7 @@ def gen_cpp_file_code(self, cpp_file_path): with open(cpp_file_path, 'w') as f: f.write( CPP_FILE_TEMPLATE.format( - body=body_code, + body=get_original_attr_map_func_code + cache_func_code, ) ) diff --git a/paddle/fluid/pir/dialect/operator/utils/shape_analysis_utils.h b/paddle/fluid/pir/dialect/operator/utils/shape_analysis_utils.h index b57f775259f138..3e170acb191d63 100644 --- a/paddle/fluid/pir/dialect/operator/utils/shape_analysis_utils.h +++ b/paddle/fluid/pir/dialect/operator/utils/shape_analysis_utils.h @@ -14,10 +14,13 @@ #pragma once +#include "paddle/pir/include/dialect/shape/utils/original_attributes_filter.h" #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" namespace paddle { namespace dialect { +std::unordered_map> +GetAllOpOriginalAttributes(); const symbol::ShapeOrDataDimExprs& GetInputShape( const pir::InferSymbolicShapeContext* infer_context, diff --git a/paddle/pir/include/dialect/shape/utils/original_attributes_filter.h b/paddle/pir/include/dialect/shape/utils/original_attributes_filter.h new file mode 100644 index 00000000000000..ec2aab8aecf21f --- /dev/null +++ b/paddle/pir/include/dialect/shape/utils/original_attributes_filter.h @@ -0,0 +1,47 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once +#include +#include +#include "paddle/pir/include/core/builtin_attribute.h" + +namespace pir { +std::map GetOrderedOriginalAttributes( + std::string op_name, + const std::unordered_map& attributes); + +class OriginalAttributesFilter { + public: + static OriginalAttributesFilter& Instance(); + + OriginalAttributesFilter(const OriginalAttributesFilter&) = delete; + OriginalAttributesFilter(OriginalAttributesFilter&&) = delete; + OriginalAttributesFilter& operator=(const OriginalAttributesFilter&) = delete; + + void SetOriginalAttributesMap( + const std::unordered_map>& + original_attributes_map) { + original_attributes_map_ = original_attributes_map; + } + + private: + OriginalAttributesFilter() {} + std::unordered_map> + original_attributes_map_; + friend std::map GetOrderedOriginalAttributes( + std::string op_name, + const std::unordered_map& attributes); +}; +} // namespace pir diff --git a/paddle/pir/include/dialect/shape/utils/shape_analysis.h b/paddle/pir/include/dialect/shape/utils/shape_analysis.h index 9b56c6f6a27ebb..359d018d190829 100644 --- a/paddle/pir/include/dialect/shape/utils/shape_analysis.h +++ b/paddle/pir/include/dialect/shape/utils/shape_analysis.h @@ -35,13 +35,13 @@ using InferSymbolicShapeCacheValue = std::vector; */ class IR_API InferSymbolicShapeCacheKey { public: - InferSymbolicShapeCacheKey( - const Operation& op, - const std::vector& input_shape_or_datas); InferSymbolicShapeCacheKey( const std::string& op_name, const std::vector& input_shape_or_datas, - const AttributeMap& attributes); + const std::map& attributes) + : op_name_(op_name), + input_shape_or_datas_(input_shape_or_datas), + attributes_(attributes) {} bool operator==(const InferSymbolicShapeCacheKey& other) const; std::size_t GetHashValue() const; friend std::ostream& operator<<(std::ostream& os, @@ -51,7 +51,7 @@ class IR_API InferSymbolicShapeCacheKey { private: std::string op_name_; std::vector input_shape_or_datas_; - std::vector> attributes_; + std::map attributes_; const std::vector& GetInputShapeOrDatas() const; void SetInputShapeOrDatas( const std::vector& input_shape_or_datas); diff --git a/paddle/pir/src/dialect/shape/transforms/shape_optimization_pass.cc b/paddle/pir/src/dialect/shape/transforms/shape_optimization_pass.cc index 87a651eeeb530f..0779bfaefb224b 100644 --- a/paddle/pir/src/dialect/shape/transforms/shape_optimization_pass.cc +++ b/paddle/pir/src/dialect/shape/transforms/shape_optimization_pass.cc @@ -23,6 +23,7 @@ #include "paddle/pir/include/dialect/shape/interface/infer_symbolic_shape/infer_symbolic_shape.h" #include "paddle/pir/include/dialect/shape/ir/shape_attribute.h" #include "paddle/pir/include/dialect/shape/ir/shape_dialect.h" +#include "paddle/pir/include/dialect/shape/utils/original_attributes_filter.h" #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #include "paddle/pir/include/pass/pass_manager.h" #include "paddle/pir/include/pass/pass_registry.h" @@ -371,7 +372,10 @@ void InferSymExprForBlock(const Block& block, input_shape_or_data.emplace_back( infer_context->GetShapeOrDataForValue(input)); } - InferSymbolicShapeCacheKey op_infer_cache_key(op, input_shape_or_data); + InferSymbolicShapeCacheKey op_infer_cache_key( + op.name(), + input_shape_or_data, + GetOrderedOriginalAttributes(op.name(), op.attributes())); InferSymExprForOp(&op, infer_context, op_infer_cache_key); CacheForwardOpSymbolicShape(&op, infer_context, op_infer_cache_key); CacheBackwardOpSymbolicShape(&op, infer_context); diff --git a/paddle/pir/src/dialect/shape/utils/original_attributes_filter.cc b/paddle/pir/src/dialect/shape/utils/original_attributes_filter.cc new file mode 100644 index 00000000000000..eb4cd8d00e3029 --- /dev/null +++ b/paddle/pir/src/dialect/shape/utils/original_attributes_filter.cc @@ -0,0 +1,50 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/pir/include/dialect/shape/utils/original_attributes_filter.h" + +namespace pir { +OriginalAttributesFilter& OriginalAttributesFilter::Instance() { + static OriginalAttributesFilter instance; + return instance; +} +std::map GetOrderedOriginalAttributes( + std::string op_name, + const std::unordered_map& attributes) { + std::map order_attributes; + + const auto& IsValidAttrName = [&](const std::string& op_name, + const std::string& attr_name) -> bool { + static const char* kOpCallStack = "op_callstack"; + static const char* kSymShapeStr = "sym_shape_str"; + static const char* kResultName = "name"; + static const char* kStopGradient = "stop_gradient"; + if (attr_name == kOpCallStack || attr_name == kSymShapeStr || + attr_name == kStopGradient || attr_name == kResultName) + return false; + const auto& original_attributes_map = + OriginalAttributesFilter::Instance().original_attributes_map_; + if (original_attributes_map.count(op_name) == 0) return true; + if (original_attributes_map.at(op_name).count(attr_name) == 0) return false; + return true; + }; + + for (const auto& [attr_name, attr_value] : attributes) { + if (!attr_value || !IsValidAttrName(op_name, attr_name)) continue; + order_attributes[attr_name] = attr_value; + } + return order_attributes; +} + +} // namespace pir diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 92568eaba9a990..2e79078430e53f 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -861,34 +861,6 @@ bool IsStaticShape(const Value& value) { return false; } -static const char* kOpCallStack = "op_callstack"; -static const char* kSymShapeStr = "sym_shape_str"; -static const char* kResultName = "name"; -static const char* kStopGradient = "stop_gradient"; - -InferSymbolicShapeCacheKey::InferSymbolicShapeCacheKey( - const Operation& op, - const std::vector& input_shape_or_datas) - : InferSymbolicShapeCacheKey( - op.name(), input_shape_or_datas, op.attributes()) {} - -InferSymbolicShapeCacheKey::InferSymbolicShapeCacheKey( - const std::string& op_name, - const std::vector& input_shape_or_datas, - const AttributeMap& attributes) - : op_name_(op_name), input_shape_or_datas_(input_shape_or_datas) { - // Keep attribute always in order. - std::map> order_attributes( - attributes.begin(), attributes.end()); - attributes_.reserve(attributes.size()); - for (const auto& [attr_name, attr_value] : order_attributes) { - if (!attr_value || attr_name == kOpCallStack || attr_name == kSymShapeStr || - attr_name == kStopGradient || attr_name == kResultName) - continue; - attributes_.emplace_back(attr_name, attr_value); - } -} - std::size_t InferSymbolicShapeCacheKey::GetHashValue() const { const auto name_hash_func = std::hash(); const auto attr_hash_func = std::hash(); @@ -907,12 +879,7 @@ std::size_t InferSymbolicShapeCacheKey::GetHashValue() const { bool InferSymbolicShapeCacheKey::operator==( const InferSymbolicShapeCacheKey& other) const { if (op_name_ != other.op_name_) return false; - if (attributes_.size() != other.attributes_.size()) return false; - for (std::size_t i = 0; i < attributes_.size(); ++i) { - if (attributes_[i].first != other.attributes_[i].first || - attributes_[i].second != other.attributes_[i].second) - return false; - } + if (attributes_ != other.attributes_) return false; if (input_shape_or_datas_.size() != other.input_shape_or_datas_.size()) return false; for (std::size_t i = 0; i < input_shape_or_datas_.size(); ++i) { @@ -927,11 +894,10 @@ std::ostream& operator<<(std::ostream& os, os << "InferSymbolicShapeCacheKey - " << info.op_name_ << std::endl; if (!info.attributes_.empty()) { os << " attrs: {"; - for (std::size_t i = 0; i < info.attributes_.size() - 1; ++i) { - ::pir::IrPrinter(os).PrintAttribute(info.attributes_[i].second); + for (const auto& attr : info.attributes_) { + ::pir::IrPrinter(os).PrintAttribute(attr.second); os << ", "; } - ::pir::IrPrinter(os).PrintAttribute(info.attributes_.back().second); os << std::endl; } if (!info.input_shape_or_datas_.empty()) { @@ -952,5 +918,4 @@ void InferSymbolicShapeCacheKey::SetInputShapeOrDatas( const std::vector& input_shape_or_datas) { this->input_shape_or_datas_ = input_shape_or_datas; } - } // namespace pir From 3a39e23e635e98d56469f7f0eb74314e9414c224 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Thu, 5 Dec 2024 17:07:02 +0800 Subject: [PATCH 191/288] [CINN]fix CauseNewSymbolicShape (#69952) --- paddle/cinn/hlir/framework/pir/utils.cc | 4 ---- 1 file changed, 4 deletions(-) diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 12bc3fe115f60c..8e023f30dbf19a 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -386,10 +386,6 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { return true; } - if (!HaveUnkDim(op)) { - return false; - } - std::unordered_set input_exprs = [&]() { std::unordered_set res; for (const auto& input_value : op.operands_source()) { From 628d01ad5bc94071937d7924b46d83bfe1e3ba1c Mon Sep 17 00:00:00 2001 From: Sylence8 <98306351+Sylence8@users.noreply.github.com> Date: Thu, 5 Dec 2024 17:17:44 +0800 Subject: [PATCH 192/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20=E7=AC=AC=E4=BA=8C=E6=9C=9F=20=E5=85=B6=E5=AE=83?= =?UTF-8?q?=E9=97=AE=E9=A2=98No.5=20=E3=80=816=E3=80=8115=E3=80=91Move=20`?= =?UTF-8?q?paddle.diagonal`=20to=20`paddle.linalg.diagonal`=20(#69934)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * 【SCU】【Paddle Tensor 第二期 其它问题No.5】'array_api_tests/test_linalg.py::test_diagonal' * codestyle fix --- python/paddle/__init__.py | 2 +- python/paddle/linalg.py | 2 + python/paddle/tensor/__init__.py | 2 +- python/paddle/tensor/linalg.py | 130 +++++++++++++++++++++++++++++++ python/paddle/tensor/math.py | 129 ------------------------------ 5 files changed, 134 insertions(+), 131 deletions(-) diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py index b9a6ac235abd24..66778e692bcbcb 100644 --- a/python/paddle/__init__.py +++ b/python/paddle/__init__.py @@ -227,6 +227,7 @@ cdist, cholesky, cross, + diagonal, dist, dot, eigvalsh, @@ -410,7 +411,6 @@ cumsum_, cumulative_trapezoid, deg2rad, - diagonal, diff, digamma, digamma_, diff --git a/python/paddle/linalg.py b/python/paddle/linalg.py index 2becdf5ab62753..06e978572a26ee 100644 --- a/python/paddle/linalg.py +++ b/python/paddle/linalg.py @@ -22,6 +22,7 @@ cov, cross, det, + diagonal, eig, eigh, eigvals, @@ -90,4 +91,5 @@ 'lstsq', 'ormqr', 'fp8_fp8_half_gemm_fused', + 'diagonal', ] diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 815f0b498fdb0b..84666c71ceffe6 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -73,6 +73,7 @@ corrcoef, cov, cross, + diagonal, dist, dot, eig, @@ -282,7 +283,6 @@ cumsum_, cumulative_trapezoid, deg2rad, - diagonal, diff, digamma, digamma_, diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index f070246493cbd6..de8fe29018ee09 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -5828,3 +5828,133 @@ def cholesky_inverse( else: A = x @ x.T return paddle.linalg.inv(A) + + +def diagonal( + x: Tensor, + offset: int = 0, + axis1: int = 0, + axis2: int = 1, + name: str | None = None, +) -> Tensor: + """ + Computes the diagonals of the input tensor x. + + If ``x`` is 2D, returns the diagonal. + If ``x`` has larger dimensions, diagonals be taken from the 2D planes specified by axis1 and axis2. + By default, the 2D planes formed by the first and second axis of the input tensor x. + + The argument ``offset`` determines where diagonals are taken from input tensor x: + + - If offset = 0, it is the main diagonal. + - If offset > 0, it is above the main diagonal. + - If offset < 0, it is below the main diagonal. + + Args: + x (Tensor): The input tensor x. Must be at least 2-dimensional. The input data type should be bool, int32, + int64, bfloat16, float16, float32, float64. + offset (int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals). + axis1 (int, optional): The first axis with respect to take diagonal. Default: 0. + axis2 (int, optional): The second axis with respect to take diagonal. Default: 1. + name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. + + Returns: + Tensor: a partial view of input tensor in specify two dimensions, the output data type is the same as input data type. + + Examples: + .. code-block:: python + + >>> import paddle + + >>> paddle.seed(2023) + >>> x = paddle.rand([2, 2, 3],'float32') + >>> print(x) + Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, + [[[0.86583614, 0.52014720, 0.25960937], + [0.90525323, 0.42400089, 0.40641287]], + [[0.97020894, 0.74437362, 0.51785129], + [0.73292869, 0.97786582, 0.04315904]]]) + + >>> out1 = paddle.diagonal(x) + >>> print(out1) + Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + [[0.86583614, 0.73292869], + [0.52014720, 0.97786582], + [0.25960937, 0.04315904]]) + + >>> out2 = paddle.diagonal(x, offset=0, axis1=2, axis2=1) + >>> print(out2) + Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + [[0.86583614, 0.42400089], + [0.97020894, 0.97786582]]) + + >>> out3 = paddle.diagonal(x, offset=1, axis1=0, axis2=1) + >>> print(out3) + Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True, + [[0.90525323], + [0.42400089], + [0.40641287]]) + + >>> out4 = paddle.diagonal(x, offset=0, axis1=1, axis2=2) + >>> print(out4) + Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, + [[0.86583614, 0.42400089], + [0.97020894, 0.97786582]]) + + """ + if in_dynamic_or_pir_mode(): + return _C_ops.diagonal(x, offset, axis1, axis2) + else: + + def __check_input(x, offset, axis1, axis2): + check_dtype( + x.dtype, + 'Input', + [ + 'bool', + 'int32', + 'int64', + 'float16', + 'uint16', + 'float32', + 'float64', + ], + 'diagonal', + ) + + input_shape = list(x.shape) + assert len(input_shape) >= 2, ( + "The x must be at least 2-dimensional, " + f"But received Input x's dimensional: {len(input_shape)}.\n" + ) + + axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1 + axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2 + + assert axis1_ < len(input_shape), ( + "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n" + % (-(len(input_shape)), len(input_shape) - 1, axis1) + ) + + assert axis2_ < len(input_shape), ( + "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n" + % (-(len(input_shape)), len(input_shape) - 1, axis2) + ) + + assert axis1_ != axis2_, ( + "axis1 and axis2 cannot be the same axis." + "But received axis1 = %d, axis2 = %d\n" % (axis1, axis2) + ) + + __check_input(x, offset, axis1, axis2) + helper = LayerHelper('diagonal', **locals()) + out = helper.create_variable_for_type_inference(dtype=x.dtype) + + helper.append_op( + type='diagonal', + inputs={'Input': [x]}, + attrs={'offset': offset, 'axis1': axis1, 'axis2': axis2}, + outputs={'Out': [out]}, + ) + + return out diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index 829dd44133cc3b..ea527f34e0c089 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -3988,135 +3988,6 @@ def __check_input(x, offset, axis1, axis2): return out -def diagonal( - x: Tensor, - offset: int = 0, - axis1: int = 0, - axis2: int = 1, - name: str | None = None, -) -> Tensor: - """ - Computes the diagonals of the input tensor x. - - If ``x`` is 2D, returns the diagonal. - If ``x`` has larger dimensions, diagonals be taken from the 2D planes specified by axis1 and axis2. - By default, the 2D planes formed by the first and second axis of the input tensor x. - - The argument ``offset`` determines where diagonals are taken from input tensor x: - - - If offset = 0, it is the main diagonal. - - If offset > 0, it is above the main diagonal. - - If offset < 0, it is below the main diagonal. - - Args: - x (Tensor): The input tensor x. Must be at least 2-dimensional. The input data type should be bool, int32, - int64, bfloat16, float16, float32, float64. - offset (int, optional): Which diagonals in input tensor x will be taken. Default: 0 (main diagonals). - axis1 (int, optional): The first axis with respect to take diagonal. Default: 0. - axis2 (int, optional): The second axis with respect to take diagonal. Default: 1. - name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. - - Returns: - Tensor: a partial view of input tensor in specify two dimensions, the output data type is the same as input data type. - - Examples: - .. code-block:: python - - >>> import paddle - - >>> paddle.seed(2023) - >>> x = paddle.rand([2, 2, 3],'float32') - >>> print(x) - Tensor(shape=[2, 2, 3], dtype=float32, place=Place(cpu), stop_gradient=True, - [[[0.86583614, 0.52014720, 0.25960937], - [0.90525323, 0.42400089, 0.40641287]], - [[0.97020894, 0.74437362, 0.51785129], - [0.73292869, 0.97786582, 0.04315904]]]) - - >>> out1 = paddle.diagonal(x) - >>> print(out1) - Tensor(shape=[3, 2], dtype=float32, place=Place(cpu), stop_gradient=True, - [[0.86583614, 0.73292869], - [0.52014720, 0.97786582], - [0.25960937, 0.04315904]]) - - >>> out2 = paddle.diagonal(x, offset=0, axis1=2, axis2=1) - >>> print(out2) - Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, - [[0.86583614, 0.42400089], - [0.97020894, 0.97786582]]) - - >>> out3 = paddle.diagonal(x, offset=1, axis1=0, axis2=1) - >>> print(out3) - Tensor(shape=[3, 1], dtype=float32, place=Place(cpu), stop_gradient=True, - [[0.90525323], - [0.42400089], - [0.40641287]]) - - >>> out4 = paddle.diagonal(x, offset=0, axis1=1, axis2=2) - >>> print(out4) - Tensor(shape=[2, 2], dtype=float32, place=Place(cpu), stop_gradient=True, - [[0.86583614, 0.42400089], - [0.97020894, 0.97786582]]) - - """ - if in_dynamic_or_pir_mode(): - return _C_ops.diagonal(x, offset, axis1, axis2) - else: - - def __check_input(x, offset, axis1, axis2): - check_dtype( - x.dtype, - 'Input', - [ - 'bool', - 'int32', - 'int64', - 'float16', - 'uint16', - 'float32', - 'float64', - ], - 'diagonal', - ) - - input_shape = list(x.shape) - assert len(input_shape) >= 2, ( - "The x must be at least 2-dimensional, " - f"But received Input x's dimensional: {len(input_shape)}.\n" - ) - - axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1 - axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2 - - assert axis1_ < len(input_shape), ( - "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis1) - ) - - assert axis2_ < len(input_shape), ( - "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis2) - ) - - assert axis1_ != axis2_, ( - "axis1 and axis2 cannot be the same axis." - "But received axis1 = %d, axis2 = %d\n" % (axis1, axis2) - ) - - __check_input(x, offset, axis1, axis2) - helper = LayerHelper('diagonal', **locals()) - out = helper.create_variable_for_type_inference(dtype=x.dtype) - - helper.append_op( - type='diagonal', - inputs={'Input': [x]}, - attrs={'offset': offset, 'axis1': axis1, 'axis2': axis2}, - outputs={'Out': [out]}, - ) - return out - - def kron(x: Tensor, y: Tensor, name: str | None = None) -> Tensor: r""" Compute the Kronecker product of two tensors, a From 18e8791f6af4836a9240095bbb3d49fa7503dc09 Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Thu, 5 Dec 2024 18:38:39 +0800 Subject: [PATCH 193/288] =?UTF-8?q?[CINN]=20refine=20recompute=20for=20dat?= =?UTF-8?q?a=EF=BC=8Cparameter=20(#69954)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix some cinn bug --- python/paddle/decomposition/recompute.py | 9 +++++++-- python/paddle/jit/dy2static/pir_partial_program.py | 9 ++++----- 2 files changed, 11 insertions(+), 7 deletions(-) diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index 2caf85769f7d5a..a34551fc910f10 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -736,12 +736,17 @@ def getIdx(program, op): define_op = recompute_value.get_defining_op() if define_op in marked_recompute_ops or define_op is None: return + if define_op.name() in [ + "builtin.parameter", + "pd_op.data", + ]: + if recompute_value not in needed_saved_values: + needed_saved_values.add(recompute_value) + return op_inputs = define_op.operands_source() if len(op_inputs) == 0 and define_op.name() not in [ "pd_op.full", "pd_op.full_int_array", - "builtin.parameter", - "pd_op.data", ]: def getIdx(program, op): diff --git a/python/paddle/jit/dy2static/pir_partial_program.py b/python/paddle/jit/dy2static/pir_partial_program.py index d6956f4089ed84..f39389fb49af50 100644 --- a/python/paddle/jit/dy2static/pir_partial_program.py +++ b/python/paddle/jit/dy2static/pir_partial_program.py @@ -774,7 +774,7 @@ def get_kwargs_forward_matched_value(kw_name, kw_value): elif kw_name in forward_name_value_map: return forward_name_value_map[kw_name] else: - return None + raise Exception(f"kw_args: {kw_name} not found") for [kw_name, kw_value] in ( backward_program.global_block().kwargs().items() @@ -782,10 +782,9 @@ def get_kwargs_forward_matched_value(kw_name, kw_value): forward_matched_value = ( get_kwargs_forward_matched_value(kw_name, kw_value) ) - if forward_matched_value is not None: - share_symbol_shape_from_forward_to_backward( - forward_matched_value, kw_value - ) + share_symbol_shape_from_forward_to_backward( + forward_matched_value, kw_value + ) if cse_is_enabled(): paddle.base.libpaddle.pir.apply_cse_pass(forward_program) From ce9811d4539714a406555528dd1ab82aea00397c Mon Sep 17 00:00:00 2001 From: XiangGao Date: Thu, 5 Dec 2024 19:52:38 +0800 Subject: [PATCH 194/288] add auto parallel high level api formally (#69946) * add auto parallel high level api formally * fix issue of dataloader * add documents and example code, delete unnecessary annotates * fix issue of circular import --- .../auto_parallel/high_level_api.py | 400 +++++++++++++++--- .../tuner/to_distributed_api_patterns.py | 35 +- .../test_to_distributed_api_for_llama.py | 9 +- .../to_distributed_api_for_llama.py | 54 ++- 4 files changed, 415 insertions(+), 83 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/high_level_api.py b/python/paddle/distributed/auto_parallel/high_level_api.py index 3bf668846ef720..25e81f57fe1924 100644 --- a/python/paddle/distributed/auto_parallel/high_level_api.py +++ b/python/paddle/distributed/auto_parallel/high_level_api.py @@ -11,10 +11,14 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. +from __future__ import annotations +import logging import math import warnings +import numpy as np + import paddle import paddle.distributed as dist from paddle.base import ( @@ -30,6 +34,8 @@ register_used_patterns, ) +logger = logging.getLogger(__name__) + class ToDistributedConfig: def __init__(self): @@ -37,6 +43,43 @@ def __init__(self): self.sequence_parallel = False +def cost_model(matched_programs, device_num, node_num): + # TODO(jeff41404): multi-node will be supported later + assert ( + node_num == 1 + ), "we only support single node now, multi-node will be supported later" + + # TODO(jeff41404): will evaluate the best combination of parallel strategies + # based on cost_model and return global_mesh, currently using pre-defined parallel strategy + if device_num % 2 == 0: + if device_num == 8: + return dist.ProcessMesh( + np.arange(device_num).reshape(2, 2, 2).tolist(), + dim_names=["pp", "dp", "mp"], + ) + elif device_num == 6: + return dist.ProcessMesh( + np.arange(device_num).reshape(3, 2).tolist(), + dim_names=["dp", "mp"], + ) + elif device_num == 4: + return dist.ProcessMesh( + np.arange(device_num).reshape(2, 2).tolist(), + dim_names=["dp", "mp"], + ) + elif device_num == 2: + return dist.ProcessMesh(list(range(device_num)), dim_names=["dp"]) + else: + raise ValueError( + f"device_num must be an even number to be able to use at least 2 parallel strategies, but got: {device_num}" + ) + else: + logger.debug( + f'device_num must be an even number to be able to use at least 2 parallel strategies, but got: {device_num}, only use data parallel.' + ) + return dist.ProcessMesh(list(range(device_num)), dim_names=["dp"]) + + def record_program_ops_pre_hook(layer, inputs): """ A pre-hook to mark op numbers before enter layer.forward. @@ -207,32 +250,225 @@ def get_layer_pp_info(mesh, num_hidden_layers, layer_index): return None -# mesh, config: input_spec -def to_distributed(model, dataloader, optimizer, mesh, config): - paddle.distributed.init_parallel_env() - - with_pp = True if "pp" in mesh.dim_names else False - with_sp = True if config.sequence_parallel else False - - # # Data Parallel - # # step_0: shard dataloader - if with_pp: - first_stage_mesh = mesh.get_mesh_with_dim("pp", 0) - last_stage_mesh = mesh.get_mesh_with_dim("pp", 1) - loader = dist.shard_dataloader( - dataloader, - meshes=[first_stage_mesh, last_stage_mesh], - shard_dims="dp", - ) - else: - loader = dist.shard_dataloader( - dataloader, meshes=[mesh], shard_dims="dp" - ) +def to_distributed( + model: paddle.nn.Layer, + optimizer: paddle.optimizer.Optimizer, + dataloader: paddle.io.DataLoader, + device_num: int, + node_num: int | None = 1, + config: ToDistributedConfig | None = None, +) -> tuple[paddle.nn.Layer, paddle.optimizer.Optimizer, paddle.io.DataLoader]: + """ + `to_distributed` can automatically convert neural networks, optimizer, and dataloader + that do not contain any distributed code into neural networks, optimizers, and dataloader + that are suitable for distributed training and ensure their correctness. + At the same time, during the transformation process, the optimal distributed strategy + will be automatically selected based on `node_num` and `device_num` to maximize performance. + + Args: + model(paddle.nn.Layer): The model in dygraph mode, whose parameters + are ordinary tensors, do not contain any distributed code. + If one device has sufficient memory, it can train directly. + optimizer(paddle.optimizer.Optimizer): The optimizer for training. + one instance of a regular optimizer, e.g. `paddle.optimizer.Adam` etc. + dataloader(paddle.io.DataLoader): The dataloader used in dygraph mode, + It is instantiated through regular `paddle.io.Dataset` and `paddle.io.Sampler`, + not `paddle.io.DistributedBatchSampler`. + device_num(int): the number of devices on each node or machine. + node_num(int|None, optional): the number of nodes or machines. + config(ToDistributedConfig| None = None): Configs for input_spec and sequence_parallel. + The custom input specs specify the shape, dtype, and name information + of each model inputs. If it is not None, the input specs and + will be inferred from the custom input specs. The custom + input specs should be a list of `paddle.static.InputSpec`. Default: None. + sequence_parallel indicates whether to use sequence parallel. Default: False. + + Returns: + model: The model in dygraph mode but contain distributed attributes. + optimizer: The optimizer for training and may be sharded states. + dataloader: The dataloader can be used in distributed training. + + Examples: + .. code-block:: python + >>> import numpy as np + >>> import paddle + >>> import paddle.distributed as dist + >>> from paddle import nn + + >>> EPOCHES = 1 + >>> VOCAB_SIZE = 8000 + >>> BATCH_NUM = 2 + >>> BATCH_SIZE = 4 + >>> HIDDEN_SIZE = 2048 + >>> INTERMEDIATE_SIZE = 4096 + >>> SEQ_LENGTH = 1024 + >>> NUM_HIDDEN_LAYERS = 4 + >>> class RandomDataset(paddle.io.Dataset): # type: ignore[type-arg] + ... def __init__(self, inputs, labels, num_samples): + ... self.inputs = inputs + ... self.labels = labels + ... self.num_samples = num_samples + ... def __getitem__(self, idx): + ... return self.inputs[idx], self.labels[idx] + ... def __len__(self): + ... return self.num_samples + + >>> class Mlp(nn.Layer): + ... def __init__( + ... self, + ... hidden_size=HIDDEN_SIZE, + ... intermediate_size=INTERMEDIATE_SIZE, + ... ): + ... super().__init__() + ... self.hidden_size = hidden_size + ... self.intermediate_size = intermediate_size + ... self.gate_proj = nn.Linear( + ... hidden_size, intermediate_size, bias_attr=False + ... ) + ... self.up_proj = nn.Linear( + ... hidden_size, intermediate_size, bias_attr=False + ... ) + ... self.down_proj = nn.Linear( + ... intermediate_size, hidden_size, bias_attr=False + ... ) + + ... def forward(self, x): + ... x = paddle.incubate.nn.functional.swiglu( + ... self.gate_proj(x), self.up_proj(x) + ... ) + ... out = self.down_proj(x) + ... return out + + >>> class DecoderLayer(nn.Layer): + ... def __init__( + ... self, + ... hidden_size=HIDDEN_SIZE, + ... intermediate_size=INTERMEDIATE_SIZE, + ... ): + ... super().__init__() + ... self.hidden_size = hidden_size + ... self.intermediate_size = intermediate_size + ... self.mlp = Mlp() + + ... def forward( + ... self, + ... hidden_states, + ... ): + ... residual = hidden_states + ... hidden_states = self.mlp(hidden_states) + ... hidden_states = residual + hidden_states + ... return hidden_states + + >>> class DemoNet(nn.Layer): + ... def __init__( + ... self, + ... vocab_size=VOCAB_SIZE, + ... hidden_size=HIDDEN_SIZE, + ... intermediate_size=INTERMEDIATE_SIZE, + ... labels=None, + ... ): + ... super().__init__() + ... self.embed_tokens = nn.Embedding( + ... vocab_size, + ... hidden_size, + ... ) + ... self.layers = nn.LayerList( + ... [ + ... DecoderLayer() + ... for i in range(NUM_HIDDEN_LAYERS) + ... ] + ... ) + ... self.weight = self.create_parameter( + ... shape=[hidden_size, vocab_size], + ... dtype=paddle.get_default_dtype(), + ... ) + ... self.ignore_index = -100 + ... self.loss_func = paddle.nn.CrossEntropyLoss( + ... reduction="none", ignore_index=self.ignore_index + ... ) + + ... def forward( + ... self, + ... input_ids=None, + ... labels=None, + ... ): + ... batch_size, seq_length = input_ids.shape + ... hidden_states = self.embed_tokens(input_ids) + ... for idx, (decoder_layer) in enumerate(self.layers): + ... layer_outputs = decoder_layer( + ... hidden_states, + ... ) + ... hidden_states = layer_outputs + ... logits = paddle.matmul(hidden_states, self.weight) + ... loss = None + ... if labels is not None: + ... masked_lm_loss = self.loss_func( + ... logits.astype("float32"), + ... labels.unsqueeze(2), + ... ) + ... binary_sequence = paddle.where( + ... masked_lm_loss > 0, + ... paddle.ones_like(masked_lm_loss), + ... paddle.zeros_like(masked_lm_loss), + ... ) + ... count = paddle.sum(binary_sequence) + ... if count == 0: + ... loss = paddle.sum(masked_lm_loss * binary_sequence) + ... else: + ... loss = paddle.sum(masked_lm_loss * binary_sequence) / count + ... return (loss, logits) + >>> model = DemoNet() + >>> input_seqs = np.random.randint( + ... low=0, high=1024, size=(BATCH_SIZE * BATCH_NUM, SEQ_LENGTH) + ... ).astype("int64") + >>> labels = np.random.randint( + ... low=0, high=1024, size=(BATCH_SIZE * BATCH_NUM, SEQ_LENGTH) + ... ).astype("int64") + >>> dataset = RandomDataset( + ... input_seqs, labels, BATCH_SIZE * BATCH_NUM + ... ) + >>> sampler = paddle.io.BatchSampler( + ... dataset, batch_size=BATCH_SIZE, shuffle=False, drop_last=True + ... ) + >>> loader = paddle.io.DataLoader( + ... dataset, batch_sampler=sampler + ... ) + >>> opt = paddle.optimizer.SGD( + ... learning_rate=0.1, parameters=model.parameters() + ... ) + >>> input_seq_spec = paddle.static.InputSpec( + ... [BATCH_SIZE, SEQ_LENGTH], 'float32', 'input_seq', True + ... ) + >>> dist_config = ToDistributedConfig() + >>> dist_config.input_spec = [input_seq_spec] + >>> dist_config.sequence_parallel = True + + >>> # # wrap model by using **to_distributed** + >>> dist_model, dist_opt, dist_loader = to_distributed( + ... model, + ... opt, + ... loader, + ... device_num, + ... node_num, + ... dist_config, + ... ) + + >>> for epoch in range(EPOCHES): + ... dist_model.train() + ... for i, data in enumerate(dist_loader()): + ... inputs, labels = data + ... loss, _ = dist_model(inputs, labels=labels) + ... print(f"epoch {epoch}, step {i}: loss {loss}") + ... loss.backward() + ... dist_opt.step() + ... dist_opt.clear_grad() - # Sharding Parallel - # # step_1: shard optimizer + """ + logger.debug(f'input model: {model}') + # paddle.distributed.init_parallel_env() - # # step_2: register pre-hooks and post-hooks, thus recording corresponding static ops in following paddle.jit.to_static + # step 1: identifying network structure and pattern recogincation + # step 1.1: register pre-hooks and post-hooks, thus recording corresponding static ops in following paddle.jit.to_static for layer in model.sublayers(): pre_hook_helper = layer.register_forward_pre_hook( record_program_ops_pre_hook @@ -243,15 +479,19 @@ def to_distributed(model, dataloader, optimizer, mesh, config): layer._op_recorder.hooks.append(pre_hook_helper) layer._op_recorder.hooks.append(post_hook_helper) - # # step_3: call @to_static, get program, and corresponding static ops of each layer - # (1) with FLAGS_enable_pir_api=False, get program based on var and op, default to False - # (2) with FLAGS_enable_pir_api=True, get pir program + # step 1.2: call @to_static, get program, and corresponding static ops of each layer static_func = paddle.jit.to_static( model.forward, input_spec=config.input_spec, full_graph=True ) program = static_func.concrete_program.main_program + # currently, paddle.jit.to_static has side effects that will affect model. + # After fixing it, one line of code below can be dropped + static_func.rollback() + logger.debug( + f'Converted model to pir program: {program}, for pattern matching' + ) - # # step_4: get the mapping [dynamic-layers : static ops] + # step 1.3: get the mapping [dynamic-layers : static ops] op_to_id = {} for idx, op in enumerate(program.global_block().ops): op_to_id[op] = idx @@ -268,10 +508,11 @@ def to_distributed(model, dataloader, optimizer, mesh, config): ops_id.append(op_id) ops_id_to_layer[tuple(ops_id)] = layer - # # step_5: pattern recogincation + # step 1.4: pattern recogincation DECODER_LAYER_NAME = 'decoder_layer' register_used_patterns(DECODER_LAYER_NAME) results = match_all_patterns(program) + logger.debug(f'Matched decoder layer patterns are: {results}') matched_programs = {} for pattern_name, matched_patterns in results.items(): @@ -289,42 +530,54 @@ def to_distributed(model, dataloader, optimizer, mesh, config): for pattern_op_id in pattern_ops_id: assert ( pattern_op_id in matched_pattern.keys() - ), "pattern not matched" + ), f"please check ops_dist_infos of {pattern_name}, {pattern_op_id} not in matched_pattern: {matched_pattern.keys()}" program_op_id = matched_pattern[pattern_op_id] program_ops_id.append(program_op_id) program_ops_dist_infos[tuple(program_ops_id)] = op_dist_info processed_patterns.append(program_ops_dist_infos) matched_programs[pattern_name] = processed_patterns - # Tensor Parallel - # # step_6: shard weight tensors in decoder blocks - num_hidden_layers = len(matched_programs[DECODER_LAYER_NAME]) - for pattern_name, processed_patterns in matched_programs.items(): - assert ( - len(processed_patterns) == num_hidden_layers - ), "transformer patterns matched are incomplete" - for idx, processed_pattern in enumerate(processed_patterns): - local_mesh = mesh - if with_pp: - pp_stage_id = get_layer_pp_info(mesh, num_hidden_layers, idx) - local_mesh = mesh.get_mesh_with_dim("pp", pp_stage_id) - - for program_ops_id, dist_infos in processed_pattern.items(): - assert ( - program_ops_id in ops_id_to_layer.keys() - ), f"program_ops: {program_ops_id} is not corresponding to a dynamic layer" - dynamic_layer = ops_id_to_layer[program_ops_id] - mesh_num_dims = len(local_mesh.shape) - sharding_info = dist_infos.get_dist_info(mesh_num_dims) - dynamic_layer.weight = dist.shard_tensor( - dynamic_layer.weight, local_mesh, sharding_info[0] - ) - if dynamic_layer.bias is not None: - dynamic_layer.bias = dist.shard_tensor( - dynamic_layer.bias, local_mesh, sharding_info[1] + # step 2: calculate the optimal parallel strategies based on the network structure + mesh = cost_model(matched_programs, device_num, node_num) + logger.debug(f'mesh: {mesh}') + + with_pp = True if "pp" in mesh.dim_names else False + with_mp = True if "mp" in mesh.dim_names else False + with_dp = True if "dp" in mesh.dim_names else False + with_sp = True if config.sequence_parallel else False + + # step 3: processing tensor parallel if necessary, according to the optimal parallel strategies shard weight tensors in decoder blocks + if with_mp: + num_hidden_layers = len(matched_programs[DECODER_LAYER_NAME]) + for pattern_name, processed_patterns in matched_programs.items(): + assert ( + len(processed_patterns) == num_hidden_layers + ), "transformer patterns matched are incomplete" + for idx, processed_pattern in enumerate(processed_patterns): + local_mesh = mesh + if with_pp: + pp_stage_id = get_layer_pp_info( + mesh, num_hidden_layers, idx ) - # Pipeline Parallel - # # step_7: reshard inputs of decoder blocks to next pp mesh b when switching from pp stage a to pp stage b + local_mesh = mesh.get_mesh_with_dim("pp", pp_stage_id) + + for program_ops_id, dist_infos in processed_pattern.items(): + assert ( + program_ops_id in ops_id_to_layer.keys() + ), f"program_ops: {program_ops_id} is not corresponding to a dynamic layer" + dynamic_layer = ops_id_to_layer[program_ops_id] + mesh_num_dims = len(local_mesh.shape) + sharding_info = dist_infos.get_dist_info(mesh_num_dims) + dynamic_layer.weight = dist.shard_tensor( + dynamic_layer.weight, local_mesh, sharding_info[0] + ) + if dynamic_layer.bias is not None: + dynamic_layer.bias = dist.shard_tensor( + dynamic_layer.bias, local_mesh, sharding_info[1] + ) + logger.debug(f'after tensor parallel, model: {model}') + + # step 4: processing pipeline parallel if necessary, reshard inputs of decoder blocks to next pp mesh b when switching from pp stage a to pp stage b if with_pp: decoder_layers = [] for pattern_name, matched_all_patterns in results.items(): @@ -342,7 +595,7 @@ def to_distributed(model, dataloader, optimizer, mesh, config): num_decoder_blocks = len(decoder_layers) assert ( num_decoder_blocks == num_hidden_layers - ), "decoder pattern layers matched are incomplete" + ), f"decoder pattern layers matched are incomplete, num_decoder_blocks: {num_decoder_blocks} should be equal to num_hidden_layers: {num_hidden_layers}" pp_degree = mesh.get_dim_size("pp") num_blocks_per_stage = num_decoder_blocks // pp_degree @@ -354,9 +607,9 @@ def to_distributed(model, dataloader, optimizer, mesh, config): pre_hook_helper = decoder_layer.register_forward_pre_hook( reshard_all_inputs ) + logger.debug(f'after pipeline parallel, model: {model}') - # Sequence Parallel - # # step_8: reshard or transpose sequence dims for inputs of attention/mlp inputs + # step 5: processing sequence parallel if necessary, reshard or transpose sequence dims for inputs of attention/mlp inputs if with_sp: clear_used_patterns() EMBEDDING_LAYER_NAME = "embedding" @@ -389,6 +642,7 @@ def to_distributed(model, dataloader, optimizer, mesh, config): ops_id_to_layer[tuple(sorted(program_ops_id))] ] + logger.debug(f'Matched attention/mlp layers are: {matched_layers}') # init mesh GLOBAL_MESH = [] if with_pp: @@ -457,9 +711,29 @@ def to_distributed(model, dataloader, optimizer, mesh, config): reshard_transpose_rms_norm_layer_output ) - # # step_9: clean layer_op recorder hooks + # step 6: processing data parallel if necessary, shard dataloader + # TODO(jeff41404): shard optimizer + if with_dp: + if with_pp: + first_stage_mesh = mesh.get_mesh_with_dim("pp", 0) + last_stage_mesh = mesh.get_mesh_with_dim("pp", 1) + loader = dist.shard_dataloader( + dataloader, + meshes=[first_stage_mesh, last_stage_mesh], + shard_dims="dp", + ) + else: + loader = dist.shard_dataloader( + dataloader, meshes=[mesh], shard_dims="dp" + ) + else: + loader = dist.shard_dataloader( + dataloader, meshes=[mesh], shard_dims=None + ) + + # step 7: clean layer_op recorder hooks for layer in model.sublayers(): for hook_helper in layer._op_recorder.hooks: hook_helper.remove() - return model, loader, optimizer + return model, optimizer, loader diff --git a/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py b/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py index d55d7de9c0e561..62d48522e2d752 100644 --- a/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py +++ b/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py @@ -12,6 +12,7 @@ # See the License for the specific language governing permissions and # limitations under the License. +import logging import math from abc import abstractmethod @@ -22,6 +23,8 @@ _ALL_PATTERNS = {} _USED_PATTERNS = [] +logger = logging.getLogger(__name__) + def register_pattern(cls): """Register a pattern""" @@ -661,7 +664,7 @@ def build(self): (9,): qkv_linear_dist_infos, (10,): qkv_linear_dist_infos, (11,): qkv_linear_dist_infos, - (77,): out_linear_dist_infos, + (76,): out_linear_dist_infos, } self.ops_dist_infos = ops_dist_infos @@ -858,13 +861,13 @@ def build(self): down_linear_dist_infos = MpDistInfos("row") # # # build ops dist infos # # # ops_dist_infos = { + (21,): qkv_linear_dist_infos, (22,): qkv_linear_dist_infos, (23,): qkv_linear_dist_infos, - (24,): qkv_linear_dist_infos, - (90,): out_linear_dist_infos, - (100,): up_linear_dist_infos, - (101,): up_linear_dist_infos, - (103,): down_linear_dist_infos, + (88,): out_linear_dist_infos, + (97,): up_linear_dist_infos, + (98,): up_linear_dist_infos, + (100,): down_linear_dist_infos, } self.ops_dist_infos = ops_dist_infos @@ -1272,6 +1275,9 @@ def _match_core(src, tgt, is_op): return if is_op: + logger.debug( + f'comparing src op {src.name()} with tgt op {tgt.name()}' + ) # skip comparing data_op if src.name() == "pd_op.data" or src.name() == "builtin.parameter": return @@ -1306,7 +1312,7 @@ def _match_core(src, tgt, is_op): _match_core(src_result, tgt_result, is_op=False) else: - + logger.debug('comparing operands') # as input for op node src_as_input_ops = src.all_used_ops() tgt_as_input_ops = tgt.all_used_ops() @@ -1352,14 +1358,26 @@ def _match_core(src, tgt, is_op): break # src_start_op = src_ops[0] # to be done, need to check pattern start op assert src_start_op is not None, "src_start_op is none" + logger.debug( + f'in match_pattern func, Matching Pattern {pattern.name}, start op is {src_start_op.name()}' + ) tgt_ops = program.global_block().ops for idx, tgt_op in enumerate(tgt_ops): if tgt_op.name() == src_start_op.name(): + tgt_op_id = tgt_op.get_parent_block().ops.index(tgt_op) + if tgt_op_id in matched_op_node_ids: + continue + logger.debug( + f'in match_pattern func, Matching Pattern {pattern.name}, tgt_op is {tgt_op.name()}' + ) not_matched = False result = {} _match_core(src_start_op, tgt_op, is_op=True) if not not_matched: + logger.debug( + f'in match_pattern func, Matched Pattern {pattern.name}, pattern.program is {pattern.program} result is {result}' + ) need_to_append = True for value in result.values(): if value in matched_op_node_ids: @@ -1384,6 +1402,9 @@ def match_all_patterns(program): matched_ids = set() for pattern_name in _ALL_PATTERNS: if pattern_name in _USED_PATTERNS: + logger.debug( + f'in match_all_patterns func, Matching Pattern {pattern_name}' + ) pattern = _ALL_PATTERNS[pattern_name] results, matched = match_pattern(pattern, program) for result in results: diff --git a/test/auto_parallel/test_to_distributed_api_for_llama.py b/test/auto_parallel/test_to_distributed_api_for_llama.py index 8eb9a1003322ea..e42471fe9abc4e 100644 --- a/test/auto_parallel/test_to_distributed_api_for_llama.py +++ b/test/auto_parallel/test_to_distributed_api_for_llama.py @@ -20,11 +20,16 @@ class TestToDistributedApiForLlamaBasic(test_base.CommunicationTestDistBase): def setUp(self): + self._num_of_devices = 8 super().setUp( - num_of_devices=8, + num_of_devices=self._num_of_devices, timeout=120, ) - self._default_envs = {"dtype": "float32", "seed": "2023"} + self._default_envs = { + "dtype": "float32", + "seed": "2023", + "num_of_devices": self._num_of_devices, + } self._changeable_envs = {"backend": ["cpu", "gpu"]} def test_llama(self): diff --git a/test/auto_parallel/to_distributed_api_for_llama.py b/test/auto_parallel/to_distributed_api_for_llama.py index aaf05ac821672d..4e8d8b4637762d 100644 --- a/test/auto_parallel/to_distributed_api_for_llama.py +++ b/test/auto_parallel/to_distributed_api_for_llama.py @@ -26,8 +26,9 @@ to_distributed, ) +EPOCHES = 1 VOCAB_SIZE = 8000 -BATCH_NUM = 3 +BATCH_NUM = 2 BATCH_SIZE = 4 HIDDEN_SIZE = 2048 INTERMEDIATE_SIZE = 4096 @@ -555,13 +556,13 @@ def forward( class RandomDataset(paddle.io.Dataset): - def __init__(self, images, labels, num_samples): - self.images = images + def __init__(self, inputs, labels, num_samples): + self.inputs = inputs self.labels = labels self.num_samples = num_samples def __getitem__(self, idx): - return self.images[idx], self.labels[idx] + return self.inputs[idx], self.labels[idx] def __len__(self): return self.num_samples @@ -573,11 +574,25 @@ def __init__(self): self._backend = os.getenv("backend", "gpu") self._seed = eval(os.getenv("seed", "2023")) - self._mesh = mesh = dist.ProcessMesh( - [[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["pp", "dp", "mp"] - ) + self._device_num = os.getenv("num_of_devices", 8) + self._node_num = 1 + + np.random.seed(self._seed) + paddle.seed(self._seed) self._model = LlamaForCausalLM("demo_llama") + # ensure that input data between dp is different and data within dp is the same + self._mesh = dist.ProcessMesh( + [[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["pp", "dp", "mp"] + ) + if "dp" in self._mesh.dim_names: + dp_seed = self._mesh.get_rank_by_dim_and_process_id( + "dp", dist.get_rank() + ) + else: + dp_seed = 0 + np.random.seed(self._seed + dp_seed) + paddle.seed(self._seed + dp_seed) self._input_seqs = np.random.randint( low=0, high=1024, size=(BATCH_SIZE * BATCH_NUM, SEQ_LENGTH) ).astype("int64") @@ -587,8 +602,11 @@ def __init__(self): self._dataset = RandomDataset( self._input_seqs, self._labels, BATCH_SIZE * BATCH_NUM ) + self._sampler = paddle.io.BatchSampler( + self._dataset, batch_size=BATCH_SIZE, shuffle=False, drop_last=True + ) self._loader = paddle.io.DataLoader( - self._dataset, batch_size=BATCH_SIZE + self._dataset, batch_sampler=self._sampler ) self._opt = paddle.optimizer.SGD( learning_rate=0.1, parameters=self._model.parameters() @@ -606,9 +624,23 @@ def test_to_distributed_api(self): dist_config.sequence_parallel = True # # wrap model by using **to_distributed** - dist_model, dist_loader, dist_opt = to_distributed( - self._model, self._loader, self._opt, self._mesh, dist_config - ) + dist_model, dist_opt, dist_loader = to_distributed( + self._model, + self._opt, + self._loader, + self._device_num, + self._node_num, + dist_config, + ) + + for epoch in range(EPOCHES): + dist_model.train() + for i, data in enumerate(dist_loader()): + inputs, labels = data + loss, _ = dist_model(inputs, labels=labels) + loss.backward() + dist_opt.step() + dist_opt.clear_grad() def run_test_case(self): if self._backend == "gpu": From a162cf0aa8aaded0a7310ce8b3af59d981d0236b Mon Sep 17 00:00:00 2001 From: cubehan3 Date: Thu, 5 Dec 2024 22:23:30 +0800 Subject: [PATCH 195/288] fast inverse_sort_op (#69980) --- python/paddle/autograd/backward_utils.py | 10 ++++------ 1 file changed, 4 insertions(+), 6 deletions(-) diff --git a/python/paddle/autograd/backward_utils.py b/python/paddle/autograd/backward_utils.py index 6f33200fc040ae..5b6e1523cae800 100644 --- a/python/paddle/autograd/backward_utils.py +++ b/python/paddle/autograd/backward_utils.py @@ -428,18 +428,16 @@ def inverse_sort_op(old_ops): # pending edges for its grad_op pending_count = collections.defaultdict(int) - ops = [] - [ops.append(x) for x in old_ops if x not in ops] - ops_set = set(ops) + ops_set = set(old_ops) sorted_list = [] - for op in ops: + for op in ops_set: for x in get_real_op_inputs(op): if not pir.is_fake_value(x) and x.get_defining_op() in ops_set: pending_count[x.get_defining_op()] += 1 queue = collections.deque() - for op in ops: + for op in ops_set: if pending_count[op] == 0: queue.append(op) @@ -452,7 +450,7 @@ def inverse_sort_op(old_ops): if pending_count[x_op] == 0: queue.append(x_op) - if len(sorted_list) != len(ops): + if len(sorted_list) != len(ops_set): raise ValueError( "inverse_sort_op wrong, sorted_list size is not equal to origin_list size" ) From 4188f4ce5834f37622f0fc290f96fd433e4ccf5b Mon Sep 17 00:00:00 2001 From: waliwali777 Date: Thu, 5 Dec 2024 22:39:41 +0800 Subject: [PATCH 196/288] [PIR-Auto-Parallel]refactor recompute pass in PIR mode (#69681) --- .../distributed/auto_parallel/interface.py | 10 + .../auto_parallel/static/engine.py | 8 + python/paddle/distributed/passes/__init__.py | 3 + .../passes/auto_parallel_recompute_pir.py | 152 ++++++++++++ test/auto_parallel/pir/CMakeLists.txt | 5 + ...to_parallel_recompute_pir_pass_unittest.py | 233 ++++++++++++++++++ .../test_auto_parallel_recompute_pir_pass.py | 46 ++++ 7 files changed, 457 insertions(+) create mode 100644 python/paddle/distributed/passes/auto_parallel_recompute_pir.py create mode 100644 test/auto_parallel/pir/auto_parallel_recompute_pir_pass_unittest.py create mode 100644 test/auto_parallel/pir/test_auto_parallel_recompute_pir_pass.py diff --git a/python/paddle/distributed/auto_parallel/interface.py b/python/paddle/distributed/auto_parallel/interface.py index 595c9ff13714bd..99b7c560ae73c1 100644 --- a/python/paddle/distributed/auto_parallel/interface.py +++ b/python/paddle/distributed/auto_parallel/interface.py @@ -216,6 +216,9 @@ def __init__(self, op): self._op = op def __call__(self, *args, **kwargs): + block = paddle.static.default_main_program().global_block() + rc_begin_id = len(block.ops) + with paddle.static.name_scope( f'/auto_parallel/rc_{_g_recompute_idx}' ): @@ -228,6 +231,13 @@ def __call__(self, *args, **kwargs): else: output = self._op(*args, **kwargs) + if paddle.framework.in_pir_mode(): + block = paddle.static.default_main_program().global_block() + rc_end_id = len(block.ops) + for idx in range(rc_begin_id, rc_end_id): + rc_op = block.ops[idx] + rc_op.set_int_attr("fwd_recompute_id", _g_recompute_idx) + return output return RecomputeOperator(op) diff --git a/python/paddle/distributed/auto_parallel/static/engine.py b/python/paddle/distributed/auto_parallel/static/engine.py index f8499e5deec395..d224f7915e0620 100644 --- a/python/paddle/distributed/auto_parallel/static/engine.py +++ b/python/paddle/distributed/auto_parallel/static/engine.py @@ -805,6 +805,14 @@ def _parallel_pir(self, mode): # re-run apply_mix2dist_pass to dist accumulator. apply_mix2dist_pass(dist_program) + if mode == "train" and self._strategy.recompute.enable: + auto_parallel_recompute_pir_pass = new_pass( + "auto_parallel_recompute_pir", {} + ) + auto_parallel_recompute_pir_pass.apply( + [dist_program], [startup_program] + ) + # Part 2: Parallelism search (for full auto-parallel) # NOTE make all parallelis search logic work as Pass, # and all the Pass in this Part should be optional to allow consistence in dynamic and static mode. diff --git a/python/paddle/distributed/passes/__init__.py b/python/paddle/distributed/passes/__init__.py index a85e415ca0d543..dbee28538d94c0 100644 --- a/python/paddle/distributed/passes/__init__.py +++ b/python/paddle/distributed/passes/__init__.py @@ -55,6 +55,9 @@ RecomputePass, RecomputeState, ) +from .auto_parallel_recompute_pir import ( # noqa: F401 + AutoParallelRecomputePIRPass, +) from .auto_parallel_replace_with_parallel_cross_entropy import ( # noqa: F401 AutoParallelReplaceWithParallelCrossEntropyPass, ) diff --git a/python/paddle/distributed/passes/auto_parallel_recompute_pir.py b/python/paddle/distributed/passes/auto_parallel_recompute_pir.py new file mode 100644 index 00000000000000..d8a1255461de52 --- /dev/null +++ b/python/paddle/distributed/passes/auto_parallel_recompute_pir.py @@ -0,0 +1,152 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import logging + +import paddle +from paddle.base import core + +OpRole = core.op_proto_and_checker_maker.OpRole + +from paddle.autograd import backward_utils + +from ..auto_parallel.static.utils import ( + get_logger, +) +from .pass_base import PassBase, register_pass + +logger = get_logger(logging.INFO) + + +@register_pass("auto_parallel_recompute_pir") +class AutoParallelRecomputePIRPass(PassBase): + def __init__(self): + super().__init__() + + def _check_self(self): + return True + + def _check_conflict(self, other_pass): + return True + + def get_fwd_bwd_ops(self, program): + fwd_ops = [] + bwd_ops = [] + for op in program.global_block().ops: + if op.op_role == int(OpRole.Forward): + fwd_ops.append(op) + elif op.op_role == int(OpRole.Backward): + bwd_ops.append(op) + assert len(fwd_ops) and len(bwd_ops) + return fwd_ops, bwd_ops + + def get_first_bwd_used_op(self, fwd_op, bwd_ops): + # Find the first user op of the op result in backward op list. + first_op = bwd_ops[-1] + for res in fwd_op.results(): + for user_op in res.all_used_ops(): + if user_op in bwd_ops and first_op.id() >= user_op.id(): + first_op = user_op + return first_op + + def is_seed_used_by_dropout(self, seed_op): + # Ensure that the random operator has the same output in backward recompute. + if seed_op.name() != "seed": + return False + seed_value = seed_op.results()[0] + dropout_ops = ["pd_op.dropout", "pd_op.fused_dropout_add"] + return any( + True + for used_op in seed_value.all_used_ops() + if used_op.name() in dropout_ops + ) + + def get_segments(self, program): + # `fwd_recompute_id` indicates the ID assigned to the segment for + # which the OP requires recompute. + # A segment comprises all OPs within a program, ranging from the OP + # with the minimum index to the OP with the maximum index, and all + # these operations share the same `fwd_recompute_id`. + segment_beg = {} + segment_end = {} + max_op_id = len(program.global_block().ops) + for idx, op in enumerate(program.global_block().ops): + if not op.has_attr("fwd_recompute_id"): + continue + rc_id = op.attrs()["fwd_recompute_id"] + if rc_id not in segment_beg: + segment_beg[rc_id] = max_op_id + segment_end[rc_id] = 0 + segment_beg[rc_id] = min(segment_beg[rc_id], idx) + segment_end[rc_id] = max(segment_end[rc_id], idx) + + segments = {} + idx = 0 + assert len(segment_beg.keys()) == len(segment_end.keys()) + for segment_id, beg_id in segment_beg.items(): + assert segment_id in segment_end.keys() + end_id = segment_end[segment_id] + assert beg_id <= end_id + segment = [] + for p_id in range(beg_id, end_id - 1): + segment.append(p_id) + segments[idx] = segment + idx += 1 + return segments + + def _apply_single_impl(self, main_program, startup_program, context=None): + segments = self.get_segments(main_program) + if len(segments) == 0: + logger.info("No segments found in PIR recompite pass.") + return + + fwd_ops, bwd_ops = self.get_fwd_bwd_ops(main_program) + + input_value = main_program.list_vars() + value_map = paddle.pir.IrMapping() + for val in input_value: + value_map.add(val, val) + + for rc_id, segment in segments.items(): + first_bwd_used_op = bwd_ops[-1] + for idx in segment: + op = main_program.global_block().ops[idx] + bwd_used_op = self.get_first_bwd_used_op(op, bwd_ops) + if first_bwd_used_op.id() > bwd_used_op.id(): + first_bwd_used_op = bwd_used_op + + ori_segment_outputs = backward_utils.ValueSet() + paddle.pir.set_insertion_point(first_bwd_used_op) + + for idx in segment: + op = main_program.global_block().ops[idx] + ori_segment_outputs.update(op.results()) + + if self.is_seed_used_by_dropout(op): + continue + + rc_op = op.clone( + value_map, paddle.pir.CloneOptions(False, True, True) + ) + rc_op.set_int_attr("bwd_recompute_id", rc_id) + + if first_bwd_used_op.has_attr('op_role'): + rc_op.set_int_attr("op_role", first_bwd_used_op.op_role) + + if first_bwd_used_op.has_attr('chunk_id'): + rc_op.set_int_attr("chunk_id", first_bwd_used_op.chunk_id) + + for ori_value in ori_segment_outputs: + rc_value = value_map.look_up(ori_value) + ori_value.replace_grad_users_with(rc_value, set(bwd_ops)) diff --git a/test/auto_parallel/pir/CMakeLists.txt b/test/auto_parallel/pir/CMakeLists.txt index 6849e8d92f5252..ecf15bd6a0d9e9 100644 --- a/test/auto_parallel/pir/CMakeLists.txt +++ b/test/auto_parallel/pir/CMakeLists.txt @@ -31,6 +31,9 @@ if(WITH_DISTRIBUTE AND WITH_GPU) test_auto_parallel_replace_with_parallel_cross_entropy_pass MODULES test_auto_parallel_replace_with_parallel_cross_entropy_pass ENVS FLAGS_enable_pir_api=1 FLAGS_dist_prim_all=1) + py_test_modules( + test_auto_parallel_recompute_pir_pass MODULES + test_auto_parallel_recompute_pir_pass ENVS FLAGS_enable_pir_api=1) py_test_modules(test_reshard MODULES test_reshard ENVS FLAGS_enable_pir_api=1) py_test_modules(test_learning_rate MODULES test_learning_rate ENVS FLAGS_enable_pir_api=1) @@ -47,6 +50,8 @@ if(WITH_DISTRIBUTE AND WITH_GPU) set_tests_properties( test_auto_parallel_replace_with_parallel_cross_entropy_pass PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE" TIMEOUT 60) + set_tests_properties(test_auto_parallel_recompute_pir_pass + PROPERTIES LABELS "RUN_TYPE=EXCLUSIVE" TIMEOUT 60) py_test_modules( test_eliminate_transpose_pass MODULES test_eliminate_transpose_pass ENVS FLAGS_enable_pir_in_executor=1) diff --git a/test/auto_parallel/pir/auto_parallel_recompute_pir_pass_unittest.py b/test/auto_parallel/pir/auto_parallel_recompute_pir_pass_unittest.py new file mode 100644 index 00000000000000..167dfd8700dabd --- /dev/null +++ b/test/auto_parallel/pir/auto_parallel_recompute_pir_pass_unittest.py @@ -0,0 +1,233 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import hashlib +import os +import sys + +sys.path.append("../hybrid_strategy/") + +import random +from functools import reduce + +import numpy as np +from semi_auto_parallel_llama_model import ( + LlamaForCausalLMAuto, + LlamaPretrainingCriterionAuto, + get_mesh, +) + +import paddle +import paddle.distributed as dist +from paddle.io import BatchSampler, DataLoader, Dataset + + +class Config: + vocab_size = 320 + hidden_size = 8 + intermediate_size = 64 + max_position_embeddings = 8 + seq_length = 8 + + num_hidden_layers = 4 + num_attention_heads = 4 + num_key_value_heads = 4 + initializer_range = 0.02 + rms_norm_eps = 1e-6 + use_cache = True + use_flash_attention = False + sequence_parallel = False + rope = True + + +class RandomDataset(Dataset): + def __init__(self, seq_len, num_samples=100): + super().__init__() + self.seq_len = seq_len + self.num_samples = num_samples + + def __getitem__(self, index): + input = np.full([self.seq_len], index, dtype="int64") + label = np.array([index] * 8) + + return input, label + + def __len__(self): + return self.num_samples + + +def create_optimizer(model, lr_scheduler): + decay_parameters = [ + p.name + for n, p in model.named_parameters() + if not any(nd in n for nd in ["bias", "norm"]) + ] + + def apply_decay_param_fun(x): + return x in decay_parameters + + optimizer = paddle.optimizer.adamw.AdamW( + learning_rate=lr_scheduler, + apply_decay_param_fun=apply_decay_param_fun, + parameters=model.parameters(), + weight_decay=0.01, + grad_clip=paddle.nn.ClipGradByGlobalNorm(1.0), + ) + return optimizer + + +class TestLlamaAuto: + def __init__(self): + self.config = Config() + self.dp = int(os.getenv("dp")) + self.mp = int(os.getenv("mp")) + self.pp = int(os.getenv("pp")) + + self.strategy = dist.Strategy() + + self.run_step = 10 + + def prepare_llama(self, model, model_config): + # optimizer + lr_scheduler = paddle.optimizer.lr.LinearWarmup( + learning_rate=0.0001, warmup_steps=2, start_lr=0, end_lr=0.0001 + ) + optimizer = create_optimizer(model, lr_scheduler) + optimizer = dist.shard_optimizer(optimizer) + + # dataloader + train_dataset = RandomDataset(model_config.seq_length) + train_sampler = BatchSampler( + train_dataset, + batch_size=2, + shuffle=True, + drop_last=True, + ) + train_dataloader = DataLoader( + train_dataset, + batch_sampler=train_sampler, + num_workers=0, + ) + dist_loader = dist.shard_dataloader( + dataloader=train_dataloader, + meshes=[get_mesh(0), get_mesh(1)], + shard_dims="dp", + ) + return optimizer, dist_loader + + def run_llama(self, model_config): + self.init_dist_env() + # model + model = LlamaForCausalLMAuto(model_config) + criterion = LlamaPretrainingCriterionAuto(model_config) + + optimizer, dist_loader = self.prepare_llama(model, model_config) + + model = dist.to_static( + model, dist_loader, criterion, optimizer, strategy=self.strategy + ) + model.train() + + md5_losses = [] + for step, inputs in enumerate(dist_loader()): + if step >= self.run_step: + break + input_ids, labels = inputs + loss = model(input_ids, labels) + array_bytes = np.array(loss).tobytes() + md5_losses.append(hashlib.md5(array_bytes).hexdigest()) + return md5_losses, model + + def init_dist_env(self): + order = ["dp", "pp", "mp"] + dp_degree = self.dp + mp_degree = self.mp + pp_degree = self.pp + degree = [dp_degree, pp_degree, mp_degree] + mesh_dims = list(filter(lambda x: x[1] > 1, list(zip(order, degree)))) + if not mesh_dims: + mesh_dims = [("dp", 1)] + dim_names = [mesh_dim[0] for mesh_dim in mesh_dims] + mesh_shape = [mesh_dim[1] for mesh_dim in mesh_dims] + mesh_arr = np.arange( + 0, reduce(lambda x, y: x * y, mesh_shape, 1) + ).reshape(mesh_shape) + global_mesh = dist.ProcessMesh(mesh_arr, dim_names) + dist.auto_parallel.set_mesh(global_mesh) + paddle.seed(1024) + np.random.seed(1024) + random.seed(1024) + + def check_loss(self, losses_1, losses_2): + np.testing.assert_equal(len(losses_1), len(losses_2)) + for idx in range(len(losses_1)): + np.testing.assert_equal(losses_1[idx], losses_2[idx]) + + def get_recompute_message(self, program): + segment_num = set() + fwd_rc_op_num = 0 + for block in program.blocks: + for op in block.ops: + if op.has_attr("fwd_recompute_id"): + idx = op.attrs()["fwd_recompute_id"] + segment_num.add(idx) + fwd_rc_op_num += 1 + return len(segment_num), fwd_rc_op_num + + def run_test_cases(self): + self.strategy._recompute.enable = False + self.config.recompute = False + base_losses, base_model = self.run_llama(self.config) + + self.strategy._recompute.enable = True + self.config.recompute = True + self.config.recompute_granularity = "core_attn" + losses_1, model_1 = self.run_llama(self.config) + + self.config.recompute = True + self.config.recompute_granularity = "full_attn" + losses_2, model_2 = self.run_llama(self.config) + + self.config.recompute = True + self.config.recompute_granularity = "full" + losses_3, model_3 = self.run_llama(self.config) + + # check loss + self.check_loss(base_losses, losses_1) + self.check_loss(base_losses, losses_2) + self.check_loss(base_losses, losses_3) + + # check program + base_prog = base_model.dist_main_program() + prog_1 = model_1.dist_main_program() + prog_2 = model_2.dist_main_program() + prog_3 = model_3.dist_main_program() + base_segment_num, base_rc_op_num = self.get_recompute_message(base_prog) + segment_num_1, fwd_rc_op_num_1 = self.get_recompute_message(prog_1) + segment_num_2, fwd_rc_op_num_2 = self.get_recompute_message(prog_2) + segment_num_3, fwd_rc_op_num_3 = self.get_recompute_message(prog_3) + + assert base_segment_num == 0 + assert segment_num_1 == 2 + assert segment_num_2 == 2 + assert segment_num_3 == 2 + + assert base_rc_op_num == 0 + assert fwd_rc_op_num_1 == 60 + assert fwd_rc_op_num_2 == 204 + assert fwd_rc_op_num_3 == 288 + + +if __name__ == '__main__': + TestLlamaAuto().run_test_cases() diff --git a/test/auto_parallel/pir/test_auto_parallel_recompute_pir_pass.py b/test/auto_parallel/pir/test_auto_parallel_recompute_pir_pass.py new file mode 100644 index 00000000000000..3e39afc91a7b41 --- /dev/null +++ b/test/auto_parallel/pir/test_auto_parallel_recompute_pir_pass.py @@ -0,0 +1,46 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import collective.test_communication_api_base as test_base + + +class TestSemiAutoParallelLlamaACCTest(test_base.CommunicationTestDistBase): + def setUp(self): + super().setUp(num_of_devices=8, timeout=200, nnode=1) + + def test_simple_net_hybrid_strategy_acc(self): + _default_envs = { + "dp": "2", + "mp": "2", + "pp": "2", + "FLAGS_embedding_deterministic": "1", + "FLAGS_cudnn_deterministic": "1", + } + _changeable_envs = { + "backend": ["gpu"], + } + envs_list = test_base.gen_product_envs_list( + _default_envs, _changeable_envs + ) + for envs in envs_list: + self.run_test_case( + "auto_parallel_recompute_pir_pass_unittest.py", + user_defined_envs=envs, + ) + + +if __name__ == "__main__": + unittest.main() From f075c0aa401634e2cbd834499134ca1ed3c1259f Mon Sep 17 00:00:00 2001 From: nizne <97940276+nizne9@users.noreply.github.com> Date: Thu, 5 Dec 2024 07:56:34 -0800 Subject: [PATCH 197/288] [CodeStyle][Typos][B-2] Fix typos (`beacuse`, `becasue`, `Becasue`, `becuase`) (#69983) --- _typos.toml | 4 ---- paddle/cinn/common/simplify_special_pattern.cc | 2 +- paddle/phi/kernels/gpudnn/conv_cudnn_v7.h | 6 +++--- python/paddle/jit/dy2static/program_translator.py | 2 +- test/legacy_test/test_assign_pos_op.py | 2 +- test/legacy_test/test_assign_pos_op_dygraph.py | 2 +- 6 files changed, 7 insertions(+), 11 deletions(-) diff --git a/_typos.toml b/_typos.toml index 8052ec5a4a6611..64693adf7534cb 100644 --- a/_typos.toml +++ b/_typos.toml @@ -26,10 +26,6 @@ UE = "UE" unpacket = "unpacket" # These words need to be fixed -beacuse = 'beacuse' -becasue = 'becasue' -Becasue = 'Becasue' -becuase = 'becuase' blokc = 'blokc' blcok = 'blcok' bootom = 'bootom' diff --git a/paddle/cinn/common/simplify_special_pattern.cc b/paddle/cinn/common/simplify_special_pattern.cc index 275c36e2c199cc..00c9b4f31aad28 100644 --- a/paddle/cinn/common/simplify_special_pattern.cc +++ b/paddle/cinn/common/simplify_special_pattern.cc @@ -30,7 +30,7 @@ std::optional DivMulAddModCornerCase(const ir::IndexExpr& lhs, auto rhsMod = rhs.As(); if (!lhsMul || !rhsMod) return std::nullopt; - // Why inner is lhs of Mul? beacuse we sort by expr length, and the length of + // Why inner is lhs of Mul? because we sort by expr length, and the length of // inner is longer in this case. auto inner = lhsMul->a().as_index(); auto mult_outer = lhsMul->b().as_index(); diff --git a/paddle/phi/kernels/gpudnn/conv_cudnn_v7.h b/paddle/phi/kernels/gpudnn/conv_cudnn_v7.h index ca5e8ad110cded..5940e23ba90666 100644 --- a/paddle/phi/kernels/gpudnn/conv_cudnn_v7.h +++ b/paddle/phi/kernels/gpudnn/conv_cudnn_v7.h @@ -163,7 +163,7 @@ struct SearchAlgorithmBase { perf_results, workspace_size_limit, &result); #else VLOG(3) << "Fallback to non-v7 method to find conv algorithm " - "becasue the workspace size request(" + "because the workspace size request(" << result.workspace_size << ") exceeds the limit(" << workspace_size_limit << ")"; PADDLE_ENFORCE_GPU_SUCCESS( @@ -346,7 +346,7 @@ struct SearchAlgorithmBase { ChooseAlgoByWorkspace( perf_results, workspace_size_limit, &result); #else - VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue " + VLOG(1) << "Fallback to non-v7 method to find conv algorithm because " "the workspace size request(" << result.workspace_size << ") exceeds the limit(" << workspace_size_limit << ")"; @@ -518,7 +518,7 @@ struct SearchAlgorithmBase { ChooseAlgoByWorkspace( perf_results, workspace_size_limit, &result); #else - VLOG(1) << "Fallback to non-v7 method to find conv algorithm becasue " + VLOG(1) << "Fallback to non-v7 method to find conv algorithm because " "the workspace size request(" << result.workspace_size << ") exceeds the limit(" << workspace_size_limit << ")"; diff --git a/python/paddle/jit/dy2static/program_translator.py b/python/paddle/jit/dy2static/program_translator.py index 435150629a9701..651c91e0489590 100644 --- a/python/paddle/jit/dy2static/program_translator.py +++ b/python/paddle/jit/dy2static/program_translator.py @@ -700,7 +700,7 @@ def rollback_impl(class_instance): def __deepcopy__(self, memo): """ Customized behavior for copy.deepcopy, return original decorated function instead - of a new StaticFunction Object. StaticFunction itself is not copyable becuase it's + of a new StaticFunction Object. StaticFunction itself is not copyable because it's associated with class_instance. We add __deepcopy__ here only for the following usage: diff --git a/test/legacy_test/test_assign_pos_op.py b/test/legacy_test/test_assign_pos_op.py index 2133aa236c4432..61e899a3b9949b 100644 --- a/test/legacy_test/test_assign_pos_op.py +++ b/test/legacy_test/test_assign_pos_op.py @@ -43,7 +43,7 @@ def count(x, upper_num): # why defining the assert function specially? -# Becasue assign_pos_op is multithread-op, which can make the order of numbers +# Because assign_pos_op is multithread-op, which can make the order of numbers # in each counter(bin) is random. But the numbers set is certain in each counter(bin). np_allclose = np.allclose diff --git a/test/legacy_test/test_assign_pos_op_dygraph.py b/test/legacy_test/test_assign_pos_op_dygraph.py index 9f5476aeb496aa..5a3cea592e6c0f 100644 --- a/test/legacy_test/test_assign_pos_op_dygraph.py +++ b/test/legacy_test/test_assign_pos_op_dygraph.py @@ -42,7 +42,7 @@ def count(x, upper_num): # why defining the assert function specially? -# Becasue assign_pos_op is multithread-op, which can make the order of numbers +# Because assign_pos_op is multithread-op, which can make the order of numbers # in each counter(bin) is random. But the numbers set is certain in each counter(bin). np_allclose = np.allclose From 462c65c217205e4aba304fd2ebea7b5fe8d16aa2 Mon Sep 17 00:00:00 2001 From: co63oc Date: Fri, 6 Dec 2024 09:02:16 +0800 Subject: [PATCH 198/288] Fix (#69976) --- paddle/fluid/inference/utils/io_utils.cc | 11 ++++++----- 1 file changed, 6 insertions(+), 5 deletions(-) diff --git a/paddle/fluid/inference/utils/io_utils.cc b/paddle/fluid/inference/utils/io_utils.cc index 9898330bf99d91..95af37a992fb25 100644 --- a/paddle/fluid/inference/utils/io_utils.cc +++ b/paddle/fluid/inference/utils/io_utils.cc @@ -88,12 +88,13 @@ void DeserializePDTensorToStream(std::istream &is, PaddleTensor *tensor) { std::vector bytes(name_bytes); is.read(bytes.data(), name_bytes); // NOLINT tensor->name = std::string(bytes.data(), name_bytes); - // 3. LoD - uint64_t lod_level = 0; - is.read(reinterpret_cast(&lod_level), sizeof(lod_level)); + // 3. LegacyLoD + uint64_t legacy_lod_level = 0; + is.read(reinterpret_cast(&legacy_lod_level), + sizeof(legacy_lod_level)); auto *lod = &(tensor->lod); - lod->resize(lod_level); - for (uint64_t i = 0; i < lod_level; ++i) { + lod->resize(legacy_lod_level); + for (uint64_t i = 0; i < legacy_lod_level; ++i) { uint64_t size = 0; is.read(reinterpret_cast(&size), sizeof(size)); std::vector tmp(size / sizeof(size_t)); From 0646a8f4f6c3414e136c32666fff3028dbf36709 Mon Sep 17 00:00:00 2001 From: XiangGao Date: Fri, 6 Dec 2024 09:34:46 +0800 Subject: [PATCH 199/288] improve documents of auto parallel high level api (#69989) --- .../auto_parallel/high_level_api.py | 232 +++++++++++++++++- 1 file changed, 220 insertions(+), 12 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/high_level_api.py b/python/paddle/distributed/auto_parallel/high_level_api.py index 25e81f57fe1924..7ca20090fed703 100644 --- a/python/paddle/distributed/auto_parallel/high_level_api.py +++ b/python/paddle/distributed/auto_parallel/high_level_api.py @@ -290,10 +290,15 @@ def to_distributed( Examples: .. code-block:: python + >>> import math >>> import numpy as np >>> import paddle - >>> import paddle.distributed as dist + >>> import paddle.nn.functional as F >>> from paddle import nn + >>> from paddle.distributed.auto_parallel.high_level_api import ( + >>> ToDistributedConfig, + >>> to_distributed, + >>> ) >>> EPOCHES = 1 >>> VOCAB_SIZE = 8000 @@ -302,8 +307,9 @@ def to_distributed( >>> HIDDEN_SIZE = 2048 >>> INTERMEDIATE_SIZE = 4096 >>> SEQ_LENGTH = 1024 + >>> N_HEAD = 32 >>> NUM_HIDDEN_LAYERS = 4 - >>> class RandomDataset(paddle.io.Dataset): # type: ignore[type-arg] + >>> class RandomDataset(paddle.io.Dataset): ... def __init__(self, inputs, labels, num_samples): ... self.inputs = inputs ... self.labels = labels @@ -313,6 +319,136 @@ def to_distributed( ... def __len__(self): ... return self.num_samples + >>> class RotaryEmbedding(nn.Layer): + ... def __init__(self, dim, max_position_embeddings=2048, base=10000): + ... super().__init__() + ... self.dim = dim + ... self.max_position_embeddings = max_position_embeddings + ... self.base = base + ... self.inv_freq = 1.0 / ( + ... self.base + ... ** ( + ... paddle.cast(paddle.arange(0, self.dim, 2), dtype="float32") + ... / self.dim + ... ) + ... ) + ... self._set_cos_sin_cache(seq_len=max_position_embeddings) + + ... def _set_cos_sin_cache(self, seq_len): + ... self.max_seq_len_cached = seq_len + ... t = paddle.arange(seq_len, dtype="float32") + ... freqs = paddle.einsum("i,j->ij", t, self.inv_freq) + ... emb = paddle.concat([freqs, freqs], axis=-1) + ... self.cos_cached = emb.cos()[None, :, None, :] + ... self.sin_cached = emb.sin()[None, :, None, :] + + ... def forward(self, x, seq_len=None): + ... cos = self.cos_cached[:, :seq_len, :, :] + ... sin = self.sin_cached[:, :seq_len, :, :] + ... return ( + ... cos.cast(x.dtype) if cos.dtype != x.dtype else cos, + ... sin.cast(x.dtype) if sin.dtype != x.dtype else sin, + ... ) + + >>> def rotate_half(x): + ... x1 = x[..., : x.shape[-1] // 2] + ... x2 = x[..., x.shape[-1] // 2 :] + ... return paddle.concat([-x2, x1], axis=-1) + + >>> def apply_rotary_pos_emb(q, k, cos, sin, position_ids): + ... if position_ids is None: + ... cos = cos[:, : q.shape[1], :, :] + ... sin = sin[:, : q.shape[1], :, :] + ... else: + ... cos = cos.squeeze(axis=[0, 2]) + ... sin = sin.squeeze(axis=[0, 2]) + ... cos = cos[position_ids].unsqueeze(2) + ... sin = sin[position_ids].unsqueeze(2) + ... q_embed = (q * cos) + (rotate_half(q) * sin) + ... k_embed = (k * cos) + (rotate_half(k) * sin) + ... return q_embed, k_embed + + >>> def scaled_dot_product_attention( + ... query_states, + ... key_states, + ... value_states, + ... attention_mask, + ... ): + ... bsz, q_len, num_heads, head_dim = query_states.shape + ... _, kv_seq_len, _, _ = value_states.shape + ... query_states = paddle.transpose(query_states, [0, 2, 1, 3]) + ... key_states = paddle.transpose(key_states, [0, 2, 1, 3]) + ... value_states = paddle.transpose(value_states, [0, 2, 1, 3]) + ... attn_weights = paddle.matmul( + ... query_states / math.sqrt(head_dim), key_states.transpose([0, 1, 3, 2]) + ... ) + ... attention_mask = attention_mask.reshape([bsz, 1, q_len, kv_seq_len]) + ... attn_weights = attn_weights + attention_mask + ... if not paddle.in_dynamic_mode(): + ... attn_weights = F.softmax(attn_weights, axis=-1, dtype="float32").astype( + ... query_states.dtype + ... ) + ... else: + ... with paddle.amp.auto_cast(False): + ... attn_weights = F.softmax( + ... attn_weights, axis=-1, dtype="float32" + ... ).astype(query_states.dtype) + ... attn_output = paddle.matmul(attn_weights, value_states) + ... attn_output = attn_output.transpose([0, 2, 1, 3]) + ... attn_output = attn_output.reshape([bsz, q_len, head_dim * num_heads]) + ... return attn_output + + >>> class Attention(nn.Layer): + ... def __init__(self, hidden_size=HIDDEN_SIZE, n_head=N_HEAD): + ... super().__init__() + ... self.hidden_size = hidden_size + ... self.num_heads = n_head + ... self.head_dim = hidden_size // n_head + ... self.q_proj = nn.Linear( + ... hidden_size, hidden_size, bias_attr=False + ... ) + ... self.k_proj = nn.Linear( + ... hidden_size, hidden_size, bias_attr=False + ... ) + ... self.v_proj = nn.Linear( + ... hidden_size, hidden_size, bias_attr=False + ... ) + ... self.o_proj = nn.Linear( + ... hidden_size, hidden_size, bias_attr=False + ... ) + ... self.rotary_emb = RotaryEmbedding( + ... self.head_dim, max_position_embeddings=SEQ_LENGTH, base=10000 + ... ) + + ... def forward( + ... self, + ... hidden_states, + ... position_ids=None, + ... attention_mask=None, + ... ): + ... query_states = self.q_proj(hidden_states) + ... key_states = self.k_proj(hidden_states) + ... value_states = self.v_proj(hidden_states) + ... target_query_shape = [0, 0, self.num_heads, self.head_dim] + ... target_key_value_shape = [0, 0, self.num_heads, self.head_dim] + ... query_states = query_states.reshape(shape=target_query_shape) + ... key_states = key_states.reshape(shape=target_key_value_shape) + ... value_states = value_states.reshape(shape=target_key_value_shape) + ... kv_seq_len = key_states.shape[-3] + ... cos, sin = self.rotary_emb(value_states, seq_len=kv_seq_len) + ... query_states, key_states = apply_rotary_pos_emb( + ... query_states, key_states, cos, sin, position_ids + ... ) + ... output = scaled_dot_product_attention( + ... query_states, + ... key_states, + ... value_states, + ... attention_mask, + ... ) + ... attn_output = output + ... attn_output = self.o_proj(attn_output) + ... return attn_output + >>> class Mlp(nn.Layer): ... def __init__( ... self, @@ -339,6 +475,28 @@ def to_distributed( ... out = self.down_proj(x) ... return out + >>> class RMSNorm(nn.Layer): + ... def __init__(self, hidden_size=HIDDEN_SIZE): + ... super().__init__() + ... self.hidden_size = hidden_size + ... self.weight = paddle.create_parameter( + ... shape=[self.hidden_size], + ... dtype=paddle.get_default_dtype(), + ... default_initializer=nn.initializer.Constant(1.0), + ... ) + ... self.variance_epsilon = 1.0 + + ... def forward(self, hidden_states): + ... with paddle.amp.auto_cast(False): + ... hidden_states = hidden_states.astype("float32") + ... variance = hidden_states.pow(2).mean(-1, keepdim=True) + ... hidden_states = ( + ... paddle.rsqrt(variance + self.variance_epsilon) * hidden_states + ... ) + ... if self.weight.dtype in [paddle.float16, paddle.bfloat16]: + ... hidden_states = paddle.cast(hidden_states, self.weight.dtype) + ... return hidden_states * self.weight + >>> class DecoderLayer(nn.Layer): ... def __init__( ... self, @@ -348,26 +506,58 @@ def to_distributed( ... super().__init__() ... self.hidden_size = hidden_size ... self.intermediate_size = intermediate_size + ... self.self_attn = Attention(hidden_size) ... self.mlp = Mlp() + ... self.input_layernorm = RMSNorm(hidden_size) + ... self.post_attn_layernorm = RMSNorm(hidden_size) ... def forward( ... self, ... hidden_states, + ... position_ids=None, + ... attention_mask=None, ... ): ... residual = hidden_states + ... hidden_states = self.input_layernorm(hidden_states) + ... hidden_states = self.self_attn( + ... hidden_states, position_ids, attention_mask + ... ) + ... hidden_states = residual + hidden_states + ... residual = hidden_states + ... hidden_states = self.post_attn_layernorm(hidden_states) ... hidden_states = self.mlp(hidden_states) ... hidden_states = residual + hidden_states ... return hidden_states - >>> class DemoNet(nn.Layer): + >>> def _prepare_decoder_attention_mask( + ... attention_mask, input_shape, dtype + ... ): + ... batch_size, src_length = attention_mask.shape[0], attention_mask.shape[-1] + ... batch_size, target_length = input_shape + ... attention_mask = attention_mask[:, None, None, :].astype("bool") + ... attention_mask.stop_gradient = True + ... expanded_attn_mask = attention_mask.expand([batch_size, 1, target_length, src_length]) + ... mask = paddle.tril(paddle.ones((target_length, target_length), dtype="bool")) + ... combined_attention_mask = mask[None, None, :, :].expand( + ... [batch_size, 1, target_length, target_length] + ... ) + ... expanded_attn_mask = (expanded_attn_mask & combined_attention_mask) + ... expanded_attn_mask = paddle.where( + ... expanded_attn_mask, 0.0, paddle.finfo(dtype).min + ... ).astype(dtype) + ... return expanded_attn_mask + + >>> class Model(nn.Layer): ... def __init__( ... self, ... vocab_size=VOCAB_SIZE, ... hidden_size=HIDDEN_SIZE, ... intermediate_size=INTERMEDIATE_SIZE, - ... labels=None, ... ): ... super().__init__() + ... self.vocab_size = vocab_size + ... self.hidden_size = hidden_size + ... self.intermediate_size = intermediate_size ... self.embed_tokens = nn.Embedding( ... vocab_size, ... hidden_size, @@ -378,6 +568,7 @@ def to_distributed( ... for i in range(NUM_HIDDEN_LAYERS) ... ] ... ) + ... self.norm = RMSNorm(hidden_size) ... self.weight = self.create_parameter( ... shape=[hidden_size, vocab_size], ... dtype=paddle.get_default_dtype(), @@ -390,15 +581,33 @@ def to_distributed( ... def forward( ... self, ... input_ids=None, + ... position_ids=None, + ... attention_mask=None, ... labels=None, ... ): ... batch_size, seq_length = input_ids.shape - ... hidden_states = self.embed_tokens(input_ids) + ... inputs_embeds = self.embed_tokens(input_ids) + ... attention_mask = paddle.ones( + ... (batch_size, seq_length), dtype=paddle.bool + ... ) + ... if position_ids is None: + ... position_ids = paddle.arange(seq_length, dtype="int64").expand( + ... (batch_size, seq_length) + ... ) + ... attention_mask = _prepare_decoder_attention_mask( + ... attention_mask, + ... (batch_size, seq_length), + ... inputs_embeds.dtype, + ... ) + ... hidden_states = inputs_embeds ... for idx, (decoder_layer) in enumerate(self.layers): ... layer_outputs = decoder_layer( ... hidden_states, + ... position_ids, + ... attention_mask, ... ) ... hidden_states = layer_outputs + ... hidden_states = self.norm(hidden_states) ... logits = paddle.matmul(hidden_states, self.weight) ... loss = None ... if labels is not None: @@ -417,7 +626,8 @@ def to_distributed( ... else: ... loss = paddle.sum(masked_lm_loss * binary_sequence) / count ... return (loss, logits) - >>> model = DemoNet() + + >>> model = Model() # There is no distributed code or markup in Model >>> input_seqs = np.random.randint( ... low=0, high=1024, size=(BATCH_SIZE * BATCH_NUM, SEQ_LENGTH) ... ).astype("int64") @@ -441,16 +651,15 @@ def to_distributed( ... ) >>> dist_config = ToDistributedConfig() >>> dist_config.input_spec = [input_seq_spec] - >>> dist_config.sequence_parallel = True - >>> # # wrap model by using **to_distributed** + >>> # wrap model, opt, dataloader by using **to_distributed** >>> dist_model, dist_opt, dist_loader = to_distributed( ... model, ... opt, ... loader, - ... device_num, - ... node_num, - ... dist_config, + ... device_num=8, + ... node_num=1, + ... config=dist_config, ... ) >>> for epoch in range(EPOCHES): @@ -462,7 +671,6 @@ def to_distributed( ... loss.backward() ... dist_opt.step() ... dist_opt.clear_grad() - """ logger.debug(f'input model: {model}') # paddle.distributed.init_parallel_env() From e9e419f46f538d85b07c5f3091f94ea14bd3abac Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Fri, 6 Dec 2024 10:29:06 +0800 Subject: [PATCH 200/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20No.24=E3=80=91=E8=A1=A5=E5=85=85=20matrix=5Ftranspose?= =?UTF-8?q?=20=E5=88=B0=20tensor.=5F=5Finit=5F=5F=20(#69982)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit --- python/paddle/tensor/__init__.py | 1 + 1 file changed, 1 insertion(+) diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 84666c71ceffe6..5874f8507ffa2b 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -500,6 +500,7 @@ 'bincount', 'mv', 'matrix_power', + 'matrix_transpose', 'qr', 'householder_product', 'pca_lowrank', From c2e864eb41a098ee9d6f85564184da1bca104bc1 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Fri, 6 Dec 2024 10:31:03 +0800 Subject: [PATCH 201/288] [Win] Restore slow code in windows (#69948) * restore slow code in win32 * fix --- paddle/phi/kernels/funcs/p_norm_utils.h | 3 +++ paddle/phi/kernels/gpu/p_norm_kernel.cu | 14 ++++++++++++++ 2 files changed, 17 insertions(+) diff --git a/paddle/phi/kernels/funcs/p_norm_utils.h b/paddle/phi/kernels/funcs/p_norm_utils.h index d1a492b30d3a8d..cde20dd63c73aa 100644 --- a/paddle/phi/kernels/funcs/p_norm_utils.h +++ b/paddle/phi/kernels/funcs/p_norm_utils.h @@ -61,6 +61,8 @@ __device__ __forceinline__ double inline_pow(double base, double exponent) { return pow(base, exponent); } +#ifndef _WIN32 +// To avoid large .so size in Windows cuda11.8 __device__ __forceinline__ dtype::float16 inline_fabs(dtype::float16 x) { return static_cast(fabs(static_cast(x))); } @@ -96,4 +98,5 @@ __device__ __forceinline__ float inline_fabs_cubic(float x) { __device__ __forceinline__ double inline_fabs_cubic(double x) { return fabs(x * x * x); } +#endif } // namespace phi diff --git a/paddle/phi/kernels/gpu/p_norm_kernel.cu b/paddle/phi/kernels/gpu/p_norm_kernel.cu index 0793ca55f5f24c..b160026b2bbde8 100644 --- a/paddle/phi/kernels/gpu/p_norm_kernel.cu +++ b/paddle/phi/kernels/gpu/p_norm_kernel.cu @@ -49,6 +49,8 @@ struct UnsignedPowFunctor { float porder; }; +#ifndef _WIN32 +// To avoid large .so size in Windows cuda11.8 template struct FabsFunctor { HOSTDEVICE explicit inline FabsFunctor() = default; @@ -72,6 +74,7 @@ struct FabsCubicFunctor { return static_cast(inline_fabs_cubic(x)); } }; +#endif template void PNormKernel(const Context& dev_ctx, @@ -108,6 +111,16 @@ void PNormKernel(const Context& dev_ctx, phi::funcs::ReduceKernel>( dev_ctx, *in_x, out_norm, AbsFunctor(), reduce_axis); } else { +#ifdef _WIN32 + phi::funcs::ReduceKernel>( + dev_ctx, *in_x, out_norm, UnsignedPowFunctor(porder), reduce_axis); + + const DenseTensor* tmp_norm = out_norm; + std::vector ins = {tmp_norm}; + std::vector outs = {out_norm}; + phi::funcs::ElementwiseKernel( + dev_ctx, ins, &outs, UnsignedPowFunctor(1. / porder)); +#else if (porder == 1.0) { // fast 1-norm phi::funcs::ReduceKernel>( @@ -134,6 +147,7 @@ void PNormKernel(const Context& dev_ctx, phi::funcs::ElementwiseKernel( dev_ctx, ins, &outs, UnsignedPowFunctor(1. / porder)); } +#endif } } } // namespace phi From 818ed8aea6e7dc06b6ab900fcc32291aee8af744 Mon Sep 17 00:00:00 2001 From: zyfncg Date: Fri, 6 Dec 2024 10:32:22 +0800 Subject: [PATCH 202/288] fix bug of cinn unittest (#69987) --- .../symbolic/test_sub_graph_stable_diffusion_24_st.py | 9 +++------ 1 file changed, 3 insertions(+), 6 deletions(-) diff --git a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_24_st.py b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_24_st.py index 1168be001862cd..31f730c9ead168 100644 --- a/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_24_st.py +++ b/test/ir/pir/cinn/symbolic/test_sub_graph_stable_diffusion_24_st.py @@ -33,14 +33,14 @@ def __init__(self): shape=[640], dtype=paddle.float32, ) + self.size = [3, 8] def forward( self, var_0, # (shape: [1, 640, 1, 1], dtype: paddle.float32, stop_gradient: False) - var_1, # (shape: [2], dtype: paddle.int32, stop_gradient: True) ): var_2 = paddle.nn.functional.common.interpolate( - var_0, size=var_1, mode='nearest' + var_0, size=self.size, mode='nearest' ) var_3 = paddle.nn.functional.conv.conv2d( var_2, self.parameter_0, self.parameter_1, [1, 1], 1, [1, 1], 1 @@ -49,10 +49,7 @@ def forward( def create_paddle_inputs(): - inputs = ( - paddle.rand(shape=[1, 640, 1, 1], dtype=paddle.float32), - paddle.randint(low=0, high=10, shape=[2], dtype=paddle.int32), - ) + inputs = (paddle.rand(shape=[1, 640, 1, 1], dtype=paddle.float32),) return inputs From 09ba5479cb9bc2720dd8d3b107778cb884386beb Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Fri, 6 Dec 2024 10:32:32 +0800 Subject: [PATCH 203/288] [CodeStyle][Typos][B-14,B-[17-19]] Fix typos(`Broardcast`,`Bradcast`,`Boardcast`,`buitin`,`buitlin`,`Buitin`,`builded`,`ba`) (#69966) * [CodeStyle][Typos][B-14,B-[17-19]] Fix typos * [CodeStyle][Typos][B-14,B-[17-19]] Fix typos(Broardcast,Bradcast,Boardcast,buitin,buitlin,Buitin,builded,ba) --- _typos.toml | 9 +---- .../new_executor/standalone_executor.cc | 4 +-- .../include/serialize_utils.h | 2 +- .../fluid/pybind/manual_static_op_function.h | 2 +- .../paddle/jit/dy2static/partial_program.py | 36 +++++++++---------- python/paddle/tensorrt/export.py | 4 +-- python/paddle/tensorrt/util.py | 2 +- .../test_eager_run_program_deprecated.py | 4 +-- .../test_run_program_op_deprecated.py | 4 +-- test/legacy_test/test_elementwise_add_op.py | 4 +-- .../test_imperative_triple_grad.py | 2 +- test/tensorrt/tensorrt_test_base.py | 4 +-- 12 files changed, 33 insertions(+), 44 deletions(-) diff --git a/_typos.toml b/_typos.toml index 64693adf7534cb..8697561e4d9464 100644 --- a/_typos.toml +++ b/_typos.toml @@ -12,6 +12,7 @@ extend-exclude = [ anc = 'anc' arange = "arange" astroid = 'astroid' +ba = 'ba' Clas = 'Clas' clen = 'clen' dout = "dout" @@ -31,14 +32,6 @@ blcok = 'blcok' bootom = 'bootom' bondary = 'bondary' branchs = 'branchs' -Broardcast = 'Broardcast' -Bradcast = 'Bradcast' -Boardcast = 'Boardcast' -Buitin = 'Buitin' -buitlin = 'buitlin' -buitin = 'buitin' -builded = 'builded' -ba = 'ba' cahe = 'cahe' Caculate = 'Caculate' caculate = 'caculate' diff --git a/paddle/fluid/framework/new_executor/standalone_executor.cc b/paddle/fluid/framework/new_executor/standalone_executor.cc index c257b87cc45200..a97601b563ab9c 100644 --- a/paddle/fluid/framework/new_executor/standalone_executor.cc +++ b/paddle/fluid/framework/new_executor/standalone_executor.cc @@ -159,9 +159,9 @@ StandaloneExecutor::StandaloneExecutor(const phi::Place& place, common::errors::InvalidArgument( "When using pipeline strategy in auto " "prarallelism with new executor, " - "the backward subprogram must be builded in real " + "the backward subprogram must be built in real " "static build mode, but it can not " - "be staticly builded in this case. You can " + "be staticly built in this case. You can " "enable 'GLOG_v=1' to obtain log information.")); } } diff --git a/paddle/fluid/pir/serialize_deserialize/include/serialize_utils.h b/paddle/fluid/pir/serialize_deserialize/include/serialize_utils.h index 707c0de0aaf9e1..fffc9ceb6fb367 100644 --- a/paddle/fluid/pir/serialize_deserialize/include/serialize_utils.h +++ b/paddle/fluid/pir/serialize_deserialize/include/serialize_utils.h @@ -420,7 +420,7 @@ Json AttrTypeWriter::WriteBuiltInAttr(const pir::Attribute& attr) { } else { PADDLE_ENFORCE(false, common::errors::InvalidArgument( - "Unknown Attr %s when write Buitin dialect attr")); + "Unknown Attr %s when write Builtin dialect attr")); } return attr_json; } diff --git a/paddle/fluid/pybind/manual_static_op_function.h b/paddle/fluid/pybind/manual_static_op_function.h index 25f0dba0bfbe83..5954d9d9a232eb 100644 --- a/paddle/fluid/pybind/manual_static_op_function.h +++ b/paddle/fluid/pybind/manual_static_op_function.h @@ -966,7 +966,7 @@ static PyObject *builtin_combine_op(PyObject *self, PyObject *args, PyObject *kwargs) { try { - VLOG(6) << "Add buitin_combine op into program"; + VLOG(6) << "Add builtin_combine op into program"; VLOG(8) << "args count: " << (PyTuple_Size(args) / 2); // Get Value from args PyObject *x_obj = PyTuple_GET_ITEM(args, 0); diff --git a/python/paddle/jit/dy2static/partial_program.py b/python/paddle/jit/dy2static/partial_program.py index b870f1bbf4f182..51ec53b968e245 100644 --- a/python/paddle/jit/dy2static/partial_program.py +++ b/python/paddle/jit/dy2static/partial_program.py @@ -808,15 +808,15 @@ def _prepare_attributes(self): @switch_to_static_graph def _build_infer_program(self, infer_program, forward_end_op_index): forward_skip_vars = self._parse_skip_gc_vars(infer_program) - builded_infer_program = add_build_strategy_for( + built_infer_program = add_build_strategy_for( infer_program, 0, forward_end_op_index, self._build_strategy, forward_skip_vars, ) - self._apply_inplace_pass(builded_infer_program, None) - return builded_infer_program + self._apply_inplace_pass(built_infer_program, None) + return built_infer_program @switch_to_static_graph def _get_forward_backward_program_form( @@ -833,7 +833,7 @@ def _get_forward_backward_program_form( backward_skip_vars = self._parse_skip_gc_vars( whole_program ) + self._grad_var_names.get('param', []) - backward_builded_program = add_build_strategy_for( + backward_built_program = add_build_strategy_for( whole_program, backward_start_op_index, backward_end_op_index, @@ -842,9 +842,9 @@ def _get_forward_backward_program_form( ) forward_skip_vars = self._parse_skip_gc_vars( - whole_program, backward_builded_program + whole_program, backward_built_program ) - forward_builded_program = add_build_strategy_for( + forward_built_program = add_build_strategy_for( whole_program, 0, forward_end_op_index, @@ -852,27 +852,25 @@ def _get_forward_backward_program_form( forward_skip_vars, ) - self._apply_inplace_pass( - forward_builded_program, backward_builded_program - ) + self._apply_inplace_pass(forward_built_program, backward_built_program) # NOTE(Aurelius84): Export forward/backward program for SubGraphChecker, # see export_subgraph for detail. pir_exporter( self, - forward_builded_program, + forward_built_program, SubGraphRole.Forward, set(), set(forward_skip_vars), ) pir_exporter( self, - backward_builded_program, + backward_built_program, SubGraphRole.Backward, set(forward_skip_vars), set(backward_skip_vars), ) - return [forward_builded_program, backward_builded_program] + return [forward_built_program, backward_built_program] def _apply_inplace_pass(self, forward_program, backward_program): attr_types = { @@ -1157,19 +1155,17 @@ def add_build_strategy_for( core.Scope(), framework._current_expected_place() ) ir_graph = framework.IrGraph(compiled_program._graph) - builded_program = ir_graph.to_program() + built_program = ir_graph.to_program() if hasattr(compiled_program._program, 'lr_scheduler'): - builded_program.lr_scheduler = ( - compiled_program._program.lr_scheduler - ) + built_program.lr_scheduler = compiled_program._program.lr_scheduler else: # can't just create a new program, we need copy the vardesc. - builded_program = paddle.static.Program() + built_program = paddle.static.Program() for var in program.block(0).vars.values(): - builded_program.block(0)._clone_variable(var, False) + built_program.block(0)._clone_variable(var, False) # set back the parent_idx of blocks - for origin, current in zip(program.blocks, builded_program.blocks): + for origin, current in zip(program.blocks, built_program.blocks): current.desc.set_parent_idx(origin.desc.parent) - return builded_program + return built_program diff --git a/python/paddle/tensorrt/export.py b/python/paddle/tensorrt/export.py index 502d4b9dd3af77..b8e20ff4b23c97 100644 --- a/python/paddle/tensorrt/export.py +++ b/python/paddle/tensorrt/export.py @@ -37,7 +37,7 @@ from paddle.tensorrt.converter import PaddleToTensorRTConverter from paddle.tensorrt.util import ( forbid_op_lower_trt, - mark_buitlin_op, + mark_builtin_op, run_pir_pass, warmup_shape_infer, ) @@ -267,7 +267,7 @@ def convert_to_trt(program, trt_config, scope): forbid_op_lower_trt(program, trt_config.disable_ops) # Adding marker labels to builtin ops facilitates convert processing, but they ultimately do not enter the TensorRT subgraph. - mark_buitlin_op(program) + mark_builtin_op(program) # run pir pass (including trt_sub_graph_extract_pass) program_with_pir = run_pir_pass(program, partition_mode=True) diff --git a/python/paddle/tensorrt/util.py b/python/paddle/tensorrt/util.py index b402adbd2290ea..8f815db287afb2 100644 --- a/python/paddle/tensorrt/util.py +++ b/python/paddle/tensorrt/util.py @@ -129,7 +129,7 @@ def get_trt_version_list(): # Adding marker labels to builtin ops facilitates convert processing, but they ultimately do not enter the TensorRT subgraph. -def mark_buitlin_op(program): +def mark_builtin_op(program): for op in program.global_block().ops: if op.name() == "builtin.split": defining_op = op.operands()[0].source().get_defining_op() diff --git a/test/deprecated/legacy_test/test_eager_run_program_deprecated.py b/test/deprecated/legacy_test/test_eager_run_program_deprecated.py index 00b29d9c0068bb..4960b8a587f315 100644 --- a/test/deprecated/legacy_test/test_eager_run_program_deprecated.py +++ b/test/deprecated/legacy_test/test_eager_run_program_deprecated.py @@ -78,8 +78,8 @@ def _add_build_strategy_for(input_program, start_op_index, end_op_index): core.Scope(), paddle.framework._current_expected_place() ) ir_graph = paddle.base.framework.IrGraph(compiled_program._graph) - builded_program = ir_graph.to_program() - return builded_program + built_program = ir_graph.to_program() + return built_program class TestRunProgram(unittest.TestCase): diff --git a/test/deprecated/legacy_test/test_run_program_op_deprecated.py b/test/deprecated/legacy_test/test_run_program_op_deprecated.py index 7b8b8413689657..0e84d9227add17 100644 --- a/test/deprecated/legacy_test/test_run_program_op_deprecated.py +++ b/test/deprecated/legacy_test/test_run_program_op_deprecated.py @@ -47,8 +47,8 @@ def _add_build_strategy_for(input_program, start_op_index, end_op_index): core.Scope(), paddle.framework._current_expected_place() ) ir_graph = paddle.base.framework.IrGraph(compiled_program._graph) - builded_program = ir_graph.to_program() - return builded_program + built_program = ir_graph.to_program() + return built_program @switch_to_static_graph diff --git a/test/legacy_test/test_elementwise_add_op.py b/test/legacy_test/test_elementwise_add_op.py index a3ccad172f7c20..0cf79f4ad7165f 100644 --- a/test/legacy_test/test_elementwise_add_op.py +++ b/test/legacy_test/test_elementwise_add_op.py @@ -986,7 +986,7 @@ def if_enable_cinn(self): pass -class TestElementwiseAddOpAutoParallelXShardBoardcast( +class TestElementwiseAddOpAutoParallelXShardBroadcast( TestElementwiseAddOpAutoParallel ): def init_placements(self): @@ -1023,7 +1023,7 @@ def init_input_output(self): self.out = np.add(self.x, self.y) -class TestElementwiseAddOpAutoParallelXYShardBroardcast( +class TestElementwiseAddOpAutoParallelXYShardBroadcast( TestElementwiseAddOpAutoParallelXYShard ): def init_placements(self): diff --git a/test/legacy_test/test_imperative_triple_grad.py b/test/legacy_test/test_imperative_triple_grad.py index 09372aaf9c9767..60425c31c955e6 100644 --- a/test/legacy_test/test_imperative_triple_grad.py +++ b/test/legacy_test/test_imperative_triple_grad.py @@ -227,7 +227,7 @@ def test_all_cases(self): self.func_example_with_gradient_and_create_graph() -class TestDygraphTripleGradBradcastCase(TestCase): +class TestDygraphTripleGradBroadcastCase(TestCase): def setUp(self): self.sort_sum_gradient = False self.x_shape = [3, 2, 2] diff --git a/test/tensorrt/tensorrt_test_base.py b/test/tensorrt/tensorrt_test_base.py index bef4f87968b09a..0eef389fd42e2d 100755 --- a/test/tensorrt/tensorrt_test_base.py +++ b/test/tensorrt/tensorrt_test_base.py @@ -25,7 +25,7 @@ TensorRTConfig, ) from paddle.tensorrt.util import ( - mark_buitlin_op, + mark_builtin_op, run_pir_pass, warmup_shape_infer, ) @@ -247,7 +247,7 @@ def check_trt_result(self, rtol=1e-5, atol=1e-5): main_program = run_pir_pass(main_program, partition_mode=False) # Adding marker labels to builtin ops facilitates convert processing, but they ultimately do not enter the TensorRT subgraph. - mark_buitlin_op(main_program) + mark_builtin_op(main_program) # run trt_sub_graph_extract_pass() program_with_trt = run_pir_pass(main_program, partition_mode=True) From 8b44677ae7ad8a54849015e3cd8b58bf0076ca4c Mon Sep 17 00:00:00 2001 From: wwwuyan <90775351+wwwuyan@users.noreply.github.com> Date: Fri, 6 Dec 2024 10:33:29 +0800 Subject: [PATCH 204/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Add=20API?= =?UTF-8?q?=20Legend=20No.54=E3=80=91Add=20the=20legend=20of=20diagonal=5F?= =?UTF-8?q?scatter=5Fen=20(#69895)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * diagonal_scatter_en * Update python/paddle/tensor/manipulation.py --------- Co-authored-by: zachary sun <70642955+sunzhongkai588@users.noreply.github.com> --- python/paddle/tensor/manipulation.py | 6 ++++++ 1 file changed, 6 insertions(+) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index 1b8414ac520837..d4375c7b918dcd 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -7343,6 +7343,12 @@ def diagonal_scatter( Note: ``y`` should have the same shape as :ref:`paddle.diagonal `. + The image below demonstrates the example: A 2D tensor with a shape of [2, 3] is ``diagonal_scatter`` along its main diagonal (``offset = 0``) within ``axis1 = 0`` and ``axis2 = 1`` using a 1D tensor filled with ones. + + .. image:: https://githubraw.cdn.bcebos.com/PaddlePaddle/docs/develop/docs/images/api_legend/diagonal_scatter.png + :width: 500 + :alt: legend of diagonal_scatter API + Args: x (Tensor): ``x`` is the original Tensor. Must be at least 2-dimensional. y (Tensor): ``y`` is the Tensor to embed into ``x`` From 163d7e80008a1fc2b7100d01cb2fd59693fb0c2e Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Fri, 6 Dec 2024 10:36:20 +0800 Subject: [PATCH 205/288] [CINN] Add program id to compilation cache key (#69994) --- paddle/cinn/hlir/framework/pir/fusion_info.cc | 15 +++++++++++++ paddle/cinn/hlir/framework/pir/fusion_info.h | 22 +++++++++++++++---- 2 files changed, 33 insertions(+), 4 deletions(-) diff --git a/paddle/cinn/hlir/framework/pir/fusion_info.cc b/paddle/cinn/hlir/framework/pir/fusion_info.cc index f58bcb97e78c3b..d445910e0909e7 100644 --- a/paddle/cinn/hlir/framework/pir/fusion_info.cc +++ b/paddle/cinn/hlir/framework/pir/fusion_info.cc @@ -124,9 +124,19 @@ std::ostream& operator<<(std::ostream& os, const FusionOpInfo& info) { return os; } +ProgramInfo::ProgramInfo(const ::pir::Program& program) { id_ = program.id(); } + +std::size_t ProgramInfo::hash() const { return std::hash{}(id_); } + +std::ostream& operator<<(std::ostream& os, const ProgramInfo& info) { + os << "ProgramInfo - " << info.hash(); + return os; +} + FusionInfo::FusionInfo(const OpLoweringGroup& group) { ParseOpInfos(group); ParseInputDimExprs(group); + ParseProgramInfo(group); } void FusionInfo::ParseOpInfos(const OpLoweringGroup& group) { @@ -191,6 +201,10 @@ void FusionInfo::ParseInputDimExprs(const OpLoweringGroup& group) { } } +void FusionInfo::ParseProgramInfo(const OpLoweringGroup& group) { + program_info_ = std::make_shared(*group.GetParentProgram()); +} + std::size_t FusionInfo::hash() const { if (cached_hash_value_ != 0U) { return cached_hash_value_; @@ -198,6 +212,7 @@ std::size_t FusionInfo::hash() const { std::size_t seed = 2153; for (const auto& info : op_infos_) hash_combine(seed, info); for (const auto& dim_expr : input_dim_exprs_) hash_combine(seed, dim_expr); + hash_combine(seed, *program_info_); if (!FLAGS_enable_cinn_compile_cache) hash_combine(seed, unique_fn_name_); return seed; diff --git a/paddle/cinn/hlir/framework/pir/fusion_info.h b/paddle/cinn/hlir/framework/pir/fusion_info.h index c6826f73ea83d3..a9baf01f525216 100644 --- a/paddle/cinn/hlir/framework/pir/fusion_info.h +++ b/paddle/cinn/hlir/framework/pir/fusion_info.h @@ -90,6 +90,17 @@ class FusionOpInfo { std::map inner_deps_; }; +class ProgramInfo { + public: + explicit ProgramInfo(const ::pir::Program &program); + + std::size_t hash() const; + friend std::ostream &operator<<(std::ostream &os, const ProgramInfo &info); + + private: + uint64_t id_; +}; + class FusionInfo { using IntArgsMap = std::map; @@ -109,9 +120,11 @@ class FusionInfo { private: void ParseOpInfos(const OpLoweringGroup &group); void ParseInputDimExprs(const OpLoweringGroup &group); + void ParseProgramInfo(const OpLoweringGroup &group); std::vector op_infos_; std::vector<::symbol::ShapeOrDataDimExprs> input_dim_exprs_; + std::shared_ptr program_info_; std::size_t cached_hash_value_{0}; // Used to make same subgraphs have unique FusionInfo while @@ -149,10 +162,11 @@ namespace std { } \ }; -REGISTER_STD_HASH(AttributeInfo); -REGISTER_STD_HASH(ValueInfo); -REGISTER_STD_HASH(OperationInfo); +REGISTER_STD_HASH(AttributeInfo) +REGISTER_STD_HASH(ValueInfo) +REGISTER_STD_HASH(OperationInfo) REGISTER_STD_HASH(OpDepInfo) -REGISTER_STD_HASH(FusionOpInfo); +REGISTER_STD_HASH(FusionOpInfo) +REGISTER_STD_HASH(ProgramInfo) REGISTER_STD_HASH(FusionInfo) } // namespace std From e81f314b037cec0b6434bfba477c8dc5f7d43680 Mon Sep 17 00:00:00 2001 From: ooo oo <106524776+ooooo-create@users.noreply.github.com> Date: Fri, 6 Dec 2024 10:53:03 +0800 Subject: [PATCH 206/288] [CINN] fix matual_with_flatten InferSymbolicShapeInterface location (#69964) * fix some bugs * Empty commit --- paddle/phi/ops/yaml/inconsistent/static_ops.yaml | 2 +- paddle/phi/ops/yaml/legacy/static_ops.yaml | 1 - 2 files changed, 1 insertion(+), 2 deletions(-) diff --git a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml index a714a81f72379c..798fe5ed8c87de 100644 --- a/paddle/phi/ops/yaml/inconsistent/static_ops.yaml +++ b/paddle/phi/ops/yaml/inconsistent/static_ops.yaml @@ -569,7 +569,7 @@ func : matmul_with_flatten data_type : x backward : matmul_with_flatten_grad - # interfaces : paddle::dialect::InferSymbolicShapeInterface + interfaces : paddle::dialect::InferSymbolicShapeInterface - op : maximum args : (Tensor x, Tensor y) diff --git a/paddle/phi/ops/yaml/legacy/static_ops.yaml b/paddle/phi/ops/yaml/legacy/static_ops.yaml index 0ca7f156b34744..c5891880cd70ee 100755 --- a/paddle/phi/ops/yaml/legacy/static_ops.yaml +++ b/paddle/phi/ops/yaml/legacy/static_ops.yaml @@ -519,7 +519,6 @@ func : matmul_with_flatten data_type : x backward : matmul_with_flatten_grad - interfaces : paddle::dialect::InferSymbolicShapeInterface - op : matrix_rank args : (Tensor x, Tensor tol_tensor, float tol=0.0f, bool hermitian=false, bool use_default_tol=true) From 915f6446d711c060eb00ade0f326ae96c9a2576e Mon Sep 17 00:00:00 2001 From: lijin23 <41257772+lj970926@users.noreply.github.com> Date: Fri, 6 Dec 2024 10:55:45 +0800 Subject: [PATCH 207/288] [XPU] fix missed link to xpuml in local build (#69990) --- tools/xpu/pack_paddle_dependence.sh | 2 ++ 1 file changed, 2 insertions(+) diff --git a/tools/xpu/pack_paddle_dependence.sh b/tools/xpu/pack_paddle_dependence.sh index 0d2165a5f64c80..1e31c0d3593ae8 100644 --- a/tools/xpu/pack_paddle_dependence.sh +++ b/tools/xpu/pack_paddle_dependence.sh @@ -142,6 +142,8 @@ function local_assemble() { cp -r ${LOCAL_PATH}/${XHPC_DIR_NAME}/xpudnn/include/* xpu/include/xhpc/xpudnn cp -r ${LOCAL_PATH}/${XHPC_DIR_NAME}/xpudnn/so/libxpu_dnn.so xpu/lib/ + # FIXME(yangjianbang): 待bkcl增加RPATH后, 删除以下代码 + patchelf --set-rpath '$ORIGIN/' xpu/lib/libbkcl.so fi } From 7a9149517da9ec520e5f61439dbd9fc360e39d9c Mon Sep 17 00:00:00 2001 From: wanghuancoder Date: Fri, 6 Dec 2024 12:53:43 +0800 Subject: [PATCH 208/288] roi_align_grad boxes_num support int64 (#70003) --- .../phi/kernels/cpu/roi_align_grad_kernel.cc | 23 ++++++++--- .../phi/kernels/gpu/roi_align_grad_kernel.cu | 41 +++++++++++++------ .../phi/kernels/xpu/roi_align_grad_kernel.cc | 34 ++++++++++----- 3 files changed, 70 insertions(+), 28 deletions(-) diff --git a/paddle/phi/kernels/cpu/roi_align_grad_kernel.cc b/paddle/phi/kernels/cpu/roi_align_grad_kernel.cc index f6599b2ed47333..12133d6773964b 100644 --- a/paddle/phi/kernels/cpu/roi_align_grad_kernel.cc +++ b/paddle/phi/kernels/cpu/roi_align_grad_kernel.cc @@ -97,13 +97,24 @@ void RoiAlignGradKernel(const Context& dev_ctx, int boxes_batch_size = 0; if (boxes_num) { boxes_batch_size = static_cast(boxes_num->numel()); - auto* boxes_num_data = boxes_num->data(); - int start = 0; - for (int n = 0; n < boxes_batch_size; ++n) { - for (int i = start; i < start + boxes_num_data[n]; ++i) { - box_batch_id_data[i] = n; + if (boxes_num->dtype() == phi::DataType::INT64) { + auto* boxes_num_data = boxes_num->data(); + int64_t start = 0; + for (int64_t n = 0; n < boxes_batch_size; ++n) { + for (int64_t i = start; i < start + boxes_num_data[n]; ++i) { + box_batch_id_data[i] = n; + } + start += boxes_num_data[n]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + auto* boxes_num_data = boxes_num->data(); + int start = 0; + for (int n = 0; n < boxes_batch_size; ++n) { + for (int i = start; i < start + boxes_num_data[n]; ++i) { + box_batch_id_data[i] = n; + } + start += boxes_num_data[n]; } - start += boxes_num_data[n]; } } else { auto boxes_lod = boxes.lod().back(); diff --git a/paddle/phi/kernels/gpu/roi_align_grad_kernel.cu b/paddle/phi/kernels/gpu/roi_align_grad_kernel.cu index 9eb4637aab7d9b..f388ede1fe73ca 100644 --- a/paddle/phi/kernels/gpu/roi_align_grad_kernel.cu +++ b/paddle/phi/kernels/gpu/roi_align_grad_kernel.cu @@ -193,19 +193,36 @@ void RoiAlignGradKernel(const Context& dev_ctx, auto gplace = dev_ctx.GetPlace(); if (boxes_num) { int boxes_batch_size = boxes_num->numel(); - std::vector boxes_num_list(boxes_batch_size); - memory_utils::Copy(cplace, - boxes_num_list.data(), - gplace, - boxes_num->data(), - sizeof(int) * boxes_batch_size, - 0); - int start = 0; - for (int n = 0; n < boxes_batch_size; ++n) { - for (size_t i = start; i < start + boxes_num_list[n]; ++i) { - box_batch_size[i] = n; + if (boxes_num->dtype() == phi::DataType::INT64) { + std::vector boxes_num_list(boxes_batch_size); + memory_utils::Copy(cplace, + boxes_num_list.data(), + gplace, + boxes_num->data(), + sizeof(int64_t) * boxes_batch_size, + 0); + int64_t start = 0; + for (int64_t n = 0; n < boxes_batch_size; ++n) { + for (int64_t i = start; i < start + boxes_num_list[n]; ++i) { + box_batch_size[i] = n; + } + start += boxes_num_list[n]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + std::vector boxes_num_list(boxes_batch_size); + memory_utils::Copy(cplace, + boxes_num_list.data(), + gplace, + boxes_num->data(), + sizeof(int) * boxes_batch_size, + 0); + int start = 0; + for (int n = 0; n < boxes_batch_size; ++n) { + for (size_t i = start; i < start + boxes_num_list[n]; ++i) { + box_batch_size[i] = n; + } + start += boxes_num_list[n]; } - start += boxes_num_list[n]; } } else { auto boxes_lod = boxes.lod().back(); diff --git a/paddle/phi/kernels/xpu/roi_align_grad_kernel.cc b/paddle/phi/kernels/xpu/roi_align_grad_kernel.cc index ff1cfe36440514..6d126a8a7ec700 100644 --- a/paddle/phi/kernels/xpu/roi_align_grad_kernel.cc +++ b/paddle/phi/kernels/xpu/roi_align_grad_kernel.cc @@ -50,16 +50,30 @@ void RoiAlignGradKernel(const Context& dev_ctx, int* cpu_lod = nullptr; if (boxes_num) { rois_batch_size = boxes_num->numel(); - std::vector rois_num_list(rois_batch_size); - memory_utils::Copy(cplace, - rois_num_list.data(), - xplace, - boxes_num->data(), - sizeof(int) * rois_batch_size); - cpu_lod = new int[rois_batch_size + 1]; - cpu_lod[0] = 0; - for (int i = 0; i < rois_batch_size; i++) { - cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + if (boxes_num->dtype() == phi::DataType::INT64) { + std::vector rois_num_list(rois_batch_size); + memory_utils::Copy(cplace, + rois_num_list.data(), + xplace, + boxes_num->data(), + sizeof(int64_t) * rois_batch_size); + cpu_lod = new int[rois_batch_size + 1]; + cpu_lod[0] = 0; + for (int64_t i = 0; i < rois_batch_size; i++) { + cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + } + } else if (boxes_num->dtype() == phi::DataType::INT32) { + std::vector rois_num_list(rois_batch_size); + memory_utils::Copy(cplace, + rois_num_list.data(), + xplace, + boxes_num->data(), + sizeof(int) * rois_batch_size); + cpu_lod = new int[rois_batch_size + 1]; + cpu_lod[0] = 0; + for (int i = 0; i < rois_batch_size; i++) { + cpu_lod[i + 1] = cpu_lod[i] + rois_num_list[i]; + } } } else { auto rois_lod = boxes.lod().back(); From b6f4511b7d69fee12555c64b87590e4943bf8790 Mon Sep 17 00:00:00 2001 From: Yuang Liu Date: Fri, 6 Dec 2024 13:15:43 +0800 Subject: [PATCH 209/288] [Auto Parallel] Public mesh get/set method. (#69999) --- python/paddle/distributed/__init__.py | 3 ++ .../distributed/auto_parallel/interface.py | 42 ++++++++++++++++++- 2 files changed, 43 insertions(+), 2 deletions(-) diff --git a/python/paddle/distributed/__init__.py b/python/paddle/distributed/__init__.py index 4747925e2b47b8..a4f63508ba3089 100644 --- a/python/paddle/distributed/__init__.py +++ b/python/paddle/distributed/__init__.py @@ -45,6 +45,7 @@ to_static, unshard_dtensor, ) +from .auto_parallel.interface import get_mesh, set_mesh from .auto_parallel.intermediate.parallelize import parallelize from .auto_parallel.intermediate.pipeline_parallel import SplitPoint from .auto_parallel.intermediate.tensor_parallel import ( @@ -199,4 +200,6 @@ "PrepareLayerOutput", "PrepareLayerInput", "SplitPoint", + "set_mesh", + "get_mesh", ] diff --git a/python/paddle/distributed/auto_parallel/interface.py b/python/paddle/distributed/auto_parallel/interface.py index 99b7c560ae73c1..4c07e11b901467 100644 --- a/python/paddle/distributed/auto_parallel/interface.py +++ b/python/paddle/distributed/auto_parallel/interface.py @@ -322,12 +322,50 @@ def fetch(tensor, name=None, logging=False): _g_mesh = None -def get_mesh(): +def get_mesh() -> paddle.distributed.ProcessMesh: + """ + Get the global mesh set by set_mesh. + + Returns: + mesh (paddle.distributed.ProcessMesh): the global mesh. + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + >>> mesh = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["dp", "mp", "pp"]) + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> dist.auto_parallel.set_mesh(mesh) + >>> mesh = dist.auto_parallel.get_mesh() + >>> # This case need to be executed in multi-card environment + >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py + """ global _g_mesh return _g_mesh -def set_mesh(mesh): +def set_mesh(mesh: paddle.distributed.ProcessMesh) -> None: + """ + Set the global mesh. + + Args: + mesh (paddle.distributed.ProcessMesh): global mesh to be set. + + Returns: + None + + Examples: + .. code-block:: python + + >>> import paddle + >>> import paddle.distributed as dist + >>> mesh = dist.ProcessMesh([[[0, 1], [2, 3]], [[4, 5], [6, 7]]], dim_names=["dp", "mp", "pp"]) + >>> # doctest: +REQUIRES(env:DISTRIBUTED) + >>> dist.auto_parallel.set_mesh(mesh) + >>> # This case need to be executed in multi-card environment + >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py + """ global _g_mesh _g_mesh = mesh From 44364db4e21eca7033b2f742e9c5e37cdcbbfa7f Mon Sep 17 00:00:00 2001 From: co63oc Date: Fri, 6 Dec 2024 14:14:34 +0800 Subject: [PATCH 210/288] Fix (#70002) --- paddle/fluid/framework/var_type_inference.h | 17 ----------------- .../fluid/framework/var_type_inference_test.cc | 3 --- 2 files changed, 20 deletions(-) diff --git a/paddle/fluid/framework/var_type_inference.h b/paddle/fluid/framework/var_type_inference.h index 03e0671462f712..d9699bcd56d9db 100644 --- a/paddle/fluid/framework/var_type_inference.h +++ b/paddle/fluid/framework/var_type_inference.h @@ -203,23 +203,6 @@ class InferVarTypeContext { this->SetVarShape(var_name, dims); } - virtual int32_t GetInputLoDLevel(const std::string& name, - const int& index = 0) const { - PADDLE_ENFORCE_NOT_NULL( - op_, common::errors::PreconditionNotMet("op_ should not be null")); - auto& var_name = op_->Input(name).at(index); - return this->GetVarLoDLevel(var_name); - } - - virtual void SetOutputLoDLevel(const std::string& name, - int32_t lod_level, - const int& index = 0) { - PADDLE_ENFORCE_NOT_NULL( - op_, common::errors::PreconditionNotMet("op_ should not be null")); - auto& var_name = op_->Output(name).at(index); - this->SetVarLoDLevel(var_name, lod_level); - } - // add a special API for save_op // avoid use this API for common logic virtual void InsertVar(const std::string& var_name, diff --git a/test/cpp/fluid/framework/var_type_inference_test.cc b/test/cpp/fluid/framework/var_type_inference_test.cc index f466e73e39ad71..ed851cece22b77 100644 --- a/test/cpp/fluid/framework/var_type_inference_test.cc +++ b/test/cpp/fluid/framework/var_type_inference_test.cc @@ -294,9 +294,6 @@ TEST(InferVarType, test_enforce_check) { ASSERT_ANY_THROW(ctx.GetInputShape("X")); ASSERT_ANY_THROW(ctx.SetOutputShape("Out", {})); - ASSERT_ANY_THROW(ctx.GetInputLoDLevel("X")); - ASSERT_ANY_THROW(ctx.SetOutputLoDLevel("Out", 1)); - ASSERT_ANY_THROW(ctx.InsertVar("var", proto::VarType::DENSE_TENSOR)); } From a7bdc356313286c2da6f15a73ac836ae025194c8 Mon Sep 17 00:00:00 2001 From: Lei Ding <69283446+Dmovic@users.noreply.github.com> Date: Fri, 6 Dec 2024 14:30:08 +0800 Subject: [PATCH 211/288] [CINN] Remove cinn_bucket_compile flag (#69311) * [CINN] Remove cinn_bucket_compile flag * polish code * remove annotation --- paddle/cinn/ast_gen_ius/ast_gen.cc | 4 +- paddle/cinn/backends/codegen_cuda_host.cc | 172 ------------------ paddle/cinn/backends/codegen_cuda_host.h | 22 +-- paddle/cinn/backends/codegen_device_util.cc | 8 +- .../hlir/framework/pir/op_lowering_impl.cc | 41 ++--- paddle/cinn/hlir/op/reduction.cc | 2 - paddle/cinn/hlir/pe/broadcast.cc | 1 - .../dy_shape_group_scheduler.cc | 2 - paddle/cinn/ir/tensor.cc | 56 +----- .../eliminate_common_factor_of_local_index.cc | 9 +- paddle/cinn/runtime/flags.cc | 4 - .../instruction/cinn_jit_instruction.cc | 3 +- test/cinn/test_same_input_fusion.py | 1 - test/cpp/pir/cinn/CMakeLists.txt | 2 +- test/cpp/pir/cinn/compilation_task_test.cc | 4 - test/cpp/pir/cinn/symbolic_lower_test.cc | 2 - test/ir/pir/cinn/CMakeLists.txt | 47 ++--- test/ir/pir/cinn/adt/CMakeLists.txt | 3 +- test/ir/pir/cinn/inference/CMakeLists.txt | 3 +- .../cinn/inference/test_llama_full_graph.py | 1 - test/ir/pir/cinn/performance/CMakeLists.txt | 3 +- .../pir/cinn/sub_graphs/test_sub_graph_15.py | 1 - test/ir/pir/cinn/symbolic/CMakeLists.txt | 65 +++---- test/ir/pir/cinn/symbolic/test_if_st.py | 1 - test/ir/pir/cinn/symbolic/test_llama_if_dy.py | 1 - .../cinn/symbolic/test_reshape_zero_shape.py | 1 - test/ir/pir/cinn/symbolic/test_while_st.py | 1 - test/ir/pir/cinn/test_anchor_fusion.py | 1 - test/ir/pir/cinn/test_dynamic_shape.py | 1 - .../ir/pir/cinn/test_expr_multi_downstream.py | 1 - .../ir/pir/cinn/test_fusion_reduce_trivial.py | 1 - .../pir/cinn/test_fusion_softmax_subgraph.py | 1 - test/ir/pir/cinn/test_graph.py | 1 - test/ir/pir/cinn/test_reduce_fusion.py | 1 - test/ir/pir/cinn/test_trivial_fusion.py | 1 - test/prim/pir_prim/CMakeLists.txt | 1 - 36 files changed, 82 insertions(+), 387 deletions(-) diff --git a/paddle/cinn/ast_gen_ius/ast_gen.cc b/paddle/cinn/ast_gen_ius/ast_gen.cc index e44eb635459b59..66d0d5345081a1 100644 --- a/paddle/cinn/ast_gen_ius/ast_gen.cc +++ b/paddle/cinn/ast_gen_ius/ast_gen.cc @@ -22,7 +22,6 @@ #include "paddle/cinn/optim/replace_var_with_expr.h" PD_DECLARE_bool(group_schedule_tiling_first); -PD_DECLARE_bool(cinn_bucket_compile); namespace cinn { namespace ast_gen_ius { @@ -239,8 +238,7 @@ ir::Expr AstGen::Build(const ir::Tensor& tensor, TensorGroup* tensor_group) { // Put the two parts together ir::Expr body = ir::Block::Make({init_body, reduce_body}); for (int i = static_cast(axis_len) - 1; i >= 0; --i) { - if ((!FLAGS_group_schedule_tiling_first || !FLAGS_cinn_bucket_compile) && - shape[i] == Expr(1)) { + if (!FLAGS_group_schedule_tiling_first && shape[i] == Expr(1)) { continue; } ir::Var loop_var = axis[i]; diff --git a/paddle/cinn/backends/codegen_cuda_host.cc b/paddle/cinn/backends/codegen_cuda_host.cc index c6a8e3ee963312..27370f01b424d6 100644 --- a/paddle/cinn/backends/codegen_cuda_host.cc +++ b/paddle/cinn/backends/codegen_cuda_host.cc @@ -30,178 +30,6 @@ namespace backends { using cinn::common::bfloat16; using cinn::common::float16; -const int kArgsArrayMaxLen = 20; - -llvm::Value* CodeGenGpuHost::LowerGPUKernelLauncher( - const ir::_LoweredFunc_* func) { - auto body = func->body; - auto* call_ir = body.As(); - PADDLE_ENFORCE_EQ( - call_ir, - nullptr, - ::common::errors::InvalidArgument("The 'call_ir' must be true.")); - - // Create the function - // @{ - auto* function_type = GenFunctionTypeFromCinnFunction(func, true); - llvm::Function* function = llvm::Function::Create( - function_type, llvm::Function::ExternalLinkage, func->name, m_); - function->setCallingConv(llvm::CallingConv::C); - function->setHasUWTable(); - - std::vector ll_function_args; - std::transform(function->arg_begin(), - function->arg_end(), - std::back_inserter(ll_function_args), - [](auto& arg) { return std::addressof(arg); }); - // @} - - llvm::BasicBlock* entry = llvm::BasicBlock::Create( - /*Context=*/b_->getContext(), - /*Name=*/"entry", - /*Parent=*/function, - /*InsertBefore=*/nullptr); - b_->SetInsertPoint(entry); - - auto* kernel_args = ll_function_args[0]; - auto* kernel_args_count = ll_function_args[1]; - llvm::Value* kernel_stream = nullptr; - if (ll_function_args.size() == 3) { - kernel_stream = ll_function_args[2]; - PADDLE_ENFORCE_EQ( - kernel_stream->getType(), - ll_void_p_ty(), - ::common::errors::InvalidArgument( - "The type of kernel_stream should be void*")); // void* stream - } - PADDLE_ENFORCE_EQ( - kernel_args->getType(), - ll_void_p_ty(), - ::common::errors::InvalidArgument( - "The type of kernel_args should be void*")); // void* args - PADDLE_ENFORCE_EQ( - kernel_args_count->getType(), - ll_int32_ty(), - ::common::errors::InvalidArgument( - "The type of kernel_args_count should be int32")); // int32 - - std::unordered_map global_args = { - {KERNEL_ARGS, kernel_args}, - {KERNEL_ARGS_NUM, kernel_args_count}, - {KERNEL_STREAM, kernel_stream}}; - - auto ret_type = CinnTypeToLLVMType(Void(), m_); - std::vector args_type; - for (auto r_arg : call_ir->read_args) { - if (r_arg.is_var()) { - if (r_arg.as_var()->type().is_cpp_handle() || - r_arg.as_var()->type().is_string()) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.as_var()->type().is_int(32)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else { - CINN_NOT_IMPLEMENTED; - } - } else { - if (r_arg.type().is_bool()) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_uint(8)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_uint(16)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_uint(32)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_uint(64)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_int(8)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_int(16)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_int(32)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_int(64)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_float(32)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_float(64)) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_bfloat16()) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else if (r_arg.type().is_float16()) { - args_type.push_back(CinnTypeToLLVMType(type_of(), m_)); - } else { - CINN_NOT_IMPLEMENTED; - } - } - } - auto func_type = llvm::FunctionType::get(ret_type, args_type, false); - auto call_func = m_->getOrInsertFunction(call_ir->name, func_type); - - std::vector call_args; - for (auto& r_arg : call_ir->read_args) { - if (r_arg.is_var()) { - if (r_arg.as_var()->type().is_string()) { - auto kvalue = m_->getOrInsertGlobal(r_arg.as_var()->name + "_ptr_", - b_->getInt8PtrTy()); - call_args.push_back(b_->CreateLoad( - b_->getInt8PtrTy(), kvalue, r_arg.as_var()->name + "_ptr_load")); - } else if (r_arg.as_var()->type().is_cpp_handle() || - r_arg.as_var()->type().is_int(32)) { - PADDLE_ENFORCE_EQ( - global_args.count(r_arg.as_var()->name), - 1, - ::common::errors::InvalidArgument( - "The argument '%s' must be present in global_args.", - r_arg.as_var()->name.c_str())); - call_args.push_back(global_args[r_arg.as_var()->name]); - } else { - CINN_NOT_IMPLEMENTED; - } - } else { - if (r_arg.type().is_bool()) { - call_args.push_back(b_->getInt1(r_arg.as_bool())); - } else if (r_arg.type().is_int(8)) { - call_args.push_back(b_->getInt8(r_arg.as_int8())); - } else if (r_arg.type().is_int(16)) { - call_args.push_back(b_->getInt16(r_arg.as_int16())); - } else if (r_arg.type().is_int(32)) { - call_args.push_back(b_->getInt32(r_arg.as_int32())); - } else if (r_arg.type().is_int(64)) { - call_args.push_back(b_->getInt64(r_arg.as_int64())); - } else if (r_arg.type().is_uint(8)) { - call_args.push_back(b_->getInt8(r_arg.as_uint8())); - } else if (r_arg.type().is_uint(16)) { - call_args.push_back(b_->getInt16(r_arg.as_uint16())); - } else if (r_arg.type().is_uint(32)) { - call_args.push_back(b_->getInt32(r_arg.as_uint32())); - } else if (r_arg.type().is_uint(64)) { - call_args.push_back(b_->getInt64(r_arg.as_uint64())); - } else if (r_arg.type().is_float(32)) { - call_args.push_back(llvm::ConstantFP::get( - b_->getFloatTy(), llvm::APFloat(r_arg.as_float()))); - } else if (r_arg.type().is_float(64)) { - call_args.push_back(llvm::ConstantFP::get( - b_->getDoubleTy(), llvm::APFloat(r_arg.as_double()))); - } else if (r_arg.type().is_bfloat16()) { - call_args.push_back(llvm::ConstantFP::get( - b_->getBFloatTy(), - llvm::APFloat(static_cast(r_arg.as_bfloat16())))); - } else if (r_arg.type().is_float16()) { - call_args.push_back(llvm::ConstantFP::get( - b_->getHalfTy(), - llvm::APFloat(static_cast(r_arg.as_float16())))); - } else { - CINN_NOT_IMPLEMENTED; - } - } - } - - b_->CreateCall(call_func, call_args); - RetVoid(); - - return function; -} - llvm::Value* CodeGenGpuHost::LowerGPUKernelCall(const ir::Call* call_ir) { std::vector ll_function_args; std::transform(f_->arg_begin(), diff --git a/paddle/cinn/backends/codegen_cuda_host.h b/paddle/cinn/backends/codegen_cuda_host.h index f6f279b52e9ebe..b78add6b1636e9 100644 --- a/paddle/cinn/backends/codegen_cuda_host.h +++ b/paddle/cinn/backends/codegen_cuda_host.h @@ -18,8 +18,6 @@ #include "paddle/cinn/backends/codegen_invoke_module.h" #include "paddle/cinn/runtime/intrinsic.h" -PD_DECLARE_bool(cinn_bucket_compile); - namespace cinn { namespace backends { @@ -36,10 +34,7 @@ class CodeGenGpuHost : public CodeGenHost { // TODO(Hongqing-work): remove this after we clear some old codes. llvm::Value *Visit(const ir::_LoweredFunc_ *func) { - if (FLAGS_cinn_bucket_compile) { - return CodeGenHost::Visit(func); - } - return LowerGPUKernelLauncher(func); + return CodeGenHost::Visit(func); } llvm::Value *Visit(const ir::Call *op) override { @@ -64,21 +59,6 @@ class CodeGenGpuHost : public CodeGenHost { } private: - /** - * Lower a CUDA/HIP kernel launcher. - * - * We launch a CUDA/HIP kernel in the following way: - * - * 1. a GPU function (called fn) will compiled to PTX and lower by CUDA driver - * to a function pointer, which we store as a `void*` type global variable - * [fn_kernel_ptr] in LLVM module. - * 2. when lower the host launcher, we replace the Call of the original kernel - * [fn] to a Call of `cinn_call_cuda_kernel` method which is registered as an - * external function. - * - */ - llvm::Value *LowerGPUKernelLauncher(const ir::_LoweredFunc_ *func); - llvm::Value *LowerGPUKernelCall(const ir::Call *op); }; diff --git a/paddle/cinn/backends/codegen_device_util.cc b/paddle/cinn/backends/codegen_device_util.cc index 41b7b9940c15d7..78f269c464659b 100644 --- a/paddle/cinn/backends/codegen_device_util.cc +++ b/paddle/cinn/backends/codegen_device_util.cc @@ -18,16 +18,12 @@ #include "paddle/cinn/common/cas.h" #include "paddle/cinn/ir/ir_mutator.h" #include "paddle/common/enforce.h" -PD_DECLARE_bool(cinn_bucket_compile); + namespace cinn { namespace backends { std::tuple SplitDeviceAndHostModule(ir::Module module) { - if (FLAGS_cinn_bucket_compile) { - detail::CollectBucketStrategyHostFunctionVisitor visitor(module->name); - return visitor(module); - } - detail::CollectHostFunctionVisitor visitor(module->name); + detail::CollectBucketStrategyHostFunctionVisitor visitor(module->name); return visitor(module); } diff --git a/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc b/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc index 4124071fafb7e1..1efe7f1196130b 100644 --- a/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc +++ b/paddle/cinn/hlir/framework/pir/op_lowering_impl.cc @@ -48,7 +48,6 @@ #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" PD_DECLARE_bool(cinn_use_cuda_vectorize); -PD_DECLARE_bool(cinn_bucket_compile); PD_DECLARE_bool(cinn_check_tensor_buffer_map); const int default_priority = 100; @@ -582,31 +581,25 @@ ir::Tensor OpLowererImpl::GetTensor(const OpLoweringGroupPtr& group, } }; - if (FLAGS_cinn_bucket_compile) { - std::vector sym_shape; - ForEachDimExpr( - [&](const auto& sym) { sym_shape.emplace_back(input_id, sym); }); - if (sym_shape.empty()) { - sym_shape.emplace_back(input_id, symbol::DimExpr{1}); - } - auto tensor = lang::CreatePlaceHolder( - sym_shape, CompatibleInfo::ConvertIRType(dtype), input_id); - auto IsIntType = [](const ::pir::Type& t) { - return t.isa<::pir::Int32Type>() || t.isa<::pir::Int64Type>(); - }; - if (IsIntType(dtype) && group->HasShapeOrDataExprs(value)) { - const auto& tensor_value = details::GetTensorValueFromShapeOrData( - group->GetShapeOrDataExprs(value)); - if (tensor_value.has_value()) { - tensor->set_value(*tensor_value); - } + std::vector sym_shape; + ForEachDimExpr( + [&](const auto& sym) { sym_shape.emplace_back(input_id, sym); }); + if (sym_shape.empty()) { + sym_shape.emplace_back(input_id, symbol::DimExpr{1}); + } + auto tensor = lang::CreatePlaceHolder( + sym_shape, CompatibleInfo::ConvertIRType(dtype), input_id); + auto IsIntType = [](const ::pir::Type& t) { + return t.isa<::pir::Int32Type>() || t.isa<::pir::Int64Type>(); + }; + if (IsIntType(dtype) && group->HasShapeOrDataExprs(value)) { + const auto& tensor_value = details::GetTensorValueFromShapeOrData( + group->GetShapeOrDataExprs(value)); + if (tensor_value.has_value()) { + tensor->set_value(*tensor_value); } - return tensor; - } else { - auto shape = ::common::vectorize(type_info.dims()); - return lang::CreatePlaceHolder( - shape, CompatibleInfo::ConvertIRType(dtype), input_id); } + return tensor; } std::vector OpLowererImpl::CollectInputTensor( diff --git a/paddle/cinn/hlir/op/reduction.cc b/paddle/cinn/hlir/op/reduction.cc index e9fad715cd6417..e7e08a8b364541 100644 --- a/paddle/cinn/hlir/op/reduction.cc +++ b/paddle/cinn/hlir/op/reduction.cc @@ -32,8 +32,6 @@ PD_DECLARE_bool(cinn_enable_map_expr); -PD_DECLARE_bool(cinn_bucket_compile); - namespace cinn { namespace hlir { namespace op { diff --git a/paddle/cinn/hlir/pe/broadcast.cc b/paddle/cinn/hlir/pe/broadcast.cc index db10494dab30af..2c400c5393f713 100644 --- a/paddle/cinn/hlir/pe/broadcast.cc +++ b/paddle/cinn/hlir/pe/broadcast.cc @@ -25,7 +25,6 @@ #include "paddle/cinn/lang/compute.h" #include "paddle/common/enforce.h" #include "paddle/common/errors.h" -PD_DECLARE_bool(cinn_bucket_compile); namespace cinn { namespace hlir { diff --git a/paddle/cinn/ir/group_schedule/dy_shape_group_scheduler.cc b/paddle/cinn/ir/group_schedule/dy_shape_group_scheduler.cc index eb1d0c77d9a59b..8813a638b9c8ec 100644 --- a/paddle/cinn/ir/group_schedule/dy_shape_group_scheduler.cc +++ b/paddle/cinn/ir/group_schedule/dy_shape_group_scheduler.cc @@ -22,8 +22,6 @@ #include "paddle/cinn/ir/op/ir_operators.h" #include "paddle/common/enforce.h" -PD_DECLARE_bool(cinn_bucket_compile); - namespace cinn { namespace ir { diff --git a/paddle/cinn/ir/tensor.cc b/paddle/cinn/ir/tensor.cc index b272e4da03f84a..f05da6ce6dcfeb 100644 --- a/paddle/cinn/ir/tensor.cc +++ b/paddle/cinn/ir/tensor.cc @@ -34,8 +34,6 @@ #include "paddle/cinn/poly/stage.h" #include "paddle/common/enforce.h" -PD_DECLARE_bool(cinn_bucket_compile); - namespace cinn { namespace ir { @@ -247,52 +245,6 @@ bool _Tensor_::has_expression() const { (!is_buffer_shared_node()); } -isl::set _Tensor_::GenerateIslDomain() const { - // include the reduce axis. - std::vector dims; - - if (has_expression()) { - if (axis_.empty()) InitAxis(); - auto domain = domain_with_reduce_axis(); - PADDLE_ENFORCE_EQ( - axis_with_reduce().size(), - domain.size(), - ::common::errors::PreconditionNotMet( - "Required axis_with_reduce and domain shall be with same size.")); - auto _axis_with_reduce = axis_with_reduce(); - for (int i = 0; i < domain.size(); i++) { - auto dim = domain[i]; - if (dim.type() == type_of()) { - if (dim.is_constant()) { - dims.emplace_back(_axis_with_reduce[i]->name, - static_cast(0), - static_cast(dim.as_int64() - 1)); - } else { - dims.emplace_back( - _axis_with_reduce[i]->name, - Expr(static_cast(0)), - Sub::Make(dim, - cinn::common::make_const(static_cast(1)))); - } - } else { - if (dim.is_constant()) { - dims.emplace_back(_axis_with_reduce[i]->name, - static_cast(0), - dim.as_int32() - 1); - } else { - dims.emplace_back(_axis_with_reduce[i]->name, - Expr(0), - Sub::Make(dim, cinn::common::make_const(1))); - } - } - } - } - - poly::Domain isl_domain(Context::isl_ctx(), name, dims); - VLOG(1) << "name:" << this->name << ", domain: " << isl_domain.__str__(); - return isl_domain.to_isl(); -} - std::vector _Tensor_::expr_fields() { std::vector res; const char *func_type = operation->as()->func_type(); @@ -664,12 +616,8 @@ Shared CreateStage(Tensor tensor) { // use it. But it has not been completely removed in the process. it cannot be // supported here under dynamic shape. Therefore, we temporarily use fake // domain. - if (FLAGS_cinn_bucket_compile) { - poly::Domain fake_domain(Context::isl_ctx(), "fake_domain", {}); - isl_domain = fake_domain.to_isl(); - } else { - isl_domain = tensor->GenerateIslDomain(); - } + poly::Domain fake_domain(Context::isl_ctx(), "fake_domain", {}); + isl_domain = fake_domain.to_isl(); return poly::Stage::New(isl_domain, tensor->body(), tensor.self()); } diff --git a/paddle/cinn/optim/eliminate_common_factor_of_local_index.cc b/paddle/cinn/optim/eliminate_common_factor_of_local_index.cc index 77c4db7c5b07a5..4a3c101f3c325f 100644 --- a/paddle/cinn/optim/eliminate_common_factor_of_local_index.cc +++ b/paddle/cinn/optim/eliminate_common_factor_of_local_index.cc @@ -285,11 +285,10 @@ std::vector CalculateIndexCommonFactor( "should at least load and store once. ")); for (std::size_t i = 1; i < indexes.size(); ++i) { // NOTE(Hongyu Jia): Ideally, we can guarantee the size of indexes are equal - // under flags FLAGS_cinn_bucket_compile=1. However, some unit tests (e.g. - // test_resnet_cinn, test_instance_norm_op) are still running with the - // deprecated OpScheduler, and the ir::Expr will break this guarantee after - // IRGpuScheduleBlockReduce function. So we have to relax the restriction - // here. + // However, some unit tests (e.g. test_resnet_cinn, test_instance_norm_op + // are still running with the deprecated OpScheduler, and the ir::Expr + // will break this guarantee after IRGpuScheduleBlockReduce function. + // So we have to relax the restriction here. if (indexes[i].size() != indexes[0].size()) { LOG(WARNING) << "Not supported for calculating common factor, local var = " diff --git a/paddle/cinn/runtime/flags.cc b/paddle/cinn/runtime/flags.cc index b610f5a0b3b9b7..24efba3131cefc 100644 --- a/paddle/cinn/runtime/flags.cc +++ b/paddle/cinn/runtime/flags.cc @@ -89,10 +89,6 @@ PD_DEFINE_bool( BoolFromEnv("FLAGS_cinn_bc_branch_optimize", true), "Whether to open the broadcast branch optimization in frontend."); -PD_DEFINE_bool(cinn_bucket_compile, - BoolFromEnv("FLAGS_cinn_bucket_compile", true), - "Whether to enable bucket compile for dynamic shape."); - PD_DEFINE_bool(group_schedule_tiling_first, BoolFromEnv("FLAGS_group_schedule_tiling_first", true), "Whether to enable new group scheduler tiling first strategy."); diff --git a/paddle/fluid/framework/new_executor/instruction/cinn_jit_instruction.cc b/paddle/fluid/framework/new_executor/instruction/cinn_jit_instruction.cc index c1126f0db82f73..f235216c61bc0c 100644 --- a/paddle/fluid/framework/new_executor/instruction/cinn_jit_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/cinn_jit_instruction.cc @@ -25,7 +25,6 @@ #if defined(PADDLE_WITH_CUDA) #include "paddle/cinn/runtime/cinn_runtime.h" #endif -PD_DECLARE_bool(cinn_bucket_compile); PD_DECLARE_bool(cinn_measure_kernel_time); PD_DECLARE_string(tile_config_policy); PD_DECLARE_string(cinn_kernel_execution_label); @@ -289,7 +288,7 @@ void CinnJitInstruction::Run() { // 1. prepare kernel arguments fn_ptr_impl_->InitFuncArgs(tensor_args_); - if (FLAGS_cinn_bucket_compile && need_update_shape) { + if (need_update_shape) { fn_ptr_impl_->InferShape( tensor_args_, input_tensor_size, output_tensor_size); } diff --git a/test/cinn/test_same_input_fusion.py b/test/cinn/test_same_input_fusion.py index 616b1a8d9d2719..834652b402e6f3 100644 --- a/test/cinn/test_same_input_fusion.py +++ b/test/cinn/test_same_input_fusion.py @@ -23,7 +23,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' import paddle diff --git a/test/cpp/pir/cinn/CMakeLists.txt b/test/cpp/pir/cinn/CMakeLists.txt index 0d690fb072fe15..846f6ce2c783a9 100644 --- a/test/cpp/pir/cinn/CMakeLists.txt +++ b/test/cpp/pir/cinn/CMakeLists.txt @@ -64,7 +64,7 @@ if(WITH_TESTING AND WITH_CINN) TEST ${test_name} PROPERTY ENVIRONMENT) set_property( - TEST ${test_name} PROPERTY ENVIRONMENT "FLAGS_cinn_bucket_compile=1" + TEST ${test_name} PROPERTY ENVIRONMENT "FLAGS_group_schedule_tiling_first=1" ${env}) set_tests_properties(${test_name} PROPERTIES LABELS "RUN_TYPE=CINN") endforeach() diff --git a/test/cpp/pir/cinn/compilation_task_test.cc b/test/cpp/pir/cinn/compilation_task_test.cc index 49680042871d2b..f77d7683bf3b61 100644 --- a/test/cpp/pir/cinn/compilation_task_test.cc +++ b/test/cpp/pir/cinn/compilation_task_test.cc @@ -32,8 +32,6 @@ #include "paddle/pir/include/core/ir_context.h" #include "paddle/pir/include/core/program.h" -PD_DECLARE_bool(cinn_bucket_compile); - using cinn::hlir::framework::pir::CompatibleInfo; using cinn::hlir::framework::pir::OpLoweringGroup; using cinn::hlir::framework::pir::OpLoweringGroupPtr; @@ -63,7 +61,6 @@ ProgramInfo BuildProgram(std::vector input_shape) { // TODO(LiuYang): This test is temporarily // TEST(CompilationTask, Basic) { -// FLAGS_cinn_bucket_compile = true; // auto prog_info = BuildProgram({4096, 128}); // std::shared_ptr<::pir::Program> program = std::get<0>(prog_info); // LOG(INFO) << program->block()->size(); @@ -89,7 +86,6 @@ ProgramInfo BuildProgram(std::vector input_shape) { // } // TEST(CompilationTask, CompileGroup) { -// FLAGS_cinn_bucket_compile = true; // // Step 1: Construct pir::Program // int M = 4096, N = 128; // auto prog_info = BuildProgram({M, N}); diff --git a/test/cpp/pir/cinn/symbolic_lower_test.cc b/test/cpp/pir/cinn/symbolic_lower_test.cc index 810e0bb230f100..c73142a1c336e0 100644 --- a/test/cpp/pir/cinn/symbolic_lower_test.cc +++ b/test/cpp/pir/cinn/symbolic_lower_test.cc @@ -36,8 +36,6 @@ #include "paddle/pir/include/dialect/control_flow/ir/cf_op.h" #include "paddle/pir/include/dialect/shape/utils/shape_or_data_expr.h" -PD_DECLARE_bool(cinn_bucket_compile); - using cinn::hlir::framework::pir::CompatibleInfo; using cinn::hlir::framework::pir::OpLoweringGroup; using cinn::hlir::framework::pir::OpLoweringGroupPtr; diff --git a/test/ir/pir/cinn/CMakeLists.txt b/test/ir/pir/cinn/CMakeLists.txt index 32d5a81b1aeafc..5f02ff5fde7047 100644 --- a/test/ir/pir/cinn/CMakeLists.txt +++ b/test/ir/pir/cinn/CMakeLists.txt @@ -20,8 +20,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_enable_pir_api=1 FLAGS_prim_all=True FLAGS_check_infer_symbolic=1 - FLAGS_cinn_bucket_compile=1 FLAGS_group_schedule_tiling_first=1 - ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${cinn_pir_test_name}.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(${cinn_pir_test_name} PROPERTIES LABELS @@ -34,9 +33,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} CUDA_VISIBLE_DEVICES=0,1 FLAGS_enable_pir_api=1 FLAGS_prim_all=True - FLAGS_cinn_new_group_scheduler=1 FLAGS_cinn_bucket_compile=1 - FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_multi_device.py + FLAGS_cinn_new_group_scheduler=1 FLAGS_group_schedule_tiling_first=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_multi_device.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_cinn_multi_device PROPERTIES LABELS "RUN_TYPE=CINN") @@ -47,9 +45,8 @@ if(WITH_GPU) PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} CUDA_VISIBLE_DEVICES=0,1 FLAGS_enable_pir_api=1 FLAGS_enable_pir_in_executor=1 FLAGS_prim_all=True FLAGS_use_cinn=true - FLAGS_cinn_new_group_scheduler=1 FLAGS_cinn_bucket_compile=1 - FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} -u -m - paddle.distributed.launch --gpus "0,1" + FLAGS_cinn_new_group_scheduler=1 FLAGS_group_schedule_tiling_first=1 + ${PYTHON_EXECUTABLE} -u -m paddle.distributed.launch --gpus "0,1" ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_auto_parallel.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_cinn_auto_parallel PROPERTIES LABELS @@ -61,9 +58,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_enable_pir_api=1 FLAGS_prim_all=True - FLAGS_cinn_new_group_scheduler=1 FLAGS_cinn_bucket_compile=1 - FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_sub_graph.py + FLAGS_cinn_new_group_scheduler=1 FLAGS_group_schedule_tiling_first=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_sub_graph.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_cinn_sub_graph_stride_read PROPERTIES LABELS "RUN_TYPE=CINN") @@ -74,7 +70,7 @@ if(WITH_GPU) # ${CMAKE_COMMAND} -E env # PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} # FLAGS_enable_pir_api=1 FLAGS_prim_all=True - # FLAGS_cinn_new_group_scheduler=1 FLAGS_cinn_bucket_compile=1 + # FLAGS_cinn_new_group_scheduler=1 # FLAGS_group_schedule_tiling_first=1 # ${PYTHON_EXECUTABLE} # ${CMAKE_CURRENT_SOURCE_DIR}/test_subgraph_checker.py @@ -86,9 +82,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_rms_norm_seq_len_symbolic PROPERTIES LABELS "RUN_TYPE=CINN") @@ -98,9 +93,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=7:S1 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_rms_norm_bs_symbolic PROPERTIES LABELS "RUN_TYPE=CINN") @@ -110,7 +104,7 @@ if(WITH_GPU) # ${CMAKE_COMMAND} -E env # PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} # FLAGS_cinn_convert_static_dim_to_dynamic_dim=768:S0 - # FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + # FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} # ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py # WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) # set_tests_properties(test_rms_norm_reduce_symbolic PROPERTIES LABELS @@ -121,9 +115,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0,7:S1 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_rms_norm.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_rms_norm_symbolic PROPERTIES LABELS "RUN_TYPE=CINN") add_test( @@ -132,8 +125,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S1 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 FLAGS_prim_all=True ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + FLAGS_prim_all=True ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_rope.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_rope_seq_len_symbolic PROPERTIES LABELS @@ -145,7 +138,7 @@ if(WITH_GPU) # ${CMAKE_COMMAND} -E env # PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} # FLAGS_cinn_convert_static_dim_to_dynamic_dim=61:S0 - # FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 FLAGS_prim_all=True + # FLAGS_enable_pir_api=1 FLAGS_prim_all=True # FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} # ${CMAKE_CURRENT_SOURCE_DIR}/test_rope.py # WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) @@ -157,8 +150,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=61:S0,2048:S1 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 FLAGS_prim_all=True ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + FLAGS_prim_all=True ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_rope.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_rope_symbolic PROPERTIES LABELS "RUN_TYPE=CINN") diff --git a/test/ir/pir/cinn/adt/CMakeLists.txt b/test/ir/pir/cinn/adt/CMakeLists.txt index 434f50a0bbc594..b1ddc033844f32 100644 --- a/test/ir/pir/cinn/adt/CMakeLists.txt +++ b/test/ir/pir/cinn/adt/CMakeLists.txt @@ -12,8 +12,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_enable_pir_api=1 FLAGS_prim_all=True FLAGS_cinn_enable_map_expr=1 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=1 - ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${cinn_pir_test_name}.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(${cinn_pir_test_name} PROPERTIES LABELS diff --git a/test/ir/pir/cinn/inference/CMakeLists.txt b/test/ir/pir/cinn/inference/CMakeLists.txt index 18b4fbcb321454..16ac83f763ca54 100644 --- a/test/ir/pir/cinn/inference/CMakeLists.txt +++ b/test/ir/pir/cinn/inference/CMakeLists.txt @@ -13,8 +13,7 @@ if(WITH_GPU) PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_prim_enable_dynamic=True FLAGS_prim_all=True FLAGS_check_infer_symbolic=1 FLAGS_enable_pir_api=1 - FLAGS_cinn_bucket_compile=True FLAGS_group_schedule_tiling_first=1 - ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${cinn_pir_test_name}.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(${cinn_pir_test_name} PROPERTIES LABELS diff --git a/test/ir/pir/cinn/inference/test_llama_full_graph.py b/test/ir/pir/cinn/inference/test_llama_full_graph.py index 6f51b2e13310b0..9f2bfa1fc74fe4 100644 --- a/test/ir/pir/cinn/inference/test_llama_full_graph.py +++ b/test/ir/pir/cinn/inference/test_llama_full_graph.py @@ -23,7 +23,6 @@ os.environ['FLAGS_prim_enable_dynamic'] = 'true' # os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_cinn_new_cluster_op_method'] = '1' os.environ['FLAGS_prim_forward_blacklist'] = 'pd_op.embedding' diff --git a/test/ir/pir/cinn/performance/CMakeLists.txt b/test/ir/pir/cinn/performance/CMakeLists.txt index a8145d0c4083d5..d00b7056b6aa74 100644 --- a/test/ir/pir/cinn/performance/CMakeLists.txt +++ b/test/ir/pir/cinn/performance/CMakeLists.txt @@ -12,8 +12,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_check_infer_symbolic=1 FLAGS_enable_pir_api=1 - FLAGS_cinn_bucket_compile=True FLAGS_prim_enable_dynamic=true - FLAGS_pir_apply_shape_optimization_pass=1 + FLAGS_prim_enable_dynamic=true FLAGS_pir_apply_shape_optimization_pass=1 FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_new_group_scheduler=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${cinn_pir_test_name}.py diff --git a/test/ir/pir/cinn/sub_graphs/test_sub_graph_15.py b/test/ir/pir/cinn/sub_graphs/test_sub_graph_15.py index c5050e5cb9d559..030dc86ee1d69f 100644 --- a/test/ir/pir/cinn/sub_graphs/test_sub_graph_15.py +++ b/test/ir/pir/cinn/sub_graphs/test_sub_graph_15.py @@ -27,7 +27,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' # os.environ['GLOG_vmodule'] = 'op_lowering_impl=4' diff --git a/test/ir/pir/cinn/symbolic/CMakeLists.txt b/test/ir/pir/cinn/symbolic/CMakeLists.txt index 353d98719f4f99..de393baaa261c3 100644 --- a/test/ir/pir/cinn/symbolic/CMakeLists.txt +++ b/test/ir/pir/cinn/symbolic/CMakeLists.txt @@ -34,8 +34,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_check_infer_symbolic=1 FLAGS_enable_pir_api=1 - FLAGS_cinn_bucket_compile=True FLAGS_prim_enable_dynamic=true - FLAGS_prim_all=True FLAGS_pir_apply_shape_optimization_pass=1 + FLAGS_prim_enable_dynamic=true FLAGS_prim_all=True + FLAGS_pir_apply_shape_optimization_pass=1 FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_new_group_scheduler=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/${cinn_pir_test_name}.py @@ -50,7 +50,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0 - FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_if_st.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_if_st PROPERTIES LABELS "RUN_TYPE=CINN") @@ -60,7 +60,7 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_if_dy.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_if_dy PROPERTIES LABELS "RUN_TYPE=CINN") @@ -70,10 +70,9 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_prim_all=true FLAGS_cinn_bucket_compile=false - FLAGS_pir_apply_shape_optimization_pass=true FLAGS_enable_pir_api=true - FLAGS_prim_enable_dynamic=true ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_llama_if_dy.py + FLAGS_prim_all=true FLAGS_pir_apply_shape_optimization_pass=true + FLAGS_enable_pir_api=true FLAGS_prim_enable_dynamic=true + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_llama_if_dy.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_llama_if_dy PROPERTIES LABELS "RUN_TYPE=CINN") @@ -83,9 +82,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_prim_enable_dynamic=true FLAGS_prim_check_ops=true - FLAGS_enable_pir_api=true FLAGS_cinn_bucket_compile=false - FLAGS_pir_apply_shape_optimization_pass=false ${PYTHON_EXECUTABLE} - ${CMAKE_CURRENT_SOURCE_DIR}/test_simple_llama_dy.py + FLAGS_enable_pir_api=true FLAGS_pir_apply_shape_optimization_pass=false + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_simple_llama_dy.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_simple_llama_dy PROPERTIES LABELS "RUN_TYPE=CINN") @@ -94,7 +92,7 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_pir_apply_shape_optimization_pass=1 FLAGS_cinn_bucket_compile=True + FLAGS_pir_apply_shape_optimization_pass=1 FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_cinn_reduce_symbolic_demo.py @@ -108,9 +106,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=64:S0 - FLAGS_cinn_bucket_compile=True FLAGS_group_schedule_tiling_first=1 - FLAGS_enable_pir_api=1 FLAGS_pir_apply_shape_optimization_pass=1 - ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + FLAGS_pir_apply_shape_optimization_pass=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_sub_graph_for_backend.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_sub_graph_for_backend PROPERTIES LABELS @@ -121,8 +118,8 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_cinn_bucket_compile=True FLAGS_group_schedule_tiling_first=1 - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_sub_graph_for_frontend.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_sub_graph_for_frontend PROPERTIES LABELS @@ -133,9 +130,8 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_check_infer_symbolic=True FLAGS_cinn_bucket_compile=True - FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 - ${PYTHON_EXECUTABLE} + FLAGS_check_infer_symbolic=True FLAGS_group_schedule_tiling_first=1 + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_check_infer_symbolic.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_check_infer_symbolic PROPERTIES LABELS @@ -147,8 +143,8 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0 - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_multiple_subgraph_st.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_multiple_subgraph_st PROPERTIES LABELS @@ -159,8 +155,8 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_group_schedule_tiling_first=1 FLAGS_cinn_bucket_compile=True - FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 + ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_multiple_subgraph_dy.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_multiple_subgraph_dy PROPERTIES LABELS @@ -172,7 +168,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0 - FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_llama_mlp_st.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_llama_mlp_st PROPERTIES LABELS "RUN_TYPE=CINN") @@ -182,9 +178,9 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_prim_all=true FLAGS_cinn_bucket_compile=True - FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_llama_mlp_dy.py + FLAGS_prim_all=true FLAGS_group_schedule_tiling_first=1 + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + ${CMAKE_CURRENT_SOURCE_DIR}/test_llama_mlp_dy.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_llama_mlp_dy PROPERTIES LABELS "RUN_TYPE=CINN") @@ -194,7 +190,7 @@ if(WITH_GPU) ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} FLAGS_cinn_convert_static_dim_to_dynamic_dim=2048:S0 - FLAGS_cinn_bucket_compile=True FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_while_st.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_while_st PROPERTIES LABELS "RUN_TYPE=CINN") @@ -204,9 +200,9 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_prim_all=true FLAGS_cinn_bucket_compile=True - FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 - ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_dyshape_cast.py + FLAGS_prim_all=true FLAGS_group_schedule_tiling_first=1 + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} + ${CMAKE_CURRENT_SOURCE_DIR}/test_dyshape_cast.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_dyshape_cast PROPERTIES LABELS "RUN_TYPE=CINN") @@ -215,9 +211,8 @@ if(WITH_GPU) COMMAND ${CMAKE_COMMAND} -E env PYTHONPATH=${CMAKE_BINARY_DIR}:${CMAKE_BINARY_DIR}/python/:$ENV{PYTHONPATH} - FLAGS_prim_all=true FLAGS_cinn_bucket_compile=True - FLAGS_group_schedule_tiling_first=1 FLAGS_enable_pir_api=1 - ${PYTHON_EXECUTABLE} + FLAGS_prim_all=true FLAGS_group_schedule_tiling_first=1 + FLAGS_enable_pir_api=1 ${PYTHON_EXECUTABLE} ${CMAKE_CURRENT_SOURCE_DIR}/test_dyshape_split_with_num.py WORKING_DIRECTORY ${CMAKE_BINARY_DIR}) set_tests_properties(test_dyshape_split_with_num PROPERTIES LABELS diff --git a/test/ir/pir/cinn/symbolic/test_if_st.py b/test/ir/pir/cinn/symbolic/test_if_st.py index 85b9a013d46645..10f7e107acfffd 100644 --- a/test/ir/pir/cinn/symbolic/test_if_st.py +++ b/test/ir/pir/cinn/symbolic/test_if_st.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' import numpy as np diff --git a/test/ir/pir/cinn/symbolic/test_llama_if_dy.py b/test/ir/pir/cinn/symbolic/test_llama_if_dy.py index af47b915cc08c8..8623bc212b220c 100644 --- a/test/ir/pir/cinn/symbolic/test_llama_if_dy.py +++ b/test/ir/pir/cinn/symbolic/test_llama_if_dy.py @@ -25,7 +25,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' import paddle from paddle import nn diff --git a/test/ir/pir/cinn/symbolic/test_reshape_zero_shape.py b/test/ir/pir/cinn/symbolic/test_reshape_zero_shape.py index acf2f3742018d5..1b5f101b8626f2 100644 --- a/test/ir/pir/cinn/symbolic/test_reshape_zero_shape.py +++ b/test/ir/pir/cinn/symbolic/test_reshape_zero_shape.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' diff --git a/test/ir/pir/cinn/symbolic/test_while_st.py b/test/ir/pir/cinn/symbolic/test_while_st.py index e352c538793c0c..348ee358e40b6c 100644 --- a/test/ir/pir/cinn/symbolic/test_while_st.py +++ b/test/ir/pir/cinn/symbolic/test_while_st.py @@ -25,7 +25,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' diff --git a/test/ir/pir/cinn/test_anchor_fusion.py b/test/ir/pir/cinn/test_anchor_fusion.py index cb50b5777757d7..b120647da8c371 100644 --- a/test/ir/pir/cinn/test_anchor_fusion.py +++ b/test/ir/pir/cinn/test_anchor_fusion.py @@ -25,7 +25,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_cinn_new_cluster_op_method'] = '1' import paddle diff --git a/test/ir/pir/cinn/test_dynamic_shape.py b/test/ir/pir/cinn/test_dynamic_shape.py index 2754e296f90f77..4e262ee8490485 100644 --- a/test/ir/pir/cinn/test_dynamic_shape.py +++ b/test/ir/pir/cinn/test_dynamic_shape.py @@ -25,7 +25,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' import paddle diff --git a/test/ir/pir/cinn/test_expr_multi_downstream.py b/test/ir/pir/cinn/test_expr_multi_downstream.py index 4dd62f3470eccb..570e9f0b1bb1ca 100644 --- a/test/ir/pir/cinn/test_expr_multi_downstream.py +++ b/test/ir/pir/cinn/test_expr_multi_downstream.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_cinn_new_cluster_op_method'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' diff --git a/test/ir/pir/cinn/test_fusion_reduce_trivial.py b/test/ir/pir/cinn/test_fusion_reduce_trivial.py index d06587c7c15afc..d6540d3cc633b7 100644 --- a/test/ir/pir/cinn/test_fusion_reduce_trivial.py +++ b/test/ir/pir/cinn/test_fusion_reduce_trivial.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' from utils import check_jit_kernel_number diff --git a/test/ir/pir/cinn/test_fusion_softmax_subgraph.py b/test/ir/pir/cinn/test_fusion_softmax_subgraph.py index a73eca5f044582..e7ff1046d8c46a 100644 --- a/test/ir/pir/cinn/test_fusion_softmax_subgraph.py +++ b/test/ir/pir/cinn/test_fusion_softmax_subgraph.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' from utils import check_jit_kernel_number diff --git a/test/ir/pir/cinn/test_graph.py b/test/ir/pir/cinn/test_graph.py index 99f3b3f44ea9f2..faa9b94a9f4b47 100644 --- a/test/ir/pir/cinn/test_graph.py +++ b/test/ir/pir/cinn/test_graph.py @@ -26,7 +26,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_deny_cinn_ops'] = 'slice;' diff --git a/test/ir/pir/cinn/test_reduce_fusion.py b/test/ir/pir/cinn/test_reduce_fusion.py index bcdcc697766c3d..d534fda33a0d41 100644 --- a/test/ir/pir/cinn/test_reduce_fusion.py +++ b/test/ir/pir/cinn/test_reduce_fusion.py @@ -25,7 +25,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_cinn_new_cluster_op_method'] = '1' import paddle diff --git a/test/ir/pir/cinn/test_trivial_fusion.py b/test/ir/pir/cinn/test_trivial_fusion.py index fe4f41e22374ba..c8f968a47a8e37 100644 --- a/test/ir/pir/cinn/test_trivial_fusion.py +++ b/test/ir/pir/cinn/test_trivial_fusion.py @@ -24,7 +24,6 @@ os.environ['FLAGS_print_ir'] = '1' os.environ['FLAGS_enable_pir_api'] = '1' os.environ['FLAGS_use_cinn'] = '1' -os.environ['FLAGS_cinn_bucket_compile'] = '1' os.environ['FLAGS_cinn_new_cluster_op_method'] = '1' import paddle diff --git a/test/prim/pir_prim/CMakeLists.txt b/test/prim/pir_prim/CMakeLists.txt index cb440c201938a8..a973a7a5277d5a 100644 --- a/test/prim/pir_prim/CMakeLists.txt +++ b/test/prim/pir_prim/CMakeLists.txt @@ -84,7 +84,6 @@ if(WITH_CINN) FLAGS_enable_pir_api=true FLAGS_prim_enable_dynamic=true FLAGS_prim_vjp_skip_default_ops=false - FLAGS_cinn_bucket_compile=True FLAGS_pir_apply_shape_optimization_pass=1) set_tests_properties(${target} PROPERTIES LABELS "RUN_TYPE=CINN") endforeach() From 973924ca3a2b230a1cc0393a22731023878f3b51 Mon Sep 17 00:00:00 2001 From: lijin23 <41257772+lj970926@users.noreply.github.com> Date: Fri, 6 Dec 2024 14:30:54 +0800 Subject: [PATCH 212/288] [XPU] add bf16/fp16 support for index_put/_grad (#69970) * add bf16/fp16 support for index_put/_grad for XPU * fix bugs in tests * fix bugs in kl2 ci --------- Co-authored-by: chenqingshu --- paddle/phi/backends/xpu/xpu3_op_list.cc | 4 + .../phi/kernels/xpu/index_put_grad_kernel.cc | 4 +- paddle/phi/kernels/xpu/index_put_kernel.cc | 11 +- test/legacy_test/op_test.py | 1 + test/xpu/test_index_put_op_xpu.py | 105 ++++++++++-------- 5 files changed, 78 insertions(+), 47 deletions(-) diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index f1ea5e21b3d1b3..22eaa2171306ee 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -660,10 +660,14 @@ XPUOpMap& get_kl3_ops() { {"index_put", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16, phi::DataType::INT64})}, {"index_put_grad", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::INT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16, phi::DataType::INT64})}, {"index_sample_grad", XPUKernelSet({phi::DataType::FLOAT32})}, {"index_sample", diff --git a/paddle/phi/kernels/xpu/index_put_grad_kernel.cc b/paddle/phi/kernels/xpu/index_put_grad_kernel.cc index d05eeb70e04249..664cc71845e2ad 100644 --- a/paddle/phi/kernels/xpu/index_put_grad_kernel.cc +++ b/paddle/phi/kernels/xpu/index_put_grad_kernel.cc @@ -73,7 +73,6 @@ void IndexPutGradKernel(const Context& dev_ctx, std::copy(xshape.begin() + int_indices_v.size(), xshape.end(), value_shape_bd.begin() + index_shape.size() - 1); - auto value_shape = common::vectorize(value_grad->dims()); int ret = xpu::SUCCESS; using XPUType = typename XPUTypeTrait::Type; if (x_grad) { @@ -95,6 +94,7 @@ void IndexPutGradKernel(const Context& dev_ctx, } } if (value_grad) { + auto value_shape = common::vectorize(value_grad->dims()); dev_ctx.template Alloc(value_grad); if (value_shape != value_shape_bd) { std::vector compress_dims; @@ -140,5 +140,7 @@ PD_REGISTER_KERNEL(index_put_grad, ALL_LAYOUT, phi::IndexPutGradKernel, float, + phi::dtype::float16, + phi::dtype::bfloat16, int, int64_t) {} diff --git a/paddle/phi/kernels/xpu/index_put_kernel.cc b/paddle/phi/kernels/xpu/index_put_kernel.cc index 1ca4ef3a0db342..a265489ff39b4e 100644 --- a/paddle/phi/kernels/xpu/index_put_kernel.cc +++ b/paddle/phi/kernels/xpu/index_put_kernel.cc @@ -100,5 +100,12 @@ void IndexPutKernel(const Context& dev_ctx, } } // namespace phi -PD_REGISTER_KERNEL( - index_put, XPU, ALL_LAYOUT, phi::IndexPutKernel, float, int, int64_t) {} +PD_REGISTER_KERNEL(index_put, + XPU, + ALL_LAYOUT, + phi::IndexPutKernel, + float, + phi::dtype::float16, + phi::dtype::bfloat16, + int, + int64_t) {} diff --git a/test/legacy_test/op_test.py b/test/legacy_test/op_test.py index e1b677834f97dd..797f2fcf32a100 100644 --- a/test/legacy_test/op_test.py +++ b/test/legacy_test/op_test.py @@ -525,6 +525,7 @@ def is_complex_test(): not cls.input_shape_is_large and cls.op_type not in check_shape_white_list.NEED_TO_FIX_OP_LIST + and not is_xpu_op_test() ): raise AssertionError( "Number of element(s) of input should be large than or equal to 100 for " diff --git a/test/xpu/test_index_put_op_xpu.py b/test/xpu/test_index_put_op_xpu.py index d2db6fcdc85852..2b309a1e1b6223 100644 --- a/test/xpu/test_index_put_op_xpu.py +++ b/test/xpu/test_index_put_op_xpu.py @@ -21,6 +21,7 @@ create_test_class, get_xpu_op_support_types, ) +from op_test import convert_float_to_uint16, convert_uint16_to_float from op_test_xpu import XPUOpTest import paddle @@ -104,11 +105,18 @@ def set_case(self): def init_data(self): x_np = ((np.random.random(self.x_shape) - 0.5) * 10.0).astype( - self.dtype + "float32" ) value_np = ( (np.random.random(self.value_shape) - 0.5) * 10.0 - ).astype(self.dtype) + ).astype("float32") + + if self.dtype == np.uint16: + x_np = convert_float_to_uint16(x_np) + value_np = convert_float_to_uint16(value_np) + else: + x_np = x_np.astype(self.dtype) + value_np = value_np.astype(self.dtype) if self.mixed_indices: tmp_indices_np1 = gen_indices_np( @@ -149,12 +157,21 @@ def init_data(self): if self.is_all_false: out_np = x_np else: - out_np = compute_index_put_ref( - copy.deepcopy(x_np), - self.indices_np, - value_np, - self.accumulate, - ) + if self.dtype == np.uint16: + out_np = compute_index_put_ref( + convert_uint16_to_float(copy.deepcopy(x_np)), + self.indices_np, + convert_uint16_to_float(value_np), + self.accumulate, + ) + out_np = convert_float_to_uint16(out_np) + else: + out_np = compute_index_put_ref( + copy.deepcopy(x_np), + self.indices_np, + value_np, + self.accumulate, + ) self.outputs = {'out': out_np} def get_indices_names(self): @@ -172,49 +189,49 @@ def test_check_grad(self): class TestXPUIndexPut1(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = [(16, 16), (16, 16), (1, 16), (1, 16)] - self.value_shape = (16, 16) + self.x_shape = (48, 26, 56) + self.indices_shapes = [(16, 16), (16, 16), (1, 16)] + self.value_shape = [16, 16] self.accumulate = False class TestXPUIndexPut2(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = [(16, 16), (16, 16), (1, 16), (1, 16)] + self.x_shape = (48, 26, 56) + self.indices_shapes = [(16, 16), (16, 16), (1, 16)] self.value_shape = (16, 16) self.accumulate = True class TestXPUIndexPut3(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.bool_ - self.x_shape = (110, 94) - self.indices_shapes = [(110, 94)] - self.value_shape = (5170,) + self.x_shape = (12, 94) + self.indices_shapes = [(12, 94)] + self.value_shape = (564,) self.accumulate = False class TestXPUIndexPut4(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.bool_ - self.x_shape = (110, 94) - self.indices_shapes = [(110, 94)] - self.value_shape = (5170,) + self.x_shape = (11, 94) + self.indices_shapes = [(11, 94)] + self.value_shape = (564,) self.accumulate = True class TestXPUIndexPut5(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int32 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) - self.value_shape = (16, 16, 56) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (1, 8)) + self.value_shape = (8, 8, 36) self.accumulate = False class TestXPUIndexPut6(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int32 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) - self.value_shape = (16, 16, 56) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (1, 8)) + self.value_shape = (8, 8, 36) self.accumulate = True class TestXPUIndexPut7(TestXPUIndexPutOp): @@ -237,32 +254,32 @@ def set_case(self): class TestXPUIndexPut9(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) - self.value_shape = (56,) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (1, 8)) + self.value_shape = (36,) self.accumulate = False class TestXPUIndexPut10(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) - self.value_shape = (56,) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (8, 8)) + self.value_shape = (36,) self.accumulate = True class TestXPUIndexPut11(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (8, 8)) self.value_shape = (1,) self.accumulate = False class TestXPUIndexPut12(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int64 - self.x_shape = (110, 42, 56, 56) - self.indices_shapes = ((16, 16), (16, 16), (1, 16)) + self.x_shape = (17, 32, 26, 36) + self.indices_shapes = ((8, 8), (8, 8), (1, 8)) self.value_shape = (1,) self.accumulate = True @@ -317,26 +334,26 @@ def set_case(self): class TestXPUIndexPutMixedIndices(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int32 - self.x_shape = (110, 42, 32, 56) - self.indices_shapes = ((16, 16), (16, 16)) - self.value_shape = (16, 16, 56) + self.x_shape = (17, 32, 16, 36) + self.indices_shapes = ((8, 8), (8, 8)) + self.value_shape = (8, 8, 36) self.accumulate = False self.mixed_indices = True self.index_dtype1 = np.bool_ - self.indices_shapes1 = [(32,)] + self.indices_shapes1 = [(16,)] class TestXPUIndexPutMixedIndices1(TestXPUIndexPutOp): def set_case(self): self.index_dtype = np.int32 - self.x_shape = (110, 42, 32, 56) - self.indices_shapes = ((16, 16), (16, 16)) - self.value_shape = (16, 16, 56) + self.x_shape = (17, 32, 16, 36) + self.indices_shapes = ((8, 8), (8, 8)) + self.value_shape = (8, 8, 36) self.accumulate = True self.mixed_indices = True self.index_dtype1 = np.bool_ - self.indices_shapes1 = [(32,)] + self.indices_shapes1 = [(16,)] supported_type = get_xpu_op_support_types("index_put") @@ -357,7 +374,7 @@ def setUp(self): def init_dtype_type(self): self.dtype_np = np.float32 self.index_type_np = np.int64 - self.x_shape = (100, 110) + self.x_shape = (50, 55) self.indices_shapes = [(21,), (21,)] self.value_shape = (21,) self.dtype_pd = paddle.float32 From d68d9ddc068adfafc0e3fbceea51e0d75e7e3f2e Mon Sep 17 00:00:00 2001 From: tianshuo78520a <707759223@qq.com> Date: Fri, 6 Dec 2024 16:10:35 +0800 Subject: [PATCH 213/288] Update approval;test=document_fix (#70006) --- paddle/scripts/paddle_build.sh | 4 ++-- tools/check_file_diff_approvals.sh | 8 ++++---- 2 files changed, 6 insertions(+), 6 deletions(-) diff --git a/paddle/scripts/paddle_build.sh b/paddle/scripts/paddle_build.sh index eb0dc4fe780e7b..2e971c680c87f5 100644 --- a/paddle/scripts/paddle_build.sh +++ b/paddle/scripts/paddle_build.sh @@ -1116,12 +1116,12 @@ function check_whl_size() { whldiffSize=`echo $(($pr_whl_size - $dev_whl_size))` if [ ${whldiffSize} -gt 10 ]; then approval_line=`curl -H "Authorization: token ${GITHUB_API_TOKEN}" https://api.github.com/repos/PaddlePaddle/Paddle/pulls/${GIT_PR_ID}/reviews?per_page=10000` - APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 22334008 22361972` + APPROVALS=`echo ${approval_line}|python ${PADDLE_ROOT}/tools/check_pr_approval.py 1 zhangbo9674 risemeup1 phlrain` echo "current pr ${GIT_PR_ID} got approvals: ${APPROVALS}" if [ "${APPROVALS}" == "FALSE" ]; then echo "==========================================================================================" echo "This PR make the release paddlepaddle whl size growth exceeds 10 M." - echo "Then you must have one RD (jim19930609 (Recommend) or JiabinYang) approval for this PR\n" + echo "Then you must have one RD (zhangbo9674 or risemeup1 or phlrain) approval for this PR\n" echo "==========================================================================================" exit 6 fi diff --git a/tools/check_file_diff_approvals.sh b/tools/check_file_diff_approvals.sh index 5933c864c6f808..6ffd8d4870a81e 100644 --- a/tools/check_file_diff_approvals.sh +++ b/tools/check_file_diff_approvals.sh @@ -400,8 +400,8 @@ for CHANGE_FILE in ${ALL_CHANGE_FILES}; do fi done if [ "${ALL_OPTEST_BAN_DYGRAPH_MESSAGE}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then - echo_line="Developers are not allowed to set the check_dygraph field directly, which is set to True by default. If you need to change the check_dygraph field, you must have one RD (phlrain (Recommend), fuyinno4, QingshuChen (Recommend for kunlun) review and approve. \nThe code that do not meet the specification are as follows:\n${ALL_OPTEST_BAN_DYGRAPH_MESSAGE}\n" - check_approval 1 phlrain fuyinno4 QingshuChen + echo_line="Developers are not allowed to set the check_dygraph field directly, which is set to True by default. If you need to change the check_dygraph field, you must have one RD (phlrain (Recommend), QingshuChen (Recommend for kunlun) review and approve. \nThe code that do not meet the specification are as follows:\n${ALL_OPTEST_BAN_DYGRAPH_MESSAGE}\n" + check_approval 1 phlrain QingshuChen fi ALL_CHANGE_YAML_FILES=`git diff --numstat upstream/$BRANCH | awk '{print $3}' | grep ".yaml"` @@ -482,8 +482,8 @@ if [ "${NEW_OP_TEST_ADDED}" != "" ] && [ "${GIT_PR_ID}" != "" ]; then CHECK_WHOLE=$CHECK_OUTPUT$CHECK_OUTPUT_WITH_PLACE$CHECK_GRAD$CHECK_GRAD_CHECK if [ "${CHECK_WHOLE}" != "" ] ; then CHECK_OP=${CHECK_WHOLE//+/'\n+'} - echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), fuyinno4, QingshuChen(Recommend for kunlun), zhiqiu, luotao1, phlrain or ZzSean) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n" - check_approval 1 Xreki fuyinno4 QingshuChen zhiqiu luotao1 phlrain + echo_line="Please use the default precision parameters of 'atol, rtol, eps, max_relative_error'. If you don't use the default value, you must have one RD (Xreki (Recommend), QingshuChen(Recommend for kunlun), zhiqiu, luotao1, phlrain or ZzSean) approval for the usage of other values. The detailed information is in the link: https://github.cor/PaddlePaddle/Paddle/wiki/OP-test-accuracy-requirements. The error line is ${CHECK_OP}\n" + check_approval 1 Xreki QingshuChen zhiqiu luotao1 phlrain fi fi From 6eba01b900e43ea2164a88474ddf09c71e38b9b8 Mon Sep 17 00:00:00 2001 From: Ayakouji <148307532+aquagull@users.noreply.github.com> Date: Fri, 6 Dec 2024 16:55:30 +0800 Subject: [PATCH 214/288] =?UTF-8?q?=20=E3=80=90Paddle=20Tensor=20No.27?= =?UTF-8?q?=E3=80=91=20`paddle.to=5Ftensor`=20=E9=80=82=E9=85=8D=20`=5F=5F?= =?UTF-8?q?cuda=5Farray=5Finterface=5F=5F`=20(#69913)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add tensor_from_cuda_array_interface * fix * fix * fix * fix * fix * fix * fix * fix * fix * fix api and add test * fix bug in creation * fix --- paddle/fluid/framework/tensor_util.cc | 26 +++++++ paddle/fluid/framework/tensor_util.h | 2 + paddle/fluid/pybind/pybind.cc | 98 +++++++++++++++++++++++++++ python/paddle/tensor/creation.py | 13 ++++ test/legacy_test/test_eager_tensor.py | 18 +++++ 5 files changed, 157 insertions(+) diff --git a/paddle/fluid/framework/tensor_util.cc b/paddle/fluid/framework/tensor_util.cc index 42f56be54472f5..1fd3276ad8f1ec 100644 --- a/paddle/fluid/framework/tensor_util.cc +++ b/paddle/fluid/framework/tensor_util.cc @@ -864,6 +864,32 @@ void DeleterBridge(phi::Allocation* alloc) { } } +phi::DataType ConvertToPDDataType(const std::string& typestr) { + static const std::unordered_map type_map = { + {"second; +} + phi::DenseTensor from_blob(void* data, DLManagedTensor* src, const phi::DDim& shape, diff --git a/paddle/fluid/framework/tensor_util.h b/paddle/fluid/framework/tensor_util.h index 26ef35de213e92..7c3d7284ad689f 100644 --- a/paddle/fluid/framework/tensor_util.h +++ b/paddle/fluid/framework/tensor_util.h @@ -53,6 +53,8 @@ class PrintOptions { PrintOptions() {} }; +phi::DataType ConvertToPDDataType(const std::string& typestr); + TEST_API void TensorToStream(std::ostream& os, const phi::DenseTensor& tensor, const phi::DeviceContext& dev_ctx); diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 9a27f20bc1483f..53b53d85ce3fb3 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -36,6 +36,7 @@ limitations under the License. */ #include #include +#include "paddle/common/ddim.h" #include "paddle/fluid/framework/compiled_program.h" #include "paddle/fluid/framework/convert_utils.h" #include "paddle/fluid/framework/custom_operator.h" @@ -75,9 +76,11 @@ limitations under the License. */ #include "paddle/fluid/prim/utils/utils.h" #include "paddle/phi/common/bfloat16.h" #include "paddle/phi/common/float16.h" +#include "paddle/phi/common/int_array.h" #include "paddle/phi/core/framework/reader.h" #include "paddle/phi/core/memory/allocation/allocator_strategy.h" #include "paddle/phi/core/raw_tensor.h" +#include "paddle/phi/core/tensor_meta.h" #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) #include "paddle/phi/core/memory/allocation/auto_growth_best_fit_allocator_v2.h" #include "paddle/phi/core/memory/allocation/cuda_ipc_allocator.h" @@ -1259,6 +1262,101 @@ PYBIND11_MODULE(libpaddle, m) { return ptensor; }); + m.def("tensor_from_cuda_array_interface", [](py::object obj) { + // We use CUDA Array Interface (Version 2) protocol: + // https://numba.pydata.org/numba-doc/dev/cuda/cuda_array_interface.html + py::object cuda_array_interface = obj.attr("__cuda_array_interface__"); + PADDLE_ENFORCE_EQ(py::isinstance(cuda_array_interface), + true, + common::errors::InvalidArgument( + "`__cuda_array_interface` must be a dict")); + py::dict cuda_dict = cuda_array_interface.cast(); + + // Extract the `obj.__cuda_array_interface__['shape']` attribute + PADDLE_ENFORCE_EQ( + cuda_dict.contains("shape"), + true, + common::errors::InvalidArgument( + "The 'shape' key is missing in the __cuda_array_interface__ " + "dict.")); + py::object shape_obj = cuda_dict["shape"]; + PADDLE_ENFORCE_EQ( + py::isinstance(shape_obj) || + py::isinstance(shape_obj), + true, + common::errors::InvalidArgument("Shape must be a tuple or list")); + std::vector shapes; + shapes = shape_obj.cast>(); + phi::IntArray shapeIntArray = phi::IntArray(shapes); + + // Extract the `obj.__cuda_array_interface__['typestr'] attribute + PADDLE_ENFORCE_EQ( + cuda_dict.contains("typestr"), + true, + common::errors::InvalidArgument( + "The 'typestr' key is missing in the __cuda_array_interface__ " + "dict.")); + py::object typestr_obj = cuda_dict["typestr"]; + std::string typestr = typestr_obj.cast(); + phi::DataType dtype = paddle::framework::ConvertToPDDataType(typestr); + + // Extract the `obj.__cuda_array_interface__['data']` attribute + PADDLE_ENFORCE_EQ( + cuda_dict.contains("data"), + true, + common::errors::InvalidArgument( + "The 'data' key is missing in the __cuda_array_interface__ " + "dict.")); + py::object data_obj = cuda_dict["data"]; + py::tuple data_tuple = data_obj.cast(); + + // Data tuple(ptr_as_int, read_only_flag). + // The ptr_as_int stands for data pointer but in Python it is a integer. + // It need to be converted to a large enough integral type first + // and then convert to void* + void *data_ptr = reinterpret_cast(data_tuple[0].cast()); + PADDLE_ENFORCE_NE( + data_tuple[1].cast(), + true, + common::errors::InvalidArgument("Read-only array is not supported")); + + // Extract the `obj.__cuda_array_interface__['strides']` attribute + phi::IntArray stridesIntArray; + if (cuda_dict.contains("strides") && !cuda_dict["strides"].is_none()) { + std::vector strides_vec = + cuda_dict["strides"].cast>(); + + // __cuda_array_interface__ strides uses bytes + size_t element_size = phi::SizeOf(dtype); + for (auto &stride : strides_vec) { + PADDLE_ENFORCE_NE( + stride % element_size, + 0, + common::errors::InvalidArgument( + "strides must be a multiple of the element size.")); + stride /= element_size; + } + stridesIntArray = phi::IntArray(strides_vec); + } else { + DDim ddim_strides = + phi::DenseTensorMeta::calc_strides(common::make_ddim(shapes)); + int rank = ddim_strides.size(); + const int64_t *ddim_data = ddim_strides.Get(); + std::vector strides_vec(ddim_data, ddim_data + rank); + stridesIntArray = phi::IntArray(strides_vec); + } + return paddle::from_blob(data_ptr, + shapeIntArray, + stridesIntArray, + dtype, + phi::DataLayout::NCHW, + phi::Place(), + [obj](void *data) { + py::gil_scoped_acquire gil; + obj.dec_ref(); + }); + }); + m.def("_create_loaded_parameter", [](const py::handle &vec_var_list, const Scope &scope, diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index 24697cf78367f6..28cdf43a4121e2 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -16,6 +16,7 @@ import math import re +import warnings from typing import TYPE_CHECKING, Any, overload import numpy as np @@ -931,6 +932,18 @@ def to_tensor( if place is None: place = _current_expected_place_() if in_dynamic_mode(): + is_tensor = paddle.is_tensor(data) + if not is_tensor and hasattr(data, "__cuda_array_interface__"): + if not core.is_compiled_with_cuda(): + raise RuntimeError( + "PaddlePaddle is not compiled with CUDA, but trying to create a Tensor from a CUDA array." + ) + return core.tensor_from_cuda_array_interface(data) + if is_tensor: + warnings.warn( + "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), " + "rather than paddle.to_tensor(sourceTensor)." + ) return _to_tensor_non_static(data, dtype, place, stop_gradient) # call assign for static graph diff --git a/test/legacy_test/test_eager_tensor.py b/test/legacy_test/test_eager_tensor.py index 7384034a87370c..102869848e0131 100644 --- a/test/legacy_test/test_eager_tensor.py +++ b/test/legacy_test/test_eager_tensor.py @@ -15,6 +15,7 @@ import copy import itertools import unittest +import warnings import numpy as np from utils import dygraph_guard @@ -1293,6 +1294,23 @@ def test___cuda_array_interface__(self): self.assertIn("version", interface) self.assertEqual(interface["version"], 2) + def test_to_tensor_from___cuda_array_interface__(self): + # only test warning message here for cuda tensor of other framework is not supported in Paddle test, more tests code can be referenced: https://github.com/PaddlePaddle/Paddle/pull/69913 + with dygraph_guard(): + with warnings.catch_warnings(record=True) as w: + x = paddle.to_tensor([1, 2, 3]) + paddle.to_tensor(x) + flag = False + for warn in w: + if ( + issubclass(warn.category, UserWarning) + ) and "To copy construct from a tensor, it is recommended to use sourceTensor.clone().detach(), rather than paddle.to_tensor(sourceTensor)." in str( + warn.message + ): + flag = True + break + self.assertTrue(flag) + def test_dlpack_device(self): """test Tensor.__dlpack_device__""" with dygraph_guard(): From 5cbe145cf61185ef33887c1c20de87fd5bb27aa6 Mon Sep 17 00:00:00 2001 From: Guoxia Wang Date: Fri, 6 Dec 2024 17:04:58 +0800 Subject: [PATCH 215/288] FlashAttention build skip when bos has cache (#69961) * FlashAttention build skip when bos has cache * fix --- cmake/external/flashattn.cmake | 105 ++++++++++++++++++++++++++++++++- third_party/flashattn | 2 +- 2 files changed, 104 insertions(+), 3 deletions(-) diff --git a/cmake/external/flashattn.cmake b/cmake/external/flashattn.cmake index 95b995945f7336..f660e567289a34 100644 --- a/cmake/external/flashattn.cmake +++ b/cmake/external/flashattn.cmake @@ -80,12 +80,20 @@ if(WITH_ROCM) else() add_definitions(-DPADDLE_WITH_FLASHATTN) + option(FA_BUILD_WITH_CACHE "Download cache so files from bos" ON) set(FLASHATTN_PREFIX_DIR ${THIRD_PARTY_PATH}/flashattn) set(FLASHATTN_SOURCE_SUBDIR csrc) set(FLASHATTN_INSTALL_DIR ${THIRD_PARTY_PATH}/install/flashattn) set(SOURCE_DIR ${PADDLE_SOURCE_DIR}/third_party/flashattn) - set(FLASHATTN_TAG 5fc132ac11e78d26471ca09e5ba0cd817c3424d8) + + # get FA git commit + execute_process( + COMMAND git rev-parse HEAD + WORKING_DIRECTORY ${SOURCE_DIR} + OUTPUT_VARIABLE FLASHATTN_TAG + OUTPUT_STRIP_TRAILING_WHITESPACE) + message(STATUS "flashattn git commit: ${FLASHATTN_TAG}") set(FLASHATTN_INCLUDE_DIR "${FLASHATTN_INSTALL_DIR}/include" @@ -166,6 +174,99 @@ else() endif() endforeach() + set(BASE_URL + "https://xly-devops.bj.bcebos.com/gpups/flash-attention/cu${FA_NVCC_ARCH_BIN}" + ) + set(TAR_FILE_NAME "flashattn_libs_${FLASHATTN_TAG}.tar") + set(TAR_FILE_URL "${BASE_URL}/${TAR_FILE_NAME}") + set(FA_BUILD_DIR "${FLASHATTN_PREFIX_DIR}/src/extern_flashattn-build/") + set(CACHE_TAR_PATH "${FA_BUILD_DIR}/${TAR_FILE_NAME}") + set(CACHE_TAR_DIR "${FA_BUILD_DIR}/flashattn_libs_${FLASHATTN_TAG}") + + set(SKIP_BUILD_FA OFF) + if(FA_BUILD_WITH_CACHE) + + message(STATUS "Downloading ${TAR_FILE_URL} to ${CACHE_TAR_PATH}") + file( + DOWNLOAD "${TAR_FILE_URL}" "${CACHE_TAR_PATH}" + STATUS DOWNLOAD_STATUS + LOG DOWNLOAD_LOG) + list(GET DOWNLOAD_STATUS 0 DOWNLOAD_RESULT) + + if(DOWNLOAD_RESULT EQUAL 0) + message(STATUS "Download Successful") + + file(MAKE_DIRECTORY ${FA_BUILD_DIR}) + + execute_process( + COMMAND ${CMAKE_COMMAND} -E tar xf ${CACHE_TAR_PATH} + WORKING_DIRECTORY ${FA_BUILD_DIR} + RESULT_VARIABLE TAR_RESULT) + + if(NOT TAR_RESULT EQUAL 0) + message(FATAL_ERROR "Failed to extract ${CACHE_TAR_PATH}") + endif() + + file(STRINGS ${CACHE_TAR_DIR}/MD5.txt FILE_MD5) + + # Strip any leading or trailing whitespace + string(STRIP ${FILE_MD5} FILE_MD5) + + file(MD5 ${CACHE_TAR_DIR}/fa_libs.tar FILE_MD5_ACTUAL) + + message(STATUS "Expected MD5: ${FILE_MD5}") + message(STATUS "Actual MD5: ${FILE_MD5_ACTUAL}") + + if(NOT "${FILE_MD5}" STREQUAL "${FILE_MD5_ACTUAL}") + message( + FATAL_ERROR "MD5 checksum mismatch! The download may be corrupted.") + else() + message(STATUS "MD5 checksum verified successfully.") + endif() + + execute_process( + COMMAND ${CMAKE_COMMAND} -E tar xf ${CACHE_TAR_DIR}/fa_libs.tar + WORKING_DIRECTORY ${CACHE_TAR_DIR} + RESULT_VARIABLE TAR_RESULT) + + if(NOT TAR_RESULT EQUAL 0) + message(FATAL_ERROR "Failed to extract ${CACHE_TAR_PATH}/fa_libs.tar") + endif() + + file(GLOB_RECURSE SO_FILES "${CACHE_TAR_DIR}/fa_libs/*.so") + foreach(so_file ${SO_FILES}) + message(STATUS "Copy ${so_file} to ${FA_BUILD_DIR}") + message(STATUS "Copy ${so_file} to ${FLASHATTN_LIB_DIR}") + file(COPY "${so_file}" DESTINATION "${FA_BUILD_DIR}") + file(COPY "${so_file}" DESTINATION "${FLASHATTN_LIB_DIR}") + endforeach() + + file(REMOVE_RECURSE ${CACHE_TAR_DIR}) + message(STATUS "Extraction completed in ${FA_BUILD_DIR}") + + set(SKIP_BUILD_FA ON) + + elseif(DOWNLOAD_RESULT EQUAL 6) + message( + STATUS + "Could not resolve host. The given remote host was not resolvable.") + elseif(DOWNLOAD_RESULT EQUAL 7) + message(STATUS "Failed to connect to host.") + elseif(DOWNLOAD_RESULT EQUAL 22) + message( + STATUS + "HTTP page not retrieved. The requested URL was not found or a server returned a 4xx (client error) or 5xx (server error) response." + ) + elseif(DOWNLOAD_RESULT EQUAL 28) + message( + STATUS + "Operation timeout. The specified time-out period was reached according to the conditions." + ) + else() + message(STATUS "An error occurred. Error code: ${DOWNLOAD_RESULT}") + endif() + endif() + ExternalProject_Add( extern_flashattn ${EXTERNAL_PROJECT_LOG_ARGS} @@ -196,13 +297,13 @@ else() -DCMAKE_JOB_POOLS:STRING=compile=${FA_JOB_POOLS_COMPILE} -DNVCC_ARCH_BIN=${FA_NVCC_ARCH_BIN} -DWITH_FLASHATTN_V3=${WITH_FLASHATTN_V3} + -DSKIP_BUILD_FA=${SKIP_BUILD_FA} ${EXTERNAL_OPTIONAL_ARGS} CMAKE_CACHE_ARGS -DCMAKE_BUILD_TYPE:STRING=${THIRD_PARTY_BUILD_TYPE} -DCMAKE_POSITION_INDEPENDENT_CODE:BOOL=ON -DCMAKE_INSTALL_PREFIX:PATH=${FLASHATTN_INSTALL_DIR} BUILD_BYPRODUCTS ${BUILD_BYPRODUCTS_LIST}) - endif() message(STATUS "flash-attn library: ${FLASHATTN_LIBRARIES}") diff --git a/third_party/flashattn b/third_party/flashattn index 6ea759b3ea9563..6f8ae73cd96415 160000 --- a/third_party/flashattn +++ b/third_party/flashattn @@ -1 +1 @@ -Subproject commit 6ea759b3ea9563b49d92f1ae0c4cb0fb26a7b365 +Subproject commit 6f8ae73cd96415c50ccc301de2696aaf5481c639 From 6057335281e60a4f6df2428ad211060432eee39a Mon Sep 17 00:00:00 2001 From: Guoxia Wang Date: Sat, 7 Dec 2024 16:53:45 +0800 Subject: [PATCH 216/288] fix bug of FA2 densemask when casual=True (#70019) --- third_party/flashattn | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third_party/flashattn b/third_party/flashattn index 6f8ae73cd96415..6c165641f31504 160000 --- a/third_party/flashattn +++ b/third_party/flashattn @@ -1 +1 @@ -Subproject commit 6f8ae73cd96415c50ccc301de2696aaf5481c639 +Subproject commit 6c165641f3150420b7351735ba82455ffe27d79c From 86bd061276d181d40dec3934bfdbcc30d6ca80d6 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sat, 7 Dec 2024 23:04:55 +0800 Subject: [PATCH 217/288] [CodeStyle][UP031] Use f-string instead of percent format in some framework dirs (part20) (#70031) --- .../gather_gemm_scatter_operation.py | 2 +- python/paddle/amp/auto_cast.py | 15 ++------------- python/paddle/amp/debugging.py | 3 +-- python/paddle/audio/features/layers.py | 2 +- python/paddle/autograd/ir_backward.py | 6 ++---- python/paddle/base/backward.py | 2 +- .../paddle/base/dygraph/tensor_patch_methods.py | 3 +-- python/paddle/base/executor.py | 16 ++++++---------- python/paddle/base/framework.py | 15 ++++++--------- python/paddle/base/layer_helper.py | 4 +--- python/paddle/base/variable_index.py | 3 +-- python/paddle/dataset/flowers.py | 2 +- python/paddle/dataset/image.py | 4 ++-- python/paddle/dataset/movielens.py | 14 +++----------- python/paddle/dataset/wmt16.py | 4 ++-- 15 files changed, 31 insertions(+), 64 deletions(-) diff --git a/paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_operation.py b/paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_operation.py index b8f3254292bb49..82ef666b53e9b8 100644 --- a/paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_operation.py +++ b/paddle/phi/kernels/sparse/gpu/cutlass_generator/gather_gemm_scatter_operation.py @@ -158,7 +158,7 @@ def emit(self, operation): 'opcode_class': OpcodeClassTag[ operation.tile_description.math_instruction.opcode_class ], - 'arch': "cutlass::arch::Sm%d" % operation.arch, + 'arch': f"cutlass::arch::Sm{operation.arch}", 'threadblock_shape_m': str( operation.tile_description.threadblock_shape[0] ), diff --git a/python/paddle/amp/auto_cast.py b/python/paddle/amp/auto_cast.py index e222f5fec2f694..e93a08f51c61f9 100644 --- a/python/paddle/amp/auto_cast.py +++ b/python/paddle/amp/auto_cast.py @@ -632,25 +632,14 @@ def amp_guard( if (dtype == 'float16') and not _is_gpu_float16_supported(): prop = paddle.device.cuda.get_device_capability() warnings.warn( - "For float16, amp only support NVIDIA GPU with Compute Capability 7.0 or higher, current GPU is: %s, with Compute Capability: %d.%d." - % ( - paddle.device.cuda.get_device_name(), - prop[0], - prop[1], - ) + f"For float16, amp only support NVIDIA GPU with Compute Capability 7.0 or higher, current GPU is: {paddle.device.cuda.get_device_name()}, with Compute Capability: {prop[0]}.{prop[1]}." ) enable = False elif (dtype == 'bfloat16') and not _is_gpu_bfloat16_supported(): prop = paddle.device.cuda.get_device_capability() cuda_version = paddle.version.cuda() warnings.warn( - "For bfloat16, amp only support NVIDIA GPU with Compute Capability 8.0 or higher and CUDA Version 11.0 or higher, current GPU is: %s, with Compute Capability: %d.%d, current CUDA Version is: %s." - % ( - paddle.device.cuda.get_device_name(), - prop[0], - prop[1], - cuda_version, - ) + f"For bfloat16, amp only support NVIDIA GPU with Compute Capability 8.0 or higher and CUDA Version 11.0 or higher, current GPU is: {paddle.device.cuda.get_device_name()}, with Compute Capability: {prop[0]}.{prop[1]}, current CUDA Version is: {cuda_version}." ) enable = False diff --git a/python/paddle/amp/debugging.py b/python/paddle/amp/debugging.py index 14b7ef4da535a5..7bdc30bdc33897 100644 --- a/python/paddle/amp/debugging.py +++ b/python/paddle/amp/debugging.py @@ -471,8 +471,7 @@ def _print_operator_stats(op_count_dict: dict[str, str | list[int]]) -> None: f"Input {value} is expected to be a list of str, but received {type(value)}." ) print( - " %-40s| %-17s| %-17s| %-17s| %-17s" - % (op_type, called[0], called[1], called[2], called[3]) + f" {op_type:<40}| {called[0]:<17}| {called[1]:<17}| {called[2]:<17}| {called[3]:<17}" ) total_ops += 1 print("<{:-^120}>\n".format(" op count: " + str(total_ops) + " ")) diff --git a/python/paddle/audio/features/layers.py b/python/paddle/audio/features/layers.py index 1f578f072b8e51..cbd09e4498a121 100644 --- a/python/paddle/audio/features/layers.py +++ b/python/paddle/audio/features/layers.py @@ -412,7 +412,7 @@ def __init__( super().__init__() assert ( n_mfcc <= n_mels - ), 'n_mfcc cannot be larger than n_mels: %d vs %d' % (n_mfcc, n_mels) + ), f'n_mfcc cannot be larger than n_mels: {n_mfcc} vs {n_mels}' self._log_melspectrogram = LogMelSpectrogram( sr=sr, n_fft=n_fft, diff --git a/python/paddle/autograd/ir_backward.py b/python/paddle/autograd/ir_backward.py index 33c4c8cfcf0507..a9e8994850a862 100644 --- a/python/paddle/autograd/ir_backward.py +++ b/python/paddle/autograd/ir_backward.py @@ -233,13 +233,11 @@ def prepare_grad_outputs(grad_outputs, outputs, state): else: if output.shape != grad.shape: raise ValueError( - "The shape of grad_output[%d] %s should be the same as the shape of output[%d] %s" - % (i, str(grad.shape), i, str(output.shape)) + f"The shape of grad_output[{i}] {grad.shape} should be the same as the shape of output[{i}] {output.shape}" ) if output.dtype != grad.dtype: warnings.warn( - "The dtype of grad_output[%d] %s is not same as the dtype of output[%d] %s" - % (i, str(grad.dtype), i, str(output.dtype)) + f"The dtype of grad_output[{i}] {grad.dtype} is not same as the dtype of output[{i}] {output.dtype}" ) feedop = grad.get_defining_op() update_bwdop_structure( diff --git a/python/paddle/base/backward.py b/python/paddle/base/backward.py index 1ae69767335df9..8a34c5703978d6 100755 --- a/python/paddle/base/backward.py +++ b/python/paddle/base/backward.py @@ -1146,7 +1146,7 @@ def _append_backward_ops_with_checkpoints_( grad_to_var.update(op_grad_to_var) ff_ops = ops[segment[0] : segment[1]] - var_suffix = ".subprog_%d" % i + var_suffix = f".subprog_{i}" for op in ff_ops: if op.has_attr("sub_block"): diff --git a/python/paddle/base/dygraph/tensor_patch_methods.py b/python/paddle/base/dygraph/tensor_patch_methods.py index 15de98a154e072..e4120be7e6e4e6 100644 --- a/python/paddle/base/dygraph/tensor_patch_methods.py +++ b/python/paddle/base/dygraph/tensor_patch_methods.py @@ -76,8 +76,7 @@ def remove(self) -> bool: return True else: warnings.warn( - "The backward hook (ID: %d) of Tensor `%s` you want to remove does not exist or has been removed." - % (self._hook_id, tensor.name), + f"The backward hook (ID: {self._hook_id}) of Tensor `{tensor.name}` you want to remove does not exist or has been removed.", RuntimeWarning, ) return False diff --git a/python/paddle/base/executor.py b/python/paddle/base/executor.py index 4b09c7b9bafcce..a493621074c86a 100755 --- a/python/paddle/base/executor.py +++ b/python/paddle/base/executor.py @@ -270,9 +270,8 @@ def check_feed_shape_type(var, feed, num_places=1): diff_shape = core.diff_tensor_shape(feed, var.desc, num_places) if diff_shape is not None: raise ValueError( - 'The fed Variable %r should have dimensions = %d, shape = ' - '%r, but received fed shape %r on each device' - % (var.name, len(var.shape), var.shape, diff_shape) + f'The fed Variable {var.name!r} should have dimensions = {len(var.shape)}, shape = ' + f'{var.shape!r}, but received fed shape {diff_shape!r} on each device' ) if not dtype_is_compatible_with(feed._dtype(), var.dtype): var_dtype_format = ( @@ -318,9 +317,8 @@ def pir_check_feed_shape_type(feed, name, target_shape, dtype, num_places=1): diff_shape = core.diff_tensor_shape(feed, target_shape, num_places) if diff_shape is not None: warnings.warn( - 'The fed Variable %r should have dimensions = %d, shape = ' - '%r, but received fed shape %r on each device' - % (name, len(target_shape), target_shape, diff_shape) + f'The fed Variable {name!r} should have dimensions = {len(target_shape)}, shape = ' + f'{target_shape!r}, but received fed shape {diff_shape!r} on each device' ) if not dtype_is_compatible_with(feed._dtype(), dtype): var_dtype_format = ( @@ -2277,13 +2275,11 @@ def _adjust_pipeline_resource(self, pipeline_opt, dataset, pipeline_num): if filelist_length < pipeline_num: pipeline_num = filelist_length print( - "Pipeline training: setting the pipeline num to %d is enough because there are only %d files" - % (filelist_length, filelist_length) + f"Pipeline training: setting the pipeline num to {filelist_length} is enough because there are only {filelist_length} files" ) if filelist_length < pipeline_num * pipeline_opt["concurrency_list"][0]: print( - "Pipeline training: setting the 1st element in concurrency_list to %d is enough because there are only %d files" - % (filelist_length // pipeline_num, filelist_length) + f"Pipeline training: setting the 1st element in concurrency_list to {filelist_length // pipeline_num} is enough because there are only {filelist_length} files" ) pipeline_opt["concurrency_list"][0] = ( filelist_length // pipeline_num diff --git a/python/paddle/base/framework.py b/python/paddle/base/framework.py index 4c6080a97ac763..2429e8e9aaa68b 100644 --- a/python/paddle/base/framework.py +++ b/python/paddle/base/framework.py @@ -1173,7 +1173,7 @@ def child(self, prefix): self._children[prefix] = [new_child] else: new_child = NameScope( - prefix + "_%d" % len(self._children[prefix]), self + f"{prefix}_{len(self._children[prefix])}", self ) self._children[prefix].append(new_child) return new_child @@ -1264,7 +1264,7 @@ def child(self, prefix): self._children[prefix] = [new_child] else: new_child = NameStruct( - prefix + "_%d" % len(self._children[prefix]), self + f"{prefix}_{len(self._children[prefix])}", self ) self._children[prefix].append(new_child) return new_child @@ -3315,8 +3315,7 @@ def find_name(var_list, name): in_args = [in_args] if not in_proto.duplicable and len(in_args) > 1: raise ValueError( - "Input %s expects only one input, but %d are given." - % (in_proto.name, len(in_args)) + f"Input {in_proto.name} expects only one input, but {len(in_args)} are given." ) in_arg_names = [] for index, arg in enumerate(in_args): @@ -3370,8 +3369,7 @@ def find_name(var_list, name): out_args = [out_args] if not out_proto.duplicable and len(out_args) > 1: raise ValueError( - "Output %s expects only one output, but %d are given." - % (out_proto.name, len(out_args)) + f"Output {out_proto.name} expects only one output, but {len(out_args)} are given." ) out_arg_names = [] for arg in out_args: @@ -4327,9 +4325,8 @@ def to_string(self, throw_on_error, with_details=False): ) if with_details: re_add_indent = re.compile(r"\n(.)") - res_str = "blocks {\n idx: %d\n parent_idx: %d" % ( - self.idx, - self.parent_idx, + res_str = ( + f"blocks {{\n idx: {self.idx}\n parent_idx: {self.parent_idx}" ) for var in list(self.vars.values()): res_str += "\n vars {{\n {} }}".format( diff --git a/python/paddle/base/layer_helper.py b/python/paddle/base/layer_helper.py index ca8b7c2d3f7766..45350ab8f19f24 100644 --- a/python/paddle/base/layer_helper.py +++ b/python/paddle/base/layer_helper.py @@ -111,9 +111,7 @@ def input_dtype( if dtype is None: dtype = each.dtype elif dtype != each.dtype: - raise ValueError( - "Data Type mismatch: %d to %d" % (dtype, each.dtype) - ) + raise ValueError(f"Data Type mismatch: {dtype} to {each.dtype}") return dtype def get_parameter(self, name: str) -> Tensor: diff --git a/python/paddle/base/variable_index.py b/python/paddle/base/variable_index.py index b2b74bd524b1f0..4cbe8bc49b29c3 100644 --- a/python/paddle/base/variable_index.py +++ b/python/paddle/base/variable_index.py @@ -297,8 +297,7 @@ def parse_index(x, indices): # the unpack size would cause error. # We raises IndexError here to support grammar like `a, b = var` raise IndexError( - "slice_item %d at dim %d should be >= 0 and < x.shape[%d]: %d" - % (slice_item, dim, dim, x.shape[dim]) + f"slice_item {slice_item} at dim {dim} should be >= 0 and < x.shape[{dim}]: {x.shape[dim]}" ) # not calculate result to reduce call times for slice OP. decrease_axes.append(dim) diff --git a/python/paddle/dataset/flowers.py b/python/paddle/dataset/flowers.py index c0d3aff0b475ed..208766e10b186b 100644 --- a/python/paddle/dataset/flowers.py +++ b/python/paddle/dataset/flowers.py @@ -114,7 +114,7 @@ def reader(): img2label = {} for i in indexes: - img = "jpg/image_%05d.jpg" % i + img = f"jpg/image_{i:05}.jpg" img2label[img] = labels[i - 1] tf = tarfile.open(data_file) diff --git a/python/paddle/dataset/image.py b/python/paddle/dataset/image.py index 771fb189432f33..02b71c240b621d 100755 --- a/python/paddle/dataset/image.py +++ b/python/paddle/dataset/image.py @@ -97,7 +97,7 @@ def batch_images_from_tar( output = {'label': labels, 'data': data} pickle.dump( output, - open('%s/batch_%d' % (out_path, file_id), 'wb'), + open(f'{out_path}/batch_{file_id}', 'wb'), protocol=2, ) file_id += 1 @@ -106,7 +106,7 @@ def batch_images_from_tar( if len(data) > 0: output = {'label': labels, 'data': data} pickle.dump( - output, open('%s/batch_%d' % (out_path, file_id), 'wb'), protocol=2 + output, open(f'{out_path}/batch_{file_id}', 'wb'), protocol=2 ) with open(meta_file, mode='a') as meta: diff --git a/python/paddle/dataset/movielens.py b/python/paddle/dataset/movielens.py index bcf089bfe97757..079f825c8763a3 100644 --- a/python/paddle/dataset/movielens.py +++ b/python/paddle/dataset/movielens.py @@ -61,11 +61,7 @@ def value(self): ] def __str__(self): - return "" % ( - self.index, - self.title, - self.categories, - ) + return f"" def __repr__(self): return self.__str__() @@ -89,12 +85,8 @@ def value(self): return [self.index, 0 if self.is_male else 1, self.age, self.job_id] def __str__(self): - return "" % ( - self.index, - "M" if self.is_male else "F", - age_table[self.age], - self.job_id, - ) + gender = "M" if self.is_male else "F" + return f"" def __repr__(self): return str(self) diff --git a/python/paddle/dataset/wmt16.py b/python/paddle/dataset/wmt16.py index 5f9ba9b19d500f..22f5cb892327f0 100644 --- a/python/paddle/dataset/wmt16.py +++ b/python/paddle/dataset/wmt16.py @@ -73,7 +73,7 @@ def __build_dict(tar_file, dict_size, save_path, lang): def __load_dict(tar_file, dict_size, lang, reverse=False): dict_path = os.path.join( - paddle.dataset.common.DATA_HOME, "wmt16/%s_%d.dict" % (lang, dict_size) + paddle.dataset.common.DATA_HOME, f"wmt16/{lang}_{dict_size}.dict" ) if not os.path.exists(dict_path) or ( len(open(dict_path, "rb").readlines()) != dict_size @@ -349,7 +349,7 @@ def get_dict(lang, dict_size, reverse=False): dict_size = min(dict_size, TOTAL_DE_WORDS) dict_path = os.path.join( - paddle.dataset.common.DATA_HOME, "wmt16/%s_%d.dict" % (lang, dict_size) + paddle.dataset.common.DATA_HOME, f"wmt16/{lang}_{dict_size}.dict" ) assert os.path.exists(dict_path), "Word dictionary does not exist. " "Please invoke paddle.dataset.wmt16.train/test/validation first " From 2fc83bed70dcd253537fdf86a8aa861a19c1a837 Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Sun, 8 Dec 2024 06:16:07 +0800 Subject: [PATCH 218/288] [CodeStyle][Typos][C-[4-9]] Fix typos(`cacl`,`cll`,`cadidate`,`candiate`,`connot`,`Cann`,`CANN`,`cann`,`vart`) (#70030) --- _typos.toml | 12 +++--------- paddle/cinn/hlir/framework/pir/trivial_op_util.cc | 2 +- paddle/fluid/operators/data_norm_op.cu | 2 +- .../pir/dialect/distributed/ir/dist_attribute.cc | 2 +- .../core/distributed/auto_parallel/dist_tensor.cc | 2 +- paddle/pir/src/core/op_info_impl.cc | 2 +- python/paddle/base/backward.py | 2 +- python/paddle/base/framework.py | 6 +++--- python/paddle/distribution/kl.py | 2 +- python/paddle/optimizer/optimizer.py | 2 +- test/deprecated/legacy_test/auto_parallel_op_test.py | 2 +- test/ir/inference/test_fc_fuse_pass.py | 2 +- test/legacy_test/auto_parallel_op_test.py | 2 +- test/legacy_test/test_nanmedian.py | 8 ++++---- 14 files changed, 21 insertions(+), 27 deletions(-) diff --git a/_typos.toml b/_typos.toml index 8697561e4d9464..86b9f2bb7cd4b4 100644 --- a/_typos.toml +++ b/_typos.toml @@ -13,8 +13,11 @@ anc = 'anc' arange = "arange" astroid = 'astroid' ba = 'ba' +cacl = 'cacl' +CANN = 'CANN' Clas = 'Clas' clen = 'clen' +cll = 'cll' dout = "dout" eles = 'eles' grad = "grad" @@ -36,15 +39,6 @@ cahe = 'cahe' Caculate = 'Caculate' caculate = 'caculate' calcualtion = 'calcualtion' -cacl = 'cacl' -cll = 'cll' -candiate = 'candiate' -cadidate = 'cadidate' -connot = 'connot' -CANN = 'CANN' -Cann = 'Cann' -cann = 'cann' -vart = 'vart' checkings = 'checkings' childs = 'childs' comsume = 'comsume' diff --git a/paddle/cinn/hlir/framework/pir/trivial_op_util.cc b/paddle/cinn/hlir/framework/pir/trivial_op_util.cc index 5d7d4d35d910a8..aa61b5e5d41f94 100644 --- a/paddle/cinn/hlir/framework/pir/trivial_op_util.cc +++ b/paddle/cinn/hlir/framework/pir/trivial_op_util.cc @@ -160,7 +160,7 @@ ir::Expr CopyedReplaceExpr(const Expr& source, candidates.size(), ::common::errors::InvalidArgument( "In ReplaceExpr, the size of Vars to be replaced must be equal to " - "the size of cadidate Exprs! Please check.")); + "the size of candidate Exprs! Please check.")); auto copyed_source = ir::ir_utils::IRCopy(source); if (replaced.empty()) return copyed_source; std::map replacing_map; diff --git a/paddle/fluid/operators/data_norm_op.cu b/paddle/fluid/operators/data_norm_op.cu index 179c13d9d36fb6..7b3fc74d2a0d27 100644 --- a/paddle/fluid/operators/data_norm_op.cu +++ b/paddle/fluid/operators/data_norm_op.cu @@ -300,7 +300,7 @@ class DataNormGradKernel : public framework::OpKernel { phi::backends::gpu::GpuStreamSync(stream); #else PADDLE_THROW(common::errors::PreconditionNotMet( - "PaddlePaddle should compile with GPU, and need_sync_stats connot be " + "PaddlePaddle should compile with GPU, and need_sync_stats cannot be " "supported on windows now.")); #endif } diff --git a/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc b/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc index 16db2c543e2c59..e2bcbf3d718c5d 100644 --- a/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc +++ b/paddle/fluid/pir/dialect/distributed/ir/dist_attribute.cc @@ -77,7 +77,7 @@ phi::distributed::Placements TensorDistAttribute::placements() const { auto& p = placements[mesh_id]; if (p->is_shard()) { PADDLE_THROW(common::errors::PreconditionNotMet( - "ProcessMesh dimension cann't be mapped to two dimension of the " + "ProcessMesh dimension can't be mapped to two dimension of the " "same tensor: {%d} and {%d}", i, dynamic_cast(*p).get_dim())); diff --git a/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc b/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc index cc22d17867ef96..50e3a6cca00d6f 100644 --- a/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc +++ b/paddle/phi/core/distributed/auto_parallel/dist_tensor.cc @@ -104,7 +104,7 @@ Placements ToPlacements(const TensorDistAttr& dist_attr) { if (p->is_shard()) { PADDLE_THROW(common::errors::PreconditionNotMet( - "ProcessMesh dimension cann't be mapped to two dimension of the " + "ProcessMesh dimension can't be mapped to two dimension of the " "same tensor: {%d} and {%d}", i, dynamic_cast(*p).get_dim())); diff --git a/paddle/pir/src/core/op_info_impl.cc b/paddle/pir/src/core/op_info_impl.cc index 08978ff061a655..8c262326ee5162 100644 --- a/paddle/pir/src/core/op_info_impl.cc +++ b/paddle/pir/src/core/op_info_impl.cc @@ -23,7 +23,7 @@ namespace pir { void OpInfo::AttachInterface(InterfaceValue &&interface_value) { PADDLE_ENFORCE_NOT_NULL(impl_, common::errors::InvalidArgument( - "Cann't attach interface to a nullptr OpInfo")); + "Can't attach interface to a nullptr OpInfo")); impl_->AttachInterface(std::move(interface_value)); } diff --git a/python/paddle/base/backward.py b/python/paddle/base/backward.py index 8a34c5703978d6..a27dd8b8cd86bf 100755 --- a/python/paddle/base/backward.py +++ b/python/paddle/base/backward.py @@ -1840,7 +1840,7 @@ def infershape_for_composite(block, grad_op_desc): for name, args in grad_op_desc.outputs().items() }, # NOTE Runtime attr will be ignore as the c++ GetRuntimeAttr - # interface cann't be exported to python. Please note the WARNING + # interface can't be exported to python. Please note the WARNING # message logged in RuntimeAttrs of composite_grad_desc_maker.h attrs=grad_op_desc.get_attr_map(), ) diff --git a/python/paddle/base/framework.py b/python/paddle/base/framework.py index 2429e8e9aaa68b..0489710364c1f5 100644 --- a/python/paddle/base/framework.py +++ b/python/paddle/base/framework.py @@ -5553,7 +5553,7 @@ def create_persistable_node(self, name, var_type, shape, var_dtype): Args: name(str): the name of the persistable variable node. - vart_type(core.VarDesc.VarType): the type of the persistable variable node. + var_type(core.VarDesc.VarType): the type of the persistable variable node. shape(list): the shape of the persistable variable node. var_dtype(core.VarDesc.VarType): the data type of the persistable variable node. @@ -5574,7 +5574,7 @@ def create_var_node(self, name, var_type, shape, var_dtype): Args: name(str): the name of the variable node. - vart_type(core.VarDesc.VarType): the type of the variable node. + var_type(core.VarDesc.VarType): the type of the variable node. shape(list): the shape of the variable node. var_dtype(core.VarDesc.VarType): the data type of the variable node. @@ -6849,7 +6849,7 @@ def _remove_training_info(self, clip_extra=True): res.blocks = [Block(res, i) for i in range(res.desc.num_blocks())] res._sync_with_cpp() - # Note: The op_role and op_role_var cann't be deleted currently, + # Note: The op_role and op_role_var can't be deleted currently, # and we will try to remove them in the future. common_clipped_attrs_list = ["op_callstack", "with_quant_attr"] diff --git a/python/paddle/distribution/kl.py b/python/paddle/distribution/kl.py index de8e12ff0071a1..53c6e7778c69b5 100644 --- a/python/paddle/distribution/kl.py +++ b/python/paddle/distribution/kl.py @@ -264,7 +264,7 @@ def _kl_expfamily_expfamily(p, q): p_grads = paddle.static.gradients(p_log_norm, p_natural_params) except RuntimeError as e: raise TypeError( - "Cann't compute kl_divergence({cls_p}, {cls_q}) use bregman divergence. Please register_kl({cls_p}, {cls_q}).".format( + "Can't compute kl_divergence({cls_p}, {cls_q}) use bregman divergence. Please register_kl({cls_p}, {cls_q}).".format( cls_p=type(p).__name__, cls_q=type(q).__name__ ) ) from e diff --git a/python/paddle/optimizer/optimizer.py b/python/paddle/optimizer/optimizer.py index 79420f892f7499..f9fe375a6810a3 100644 --- a/python/paddle/optimizer/optimizer.py +++ b/python/paddle/optimizer/optimizer.py @@ -803,7 +803,7 @@ def _global_learning_rate(self, program=None): def _append_optimize_op(self, block, param_and_grad): """append optimize operator to block and return all the added optimize_op""" raise NotImplementedError( - 'Class "Optimizer" connot be used directly as an optimizer, please use its subclasses such as "Adam"' + 'Class "Optimizer" cannot be used directly as an optimizer, please use its subclasses such as "Adam"' ) def _create_param_lr(self, param_and_grad): diff --git a/test/deprecated/legacy_test/auto_parallel_op_test.py b/test/deprecated/legacy_test/auto_parallel_op_test.py index 1e6c9dfe4547d6..5efe97b6e8c970 100644 --- a/test/deprecated/legacy_test/auto_parallel_op_test.py +++ b/test/deprecated/legacy_test/auto_parallel_op_test.py @@ -404,7 +404,7 @@ def dims_map_to_placements( if placement.is_shard(): placement = cast(dist.Shard, placement) raise RuntimeError( - f"DeviceMesh dimension cann't be mapped to two dimension of the same tensor: {i} and {placement.dim}" + f"DeviceMesh dimension can't be mapped to two dimension of the same tensor: {i} and {placement.dim}" ) elif placement.is_partial(): raise RuntimeError( diff --git a/test/ir/inference/test_fc_fuse_pass.py b/test/ir/inference/test_fc_fuse_pass.py index 237faff87149e7..2af6732700f675 100644 --- a/test/ir/inference/test_fc_fuse_pass.py +++ b/test/ir/inference/test_fc_fuse_pass.py @@ -55,7 +55,7 @@ def teller1(program_config, predictor_config): bias_shape = list(program_config.weights["bias"].shape) if predictor_config.tensorrt_engine_enabled(): - # TensorRT cann't handle all the situation of elementwise_add + # TensorRT can't handle all the situation of elementwise_add # disable it until this problem fixed predictor_config.exp_disable_tensorrt_ops(["elementwise_add"]) diff --git a/test/legacy_test/auto_parallel_op_test.py b/test/legacy_test/auto_parallel_op_test.py index c74ee27e07ff6e..5265ecbdfeda90 100644 --- a/test/legacy_test/auto_parallel_op_test.py +++ b/test/legacy_test/auto_parallel_op_test.py @@ -403,7 +403,7 @@ def dims_map_to_placements( if placement.is_shard(): placement = cast(dist.Shard, placement) raise RuntimeError( - f"DeviceMesh dimension cann't be mapped to two dimension of the same tensor: {i} and {placement.dim}" + f"DeviceMesh dimension can't be mapped to two dimension of the same tensor: {i} and {placement.dim}" ) elif placement.is_partial(): raise RuntimeError( diff --git a/test/legacy_test/test_nanmedian.py b/test/legacy_test/test_nanmedian.py index 3507928b9014b2..9695c0da351797 100644 --- a/test/legacy_test/test_nanmedian.py +++ b/test/legacy_test/test_nanmedian.py @@ -147,7 +147,7 @@ def setUp(self): if core.is_compiled_with_cuda() else paddle.CPUPlace() ) - self.axis_candiate_list = [ + self.axis_candidate_list = [ None, 0, 2, @@ -231,7 +231,7 @@ def test_axis_case(data, axis): for name, data in self.fake_data.items(): test_data_case(data, name) - for axis in self.axis_candiate_list: + for axis in self.axis_candidate_list: test_axis_case(self.fake_data["row_nan_even"], axis) test_axis_case(self.fake_data["col_nan_odd"], axis) @@ -402,7 +402,7 @@ def setUp(self): if core.is_compiled_with_cuda() else paddle.CPUPlace() ) - self.axis_candiate_list = [ + self.axis_candidate_list = [ None, 0, 2, @@ -480,7 +480,7 @@ def test_axis_case(data, axis): for name, data in self.fake_data.items(): test_data_case(data, name) - for axis in self.axis_candiate_list: + for axis in self.axis_candidate_list: test_axis_case(self.fake_data["row_nan_even"], axis) test_axis_case(self.fake_data["col_nan_odd"], axis) From dc04bb77ce5025d40227a00c1f25547435efa1be Mon Sep 17 00:00:00 2001 From: zhengzhonghui Date: Sun, 8 Dec 2024 07:59:17 +0800 Subject: [PATCH 219/288] [Auto Parallel] do not fold global to sub reshard (#70023) --- .../auto_parallel/static/pir_pass.py | 18 +++++++++++++----- 1 file changed, 13 insertions(+), 5 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/static/pir_pass.py b/python/paddle/distributed/auto_parallel/static/pir_pass.py index a60baa1ed04713..7fffc834e7b913 100644 --- a/python/paddle/distributed/auto_parallel/static/pir_pass.py +++ b/python/paddle/distributed/auto_parallel/static/pir_pass.py @@ -63,11 +63,19 @@ def reshard_single_value(program, op, operand, attr): # fold reshard if prev_var.get_defining_op().name() == 'dist_op.reshard': prev_reshard = prev_var.get_defining_op() - prev_var = prev_reshard.operand_source(0) - if prev_var.dist_attr() == operand_attr: - return prev_var - reshard_var = paddle._C_ops.reshard_v2(prev_var, operand_attr) - return reshard_var + prev_reshard_input = prev_reshard.operand_source(0) + prev_reshard_result = prev_reshard.result(0) + # skil global to sub mesh reshard + if ( + prev_reshard_input.dist_attr().process_mesh.ndim + == prev_reshard_result.dist_attr().process_mesh.ndim + ): + if prev_reshard_input.dist_attr() == operand_attr: + return prev_reshard_input + reshard_var = paddle._C_ops.reshard_v2( + prev_reshard_input, operand_attr + ) + return reshard_var # insert reshard reshard_var = paddle._C_ops.reshard_v2(prev_var, operand_attr) return reshard_var From 76d8566707eaf589d65657f3f6bb94ef26ac9f9d Mon Sep 17 00:00:00 2001 From: zhengzhonghui Date: Sun, 8 Dec 2024 07:59:39 +0800 Subject: [PATCH 220/288] [Auto Parallel] process attn_mask for flash_attn spmd rule (#69991) * [Auto Parallel] process attn_mask for flash_attn spmd rule * fix --- .../infermeta/spmd_rules/flash_attention.cc | 50 +++++++++++++++---- 1 file changed, 41 insertions(+), 9 deletions(-) diff --git a/paddle/phi/infermeta/spmd_rules/flash_attention.cc b/paddle/phi/infermeta/spmd_rules/flash_attention.cc index 74796baa4564cc..e755138d6d0e45 100644 --- a/paddle/phi/infermeta/spmd_rules/flash_attention.cc +++ b/paddle/phi/infermeta/spmd_rules/flash_attention.cc @@ -227,6 +227,9 @@ SpmdInfo FlashAttInferSpmd(const DistMetaTensor& q, // [batch_size, seq_len_kv, num_heads, head_dim_v] std::string v_axes = { batch_axis, seq_len_kv_axis, num_heads_axis, head_dim_v_axis}; + // [batch_size, num_heads, seq_len_q, seq_len_kv] + std::string attn_mask_axes = { + batch_axis, num_heads_axis, seq_len_q_axis, seq_len_kv_axis}; // [batch_size, seq_len_q, num_heads, head_dim_v] std::string out_axes = { batch_axis, seq_len_q_axis, num_heads_axis, head_dim_v_axis}; @@ -239,11 +242,18 @@ SpmdInfo FlashAttInferSpmd(const DistMetaTensor& q, auto q_dist_attr_dst = UnShardTensorDims(q_dist_attr, {1, 3}); auto k_dist_attr_dst = UnShardTensorDims(k_dist_attr, {1, 3}); auto v_dist_attr_dst = UnShardTensorDims(k_dist_attr, {1, 3}); + auto attn_mask_dist_attr_dst = attn_mask_dist_attr; + if (!IsEmpty(attn_mask_shape)) { + attn_mask_dist_attr_dst = UnShardTensorDims(attn_mask_dist_attr, {2, 3}); + } if (!is_same_num_heads && !is_divisible) { - q_dist_attr_dst = UnShardTensorDims(q_dist_attr, {2}); - k_dist_attr_dst = UnShardTensorDims(k_dist_attr, {2}); - v_dist_attr_dst = UnShardTensorDims(k_dist_attr, {2}); + q_dist_attr_dst = UnShardTensorDims(q_dist_attr_dst, {2}); + k_dist_attr_dst = UnShardTensorDims(k_dist_attr_dst, {2}); + v_dist_attr_dst = UnShardTensorDims(v_dist_attr_dst, {2}); + if (!IsEmpty(attn_mask_shape)) { + attn_mask_dist_attr_dst = UnShardTensorDims(attn_mask_dist_attr_dst, {1}); + } } std::vector>> axes_sharding_info; @@ -251,16 +261,23 @@ SpmdInfo FlashAttInferSpmd(const DistMetaTensor& q, axes_sharding_info.emplace_back(q_axes, q_dist_attr_dst.dims_mapping()); axes_sharding_info.emplace_back(k_axes, k_dist_attr_dst.dims_mapping()); axes_sharding_info.emplace_back(v_axes, v_dist_attr_dst.dims_mapping()); + if (!IsEmpty(attn_mask_shape)) { + axes_sharding_info.emplace_back(attn_mask_axes, + attn_mask_dist_attr_dst.dims_mapping()); + } auto axis_to_dim_map = ShardingMergeForTensors(axes_sharding_info); q_dist_attr_dst = MapDims(q_dist_attr, axis_to_dim_map, q_axes); k_dist_attr_dst = MapDims(k_dist_attr, axis_to_dim_map, k_axes); v_dist_attr_dst = MapDims(v_dist_attr, axis_to_dim_map, v_axes); + if (!IsEmpty(attn_mask_shape)) { + attn_mask_dist_attr_dst = + MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + } - // TODO(liuzhenhai): process fixed_seed and attn_mask + // TODO(liuzhenhai): process fixed_seed auto fixed_seed_offset_dist_attr_dst = fixed_seed_offset_dist_attr; - auto attn_mask_dist_attr_dst = attn_mask_dist_attr; auto out = MapDims(q_dist_attr, axis_to_dim_map, out_axes); auto softmax = MapDims(q_dist_attr, axis_to_dim_map, softmax_axes); @@ -459,6 +476,11 @@ SpmdInfo FlashAttInferSpmdReverse(const DistMetaTensor& q, // [batch_size, seq_len_kv, num_heads, head_dim_v] std::string v_axes = { batch_axis, seq_len_kv_axis, num_heads_axis, head_dim_v_axis}; + + // [batch_size, num_heads, seq_len_q, seq_len_kv] + std::string attn_mask_axes = { + batch_axis, num_heads_axis, seq_len_q_axis, seq_len_kv_axis}; + // [batch_size, seq_len_q, num_heads, head_dim_v] std::string out_axes = { batch_axis, seq_len_q_axis, num_heads_axis, head_dim_v_axis}; @@ -501,11 +523,14 @@ SpmdInfo FlashAttInferSpmdReverse(const DistMetaTensor& q, out_dist_attr_dst = MapDims(out_dist_attr_dst, axis_to_dim_map, out_axes); softmax_lse_dist_attr_dst = MapDims(softmax_lse_dist_attr_dst, axis_to_dim_map, softmax_lse_axes); + auto attn_mask_dist_attr_dst = attn_mask_dist_attr; + if (!IsEmpty(attn_mask_shape)) { + attn_mask_dist_attr_dst = + MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + } - // TODO(liuzhenhai): process fixed_seed and attn_mask - + // TODO(liuzhenhai): process fixed_seed auto fixed_seed_offset_dist_attr_dst = fixed_seed_offset_dist_attr; - auto attn_mask_dist_attr_dst = attn_mask_dist_attr; auto softmax_dist_attr_dst = softmax_dist_attr; auto seed_offset_dist_attr_dst = seed_offset_dist_attr; @@ -716,6 +741,9 @@ SpmdInfo FlashAttGradInferSpmd(const DistMetaTensor& q, // [batch_size, seq_len_kv, num_heads, head_dim_v] std::string v_axes = { batch_axis, seq_len_kv_axis, num_heads_axis, head_dim_v_axis}; + // [batch_size, num_heads, seq_len_q, seq_len_kv] + std::string attn_mask_axes = { + batch_axis, num_heads_axis, seq_len_q_axis, seq_len_kv_axis}; // [batch_size, seq_len_q, num_heads, head_dim_v] std::string out_axes = { batch_axis, seq_len_q_axis, num_heads_axis, head_dim_v_axis}; @@ -768,10 +796,14 @@ SpmdInfo FlashAttGradInferSpmd(const DistMetaTensor& q, out_dist_attr_dst = MapDims(out_dist_attr, axis_to_dim_map, out_axes); softmax_lse_dist_attr_dst = MapDims(softmax_lse_dist_attr, axis_to_dim_map, softmax_lse_axes); + auto attn_mask_dist_attr_dst = attn_mask_dist_attr; + if (!IsEmpty(attn_mask_shape)) { + attn_mask_dist_attr_dst = + MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + } // TODO(liuzhenhai): process seed and attn_mask auto& seed_offset_dist_attr_dst = seed_offset_dist_attr; - auto& attn_mask_dist_attr_dst = attn_mask_dist_attr; out_grad_dist_attr_dst = MapDims(out_dist_attr, axis_to_dim_map, out_axes); auto q_grad = MapDims(q_dist_attr, axis_to_dim_map, q_axes); From f553a716e156860a1f61c157c6007ba6e64b87b4 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 11:45:43 +0800 Subject: [PATCH 221/288] [CodeStyle][UP031] Use f-string instead of percent format in some framework dirs (part23) (#70035) --- python/paddle/hapi/model_summary.py | 2 +- python/paddle/hapi/progressbar.py | 14 ++++---- python/paddle/jit/translated_layer.py | 2 +- python/paddle/nn/functional/loss.py | 5 ++- python/paddle/nn/layer/layers.py | 3 +- python/paddle/nn/layer/transformer.py | 6 ++-- python/paddle/static/io.py | 4 +-- python/paddle/tensor/creation.py | 16 ++++----- python/paddle/tensor/einsum.py | 9 +++-- python/paddle/tensor/linalg.py | 34 ++++++++----------- python/paddle/tensor/manipulation.py | 24 +++++-------- python/paddle/tensor/math.py | 26 +++++++------- python/paddle/tensorrt/converter_utils.py | 6 ++-- python/paddle/tensorrt/impls/manipulation.py | 17 +++------- python/paddle/text/datasets/movielens.py | 14 ++------ python/paddle/text/datasets/wmt16.py | 4 +-- .../utils/cpp_extension/extension_utils.py | 3 +- python/paddle/utils/layers_utils.py | 3 +- python/paddle/vision/datasets/flowers.py | 2 +- 19 files changed, 78 insertions(+), 116 deletions(-) diff --git a/python/paddle/hapi/model_summary.py b/python/paddle/hapi/model_summary.py index 8ee3218865b022..144677d9a32d9e 100644 --- a/python/paddle/hapi/model_summary.py +++ b/python/paddle/hapi/model_summary.py @@ -466,7 +466,7 @@ def hook(layer, input, output): except: layer_idx = len(summary) - m_key = "%s-%i" % (class_name, layer_idx + 1) + m_key = f"{class_name}-{layer_idx + 1}" summary[m_key] = OrderedDict() try: diff --git a/python/paddle/hapi/progressbar.py b/python/paddle/hapi/progressbar.py index 00f5aa8ca091b7..167a8a9dc8b037 100644 --- a/python/paddle/hapi/progressbar.py +++ b/python/paddle/hapi/progressbar.py @@ -129,7 +129,7 @@ def convert_uint16_to_float(in_list): bar_chars += '.' * (self._width - prog_width) bar_chars += ']' else: - bar_chars = self.name + ' %3d' % current_num + bar_chars = f'{self.name} {current_num:3}' self._total_width = len(bar_chars) sys.stdout.write(bar_chars) @@ -149,15 +149,13 @@ def convert_uint16_to_float(in_list): if self._num is not None and current_num < self._num: eta = time_per_unit * (self._num - current_num) if eta > 3600: - eta_format = '%d:%02d:%02d' % ( - eta // 3600, - (eta % 3600) // 60, - eta % 60, + eta_format = ( + f'{eta // 3600}:{(eta % 3600) // 60:02}:{eta % 60:02}' ) elif eta > 60: - eta_format = '%d:%02d' % (eta // 60, eta % 60) + eta_format = f'{eta // 60}:{eta % 60:02}' else: - eta_format = '%ds' % eta + eta_format = f'{eta}s' info += f' - ETA: {eta_format}' @@ -183,7 +181,7 @@ def convert_uint16_to_float(in_list): self._num, ) else: - count = self.name + ' %3d' % current_num + count = f'{self.name} {current_num:3}' info = count + info for k, val in values: diff --git a/python/paddle/jit/translated_layer.py b/python/paddle/jit/translated_layer.py index 4a880d214b5fa8..488beae656c0c2 100644 --- a/python/paddle/jit/translated_layer.py +++ b/python/paddle/jit/translated_layer.py @@ -51,7 +51,7 @@ def _load_program_desc(model_file_path): program_desc = core.ProgramDesc(program_desc_str) if not core._is_program_version_supported(program_desc._version()): raise ValueError( - "Unsupported program version: %d\n" % program_desc._version() + f"Unsupported program version: {program_desc._version()}\n" ) return program_desc diff --git a/python/paddle/nn/functional/loss.py b/python/paddle/nn/functional/loss.py index 4f39cd0395635c..e99f584712fef0 100644 --- a/python/paddle/nn/functional/loss.py +++ b/python/paddle/nn/functional/loss.py @@ -101,12 +101,11 @@ def dice_loss( ), "The rank of input should be greater than or equal to 2." assert len(input.shape) == len(label.shape), ( "The rank of input and label should be equal, " - "but received input: %d, label: %d." - % (len(input.shape), len(label.shape)) + f"but received input: {len(input.shape)}, label: {len(label.shape)}." ) assert label.shape[-1] == 1, ( "The last dimension of label should be 1, " - "but received %d." % label.shape[-1] + f"but received {label.shape[-1]}." ) assert ( input.shape[:-1] == label.shape[:-1] diff --git a/python/paddle/nn/layer/layers.py b/python/paddle/nn/layer/layers.py index 2ac94dd9aa7c0d..2b9e94472914a8 100644 --- a/python/paddle/nn/layer/layers.py +++ b/python/paddle/nn/layer/layers.py @@ -246,8 +246,7 @@ def input_dtype(self, inputs_in): dtype = each.dtype elif dtype != each.dtype: raise ValueError( - "Data Type mismatch: %d to %d in %s" - % (dtype, each.dtype, self.name) + f"Data Type mismatch: {dtype} to {each.dtype} in {self.name}" ) return dtype diff --git a/python/paddle/nn/layer/transformer.py b/python/paddle/nn/layer/transformer.py index d3eed7940ce73a..539aa3d68f531d 100644 --- a/python/paddle/nn/layer/transformer.py +++ b/python/paddle/nn/layer/transformer.py @@ -70,9 +70,9 @@ def _convert_param_attr_to_list(param_attr, n): list: A list composed of each including cell's `param_attr`. """ if isinstance(param_attr, (list, tuple)): - assert len(param_attr) == n, ( - "length of param_attr should be %d when it is a list/tuple" % n - ) + assert ( + len(param_attr) == n + ), f"length of param_attr should be {n} when it is a list/tuple" param_attrs = [] for attr in param_attr: if isinstance(attr, bool): diff --git a/python/paddle/static/io.py b/python/paddle/static/io.py index b05dc4aa65ad0d..feca1a3ac58027 100644 --- a/python/paddle/static/io.py +++ b/python/paddle/static/io.py @@ -692,9 +692,7 @@ def deserialize_program(data: bytes) -> Program: """ program = Program.parse_from_string(data) if not core._is_program_version_supported(program._version()): - raise ValueError( - "Unsupported program version: %d\n" % program._version() - ) + raise ValueError(f"Unsupported program version: {program._version()}\n") return program diff --git a/python/paddle/tensor/creation.py b/python/paddle/tensor/creation.py index 28cdf43a4121e2..c02da17888cd94 100644 --- a/python/paddle/tensor/creation.py +++ b/python/paddle/tensor/creation.py @@ -2066,21 +2066,19 @@ def __check_input(input, offset, dim1, dim2): f"But received Input's dimensional: {len(input_shape)}.\n" ) - assert np.abs(dim1) <= len(input_shape), ( - "Dim1 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape) + 1), len(input_shape), dim1) - ) + assert np.abs(dim1) <= len( + input_shape + ), f"Dim1 is out of range (expected to be in range of [{-(len(input_shape) + 1)}, {len(input_shape)}], but got {dim1}).\n" - assert np.abs(dim2) <= len(input_shape), ( - "Dim2 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape) + 1), len(input_shape), dim2) - ) + assert np.abs(dim2) <= len( + input_shape + ), f"Dim2 is out of range (expected to be in range of [{-(len(input_shape) + 1)}, {len(input_shape)}], but got {dim2}).\n" dim1_ = dim1 if dim1 >= 0 else len(input_shape) + dim1 + 1 dim2_ = dim2 if dim2 >= 0 else len(input_shape) + dim2 + 1 assert dim1_ != dim2_, ( "dim1 and dim2 cannot be the same dimension." - "But received dim1 = %d, dim2 = %d\n" % (dim1, dim2) + f"But received dim1 = {dim1}, dim2 = {dim2}\n" ) __check_input(input, offset, dim1, dim2) diff --git a/python/paddle/tensor/einsum.py b/python/paddle/tensor/einsum.py index 086b7bf72a923f..b5eebf10050348 100644 --- a/python/paddle/tensor/einsum.py +++ b/python/paddle/tensor/einsum.py @@ -838,10 +838,9 @@ def fake_shape(ori_label: str, label: str, op: Tensor) -> Shaped: 1. ori_label is the original labels, not aligned by '....' 2. if the '...' is evaluated to empty list, there is no '.' in label """ - assert len(op.shape) == len(label), ( - "length of shape and length of label must be the same, but received %d != %d" - % (len(op.shape), len(label)) - ) + assert len(op.shape) == len( + label + ), f"length of shape and length of label must be the same, but received {len(op.shape)} != {len(label)}" fakes = [s for i, (l, s) in enumerate(zip(label, op.shape))] fakes = list(map(abs, fakes)) # make -1 -> 1 if '.' in ori_label: @@ -913,7 +912,7 @@ def einsum_v2(equation: str, *operands: Tensor) -> Tensor: var_list.append(gen_einsum_op(eq, *var_s)) assert ( len(var_list) == 1 - ), "There must be one elements in list, but received %d." % len(var_list) + ), f"There must be one elements in list, but received {len(var_list)}." return var_list[0] diff --git a/python/paddle/tensor/linalg.py b/python/paddle/tensor/linalg.py index de8fe29018ee09..93d7d279bf5e76 100644 --- a/python/paddle/tensor/linalg.py +++ b/python/paddle/tensor/linalg.py @@ -162,8 +162,8 @@ def transpose( if dim >= len(x.shape): raise ValueError( "Each element in Input(perm) should be less than Input(x)'s dimension, " - "but %d-th element in Input(perm) is %d which exceeds Input(x)'s " - "dimension %d." % (idx, perm[idx], len(x.shape)) + f"but {idx}-th element in Input(perm) is {perm[idx]} which exceeds Input(x)'s " + f"dimension {len(x.shape)}." ) helper = LayerHelper('transpose', **locals()) @@ -5560,7 +5560,7 @@ def __check_ranges(D, ranges): check_type(ranges, 'ranges', (list, tuple), 'histogramdd') assert D * 2 == len( ranges - ), "The length of ranges list must be %d\n" % (D * 2) + ), f"The length of ranges list must be {D * 2}\n" check_type(density, 'density', bool, 'histogramdd') @@ -5574,9 +5574,7 @@ def __check_ranges(D, ranges): if weights is not None: weights = weights.astype(x.dtype) reshaped_weights = weights.reshape([N]) - assert reshaped_weights.shape[0] == N, ( - "The size of weight must be %d" % N - ) + assert reshaped_weights.shape[0] == N, f"The size of weight must be {N}" # ranges __check_ranges(D, ranges) if ranges is None: @@ -5599,13 +5597,13 @@ def __check_ranges(D, ranges): if isinstance(bins, (int, list)): # int or int[] if isinstance(bins, int): bins = [bins] * D - assert len(bins) == D, ( - "The length of bins must be %d when bins is a list.\n" % D - ) + assert ( + len(bins) == D + ), f"The length of bins must be {D} when bins is a list.\n" for idx, r in enumerate(ranges): if not isinstance(bins[idx], int): raise ValueError( - "The type of %d-th element in bins list must be int." % idx + f"The type of {idx}-th element in bins list must be int." ) e = paddle.linspace(r[0], r[1], bins[idx] + 1, x.dtype) edges.append(e) @@ -5931,19 +5929,17 @@ def __check_input(x, offset, axis1, axis2): axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1 axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2 - assert axis1_ < len(input_shape), ( - "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis1) - ) + assert axis1_ < len( + input_shape + ), f"The argument axis1 is out of range (expected to be in range of [{-(len(input_shape))}, {len(input_shape) - 1}], but got {axis1}).\n" - assert axis2_ < len(input_shape), ( - "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis2) - ) + assert axis2_ < len( + input_shape + ), f"The argument axis2 is out of range (expected to be in range of [{-(len(input_shape))}, {len(input_shape) - 1}], but got {axis2}).\n" assert axis1_ != axis2_, ( "axis1 and axis2 cannot be the same axis." - "But received axis1 = %d, axis2 = %d\n" % (axis1, axis2) + f"But received axis1 = {axis1}, axis2 = {axis2}\n" ) __check_input(x, offset, axis1, axis2) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index d4375c7b918dcd..a20f4e3e0ea3bc 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -613,8 +613,8 @@ def transpose( if dim >= len(x.shape): raise ValueError( "Each element in Input(perm) should be less than Input(x)'s dimension, " - "but %d-th element in Input(perm) is %d which exceeds Input(x)'s " - "dimension %d." % (idx, perm[idx], len(x.shape)) + f"but {idx}-th element in Input(perm) is {perm[idx]} which exceeds Input(x)'s " + f"dimension {len(x.shape)}." ) helper = LayerHelper('transpose', **locals()) @@ -748,7 +748,7 @@ def shard_index( helper = LayerHelper(op_type, **locals()) if shard_id < 0 or shard_id >= nshards: raise ValueError( - 'The shard_id(%d) should be in [0, %d)' % (shard_id, nshards) + f'The shard_id({shard_id}) should be in [0, {nshards})' ) out = helper.create_variable_for_type_inference(dtype=input.dtype) @@ -2788,8 +2788,7 @@ def split( assert input_shape[dim] % num_or_sections == 0, ( "The input's size along the split dimension " "must be evenly divisible by Attr(num_or_sections). " - "But %d is not evenly divisible by %d. " - % (num_or_sections, input_shape[dim]) + f"But {num_or_sections} is not evenly divisible by {input_shape[dim]}. " ) return _C_ops.split_with_num(input, num_or_sections, dim) else: @@ -2848,8 +2847,7 @@ def _get_SectionsTensorList(one_list): if dim_size == -1: assert unk_dim_idx == -1, ( "Only one value of 'num_or_section' in split can " - "be -1. But received num_or_section[%d] is also -1." - % idx + f"be -1. But received num_or_section[{idx}] is also -1." ) unk_dim_idx = idx temp_out = helper.create_variable_for_type_inference( @@ -2875,8 +2873,7 @@ def _get_SectionsTensorList(one_list): assert input_shape[dim] % num_or_sections == 0, ( "The input's size along the split dimension " "must be evenly divisible by Attr(num_or_sections). " - "But %d is not evenly divisible by %d. " - % (num_or_sections, input_shape[dim]) + f"But {num_or_sections} is not evenly divisible by {input_shape[dim]}. " ) num = num_or_sections else: @@ -4953,14 +4950,13 @@ def get_attr_shape(list_shape): if dim_size == -1: assert unk_dim_idx == -1, ( "Only one dimension value of 'shape' in reshape can " - "be -1. But received shape[%d] is also -1.\n" + f"be -1. But received shape[{dim_idx}] is also -1.\n" "\n\t# N = x.shape()[2]\t\t# N is an int. " "(NOT recommend under @to_static)\n\tN = paddle.shape(x)[2]\t\t" "# N is a Tensor. (Recommend)\n\tz = paddle.reshape([N, -1, 4])" "\t# z.shape is [-1, -1, 4]\n\n" " If your target shape in Reshape represents dynamic shape, " "please turn it into a Tensor under @to_static. See above example for details." - % dim_idx ) unk_dim_idx = dim_idx elif dim_size == 0: @@ -4968,15 +4964,13 @@ def get_attr_shape(list_shape): assert dim_idx < len(x.shape), ( "The index of 0 in `shape` must be less than " "the input tensor X's dimensions. " - "But received shape[%d] = 0, X's dimensions = %d." - % (dim_idx, len(x.shape)) + f"But received shape[{dim_idx}] = 0, X's dimensions = {len(x.shape)}." ) else: assert dim_size > 0, ( "Each dimension value of 'shape' in reshape must not " "be negative except one unknown dimension. " - "But received shape[%d] = %s." - % (dim_idx, str(dim_size)) + f"But received shape[{dim_idx}] = {dim_size!s}." ) return attrs_shape diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index ea527f34e0c089..6172cd7849ae68 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -2365,8 +2365,8 @@ def __check_input(x, y): raise ValueError( "When the matrix is larger than 2 dimensions, the higher " "dimensional values of the two matrices need to be equal. " - "But received x_shape[%d] != y_shape[%d]. X's shape: %s, " - "Y's shape: %s.\n" % (i, i, x_shape, y_shape) + f"But received x_shape[{i}] != y_shape[{i}]. X's shape: {x_shape}, " + f"Y's shape: {y_shape}.\n" ) __check_input(input, mat2) @@ -2890,8 +2890,8 @@ def _check_input(x): if len(x.shape) < 2: raise ValueError( "The input of inverse is expected to be a Tensor whose number " - "of dimensions is no less than 2. But received: %d, " - "x's shape: %s." % (len(x.shape), x.shape) + f"of dimensions is no less than 2. But received: {len(x.shape)}, " + f"x's shape: {x.shape}." ) _check_input(x) @@ -3956,19 +3956,17 @@ def __check_input(x, offset, axis1, axis2): axis1_ = axis1 if axis1 >= 0 else len(input_shape) + axis1 axis2_ = axis2 if axis2 >= 0 else len(input_shape) + axis2 - assert (0 <= axis1_) and (axis1_ < len(input_shape)), ( - "The argument axis1 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis1) - ) + assert (0 <= axis1_) and ( + axis1_ < len(input_shape) + ), f"The argument axis1 is out of range (expected to be in range of [{-(len(input_shape))}, {len(input_shape) - 1}], but got {axis1}).\n" - assert (0 <= axis2_) and (axis2_ < len(input_shape)), ( - "The argument axis2 is out of range (expected to be in range of [%d, %d], but got %d).\n" - % (-(len(input_shape)), len(input_shape) - 1, axis2) - ) + assert (0 <= axis2_) and ( + axis2_ < len(input_shape) + ), f"The argument axis2 is out of range (expected to be in range of [{-(len(input_shape))}, {len(input_shape) - 1}], but got {axis2}).\n" assert axis1_ != axis2_, ( "axis1 and axis2 cannot be the same axis." - "But received axis1 = %d, axis2 = %d\n" % (axis1, axis2) + f"But received axis1 = {axis1}, axis2 = {axis2}\n" ) if in_dynamic_or_pir_mode(): @@ -7206,7 +7204,7 @@ def vander( if x.dim() != 1: raise ValueError( "The input of x is expected to be a 1-D Tensor." - "But now the dims of Input(X) is %d." % x.dim() + f"But now the dims of Input(X) is {x.dim()}." ) if n is None: diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index 49a0346e236bfe..dde1bf1f9bd3af 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -263,9 +263,9 @@ def trt_reshape(network, input, new_shape, name="", is_shape_tensor=False): # Get element tensor of 1D shape tensor def get_shape_tensor_element(network, x, index, is_scalar=False): - assert index >= 0, ( - "The index should be greater or equal than 0, but got %d" % index - ) + assert ( + index >= 0 + ), f"The index should be greater or equal than 0, but got {index}" index_tensor = add_1D_constant_layer(network, index, is_scalar=is_scalar) gather_layer = network.add_gather(input=x, indices=index_tensor, axis=0) return gather_layer.get_output(0) diff --git a/python/paddle/tensorrt/impls/manipulation.py b/python/paddle/tensorrt/impls/manipulation.py index 255104b51a17f7..76016bad3b5870 100644 --- a/python/paddle/tensorrt/impls/manipulation.py +++ b/python/paddle/tensorrt/impls/manipulation.py @@ -193,10 +193,9 @@ def unsqueeze_converter(network, paddle_op, inputs): x = inputs[0] input_dims = x.shape axes = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] - assert len(axes) > 0, ( - "axes size should be > 0 in when convert unsqueeze op in TensorRT, but received len(axes) = %d." - % (len(axes)) - ) + assert ( + len(axes) > 0 + ), f"axes size should be > 0 in when convert unsqueeze op in TensorRT, but received len(axes) = {len(axes)}." should_unsqueeze = [False] * (len(input_dims) + len(axes)) cur_out_rank = len(input_dims) @@ -352,10 +351,7 @@ def slice_converter(network, paddle_op, inputs): starts = starts_op.attrs()["value"] assert len(starts) == len( axes - ), "The size of this starts: %d must be equal to the axes: %d." % ( - len(starts), - len(axes), - ) + ), f"The size of this starts: {len(starts)} must be equal to the axes: {len(axes)}." for idx in range(len(axes)): if starts[idx] < 0: starts_tensor[axes[idx]] = trt_max( @@ -388,10 +384,7 @@ def slice_converter(network, paddle_op, inputs): ends = ends_op.attrs()["value"] assert len(ends) == len( axes - ), "The size of this ends: %d must be equal to the axes: %d." % ( - len(ends), - len(axes), - ) + ), f"The size of this ends: {len(ends)} must be equal to the axes: {len(axes)}." for idx in range(len(axes)): if ends[idx] < 0: ends_tensor[axes[idx]] = trt_max( diff --git a/python/paddle/text/datasets/movielens.py b/python/paddle/text/datasets/movielens.py index 405a31aca83e20..65c8c6cebedd58 100644 --- a/python/paddle/text/datasets/movielens.py +++ b/python/paddle/text/datasets/movielens.py @@ -59,11 +59,7 @@ def value(self, categories_dict, movie_title_dict): ] def __str__(self) -> str: - return "" % ( - self.index, - self.title, - self.categories, - ) + return f"" def __repr__(self) -> str: return self.__str__() @@ -97,12 +93,8 @@ def value(self): ] def __str__(self) -> str: - return "" % ( - self.index, - "M" if self.is_male else "F", - age_table[self.age], - self.job_id, - ) + gender = "M" if self.is_male else "F" + return f"" def __repr__(self) -> str: return str(self) diff --git a/python/paddle/text/datasets/wmt16.py b/python/paddle/text/datasets/wmt16.py index 798f4bcd176b00..839d731bfaba86 100644 --- a/python/paddle/text/datasets/wmt16.py +++ b/python/paddle/text/datasets/wmt16.py @@ -192,7 +192,7 @@ def _load_dict( def _load_dict(self, lang, dict_size, reverse=False): dict_path = os.path.join( paddle.dataset.common.DATA_HOME, - "wmt16/%s_%d.dict" % (lang, dict_size), + f"wmt16/{lang}_{dict_size}.dict", ) dict_found = False if os.path.exists(dict_path): @@ -331,7 +331,7 @@ def get_dict(self, lang, reverse=False): dict_path = os.path.join( paddle.dataset.common.DATA_HOME, - "wmt16/%s_%d.dict" % (lang, dict_size), + f"wmt16/{lang}_{dict_size}.dict", ) assert os.path.exists(dict_path), "Word dictionary does not exist. " "Please invoke paddle.dataset.wmt16.train/test/validation first " diff --git a/python/paddle/utils/cpp_extension/extension_utils.py b/python/paddle/utils/cpp_extension/extension_utils.py index f9cb07456f20da..bdd01e1304a37b 100644 --- a/python/paddle/utils/cpp_extension/extension_utils.py +++ b/python/paddle/utils/cpp_extension/extension_utils.py @@ -225,8 +225,7 @@ def __bootstrap__(): for op_name in new_custom_ops: api_content.append(_custom_api_content(op_name)) print( - "Received len(custom_op) = %d, using custom operator" - % len(new_custom_ops) + f"Received len(custom_op) = {len(new_custom_ops)}, using custom operator" ) with open(pyfile, 'w') as f: diff --git a/python/paddle/utils/layers_utils.py b/python/paddle/utils/layers_utils.py index fae4eeb0b05d20..5cafe48bb57d0d 100644 --- a/python/paddle/utils/layers_utils.py +++ b/python/paddle/utils/layers_utils.py @@ -228,8 +228,7 @@ def pack_sequence_as(structure, flat_sequence): if not is_sequence(structure): if len(flat_sequence) != 1: raise ValueError( - "Structure is a scalar but len(flat_sequence) == %d > 1" - % len(flat_sequence) + f"Structure is a scalar but len(flat_sequence) == {len(flat_sequence)} > 1" ) return flat_sequence[0] flat_structure = flatten(structure) diff --git a/python/paddle/vision/datasets/flowers.py b/python/paddle/vision/datasets/flowers.py index ec95f78895f2ae..6e4966e778debf 100644 --- a/python/paddle/vision/datasets/flowers.py +++ b/python/paddle/vision/datasets/flowers.py @@ -194,7 +194,7 @@ def __getitem__( ) -> tuple[_ImageDataType, npt.NDArray[np.int64]]: index = self.indexes[idx] label = np.array([self.labels[index - 1]]) - img_name = "jpg/image_%05d.jpg" % index + img_name = f"jpg/image_{index:05}.jpg" image = os.path.join(self.data_path, img_name) if self.backend == 'pil': image = Image.open(image) From 79e47171482dc7dcbc9842942badf397e2f53a5c Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 12:33:00 +0800 Subject: [PATCH 222/288] [CodeStyle][UP031] Use f-string instead of percent format in dy2st uts (part25) (#70037) --- test/dygraph_to_static/darknet.py | 6 ++-- .../dygraph_to_static/simnet_dygraph_model.py | 2 +- test/dygraph_to_static/test_bert.py | 6 ++-- test/dygraph_to_static/test_cycle_gan.py | 2 +- test/dygraph_to_static/test_mobile_net.py | 16 +++------ test/dygraph_to_static/test_ptb_lm.py | 15 +++------ test/dygraph_to_static/test_resnet.py | 16 ++++----- test/dygraph_to_static/test_resnet_amp.py | 14 +++----- .../test_resnet_pure_fp16.py | 14 +++----- test/dygraph_to_static/test_se_resnet.py | 28 ++++++---------- test/dygraph_to_static/test_seq2seq.py | 14 +++----- test/dygraph_to_static/test_transformer.py | 33 +++++-------------- test/dygraph_to_static/test_tsm.py | 2 +- test/dygraph_to_static/test_word2vec.py | 6 ++-- .../transformer_dygraph_model.py | 6 ++-- test/dygraph_to_static/yolov3.py | 6 ++-- 16 files changed, 63 insertions(+), 123 deletions(-) diff --git a/test/dygraph_to_static/darknet.py b/test/dygraph_to_static/darknet.py index 7606407a186151..5f1afdb1cae699 100644 --- a/test/dygraph_to_static/darknet.py +++ b/test/dygraph_to_static/darknet.py @@ -124,7 +124,7 @@ def __init__(self, ch_in, ch_out, count, is_test=True): self.res_out_list = [] for i in range(1, count): res_out = self.add_sublayer( - "basic_block_%d" % (i), + f"basic_block_{i}", BasicBlock(ch_out * 2, ch_out, is_test=is_test), ) self.res_out_list.append(res_out) @@ -161,13 +161,13 @@ def __init__(self, ch_in=3, is_test=True): ch_in = [64, 128, 256, 512, 1024] for i, stage in enumerate(self.stages): conv_block = self.add_sublayer( - "stage_%d" % (i), + f"stage_{i}", LayerWarp(int(ch_in[i]), 32 * (2**i), stage, is_test=is_test), ) self.darknet53_conv_block_list.append(conv_block) for i in range(len(self.stages) - 1): downsample = self.add_sublayer( - "stage_%d_downsample" % i, + f"stage_{i}_downsample", DownSample( ch_in=32 * (2 ** (i + 1)), ch_out=32 * (2 ** (i + 2)), diff --git a/test/dygraph_to_static/simnet_dygraph_model.py b/test/dygraph_to_static/simnet_dygraph_model.py index abcc49a84ed29e..35262bd77e8397 100644 --- a/test/dygraph_to_static/simnet_dygraph_model.py +++ b/test/dygraph_to_static/simnet_dygraph_model.py @@ -345,7 +345,7 @@ def _build_once(self, input): ] self.__w.append( self.add_parameter( - '_w%d' % i, + f'_w{i}', self.create_parameter( attr=param, shape=param_shape, diff --git a/test/dygraph_to_static/test_bert.py b/test/dygraph_to_static/test_bert.py index e20371c9812d2a..552cb780efd57d 100644 --- a/test/dygraph_to_static/test_bert.py +++ b/test/dygraph_to_static/test_bert.py @@ -147,16 +147,14 @@ def train(self, bert_config, data_reader, to_static): if step_idx % PRINT_STEP == 0: if step_idx == 0: print( - "Step: %d, loss: %f, ppl: %f, next_sent_acc: %f" - % (step_idx, loss, ppl, acc) + f"Step: {step_idx}, loss: {loss:f}, ppl: {ppl:f}, next_sent_acc: {acc:f}" ) avg_batch_time = time.time() else: speed = PRINT_STEP / (time.time() - avg_batch_time) speed_list.append(speed) print( - "Step: %d, loss: %f, ppl: %f, next_sent_acc: %f, speed: %.3f steps/s" - % (step_idx, loss, ppl, acc, speed) + f"Step: {step_idx}, loss: {loss:f}, ppl: {ppl:f}, next_sent_acc: {acc:f}, speed: {speed:.3f} steps/s" ) avg_batch_time = time.time() diff --git a/test/dygraph_to_static/test_cycle_gan.py b/test/dygraph_to_static/test_cycle_gan.py index 345f9e01c5f81a..c6e8d821bc3bc0 100644 --- a/test/dygraph_to_static/test_cycle_gan.py +++ b/test/dygraph_to_static/test_cycle_gan.py @@ -207,7 +207,7 @@ def __init__(self, input_channel): dim = 128 for i in range(9): Build_Resnet_Block = self.add_sublayer( - "generator_%d" % (i + 1), build_resnet_block(dim) + f"generator_{i + 1}", build_resnet_block(dim) ) self.build_resnet_block_list.append(Build_Resnet_Block) self.deconv0 = DeConv2D( diff --git a/test/dygraph_to_static/test_mobile_net.py b/test/dygraph_to_static/test_mobile_net.py index a9e9afb7050c25..9e73cf4d7a5fc8 100644 --- a/test/dygraph_to_static/test_mobile_net.py +++ b/test/dygraph_to_static/test_mobile_net.py @@ -578,18 +578,10 @@ def train_mobilenet(args, to_static): train_batch_elapse = t2 - t1 if batch_id % args.print_step == 0: print( - "epoch id: %d, batch step: %d, avg_loss %0.5f acc_top1 %0.5f acc_top5 %0.5f %2.4f sec net_t:%2.4f back_t:%2.4f read_t:%2.4f" - % ( - eop, - batch_id, - avg_loss.numpy(), - acc_top1.numpy(), - acc_top5.numpy(), - train_batch_elapse, - t_end - t_start, - t_end_back - t_start_back, - t1 - t_last, - ) + f"epoch id: {eop}, batch step: {batch_id}, avg_loss {avg_loss.numpy():0.5f} " + f"acc_top1 {acc_top1.numpy():0.5f} acc_top5 {acc_top5.numpy():0.5f} " + f"{train_batch_elapse:2.4f} sec net_t:{t_end - t_start:2.4f} " + f"back_t:{t_end_back - t_start_back:2.4f} read_t:{t1 - t_last:2.4f}" ) batch_id += 1 t_last = time.time() diff --git a/test/dygraph_to_static/test_ptb_lm.py b/test/dygraph_to_static/test_ptb_lm.py index dfc23312cdb965..a254e9be7bf84c 100644 --- a/test/dygraph_to_static/test_ptb_lm.py +++ b/test/dygraph_to_static/test_ptb_lm.py @@ -61,7 +61,7 @@ def __init__( low=-self._init_scale, high=self._init_scale ), ) - self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) + self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1)) bias_1 = self.create_parameter( attr=paddle.ParamAttr( initializer=paddle.nn.initializer.Uniform( @@ -72,7 +72,7 @@ def __init__( dtype="float32", default_initializer=paddle.nn.initializer.Constant(0.0), ) - self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) + self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): cell_array = [] @@ -292,20 +292,13 @@ def train(): if step_id % PRINT_STEP == 0: if step_id == 0: logging.info( - "epoch %d | step %d, loss %0.3f" - % (epoch_id, step_id, total_loss / total_sample) + f"epoch {epoch_id} | step {step_id}, loss {total_loss / total_sample:0.3f}" ) avg_batch_time = time.time() else: speed = PRINT_STEP / (time.time() - avg_batch_time) logging.info( - "epoch %d | step %d, loss %0.3f, speed %.3f steps/s" - % ( - epoch_id, - step_id, - total_loss / total_sample, - speed, - ) + f"epoch {epoch_id} | step {step_id}, loss {total_loss / total_sample:0.3f}, speed {speed:.3f} steps/s" ) avg_batch_time = time.time() diff --git a/test/dygraph_to_static/test_resnet.py b/test/dygraph_to_static/test_resnet.py index 3cc6fbfc556ce2..dc6c412e552f09 100644 --- a/test/dygraph_to_static/test_resnet.py +++ b/test/dygraph_to_static/test_resnet.py @@ -177,7 +177,7 @@ def __init__(self, layers=50, class_dim=102): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=( num_channels[block] @@ -333,15 +333,11 @@ def train(self, to_static, build_strategy=None): end_time = time.time() if batch_id % 2 == 0: print( - "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - epoch, - batch_id, - total_loss.numpy() / total_sample, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"epoch {epoch} | batch step {batch_id}, " + f"loss {total_loss.numpy() / total_sample:0.3f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id == 10: if to_static: diff --git a/test/dygraph_to_static/test_resnet_amp.py b/test/dygraph_to_static/test_resnet_amp.py index 2aa3fad362f079..4281613e7b205b 100644 --- a/test/dygraph_to_static/test_resnet_amp.py +++ b/test/dygraph_to_static/test_resnet_amp.py @@ -95,15 +95,11 @@ def train(build_strategy=None): end_time = time.time() if batch_id % 2 == 0: print( - "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - epoch, - batch_id, - total_loss.numpy() / total_sample, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"epoch {epoch} | batch step {batch_id}, " + f"loss {total_loss.numpy() / total_sample:0.3f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id == 10: break diff --git a/test/dygraph_to_static/test_resnet_pure_fp16.py b/test/dygraph_to_static/test_resnet_pure_fp16.py index 439a6d3129b611..9c0f556991f58a 100644 --- a/test/dygraph_to_static/test_resnet_pure_fp16.py +++ b/test/dygraph_to_static/test_resnet_pure_fp16.py @@ -98,15 +98,11 @@ def train(build_strategy=None): end_time = time.time() if batch_id % 2 == 0: print( - "epoch %d | batch step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - epoch, - batch_id, - total_loss.numpy() / total_sample, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"epoch {epoch} | batch step {batch_id}, " + f"loss {total_loss.numpy() / total_sample:0.3f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id == 10: break diff --git a/test/dygraph_to_static/test_se_resnet.py b/test/dygraph_to_static/test_se_resnet.py index 9fa2d57f89e388..ef56e7b82184ed 100644 --- a/test/dygraph_to_static/test_se_resnet.py +++ b/test/dygraph_to_static/test_se_resnet.py @@ -295,7 +295,7 @@ def __init__(self, layers=50, class_dim=102): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=num_channels, num_filters=num_filters[block], @@ -424,29 +424,21 @@ def train(self, train_reader, to_static): if step_id % PRINT_STEP == 0: if step_id == 0: logging.info( - "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f" - % ( - epoch_id, - step_id, - total_loss / total_sample, - total_acc1 / total_sample, - total_acc5 / total_sample, - ) + f"epoch {epoch_id} | step {step_id}, " + f"loss {total_loss / total_sample:0.3f}, " + f"acc1 {total_acc1 / total_sample:0.3f}, " + f"acc5 {total_acc5 / total_sample:0.3f}" ) avg_batch_time = time.time() else: speed = PRINT_STEP / (time.time() - avg_batch_time) speed_list.append(speed) logging.info( - "epoch %d | step %d, loss %0.3f, acc1 %0.3f, acc5 %0.3f, speed %.3f steps/s" - % ( - epoch_id, - step_id, - total_loss / total_sample, - total_acc1 / total_sample, - total_acc5 / total_sample, - speed, - ) + f"epoch {epoch_id} | step {step_id}, " + f"loss {total_loss / total_sample:0.3f}, " + f"acc1 {total_acc1 / total_sample:0.3f}, " + f"acc5 {total_acc5 / total_sample:0.3f}, " + f"speed {speed:.3f} steps/s" ) avg_batch_time = time.time() diff --git a/test/dygraph_to_static/test_seq2seq.py b/test/dygraph_to_static/test_seq2seq.py index c93c41b7e49807..0c14b4d3985362 100644 --- a/test/dygraph_to_static/test_seq2seq.py +++ b/test/dygraph_to_static/test_seq2seq.py @@ -105,15 +105,11 @@ def train(args, attn_model=False): batch_times.append(batch_time) if batch_id % PRINT_STEP == 0: print( - "Batch:[%d]; Time: %.5f s; loss: %.5f; total_loss: %.5f; word num: %.5f; ppl: %.5f" - % ( - batch_id, - batch_time, - loss.numpy(), - total_loss.numpy(), - word_count, - np.exp(total_loss.numpy() / word_count), - ) + f"Batch:[{batch_id}]; Time: {batch_time:.5f}s; " + f"loss: {loss.numpy():.5f}; " + f"total_loss: {total_loss.numpy():.5f}; " + f"word num: {word_count:.5f}; " + f"ppl: {np.exp(total_loss.numpy() / word_count):.5f}" ) if attn_model: diff --git a/test/dygraph_to_static/test_transformer.py b/test/dygraph_to_static/test_transformer.py index 5fc11f3b05e055..1a0a82e4a64000 100644 --- a/test/dygraph_to_static/test_transformer.py +++ b/test/dygraph_to_static/test_transformer.py @@ -132,31 +132,16 @@ def train_dygraph(args, batch_generator): avg_loss.append(float(total_avg_cost)) if step_idx == 0: logging.info( - "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, " - "normalized loss: %f, ppl: %f" - % ( - step_idx, - pass_id, - batch_id, - total_avg_cost, - total_avg_cost - loss_normalizer, - np.exp([min(total_avg_cost, 100)]).item(), - ) + f"step_idx: {step_idx}, epoch: {pass_id}, batch: {batch_id}, avg loss: {total_avg_cost:f}, " + f"normalized loss: {total_avg_cost - loss_normalizer:f}, ppl: {np.exp([min(total_avg_cost, 100)]).item():f}" ) avg_batch_time = time.time() else: logging.info( - "step_idx: %d, epoch: %d, batch: %d, avg loss: %f, " - "normalized loss: %f, ppl: %f, speed: %.2f steps/s" - % ( - step_idx, - pass_id, - batch_id, - total_avg_cost, - total_avg_cost - loss_normalizer, - np.exp([min(total_avg_cost, 100)]).item(), - args.print_step / (time.time() - avg_batch_time), - ) + f"step_idx: {step_idx}, epoch: {pass_id}, batch: {batch_id}, avg loss: {total_avg_cost:f}, " + f"normalized loss: {total_avg_cost - loss_normalizer:f}, " + f"ppl: {np.exp([min(total_avg_cost, 100)]).item():f}, " + f"speed: {args.print_step / (time.time() - avg_batch_time):.2f} steps/s" ) ce_ppl.append(np.exp([min(total_avg_cost, 100)])) avg_batch_time = time.time() @@ -258,16 +243,14 @@ def predict_dygraph(args, batch_generator): if step_idx % args.print_step == 0: if step_idx == 0: logging.info( - "Dygraph Predict: step_idx: %d, 1st seq_id: %d, 1st seq_score: %.2f" - % (step_idx, seq_ids[0][0][0], seq_scores[0][0]) + f"Dygraph Predict: step_idx: {step_idx}, 1st seq_id: {seq_ids[0][0][0]}, 1st seq_score: {seq_scores[0][0]:.2f}" ) avg_batch_time = time.time() else: speed = args.print_step / (time.time() - avg_batch_time) speed_list.append(speed) logging.info( - "Dygraph Predict: step_idx: %d, 1st seq_id: %d, 1st seq_score: %.2f, speed: %.3f steps/s" - % (step_idx, seq_ids[0][0][0], seq_scores[0][0], speed) + f"Dygraph Predict: step_idx: {step_idx}, 1st seq_id: {seq_ids[0][0][0]}, 1st seq_score: {seq_scores[0][0]:.2f}, speed: {speed:.3f} steps/s" ) avg_batch_time = time.time() diff --git a/test/dygraph_to_static/test_tsm.py b/test/dygraph_to_static/test_tsm.py index 8cb6a005a98a6e..3d7eaffab239d7 100644 --- a/test/dygraph_to_static/test_tsm.py +++ b/test/dygraph_to_static/test_tsm.py @@ -175,7 +175,7 @@ def __init__(self, name_scope, config, mode): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=num_channels, num_filters=num_filters[block], diff --git a/test/dygraph_to_static/test_word2vec.py b/test/dygraph_to_static/test_word2vec.py index 42dca825d003b9..cdb9fe720259e5 100644 --- a/test/dygraph_to_static/test_word2vec.py +++ b/test/dygraph_to_static/test_word2vec.py @@ -89,8 +89,7 @@ def build_dict(corpus, min_freq=3): print("there are totoally %d different words in the corpus" % vocab_size) for _, (word, word_id) in zip(range(50), word2id_dict.items()): print( - "word %s, its id %d, its word freq %d" - % (word, word_id, word2id_freq[word_id]) + f"word {word}, its id {word_id}, its word freq {word2id_freq[word_id]}" ) @@ -174,8 +173,7 @@ def build_data( dataset = build_data(corpus, word2id_dict, word2id_freq) for _, (center_word, target_word, label) in zip(range(50), dataset): print( - "center_word %s, target %s, label %d" - % (id2word_dict[center_word], id2word_dict[target_word], label) + f"center_word {id2word_dict[center_word]}, target {id2word_dict[target_word]}, label {label}" ) diff --git a/test/dygraph_to_static/transformer_dygraph_model.py b/test/dygraph_to_static/transformer_dygraph_model.py index b998cc6a8d2fba..3189e284d92c7f 100644 --- a/test/dygraph_to_static/transformer_dygraph_model.py +++ b/test/dygraph_to_static/transformer_dygraph_model.py @@ -51,7 +51,7 @@ def __init__(self, process_cmd, d_model, dropout_rate): elif cmd == "n": # add layer normalization self.functors.append( self.add_sublayer( - "layer_norm_%d" % len(list(self.children())), + f"layer_norm_{len(list(self.children()))}", paddle.nn.LayerNorm( normalized_shape=d_model, weight_attr=base.ParamAttr( @@ -252,7 +252,7 @@ def __init__( for i in range(n_layer): self.encoder_layers.append( self.add_sublayer( - "layer_%d" % i, + f"layer_{i}", EncoderLayer( n_head, d_key, @@ -446,7 +446,7 @@ def __init__( for i in range(n_layer): self.decoder_layers.append( self.add_sublayer( - "layer_%d" % i, + f"layer_{i}", DecoderLayer( n_head, d_key, diff --git a/test/dygraph_to_static/yolov3.py b/test/dygraph_to_static/yolov3.py index 657df303bc20c9..bfd79432589c91 100644 --- a/test/dygraph_to_static/yolov3.py +++ b/test/dygraph_to_static/yolov3.py @@ -228,7 +228,7 @@ def __init__(self, ch_in, is_train=True, use_random=False): ch_in_list = [1024, 768, 384] for i in range(3): yolo_block = self.add_sublayer( - "yolo_detecton_block_%d" % (i), + f"yolo_detecton_block_{i}", YoloDetectionBlock( ch_in_list[i], channel=512 // (2**i), @@ -240,7 +240,7 @@ def __init__(self, ch_in, is_train=True, use_random=False): num_filters = len(cfg.anchor_masks[i]) * (cfg.class_num + 5) block_out = self.add_sublayer( - "block_out_%d" % (i), + f"block_out_{i}", paddle.nn.Conv2D( in_channels=1024 // (2**i), out_channels=num_filters, @@ -259,7 +259,7 @@ def __init__(self, ch_in, is_train=True, use_random=False): self.block_outputs.append(block_out) if i < 2: route = self.add_sublayer( - "route2_%d" % i, + f"route2_{i}", ConvBNLayer( ch_in=512 // (2**i), ch_out=256 // (2**i), From 14d554262939319f86fb67b71ae42c50c622b6e8 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 12:46:32 +0800 Subject: [PATCH 223/288] [CodeStyle][UP031] Use f-string instead of percent format in part of distributed files (part21) (#70033) --- .../static/cost/op_runtime_cost.py | 3 +-- .../auto_parallel/static/engine.py | 2 +- .../auto_parallel/static/process_group.py | 10 +++------- .../distributed/auto_parallel/static/utils.py | 2 +- python/paddle/distributed/cloud_utils.py | 2 +- .../paddle/distributed/fleet/base/graphviz.py | 4 ++-- .../paddle/distributed/fleet/base/topology.py | 12 ++--------- .../meta_optimizers/sharding_optimizer.py | 10 ++++------ .../parallel_layers/pp_layers.py | 2 +- .../fleet/meta_parallel/pipeline_parallel.py | 13 +++--------- python/paddle/distributed/io.py | 4 +--- .../paddle/distributed/launch/plugins/test.py | 3 +-- .../passes/auto_parallel_recompute.py | 2 +- .../distributed/passes/ps_server_pass.py | 20 ++++++++----------- 14 files changed, 30 insertions(+), 59 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/static/cost/op_runtime_cost.py b/python/paddle/distributed/auto_parallel/static/cost/op_runtime_cost.py index 3aa0d86490056a..e30a312714b6ad 100644 --- a/python/paddle/distributed/auto_parallel/static/cost/op_runtime_cost.py +++ b/python/paddle/distributed/auto_parallel/static/cost/op_runtime_cost.py @@ -307,8 +307,7 @@ def measure_program_real_op_cost( op.dist_attr.run_time_us = op_runtime_us_final ( logger.info( - "%4s %32s %.1f us" - % (str(op_id), str(op.type), op_runtime_us_final) + f"{op_id!s:>4} {op.type!s:>32} {op_runtime_us_final:.1f} us" ) if verbose_level >= 1 else None diff --git a/python/paddle/distributed/auto_parallel/static/engine.py b/python/paddle/distributed/auto_parallel/static/engine.py index d224f7915e0620..ea5305ceb9df45 100644 --- a/python/paddle/distributed/auto_parallel/static/engine.py +++ b/python/paddle/distributed/auto_parallel/static/engine.py @@ -640,7 +640,7 @@ def _prepare_logger( outputs_indices = fetch_indices[group_idx] logs_out = {} for idx in outputs_indices: - logs_out["out%d" % (idx)] = outs[idx] + logs_out[f"out{idx}"] = outs[idx] logs["outputs"] = logs_out group_idx += 1 # logging user fetches diff --git a/python/paddle/distributed/auto_parallel/static/process_group.py b/python/paddle/distributed/auto_parallel/static/process_group.py index aef656629bc6d8..32dd16a1495d53 100644 --- a/python/paddle/distributed/auto_parallel/static/process_group.py +++ b/python/paddle/distributed/auto_parallel/static/process_group.py @@ -222,19 +222,15 @@ def instantiate(self): if core.is_compiled_with_cuda(): paddle.set_device( - 'gpu:%d' % paddle.distributed.ParallelEnv().dev_id + f'gpu:{paddle.distributed.ParallelEnv().dev_id}' ) elif core.is_compiled_with_xpu(): paddle.set_device( - 'xpu:%d' % paddle.distributed.ParallelEnv().dev_id + f'xpu:{paddle.distributed.ParallelEnv().dev_id}' ) elif genv.device_type in core.get_all_custom_device_type(): paddle.set_device( - '%s:%d' - % ( - paddle.distributed.ParallelEnv().device_type, - paddle.distributed.ParallelEnv().dev_id, - ), + f'{paddle.distributed.ParallelEnv().device_type!s}:{paddle.distributed.ParallelEnv().dev_id}' ) # TODO(shenliang03): This is a temporary solution to solve the problem of diff --git a/python/paddle/distributed/auto_parallel/static/utils.py b/python/paddle/distributed/auto_parallel/static/utils.py index c26faa1b85898d..204dadea739c70 100644 --- a/python/paddle/distributed/auto_parallel/static/utils.py +++ b/python/paddle/distributed/auto_parallel/static/utils.py @@ -1773,7 +1773,7 @@ def to_list(value): def debug_program(program, path, name): filename = os.path.join( - path, name + '_program' + ".%d" % (paddle.distributed.get_rank()) + path, f"{name}_program.{paddle.distributed.get_rank()}" ) with open(filename, 'w') as f: f.write(str(program)) diff --git a/python/paddle/distributed/cloud_utils.py b/python/paddle/distributed/cloud_utils.py index c384572dc04a00..0f878d7fec1f91 100644 --- a/python/paddle/distributed/cloud_utils.py +++ b/python/paddle/distributed/cloud_utils.py @@ -84,7 +84,7 @@ def get_cloud_cluster(args_node_ips, args_node_ip, args_port, selected_devices): ports = list(range(started_port, started_port + len(selected_devices))) trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in ports]) else: trainer_endpoints_ori = trainer_endpoints.split(",") trainer_endpoints = [] diff --git a/python/paddle/distributed/fleet/base/graphviz.py b/python/paddle/distributed/fleet/base/graphviz.py index 686420ea4e07e1..1fdf825e4b3368 100644 --- a/python/paddle/distributed/fleet/base/graphviz.py +++ b/python/paddle/distributed/fleet/base/graphviz.py @@ -63,7 +63,7 @@ def code(self): return self.__str__() def rank_group(self, kind, priority): - name = "rankgroup-%d" % Graph.rank_counter + name = f"rankgroup-{Graph.rank_counter}" Graph.rank_counter += 1 rank = Rank(kind, name, priority) self.rank_groups[name] = rank @@ -148,7 +148,7 @@ class Node: def __init__(self, label, prefix, description="", **attrs): self.label = label - self.name = "%s_%d" % (prefix, Node.counter) + self.name = f"{prefix}_{Node.counter}" self.description = description self.attrs = attrs Node.counter += 1 diff --git a/python/paddle/distributed/fleet/base/topology.py b/python/paddle/distributed/fleet/base/topology.py index 3f3fb9b07ea0d1..4603053ede84f4 100644 --- a/python/paddle/distributed/fleet/base/topology.py +++ b/python/paddle/distributed/fleet/base/topology.py @@ -280,16 +280,8 @@ def __init__(self, topology: CommunicateTopology) -> None: self._set_four_directions_p2p_group() debug_str = ( - "HybridParallelInfo: rank_id: %d, mp_degree: %d, " - "sharding_degree: %d, pp_degree: %d, dp_degree: %d, sep_degree: %d" - % ( - self.global_rank, - self._mp_degree, - self._sharding_degree, - self._pp_degree, - self._dp_degree, - self._sep_degree, - ) + f"HybridParallelInfo: rank_id: {self.global_rank}, mp_degree: {self._mp_degree}, " + f"sharding_degree: {self._sharding_degree}, pp_degree: {self._pp_degree}, dp_degree: {self._dp_degree}, sep_degree: {self._sep_degree}" ) debug_str += f", mp_group: {self._mp_group}, sharding_group: {self._sharding_group}, pp_group: {self._pp_group}, dp_group: {self._dp_group}, sep:group: {self._sep_group}, check/clip group: {self._check_group}" logger.info(debug_str) diff --git a/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py b/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py index 47a850265b61e5..045befd1f7bd28 100755 --- a/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py +++ b/python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py @@ -327,7 +327,7 @@ def _inner_opt_minimize( ] else: main_program = program_list[self.pp_rank] - with open("main_%d" % self.role_maker._worker_index(), 'w') as f: + with open(f"main_{self.role_maker._worker_index()}", 'w') as f: f.writelines(str(main_program)) main_block = main_program.global_block() new_params_grads = [] @@ -344,7 +344,7 @@ def _inner_opt_minimize( if self.pp_degree > 1: pp_optimizer._rename_gradient_var_name(main_block) - with open("main_%d" % self.role_maker._worker_index(), 'w') as f: + with open(f"main_{self.role_maker._worker_index()}", 'w') as f: f.writelines(str(main_program)) return optimize_ops, params_grads @@ -645,12 +645,10 @@ def _dump_program_for_debug(self): main_block = self._main_program.global_block() startup_block = self._startup_program.global_block() with open( - "start_sharding_%d" % self.role_maker._worker_index(), 'w' + f"start_sharding_{self.role_maker._worker_index()}", 'w' ) as f: f.writelines(str(startup_block.program)) - with open( - "main_sharding_%d" % self.role_maker._worker_index(), 'w' - ) as f: + with open(f"main_sharding_{self.role_maker._worker_index()}", 'w') as f: f.writelines(str(main_block.program)) def minimize_impl( diff --git a/python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py b/python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py index 4ca1337f3c6ce2..d0beee1aa1d8df 100755 --- a/python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py +++ b/python/paddle/distributed/fleet/meta_parallel/parallel_layers/pp_layers.py @@ -407,7 +407,7 @@ def __init__( if num_stages: assert ( self._num_stages == num_stages - ), "num_stages should be equal to be %d" % (self._num_stages) + ), f"num_stages should be equal to be {self._num_stages}" else: # construct default topology if world_size % num_stages != 0: diff --git a/python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py b/python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py index 0c14bee7b52183..fc30e7a2052e5a 100644 --- a/python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py +++ b/python/paddle/distributed/fleet/meta_parallel/pipeline_parallel.py @@ -125,10 +125,7 @@ def _load_micro_batch_impl(self, inputs, micro_step): if isinstance(data, list): assert ( len(data) == self._acc_steps - ), "length of data should be %d, but it is %d" % ( - self._acc_steps, - len(data), - ) + ), f"length of data should be {self._acc_steps}, but it is {len(data)}" output.append( data[micro_step].detach() if data[micro_step] is not None @@ -144,10 +141,7 @@ def _load_micro_batch_impl(self, inputs, micro_step): elif isinstance(inputs, list): assert ( len(inputs) == self._acc_steps - ), "length of data should be %d, but it is %d" % ( - self._acc_steps, - len(inputs), - ) + ), f"length of data should be {self._acc_steps}, but it is {len(inputs)}" return inputs[micro_step].detach() elif inputs is not None: self._check_data_valid(inputs) @@ -159,8 +153,7 @@ def _check_data_valid(self, data): batch_size = data.shape[0] assert self._micro_batch_size * self._acc_steps == batch_size, ( "batch_size needs to be divisible by micro_batch_size. Currently, " - "batch_size = %d, micro_batch_size = %d, accumulate_steps = %d." - % (batch_size, self._micro_batch_size, self._acc_steps) + f"batch_size = {batch_size}, micro_batch_size = {self._micro_batch_size}, accumulate_steps = {self._acc_steps}." ) diff --git a/python/paddle/distributed/io.py b/python/paddle/distributed/io.py index cc7c148cf063ed..91ea9d9eef0691 100644 --- a/python/paddle/distributed/io.py +++ b/python/paddle/distributed/io.py @@ -582,9 +582,7 @@ def load_inference_model_distributed( program = Program.parse_from_string(program_desc_str) if not core._is_program_version_supported(program._version()): - raise ValueError( - "Unsupported program version: %d\n" % program._version() - ) + raise ValueError(f"Unsupported program version: {program._version()}\n") # Binary data also need versioning. load_persistables(executor, load_dirname, program, params_filename) diff --git a/python/paddle/distributed/launch/plugins/test.py b/python/paddle/distributed/launch/plugins/test.py index 29f378ea50e8cc..25163389d4c516 100644 --- a/python/paddle/distributed/launch/plugins/test.py +++ b/python/paddle/distributed/launch/plugins/test.py @@ -90,8 +90,7 @@ def train_resnet(): resnet.clear_gradients() print( - "[Epoch %d, batch %d] loss: %.5f, acc1: %.5f, acc5: %.5f" - % (eop, batch_id, avg_loss, acc_top1, acc_top5) + f"[Epoch {eop}, batch {batch_id}] loss: {avg_loss:.5f}, acc1: {acc_top1:.5f}, acc5: {acc_top5:.5f}" ) print("Distributed training completed") diff --git a/python/paddle/distributed/passes/auto_parallel_recompute.py b/python/paddle/distributed/passes/auto_parallel_recompute.py index 05dc769cfd6173..cb4ecb9d6d62d8 100644 --- a/python/paddle/distributed/passes/auto_parallel_recompute.py +++ b/python/paddle/distributed/passes/auto_parallel_recompute.py @@ -442,7 +442,7 @@ def _apply_single_impl(self, main_program, startup_program, context): buffer_block = main_block.program._create_block() for i, segment in enumerate(segments[::-1]): fwd_ops = op_path[segment[0] : segment[1]] - var_suffix = ".subprog_%d" % i + var_suffix = f".subprog_{i}" for op in fwd_ops: input_and_output_names = [] input_and_output_names.extend(op.input_arg_names) diff --git a/python/paddle/distributed/passes/ps_server_pass.py b/python/paddle/distributed/passes/ps_server_pass.py index bd05e58cf02296..0e72ed013f7e6e 100755 --- a/python/paddle/distributed/passes/ps_server_pass.py +++ b/python/paddle/distributed/passes/ps_server_pass.py @@ -86,12 +86,11 @@ def _get_lr_scheduler_program(self, lr_scheduler, lr_decay_steps): 1.0, lr_decay_steps, lr_scheduler.gamma, True ) lr_name = lr.name - logging.warn( - "ExponentialDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"ExponentialDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) elif isinstance(lr_scheduler, NoamDecay): with paddle.static.program_guard( @@ -101,9 +100,8 @@ def _get_lr_scheduler_program(self, lr_scheduler, lr_decay_steps): lr_scheduler.d_model, lr_scheduler.warmup_steps, 1.0 ) lr_name = lr.name - logging.warn( - "NoamDecay is set, warmup steps is [ %d ]" - % lr_scheduler.warmup_steps + logging.warning( + f"NoamDecay is set, warmup steps is [ {lr_scheduler.warmup_steps} ]" ) elif isinstance(lr_scheduler, NaturalExpDecay): with paddle.static.program_guard( @@ -113,12 +111,11 @@ def _get_lr_scheduler_program(self, lr_scheduler, lr_decay_steps): 1.0, lr_scheduler.gamma ).get_lr() lr_name = lr.name - logging.warn( - "NaturalExpDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"NaturalExpDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) elif isinstance(lr_scheduler, InverseTimeDecay): with paddle.static.program_guard( @@ -128,12 +125,11 @@ def _get_lr_scheduler_program(self, lr_scheduler, lr_decay_steps): 1.0, lr_decay_steps, lr_scheduler.gamma, True ) lr_name = lr.name - logging.warn( - "InverseTimeDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"InverseTimeDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) else: raise ValueError( From 9230d397b560517404cf8bfea28726c3cebd73be Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 12:47:11 +0800 Subject: [PATCH 224/288] [CodeStyle][UP031] Use f-string instead of percent format in some uts (part24) (#70036) --- test/cinn/test_efficientnet.py | 3 +-- test/cinn/test_facedet.py | 3 +-- test/cinn/test_mobilenetv1.py | 3 +-- test/cinn/test_mobilenetv2.py | 3 +-- test/cinn/test_resnet18.py | 3 +-- test/cinn/test_resnet50.py | 3 +-- test/cinn/test_squeezenet.py | 3 +-- test/collective/process_group_nccl_pir.py | 2 +- test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py | 4 +--- test/cpp/inference/api/full_pascalvoc_test_preprocess.py | 4 +--- test/cpp_extension/test_cpp_extension_setup.py | 6 +++--- test/cpp_extension/test_mixed_extension_setup.py | 6 +++--- test/custom_op/test_custom_relu_op_setup.py | 6 +++--- test/custom_op/test_custom_relu_op_xpu_setup.py | 6 +++--- test/custom_op/test_inference_gap_setup.py | 6 +++--- test/custom_runtime/process_group_xccl.py | 2 +- test/custom_runtime/test_collective_process_group_xccl.py | 2 +- .../custom_op/test_custom_raw_op_kernel_op_deprecated.py | 6 +++--- test/dygraph_to_static/bert_dygraph_model.py | 2 +- 19 files changed, 31 insertions(+), 42 deletions(-) diff --git a/test/cinn/test_efficientnet.py b/test/cinn/test_efficientnet.py index f543cdefa987c6..9c8d116359fc07 100755 --- a/test/cinn/test_efficientnet.py +++ b/test/cinn/test_efficientnet.py @@ -76,8 +76,7 @@ def apply_test(self): end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) out.from_numpy(np.zeros(out.shape(), dtype='float32'), self.target) diff --git a/test/cinn/test_facedet.py b/test/cinn/test_facedet.py index 5c9a6f33f011b0..b057db0ef93b54 100755 --- a/test/cinn/test_facedet.py +++ b/test/cinn/test_facedet.py @@ -79,8 +79,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/cinn/test_mobilenetv1.py b/test/cinn/test_mobilenetv1.py index e46cd13a10f770..4a8a72f4f81866 100644 --- a/test/cinn/test_mobilenetv1.py +++ b/test/cinn/test_mobilenetv1.py @@ -77,8 +77,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/cinn/test_mobilenetv2.py b/test/cinn/test_mobilenetv2.py index a7a683f7f97897..eacc167131cc97 100755 --- a/test/cinn/test_mobilenetv2.py +++ b/test/cinn/test_mobilenetv2.py @@ -79,8 +79,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/cinn/test_resnet18.py b/test/cinn/test_resnet18.py index 926aef5e951bac..89fe992e9fd4b3 100755 --- a/test/cinn/test_resnet18.py +++ b/test/cinn/test_resnet18.py @@ -79,8 +79,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/cinn/test_resnet50.py b/test/cinn/test_resnet50.py index d816924b087692..7c880a4ee6c18b 100755 --- a/test/cinn/test_resnet50.py +++ b/test/cinn/test_resnet50.py @@ -83,8 +83,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/cinn/test_squeezenet.py b/test/cinn/test_squeezenet.py index 7f78539f321285..fb148211c7e767 100644 --- a/test/cinn/test_squeezenet.py +++ b/test/cinn/test_squeezenet.py @@ -77,8 +77,7 @@ def apply_test(self): self.executor.run() end5 = time.perf_counter() print( - "Repeat %d times, average Executor.run() time is: %.3f ms" - % (repeat, (end5 - end4) * 1000 / repeat) + f"Repeat {repeat} times, average Executor.run() time is: {(end5 - end4) * 1000 / repeat:.3f} ms" ) a_t.from_numpy(x_data, self.target) diff --git a/test/collective/process_group_nccl_pir.py b/test/collective/process_group_nccl_pir.py index 00442ef265d478..b021a542e169af 100644 --- a/test/collective/process_group_nccl_pir.py +++ b/test/collective/process_group_nccl_pir.py @@ -44,7 +44,7 @@ def config(self): @classmethod def setUpClass(cls): device_id = paddle.distributed.ParallelEnv().dev_id - paddle.set_device('gpu:%d' % device_id) + paddle.set_device(f'gpu:{device_id}') assert paddle.distributed.is_available() diff --git a/test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py b/test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py index 69ece9d573859a..813c623f0d83c0 100644 --- a/test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py +++ b/test/cpp/inference/api/full_ILSVRC2012_val_preprocess.py @@ -96,9 +96,7 @@ def download_concat(cache_folder, zip_path): def print_processbar(done_percentage): done_filled = done_percentage * '=' empty_filled = (100 - done_percentage) * ' ' - sys.stdout.write( - "\r[%s%s]%d%%" % (done_filled, empty_filled, done_percentage) - ) + sys.stdout.write(f"\r[{done_filled}{empty_filled}]{done_percentage}%") sys.stdout.flush() diff --git a/test/cpp/inference/api/full_pascalvoc_test_preprocess.py b/test/cpp/inference/api/full_pascalvoc_test_preprocess.py index 8c072e614ce269..843cf9d1c414af 100644 --- a/test/cpp/inference/api/full_pascalvoc_test_preprocess.py +++ b/test/cpp/inference/api/full_pascalvoc_test_preprocess.py @@ -158,9 +158,7 @@ def convert_pascalvoc_local2bin(args): def print_processbar(done_percentage): done_filled = done_percentage * '=' empty_filled = (100 - done_percentage) * ' ' - sys.stdout.write( - "\r[%s%s]%d%%" % (done_filled, empty_filled, done_percentage) - ) + sys.stdout.write(f"\r[{done_filled}{empty_filled}]{done_percentage}%") sys.stdout.flush() diff --git a/test/cpp_extension/test_cpp_extension_setup.py b/test/cpp_extension/test_cpp_extension_setup.py index 56db08ae0f0902..5baeb9d10cae92 100644 --- a/test/cpp_extension/test_cpp_extension_setup.py +++ b/test/cpp_extension/test_cpp_extension_setup.py @@ -42,9 +42,9 @@ def setUp(self): custom_egg_path = [ x for x in os.listdir(site_dir) if 'custom_cpp_extension' in x ] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) ################################# diff --git a/test/cpp_extension/test_mixed_extension_setup.py b/test/cpp_extension/test_mixed_extension_setup.py index 574a218e062ecd..6312ccd25810bc 100644 --- a/test/cpp_extension/test_mixed_extension_setup.py +++ b/test/cpp_extension/test_mixed_extension_setup.py @@ -112,9 +112,9 @@ def setUp(self): custom_egg_path = [ x for x in os.listdir(site_dir) if 'mix_relu_extension' in x ] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) ################################# diff --git a/test/custom_op/test_custom_relu_op_setup.py b/test/custom_op/test_custom_relu_op_setup.py index d63c8633844ea0..ebf7ba90f3f8b5 100644 --- a/test/custom_op/test_custom_relu_op_setup.py +++ b/test/custom_op/test_custom_relu_op_setup.py @@ -166,9 +166,9 @@ def setUp(self): custom_egg_path = [ x for x in os.listdir(site_dir) if 'custom_relu_module_setup' in x ] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) # usage: import the package directly diff --git a/test/custom_op/test_custom_relu_op_xpu_setup.py b/test/custom_op/test_custom_relu_op_xpu_setup.py index a51d61a6876db3..5d1fdef9fdf164 100644 --- a/test/custom_op/test_custom_relu_op_xpu_setup.py +++ b/test/custom_op/test_custom_relu_op_xpu_setup.py @@ -75,9 +75,9 @@ def setUp(self): for x in os.listdir(site_dir) if 'custom_relu_xpu_module_setup' in x ] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) # usage: import the package directly diff --git a/test/custom_op/test_inference_gap_setup.py b/test/custom_op/test_inference_gap_setup.py index 976da045b61dc1..d116ce670f5c6d 100644 --- a/test/custom_op/test_inference_gap_setup.py +++ b/test/custom_op/test_inference_gap_setup.py @@ -57,9 +57,9 @@ def setUp(self): custom_egg_path = [ x for x in os.listdir(site_dir) if 'gap_op_setup' in x ] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) # usage: import the package directly diff --git a/test/custom_runtime/process_group_xccl.py b/test/custom_runtime/process_group_xccl.py index 9597f8c0c78550..39287b9e3a8908 100644 --- a/test/custom_runtime/process_group_xccl.py +++ b/test/custom_runtime/process_group_xccl.py @@ -49,7 +49,7 @@ def config(self): def test_create_process_group_xccl(self): device_id = paddle.distributed.ParallelEnv().dev_id - paddle.set_device('custom_cpu:%d' % device_id) + paddle.set_device(f'custom_cpu:{device_id}') pg = init_process_group() diff --git a/test/custom_runtime/test_collective_process_group_xccl.py b/test/custom_runtime/test_collective_process_group_xccl.py index de5eaf0fc94d8b..dcad082cb186f8 100644 --- a/test/custom_runtime/test_collective_process_group_xccl.py +++ b/test/custom_runtime/test_collective_process_group_xccl.py @@ -70,7 +70,7 @@ def start_local_trainers( print(f"start trainer proc:{cmd} env:{proc_env}") - fn = open("workerlog.%d" % idx, "a") + fn = open(f"workerlog.{idx}", "a") proc = subprocess.Popen( cmd.split(" "), env=current_env, stdout=fn, stderr=fn ) diff --git a/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py b/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py index 297f6e7f55b088..686cec5457a08e 100644 --- a/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py +++ b/test/deprecated/custom_op/test_custom_raw_op_kernel_op_deprecated.py @@ -38,9 +38,9 @@ def prepare_module_path(): else: site_dir = site.getsitepackages()[0] custom_egg_path = [x for x in os.listdir(site_dir) if MODULE_NAME in x] - assert len(custom_egg_path) == 1, "Matched egg number is %d." % len( - custom_egg_path - ) + assert ( + len(custom_egg_path) == 1 + ), f"Matched egg number is {len(custom_egg_path)}." sys.path.append(os.path.join(site_dir, custom_egg_path[0])) diff --git a/test/dygraph_to_static/bert_dygraph_model.py b/test/dygraph_to_static/bert_dygraph_model.py index b557a941d3c674..706ab1b169455b 100644 --- a/test/dygraph_to_static/bert_dygraph_model.py +++ b/test/dygraph_to_static/bert_dygraph_model.py @@ -153,7 +153,7 @@ def __init__( for i in range(n_layer): self._encoder_sublayers.append( self.add_sublayer( - 'esl_%d' % i, + f'esl_{i}', EncoderSubLayer( hidden_act, n_head, From 2b488505ec3b98c3102d933d7ce90f86c3895a26 Mon Sep 17 00:00:00 2001 From: Liu Huijie <90851964+smile2game@users.noreply.github.com> Date: Sun, 8 Dec 2024 15:23:38 +0800 Subject: [PATCH 225/288] support pir_p_to_s_reshard and test (#69265) --- .../reshard_funcs/p_to_s_reshard_func.py | 168 ++++++++-- test/auto_parallel/pir/CMakeLists.txt | 2 + test/auto_parallel/pir/pir_reshard_p_to_s.py | 288 ++++++++++++++++++ .../pir/test_pir_reshard_p_to_s.py | 46 +++ 4 files changed, 479 insertions(+), 25 deletions(-) create mode 100644 test/auto_parallel/pir/pir_reshard_p_to_s.py create mode 100644 test/auto_parallel/pir/test_pir_reshard_p_to_s.py diff --git a/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py b/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py index 4c399e34c01213..7dca9f9a6c770a 100644 --- a/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py +++ b/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py @@ -13,6 +13,7 @@ # limitations under the License. import paddle +import paddle.distributed as dist from paddle.distributed.utils.stream_utils import ExecutionStreamType from ..process_group import new_process_group @@ -44,58 +45,175 @@ def is_suitable(self, src_dist_attr, dst_dist_attr): return True def reshard(self, src_dist_attr, dst_dist_attr, src_value, dst_type): - src_mesh = src_dist_attr.process_mesh src_reduce_type = src_dist_attr.partial_status[0] assert ( src_reduce_type == paddle.base.core.ReduceType.kRedSum ), f"The p to s reshard func only support sum op, but received {src_reduce_type}" - - chunk_id = -1 - if src_value.get_defining_op().dist_attr: - chunk_id = src_value.get_defining_op().dist_attr.chunk_id - split_axis = dst_dist_attr.dims_mapping.index(0) + permute = False if split_axis != 0: perm = list(range(0, len(src_value.shape))) perm[0] = split_axis perm[split_axis] = 0 src_value = paddle._C_ops.transpose(src_value, perm) + permute = True tmp_dims_mapping = dst_dist_attr.dims_mapping tmp_dims_mapping[split_axis] = -1 tmp_dims_mapping[0] = 0 dst_dist_attr = copy_dist_attr_with_new_member( dst_dist_attr, new_dims_mapping=tmp_dims_mapping ) + dst_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + src_value.type(), dst_dist_attr + ) + original_dims_mapping = dst_dist_attr.dims_mapping.copy() + original_split_axis = split_axis + split_axis = 0 - global_dst_attr = dst_type.as_dist_type().dist_attr() - global_dims_mapping = global_dst_attr.dims_mapping - axis = global_dims_mapping[0] - global_dims_mapping[0] = global_dims_mapping[split_axis] - global_dims_mapping[split_axis] = axis - global_dist_attr = copy_dist_attr_with_new_member( - global_dst_attr, new_dims_mapping=global_dims_mapping + num_of_process = len(src_dist_attr.process_mesh.process_ids) + remainder_of_padding = src_value.shape[split_axis] % num_of_process + is_balanced_split = remainder_of_padding == 0 + + if is_balanced_split: + dst_value = self.reshard_p_to_s_with_padding( + src_value, + split_axis, + src_dist_attr, + dst_dist_attr, + dst_type, ) - dst_type = paddle.base.libpaddle.pir.cvt_to_dist_type( - src_value.type(), global_dist_attr + if permute: + dst_value = paddle._C_ops.transpose(dst_value, perm) + split_axis = original_split_axis + return dst_value + else: + avg_size_on_split_axis = int( + (src_value.shape[split_axis] + num_of_process - 1) + / num_of_process + ) + padding_num = ( + avg_size_on_split_axis * num_of_process + - src_value.shape[split_axis] + ) + padding_shape = src_value._local_shape + padding_shape[split_axis] = padding_num + padding_tensor = paddle.full( + padding_shape, + 0.0, + src_value.dtype, + ) + tmp_src_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + padding_tensor.type(), src_dist_attr + ) + padding_tensor.set_type(tmp_src_type) + padding_tensor.get_defining_op().dist_attr = ( + paddle.base.libpaddle.pir.create_op_dist_attribute( + src_dist_attr.process_mesh, [], [src_dist_attr] + ) + ) + concat_value = paddle._C_ops.concat( + [src_value, padding_tensor], split_axis + ) + axis_dist_attr = ( + paddle.base.libpaddle.pir.create_tensor_dist_attribute( + src_dist_attr.process_mesh, + [-1], + {0: paddle.base.core.ReduceType.kRedSum}, + ) + ) + concat_value.get_defining_op().dist_attr = ( + paddle.base.libpaddle.pir.create_op_dist_attribute( + src_dist_attr.process_mesh, + [ + paddle.base.libpaddle.pir.create_array_attribute( + [src_dist_attr, src_dist_attr] + ), + axis_dist_attr, + ], + [src_dist_attr], + ) ) - num_of_process = len(src_mesh.process_ids) - group = new_process_group(sorted(src_mesh.process_ids)) + concat_global_shape = list(src_value.shape) + concat_global_shape[split_axis] = ( + avg_size_on_split_axis * num_of_process + ) + concat_type = paddle.pir.create_shaped_type( + src_value.type(), concat_global_shape + ) + concat_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + concat_type, src_dist_attr + ) + concat_value.set_type(concat_type) + + dst_value = self.reshard_p_to_s_with_padding( + concat_value, + split_axis, + src_dist_attr, + dst_dist_attr, + dst_type, + padding_num, + ) + if permute: + dst_value = paddle._C_ops.transpose(dst_value, perm) + split_axis = original_split_axis + return dst_value + + def reshard_p_to_s_with_padding( + self, + src_value, + split_axis, + src_dist_attr, + dst_dist_attr, + dst_type, + padding_num=0, + ): + group = new_process_group( + sorted(src_dist_attr.process_mesh.process_ids) + ) dst_value = paddle._C_ops.reduce_scatter( - src_value, group.id, num_of_process + src_value, group.id, len(src_dist_attr.process_mesh.process_ids) ) + out_global_shape = dst_type.shape + out_global_shape[split_axis] = ( + padding_num + out_global_shape[split_axis] + ) + dst_tmp_type = paddle.pir.create_shaped_type( + dst_value.type(), out_global_shape + ) + dst_tmp_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + dst_tmp_type, dst_dist_attr + ) + dst_value.set_type(dst_tmp_type) dst_value.get_defining_op().set_execution_stream( ExecutionStreamType.DefaultStream.value ) - - # set dist type and dist attr - dst_value.set_type(dst_type) dst_value.get_defining_op().dist_attr = ( paddle.base.libpaddle.pir.create_op_dist_attribute( - src_mesh, [src_dist_attr], [dst_dist_attr], chunk_id + src_dist_attr.process_mesh, + [src_dist_attr], + [dst_dist_attr], + src_value.get_defining_op().dist_attr.chunk_id, ) ) - - if split_axis != 0: - dst_value = paddle._C_ops.transpose(dst_value, perm) + if padding_num != 0: + if dist.get_rank() == dst_dist_attr.process_mesh.process_ids[-1]: + dst_value = paddle._C_ops.split( + dst_value, + [ + dst_value.shape[split_axis] - padding_num, + padding_num, + ], + 0, + )[0] + dst_value.get_defining_op().dist_attr = ( + paddle.base.libpaddle.pir.create_op_dist_attribute( + dst_dist_attr.process_mesh, + [dst_dist_attr], + [dst_dist_attr], + src_value.get_defining_op().dist_attr.chunk_id, + ) + ) + else: + dst_value.set_type(dst_type) return dst_value diff --git a/test/auto_parallel/pir/CMakeLists.txt b/test/auto_parallel/pir/CMakeLists.txt index ecf15bd6a0d9e9..bc41c345d5b4ac 100644 --- a/test/auto_parallel/pir/CMakeLists.txt +++ b/test/auto_parallel/pir/CMakeLists.txt @@ -76,3 +76,5 @@ if(WITH_DISTRIBUTE AND WITH_GPU) endif() py_test_modules(test_pir_1f1b_plan MODULES test_pir_1f1b_plan ENVS FLAGS_enable_pir_api=1) +py_test_modules(test_pir_reshard_p_to_s MODULES test_pir_reshard_p_to_s ENVS + FLAGS_enable_pir_api=1) diff --git a/test/auto_parallel/pir/pir_reshard_p_to_s.py b/test/auto_parallel/pir/pir_reshard_p_to_s.py new file mode 100644 index 00000000000000..4569e7dc4b98ea --- /dev/null +++ b/test/auto_parallel/pir/pir_reshard_p_to_s.py @@ -0,0 +1,288 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import os + +import numpy as np + +import paddle +import paddle.distributed as dist +from paddle.distributed.auto_parallel.static.pir_pass import ReshardPasses + + +class TestReshardPToS: + def __init__(self): + self._shape = eval(os.getenv("shape")) + self._dtype = os.getenv("dtype") + self._seeds = eval(os.getenv("seeds")) + self._shard = eval(os.getenv("shard")) + self._backend = os.getenv("backend") + self._mesh = dist.ProcessMesh([0, 1], dim_names=["x"]) + self.rank = dist.get_rank() + + def run_pir_test_case(self): + paddle.enable_static() + if self._backend == "gpu": + place = paddle.CUDAPlace(dist.get_rank()) + + BATCH_SIZE = 2 + SEQ_LEN = 4 + HIDDEN_SIZE = 6 + MP_SIZE = 2 + + with paddle.pir_utils.IrGuard(): + main_program = paddle.base.Program() + with paddle.base.program_guard(main_program): + w0 = paddle.pir.core.create_parameter( + dtype="float32", + shape=[SEQ_LEN, HIDDEN_SIZE], + name="w0", + initializer=paddle.nn.initializer.Uniform(), + ) + input_tensor = dist.shard_tensor( + w0, self._mesh, [dist.Partial()] + ) + reshard_tensor = paddle._C_ops.reshard( + input_tensor, + self._mesh, + [dist.Shard(self._shard)], + ) + ReshardPasses.apply_reshard_pass(main_program) + + ops = [op.name() for op in main_program.global_block().ops] + + if self._shard == 0: + np.testing.assert_equal(main_program.num_ops(), 3) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.reduce_scatter", + ] + np.testing.assert_equal( + ops, + std_ops, + ) + + if self._shard == 1: + np.testing.assert_equal(main_program.num_ops(), 5) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.transpose", + "pd_op.reduce_scatter", + "pd_op.transpose", + ] + np.testing.assert_equal( + ops, + std_ops, + ) + + for op in main_program.global_block().ops: + if op.name() == "pd_op.reduce_scatter": + assert op.dist_attr.num_operands() == 1 + assert op.dist_attr.num_results() == 1 + assert op.dist_attr.process_mesh == self._mesh + op_operand_dist_attr = op.dist_attr.operand( + 0 + ).as_tensor_dist_attr() + assert op_operand_dist_attr.process_mesh == self._mesh + assert op_operand_dist_attr.dims_mapping == [-1, -1] + assert op_operand_dist_attr.partial_status == { + 0: paddle.base.core.ReduceType.kRedSum + } + op_result_dist_attr = op.dist_attr.result( + 0 + ).as_tensor_dist_attr() + assert op_result_dist_attr.process_mesh == self._mesh + assert op_result_dist_attr.dims_mapping == [0, -1] + assert op_result_dist_attr.partial_status == {} + + op_value = op.result(0) + assert op_value.is_dense_tensor_type() + assert op_value.is_dist_dense_tensor_type() + assert op_value.dist_attr().process_mesh == self._mesh + assert op_value.dist_attr().dims_mapping == [0, -1] + assert op_value.dist_attr().partial_status == {} + + def run_pir_unbalanced_split_test_case(self): + paddle.enable_static() + if self._backend == "gpu": + place = paddle.CUDAPlace(dist.get_rank()) + + BATCH_SIZE = 2 + SEQ_LEN = 3 + HIDDEN_SIZE = 7 + MP_SIZE = 2 + + with paddle.pir_utils.IrGuard(): + main_program = paddle.base.Program() + with paddle.base.program_guard(main_program): + w1 = paddle.pir.core.create_parameter( + dtype="float32", + shape=[SEQ_LEN, HIDDEN_SIZE], + name="w1", + initializer=paddle.nn.initializer.Uniform(), + ) + input_tensor1 = dist.shard_tensor( + w1, self._mesh, [dist.Partial()] + ) + reshard_tensor1 = paddle._C_ops.reshard( + input_tensor1, + self._mesh, + [dist.Shard(self._shard)], + ) + ReshardPasses.apply_reshard_pass(main_program) + + ops = [op.name() for op in main_program.global_block().ops] + + if self._shard == 0: + if self.rank != self._mesh.process_ids[-1]: + np.testing.assert_equal(main_program.num_ops(), 7) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.full", + "pd_op.full", + "builtin.combine", + "pd_op.concat", + "pd_op.reduce_scatter", + ] + np.testing.assert_equal( + ops, + std_ops, + ) + else: + np.testing.assert_equal(main_program.num_ops(), 11) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.full", + "pd_op.full", + "builtin.combine", + "pd_op.concat", + "pd_op.reduce_scatter", + 'pd_op.full_int_array', + 'pd_op.full', + 'pd_op.split', + 'builtin.split', + ] + np.testing.assert_equal( + ops, + std_ops, + ) + + if self._shard == 1: + if self.rank != self._mesh.process_ids[-1]: + np.testing.assert_equal(main_program.num_ops(), 9) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.transpose", + "pd_op.full", + "pd_op.full", + "builtin.combine", + "pd_op.concat", + "pd_op.reduce_scatter", + "pd_op.transpose", + ] + np.testing.assert_equal( + ops, + std_ops, + ) + else: + np.testing.assert_equal(main_program.num_ops(), 13) + std_ops = [ + "builtin.parameter", + "dist_op.shard_tensor", + "pd_op.transpose", + "pd_op.full", + "pd_op.full", + "builtin.combine", + "pd_op.concat", + "pd_op.reduce_scatter", + 'pd_op.full_int_array', + 'pd_op.full', + 'pd_op.split', + 'builtin.split', + "pd_op.transpose", + ] + np.testing.assert_equal( + ops, + std_ops, + ) + + for op in main_program.global_block().ops: + if op.name() == 'pd_op.concat': + assert op.dist_attr.num_operands() == 2 + assert op.dist_attr.num_results() == 1 + assert op.dist_attr.process_mesh == self._mesh + operand_1_dist_attrs = op.dist_attr.operand(0).as_array_attr() + assert len(operand_1_dist_attrs) == 2 + operand_1_dist_attr_1 = operand_1_dist_attrs[ + 0 + ].as_tensor_dist_attr() + operand_1_dist_attr_2 = operand_1_dist_attrs[ + 1 + ].as_tensor_dist_attr() + assert operand_1_dist_attr_1.process_mesh == self._mesh + assert operand_1_dist_attr_1.dims_mapping == [-1, -1] + assert operand_1_dist_attr_1.partial_status == { + 0: paddle.base.core.ReduceType.kRedSum + } + assert operand_1_dist_attr_2.process_mesh == self._mesh + assert operand_1_dist_attr_2.dims_mapping == [-1, -1] + assert operand_1_dist_attr_2.partial_status == { + 0: paddle.base.core.ReduceType.kRedSum + } + op_result_dist_attr = op.dist_attr.result( + 0 + ).as_tensor_dist_attr() + assert op_result_dist_attr.process_mesh == self._mesh + assert op_result_dist_attr.dims_mapping == [-1, -1] + assert op_result_dist_attr.partial_status == { + 0: paddle.base.core.ReduceType.kRedSum + } + op_value = op.result(0) + assert op_value.is_dense_tensor_type() + assert op_value.is_dist_dense_tensor_type() + assert op_value.dist_attr().process_mesh == self._mesh + elif op.name() == "pd_op.reduce_scatter": + assert op.dist_attr.num_operands() == 1 + assert op.dist_attr.num_results() == 1 + assert op.dist_attr.process_mesh == self._mesh + op_operand_dist_attr = op.dist_attr.operand( + 0 + ).as_tensor_dist_attr() + assert op_operand_dist_attr.process_mesh == self._mesh + assert op_operand_dist_attr.dims_mapping == [-1, -1] + assert op_operand_dist_attr.partial_status == { + 0: paddle.base.core.ReduceType.kRedSum + } + op_result_dist_attr = op.dist_attr.result( + 0 + ).as_tensor_dist_attr() + assert op_result_dist_attr.process_mesh == self._mesh + assert op_result_dist_attr.dims_mapping == [0, -1] + assert op_result_dist_attr.partial_status == {} + op_value = op.result(0) + assert op_value.is_dense_tensor_type() + assert op_value.is_dist_dense_tensor_type() + assert op_value.dist_attr().process_mesh == self._mesh + assert op_value.dist_attr().dims_mapping == [0, -1] + assert op_value.dist_attr().partial_status == {} + + +if __name__ == '__main__': + TestReshardPToS().run_pir_test_case() + TestReshardPToS().run_pir_unbalanced_split_test_case() diff --git a/test/auto_parallel/pir/test_pir_reshard_p_to_s.py b/test/auto_parallel/pir/test_pir_reshard_p_to_s.py new file mode 100644 index 00000000000000..77711ff62c0bd5 --- /dev/null +++ b/test/auto_parallel/pir/test_pir_reshard_p_to_s.py @@ -0,0 +1,46 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + + +import unittest + +import collective.test_communication_api_base as test_base + + +class TestReshardPToS(test_base.CommunicationTestDistBase): + def setUp(self): + super().setUp(num_of_devices=2, timeout=120) + self._default_envs = { + "shape": "(11, 20)", + "dtype": "float32", + "seeds": "2024", + } + self._changeable_envs = { + "shard": ["0", "1"], + "backend": ["gpu"], + } + + def test_reshard_p_to_s(self): + envs_list = test_base.gen_product_envs_list( + self._default_envs, self._changeable_envs + ) + for envs in envs_list: + self.run_test_case( + "pir_reshard_p_to_s.py", + user_defined_envs=envs, + ) + + +if __name__ == "__main__": + unittest.main() From 21cffcf344abffb73ee8d22aaca14e96c491d0ab Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 18:29:46 +0800 Subject: [PATCH 226/288] [CodeStyle][UP031] Use f-string instead of percent format in some uts (part27) (#70044) --- test/legacy_test/test_meshgrid_op.py | 16 ++++++---------- test/legacy_test/test_rnn_decode_api.py | 2 +- test/legacy_test/test_split_op.py | 14 +++++++------- test/legacy_test/test_static_save_load.py | 4 ++-- test/legacy_test/test_unbind_op.py | 12 ++++++------ test/mkldnn/test_split_bf16_mkldnn_op.py | 2 +- test/prim/model/test_resnet_cinn.py | 15 +++++---------- test/prim/model/test_resnet_prim.py | 15 +++++---------- test/prim/model/test_resnet_prim_cinn.py | 15 +++++---------- test/ps/fl_ps_trainer.py | 3 +-- test/ps/ps_dnn_model.py | 16 ++++++++-------- test/xpu/process_group_bkcl.py | 2 +- test/xpu/test_collective_api_base.py | 4 ++-- test/xpu/test_distribute_fpn_proposals_op_xpu.py | 8 +++----- test/xpu/test_meshgrid_op_xpu.py | 4 ++-- test/xpu/test_parallel_dygraph_dataparallel.py | 8 ++++---- test/xpu/test_split_op_xpu.py | 2 +- test/xpu/test_top_k_v2_op_xpu.py | 2 +- test/xpu/test_unbind_op_xpu.py | 4 +--- 19 files changed, 62 insertions(+), 86 deletions(-) diff --git a/test/legacy_test/test_meshgrid_op.py b/test/legacy_test/test_meshgrid_op.py index 89f1a221d2f1b0..7d47b1050fc274 100644 --- a/test/legacy_test/test_meshgrid_op.py +++ b/test/legacy_test/test_meshgrid_op.py @@ -76,10 +76,8 @@ def init_inputs_and_outputs(self): out_reshape[i] = self.shape[i] out_temp = np.reshape(ins[i], out_reshape) outs.append(np.broadcast_to(out_temp, self.shape)) - self.inputs = {'X': [('x%d' % i, ins[i]) for i in range(len(ins))]} - self.outputs = { - 'Out': [('out%d' % i, outs[i]) for i in range(len(outs))] - } + self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]} + self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]} def get_x_shape(self): return [100, 200] @@ -141,13 +139,13 @@ def init_inputs_and_outputs(self): outs.append(np.broadcast_to(out_temp, self.shape)) self.inputs = { 'X': [ - ('x%d' % i, convert_float_to_uint16(ins[i])) + (f'x{i}', convert_float_to_uint16(ins[i])) for i in range(len(ins)) ] } self.outputs = { 'Out': [ - ('out%d' % i, convert_float_to_uint16(outs[i])) + (f'out{i}', convert_float_to_uint16(outs[i])) for i in range(len(outs)) ] } @@ -440,10 +438,8 @@ def init_inputs_and_outputs(self): out_reshape[i] = self.shape[i] out_temp = np.reshape(ins[i], out_reshape) outs.append(np.broadcast_to(out_temp, self.shape)) - self.inputs = {'X': [('x%d' % i, ins[i]) for i in range(len(ins))]} - self.outputs = { - 'Out': [('out%d' % i, outs[i]) for i in range(len(outs))] - } + self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]} + self.outputs = {'Out': [(f'out{i}', outs[i]) for i in range(len(outs))]} def get_x_shape(self): return [1, 2, 3] diff --git a/test/legacy_test/test_rnn_decode_api.py b/test/legacy_test/test_rnn_decode_api.py index 0786ad02d06d02..938be34f7dc71b 100644 --- a/test/legacy_test/test_rnn_decode_api.py +++ b/test/legacy_test/test_rnn_decode_api.py @@ -423,7 +423,7 @@ def __init__( for i in range(num_layers): self.lstm_cells.append( self.add_sublayer( - "lstm_%d" % i, + f"lstm_{i}", LSTMCell( input_size=input_size if i == 0 else hidden_size, hidden_size=hidden_size, diff --git a/test/legacy_test/test_split_op.py b/test/legacy_test/test_split_op.py index 822e5b45d5889c..8852b7354a13f1 100644 --- a/test/legacy_test/test_split_op.py +++ b/test/legacy_test/test_split_op.py @@ -38,7 +38,7 @@ def setUp(self): self.inputs = {'X': convert_float_to_uint16(x)} self.outputs = { 'Out': [ - ('out%d' % i, convert_float_to_uint16(out[i])) + (f'out{i}', convert_float_to_uint16(out[i])) for i in range(len(out)) ] } @@ -47,7 +47,7 @@ def setUp(self): out = np.split(x, [2, 3], axis) self.inputs = {'X': x} self.outputs = { - 'Out': [('out%d' % i, out[i]) for i in range(len(out))] + 'Out': [(f'out{i}', out[i]) for i in range(len(out))] } self.attrs = {'axis': axis, 'sections': [2, 1, 2]} @@ -90,7 +90,7 @@ def setUp(self): out = np.split(self.x, self.indices_or_sections, self.axis) self.outputs = { 'Out': [ - ('out%d' % i, convert_float_to_uint16(out[i])) + (f'out{i}', convert_float_to_uint16(out[i])) for i in range(len(out)) ] } @@ -98,7 +98,7 @@ def setUp(self): self.inputs = {'X': self.x} out = np.split(self.x, self.indices_or_sections, self.axis) self.outputs = { - 'Out': [('out%d' % i, out[i]) for i in range(len(out))] + 'Out': [(f'out{i}', out[i]) for i in range(len(out))] } def init_data(self): @@ -145,7 +145,7 @@ def setUp(self): self.attrs = {'sections': self.sections, 'num': self.num} out = np.split(self.x, self.indices_or_sections, self.axis) - self.outputs = {'Out': [('out%d' % i, out[i]) for i in range(len(out))]} + self.outputs = {'Out': [(f'out{i}', out[i]) for i in range(len(out))]} def init_data(self): self.x = np.random.random((4, 5, 6)).astype(self.dtype) @@ -192,7 +192,7 @@ def setUp(self): } out = np.split(self.x, self.indices_or_sections, self.axis) - self.outputs = {'Out': [('out%d' % i, out[i]) for i in range(len(out))]} + self.outputs = {'Out': [(f'out{i}', out[i]) for i in range(len(out))]} def init_data(self): self.x = np.random.random((4, 5, 6)).astype(self.dtype) @@ -232,7 +232,7 @@ def setUp(self): } out = np.split(self.x, self.indices_or_sections, self.axis) - self.outputs = {'Out': [('out%d' % i, out[i]) for i in range(len(out))]} + self.outputs = {'Out': [(f'out{i}', out[i]) for i in range(len(out))]} def init_data(self): self.x = np.random.random((4, 5, 6)).astype(self.dtype) diff --git a/test/legacy_test/test_static_save_load.py b/test/legacy_test/test_static_save_load.py index 384c3181045a19..2bc9804bcbd4a7 100644 --- a/test/legacy_test/test_static_save_load.py +++ b/test/legacy_test/test_static_save_load.py @@ -68,7 +68,7 @@ def __init__( low=-self._init_scale, high=self._init_scale ), ) - self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) + self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1)) bias_1 = self.create_parameter( attr=base.ParamAttr( initializer=paddle.nn.initializer.Uniform( @@ -79,7 +79,7 @@ def __init__( dtype="float32", default_initializer=paddle.nn.initializer.Constant(0.0), ) - self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) + self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): self.cell_array = [] diff --git a/test/legacy_test/test_unbind_op.py b/test/legacy_test/test_unbind_op.py index 5447c1bed0004b..f4916ec5afbf5a 100644 --- a/test/legacy_test/test_unbind_op.py +++ b/test/legacy_test/test_unbind_op.py @@ -179,11 +179,11 @@ def setUp(self): self.attrs = {'axis': self.axis} self.setAxis() self.outputs = { - 'Out': [('out%d' % i, self.out[i]) for i in range(len(self.out))] + 'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))] } self.python_api = paddle.unbind self.public_python_api = paddle.unbind - self.python_out_sig = ['out%d' % i for i in range(len(self.out))] + self.python_out_sig = [f'out{i}' for i in range(len(self.out))] def get_dtype(self): return "float64" @@ -338,9 +338,9 @@ def setUp(self): self.inputs = {'X': x} self.attrs = {'axis': self.axis} self.outputs = { - 'Out': [('out%d' % i, self.out[i]) for i in range(len(self.out))] + 'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))] } - self.python_out_sig = ['out%d' % i for i in range(len(self.out))] + self.python_out_sig = [f'out{i}' for i in range(len(self.out))] def outReshape(self): self.out[0] = self.out[0].reshape((2, 2)) @@ -371,11 +371,11 @@ def setUp(self): self.attrs = {'axis': self.axis} self.outputs = { 'Out': [ - ('out%d' % i, convert_float_to_uint16(self.out[i])) + (f'out{i}', convert_float_to_uint16(self.out[i])) for i in range(len(self.out)) ] } - self.python_out_sig = ['out%d' % i for i in range(len(self.out))] + self.python_out_sig = [f'out{i}' for i in range(len(self.out))] def outReshape(self): self.out[0] = self.out[0].reshape((2, 2)) diff --git a/test/mkldnn/test_split_bf16_mkldnn_op.py b/test/mkldnn/test_split_bf16_mkldnn_op.py index c9297de55fae57..ae8edba09fc74d 100644 --- a/test/mkldnn/test_split_bf16_mkldnn_op.py +++ b/test/mkldnn/test_split_bf16_mkldnn_op.py @@ -60,7 +60,7 @@ def setUp(self): self.inputs['SectionsTensorList'] = self.sections_tensor_list self.outputs = { - 'Out': [('out%d' % i, self.out[i]) for i in range(len(self.out))] + 'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))] } def test_check_output(self): diff --git a/test/prim/model/test_resnet_cinn.py b/test/prim/model/test_resnet_cinn.py index 826e5875f18604..7b7981f2508024 100644 --- a/test/prim/model/test_resnet_cinn.py +++ b/test/prim/model/test_resnet_cinn.py @@ -145,16 +145,11 @@ def run(model, data_loader, optimizer, mode): end_time = time.time() print( - "[%s]epoch %d | batch step %d, loss %0.8f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - mode, - epoch, - batch_id, - avg_loss, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"[{mode}]epoch {epoch} | batch step {batch_id}, " + f"loss {avg_loss:0.8f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id >= end_step: break diff --git a/test/prim/model/test_resnet_prim.py b/test/prim/model/test_resnet_prim.py index 26e99a1c1dacab..232f73edd88898 100644 --- a/test/prim/model/test_resnet_prim.py +++ b/test/prim/model/test_resnet_prim.py @@ -159,16 +159,11 @@ def run(model, data_loader, optimizer, mode): end_time = time.time() print( - "[%s]epoch %d | batch step %d, loss %0.8f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - mode, - epoch, - batch_id, - avg_loss, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"[{mode}]epoch {epoch} | batch step {batch_id}, " + f"loss {avg_loss:0.8f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id >= end_step: break diff --git a/test/prim/model/test_resnet_prim_cinn.py b/test/prim/model/test_resnet_prim_cinn.py index 0b572e8d925eff..1d6b48b9ee5944 100644 --- a/test/prim/model/test_resnet_prim_cinn.py +++ b/test/prim/model/test_resnet_prim_cinn.py @@ -146,16 +146,11 @@ def run(model, data_loader, optimizer, mode): end_time = time.time() print( - "[%s]epoch %d | batch step %d, loss %0.8f, acc1 %0.3f, acc5 %0.3f, time %f" - % ( - mode, - epoch, - batch_id, - avg_loss, - total_acc1.numpy() / total_sample, - total_acc5.numpy() / total_sample, - end_time - start_time, - ) + f"[{mode}]epoch {epoch} | batch step {batch_id}, " + f"loss {avg_loss:0.8f}, " + f"acc1 {total_acc1.numpy() / total_sample:0.3f}, " + f"acc5 {total_acc5.numpy() / total_sample:0.3f}, " + f"time {end_time - start_time:f}" ) if batch_id >= end_step: break diff --git a/test/ps/fl_ps_trainer.py b/test/ps/fl_ps_trainer.py index bbee2bcb40913a..7fb746072a2870 100755 --- a/test/ps/fl_ps_trainer.py +++ b/test/ps/fl_ps_trainer.py @@ -126,8 +126,7 @@ def fl_ps_train(): ) end_time = time.time() print( - "trainer epoch %d finished, use time=%d\n" - % ((epoch), end_time - start_time) + f"trainer epoch {epoch} finished, use time={end_time - start_time}\n" ) exe.close() _runtime_handle._stop_worker() diff --git a/test/ps/ps_dnn_model.py b/test/ps/ps_dnn_model.py index db5a6ea520bdc1..ea69ad8324b487 100755 --- a/test/ps/ps_dnn_model.py +++ b/test/ps/ps_dnn_model.py @@ -63,11 +63,11 @@ def __init__( ) ), ) - self.add_sublayer('linear_%d' % i, linear) + self.add_sublayer(f'linear_{i}', linear) self._mlp_layers.append(linear) if acts[i] == 'relu': act = paddle.nn.ReLU() - self.add_sublayer('act_%d' % i, act) + self.add_sublayer(f'act_{i}', act) self._mlp_layers.append(act) def forward(self, sparse_inputs, dense_inputs): @@ -151,10 +151,10 @@ def __init__( ) ), ) - self.add_sublayer('linear_%d' % i, linear) + self.add_sublayer(f'linear_{i}', linear) self._mlp_layers_a.append(linear) act = paddle.nn.ReLU() - self.add_sublayer('act_%d' % i, act) + self.add_sublayer(f'act_{i}', act) self._mlp_layers_a.append(act) # part_b fc @@ -170,10 +170,10 @@ def __init__( ) ), ) - self.add_sublayer('linear_%d' % i, linear) + self.add_sublayer(f'linear_{i}', linear) self._mlp_layers_b.append(linear) act = paddle.nn.ReLU() - self.add_sublayer('act_%d' % i, act) + self.add_sublayer(f'act_{i}', act) self._mlp_layers_b.append(act) # top fc @@ -189,10 +189,10 @@ def __init__( ) ), ) - self.add_sublayer('linear_%d' % i, linear) + self.add_sublayer(f'linear_{i}', linear) self._mlp_layers_top.append(linear) act = paddle.nn.ReLU() - self.add_sublayer('act_%d' % i, act) + self.add_sublayer(f'act_{i}', act) self._mlp_layers_top.append(act) def bottom_a_layer(self, sparse_inputs): diff --git a/test/xpu/process_group_bkcl.py b/test/xpu/process_group_bkcl.py index 13a7f416798cc0..d4d2a944af791f 100644 --- a/test/xpu/process_group_bkcl.py +++ b/test/xpu/process_group_bkcl.py @@ -44,7 +44,7 @@ def config(self): def test_create_process_group_bkcl(self): device_id = paddle.distributed.ParallelEnv().dev_id - paddle.set_device('xpu:%d' % device_id) + paddle.set_device(f'xpu:{device_id}') pg = init_process_group() sys.stdout.write( diff --git a/test/xpu/test_collective_api_base.py b/test/xpu/test_collective_api_base.py index b3a77101949a3f..a43a3e5b6df202 100644 --- a/test/xpu/test_collective_api_base.py +++ b/test/xpu/test_collective_api_base.py @@ -274,10 +274,10 @@ def _run_cluster(self, model_file, envs): tr0_cmd = tr_cmd % (self._python_interp, model_file) tr1_cmd = tr_cmd % (self._python_interp, model_file) path0 = os.path.join( - self.temp_dir.name, "/tmp/tr0_err_%d.log" % os.getpid() + self.temp_dir.name, f"/tmp/tr0_err_{os.getpid()}.log" ) path1 = os.path.join( - self.temp_dir.name, "/tmp/tr1_err_%d.log" % os.getpid() + self.temp_dir.name, f"/tmp/tr1_err_{os.getpid()}.log" ) tr0_pipe = open(path0, "w") tr1_pipe = open(path1, "w") diff --git a/test/xpu/test_distribute_fpn_proposals_op_xpu.py b/test/xpu/test_distribute_fpn_proposals_op_xpu.py index 230b9647f6ef14..d705b12101b7a1 100644 --- a/test/xpu/test_distribute_fpn_proposals_op_xpu.py +++ b/test/xpu/test_distribute_fpn_proposals_op_xpu.py @@ -69,8 +69,7 @@ def setUp(self): 'pixel_offset': self.pixel_offset, } output = [ - ('out%d' % i, self.rois_fpn[i]) - for i in range(len(self.rois_fpn)) + (f'out{i}', self.rois_fpn[i]) for i in range(len(self.rois_fpn)) ] self.outputs = { @@ -180,12 +179,11 @@ def setUp(self): 'pixel_offset': self.pixel_offset, } output = [ - ('out%d' % i, self.rois_fpn[i]) - for i in range(len(self.rois_fpn)) + (f'out{i}', self.rois_fpn[i]) for i in range(len(self.rois_fpn)) ] rois_num_per_level = [ ( - 'rois_num%d' % i, + f'rois_num{i}', np.array(self.rois_fpn[i][1][0]).astype('int32'), ) for i in range(len(self.rois_fpn)) diff --git a/test/xpu/test_meshgrid_op_xpu.py b/test/xpu/test_meshgrid_op_xpu.py index 6c00fa39d71bf9..20d2bab73fd174 100644 --- a/test/xpu/test_meshgrid_op_xpu.py +++ b/test/xpu/test_meshgrid_op_xpu.py @@ -66,9 +66,9 @@ def get_x_shape(self): def set_inputs(self): ins, outs = self.init_test_data() - self.inputs = {'X': [('x%d' % i, ins[i]) for i in range(len(ins))]} + self.inputs = {'X': [(f'x{i}', ins[i]) for i in range(len(ins))]} self.outputs = { - 'Out': [('out%d' % i, outs[i]) for i in range(len(outs))] + 'Out': [(f'out{i}', outs[i]) for i in range(len(outs))] } def set_output(self): diff --git a/test/xpu/test_parallel_dygraph_dataparallel.py b/test/xpu/test_parallel_dygraph_dataparallel.py index 3eed21553b7a53..e5896d8009128b 100644 --- a/test/xpu/test_parallel_dygraph_dataparallel.py +++ b/test/xpu/test_parallel_dygraph_dataparallel.py @@ -44,7 +44,7 @@ def get_cluster_from_args(selected_xpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_xpus) @@ -76,9 +76,9 @@ def start_local_trainers( "FLAGS_selected_xpus": "{}".format( ",".join([str(g) for g in t.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % t.rank, - "PADDLE_CURRENT_ENDPOINT": f"{t.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(t.rank), + "PADDLE_CURRENT_ENDPOINT": str(t.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } diff --git a/test/xpu/test_split_op_xpu.py b/test/xpu/test_split_op_xpu.py index 78f85369c3bf72..488d3220ce20f1 100644 --- a/test/xpu/test_split_op_xpu.py +++ b/test/xpu/test_split_op_xpu.py @@ -50,7 +50,7 @@ def setUp(self): out = np.split(self.x, self.indices_or_sections, self.axis) self.outputs = { - 'Out': [('out%d' % i, out[i]) for i in range(len(out))] + 'Out': [(f'out{i}', out[i]) for i in range(len(out))] } def init_dtype(self): diff --git a/test/xpu/test_top_k_v2_op_xpu.py b/test/xpu/test_top_k_v2_op_xpu.py index 8230aa0ff5d224..3a233f2b716c67 100644 --- a/test/xpu/test_top_k_v2_op_xpu.py +++ b/test/xpu/test_top_k_v2_op_xpu.py @@ -34,7 +34,7 @@ def random_unique_float(shape, dtype): arr = np.unique(arr) assert ( arr.shape[0] >= numel - ), "failed to create enough unique values: %d vs %d" % (arr.shape[0], numel) + ), f"failed to create enough unique values: {arr.shape[0]} vs {numel}" arr = arr[:numel] np.random.shuffle(arr) arr = arr.reshape(shape) diff --git a/test/xpu/test_unbind_op_xpu.py b/test/xpu/test_unbind_op_xpu.py index 6ae7f0514d7754..325b66516c5e3d 100644 --- a/test/xpu/test_unbind_op_xpu.py +++ b/test/xpu/test_unbind_op_xpu.py @@ -127,9 +127,7 @@ def setUp(self): self.attrs = {'axis': self.axis} self.setAxis() self.outputs = { - 'Out': [ - ('out%d' % i, self.out[i]) for i in range(len(self.out)) - ] + 'Out': [(f'out{i}', self.out[i]) for i in range(len(self.out))] } def _set_op_type(self): From e982d39132d7637369fa683b23ff2dfdafe80ed7 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 19:25:30 +0800 Subject: [PATCH 227/288] [CodeStyle][UP031] Use f-string instead of percent format in tools (part26) (#70043) * [CodeStyle][UP031] Use f-string instead of percent format in tools (part26) * apply copilot grammar suggestions --- tools/CrossStackProfiler/CspFileReader.py | 4 +- tools/CrossStackProfiler/CspReporter.py | 15 +++---- tools/CrossStackProfiler/DCGMFileReader.py | 21 +++------ tools/CrossStackProfiler/NetFileReader.py | 10 ++--- tools/CrossStackProfiler/ProfileFileReader.py | 45 +++++++------------ tools/continuous_integration/bisect.py | 2 +- tools/timeline.py | 17 ++++--- 7 files changed, 43 insertions(+), 71 deletions(-) diff --git a/tools/CrossStackProfiler/CspFileReader.py b/tools/CrossStackProfiler/CspFileReader.py index e6262562fcdb44..75cf430e3dbae5 100755 --- a/tools/CrossStackProfiler/CspFileReader.py +++ b/tools/CrossStackProfiler/CspFileReader.py @@ -281,7 +281,7 @@ def getDCGMInfoFileName(self, groupId, gpuId, tmpPath="./tmp"): return self.getFileName("dcgm", groupId, gpuId, tmpPath) def getFileName(self, name, groupId, gpuId, tmpPath="./tmp"): - return os.path.join(tmpPath, "%s_%d_%d.json" % (name, groupId, gpuId)) + return os.path.join(tmpPath, f"{name}_{groupId}_{gpuId}.json") def getOpInfoDict(self, groupId, gpuId, tmpPath="./tmp"): return self.getDict("opinfo", groupId, gpuId, tmpPath) @@ -292,7 +292,7 @@ def getDcgmInfoDict(self, groupId, gpuId, tmpPath="./tmp"): def getDict(self, name, groupId, gpuId, tmpPath="./tmp"): fileName = self.getFileName(name, groupId, gpuId, tmpPath) if not os.path.isfile(fileName): - raise OSError(f"[{fileName}] is not existed!") + raise OSError(f"[{fileName}] does not existed!") data = {} with open(fileName, "r") as rf: diff --git a/tools/CrossStackProfiler/CspReporter.py b/tools/CrossStackProfiler/CspReporter.py index 076cbb3cff333b..ae187dc28c3627 100755 --- a/tools/CrossStackProfiler/CspReporter.py +++ b/tools/CrossStackProfiler/CspReporter.py @@ -89,8 +89,7 @@ def __init__(self, args): def _checkArgs(self): if self._trainerNum % self._groupSize != 0: raise Exception( - "Input args error: trainerNum[%d] %% groupSize[%d] != 0" - % (self._trainerNum, self._groupSize) + f"Input args error: trainerNum[{self._trainerNum}] %% groupSize[{self._groupSize}] != 0" ) def _init_logger(self): @@ -217,16 +216,14 @@ def _generateTraceFileByGroup(self, groupId, processNum): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[traceFile]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, 1) + f"[traceFile]: process [{subproc.pid}] has been started, total task num is {1} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[traceFile]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[traceFile]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) def generateTraceFile(self, processNum=8): @@ -244,15 +241,13 @@ def generateTraceFile(self, processNum=8): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[GroupTraceFile]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, 1) + f"[GroupTraceFile]: process [{subproc.pid}] has been started, total task num is {1} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[GroupTraceFile]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[GroupTraceFile]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) diff --git a/tools/CrossStackProfiler/DCGMFileReader.py b/tools/CrossStackProfiler/DCGMFileReader.py index eb31ad7820a781..f0f77dc0e5253a 100755 --- a/tools/CrossStackProfiler/DCGMFileReader.py +++ b/tools/CrossStackProfiler/DCGMFileReader.py @@ -39,9 +39,7 @@ def parseFileByGroup(self, groupId, processNum=8): return self._parseTask(fileFist) else: - self._logger.info( - "using [%d] process to do this work!" % processNum - ) + self._logger.info(f"using [{processNum}] process to do this work!") processPool = [] pidList = [] @@ -61,16 +59,14 @@ def parseFileByGroup(self, groupId, processNum=8): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[DCGM reader]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, len(processPool)) + f"[DCGM reader]: process [{subproc.pid}] has been started, total task num is {len(processPool)} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[DCGM reader]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[DCGM reader]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) isFistProcess = True @@ -169,8 +165,7 @@ def _getDCGMTraceInfoByGpuId( self, groupId, gpuId, dcgm_data, pid_map, q=None ): self._logger.info( - "Begin to generate dcgm info, groupId = %d, gpuID = %d ..." - % (groupId, gpuId) + f"Begin to generate dcgm info, groupId = {groupId}, gpuID = {gpuId} ..." ) gpuDcgmData = dcgm_data[dcgm_data['Entity'].isin([gpuId])] @@ -198,7 +193,7 @@ def _getDCGMTraceInfoByGpuId( di['ts'] = self._align_ts(int(row['ts'])) # di['ts'] = int(row['ts']) di['cat'] = metric - di['tid'] = "%d_%d" % (groupId, trainerId) + di['tid'] = f"{groupId}_{trainerId}" di['ph'] = "C" di['id'] = trainerId @@ -244,16 +239,14 @@ def getDCGMTraceInfo(self, groupId, processNum=8): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[DCGM info]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, 1) + f"[DCGM info]: process [{subproc.pid}] has been started, total task num is {1} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[DCGM info]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[DCGM info]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) dcgmInfo = {} diff --git a/tools/CrossStackProfiler/NetFileReader.py b/tools/CrossStackProfiler/NetFileReader.py index 0aedd3efd72a37..5f55f5951a8238 100755 --- a/tools/CrossStackProfiler/NetFileReader.py +++ b/tools/CrossStackProfiler/NetFileReader.py @@ -33,14 +33,14 @@ def _parseSingleFile(self, fileNameList, tx_pid, rx_pid, q=None): metaInfo['name'] = 'process_name' metaInfo['ph'] = 'M' metaInfo['pid'] = tx_pid - metaInfo['args'] = {'name': "%02d_tx" % tx_pid} + metaInfo['args'] = {'name': f"{tx_pid:02}_tx"} traceEventList.append(metaInfo) metaInfo = {} metaInfo['name'] = 'process_name' metaInfo['ph'] = 'M' metaInfo['pid'] = rx_pid - metaInfo['args'] = {'name': "%02d_rx" % rx_pid} + metaInfo['args'] = {'name': f"{rx_pid:02}_rx"} traceEventList.append(metaInfo) @@ -98,16 +98,14 @@ def parseFileByGroup(self, groupId, processNum=8): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[Net info]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, len(processPool)) + f"[Net info]: process [{subproc.pid}] has been started, total task num is {len(processPool)} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[Net info]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[Net info]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) traceInfo = {} diff --git a/tools/CrossStackProfiler/ProfileFileReader.py b/tools/CrossStackProfiler/ProfileFileReader.py index 97de2b86a68645..4f842a2063f051 100755 --- a/tools/CrossStackProfiler/ProfileFileReader.py +++ b/tools/CrossStackProfiler/ProfileFileReader.py @@ -43,7 +43,7 @@ def _parseTask(self, taskList, q=None): for fileName in taskList: rankId = self.getRankId(fileName) - profile_dict["trainerRank.%03d" % (rankId)] = self._parseSingleFile( + profile_dict[f"trainerRank.{rankId:03}"] = self._parseSingleFile( fileName ) self._logger.info(f"I finish processing {fileName}!") @@ -147,8 +147,7 @@ def getPipeLineInfo(self, groupId, processNum=8): fileFist = self.getFileListByGroup(groupId) self._logger.info( - "using [%d] process to do this work, total task num is %d!" - % (processNum, len(fileFist)) + f"using [{processNum}] process to do this work, total task num is {len(fileFist)}!" ) processPool = [] pidList = [] @@ -169,16 +168,14 @@ def getPipeLineInfo(self, groupId, processNum=8): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[pipeline info]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, len(task)) + f"[pipeline info]: process [{subproc.pid}] has been started, total task num is {len(task)} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[pipeline info]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[pipeline info]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) pipeLineInfo = {} @@ -187,9 +184,7 @@ def getPipeLineInfo(self, groupId, processNum=8): metaInfo['name'] = 'process_name' metaInfo['ph'] = 'M' metaInfo['pid'] = 0 - metaInfo['args'] = { - 'name': "%02d_pipeLineInfo" % PIPELINEINFO_TRACE_NUM - } + metaInfo['args'] = {'name': f"{PIPELINEINFO_TRACE_NUM:02}_pipeLineInfo"} for t in processPool: for k, v in q.get().items(): @@ -220,13 +215,12 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): # -1 device id represents CUDA API(RunTime) call.(e.g. cudaLaunch, cudaMemcpy) if event.device_id == -1: chrome_trace.emit_pid( - "%02d_%s:cuda_api" % (lineNum, k), pid + f"{lineNum:02}_{k}:cuda_api", pid ) lineNum = lineNum + 1 else: chrome_trace.emit_pid( - "%02d_%s:cpu:block:%d" - % (lineNum, k, event.device_id), + f"{lineNum:02}_{k}:cpu:block:{event.device_id}", pid, ) lineNum = lineNum + 1 @@ -238,8 +232,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): devices[(k, event.device_id, "GPUKernel")] = pid chrome_trace.emit_pid( - "%02d_%s:gpu:%d" - % (lineNum, k, event.device_id), + f"{lineNum:02}_{k}:gpu:{event.device_id}", pid, ) lineNum = lineNum + 1 @@ -255,8 +248,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): mem_devices[(k, mevent.device_id, "GPU")] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:gpu:%d" - % (lineNum, k, mevent.device_id), + f"{lineNum:02}_memory usage on {k}:gpu:{mevent.device_id}", pid, ) lineNum = lineNum + 1 @@ -267,8 +259,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): mem_devices[(k, mevent.device_id, "CPU")] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:cpu:%d" - % (lineNum, k, mevent.device_id), + f"{lineNum:02}_memory usage on {k}:cpu:{mevent.device_id}", pid, ) lineNum = lineNum + 1 @@ -286,8 +277,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): (k, mevent.device_id, "CUDAPinnedPlace") ] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:cudapinnedplace:%d" - % (lineNum, k, mevent.device_id), + f"{lineNum:02}_memory usage on {k}:cudapinnedplace:{mevent.device_id}", pid, ) lineNum = lineNum + 1 @@ -297,7 +287,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): mem_devices[(k, 0, "CPU")] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:cpu:%d" % (lineNum, k, 0), pid + f"{lineNum:02}_memory usage on {k}:cpu:{0}", pid ) lineNum = lineNum + 1 if (k, 0, "GPU") not in mem_devices: @@ -307,7 +297,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): mem_devices[(k, 0, "GPU")] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:gpu:%d" % (lineNum, k, 0), pid + f"{lineNum:02}_memory usage on {k}:gpu:{0}", pid ) lineNum = lineNum + 1 if (k, 0, "CUDAPinnedPlace") not in mem_devices: @@ -316,8 +306,7 @@ def _allocate_pids(self, profile_dict, gpuId, initPid): mem_devices[(k, 0, "CUDAPinnedPlace")] = pid chrome_trace.emit_pid( - "%02d_memory usage on %s:cudapinnedplace:%d" - % (lineNum, k, 0), + f"{lineNum:02}_memory usage on {k}:cudapinnedplace:{0}", pid, ) lineNum = lineNum + 1 @@ -484,16 +473,14 @@ def getOPTraceInfo(self, groupId): subproc.start() pidList.append(subproc.pid) self._logger.info( - "[op info]: process [%d] has been started, total task num is %d ..." - % (subproc.pid, 1) + f"[op info]: process [{subproc.pid}] has been started, total task num is {1} ..." ) for t in processPool: t.join() pidList.remove(t.pid) self._logger.info( - "[op info]: process [%d] has exited! remained %d process!" - % (t.pid, len(pidList)) + f"[op info]: process [{t.pid}] has exited! remained {len(pidList)} process!" ) opInfo = {} diff --git a/tools/continuous_integration/bisect.py b/tools/continuous_integration/bisect.py index 2feaf7be5ec6ec..fa43cb6f4691bb 100644 --- a/tools/continuous_integration/bisect.py +++ b/tools/continuous_integration/bisect.py @@ -113,7 +113,7 @@ def print_arguments(): # Clean builds and compile. # We assume mainline commits should always compile. os.chdir(args.build_dir) - sys.stdout.write('eval commit %d/%d: %s\n' % (pick_idx, len(commits), pick)) + sys.stdout.write(f'eval commit {pick_idx}/{len(commits)}: {pick}\n') # Link error can happen without complete clean up. cmd = ( 'rm -rf * && ' diff --git a/tools/timeline.py b/tools/timeline.py index 5e16e0b9bf4f3b..e808edd43ba8e7 100644 --- a/tools/timeline.py +++ b/tools/timeline.py @@ -151,14 +151,14 @@ def _allocate_pids(self): self._chrome_trace.emit_pid(f"{k}:cuda_api", pid) else: self._chrome_trace.emit_pid( - "%s:cpu:block:%d" % (k, event.device_id), pid + f"{k}:cpu:block:{event.device_id}", pid ) elif event.type == profiler_pb2.Event.GPUKernel: if (k, event.device_id, "GPUKernel") not in self._devices: pid = self._allocate_pid() self._devices[(k, event.device_id, "GPUKernel")] = pid self._chrome_trace.emit_pid( - "%s:gpu:%d" % (k, event.device_id), pid + f"{k}:gpu:{event.device_id}", pid ) if not hasattr(profile_pb, "mem_events"): continue @@ -168,7 +168,7 @@ def _allocate_pids(self): pid = self._allocate_pid() self._mem_devices[(k, mevent.device_id, "GPU")] = pid self._chrome_trace.emit_pid( - "memory usage on %s:gpu:%d" % (k, mevent.device_id), + f"memory usage on {k}:gpu:{mevent.device_id}", pid, ) elif mevent.place == profiler_pb2.MemEvent.CPUPlace: @@ -176,7 +176,7 @@ def _allocate_pids(self): pid = self._allocate_pid() self._mem_devices[(k, mevent.device_id, "CPU")] = pid self._chrome_trace.emit_pid( - "memory usage on %s:cpu:%d" % (k, mevent.device_id), + f"memory usage on {k}:cpu:{mevent.device_id}", pid, ) elif mevent.place == profiler_pb2.MemEvent.CUDAPinnedPlace: @@ -190,27 +190,26 @@ def _allocate_pids(self): (k, mevent.device_id, "CUDAPinnedPlace") ] = pid self._chrome_trace.emit_pid( - "memory usage on %s:cudapinnedplace:%d" - % (k, mevent.device_id), + f"memory usage on {k}:cudapinnedplace:{mevent.device_id}", pid, ) if (k, 0, "CPU") not in self._mem_devices: pid = self._allocate_pid() self._mem_devices[(k, 0, "CPU")] = pid self._chrome_trace.emit_pid( - "memory usage on %s:cpu:%d" % (k, 0), pid + f"memory usage on {k}:cpu:{0}", pid ) if (k, 0, "GPU") not in self._mem_devices: pid = self._allocate_pid() self._mem_devices[(k, 0, "GPU")] = pid self._chrome_trace.emit_pid( - "memory usage on %s:gpu:%d" % (k, 0), pid + f"memory usage on {k}:gpu:{0}", pid ) if (k, 0, "CUDAPinnedPlace") not in self._mem_devices: pid = self._allocate_pid() self._mem_devices[(k, 0, "CUDAPinnedPlace")] = pid self._chrome_trace.emit_pid( - "memory usage on %s:cudapinnedplace:%d" % (k, 0), pid + f"memory usage on {k}:cudapinnedplace:{0}", pid ) def _allocate_events(self): From 2a02a980591cdc2331ea666287c15532fcb7375c Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Sun, 8 Dec 2024 20:37:11 +0800 Subject: [PATCH 228/288] [CodeStyle][UP031] Use f-string instead of percent format in part of distributed files (part22) (#70034) --- python/paddle/distributed/spawn.py | 37 ++++++++---------- .../transpiler/distribute_transpiler.py | 37 ++++++++---------- .../paddle/distributed/utils/launch_utils.py | 28 +++++++------- .../incubate/distributed/fleet/fleet_util.py | 38 +++++-------------- .../fleet/parameter_server/ir/pserver_pass.py | 2 +- .../fleet/parameter_server/ir/public.py | 22 +++++------ .../parameter_server/ir/vars_metatools.py | 2 +- 7 files changed, 65 insertions(+), 101 deletions(-) diff --git a/python/paddle/distributed/spawn.py b/python/paddle/distributed/spawn.py index 81425fbaa7a4c1..52c94c0e4ab511 100644 --- a/python/paddle/distributed/spawn.py +++ b/python/paddle/distributed/spawn.py @@ -202,11 +202,10 @@ def _get_subprocess_env_list(nprocs, options): if args.selected_devices is None: if len(env_devices_list) < nprocs: raise RuntimeError( - "the number of visible devices(%d) is less than the number " - "of spawn processes(%d), please ensure that the correct " + f"the number of visible devices({len(env_devices_list)}) is less than the number " + f"of spawn processes({nprocs}), please ensure that the correct " "`nprocs` argument is passed or the environment variable " "`CUDA_VISIBLE_DEVICES` is correctly configured." - % (len(env_devices_list), nprocs) ) args.selected_devices = ",".join( [str(env_devices_list[x]) for x in range(0, nprocs)] @@ -215,10 +214,9 @@ def _get_subprocess_env_list(nprocs, options): selected_device_list = args.selected_devices.split(',') if len(selected_device_list) != nprocs: raise ValueError( - "The number of selected devices(%s) is not equal to " - "the number of spawn processes(%d), please ensure that the " + f"The number of selected devices({len(selected_device_list)}) is not equal to " + f"the number of spawn processes({nprocs}), please ensure that the " "correct `nprocs` and `gpus` arguments are passed." - % (len(selected_device_list), nprocs) ) for card_id in selected_device_list: if card_id not in env_devices_list: @@ -243,11 +241,10 @@ def _get_subprocess_env_list(nprocs, options): if args.selected_devices is None: if len(env_devices_list) < nprocs: raise RuntimeError( - "the number of visible devices(%d) is less than the number " - "of spawn processes(%d), please ensure that the correct " + f"the number of visible devices({len(env_devices_list)}) is less than the number " + f"of spawn processes({nprocs}), please ensure that the correct " "`nprocs` argument is passed or the environment variable " "`XPU_VISIBLE_DEVICES` is correctly configured." - % (len(env_devices_list), nprocs) ) args.selected_devices = ",".join( [str(env_devices_list[x]) for x in range(0, nprocs)] @@ -256,10 +253,9 @@ def _get_subprocess_env_list(nprocs, options): selected_device_list = args.selected_devices.split(',') if len(selected_device_list) != nprocs: raise ValueError( - "The number of selected devices(%s) is not equal to " - "the number of spawn processes(%d), please ensure that the " + f"The number of selected devices({len(selected_device_list)}) is not equal to " + f"the number of spawn processes({nprocs}), please ensure that the " "correct `nprocs` and `xpus` arguments are passed." - % (len(selected_device_list), nprocs) ) for card_id in selected_device_list: if card_id not in env_devices_list: @@ -301,11 +297,10 @@ def _get_subprocess_env_list(nprocs, options): if len(env_devices_list) < nprocs: raise RuntimeError( - "the number of visible devices(%d) is less than the number " - "of spawn processes(%d), please ensure that the correct " + f"the number of visible devices({len(env_devices_list)}) is less than the number " + f"of spawn processes({nprocs}), please ensure that the correct " "`nprocs` argument is passed or the environment variable " - "`FLAGS_selected_%ss` is correctly configured." - % (len(env_devices_list), nprocs, custom_device_name) + f"`FLAGS_selected_{custom_device_name}s` is correctly configured." ) args.selected_devices = ",".join( [str(env_devices_list[x]) for x in range(0, nprocs)] @@ -441,20 +436,18 @@ def _throw_exception(self, error_index): if exitcode < 0: name = signal.Signals(-exitcode).name raise Exception( - "Process %d terminated with signal %s." - % (error_index, name) + f"Process {error_index} terminated with signal {name}." ) else: raise Exception( - "Process %d terminated with exit code %d." - % (error_index, exitcode) + f"Process {error_index} terminated with exit code {exitcode}." ) original_trace = self.error_queues[error_index].get() msg = ( "\n\n----------------------------------------------\n" - "Process %d terminated with the following error:\n" - "----------------------------------------------\n\n" % error_index + f"Process {error_index} terminated with the following error:\n" + "----------------------------------------------\n\n" ) msg += original_trace raise Exception(msg) diff --git a/python/paddle/distributed/transpiler/distribute_transpiler.py b/python/paddle/distributed/transpiler/distribute_transpiler.py index 43ccb562615cb1..78a6ef55625691 100644 --- a/python/paddle/distributed/transpiler/distribute_transpiler.py +++ b/python/paddle/distributed/transpiler/distribute_transpiler.py @@ -88,7 +88,7 @@ def __init__(self, varname, offset, size): self.size = size def __str__(self): - return "%s:%d:%d" % (self.varname, self.offset, self.size) + return f"{self.varname}:{self.offset}:{self.size}" def same_or_split_var(p_name, var_name): @@ -1334,7 +1334,7 @@ def get_pserver_program(self, endpoint): ): for trainer_id in range(self.trainer_num): var = pserver_program.global_block().create_var( - name="%s.trainer_%d" % (orig_var_name, trainer_id), + name=f"{orig_var_name}.trainer_{trainer_id}", persistable=False, type=v.type, dtype=v.dtype, @@ -1367,7 +1367,7 @@ def get_pserver_program(self, endpoint): for p in self.param_grad_ep_mapping[endpoint]["params"]: # each parameter should have w_bak for each trainer id for i in range(self.trainer_num): - param_bak_name = "%s.trainer_%d_bak" % (p.name, i) + param_bak_name = f"{p.name}.trainer_{i}_bak" tmpvar = pserver_program.global_block().create_var( # NOTE: this var name format is used in `request_get_handler` name=param_bak_name, @@ -1835,8 +1835,7 @@ def _update_dist_lookup_table_vars( if self.sync_mode: self.trainer_side_table_grad_list = [ program.global_block().create_var( - name="%s.trainer_%d.pserver_%d" - % (table_grad_var.name, self.trainer_id, index), + name=f"{table_grad_var.name}.trainer_{self.trainer_id}.pserver_{index}", type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype, @@ -1846,7 +1845,7 @@ def _update_dist_lookup_table_vars( else: self.trainer_side_table_grad_list = [ program.global_block().create_var( - name="%s.pserver_%d" % (table_grad_var.name, index), + name=f"{table_grad_var.name}.pserver_{index}", type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype, @@ -2178,8 +2177,7 @@ def _create_table_optimize_block( table_grad_var = self.table_param_grad[1] pserver_side_table_grad_list = [ pserver_program.global_block().create_var( - name="%s.trainer_%d.pserver_%d" - % (table_grad_var.name, index, pserver_index), + name=f"{table_grad_var.name}.trainer_{index}.pserver_{pserver_index}", type=table_grad_var.type, shape=table_grad_var.shape, dtype=table_grad_var.dtype, @@ -2281,10 +2279,7 @@ def _create_vars_from_blocklist( orig_var = program.global_block().var(varname) if len(split) == 1: if self.sync_mode and add_trainer_suffix: - new_var_name = "%s.trainer_%d" % ( - orig_var.name, - self.trainer_id, - ) + new_var_name = f"{orig_var.name}.trainer_{self.trainer_id}" program.global_block()._rename_var(varname, new_var_name) var_mapping[varname] = [ program.global_block().var(new_var_name) @@ -2310,13 +2305,11 @@ def _create_vars_from_blocklist( splited_shape.extend(orig_shape[1:]) new_var_name = "" if self.sync_mode and add_trainer_suffix: - new_var_name = "%s.block%d.trainer_%d" % ( - varname, - i, - self.trainer_id, + new_var_name = ( + f"{varname}.block{i}.trainer_{self.trainer_id}" ) else: - new_var_name = "%s.block%d" % (varname, i) + new_var_name = f"{varname}.block{i}" var = program.global_block().create_var( name=new_var_name, persistable=False, @@ -2479,7 +2472,7 @@ def _append_pserver_grad_merge_ops( ): vars2merge = [] for i in range(self.trainer_num): - per_trainer_name = "%s.trainer_%d" % (merged_var_name, i) + per_trainer_name = f"{merged_var_name}.trainer_{i}" vars2merge.append(pserver_block.vars[per_trainer_name]) optimize_block.append_op( type="sum", @@ -2864,7 +2857,7 @@ def _get_lr_ops(self): counter_var = outputs[key] all_trainer_counter_inputs = [ self.origin_program.global_block().create_var( - name="%s.trainer_%d" % (counter_var.name, id_), + name=f"{counter_var.name}.trainer_{id_}", type=counter_var.type, shape=counter_var.shape, dtype=counter_var.dtype, @@ -2887,9 +2880,9 @@ def _get_lr_ops(self): 'value', float(0.0 - self.trainer_num) ) for var in all_trainer_counter_inputs: - if var.name == "%s.trainer_%d" % ( - counter_var.name, - self.trainer_id, + if ( + var.name + == f"{counter_var.name}.trainer_{self.trainer_id}" ): self.counter_var = var self.startup_program.global_block().create_var( diff --git a/python/paddle/distributed/utils/launch_utils.py b/python/paddle/distributed/utils/launch_utils.py index 101963a1a8b087..a9d52da552dc5d 100644 --- a/python/paddle/distributed/utils/launch_utils.py +++ b/python/paddle/distributed/utils/launch_utils.py @@ -58,7 +58,7 @@ def get_cluster_from_args(args, selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) @@ -395,9 +395,9 @@ def _prepare_trainer_env(cluster, trainer, backend=None): "FLAGS_selected_xpus": "{}".format( ",".join([str(g) for g in trainer.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % trainer.rank, - "PADDLE_CURRENT_ENDPOINT": f"{trainer.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(trainer.rank), + "PADDLE_CURRENT_ENDPOINT": str(trainer.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } elif backend == 'nccl': @@ -405,17 +405,17 @@ def _prepare_trainer_env(cluster, trainer, backend=None): "FLAGS_selected_gpus": "{}".format( ",".join([str(g) for g in trainer.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % trainer.rank, - "PADDLE_CURRENT_ENDPOINT": f"{trainer.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(trainer.rank), + "PADDLE_CURRENT_ENDPOINT": str(trainer.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } elif backend == 'gloo': # NOTE (xiongkun) default fall back into cpu only proc_env = { - "PADDLE_TRAINER_ID": "%d" % trainer.rank, - "PADDLE_CURRENT_ENDPOINT": f"{trainer.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(trainer.rank), + "PADDLE_CURRENT_ENDPOINT": str(trainer.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), "PADDLE_DISTRI_BACKEND": backend, # only add here, other will be auto } @@ -427,9 +427,9 @@ def _prepare_trainer_env(cluster, trainer, backend=None): f"FLAGS_selected_{custom_device_name}s": "{}".format( ",".join([str(g) for g in trainer.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % trainer.rank, - "PADDLE_CURRENT_ENDPOINT": f"{trainer.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(trainer.rank), + "PADDLE_CURRENT_ENDPOINT": str(trainer.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } else: @@ -473,7 +473,7 @@ def start_local_trainers( fn = None if log_dir is not None: os.makedirs(log_dir, exist_ok=True) - fn = open("%s/workerlog.%d" % (log_dir, idx), "a") + fn = open(f"{log_dir}/workerlog.{idx}", "a") proc = subprocess.Popen(cmd, env=current_env, stdout=fn, stderr=fn) else: proc = subprocess.Popen(cmd, env=current_env) diff --git a/python/paddle/incubate/distributed/fleet/fleet_util.py b/python/paddle/incubate/distributed/fleet/fleet_util.py index a96172c2f38107..c80867376dc12a 100644 --- a/python/paddle/incubate/distributed/fleet/fleet_util.py +++ b/python/paddle/incubate/distributed/fleet/fleet_util.py @@ -456,13 +456,7 @@ def write_model_donefile( if fleet.worker_index() == 0: donefile_path = output_path + "/" + donefile_name - content = "%s\t%lu\t%s\t%s\t%d" % ( - day, - xbox_base_key, - model_path, - pass_id, - 0, - ) + content = f"{day}\t{xbox_base_key}\t{model_path}\t{pass_id}\t{0}" configs = { "fs.default.name": hadoop_fs_name, "hadoop.job.ugi": hadoop_fs_ugi, @@ -677,10 +671,10 @@ def write_cache_donefile( table_id = kwargs.get("table_id", 0) if pass_id != "-1": - suffix_name = "/%s/delta-%s/%03d_cache" % (day, pass_id, table_id) + suffix_name = f"/{day}/delta-{pass_id}/{table_id:03}_cache" model_path = output_path.rstrip("/") + suffix_name else: - suffix_name = "/%s/base/%03d_cache" % (day, table_id) + suffix_name = f"/{day}/base/{table_id:03}_cache" model_path = output_path.rstrip("/") + suffix_name if fleet.worker_index() == 0: @@ -695,10 +689,7 @@ def write_cache_donefile( f"not write because {donefile_path} already exists" ) else: - meta_str = "file_prefix:part\npart_num:%s\nkey_num:%d\n" % ( - file_num, - key_num, - ) + meta_str = f"file_prefix:part\npart_num:{file_num}\nkey_num:{key_num}\n" with open(donefile_name, "w") as f: f.write(meta_str) client.upload(donefile_name, model_path) @@ -1362,9 +1353,9 @@ def get_online_pass_interval( start += split_interval continue if is_data_hourly_placed: - split_path.append("%02d" % h) + split_path.append(f"{h:02}") else: - split_path.append("%02d%02d" % (h, m)) + split_path.append(f"{h:02}{m:02}") start += split_interval start = 0 @@ -2023,13 +2014,7 @@ def write_model_donefile( if fleet.worker_index() == 0: donefile_path = output_path + "/" + donefile_name - content = "%s\t%lu\t%s\t%s\t%d" % ( - day, - xbox_base_key, - model_path, - pass_id, - 0, - ) + content = f"{day}\t{xbox_base_key}\t{model_path}\t{pass_id}\t{0}" if self._afs.is_file(donefile_path): self._afs.download(donefile_path, donefile_name) pre_content = "" @@ -2230,10 +2215,10 @@ def write_cache_donefile( table_id = kwargs.get("table_id", 0) if pass_id != "-1": - suffix_name = "/%s/delta-%s/%03d_cache" % (day, pass_id, table_id) + suffix_name = f"/{day}/delta-{pass_id}/{table_id:03}_cache" model_path = output_path.rstrip("/") + suffix_name else: - suffix_name = "/%s/base/%03d_cache" % (day, table_id) + suffix_name = f"/{day}/base/{table_id:03}_cache" model_path = output_path.rstrip("/") + suffix_name if fleet.worker_index() == 0: @@ -2244,10 +2229,7 @@ def write_cache_donefile( f"not write because {donefile_path} already exists" ) else: - meta_str = "file_prefix:part\npart_num:%s\nkey_num:%d\n" % ( - file_num, - key_num, - ) + meta_str = f"file_prefix:part\npart_num:{file_num}\nkey_num:{key_num}\n" with open(donefile_name, "w") as f: f.write(meta_str) self._afs.upload(donefile_name, donefile_path) diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py index c240c1af089b5b..6f43c697b9da88 100644 --- a/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py +++ b/python/paddle/incubate/distributed/fleet/parameter_server/ir/pserver_pass.py @@ -431,7 +431,7 @@ def _append_pserver_grad_merge_ops( if config.is_sync_mode() and trainers > 1: vars2merge = [] for i in range(trainers): - per_trainer_name = "%s.trainer_%d" % (merged_var_name, i) + per_trainer_name = f"{merged_var_name}.trainer_{i}" per_trainer_var = pserver_block.create_var( name=per_trainer_name, persistable=False, diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py index 409250460a57f6..78f31f8af9c592 100755 --- a/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py +++ b/python/paddle/incubate/distributed/fleet/parameter_server/ir/public.py @@ -967,7 +967,7 @@ def _create_vars_from_blocklist(self, block_list): if len(orig_shape) >= 2: splited_shape.extend(orig_shape[1:]) - new_var_name = "%s.block%d" % (varname, i) + new_var_name = f"{varname}.block{i}" slice_var = vars_metatools.VarStruct( name=new_var_name, shape=splited_shape, @@ -1427,12 +1427,11 @@ def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps): 1.0, lr_decay_steps, lr_scheduler.gamma, True ) lr_name = lr.name - logging.warn( - "ExponentialDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"ExponentialDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) elif isinstance(lr_scheduler, NoamDecay): with paddle.static.program_guard( @@ -1442,9 +1441,8 @@ def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps): lr_scheduler.d_model, lr_scheduler.warmup_steps, 1.0 ) lr_name = lr.name - logging.warn( - "NoamDecay is set, warmup steps is [ %d ]" - % lr_scheduler.warmup_steps + logging.warning( + f"NoamDecay is set, warmup steps is [ {lr_scheduler.warmup_steps} ]" ) elif isinstance(lr_scheduler, NaturalExpDecay): with paddle.static.program_guard( @@ -1454,12 +1452,11 @@ def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps): 1.0, lr_decay_steps, lr_scheduler.gamma, True ) lr_name = lr.name - logging.warn( - "NaturalExpDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"NaturalExpDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) elif isinstance(lr_scheduler, InverseTimeDecay): with paddle.static.program_guard( @@ -1469,12 +1466,11 @@ def _get_lr_scheduler_program(lr_scheduler, lr_param_dict, lr_decay_steps): 1.0, lr_decay_steps, lr_scheduler.gamma, True ) lr_name = lr.name - logging.warn( - "InverseTimeDecay is set, staircase = True, global learning rate decay step is [ %d ], Change decay steps as follow: \n" + logging.warning( + f"InverseTimeDecay is set, staircase = True, global learning rate decay step is [ {lr_decay_steps} ], Change decay steps as follow: \n" "\t strategy = paddle.distributed.fleet.DistributedStrategy() \n " "\t strategy.a_sync = True \n" "\t strategy.a_sync_configs= { 'lr_decay_steps' : YOUR_DECAY_STEP } \n" - % lr_decay_steps ) else: raise ValueError( diff --git a/python/paddle/incubate/distributed/fleet/parameter_server/ir/vars_metatools.py b/python/paddle/incubate/distributed/fleet/parameter_server/ir/vars_metatools.py index 51c2e325bcb802..d252ad2cf10c2d 100644 --- a/python/paddle/incubate/distributed/fleet/parameter_server/ir/vars_metatools.py +++ b/python/paddle/incubate/distributed/fleet/parameter_server/ir/vars_metatools.py @@ -36,7 +36,7 @@ def __init__(self, varname, offset, size): self.size = size def __str__(self): - return "%s:%d:%d" % (self.varname, self.offset, self.size) + return f"{self.varname}:{int(self.offset)}:{int(self.size)}" def create_var_struct(var): From 4b5dbbdd402b764011a5bbb8361c721a61151e2c Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Mon, 9 Dec 2024 00:15:57 +0800 Subject: [PATCH 229/288] [CodeStyle][UP031] Use f-string instead of percent format in legacy uts (part28) (#70045) --- test/legacy_test/test_cholesky_op.py | 4 ++-- .../test_collect_fpn_proposals_op.py | 10 +++++----- test/legacy_test/test_collective_api_base.py | 4 ++-- test/legacy_test/test_dist_base.py | 6 +++--- .../test_dist_fleet_sparse_embedding_ctr.py | 2 +- test/legacy_test/test_dist_hapi_model.py | 8 ++++---- test/legacy_test/test_dist_train.py | 8 ++++---- .../test_distribute_fpn_proposals_op.py | 6 +++--- test/legacy_test/test_fleet_base_single.py | 3 +-- ...test_fusion_transpose_flatten_concat_op.py | 2 +- test/legacy_test/test_imperative_deepcf.py | 20 +++++++++---------- test/legacy_test/test_imperative_ptb_rnn.py | 4 ++-- test/legacy_test/test_imperative_resnet.py | 2 +- .../test_imperative_save_load_v2.py | 4 ++-- .../legacy_test/test_imperative_se_resnext.py | 2 +- ..._imperative_transformer_sorted_gradient.py | 4 ++-- test/legacy_test/test_listen_and_serv_op.py | 2 +- .../test_parallel_dygraph_dataparallel.py | 14 ++++++------- ...t_parallel_dygraph_dataparallel_cpuonly.py | 8 ++++---- 19 files changed, 56 insertions(+), 57 deletions(-) diff --git a/test/legacy_test/test_cholesky_op.py b/test/legacy_test/test_cholesky_op.py index c364947299351f..461464bdfeef2f 100644 --- a/test/legacy_test/test_cholesky_op.py +++ b/test/legacy_test/test_cholesky_op.py @@ -114,8 +114,8 @@ def func(self, place): if x_init: if len(x_init) != len(root): raise ValueError( - 'len(x_init) (=%d) is not the same' - ' as len(x) (= %d)' % (len(x_init), len(root)) + f'len(x_init) (={len(x_init)}) is not the same' + f' as len(x) (={len(root)})' ) # init variable in main program for var, arr in zip(root, x_init): diff --git a/test/legacy_test/test_collect_fpn_proposals_op.py b/test/legacy_test/test_collect_fpn_proposals_op.py index 4898eeecfdc875..fcd1c4e32aaccc 100644 --- a/test/legacy_test/test_collect_fpn_proposals_op.py +++ b/test/legacy_test/test_collect_fpn_proposals_op.py @@ -23,12 +23,12 @@ def set_data(self): self.init_test_case() self.make_rois() self.scores_input = [ - ('y%d' % i, (self.scores[i].reshape(-1, 1), self.rois_lod[i])) + (f'y{i}', (self.scores[i].reshape(-1, 1), self.rois_lod[i])) for i in range(self.num_level) ] self.rois, self.lod = self.calc_rois_collect() inputs_x = [ - ('x%d' % i, (self.roi_inputs[i][:, 1:], self.rois_lod[i])) + (f'x{i}', (self.roi_inputs[i][:, 1:], self.rois_lod[i])) for i in range(self.num_level) ] self.inputs = { @@ -107,16 +107,16 @@ def set_data(self): self.init_test_case() self.make_rois() self.scores_input = [ - ('y%d' % i, (self.scores[i].reshape(-1, 1), self.rois_lod[i])) + (f'y{i}', (self.scores[i].reshape(-1, 1), self.rois_lod[i])) for i in range(self.num_level) ] self.rois, self.lod = self.calc_rois_collect() inputs_x = [ - ('x%d' % i, (self.roi_inputs[i][:, 1:], self.rois_lod[i])) + (f'x{i}', (self.roi_inputs[i][:, 1:], self.rois_lod[i])) for i in range(self.num_level) ] rois_num_per_level = [ - ('rois%d' % i, np.array(self.rois_lod[i][0]).astype('int32')) + (f'rois{i}', np.array(self.rois_lod[i][0]).astype('int32')) for i in range(self.num_level) ] diff --git a/test/legacy_test/test_collective_api_base.py b/test/legacy_test/test_collective_api_base.py index e86a19f8ab8ab1..5f4b1e71540b65 100644 --- a/test/legacy_test/test_collective_api_base.py +++ b/test/legacy_test/test_collective_api_base.py @@ -280,10 +280,10 @@ def _run_cluster(self, model_file, envs): tr0_cmd = tr_cmd % (self._python_interp, model_file) tr1_cmd = tr_cmd % (self._python_interp, model_file) path0 = os.path.join( - self.temp_dir.name, "/tmp/tr0_err_%d.log" % os.getpid() + self.temp_dir.name, f"/tmp/tr0_err_{os.getpid()}.log" ) path1 = os.path.join( - self.temp_dir.name, "/tmp/tr1_err_%d.log" % os.getpid() + self.temp_dir.name, f"/tmp/tr1_err_{os.getpid()}.log" ) tr0_pipe = open(path0, "w") tr1_pipe = open(path1, "w") diff --git a/test/legacy_test/test_dist_base.py b/test/legacy_test/test_dist_base.py index 715146879238f0..9d528dd3be5af8 100755 --- a/test/legacy_test/test_dist_base.py +++ b/test/legacy_test/test_dist_base.py @@ -775,7 +775,7 @@ def run_trainer(self, args): if step_id % 10 == 0: print_to_err( type(self).__name__, - "loss at step %d: %f" % (step_id, loss.numpy()), + f"loss at step {step_id}: {loss.numpy().item():f}", ) out_losses.append(loss.numpy()) @@ -1128,9 +1128,9 @@ def _run_local( ) if batch_size != DEFAULT_BATCH_SIZE: - cmd += " --batch_size %d" % batch_size + cmd += f" --batch_size {batch_size}" if batch_merge_repeat > 1: - cmd += " --batch_merge_repeat %d" % batch_merge_repeat + cmd += f" --batch_merge_repeat {batch_merge_repeat}" if self._nccl2_reduce_layer: cmd += " --nccl2_reduce_layer_local_run 1" diff --git a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py index 0aa1b689a069ee..60bae581ea844c 100644 --- a/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/test_dist_fleet_sparse_embedding_ctr.py @@ -231,7 +231,7 @@ def net(): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/test_dist_hapi_model.py b/test/legacy_test/test_dist_hapi_model.py index c7094226783e40..9e3bc5995a1116 100644 --- a/test/legacy_test/test_dist_hapi_model.py +++ b/test/legacy_test/test_dist_hapi_model.py @@ -43,7 +43,7 @@ def get_cluster_from_args(selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) @@ -73,9 +73,9 @@ def start_local_trainers( "FLAGS_selected_gpus": "{}".format( ",".join([str(g) for g in t.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % t.rank, - "PADDLE_CURRENT_ENDPOINT": f"{t.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(t.rank), + "PADDLE_CURRENT_ENDPOINT": str(t.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } diff --git a/test/legacy_test/test_dist_train.py b/test/legacy_test/test_dist_train.py index 7e183288fbeae0..1e25f78298d86c 100644 --- a/test/legacy_test/test_dist_train.py +++ b/test/legacy_test/test_dist_train.py @@ -46,7 +46,7 @@ def test_send(self): self.ps_timeout = 5 self._wait_ps_ready(p.pid) - with open("/tmp/paddle.%d.port" % p.pid, "r") as fn: + with open(f"/tmp/paddle.{p.pid}.port", "r") as fn: selected_port = int(fn.readlines()[0]) self.init_client(place, selected_port) @@ -65,7 +65,7 @@ def _wait_ps_ready(self, pid): try: # the listen_and_serv_op would touch a file which contains the listen port # on the /tmp directory until it was ready to process all the RPC call. - os.stat("/tmp/paddle.%d.port" % pid) + os.stat(f"/tmp/paddle.{pid}.port") return except OSError: start_left_time -= sleep_time @@ -129,8 +129,8 @@ def init_client(self, place, port): # # BTW, `Send` is not a public API to users. So I set # `x.persistable = True` to be a hot fix of this unittest. - Send("127.0.0.1:%d" % port, [x]) - o = Recv("127.0.0.1:%d" % port, [get_var]) + Send(f"127.0.0.1:{port}", [x]) + o = Recv(f"127.0.0.1:{port}", [get_var]) exe = base.Executor(place) self.dist_out = exe.run(main, fetch_list=o) # o is a list diff --git a/test/legacy_test/test_distribute_fpn_proposals_op.py b/test/legacy_test/test_distribute_fpn_proposals_op.py index c66e1ef9aea0d5..baaf8cdb135d39 100644 --- a/test/legacy_test/test_distribute_fpn_proposals_op.py +++ b/test/legacy_test/test_distribute_fpn_proposals_op.py @@ -53,7 +53,7 @@ def set_data(self): 'pixel_offset': self.pixel_offset, } output = [ - ('out%d' % i, self.rois_fpn[i]) for i in range(len(self.rois_fpn)) + (f'out{i}', self.rois_fpn[i]) for i in range(len(self.rois_fpn)) ] self.outputs = { @@ -162,10 +162,10 @@ def set_data(self): 'pixel_offset': self.pixel_offset, } output = [ - ('out%d' % i, self.rois_fpn[i]) for i in range(len(self.rois_fpn)) + (f'out{i}', self.rois_fpn[i]) for i in range(len(self.rois_fpn)) ] rois_num_per_level = [ - ('rois_num%d' % i, np.array(self.rois_fpn[i][1][0]).astype('int32')) + (f'rois_num{i}', np.array(self.rois_fpn[i][1][0]).astype('int32')) for i in range(len(self.rois_fpn)) ] diff --git a/test/legacy_test/test_fleet_base_single.py b/test/legacy_test/test_fleet_base_single.py index 18d825a05dd4ff..d7c391f2f6b670 100644 --- a/test/legacy_test/test_fleet_base_single.py +++ b/test/legacy_test/test_fleet_base_single.py @@ -151,8 +151,7 @@ def test_single_run_ps_minimize(self): fetch_list=[avg_cost.name], ) print( - "worker_index: %d, step%d cost = %f" - % (fleet.worker_index(), i, cost_val[0]) + f"worker_index: {fleet.worker_index()}, step{i} cost = {cost_val[0]:f}" ) diff --git a/test/legacy_test/test_fusion_transpose_flatten_concat_op.py b/test/legacy_test/test_fusion_transpose_flatten_concat_op.py index a0ef5e25b58b69..fdbadb0613c90e 100644 --- a/test/legacy_test/test_fusion_transpose_flatten_concat_op.py +++ b/test/legacy_test/test_fusion_transpose_flatten_concat_op.py @@ -33,7 +33,7 @@ def setUp(self): for i in range(len(self.shapes)): in_shape = self.shapes[i] a = np.random.random(in_shape).astype("float32") - ins.append(("x%d" % i, a)) + ins.append((f"x{i}", a)) b = a.transpose(self.trans_axis) flat_shape = ( diff --git a/test/legacy_test/test_imperative_deepcf.py b/test/legacy_test/test_imperative_deepcf.py index 6b9ff839acb8fd..9a124f927123fc 100644 --- a/test/legacy_test/test_imperative_deepcf.py +++ b/test/legacy_test/test_imperative_deepcf.py @@ -38,7 +38,7 @@ def __init__(self): for i in range(len(self._hid_sizes)): self._user_layers.append( self.add_sublayer( - 'user_layer_%d' % i, + f'user_layer_{i}', Linear( 256 if i == 0 else self._hid_sizes[i - 1], self._hid_sizes[i], @@ -47,13 +47,13 @@ def __init__(self): ) self._user_layers.append( self.add_sublayer( - 'user_layer_act_%d' % i, + f'user_layer_act_{i}', paddle.nn.ReLU(), ) ) self._item_layers.append( self.add_sublayer( - 'item_layer_%d' % i, + f'item_layer_{i}', Linear( 256 if i == 0 else self._hid_sizes[i - 1], self._hid_sizes[i], @@ -62,7 +62,7 @@ def __init__(self): ) self._item_layers.append( self.add_sublayer( - 'item_layer_act_%d' % i, + f'item_layer_act_{i}', paddle.nn.ReLU(), ) ) @@ -87,7 +87,7 @@ def __init__(self): for i in range(len(self._hid_sizes)): self._match_layers.append( self.add_sublayer( - 'match_layer_%d' % i, + f'match_layer_{i}', Linear( 256 * 2 if i == 0 else self._hid_sizes[i - 1], self._hid_sizes[i], @@ -96,7 +96,7 @@ def __init__(self): ) self._match_layers.append( self.add_sublayer( - 'match_layer_act_%d' % i, + f'match_layer_act_{i}', paddle.nn.ReLU(), ) ) @@ -278,7 +278,7 @@ def test_deefcf(self): ) exe.run(startup) for e in range(self.num_epoches): - sys.stderr.write('epoch %d\n' % e) + sys.stderr.write(f'epoch {e}\n') for slice in range( 0, self.batch_size * self.num_batches, self.batch_size ): @@ -308,7 +308,7 @@ def test_deefcf(self): deepcf = DeepCF(num_users, num_items, matrix) adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters()) for e in range(self.num_epoches): - sys.stderr.write('epoch %d\n' % e) + sys.stderr.write(f'epoch {e}\n') for slice in range( 0, self.batch_size * self.num_batches, self.batch_size ): @@ -344,7 +344,7 @@ def test_deefcf(self): adam2 = paddle.optimizer.Adam(0.01, parameters=deepcf2.parameters()) base.set_flags({'FLAGS_sort_sum_gradient': True}) for e in range(self.num_epoches): - sys.stderr.write('epoch %d\n' % e) + sys.stderr.write(f'epoch {e}\n') for slice in range( 0, self.batch_size * self.num_batches, self.batch_size ): @@ -380,7 +380,7 @@ def test_deefcf(self): adam = paddle.optimizer.Adam(0.01, parameters=deepcf.parameters()) for e in range(self.num_epoches): - sys.stderr.write('epoch %d\n' % e) + sys.stderr.write(f'epoch {e}\n') for slice in range( 0, self.batch_size * self.num_batches, self.batch_size ): diff --git a/test/legacy_test/test_imperative_ptb_rnn.py b/test/legacy_test/test_imperative_ptb_rnn.py index afc74a45197707..cdb663722cbfcc 100644 --- a/test/legacy_test/test_imperative_ptb_rnn.py +++ b/test/legacy_test/test_imperative_ptb_rnn.py @@ -83,7 +83,7 @@ def _create_parameter(self): low=-self._init_scale, high=self._init_scale ), ) - self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) + self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1)) bias_1 = self.create_parameter( attr=base.ParamAttr( initializer=paddle.nn.initializer.Uniform( @@ -94,7 +94,7 @@ def _create_parameter(self): dtype="float32", default_initializer=paddle.nn.initializer.Constant(0.0), ) - self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) + self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): self.cell_array = [] diff --git a/test/legacy_test/test_imperative_resnet.py b/test/legacy_test/test_imperative_resnet.py index b6d6ba072397a0..fa8026be733df2 100644 --- a/test/legacy_test/test_imperative_resnet.py +++ b/test/legacy_test/test_imperative_resnet.py @@ -198,7 +198,7 @@ def __init__(self, layers=50, class_dim=102, use_cudnn=True): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=( num_channels[block] diff --git a/test/legacy_test/test_imperative_save_load_v2.py b/test/legacy_test/test_imperative_save_load_v2.py index d1387096946de3..c88f5ff235c1b4 100644 --- a/test/legacy_test/test_imperative_save_load_v2.py +++ b/test/legacy_test/test_imperative_save_load_v2.py @@ -57,7 +57,7 @@ def __init__( low=-self._init_scale, high=self._init_scale ), ) - self.weight_1_arr.append(self.add_parameter('w_%d' % i, weight_1)) + self.weight_1_arr.append(self.add_parameter(f'w_{i}', weight_1)) bias_1 = self.create_parameter( attr=base.ParamAttr( initializer=paddle.nn.initializer.Uniform( @@ -68,7 +68,7 @@ def __init__( dtype="float32", default_initializer=paddle.nn.initializer.Constant(0.0), ) - self.bias_arr.append(self.add_parameter('b_%d' % i, bias_1)) + self.bias_arr.append(self.add_parameter(f'b_{i}', bias_1)) def forward(self, input_embedding, init_hidden=None, init_cell=None): self.cell_array = [] diff --git a/test/legacy_test/test_imperative_se_resnext.py b/test/legacy_test/test_imperative_se_resnext.py index d95124d684542e..df5d8bdda37a2a 100644 --- a/test/legacy_test/test_imperative_se_resnext.py +++ b/test/legacy_test/test_imperative_se_resnext.py @@ -263,7 +263,7 @@ def __init__(self, layers=50, class_dim=102): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=num_channels, num_filters=num_filters[block], diff --git a/test/legacy_test/test_imperative_transformer_sorted_gradient.py b/test/legacy_test/test_imperative_transformer_sorted_gradient.py index 6b1ec9c428b11b..15875a616e29dc 100644 --- a/test/legacy_test/test_imperative_transformer_sorted_gradient.py +++ b/test/legacy_test/test_imperative_transformer_sorted_gradient.py @@ -643,7 +643,7 @@ def __init__( for i in range(n_layer): self._encoder_sublayers.append( self.add_sublayer( - 'esl_%d' % i, + f'esl_{i}', EncoderSubLayer( n_head, d_key, @@ -922,7 +922,7 @@ def __init__( for i in range(n_layer): self._decoder_sub_layers.append( self.add_sublayer( - 'dsl_%d' % i, + f'dsl_{i}', DecoderSubLayer( n_head, d_key, diff --git a/test/legacy_test/test_listen_and_serv_op.py b/test/legacy_test/test_listen_and_serv_op.py index 0c9b55f2c3e8d3..60bcc044a19395 100644 --- a/test/legacy_test/test_listen_and_serv_op.py +++ b/test/legacy_test/test_listen_and_serv_op.py @@ -147,7 +147,7 @@ def _wait_ps_ready(self, pid): try: # the listen_and_serv_op would touch a file which contains the listen port # on the /tmp directory until it was ready to process all the RPC call. - os.stat("/tmp/paddle.%d.port" % pid) + os.stat(f"/tmp/paddle.{pid}.port") return except OSError: start_left_time -= sleep_time diff --git a/test/legacy_test/test_parallel_dygraph_dataparallel.py b/test/legacy_test/test_parallel_dygraph_dataparallel.py index 7672a29be76ea1..6ddf6a69b53bba 100644 --- a/test/legacy_test/test_parallel_dygraph_dataparallel.py +++ b/test/legacy_test/test_parallel_dygraph_dataparallel.py @@ -43,7 +43,7 @@ def get_cluster_from_args(selected_devices): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_devices) @@ -65,9 +65,9 @@ def start_local_trainers_cpu( for rank_id, endpoint in enumerate(trainer_endpoints): proc_env = { "PADDLE_DISTRI_BACKEND": "gloo", - "PADDLE_TRAINER_ID": "%d" % rank_id, - "PADDLE_CURRENT_ENDPOINT": f"{endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % n_rank, + "PADDLE_TRAINER_ID": str(rank_id), + "PADDLE_CURRENT_ENDPOINT": str(endpoint), + "PADDLE_TRAINERS_NUM": str(n_rank), "PADDLE_TRAINER_ENDPOINTS": ",".join(trainer_endpoints), } @@ -121,9 +121,9 @@ def start_local_trainers( f"FLAGS_selected_{accelerator_type}s": "{}".format( ",".join([str(g) for g in t.gpus]) ), - "PADDLE_TRAINER_ID": "%d" % t.rank, - "PADDLE_CURRENT_ENDPOINT": f"{t.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(t.rank), + "PADDLE_CURRENT_ENDPOINT": str(t.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), } diff --git a/test/legacy_test/test_parallel_dygraph_dataparallel_cpuonly.py b/test/legacy_test/test_parallel_dygraph_dataparallel_cpuonly.py index cd1b89e064d6e6..5148c9dc3e9374 100644 --- a/test/legacy_test/test_parallel_dygraph_dataparallel_cpuonly.py +++ b/test/legacy_test/test_parallel_dygraph_dataparallel_cpuonly.py @@ -42,7 +42,7 @@ def get_cluster_from_args(selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) @@ -65,9 +65,9 @@ def start_local_trainers( procs = [] for t in pod.trainers: proc_env = { - "PADDLE_TRAINER_ID": "%d" % t.rank, - "PADDLE_CURRENT_ENDPOINT": f"{t.endpoint}", - "PADDLE_TRAINERS_NUM": "%d" % cluster.trainers_nranks(), + "PADDLE_TRAINER_ID": str(t.rank), + "PADDLE_CURRENT_ENDPOINT": str(t.endpoint), + "PADDLE_TRAINERS_NUM": str(cluster.trainers_nranks()), "PADDLE_TRAINER_ENDPOINTS": ",".join(cluster.trainers_endpoints()), "MASTER_ADDR": "127.0.0.1", "MASTER_PORT": "6170", From d5f4996a57f6604919a23af05d9248a0bd6a04f3 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Mon, 9 Dec 2024 00:36:00 +0800 Subject: [PATCH 230/288] [CodeStyle][UP031] Use f-string instead of percent format in legacy uts (part29) (#70046) --- test/ir/pass_test.py | 14 +-- test/legacy_test/auto_parallel_op_test.py | 30 ++---- test/legacy_test/ctr_dataset_reader.py | 4 +- test/legacy_test/dist_ctr.py | 2 +- test/legacy_test/dist_ctr_reader.py | 4 +- test/legacy_test/dist_fleet_ctr.py | 2 +- .../dist_fleet_heter_pipeline_ctr.py | 2 +- .../dist_fleet_sparse_embedding_ctr.py | 2 +- test/legacy_test/dist_test_utils.py | 2 +- test/legacy_test/fleet_heter_ps_training.py | 2 +- test/legacy_test/fleet_ps_training.py | 3 +- test/legacy_test/gradient_checker.py | 18 ++-- test/legacy_test/nets.py | 20 ++-- test/legacy_test/op.py | 6 +- test/legacy_test/op_test.py | 16 +-- test/legacy_test/prim_op_test.py | 101 +++++------------- 16 files changed, 70 insertions(+), 158 deletions(-) diff --git a/test/ir/pass_test.py b/test/ir/pass_test.py index cb133f9b4f98b2..e4f8db99789e27 100644 --- a/test/ir/pass_test.py +++ b/test/ir/pass_test.py @@ -166,17 +166,9 @@ def check_output_with_place(self, place, startup_on_cpu=False, atol=1e-5): offset = np.argmax(diff_mat > atol) self.assertTrue( is_allclose, - "Output (name: %s, shape: %s, dtype: %s) has diff at %s. The maximum diff is %e, first error element is %d, expected %e, but got %e" - % ( - self.fetch_list[i].name, - str(self.fetch_list[i].shape), - self.fetch_list[i].dtype, - str(place), - max_diff, - offset, - a.flatten()[offset], - b.flatten()[offset], - ), + f"Output (name: {self.fetch_list[i].name}, shape: {self.fetch_list[i].shape!s}, dtype: {self.fetch_list[i].dtype}) " + f"has diff at {place!s}. The maximum diff is {max_diff:e}, first error element is {offset}, " + f"expected {a.flatten()[offset].item():e}, but got {b.flatten()[offset].item():e}", ) def _check_fused_ops(self, program): diff --git a/test/legacy_test/auto_parallel_op_test.py b/test/legacy_test/auto_parallel_op_test.py index 5265ecbdfeda90..c5c593bd8d4d0a 100644 --- a/test/legacy_test/auto_parallel_op_test.py +++ b/test/legacy_test/auto_parallel_op_test.py @@ -512,14 +512,8 @@ def check_eager_auto_parallel(self): atol=self.rtol, err_msg=( 'Check eager auto parallel failed. Mismatch between eager auto parallel outputs ' - 'and eager outputs on %s, the eager forward output tensor\'s index is : %d \n' - 'eager auto parallel output tensor:\n%s\n eager output tensor:\n%s\n' - % ( - str(self.place), - i, - actual_ret[i], - self.eager_forward_desire[i], - ) + f'and eager outputs on {self.place!s}, the eager forward output tensor\'s index is : {i} \n' + f'eager auto parallel output tensor:\n{actual_ret[i]}\n eager output tensor:\n{self.eager_forward_desire[i]}\n' ), ) @@ -731,14 +725,8 @@ def check_eager_auto_parallel(self): atol=self.rtol, err_msg=( 'Check eager auto parallel failed. Mismatch between eager auto parallel outputs ' - 'and eager outputs on %s, the eager forward output tensor\'s index is : %d \n' - 'eager auto parallel output tensor:\n%s\n eager output tensor:\n%s\n' - % ( - str(self.place), - i, - actual_forward_res[i], - self.eager_forward_desire[i], - ) + f'and eager outputs on {self.place!s}, the eager forward output tensor\'s index is : {i} \n' + f'eager auto parallel output tensor:\n{actual_forward_res[i]}\n eager output tensor:\n{self.eager_forward_desire[i]}\n' ), ) @@ -757,14 +745,8 @@ def check_eager_auto_parallel(self): atol=self.rtol, err_msg=( 'Check eager auto parallel backward failed. Mismatch between eager auto parallel grad outputs ' - 'and eager grad outputs on %s, the eager grad output tensor\'s index is : %d \n' - 'eager auto parallel grad output tensor:\n%s\n eager grad output tensor:\n%s\n' - % ( - str(self.place), - i, - actual_grad_res[i], - self.eager_grad_desire[i], - ) + f'and eager grad outputs on {self.place!s}, the eager grad output tensor\'s index is : {i} \n' + f'eager auto parallel grad output tensor:\n{actual_grad_res[i]}\n eager grad output tensor:\n{self.eager_grad_desire[i]}\n' ), ) diff --git a/test/legacy_test/ctr_dataset_reader.py b/test/legacy_test/ctr_dataset_reader.py index 17358dfedcead4..cc888aeb810dff 100644 --- a/test/legacy_test/ctr_dataset_reader.py +++ b/test/legacy_test/ctr_dataset_reader.py @@ -119,8 +119,8 @@ def prepare_data(): res = list(res) dnn_input_dim = res[0] lr_input_dim = res[1] - logger.info('dnn input dim: %d' % dnn_input_dim) - logger.info('lr input dim: %d' % lr_input_dim) + logger.info(f'dnn input dim: {dnn_input_dim}') + logger.info(f'lr input dim: {lr_input_dim}') return dnn_input_dim, lr_input_dim, train_file_path diff --git a/test/legacy_test/dist_ctr.py b/test/legacy_test/dist_ctr.py index 4811c802fae376..164ae3f0e1e215 100644 --- a/test/legacy_test/dist_ctr.py +++ b/test/legacy_test/dist_ctr.py @@ -71,7 +71,7 @@ def get_model(self, batch_size=2): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/dist_ctr_reader.py b/test/legacy_test/dist_ctr_reader.py index 039d2c8aaf1782..dedeffbe8fa0b3 100644 --- a/test/legacy_test/dist_ctr_reader.py +++ b/test/legacy_test/dist_ctr_reader.py @@ -168,6 +168,6 @@ def load_data_meta(): ), err_info res = map(int, [_.split(':')[1] for _ in lines]) res = list(res) - logger.info('dnn input dim: %d' % res[0]) - logger.info('lr input dim: %d' % res[1]) + logger.info(f'dnn input dim: {res[0]}') + logger.info(f'lr input dim: {res[1]}') return res diff --git a/test/legacy_test/dist_fleet_ctr.py b/test/legacy_test/dist_fleet_ctr.py index 2aa4790c9427da..ec03fafd6a7d01 100644 --- a/test/legacy_test/dist_fleet_ctr.py +++ b/test/legacy_test/dist_fleet_ctr.py @@ -120,7 +120,7 @@ def net(self, args, is_train=True, batch_size=4, lr=0.01): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py index 98208a82cba726..57125a18172404 100644 --- a/test/legacy_test/dist_fleet_heter_pipeline_ctr.py +++ b/test/legacy_test/dist_fleet_heter_pipeline_ctr.py @@ -107,7 +107,7 @@ def net(self, args, batch_size=4, lr=0.01): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py index 136b8298ec486a..51b273ff5427ee 100644 --- a/test/legacy_test/dist_fleet_sparse_embedding_ctr.py +++ b/test/legacy_test/dist_fleet_sparse_embedding_ctr.py @@ -112,7 +112,7 @@ def net(self, args, batch_size=4, lr=0.01): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/dist_test_utils.py b/test/legacy_test/dist_test_utils.py index 7484d82f4a2da9..57cb89e761a369 100644 --- a/test/legacy_test/dist_test_utils.py +++ b/test/legacy_test/dist_test_utils.py @@ -25,4 +25,4 @@ def silentremove(filename): def remove_ps_flag(pid): - silentremove("/tmp/paddle.%d.port" % pid) + silentremove(f"/tmp/paddle.{pid}.port") diff --git a/test/legacy_test/fleet_heter_ps_training.py b/test/legacy_test/fleet_heter_ps_training.py index 425aabc74eed96..6fa60c9741d166 100644 --- a/test/legacy_test/fleet_heter_ps_training.py +++ b/test/legacy_test/fleet_heter_ps_training.py @@ -100,7 +100,7 @@ def net(batch_size=4, lr=0.01): weight_attr=base.ParamAttr( initializer=paddle.nn.initializer.Constant(value=0.01) ), - name='dnn-fc-%d' % i, + name=f'dnn-fc-{i}', ) dnn_out = fc diff --git a/test/legacy_test/fleet_ps_training.py b/test/legacy_test/fleet_ps_training.py index 2afb25664c8003..9563d0d773f300 100644 --- a/test/legacy_test/fleet_ps_training.py +++ b/test/legacy_test/fleet_ps_training.py @@ -51,6 +51,5 @@ program=fleet.main_program, feed=gen_data(), fetch_list=[cost.name] ) print( - "worker_index: %d, step%d cost = %f" - % (fleet.worker_index(), i, cost_val[0]) + f"worker_index: {fleet.worker_index()}, step{i} cost = {cost_val[0]:f}" ) diff --git a/test/legacy_test/gradient_checker.py b/test/legacy_test/gradient_checker.py index 3854067040f0c5..769bda2fe8416c 100644 --- a/test/legacy_test/gradient_checker.py +++ b/test/legacy_test/gradient_checker.py @@ -717,8 +717,8 @@ def get_static_double_grad( if x_init: if len(x_init) != len(x): raise ValueError( - 'len(x_init) (=%d) is not the same' - ' as len(x) (= %d)' % (len(x_init), len(x)) + f'len(x_init) (={len(x_init)}) is not the same' + f' as len(x) (={len(x)})' ) # init variable in main program for var, arr in zip(x, x_init): @@ -836,8 +836,8 @@ def get_pir_static_double_grad( if x_init: if len(x_init) != len(x): raise ValueError( - 'len(x_init) (=%d) is not the same' - ' as len(x) (= %d)' % (len(x_init), len(x)) + f'len(x_init) (={len(x_init)}) is not the same' + f' as len(x) (={len(x)})' ) # init variable in main program for var, arr in zip(x, x_init): @@ -1028,9 +1028,8 @@ def fail_test(msg): ): msg = ( 'Check eager double result fail. Mismatch between static_graph double grad ' - 'and eager double grad on %s, the output double grad tensor\'s index is : %d \n' - 'static:%s\n eager:%s\n' - % (str(place), i, static_double_grad[i], eager_double_grad[i]) + f'and eager double grad on {place!s}, the output double grad tensor\'s index is : {i} \n' + f'static:{static_double_grad[i]}\n eager:{eager_double_grad[i]}\n' ) return fail_test(msg) @@ -1293,8 +1292,7 @@ def fail_test(msg): ): msg = ( 'Check eager double result fail. Mismatch between static_graph double grad ' - 'and eager double grad on %s, the output double grad tensor\'s index is : %d \n' - 'static:%s\n eager:%s\n' - % (str(place), i, static_triple_grad[i], eager_triple_grad[i]) + f'and eager double grad on {place!s}, the output double grad tensor\'s index is : {i} \n' + f'static:{static_triple_grad[i]}\n eager:{eager_triple_grad[i]}\n' ) return fail_test(msg) diff --git a/test/legacy_test/nets.py b/test/legacy_test/nets.py index 58c15f9d96d399..9209f37d84b1c3 100644 --- a/test/legacy_test/nets.py +++ b/test/legacy_test/nets.py @@ -496,34 +496,30 @@ def scaled_dot_product_attention( if not (len(queries.shape) == len(keys.shape) == len(values.shape) == 3): raise ValueError( "Inputs queries, keys and values should all be 3-D tensors." - "But received len(queries.shape) = %d, " - "len(keys.shape) = %d, len(values.shape) = %d." - % (len(queries.shape), len(keys.shape), len(values.shape)) + f"But received len(queries.shape) = {len(queries.shape)}, " + f"len(keys.shape) = {len(keys.shape)}, len(values.shape) = {len(values.shape)}." ) if queries.shape[-1] != keys.shape[-1]: raise ValueError( "The hidden size of queries and keys should be the same." - "But received queries' hidden size = %d and keys' hidden size = %d." - % (queries.shape[-1], keys.shape[-1]) + f"But received queries' hidden size = {queries.shape[-1]} and keys' hidden size = {keys.shape[-1]}." ) if keys.shape[-2] != values.shape[-2]: raise ValueError( "The max sequence length in value batch and in key batch " "should be the same. But received max sequence length in value batch " - "= %d, in key batch = %d." % (values.shape[-2], keys.shape[-2]) + f"= {values.shape[-2]}, in key batch = {keys.shape[-2]}." ) if keys.shape[-1] % num_heads != 0: raise ValueError( - "The hidden size of keys (%d) must be divisible " - "by the number of attention heads (%d)." - % (keys.shape[-1], num_heads) + f"The hidden size of keys ({keys.shape[-1]}) must be divisible " + f"by the number of attention heads ({num_heads})." ) if values.shape[-1] % num_heads != 0: raise ValueError( - "The hidden size of values (%d) must be divisible " - "by the number of attention heads (%d)." - % (values.shape[-1], num_heads) + f"The hidden size of values ({values.shape[-1]}) must be divisible " + f"by the number of attention heads ({num_heads})." ) def __compute_qkv(queries, keys, values, num_heads): diff --git a/test/legacy_test/op.py b/test/legacy_test/op.py index 1d29b294d40ff3..c7bf6097f866d1 100644 --- a/test/legacy_test/op.py +++ b/test/legacy_test/op.py @@ -90,8 +90,7 @@ def __call__(self, *args, **kwargs): if not input_parameter.duplicable and len(input_arguments) > 1: raise ValueError( - "Input %s expects only one input, but %d are given." - % (input_parameter.name, len(input_arguments)) + f"Input {input_parameter.name} expects only one input, but {len(input_arguments)} are given." ) ipt = op_desc.inputs.add() @@ -105,8 +104,7 @@ def __call__(self, *args, **kwargs): if not output_parameter.duplicable and len(output_arguments) > 1: raise ValueError( - "Output %s expects only one output, but %d are given." - % (output_parameter.name, len(output_arguments)) + f"Output {output_parameter.name} expects only one output, but {len(output_arguments)} are given." ) out = op_desc.outputs.add() diff --git a/test/legacy_test/op_test.py b/test/legacy_test/op_test.py index 797f2fcf32a100..749bc902924f1c 100644 --- a/test/legacy_test/op_test.py +++ b/test/legacy_test/op_test.py @@ -3046,19 +3046,9 @@ def _assert_is_close( def err_msg(): offset = np.argmax(diff_mat > max_relative_error) return ( - "Operator %s error, %s variable %s (shape: %s, dtype: %s) max gradient diff %e over limit %e, " - "the first error element is %d, expected %e, but got %e." - ) % ( - self.op_type, - msg_prefix, - name, - str(a.shape), - self.dtype, - max_diff, - max_relative_error, - offset, - a.flatten()[offset], - b.flatten()[offset], + f"Operator {self.op_type} error, {msg_prefix} variable {name} (shape: {a.shape!s}, dtype: {self.dtype}) " + f"max gradient diff {max_diff:e} over limit {max_relative_error:e}, " + f"the first error element is {offset}, expected {a.flatten()[offset].item():e}, but got {b.flatten()[offset].item():e}." ) self.assertLessEqual(max_diff, max_relative_error, err_msg()) diff --git a/test/legacy_test/prim_op_test.py b/test/legacy_test/prim_op_test.py index da41479431c623..9a9ea383f70bef 100644 --- a/test/legacy_test/prim_op_test.py +++ b/test/legacy_test/prim_op_test.py @@ -120,9 +120,9 @@ def is_empty(a): return isinstance(a, Empty) def get_default(idx, defaults): - assert not isinstance(defaults[idx], Empty), ( - "%d-th params of python api don't have default value." % idx - ) + assert not isinstance( + defaults[idx], Empty + ), f"{idx}-th params of python api don't have default value." return defaults[idx] def to_defaults_list(params, defaults): @@ -703,15 +703,10 @@ def check_static_comp(self): atol=self.fw_comp_atol, err_msg=( 'Check static comp forward api out failed. Mismatch between static comp ' - 'and eager on %s, when enable_fw_comp is %s,the forward api out tensor\'s index is : %d \n' - 'static comp forward api out tensor:\n%s\n eager forward api out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - i, - ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place!s}, when enable_fw_comp is {self.enable_fw_comp},' + f'the forward api out tensor\'s index is : {i} \n' + f'static comp forward api out tensor:\n{ret[i]}\n ' + f'eager forward api out tensor:\n{self.eager_desire[i]}\n' ), ) with dygraph_guard(): @@ -788,15 +783,10 @@ def check_jit_comp(self): atol=atol, err_msg=( 'Check jit comp forward api out failed. Mismatch between jit comp ' - 'and eager on %s, when enable_fw_comp is %s,the forward api out tensor\'s index is : %d \n' - 'jit comp forward api out tensor:\n%s\n eager forward api out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - i, - ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place!s}, when enable_fw_comp is {self.enable_fw_comp},' + f'the forward api out tensor\'s index is : {i} \n' + f'jit comp forward api out tensor:\n{ret[i]}\n ' + f'eager forward api out tensor:\n{self.eager_desire[i]}\n' ), ) core._set_prim_forward_enabled(False) @@ -882,17 +872,12 @@ def check_jit_comp_with_cinn(self): rtol=rtol, atol=atol, err_msg=( - 'Check jit comp with cinn forward api out failed. Mismatch between jit comp and eager on %s, ' - 'when enable_fw_comp is %s, enable_cinn is %s, the forward api out tensor\'s index is : %d \n' - 'jit comp forward api out tensor:\n%s\n eager forward api out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - core.is_compiled_with_cinn() and self.enable_cinn, - i, - ret[i], - self.eager_desire[i], - ) + f'Check jit comp with cinn forward api out failed. Mismatch between jit comp and eager on {self.place!s}, ' + f'when enable_fw_comp is {self.enable_fw_comp}, ' + f'enable_cinn is {core.is_compiled_with_cinn() and self.enable_cinn}, ' + f'the forward api out tensor\'s index is : {i} \n' + f'jit comp forward api out tensor:\n{ret[i]}\n ' + f'eager forward api out tensor:\n{self.eager_desire[i]}\n' ), ) core._set_prim_forward_enabled(False) @@ -1084,15 +1069,9 @@ def check_eager_comp(self): atol=rtol, err_msg=( 'Check eager comp grad out failed. Mismatch between eager comp ' - 'and eager on %s, when enable_rev_comp is %s,the eager comp grad out tensor\'s index is : %d \n' - 'eager comp grad out tensor:\n%s\n eager grad out tensor:\n%s\n' - % ( - str(self.place), - self.enable_rev_comp, - i, - actual_ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place!s}, when enable_rev_comp is {self.enable_rev_comp},' + f'the eager comp grad out tensor\'s index is : {i} \n' + f'eager comp grad out tensor:\n{actual_ret[i]}\n eager grad out tensor:\n{self.eager_desire[i]}\n' ), ) core.set_prim_eager_enabled(False) @@ -1204,16 +1183,9 @@ def check_static_comp(self): atol=atol, err_msg=( 'Check static comp grad out failed. Mismatch between static comp ' - 'and eager on %s, when enable_fw_comp is %s,enable_rev_comp is %s,the forward api out tensor\'s index is : %d \n' - 'static comp grad out tensor:\n%s\n eager grad out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - self.enable_rev_comp, - i, - actual_ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place}, when enable_fw_comp is {self.enable_fw_comp},enable_rev_comp is { self.enable_rev_comp},' + f'the forward api out tensor\'s index is : {i} \n' + f'static comp grad out tensor:\n{actual_ret[i]}\n eager grad out tensor:\n{self.eager_desire[i]}\n' ), ) core._set_prim_forward_enabled(False) @@ -1320,16 +1292,9 @@ def check_jit_comp(self): atol=atol, err_msg=( 'Check jit comp grad out failed. Mismatch between jit comp ' - 'and eager on %s, when enable_fw_comp is %s, enable_rev_comp is %s,the grad out tensor\'s index is : %d \n' - 'jit comp grad out tensor:\n%s\n eager grad out out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - self.enable_rev_comp, - i, - ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place!s}, when enable_fw_comp is {self.enable_fw_comp}, ' + f'enable_rev_comp is {self.enable_rev_comp},the grad out tensor\'s index is : {i} \n' + f'jit comp grad out tensor:\n{ret[i]}\n eager grad out out tensor:\n{self.eager_desire[i]}\n' ), ) core._set_prim_forward_enabled(False) @@ -1449,17 +1414,9 @@ def check_jit_comp_with_cinn(self): atol=atol, err_msg=( 'Check jit comp with cinn grad out failed. Mismatch between jit comp with cinn ' - 'and eager on %s, when enable_fw_comp is %s, enable_rev_comp is %s, enable_cinn is %s,' - 'the grad out tensor\'s index is : %d ,jit comp with cinn grad out tensor:\n%s\n eager grad out out tensor:\n%s\n' - % ( - str(self.place), - self.enable_fw_comp, - self.enable_rev_comp, - self.enable_cinn and core.is_compiled_with_cinn(), - i, - ret[i], - self.eager_desire[i], - ) + f'and eager on {self.place!s}, when enable_fw_comp is {self.enable_fw_comp}, ' + f'enable_rev_comp is {self.enable_rev_comp}, enable_cinn is {self.enable_cinn and core.is_compiled_with_cinn()},' + f'the grad out tensor\'s index is : {i} ,jit comp with cinn grad out tensor:\n{ret[i]}\n eager grad out out tensor:\n{self.eager_desire[i]}\n' ), ) From d6b74b78d7ecd813d32531f64437cc9874b04796 Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 9 Dec 2024 09:44:18 +0800 Subject: [PATCH 231/288] Fix (#70011) --- python/paddle/base/data_feeder.py | 6 +++--- python/paddle/base/executor.py | 2 +- python/paddle/base/lod_tensor.py | 6 +++--- python/paddle/framework/io.py | 6 +++--- 4 files changed, 10 insertions(+), 10 deletions(-) diff --git a/python/paddle/base/data_feeder.py b/python/paddle/base/data_feeder.py index 155ee148e31212..9cbf280aafb6e0 100644 --- a/python/paddle/base/data_feeder.py +++ b/python/paddle/base/data_feeder.py @@ -270,7 +270,7 @@ def check_shape( check_dtype(shape.dtype, 'shape', expected_tensor_dtype, op_name) -class DataToLoDTensorConverter: +class DataToDenseTensorConverter: def __init__(self, place, lod_level, shape, dtype): self.place = place self.lod_level = lod_level @@ -337,7 +337,7 @@ def __init__(self, feed_list, place, batch_size, generator, drop_last): if not in_pir_mode(): assert var.lod_level == 0, "lod_level must be 0" self.converters.append( - DataToLoDTensorConverter( + DataToDenseTensorConverter( place=self.place, lod_level=0, shape=var.shape, @@ -509,7 +509,7 @@ def feed(self, iterable): self.feed_lod_level, self.feed_shapes, self.feed_dtypes ): converter.append( - DataToLoDTensorConverter( + DataToDenseTensorConverter( place=self.place, lod_level=lod_level, shape=shape, diff --git a/python/paddle/base/executor.py b/python/paddle/base/executor.py index a493621074c86a..7787edd87392d5 100755 --- a/python/paddle/base/executor.py +++ b/python/paddle/base/executor.py @@ -351,7 +351,7 @@ def has_feed_operators(block, feed_targets, feed_holder_name): feed_targets: a dictionary of {feed_target_name: feed_target_data} feed_holder_name: the name of the variable that holds the data of all feed targets. The type of this feed_holder variable is - FEED_MINIBATCH, which is essentially vector. + FEED_MINIBATCH, which is essentially vector. Returns: A boolean value that indicates whether a block has feed operators diff --git a/python/paddle/base/lod_tensor.py b/python/paddle/base/lod_tensor.py index d54b491d7f46ac..edbd935670b3bf 100644 --- a/python/paddle/base/lod_tensor.py +++ b/python/paddle/base/lod_tensor.py @@ -15,7 +15,7 @@ import numpy as np from . import core -from .data_feeder import DataToLoDTensorConverter +from .data_feeder import DataToDenseTensorConverter __all__ = [] @@ -71,8 +71,8 @@ def create_lod_tensor(data, recursive_seq_lens, place): return create_lod_tensor(np.array(data), recursive_seq_lens, place) elif isinstance(data, list): # dtype and shape are not important here, - # we only want to reuse code of DataToLoDTensorConverter - converter = DataToLoDTensorConverter( + # we only want to reuse code of DataToDenseTensorConverter + converter = DataToDenseTensorConverter( place=place, lod_level=len(recursive_seq_lens), shape=[], diff --git a/python/paddle/framework/io.py b/python/paddle/framework/io.py index ab448cfb7afb5d..4a09a302089bbf 100644 --- a/python/paddle/framework/io.py +++ b/python/paddle/framework/io.py @@ -431,7 +431,7 @@ def reduce_varbase(self): return (tuple, ((name, data),)) - def reduce_LoDTensor(self): + def reduce_DenseTensor(self): p = core.Place() p.set_place(paddle.CPUPlace()) if self._place().is_custom_place(): @@ -462,7 +462,7 @@ def add_dispatch_table(): # This is not a good method, because the pickle module has been modified. pickle.dispatch_table[core.eager.Tensor] = reduce_varbase pickle.dispatch_table[EagerParamBase] = reduce_varbase - pickle.dispatch_table[core.DenseTensor] = reduce_LoDTensor + pickle.dispatch_table[core.DenseTensor] = reduce_DenseTensor pickle.dispatch_table.update(dispatch_table_layer) def pop_dispatch_table(): @@ -485,7 +485,7 @@ def pop_dispatch_table(): pickler = pickle.Pickler(f, protocol) pickler.dispatch_table = copyreg.dispatch_table.copy() - pickler.dispatch_table[core.DenseTensor] = reduce_LoDTensor + pickler.dispatch_table[core.DenseTensor] = reduce_DenseTensor pickler.dispatch_table[core.eager.Tensor] = reduce_varbase pickler.dispatch_table[EagerParamBase] = reduce_varbase pickler.dispatch_table.update(dispatch_table_layer) From 84fff6cbeaf606743ea41faa4733aec195621863 Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 9 Dec 2024 09:45:45 +0800 Subject: [PATCH 232/288] Fix (#70015) --- paddle/fluid/pybind/eager.cc | 2 +- paddle/fluid/pybind/eager_utils.cc | 4 ++-- paddle/fluid/pybind/protobuf.cc | 1 - paddle/fluid/pybind/reader_py.cc | 4 ++-- paddle/fluid/pybind/tensor.cc | 8 +++----- python/paddle/base/reader.py | 6 +++--- python/paddle/io/dataloader/dataloader_iter.py | 4 ++-- 7 files changed, 13 insertions(+), 16 deletions(-) diff --git a/paddle/fluid/pybind/eager.cc b/paddle/fluid/pybind/eager.cc index 5ef4cfb58d1b79..ab61ea33cc0e73 100644 --- a/paddle/fluid/pybind/eager.cc +++ b/paddle/fluid/pybind/eager.cc @@ -106,7 +106,7 @@ void EmptyTensorInitializer(TensorObject* self, } else { VLOG(6) << "in EmptyTensorInitializer, create DenseTensor"; if (var_type == paddle::framework::proto::VarType::DENSE_TENSOR) { - // TODO(jiabin): Maybe support LOD later + // TODO(jiabin): Maybe support LegacyLoD later std::shared_ptr dense_tensor = nullptr; if (dims.size() == 1 && dims[0] == 0) { std::shared_ptr allocation_ptr = nullptr; diff --git a/paddle/fluid/pybind/eager_utils.cc b/paddle/fluid/pybind/eager_utils.cc index d092f50a974334..f5fda654362546 100644 --- a/paddle/fluid/pybind/eager_utils.cc +++ b/paddle/fluid/pybind/eager_utils.cc @@ -1914,7 +1914,7 @@ paddle::Tensor CreateTensorFromVarDesc( autograd_meta->SetStopGradient(var_desc.StopGradient()); if (var_type == paddle::framework::proto::VarType::DENSE_TENSOR) { - // TODO(jiabin): Maybe support LOD later + // TODO(jiabin): Maybe support LegacyLoD later std::shared_ptr dense_tensor = nullptr; if (dims.size() == 1 && dims[0] == 0) { std::shared_ptr allocation_ptr = nullptr; @@ -1998,7 +1998,7 @@ paddle::Tensor CreateTensorFromValue(const pir::Value& value) { autograd_meta->SetStopGradient(GetValueBoolAttr(value, kAttrStopGradients)); if (value.type().isa()) { - // TODO(jiabin): Maybe support LOD later + // TODO(jiabin): Maybe support LegacyLoD later std::shared_ptr dense_tensor = nullptr; auto dtype = paddle::dialect::TransToPhiDataType( value.type().dyn_cast().dtype()); diff --git a/paddle/fluid/pybind/protobuf.cc b/paddle/fluid/pybind/protobuf.cc index 1b5f649e8399d4..88a5a2ee9666ca 100644 --- a/paddle/fluid/pybind/protobuf.cc +++ b/paddle/fluid/pybind/protobuf.cc @@ -306,7 +306,6 @@ void BindVarDesc(pybind11::module *m) { .value("FEED_MINIBATCH", pd::proto::VarType::FEED_MINIBATCH) .value("FETCH_LIST", pd::proto::VarType::FETCH_LIST) .value("STEP_SCOPES", pd::proto::VarType::STEP_SCOPES) - .value("LOD_RANK_TABLE", pd::proto::VarType::LOD_RANK_TABLE) .value("DENSE_TENSOR_ARRAY", pd::proto::VarType::DENSE_TENSOR_ARRAY) .value("PLACE_LIST", pd::proto::VarType::PLACE_LIST) .value("READER", pd::proto::VarType::READER) diff --git a/paddle/fluid/pybind/reader_py.cc b/paddle/fluid/pybind/reader_py.cc index 8cf8613ef17dc4..3d034bb47a196b 100644 --- a/paddle/fluid/pybind/reader_py.cc +++ b/paddle/fluid/pybind/reader_py.cc @@ -425,11 +425,11 @@ void BindReader(py::module *module) { }); m.def( - "init_lod_tensor_blocking_queue", + "init_dense_tensor_blocking_queue", [](framework::Variable &var, size_t capacity, bool is_ordered) -> py::object { - VLOG(1) << "init_lod_tensor_blocking_queue"; + VLOG(1) << "init_dense_tensor_blocking_queue"; if (is_ordered) { auto *holder = var.GetMutable< reader::OrderedMultiDeviceDenseTensorBlockingQueueHolder>(); diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index 3da5ce16cd894d..d9b09bd253dddb 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -507,11 +507,9 @@ void BindTensor(pybind11::module &m) { // NOLINT return std::make_unique(new_offset_lod); })) .def(py::init([]() { return std::make_unique(); })) - // We implement offset based LOD in C++ while we use length based with - // Python API. So we changed set_lod to set_recursive_sequence_lengths - // to - // avoid misuse. - // The discussion is here: + // We implement offset based LegacyLoD in C++ while we use length based + // with Python API. So we changed set_lod to + // set_recursive_sequence_lengths to avoid misuse. The discussion is here: // https://github.com/PaddlePaddle/Paddle/issues/10855 .def( "set_lod", diff --git a/python/paddle/base/reader.py b/python/paddle/base/reader.py index f439d00ad950f4..76b1fa8f2fa3cc 100644 --- a/python/paddle/base/reader.py +++ b/python/paddle/base/reader.py @@ -590,7 +590,7 @@ def _init_iterable(self): self._shapes = [] self._dtypes = [] self._need_check_feed = [] - self._blocking_queue = core.init_lod_tensor_blocking_queue( + self._blocking_queue = core.init_dense_tensor_blocking_queue( core.Variable(), self._capacity, False ) self._reader = None @@ -854,7 +854,7 @@ def _init_iterable(self): self._need_check_feed = [ v.desc.need_check_feed() for v in self._feed_list ] - self._queue = core.init_lod_tensor_blocking_queue( + self._queue = core.init_dense_tensor_blocking_queue( core.Variable(), self._capacity, self._keep_order ) self._reader = None @@ -898,7 +898,7 @@ def _init_non_iterable(self): double_buffer_name = data_loader_unique_name_generator('double_buffer') var = global_scope().var(queue_name) - self._queue = core.init_lod_tensor_blocking_queue( + self._queue = core.init_dense_tensor_blocking_queue( var, self._capacity, self._keep_order ) diff --git a/python/paddle/io/dataloader/dataloader_iter.py b/python/paddle/io/dataloader/dataloader_iter.py index a90de3391a3f93..63dceeff30c5a6 100644 --- a/python/paddle/io/dataloader/dataloader_iter.py +++ b/python/paddle/io/dataloader/dataloader_iter.py @@ -203,7 +203,7 @@ def _init_thread(self): ] self._dtypes = [v.dtype for v in self._feed_list] # if only 1 place, do not need to keep order - self._blocking_queue = core.init_lod_tensor_blocking_queue( + self._blocking_queue = core.init_dense_tensor_blocking_queue( core.Variable(), self._blocking_queue_capacity, len(self._places) > 1, @@ -507,7 +507,7 @@ def _init_thread(self): ] self._dtypes = [v.dtype for v in self._feed_list] # if only 1 place, do not need to keep order - self._blocking_queue = core.init_lod_tensor_blocking_queue( + self._blocking_queue = core.init_dense_tensor_blocking_queue( core.Variable(), self._outstanding_capacity, len(self._places) > 1 ) core._set_max_memory_map_allocation_pool_size( From 48104b70280bd53049a01a8e194d8e35938fd044 Mon Sep 17 00:00:00 2001 From: Terry <38135104+TR666@users.noreply.github.com> Date: Mon, 9 Dec 2024 11:08:24 +0800 Subject: [PATCH 233/288] [XPU][PIR] add elementwise_mul_add_xpu_fuse_pass && support bfp16 for addcmul_xpu (#70025) --- .../inference/api/paddle_pass_builder.cc | 1 + paddle/fluid/pir/transforms/passes.h | 1 + .../xpu/elementwise_mul_add_xpu_fuse_pass.cc | 122 ++++++++++++++++++ .../xpu/elementwise_mul_add_xpu_fuse_pass.h | 26 ++++ paddle/phi/backends/xpu/xpu3_op_list.cc | 4 +- .../kernels/fusion/xpu/addcmul_xpu_kernel.cc | 3 +- .../test_elementwise_mul_add_xpu_fuse_pass.py | 90 +++++++++++++ 7 files changed, 245 insertions(+), 2 deletions(-) create mode 100644 paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.cc create mode 100644 paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.h create mode 100644 test/ir/pir/fused_pass/xpu/test_elementwise_mul_add_xpu_fuse_pass.py diff --git a/paddle/fluid/inference/api/paddle_pass_builder.cc b/paddle/fluid/inference/api/paddle_pass_builder.cc index dcca713778c424..c82125d053fd4d 100644 --- a/paddle/fluid/inference/api/paddle_pass_builder.cc +++ b/paddle/fluid/inference/api/paddle_pass_builder.cc @@ -619,6 +619,7 @@ const std::vector kPirXpuPasses{ "add_activation_xpu_fuse_pass", "add_layernorm_xpu_fuse_pass", "rms_norm_xpu_fuse_pass", + "elementwise_mul_add_xpu_fuse_pass", "conv2d_bn_xpu_fuse_pass", "conv2d_add_xpu_fuse_pass", "group_norm_silu_fuse_pass", diff --git a/paddle/fluid/pir/transforms/passes.h b/paddle/fluid/pir/transforms/passes.h index 6556d9143b2c37..f57e0161b8b824 100644 --- a/paddle/fluid/pir/transforms/passes.h +++ b/paddle/fluid/pir/transforms/passes.h @@ -95,6 +95,7 @@ USE_PIR_PASS(cpu_bf16_quantize_squash_pass); USE_PIR_PASS(add_activation_xpu_fuse_pass); USE_PIR_PASS(add_layernorm_xpu_fuse_pass); USE_PIR_PASS(rms_norm_xpu_fuse_pass); +USE_PIR_PASS(elementwise_mul_add_xpu_fuse_pass); USE_PIR_PASS(conv2d_bn_xpu_fuse_pass); USE_PIR_PASS(conv2d_add_xpu_fuse_pass); USE_PIR_PASS(fc_xpu_fuse_pass); diff --git a/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.cc b/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.cc new file mode 100644 index 00000000000000..5c8baea0ffb8be --- /dev/null +++ b/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.cc @@ -0,0 +1,122 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.h" +#include + +#include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/fluid/pir/drr/include/drr_pattern_base.h" +#include "paddle/fluid/pir/utils/general_functions.h" + +#include "paddle/fluid/framework/scope.h" +#include "paddle/phi/core/dense_tensor.h" +#include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pass/pass_registry.h" +#include "paddle/pir/include/pattern_rewrite/pattern_match.h" + +/* +fuse elementwise_mul + elementwise_mul to addcmul_xpu +For example: +graph: + x y + \ / + \ / + elementwise_mul w + \ / + \ / + elementwise_add + | + | + output +------------------------------------------------------ +After the pass is applied: + x y w + \ | / + \ | / + addcmul_xpu + | + | + output +*/ + +namespace { + +class ElementwiseMulAddXpuFusePattern : public paddle::drr::DrrPatternBase { + public: + std::string name() const override { + return "ElementwiseMulAddXpuFusePattern"; + } + + void operator()(paddle::drr::DrrPatternContext *ctx) const override { + // Source pattern + paddle::drr::SourcePattern pat = ctx->SourcePattern(); + + const auto &mul_op = pat.Op(paddle::dialect::MultiplyOp::name()); + const auto &add_op = pat.Op(paddle::dialect::AddOp::name()); + mul_op({&pat.Tensor("x"), &pat.Tensor("y")}, {&pat.Tensor("mul_out")}); + add_op({&pat.Tensor("mul_out"), &pat.Tensor("w")}, + {&pat.Tensor("add_out")}); + + // Constraints + pat.AddConstraint([&](const paddle::drr::MatchContext &match_ctx) { + auto x_shape = pir::GetShapeFromValue(match_ctx.Tensor("x")); + auto y_shape = pir::GetShapeFromValue(match_ctx.Tensor("y")); + auto w_shape = pir::GetShapeFromValue(match_ctx.Tensor("w")); + if (x_shape.size() == y_shape.size() && + y_shape.size() == w_shape.size()) { + for (size_t i = 0; i < x_shape.size(); ++i) { + if (x_shape[i] != y_shape[i] || x_shape[i] != w_shape[i] || + x_shape[i] == -1) { + return false; + } + } + } else { + return false; + } + return true; + }); + + // Result pattern + paddle::drr::ResultPattern res = pat.ResultPattern(); + + const auto &addcmul_xpu = res.Op(paddle::dialect::AddcmulXpuOp::name()); + addcmul_xpu({&res.Tensor("x"), &res.Tensor("y"), &res.Tensor("w")}, + {&res.Tensor("add_out")}); + } +}; + +class ElementwiseMulAddXpuFusePass : public pir::PatternRewritePass { + public: + ElementwiseMulAddXpuFusePass() + : pir::PatternRewritePass("elementwise_mul_add_xpu_fuse_pass", 2) {} + + pir::RewritePatternSet InitializePatterns(pir::IrContext *context) override { + pir::RewritePatternSet ps(context); + ps.Add(paddle::drr::Create(context)); + return ps; + } +}; + +} // namespace + +namespace pir { + +std::unique_ptr CreateElementwiseMulAddXpuFusePass() { + return std::make_unique(); +} + +} // namespace pir + +REGISTER_IR_PASS(elementwise_mul_add_xpu_fuse_pass, + ElementwiseMulAddXpuFusePass); diff --git a/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.h b/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.h new file mode 100644 index 00000000000000..6ddee9fc607387 --- /dev/null +++ b/paddle/fluid/pir/transforms/xpu/elementwise_mul_add_xpu_fuse_pass.h @@ -0,0 +1,26 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include "paddle/pir/include/core/dll_decl.h" + +namespace pir { + +class Pass; + +IR_API std::unique_ptr CreateElementwiseMulAddXpuFusePass(); + +} // namespace pir diff --git a/paddle/phi/backends/xpu/xpu3_op_list.cc b/paddle/phi/backends/xpu/xpu3_op_list.cc index 22eaa2171306ee..744fde5e1e2981 100644 --- a/paddle/phi/backends/xpu/xpu3_op_list.cc +++ b/paddle/phi/backends/xpu/xpu3_op_list.cc @@ -42,7 +42,9 @@ XPUOpMap& get_kl3_ops() { {"adam", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, {"adagrad", XPUKernelSet({phi::DataType::FLOAT32})}, {"addcmul_xpu", - XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16})}, + XPUKernelSet({phi::DataType::FLOAT32, + phi::DataType::FLOAT16, + phi::DataType::BFLOAT16})}, {"addmm", XPUKernelSet({phi::DataType::FLOAT32, phi::DataType::FLOAT16, diff --git a/paddle/phi/kernels/fusion/xpu/addcmul_xpu_kernel.cc b/paddle/phi/kernels/fusion/xpu/addcmul_xpu_kernel.cc index 57c71bcd4bd7da..a99774812738bb 100644 --- a/paddle/phi/kernels/fusion/xpu/addcmul_xpu_kernel.cc +++ b/paddle/phi/kernels/fusion/xpu/addcmul_xpu_kernel.cc @@ -58,4 +58,5 @@ PD_REGISTER_KERNEL(addcmul_xpu, ALL_LAYOUT, phi::fusion::AddCMulXPUKernel, float, - phi::dtype::float16) {} + phi::dtype::float16, + phi::dtype::bfloat16) {} diff --git a/test/ir/pir/fused_pass/xpu/test_elementwise_mul_add_xpu_fuse_pass.py b/test/ir/pir/fused_pass/xpu/test_elementwise_mul_add_xpu_fuse_pass.py new file mode 100644 index 00000000000000..65c550dfb12fad --- /dev/null +++ b/test/ir/pir/fused_pass/xpu/test_elementwise_mul_add_xpu_fuse_pass.py @@ -0,0 +1,90 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np +from pass_test import PassTest + +import paddle +from paddle.base import core + +paddle.enable_static() + + +class TestElementwiseMulAddXpuFusePattern(PassTest): + r""" + x y + \ / + \ / + elementwise_mul w + \ / + \ / + elementwise_add + | + | + output + """ + + def is_program_valid(self, program=None): + return True + + def build_ir_program(self): + with paddle.pir_utils.IrGuard(): + main_prog = paddle.static.Program() + start_prog = paddle.static.Program() + with paddle.pir.core.program_guard(main_prog, start_prog): + x_shape = [2, 1, 2560] + x_type = 'float32' + y_shape = [2, 1, 2560] + y_type = 'float32' + w_shape = [2, 1, 2560] + w_type = 'float32' + x = paddle.static.data(name='x', shape=x_shape, dtype=x_type) + y = paddle.static.data(name='y', shape=y_shape, dtype=y_type) + w = paddle.static.data(name='w', shape=w_shape, dtype=w_type) + + out = paddle.add(paddle.multiply(x, y), w) + out = paddle.assign(out) + self.pass_attr_list = [ + {'elementwise_mul_add_xpu_fuse_pass': {}} + ] + self.feeds = { + "x": np.random.random(x_shape).astype("float32"), + "y": np.random.random(y_shape).astype("float32"), + "w": np.random.random(w_shape).astype("float32"), + } + self.fetch_list = [out] + self.valid_op_map = { + "pd_op.multiply": 0, + "pd_op.add": 0, + "pd_op.addcmul_xpu": 1, + } + + return [main_prog, start_prog] + + def sample_program(self): + pir_program = self.build_ir_program() + yield pir_program, False + + def setUp(self): + if core.is_compiled_with_xpu(): + self.places.append(paddle.device.XPUPlace(0)) + + def test_check_output(self): + self.check_pass_correct() + + +if __name__ == "__main__": + unittest.main() From a18a81115fcdb03897d96da5fba61ad96d2cdf40 Mon Sep 17 00:00:00 2001 From: zyfncg Date: Mon, 9 Dec 2024 11:23:11 +0800 Subject: [PATCH 234/288] [CINN] Adjust order of infer_symbol_shape pass in inference (#70042) * Add op for SOT to dive-down * adjust order of infer_symbol_shape pass in inference * Revert "Add op for SOT to dive-down" This reverts commit 3b52900295d0e6d47a9231f88396ff852e04b1ef. --------- Co-authored-by: Pan Zhaowu --- .../operator/transforms/add_cinn_pass.cc | 13 ++++++--- .../operator/transforms/add_cinn_pass.h | 7 ++--- paddle/cinn/hlir/framework/pir/utils.cc | 27 ++++++++++++------- .../fluid/inference/api/analysis_predictor.cc | 12 +++++++-- .../src/dialect/shape/utils/shape_analysis.cc | 3 ++- 5 files changed, 43 insertions(+), 19 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc index 5b0493ea06f0cf..a196c5b095379d 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc @@ -260,15 +260,20 @@ int64_t GetOpCount(const ::pir::Operation* op) { return count; } -void ApplyCinnPass(::pir::Program* program, - const std::function()>& - CreatePassManager) { +void ApplyCinnPass( + ::pir::Program* program, + const std::function()>& CreatePassManager, + bool is_train_mode) { const uint32_t origin_num_ops = program->num_ops(); PirToPyCodeConverter(program) .file_name("original_programs.py") .dump_symbolic_shape(FLAGS_logging_pir_py_code_dump_symbolic_dims) .SaveIfFlagEnabled(); - ApplyShapeOptimizationPass(program, CreatePassManager); + if (is_train_mode) { + // Skip infer symbol shape in inference, because we have run this pass in + // the previous process + ApplyShapeOptimizationPass(program, CreatePassManager); + } ApplyPdToCinnPass(program, CreatePassManager); ApplyCinnPreprocessPass(program, CreatePassManager); ApplyBuildGroupOpPass(program, CreatePassManager); diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h index 4a71cbc5ee3101..acc7144dc753d0 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h @@ -26,8 +26,9 @@ class Program; namespace cinn::dialect::ir { -void ApplyCinnPass(::pir::Program* program, - const std::function()>& - CreatePassManager); +void ApplyCinnPass( + ::pir::Program* program, + const std::function()>& CreatePassManager, + bool is_train_mode = true); } // namespace cinn::dialect::ir diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 8e023f30dbf19a..306dc29f816a16 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -409,7 +409,6 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { } return false; }(); - return outputs_have_new_symbol; } @@ -445,14 +444,24 @@ bool HasHandledInPass(const ::pir::Operation& op) { // 3. it should be handled in pd_to_cinn_pass; bool IsSupportInCinn(const ::pir::Operation& op) { const bool is_denied = IsDeniedInCinn(op); - const bool is_registered = IsRegisteredInCINN(op); - const bool is_handled = HasHandledInPass(op); - const bool cause_new_symbolic_shape = CauseNewSymbolicShape(op); - VLOG(5) << op.name() << ": IsDeniedInCinn = " << is_denied - << ", IsRegisteredInCINN = " << is_registered - << ", HasHandledInPass = " << is_handled - << ", CauseNewSymbolicShape = " << cause_new_symbolic_shape; - return !is_denied && is_registered && is_handled && !cause_new_symbolic_shape; + if (IsDeniedInCinn(op)) { + VLOG(5) << op.name() << "[id:" << op.id() << "] is denied in CINN"; + return false; + } + if (!IsRegisteredInCINN(op)) { + VLOG(5) << op.name() << "[id:" << op.id() << "] isn't registered in CINN"; + return false; + } + if (!HasHandledInPass(op)) { + VLOG(5) << op.name() << "[id:" << op.id() << "] isn't handled in CINN"; + return false; + } + if (CauseNewSymbolicShape(op)) { + VLOG(5) << op.name() << "[id:" << op.id() + << "] caused new symbolic shape in CINN"; + return false; + } + return true; } } // namespace diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 0af49227a86804..891656471fbaf4 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -102,6 +102,7 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_util.h" #include "paddle/pir/include/dialect/shape/ir/shape_dialect.h" +#include "paddle/pir/include/dialect/shape/transforms/shape_optimization_pass.h" #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #endif @@ -126,7 +127,6 @@ #include "paddle/pir/include/core/block_argument.h" #include "paddle/pir/include/core/builtin_attribute.h" #include "paddle/pir/include/core/program.h" -#include "paddle/pir/include/dialect/shape/transforms/shape_optimization_pass.h" #include "paddle/pir/include/pass/pass_manager.h" #include "paddle/pir/include/pass/pass_registry.h" @@ -870,6 +870,13 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (!config_.custom_pass_only_) { ::pir::PassManager fused_op_pm(::pir::IrContext::Instance(), config_.pm_opt_level_); + auto &shape_analysis = + pir::ShapeAnalysisManager::Instance().Get(pir_program_.get()); + fused_op_pm.SetValueReplacedHook([&](pir::Value from, pir::Value to) { + shape_analysis.ShareShapeOrData(from, to); + }); + // Infer symbol shape for all ops before fused pass + fused_op_pm.AddPass(pir::CreateShapeOptimizationPass()); const std::vector FusedOpPasses{// Operator fusion pass "conv2d_bn_fuse_pass", "conv2d_add_act_fuse_pass", @@ -903,7 +910,8 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (config_.cinn_enabled()) { VLOG(4) << "[CINN] Begin ApplyCinnPass"; - cinn::dialect::ir::ApplyCinnPass(pir_program_.get(), CreatePassMgr); + cinn::dialect::ir::ApplyCinnPass( + pir_program_.get(), CreatePassMgr, false); } #endif diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 2e79078430e53f..9759c25cd6098c 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -605,7 +605,8 @@ ShapeConstraintIRAnalysis::GetShapeOrDataForValue(Value val) { SetSymbolForValueByStaticShape(val); } else { VLOG(3) << "InferShapeOrDataForValue, defining_op: " - << val.defining_op()->name() << " id:" << val.defining_op()->id(); + << val.defining_op()->name() << " id:" << val.defining_op()->id() + << " value id: " << val.impl()->id(); InferShapeOrDataForValue(val); } } From dd534374917a911a208fb94e592c56580781d934 Mon Sep 17 00:00:00 2001 From: huangjiyi <43315610+huangjiyi@users.noreply.github.com> Date: Mon, 9 Dec 2024 11:28:02 +0800 Subject: [PATCH 235/288] [CINN] Fix reshape fusion with dynamic shape (#70050) --- .../graph_transformer/matcher.h | 13 ++++++++++- paddle/cinn/operator_fusion/utils.cc | 22 ++++++++++--------- paddle/cinn/operator_fusion/utils.h | 4 ++++ 3 files changed, 28 insertions(+), 11 deletions(-) diff --git a/paddle/cinn/operator_fusion/graph_transformer/matcher.h b/paddle/cinn/operator_fusion/graph_transformer/matcher.h index 12964809edaeb8..80c205529009b1 100644 --- a/paddle/cinn/operator_fusion/graph_transformer/matcher.h +++ b/paddle/cinn/operator_fusion/graph_transformer/matcher.h @@ -226,8 +226,19 @@ struct TransposeOpMatcher { struct ReshapeOpMatcher { bool operator()(const PatternGraph& graph, const PatternNodePtr& node) { + auto has_dynamic_shape = [](const PatternNodePtr& node) { + const auto in_value = node->sink_op()->operand_source(0); + const auto out_value = node->sink_op()->result(0); + const auto in_shape = GetDimExprsFromValue(in_value); + const auto out_shape = GetDimExprsFromValue(out_value); + return GetShapeProduct(in_shape, 0, in_shape.size()) + .isa() && + GetShapeProduct(out_shape, 0, out_shape.size()) + .isa(); + }; return node->ops().size() == 1 && - node->sink_op()->name() == "cinn_op.reshape"; + node->sink_op()->name() == "cinn_op.reshape" && + has_dynamic_shape(node); } }; diff --git a/paddle/cinn/operator_fusion/utils.cc b/paddle/cinn/operator_fusion/utils.cc index ec346a76c258f5..27348e6b8750cf 100644 --- a/paddle/cinn/operator_fusion/utils.cc +++ b/paddle/cinn/operator_fusion/utils.cc @@ -177,22 +177,24 @@ std::vector> GetNonBroadCastDims(pir::Operation* op) { return res; } +symbol::DimExpr GetShapeProduct(const std::vector& shape, + int start, + int end) { + symbol::DimExpr product(1); + for (int i = start; i < end; ++i) { + product = product * shape[i]; + } + return symbol::SimplifyDimExpr(product); +} + bool ShapeProductEqual(const std::vector& in_shape, const std::vector& out_shape, int in_start, int in_end, int out_start, int out_end) { - symbol::DimExpr in_product(1); - symbol::DimExpr out_product(1); - for (int i = in_start; i < in_end; ++i) { - in_product = in_product * in_shape[i]; - } - for (int i = out_start; i < out_end; ++i) { - out_product = out_product * out_shape[i]; - } - return symbol::SimplifyDimExpr(in_product) == - symbol::SimplifyDimExpr(out_product); + return GetShapeProduct(in_shape, in_start, in_end) == + GetShapeProduct(out_shape, out_start, out_end); } std::vector> PartionReshapeAxes( diff --git a/paddle/cinn/operator_fusion/utils.h b/paddle/cinn/operator_fusion/utils.h index 1e37e85292c26e..436f34d2b36d96 100644 --- a/paddle/cinn/operator_fusion/utils.h +++ b/paddle/cinn/operator_fusion/utils.h @@ -642,6 +642,10 @@ std::vector ArangeVector(Int start, Int end, Int step = 1) { return res; } +symbol::DimExpr GetShapeProduct(const std::vector& shape, + int start, + int end); + bool ShapeProductEqual(const std::vector& in_shape, const std::vector& out_shape, int in_start, From 1a38fee97df2e19610f1faf1ef90d880df3b812d Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 9 Dec 2024 11:43:58 +0800 Subject: [PATCH 236/288] [Lod][fluid_ops] LoDTensor2BatchFunctor (#70007) * Fix * Fix --- .../new_executor/interpreter/data_transfer.cc | 6 ++--- paddle/fluid/framework/operator.cc | 8 +++--- paddle/fluid/framework/operator.h | 2 +- paddle/fluid/operators/sum_op.cc | 2 +- .../operators/tensor_array_to_tensor_op.cc | 25 ++++++++++--------- paddle/phi/kernels/cpu/gru_kernel.cc | 4 +-- paddle/phi/kernels/funcs/sequence2batch.cc | 8 +++--- paddle/phi/kernels/funcs/sequence2batch.cu | 8 +++--- paddle/phi/kernels/funcs/sequence2batch.h | 4 +-- .../cpu/fused_embedding_fc_lstm_kernel.cc | 4 +-- .../kernels/fusion/cpu/fusion_gru_kernel.cc | 4 +-- .../kernels/fusion/cpu/fusion_lstm_kernel.cc | 4 +-- paddle/phi/kernels/gpu/gru_kernel.cu | 4 +-- paddle/phi/kernels/impl/gru_kernel_impl.h | 4 +-- paddle/phi/kernels/impl/lstm_kernel_impl.h | 8 +++--- 15 files changed, 48 insertions(+), 47 deletions(-) diff --git a/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc b/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc index 0e290c96ca490b..1256abd73e5d6d 100644 --- a/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc +++ b/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc @@ -238,7 +238,7 @@ void DataTransferHelper::RunAndConstructOpFuncNode( bool IsTensorOfVarInitialized(Variable* var) { if (var->IsInitialized()) { if (var->IsType() || var->IsType()) { - return GetLoDTensorOrSelectedRowsValueFromVar(*var)->IsInitialized(); + return GetDenseTensorOrSelectedRowsValueFromVar(*var)->IsInitialized(); } else if (var->IsType()) { return static_cast( &(var->Get()[0])) @@ -509,7 +509,7 @@ void ApplyDataTransform(const OpKernelType& expected_kernel_key, const phi::DenseTensor* tensor_in = nullptr; if (var->IsType() || var->IsType()) { - tensor_in = GetLoDTensorOrSelectedRowsValueFromVar(*var); + tensor_in = GetDenseTensorOrSelectedRowsValueFromVar(*var); } else if (var->IsType()) { if (var->Get().empty()) { continue; @@ -800,7 +800,7 @@ void HandleComplexGradToRealGrad(const OpFuncNode& op_func_node, continue; } const auto* tensor = - framework::GetLoDTensorOrSelectedRowsValueFromVar(*var); + framework::GetDenseTensorOrSelectedRowsValueFromVar(*var); PADDLE_ENFORCE_NOT_NULL( tensor, common::errors::Unavailable( diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index 61a9fe5a33ef63..04dd6891761c72 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -1094,7 +1094,7 @@ void OperatorBase::GenerateTemporaryNames() { } } -const phi::DenseTensor* GetLoDTensorOrSelectedRowsValueFromVar( +const phi::DenseTensor* GetDenseTensorOrSelectedRowsValueFromVar( const Variable& var) { if (var.IsType()) { return static_cast(&(var.Get())); @@ -2467,7 +2467,7 @@ void OperatorWithKernel::TransferInplaceVarsBack( PADDLE_ENFORCE_NOT_NULL(var, common::errors::InvalidArgument( "The variable[%s] is nullptr.", var_name)); - auto* transformed_tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var); + auto* transformed_tensor = GetDenseTensorOrSelectedRowsValueFromVar(*var); original_tensor->ShareDataWith(*transformed_tensor); } } @@ -2515,7 +2515,7 @@ void OperatorWithKernel::HandleComplexGradToRealGrad( if (!VarIsTensor(*var)) { continue; } - const auto* tensor = GetLoDTensorOrSelectedRowsValueFromVar(*var); + const auto* tensor = GetDenseTensorOrSelectedRowsValueFromVar(*var); PADDLE_ENFORCE_NOT_NULL( tensor, common::errors::Unavailable( @@ -2582,7 +2582,7 @@ Scope* OperatorWithKernel::PrepareData( continue; } - auto* tensor_in = GetLoDTensorOrSelectedRowsValueFromVar(*var); + auto* tensor_in = GetDenseTensorOrSelectedRowsValueFromVar(*var); // When no_buffer_ins then checking of phi::DenseTensor::holder_ is // not a thread safe. And for infershape scenario checks diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h index cfb26188979440..c82effc9141d6a 100644 --- a/paddle/fluid/framework/operator.h +++ b/paddle/fluid/framework/operator.h @@ -131,7 +131,7 @@ inline bool VarIsTensor(const Variable& var) { return var.IsType() || var.IsType(); } -const phi::DenseTensor* GetLoDTensorOrSelectedRowsValueFromVar( +const phi::DenseTensor* GetDenseTensorOrSelectedRowsValueFromVar( const Variable& var); phi::DenseTensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var); diff --git a/paddle/fluid/operators/sum_op.cc b/paddle/fluid/operators/sum_op.cc index 57b0234633ef9b..ad06bef262274c 100644 --- a/paddle/fluid/operators/sum_op.cc +++ b/paddle/fluid/operators/sum_op.cc @@ -53,7 +53,7 @@ class SumOp : public framework::OperatorWithKernel { common::errors::NotFound("Input var[%s] should not be nullptr", x_vars_name[idx])); auto tensor = - framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_vars[idx]); + framework::GetDenseTensorOrSelectedRowsValueFromVar(*x_vars[idx]); if (!tensor->IsInitialized()) { continue; } diff --git a/paddle/fluid/operators/tensor_array_to_tensor_op.cc b/paddle/fluid/operators/tensor_array_to_tensor_op.cc index c6893c5045305b..2e57ffaa22cd09 100644 --- a/paddle/fluid/operators/tensor_array_to_tensor_op.cc +++ b/paddle/fluid/operators/tensor_array_to_tensor_op.cc @@ -73,7 +73,7 @@ void DenseTensorArrayCreateFromDenseTensorArray( } } -class LoDTensorArray2TensorOp : public framework::OperatorBase { +class DenseTensorArray2TensorOp : public framework::OperatorBase { public: using OperatorBase::OperatorBase; @@ -128,7 +128,8 @@ class LoDTensorArray2TensorOp : public framework::OperatorBase { } }; -class LoDTensorArray2TensorOpMaker : public framework::OpProtoAndCheckerMaker { +class DenseTensorArray2TensorOpMaker + : public framework::OpProtoAndCheckerMaker { public: void Make() override { AddInput("X", "Input phi::TensorArray of tensor_array_to_tensor operator."); @@ -170,7 +171,7 @@ the output Tensor. } }; -class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase { +class DenseTensorArray2TensorOpInferShape : public framework::InferShapeBase { public: void operator()(framework::InferShapeContext *ctx) const override { // in runtime, shape is determined by RunImpl @@ -195,14 +196,14 @@ class LoDTensorArray2TensorOpInferShape : public framework::InferShapeBase { } }; -class LoDTensorArray2TensorGradInferShape : public framework::InferShapeBase { +class DenseTensorArray2TensorGradInferShape : public framework::InferShapeBase { public: void operator()(framework::InferShapeContext *ctx) const override { ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X")); } }; -class LoDTensorArray2TensorGradInferVarType +class DenseTensorArray2TensorGradInferVarType : public framework::VarTypeInference { public: void operator()(framework::InferVarTypeContext *ctx) const override { @@ -212,7 +213,7 @@ class LoDTensorArray2TensorGradInferVarType } }; -class LoDTensorArray2TensorGradOp : public framework::OperatorBase { +class DenseTensorArray2TensorGradOp : public framework::OperatorBase { public: using OperatorBase::OperatorBase; @@ -298,12 +299,12 @@ USE_OP_ITSELF(concat); namespace ops = paddle::operators; REGISTER_OPERATOR( tensor_array_to_tensor, - ops::LoDTensorArray2TensorOp, - ops::LoDTensorArray2TensorOpMaker, - ops::LoDTensorArray2TensorOpInferShape, + ops::DenseTensorArray2TensorOp, + ops::DenseTensorArray2TensorOpMaker, + ops::DenseTensorArray2TensorOpInferShape, ops::TensorArrayToTensorGradOpMaker, ops::TensorArrayToTensorGradOpMaker); REGISTER_OPERATOR(tensor_array_to_tensor_grad, - ops::LoDTensorArray2TensorGradOp, - ops::LoDTensorArray2TensorGradInferShape, - ops::LoDTensorArray2TensorGradInferVarType); + ops::DenseTensorArray2TensorGradOp, + ops::DenseTensorArray2TensorGradInferShape, + ops::DenseTensorArray2TensorGradInferVarType); diff --git a/paddle/phi/kernels/cpu/gru_kernel.cc b/paddle/phi/kernels/cpu/gru_kernel.cc index 65f318f9adfa6c..5508a34a127850 100644 --- a/paddle/phi/kernels/cpu/gru_kernel.cc +++ b/paddle/phi/kernels/cpu/gru_kernel.cc @@ -69,7 +69,7 @@ void GRUCPUKernel(const Context &dev_ctx, dev_ctx.template Alloc(batch_reset_hidden_prev); dev_ctx.template Alloc(batch_hidden); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; to_batch(dev_ctx, input, batch_gate, true, is_reverse); if (bias) { @@ -231,7 +231,7 @@ void GRUCPUKernel(const Context &dev_ctx, #ifdef PADDLE_WITH_MKLML } #endif - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batch_hidden->set_lod(batch_gate->lod()); to_seq(dev_ctx, *batch_hidden, hidden); } diff --git a/paddle/phi/kernels/funcs/sequence2batch.cc b/paddle/phi/kernels/funcs/sequence2batch.cc index 9d23e3b7cfa261..91a805454dc88f 100644 --- a/paddle/phi/kernels/funcs/sequence2batch.cc +++ b/paddle/phi/kernels/funcs/sequence2batch.cc @@ -70,9 +70,9 @@ class CopyMatrixRowsFunctor { template class CopyMatrixRowsFunctor; template class CopyMatrixRowsFunctor; -template class LoDTensor2BatchFunctor; -template class LoDTensor2BatchFunctor; -template class Batch2LoDTensorFunctor; -template class Batch2LoDTensorFunctor; +template class DenseTensor2BatchFunctor; +template class DenseTensor2BatchFunctor; +template class Batch2DenseTensorFunctor; +template class Batch2DenseTensorFunctor; } // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/sequence2batch.cu b/paddle/phi/kernels/funcs/sequence2batch.cu index 7ae47ece76efea..1ed02f5d8ed37a 100644 --- a/paddle/phi/kernels/funcs/sequence2batch.cu +++ b/paddle/phi/kernels/funcs/sequence2batch.cu @@ -93,10 +93,10 @@ class CopyMatrixRowsFunctor { template class CopyMatrixRowsFunctor; template class CopyMatrixRowsFunctor; -template class LoDTensor2BatchFunctor; -template class LoDTensor2BatchFunctor; -template class Batch2LoDTensorFunctor; -template class Batch2LoDTensorFunctor; +template class DenseTensor2BatchFunctor; +template class DenseTensor2BatchFunctor; +template class Batch2DenseTensorFunctor; +template class Batch2DenseTensorFunctor; } // namespace funcs } // namespace phi diff --git a/paddle/phi/kernels/funcs/sequence2batch.h b/paddle/phi/kernels/funcs/sequence2batch.h index 2c42a76aa1b1a4..71d7506afe7145 100644 --- a/paddle/phi/kernels/funcs/sequence2batch.h +++ b/paddle/phi/kernels/funcs/sequence2batch.h @@ -44,7 +44,7 @@ class CopyMatrixRowsFunctor { }; template -class LoDTensor2BatchFunctor { +class DenseTensor2BatchFunctor { // Calculate the length of each sequence and // sort sequence index by the length. // example: sequences = {s0, s1, s2} @@ -174,7 +174,7 @@ class LoDTensor2BatchFunctor { }; template -class Batch2LoDTensorFunctor { +class Batch2DenseTensorFunctor { public: void operator()(const DeviceContext& context, const phi::DenseTensor& batch, diff --git a/paddle/phi/kernels/fusion/cpu/fused_embedding_fc_lstm_kernel.cc b/paddle/phi/kernels/fusion/cpu/fused_embedding_fc_lstm_kernel.cc index 289873d5a718d2..1d926b4553463c 100644 --- a/paddle/phi/kernels/fusion/cpu/fused_embedding_fc_lstm_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fused_embedding_fc_lstm_kernel.cc @@ -288,7 +288,7 @@ class FusedEmbeddingFCLSTMKernel { dev_ctx.template Alloc(hidden_out); dev_ctx.template Alloc(cell_out); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; auto blas = phi::funcs::GetBlas(dev_ctx); for (int64_t i = 0; i < ids_numel; ++i) { @@ -410,7 +410,7 @@ class FusedEmbeddingFCLSTMKernel { #undef MOVE_ONE_BATCH #undef DEFINE_CUR - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batched_h_out->set_lod(batched_lod); to_seq(dev_ctx, *batched_h_out, hidden_out); batched_c_out->set_lod(batched_lod); diff --git a/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc index 5c14e273fe20c1..1a695d1aa7ff5e 100644 --- a/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fusion_gru_kernel.cc @@ -205,7 +205,7 @@ void BatchCompute(const Context& dev_ctx, T* batched_out_data = dev_ctx.template Alloc(batched_out); dev_ctx.template Alloc(hidden); auto blas = phi::funcs::GetBlas(dev_ctx); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; phi::funcs::FCFunctor fc; if (M > D3) { @@ -333,7 +333,7 @@ void BatchCompute(const Context& dev_ctx, batched_input_data = cur_batched_data; } - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batched_out->set_lod(batched_lod); to_seq(dev_ctx, *batched_out, hidden); } diff --git a/paddle/phi/kernels/fusion/cpu/fusion_lstm_kernel.cc b/paddle/phi/kernels/fusion/cpu/fusion_lstm_kernel.cc index 522d7b77b559c9..c00b55f849d5e3 100644 --- a/paddle/phi/kernels/fusion/cpu/fusion_lstm_kernel.cc +++ b/paddle/phi/kernels/fusion/cpu/fusion_lstm_kernel.cc @@ -243,7 +243,7 @@ void BatchCompute(const Context &dev_ctx, dev_ctx.template Alloc(hidden_out); dev_ctx.template Alloc(cell_out); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; auto blas = phi::funcs::GetBlas(dev_ctx); phi::funcs::FCFunctor fc; if (M > D4) { @@ -342,7 +342,7 @@ void BatchCompute(const Context &dev_ctx, batched_input_data = cur_in_data; } - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batched_h_out->set_lod(batched_lod); to_seq(dev_ctx, *batched_h_out, hidden_out); batched_c_out->set_lod(batched_lod); diff --git a/paddle/phi/kernels/gpu/gru_kernel.cu b/paddle/phi/kernels/gpu/gru_kernel.cu index a582b5fc12209e..89c36539d88010 100644 --- a/paddle/phi/kernels/gpu/gru_kernel.cu +++ b/paddle/phi/kernels/gpu/gru_kernel.cu @@ -61,7 +61,7 @@ void GRUKernel(const Context &dev_ctx, dev_ctx.template Alloc(batch_reset_hidden_prev); dev_ctx.template Alloc(batch_hidden); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; to_batch(dev_ctx, input, batch_gate, true, is_reverse); if (bias) { @@ -113,7 +113,7 @@ void GRUKernel(const Context &dev_ctx, gru_value.prev_out_value = gru_value.output_value; } - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batch_hidden->set_lod(batch_gate->lod()); to_seq(dev_ctx, *batch_hidden, hidden); } diff --git a/paddle/phi/kernels/impl/gru_kernel_impl.h b/paddle/phi/kernels/impl/gru_kernel_impl.h index 07ec807513618b..0ee744d08d9450 100644 --- a/paddle/phi/kernels/impl/gru_kernel_impl.h +++ b/paddle/phi/kernels/impl/gru_kernel_impl.h @@ -63,7 +63,7 @@ void GRUGradKernel(const Context &dev_ctx, auto hidden_dims = hidden.dims(); int frame_size = hidden_dims[1]; - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; phi::DenseTensor batch_hidden_grad, batch_gate_grad, batch_reset_hidden_prev_grad; batch_hidden_grad.Resize(hidden_dims); @@ -156,7 +156,7 @@ void GRUGradKernel(const Context &dev_ctx, } if (input_grad) { dev_ctx.template Alloc(input_grad); - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batch_gate_grad.set_lod(batch_gate.lod()); to_seq(dev_ctx, batch_gate_grad, input_grad); } diff --git a/paddle/phi/kernels/impl/lstm_kernel_impl.h b/paddle/phi/kernels/impl/lstm_kernel_impl.h index 5b9ee6eba90b6b..e9343f67d3c4cc 100644 --- a/paddle/phi/kernels/impl/lstm_kernel_impl.h +++ b/paddle/phi/kernels/impl/lstm_kernel_impl.h @@ -56,7 +56,7 @@ void LSTMKernel(const Context& dev_ctx, dev_ctx.template Alloc(hidden); dev_ctx.template Alloc(cell); - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; to_batch(dev_ctx, input, batch_gate_new, true, is_reverse); auto in_dims = input.dims(); @@ -177,7 +177,7 @@ void LSTMKernel(const Context& dev_ctx, lstm_value.prev_state_value = lstm_value.state_value; } - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; batch_hidden.set_lod(batch_gate_new->lod()); // restore the output hidden in phi::DenseTensor from the batch hidden to_seq(dev_ctx, batch_hidden, hidden); @@ -292,7 +292,7 @@ void LSTMGradKernel(const Context& dev_ctx, lstm_grad.check_og_grad = nullptr; } - phi::funcs::LoDTensor2BatchFunctor to_batch; + phi::funcs::DenseTensor2BatchFunctor to_batch; auto ToBatch = [&batch_gate, &to_batch](const Context& ctx, const phi::DenseTensor& src, @@ -418,7 +418,7 @@ void LSTMGradKernel(const Context& dev_ctx, } } - phi::funcs::Batch2LoDTensorFunctor to_seq; + phi::funcs::Batch2DenseTensorFunctor to_seq; if (in_g) { /* backward data */ dev_ctx.template Alloc(in_g); From 8886e2ed0b290e1f1c4e776c0befb6bde7d6d8b8 Mon Sep 17 00:00:00 2001 From: Siming Dai <908660116@qq.com> Date: Mon, 9 Dec 2024 11:50:10 +0800 Subject: [PATCH 237/288] [Distributed] fix mp data broadcast (#70008) * update broadcast data * fix single element * Update hybrid_parallel_util.py * Update hybrid_parallel_util.py --- .../fleet/utils/hybrid_parallel_util.py | 16 +++++++--------- 1 file changed, 7 insertions(+), 9 deletions(-) diff --git a/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py b/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py index 317dec05c57387..db6ec2eb9c7056 100644 --- a/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py +++ b/python/paddle/distributed/fleet/utils/hybrid_parallel_util.py @@ -165,9 +165,7 @@ def _broadcast_object_list_help(object_list, hcg): ) -def _process_element(hcg, place, element): - cur_device = paddle.get_device() - dev = cur_device.split(":")[0] +def _process_element(hcg, dev, place, element): if isinstance(element, core.eager.Tensor): with framework.no_grad(): if ( @@ -184,16 +182,16 @@ def _process_element(hcg, place, element): _broadcast_object_list_help([element], hcg) -def _broadcast_nested_data(hcg, place, data): +def _broadcast_nested_data(hcg, dev, place, data): if isinstance(data, dict): return { - key: _process_element(hcg, place, value) + key: _process_element(hcg, dev, place, value) for key, value in data.items() } elif isinstance(data, list): - return [_process_element(hcg, place, item) for item in data] + return [_process_element(hcg, dev, place, item) for item in data] elif isinstance(data, tuple): - return tuple(_process_element(hcg, place, item) for item in data) + return tuple(_process_element(hcg, dev, place, item) for item in data) else: raise TypeError(f"Unsupported data type: {type(data)}") @@ -219,9 +217,9 @@ def broadcast_input_data(hcg, *inputs, **kwargs): place = eval(f"paddle.{dev.upper()}Place")(dev_idx) if len(inputs) > 0: - inputs = _broadcast_nested_data(hcg, place, inputs) + inputs = _broadcast_nested_data(hcg, dev, place, inputs) if len(kwargs) > 0: - kwargs = _broadcast_nested_data(hcg, place, kwargs) + kwargs = _broadcast_nested_data(hcg, dev, place, kwargs) return inputs, kwargs From 70ff032fbc7bcf879e1c6f7d2fcf04f089e09484 Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 9 Dec 2024 14:27:00 +0800 Subject: [PATCH 238/288] [Lod][fluid_ops] IsLoDTensorType (#70010) * Fix * ci --- .../framework/ir/coalesce_grad_tensor_pass.cc | 6 ++-- .../fuse_optimizer_op_pass.cc | 6 ++-- .../fuse_optimizer_op_pass.h | 2 +- .../eager_deletion_pass.cc | 10 +++--- paddle/fluid/framework/ir/pass_test_util.cc | 34 +++++++++---------- paddle/fluid/framework/ir/pass_test_util.h | 10 +++--- .../new_executor/interpreter/data_transfer.cc | 2 +- .../interpreter/interpreter_util.cc | 4 +-- .../framework/new_executor/pir_interpreter.cc | 6 ++-- .../framework/new_executor/pir_interpreter.h | 2 +- .../new_executor/program_interpreter.cc | 13 +++---- .../new_executor/program_interpreter.h | 2 +- paddle/fluid/framework/operator.cc | 7 ++-- paddle/fluid/framework/operator.h | 3 +- .../api/details/reset_tensor_array.h | 4 +-- .../tensorrt/convert/io_converter.cc | 4 +-- paddle/phi/kernels/funcs/sequence_padding.cc | 24 ++++++------- paddle/phi/kernels/funcs/sequence_padding.cu | 20 +++++------ paddle/phi/kernels/funcs/sequence_padding.h | 4 +-- paddle/phi/kernels/funcs/sequence_scale.cc | 6 ++-- paddle/phi/kernels/funcs/sequence_scale.cu | 6 ++-- paddle/phi/kernels/funcs/sequence_scale.h | 2 +- .../kernels/impl/warpctc_grad_kernel_impl.h | 4 +-- paddle/phi/kernels/impl/warpctc_kernel_impl.h | 6 ++-- test/cpp/fluid/framework/lod_tensor_test.cc | 2 +- test/cpp/fluid/framework/operator_test.cc | 9 ++--- test/cpp/inference/api/api_impl_tester.cc | 11 +++--- test/cpp/inference/api/tester_helper.h | 2 +- test/cpp/inference/test_helper.h | 16 ++++----- test/cpp/phi/kernels/sequence_padding_test.cc | 4 +-- .../test_add_position_encoding_op.py | 4 +-- test/legacy_test/test_sum_op.py | 4 +-- test/xpu/test_sum_op_xpu.py | 2 +- 33 files changed, 123 insertions(+), 118 deletions(-) diff --git a/paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc b/paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc index 762243b0131a21..dc8da2e2d0b4f3 100644 --- a/paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc +++ b/paddle/fluid/framework/ir/coalesce_grad_tensor_pass.cc @@ -94,7 +94,7 @@ class CoalesceGradTensorPass : public ir::Pass { auto vars_info = GetVarInfo(result); for (auto ¶m_grad : params_grads) { - if (IsLoDTensorType(GetTypeOfVar(vars_info, param_grad.second))) { + if (IsDenseTensorType(GetTypeOfVar(vars_info, param_grad.second))) { p_g_dense_grad.emplace_back(param_grad); } else { p_g_sparse_grad.emplace_back(param_grad); @@ -172,7 +172,7 @@ class CoalesceGradTensorPass : public ir::Pass { pinned_var_set->insert(it->Var()->Name()); } PADDLE_ENFORCE_EQ( - IsLoDTensorType(GetTypeOfVar(vars_info, p_g.second)), + IsDenseTensorType(GetTypeOfVar(vars_info, p_g.second)), true, common::errors::InvalidArgument( "Parameter@Grad %s is not phi::DenseTensor.", p_g.second)); @@ -464,7 +464,7 @@ class CoalesceGradTensorPass : public ir::Pass { } private: - bool IsLoDTensorType(const proto::VarType::Type &type) const { + bool IsDenseTensorType(const proto::VarType::Type &type) const { // Current only support DENSE_TENSOR. return type == proto::VarType::DENSE_TENSOR; } diff --git a/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc b/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc index 4a9fe6c16f1667..8e4f2ac3c604d9 100644 --- a/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc +++ b/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.cc @@ -44,7 +44,7 @@ void FuseOptimizerOpPass::ApplyImpl(ir::Graph *graph) const { "The %s operator has multiple gradient input. Expected " "it to only have one gradient input.", fuse_op_type)); - if (IsLoDTensorType(GetTypeOfVar(vars_info, grad_name[0]))) { + if (IsDenseTensorType(GetTypeOfVar(vars_info, grad_name[0]))) { opt_nodes.emplace_back(node); } ++opt_ops_num; @@ -398,7 +398,7 @@ void FuseOptimizerOpPass::FuseGradientsToContinuousSpace( iter->second.front()->Var(), common::errors::InvalidArgument("The gradient var(%s) node is null.", grad_var_name)); - PADDLE_ENFORCE_EQ(IsLoDTensorType(iter->second.front()->Var()->GetType()), + PADDLE_ENFORCE_EQ(IsDenseTensorType(iter->second.front()->Var()->GetType()), true, common::errors::InvalidArgument( "Currently the gradient(%s) type only should be " @@ -432,7 +432,7 @@ FuseOptimizerOpPass::GetVarInfo(const Graph &result) const { return vars; } -bool FuseOptimizerOpPass::IsLoDTensorType( +bool FuseOptimizerOpPass::IsDenseTensorType( const proto::VarType::Type &type) const { // Current only support DENSE_TENSOR. return type == proto::VarType::DENSE_TENSOR; diff --git a/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.h b/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.h index 8432996fd801e0..cd7043635a67be 100644 --- a/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.h +++ b/paddle/fluid/framework/ir/fuse_optimizer_ops_pass/fuse_optimizer_op_pass.h @@ -122,7 +122,7 @@ class FuseOptimizerOpPass : public ir::Pass { std::unordered_map> *aux_var_set) const; - bool IsLoDTensorType(const proto::VarType::Type &type) const; + bool IsDenseTensorType(const proto::VarType::Type &type) const; bool HasVarDepsBetweenOps(const std::vector &topo_nodes, const std::vector &opt_nodes) const; diff --git a/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc b/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc index 6981e16663e3b4..57956726d109be 100644 --- a/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc +++ b/paddle/fluid/framework/ir/memory_optimize_pass/eager_deletion_pass.cc @@ -46,7 +46,7 @@ static std::map> VarsGroupByScopeIdx( } // Check whether the variable is phi::DenseTensor based on static VarDesc info -static bool IsLoDTensor(VarDesc *var) { +static bool IsDenseTensor(VarDesc *var) { return var->Proto()->type().type() == proto::VarType::DENSE_TENSOR; } @@ -59,7 +59,7 @@ static int64_t GetMemorySize( PADDLE_ENFORCE_NOT_NULL( var_desc, common::errors::NotFound("Var(%s) can not find VarDesc.", var_name)); - PADDLE_ENFORCE_EQ(IsLoDTensor(var_desc), + PADDLE_ENFORCE_EQ(IsDenseTensor(var_desc), true, common::errors::InvalidArgument( "Var(%s) must be phi::DenseTensor.", var_name)); @@ -76,7 +76,7 @@ static int64_t GetMemorySize( // Non-phi::DenseTensor (e.g. SelectedRows, phi::TensorArray) Since partial GC // is based on static analysis of memory size of each variable So we should skip // SelectedRows and phi::TensorArray here -static void SplitIntoLoDTensorAndNonLoDTensorVars( +static void SplitIntoDenseTensorAndNonDenseTensorVars( const OpToVarNameSetMap &m, const details::GraphVars &vars, OpToVarNameSetMap *lod_tensors, @@ -88,7 +88,7 @@ static void SplitIntoLoDTensorAndNonLoDTensorVars( for (auto var_name : op_vars_pair.second) { auto *var_desc = TryGetLatestVarDesc( vars[op_vars_pair.first->GetScopeIdx()].at(var_name)); - if (IsLoDTensor(var_desc)) { + if (IsDenseTensor(var_desc)) { (*lod_tensors)[op_vars_pair.first].insert(var_name); } else { (*other_vars)[op_vars_pair.first].insert(var_name); @@ -130,7 +130,7 @@ static OpToVarNameSetMap ShrinkGCVars(const OpToVarNameSetMap &m, * We can only calculate memory size of DenseTensors */ OpToVarNameSetMap lod_tensors, other_vars; - SplitIntoLoDTensorAndNonLoDTensorVars(m, vars, &lod_tensors, &other_vars); + SplitIntoDenseTensorAndNonDenseTensorVars(m, vars, &lod_tensors, &other_vars); // Perform complete gc when fraction_of_memory_size >= 1 if (fraction_of_memory_size >= 1.0) { diff --git a/paddle/fluid/framework/ir/pass_test_util.cc b/paddle/fluid/framework/ir/pass_test_util.cc index faeda8e5326f9f..309f451e9da2df 100644 --- a/paddle/fluid/framework/ir/pass_test_util.cc +++ b/paddle/fluid/framework/ir/pass_test_util.cc @@ -177,11 +177,11 @@ bool RunPassAndAssert(Graph* graph, } template -void InitLoDTensorHolder(const Scope& scope, - const phi::Place& place, - const std::string& var_name, - const std::vector& dims, - const T* data) { +void InitDenseTensorHolder(const Scope& scope, + const phi::Place& place, + const std::string& var_name, + const std::vector& dims, + const T* data) { auto var = scope.FindLocalVar(var_name); auto tensor = var->GetMutable(); auto* tensor_mem_ptr = @@ -194,21 +194,21 @@ void InitLoDTensorHolder(const Scope& scope, } // Instantiate for below data types. -template void InitLoDTensorHolder(const Scope&, +template void InitDenseTensorHolder(const Scope&, + const phi::Place&, + const std::string&, + const std::vector&, + const float*); +template void InitDenseTensorHolder(const Scope&, const phi::Place&, const std::string&, const std::vector&, - const float*); -template void InitLoDTensorHolder(const Scope&, - const phi::Place&, - const std::string&, - const std::vector&, - const int*); -template void InitLoDTensorHolder(const Scope&, - const phi::Place&, - const std::string&, - const std::vector&, - const double*); + const int*); +template void InitDenseTensorHolder(const Scope&, + const phi::Place&, + const std::string&, + const std::vector&, + const double*); OpDesc* GetOp(const ProgramDesc& prog, const std::string& op_type, diff --git a/paddle/fluid/framework/ir/pass_test_util.h b/paddle/fluid/framework/ir/pass_test_util.h index 59e44d19d70d29..54955c2ce97b43 100644 --- a/paddle/fluid/framework/ir/pass_test_util.h +++ b/paddle/fluid/framework/ir/pass_test_util.h @@ -132,11 +132,11 @@ bool RunPassAndAssert(Graph* graph, /// @tparam T phi::DenseTensor data type. /// template -void InitLoDTensorHolder(const Scope& scope, - const phi::Place& place, - const std::string& var_name, - const std::vector& dims, - const T* data = nullptr); +void InitDenseTensorHolder(const Scope& scope, + const phi::Place& place, + const std::string& var_name, + const std::vector& dims, + const T* data = nullptr); /// /// @brief Retrieve operator descriptor from program. diff --git a/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc b/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc index 1256abd73e5d6d..ff7f28d5cc6574 100644 --- a/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc +++ b/paddle/fluid/framework/new_executor/interpreter/data_transfer.cc @@ -775,7 +775,7 @@ void HandleComplexGradToRealGrad(const OpFuncNode& op_func_node, continue; } auto* grad_tensor = - framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(grad_var); + framework::GetMutableDenseTensorOrSelectedRowsValueFromVar(grad_var); // skip nullptr tensor if (grad_tensor == nullptr || !grad_tensor->IsInitialized()) { VLOG(3) << "skip with grad_tensor not IsInitialized"; diff --git a/paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc b/paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc index e078b9c2930808..73caf5a40d4947 100644 --- a/paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc +++ b/paddle/fluid/framework/new_executor/interpreter/interpreter_util.cc @@ -951,10 +951,10 @@ void BuildOpFuncList(const phi::Place& place, // operator.cc for (auto& p : m) { auto* transformed_tensor = - GetMutableLoDTensorOrSelectedRowsValueFromVar( + GetMutableDenseTensorOrSelectedRowsValueFromVar( local_scope->FindVar(var_scope->GetNameById(p.first))); auto* original_tensor = - GetMutableLoDTensorOrSelectedRowsValueFromVar( + GetMutableDenseTensorOrSelectedRowsValueFromVar( local_scope->FindVar(var_scope->GetNameById(p.second))); // avoid overwriting valid data diff --git a/paddle/fluid/framework/new_executor/pir_interpreter.cc b/paddle/fluid/framework/new_executor/pir_interpreter.cc index 1338c5f8724362..65928aaa78fc9d 100644 --- a/paddle/fluid/framework/new_executor/pir_interpreter.cc +++ b/paddle/fluid/framework/new_executor/pir_interpreter.cc @@ -478,7 +478,7 @@ void PirInterpreter::CheckCUDAGraphBeforeRun( #endif } -void PirInterpreter::ClearLoDTensorArrayInLocalScope() { +void PirInterpreter::ClearDenseTensorArrayInLocalScope() { auto vars = local_scope_->LocalVars(); for (auto var : vars) { if (var->IsType()) { @@ -1544,7 +1544,7 @@ paddle::framework::FetchList PirInterpreter::Run( } if (HasLocalScope()) { - ClearLoDTensorArrayInLocalScope(); + ClearDenseTensorArrayInLocalScope(); } // return Fetch Tensors @@ -1623,7 +1623,7 @@ FetchList PirInterpreter::Run(const std::vector& feed_names, } if (HasLocalScope()) { - ClearLoDTensorArrayInLocalScope(); + ClearDenseTensorArrayInLocalScope(); } framework::FetchList fetch_res; diff --git a/paddle/fluid/framework/new_executor/pir_interpreter.h b/paddle/fluid/framework/new_executor/pir_interpreter.h index f00dd040d9240a..9c5e753a1899c1 100644 --- a/paddle/fluid/framework/new_executor/pir_interpreter.h +++ b/paddle/fluid/framework/new_executor/pir_interpreter.h @@ -138,7 +138,7 @@ class PirInterpreter : public InterpreterBaseImpl { void CalculateLastLiveOps(); // gc - void ClearLoDTensorArrayInLocalScope(); + void ClearDenseTensorArrayInLocalScope(); // cuda graph void CheckCUDAGraphBeforeRun(const std::vector& feed_names); diff --git a/paddle/fluid/framework/new_executor/program_interpreter.cc b/paddle/fluid/framework/new_executor/program_interpreter.cc index c2714719e2a50a..f83efd767a5d5a 100644 --- a/paddle/fluid/framework/new_executor/program_interpreter.cc +++ b/paddle/fluid/framework/new_executor/program_interpreter.cc @@ -195,7 +195,7 @@ FetchList ProgramInterpreter::Run(const std::vector& feed_names, } if (HasLocalScope()) { - ClearLoDTensorArrayInLocalScope(); + ClearDenseTensorArrayInLocalScope(); } // NOTE (liuchenghao): we need to reset "is_in_op_profiling_mode_" to false. @@ -281,7 +281,7 @@ FetchList ProgramInterpreter::Run( } if (HasLocalScope()) { - ClearLoDTensorArrayInLocalScope(); + ClearDenseTensorArrayInLocalScope(); } if (need_fetch) { @@ -669,7 +669,7 @@ void ProgramInterpreter::BuildOperatorDependences() { // At the end of each step, the holder of phi::DenseTensor in phi::TensorArray // is null. Clear these Tensors and leave phi::TensorArray empty, otherwise an // exception will occur in the next step -void ProgramInterpreter::ClearLoDTensorArrayInLocalScope() { +void ProgramInterpreter::ClearDenseTensorArrayInLocalScope() { auto vars = local_scope_->LocalVars(); for (auto var : vars) { if (var->IsType()) { @@ -1072,9 +1072,10 @@ void ProgramInterpreter::RunOperator(const Instruction& instr_node) { auto& m = instr_node.InplaceBackMap(); // NOTE(zhiqiu): same logic as TransferInplaceVarsBack() in operator.cc for (auto& p : m) { - auto* transformed_tensor = GetMutableLoDTensorOrSelectedRowsValueFromVar( - var_scope_.VarRef(p.first)); - auto* original_tensor = GetMutableLoDTensorOrSelectedRowsValueFromVar( + auto* transformed_tensor = + GetMutableDenseTensorOrSelectedRowsValueFromVar( + var_scope_.VarRef(p.first)); + auto* original_tensor = GetMutableDenseTensorOrSelectedRowsValueFromVar( var_scope_.VarRef(p.second)); original_tensor->ShareDataWith(*transformed_tensor); VLOG(4) << "Transfer inplace variable back form " diff --git a/paddle/fluid/framework/new_executor/program_interpreter.h b/paddle/fluid/framework/new_executor/program_interpreter.h index 760d3637db01e7..9300a487660930 100644 --- a/paddle/fluid/framework/new_executor/program_interpreter.h +++ b/paddle/fluid/framework/new_executor/program_interpreter.h @@ -164,7 +164,7 @@ class ProgramInterpreter : public InterpreterBaseImpl { // gc void RecordStreamForGC(const Instruction& instr); void CheckGC(const Instruction& instr); - void ClearLoDTensorArrayInLocalScope(); + void ClearDenseTensorArrayInLocalScope(); // workqueue std::shared_ptr GetWorkQueue(); diff --git a/paddle/fluid/framework/operator.cc b/paddle/fluid/framework/operator.cc index 04dd6891761c72..19391938ec6c45 100644 --- a/paddle/fluid/framework/operator.cc +++ b/paddle/fluid/framework/operator.cc @@ -1107,7 +1107,8 @@ const phi::DenseTensor* GetDenseTensorOrSelectedRowsValueFromVar( } } -phi::DenseTensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var) { +phi::DenseTensor* GetMutableDenseTensorOrSelectedRowsValueFromVar( + Variable* var) { if (var->IsType()) { return var->GetMutable(); } else if (var->IsType()) { @@ -2462,7 +2463,7 @@ void OperatorWithKernel::TransferInplaceVarsBack( common::errors::InvalidArgument( "The variable[%s] is nullptr.", var_name)); auto* original_tensor = - GetMutableLoDTensorOrSelectedRowsValueFromVar(origin_var); + GetMutableDenseTensorOrSelectedRowsValueFromVar(origin_var); auto* var = transfer_scope.FindVar(var_name); PADDLE_ENFORCE_NOT_NULL(var, common::errors::InvalidArgument( @@ -2495,7 +2496,7 @@ void OperatorWithKernel::HandleComplexGradToRealGrad( continue; } auto* grad_tensor = - GetMutableLoDTensorOrSelectedRowsValueFromVar(grad_var); + GetMutableDenseTensorOrSelectedRowsValueFromVar(grad_var); // skip nullptr tensor if (grad_tensor == nullptr || !grad_tensor->IsInitialized()) { continue; diff --git a/paddle/fluid/framework/operator.h b/paddle/fluid/framework/operator.h index c82effc9141d6a..cc5f0be7de04a2 100644 --- a/paddle/fluid/framework/operator.h +++ b/paddle/fluid/framework/operator.h @@ -133,7 +133,8 @@ inline bool VarIsTensor(const Variable& var) { const phi::DenseTensor* GetDenseTensorOrSelectedRowsValueFromVar( const Variable& var); -phi::DenseTensor* GetMutableLoDTensorOrSelectedRowsValueFromVar(Variable* var); +phi::DenseTensor* GetMutableDenseTensorOrSelectedRowsValueFromVar( + Variable* var); class ExecutionContext; class OperatorBase; diff --git a/paddle/fluid/inference/api/details/reset_tensor_array.h b/paddle/fluid/inference/api/details/reset_tensor_array.h index b436a117886e8e..b7e35e7b236e74 100644 --- a/paddle/fluid/inference/api/details/reset_tensor_array.h +++ b/paddle/fluid/inference/api/details/reset_tensor_array.h @@ -40,14 +40,14 @@ namespace details { struct TensorArrayBatchCleaner { TensorArrayBatchCleaner() { constexpr auto kTensorId = framework::VarTypeTrait::kId; - constexpr auto kLoDTensorId = + constexpr auto kDenseTensorId = framework::VarTypeTrait::kId; constexpr auto kSelectedRowsId = framework::VarTypeTrait::kId; constexpr auto kFetchListId = framework::VarTypeTrait::kId; valid_types_.insert(kTensorId); - valid_types_.insert(kLoDTensorId); + valid_types_.insert(kDenseTensorId); valid_types_.insert(kSelectedRowsId); valid_types_.insert(kFetchListId); } diff --git a/paddle/fluid/inference/tensorrt/convert/io_converter.cc b/paddle/fluid/inference/tensorrt/convert/io_converter.cc index cb99c36efd8873..298c2658c6322e 100644 --- a/paddle/fluid/inference/tensorrt/convert/io_converter.cc +++ b/paddle/fluid/inference/tensorrt/convert/io_converter.cc @@ -29,7 +29,7 @@ class DefaultIOConverter : public EngineIOConverter { public: DefaultIOConverter() {} // NOTE out is GPU memory. - virtual void operator()(const LoDTensor& in, + virtual void operator()(const DenseTensor& in, void* out, size_t max_size) override { PADDLE_ENFORCE_NOT_NULL(out, @@ -66,7 +66,7 @@ class DefaultIOConverter : public EngineIOConverter { } // NOTE in is GPU memory. virtual void operator()(const void* in, - LoDTensor* out, + DenseTensor* out, size_t max_size) override { PADDLE_ENFORCE_NOT_NULL(in, common::errors::InvalidArgument( diff --git a/paddle/phi/kernels/funcs/sequence_padding.cc b/paddle/phi/kernels/funcs/sequence_padding.cc index 6402f266fcfbf8..3d5ec671a7cc2a 100644 --- a/paddle/phi/kernels/funcs/sequence_padding.cc +++ b/paddle/phi/kernels/funcs/sequence_padding.cc @@ -95,7 +95,7 @@ static void fast_mem_init(void* dest, } template -class PaddingLoDTensorFunctor { +class PaddingDenseTensorFunctor { public: void operator()(const phi::CPUContext& context UNUSED, const phi::DenseTensor& seq_tensor, @@ -155,7 +155,7 @@ class PaddingLoDTensorFunctor { }; template -class UnpaddingLoDTensorFunctor { +class UnpaddingDenseTensorFunctor { public: void operator()(const phi::CPUContext& context UNUSED, const phi::DenseTensor& pad_tensor, @@ -192,7 +192,7 @@ class UnpaddingLoDTensorFunctor { #ifdef PADDLE_WITH_XPU template -class UnpaddingLoDTensorFunctor { +class UnpaddingDenseTensorFunctor { public: void operator()(const phi::XPUContext& context, const phi::DenseTensor& pad_tensor, @@ -234,18 +234,18 @@ class UnpaddingLoDTensorFunctor { }; #endif -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; #ifdef PADDLE_WITH_XPU -template class UnpaddingLoDTensorFunctor; +template class UnpaddingDenseTensorFunctor; #endif } // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/sequence_padding.cu b/paddle/phi/kernels/funcs/sequence_padding.cu index 61a9bf63ad39a6..2f3e276aaf575f 100644 --- a/paddle/phi/kernels/funcs/sequence_padding.cu +++ b/paddle/phi/kernels/funcs/sequence_padding.cu @@ -57,7 +57,7 @@ __global__ void SequencePaddingKernel(T* dst, } template -class PaddingLoDTensorFunctor { +class PaddingDenseTensorFunctor { public: void operator()(const phi::GPUContext& context, const phi::DenseTensor& seq_tensor, @@ -139,7 +139,7 @@ class PaddingLoDTensorFunctor { }; template -class UnpaddingLoDTensorFunctor { +class UnpaddingDenseTensorFunctor { public: void operator()(const phi::GPUContext& context, const phi::DenseTensor& pad_tensor, @@ -205,15 +205,15 @@ class UnpaddingLoDTensorFunctor { } }; -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; -template class PaddingLoDTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; +template class PaddingDenseTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; -template class UnpaddingLoDTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; +template class UnpaddingDenseTensorFunctor; } // namespace funcs } // namespace phi diff --git a/paddle/phi/kernels/funcs/sequence_padding.h b/paddle/phi/kernels/funcs/sequence_padding.h index c6f9c0999ce5b8..fa6e4257ce848b 100644 --- a/paddle/phi/kernels/funcs/sequence_padding.h +++ b/paddle/phi/kernels/funcs/sequence_padding.h @@ -106,7 +106,7 @@ inline static void CheckDims(const phi::DDim& seq_tensor_dims, * \note transposition is also done in this functor. */ template -class PaddingLoDTensorFunctor { +class PaddingDenseTensorFunctor { public: void operator()(const DeviceContext& context, const phi::DenseTensor& seq_tensor, @@ -119,7 +119,7 @@ class PaddingLoDTensorFunctor { }; template -class UnpaddingLoDTensorFunctor { +class UnpaddingDenseTensorFunctor { public: void operator()(const DeviceContext& context, const phi::DenseTensor& pad_tensor, diff --git a/paddle/phi/kernels/funcs/sequence_scale.cc b/paddle/phi/kernels/funcs/sequence_scale.cc index 8b4da0a33e966b..7faec99f3095cc 100644 --- a/paddle/phi/kernels/funcs/sequence_scale.cc +++ b/paddle/phi/kernels/funcs/sequence_scale.cc @@ -22,7 +22,7 @@ class DenseTensor; namespace phi::funcs { template -class ScaleLoDTensorFunctor { +class ScaleDenseTensorFunctor { public: void operator()(const phi::CPUContext& context, const T* scales, @@ -44,7 +44,7 @@ class ScaleLoDTensorFunctor { } }; -template class ScaleLoDTensorFunctor; -template class ScaleLoDTensorFunctor; +template class ScaleDenseTensorFunctor; +template class ScaleDenseTensorFunctor; } // namespace phi::funcs diff --git a/paddle/phi/kernels/funcs/sequence_scale.cu b/paddle/phi/kernels/funcs/sequence_scale.cu index 434c82bf362bf0..b1c70b107a6610 100644 --- a/paddle/phi/kernels/funcs/sequence_scale.cu +++ b/paddle/phi/kernels/funcs/sequence_scale.cu @@ -36,7 +36,7 @@ __global__ void SequenceScaleKernel(T* seq, } template -class ScaleLoDTensorFunctor { +class ScaleDenseTensorFunctor { public: void operator()(const phi::GPUContext& context, const T* scales, @@ -72,8 +72,8 @@ class ScaleLoDTensorFunctor { } }; -template class ScaleLoDTensorFunctor; -template class ScaleLoDTensorFunctor; +template class ScaleDenseTensorFunctor; +template class ScaleDenseTensorFunctor; } // namespace funcs } // namespace phi diff --git a/paddle/phi/kernels/funcs/sequence_scale.h b/paddle/phi/kernels/funcs/sequence_scale.h index 18e1f727671223..4bf7c9f1048828 100644 --- a/paddle/phi/kernels/funcs/sequence_scale.h +++ b/paddle/phi/kernels/funcs/sequence_scale.h @@ -47,7 +47,7 @@ namespace funcs { */ template -class ScaleLoDTensorFunctor { +class ScaleDenseTensorFunctor { public: void operator()(const DeviceContext& context, const T* scales, diff --git a/paddle/phi/kernels/impl/warpctc_grad_kernel_impl.h b/paddle/phi/kernels/impl/warpctc_grad_kernel_impl.h index 0c4d731b263f7f..ad1c6f846c0248 100644 --- a/paddle/phi/kernels/impl/warpctc_grad_kernel_impl.h +++ b/paddle/phi/kernels/impl/warpctc_grad_kernel_impl.h @@ -69,7 +69,7 @@ void WarpctcGradKernel(const Context& dev_ctx, logits_grad_e.device(*place) = logits_g; } } else { - phi::funcs::UnpaddingLoDTensorFunctor()( + phi::funcs::UnpaddingDenseTensorFunctor()( dev_ctx, warpctcgrad, logits_grad, @@ -79,7 +79,7 @@ void WarpctcGradKernel(const Context& dev_ctx, phi::funcs::kLengthBatchWidth); const T* loss_grad_data = loss_grad.data(); - phi::funcs::ScaleLoDTensorFunctor()( + phi::funcs::ScaleDenseTensorFunctor()( dev_ctx, loss_grad_data, logits_grad); } } diff --git a/paddle/phi/kernels/impl/warpctc_kernel_impl.h b/paddle/phi/kernels/impl/warpctc_kernel_impl.h index 46a1376cd8e6a4..d6bc1ac92a4b0c 100644 --- a/paddle/phi/kernels/impl/warpctc_kernel_impl.h +++ b/paddle/phi/kernels/impl/warpctc_kernel_impl.h @@ -360,7 +360,7 @@ void WarpctcKernel(const Context& dev_ctx, phi::Copy(dev_ctx, cpu_pad_value, dev_ctx.GetPlace(), true, &pad_value); } - phi::funcs::PaddingLoDTensorFunctor()( + phi::funcs::PaddingDenseTensorFunctor()( dev_ctx, logits, &warpctc_logits, @@ -400,7 +400,7 @@ void WarpctcKernel(const Context& dev_ctx, warpctc_label.set_lod(lod); if (dev_ctx.GetPlace() == phi::CPUPlace()) { - phi::funcs::UnpaddingLoDTensorFunctor()( + phi::funcs::UnpaddingDenseTensorFunctor()( dev_ctx, label, &warpctc_label, @@ -415,7 +415,7 @@ void WarpctcKernel(const Context& dev_ctx, 1}); dev_ctx.template Alloc(&gpu_label); gpu_label.set_lod(lod); - phi::funcs::UnpaddingLoDTensorFunctor()( + phi::funcs::UnpaddingDenseTensorFunctor()( dev_ctx, label, &gpu_label, diff --git a/test/cpp/fluid/framework/lod_tensor_test.cc b/test/cpp/fluid/framework/lod_tensor_test.cc index 4d8b3488cfc1ae..2e6f2f4de74ff5 100644 --- a/test/cpp/fluid/framework/lod_tensor_test.cc +++ b/test/cpp/fluid/framework/lod_tensor_test.cc @@ -22,7 +22,7 @@ namespace paddle { namespace framework { -TEST(LegacyLoD, PrintLoDTensor) { +TEST(LegacyLoD, PrintDenseTensor) { phi::DenseTensor tensor1; tensor1.Resize({2}); tensor1.mutable_data(phi::CPUPlace()); diff --git a/test/cpp/fluid/framework/operator_test.cc b/test/cpp/fluid/framework/operator_test.cc index 64230dc174adf9..26cee1152d8930 100644 --- a/test/cpp/fluid/framework/operator_test.cc +++ b/test/cpp/fluid/framework/operator_test.cc @@ -318,7 +318,7 @@ TEST(VarNameTest, all) { namespace paddle { namespace framework { -class IndicateLoDTensorDataTypeTest : public OperatorWithKernel { +class IndicateDenseTensorDataTypeTest : public OperatorWithKernel { public: using OperatorWithKernel::OperatorWithKernel; @@ -332,7 +332,8 @@ class IndicateLoDTensorDataTypeTest : public OperatorWithKernel { } }; -class IndicateLoDTensorDataTypeTestProtoMaker : public OpProtoAndCheckerMaker { +class IndicateDenseTensorDataTypeTestProtoMaker + : public OpProtoAndCheckerMaker { public: void Make() override { AddInput("phi::DenseTensor", "Input of phi::DenseTensor type Variable."); @@ -393,8 +394,8 @@ class EmptyTestKernel : public OpKernel { REGISTER_OP_WITHOUT_GRADIENT( indicate_lod_tensor_data_type_test, - paddle::framework::IndicateLoDTensorDataTypeTest, - paddle::framework::IndicateLoDTensorDataTypeTestProtoMaker); + paddle::framework::IndicateDenseTensorDataTypeTest, + paddle::framework::IndicateDenseTensorDataTypeTestProtoMaker); REGISTER_OP_WITHOUT_GRADIENT( indicate_selected_rows_data_type_test, paddle::framework::IndicateSelectedRowsDataTypeTest, diff --git a/test/cpp/inference/api/api_impl_tester.cc b/test/cpp/inference/api/api_impl_tester.cc index c9275c44c05f16..a3eb738b44395a 100644 --- a/test/cpp/inference/api/api_impl_tester.cc +++ b/test/cpp/inference/api/api_impl_tester.cc @@ -77,10 +77,10 @@ void MainWord2Vec(const ::paddle::PaddlePlace& place) { phi::LegacyLoD lod{{0, 1}}; int64_t dict_size = 2073; // The size of dictionary - SetupLoDTensor(&first_word, lod, static_cast(0), dict_size - 1); - SetupLoDTensor(&second_word, lod, static_cast(0), dict_size - 1); - SetupLoDTensor(&third_word, lod, static_cast(0), dict_size - 1); - SetupLoDTensor(&fourth_word, lod, static_cast(0), dict_size - 1); + SetupDenseTensor(&first_word, lod, static_cast(0), dict_size - 1); + SetupDenseTensor(&second_word, lod, static_cast(0), dict_size - 1); + SetupDenseTensor(&third_word, lod, static_cast(0), dict_size - 1); + SetupDenseTensor(&fourth_word, lod, static_cast(0), dict_size - 1); std::vector paddle_tensor_feeds; paddle_tensor_feeds.push_back(LodTensorToPaddleTensor(&first_word)); @@ -180,7 +180,8 @@ void MainThreadsWord2Vec(const ::paddle::PaddlePlace& place) { for (size_t j = 0; j < 4; ++j) { phi::LegacyLoD lod{{0, 1}}; int64_t dict_size = 2073; // The size of dictionary - SetupLoDTensor(&jobs[i][j], lod, static_cast(0), dict_size - 1); + SetupDenseTensor( + &jobs[i][j], lod, static_cast(0), dict_size - 1); paddle_tensor_feeds[i].push_back(LodTensorToPaddleTensor(&jobs[i][j])); } diff --git a/test/cpp/inference/api/tester_helper.h b/test/cpp/inference/api/tester_helper.h index 14491b02f1e047..49f6aa06dae259 100644 --- a/test/cpp/inference/api/tester_helper.h +++ b/test/cpp/inference/api/tester_helper.h @@ -1034,7 +1034,7 @@ void CompareAnalysisAndZeroCopy( } template -std::string LoDTensorSummary(const phi::DenseTensor &tensor) { +std::string DenseTensorSummary(const phi::DenseTensor &tensor) { std::stringstream ss; ss << "\n---- tensor ---" << '\n'; ss << "lod: ["; diff --git a/test/cpp/inference/test_helper.h b/test/cpp/inference/test_helper.h index 755fda6c3add69..2c10fdbc162ca6 100644 --- a/test/cpp/inference/test_helper.h +++ b/test/cpp/inference/test_helper.h @@ -68,20 +68,20 @@ void SetupTensor(phi::DenseTensor* input, } template -void SetupLoDTensor(phi::DenseTensor* input, - const phi::LegacyLoD& lod, - T lower, - T upper) { +void SetupDenseTensor(phi::DenseTensor* input, + const phi::LegacyLoD& lod, + T lower, + T upper) { input->set_lod(lod); int dim = lod[0][lod[0].size() - 1]; SetupTensor(input, {dim, 1}, lower, upper); } template -void SetupLoDTensor(phi::DenseTensor* input, - phi::DDim dims, - const phi::LegacyLoD lod, - const std::vector& data) { +void SetupDenseTensor(phi::DenseTensor* input, + phi::DDim dims, + const phi::LegacyLoD lod, + const std::vector& data) { const size_t level = lod.size() - 1; PADDLE_ENFORCE_EQ(dims[0], static_cast((lod[level]).back()), diff --git a/test/cpp/phi/kernels/sequence_padding_test.cc b/test/cpp/phi/kernels/sequence_padding_test.cc index c1d8c47d14eca4..297395421f2c2a 100644 --- a/test/cpp/phi/kernels/sequence_padding_test.cc +++ b/test/cpp/phi/kernels/sequence_padding_test.cc @@ -72,7 +72,7 @@ void TestSequencePadding(const DeviceContext &context, phi::Copy(context, cpu_pad_value, place, true, &pad_value); } - phi::funcs::PaddingLoDTensorFunctor()( + phi::funcs::PaddingDenseTensorFunctor()( context, seq, &padding, @@ -85,7 +85,7 @@ void TestSequencePadding(const DeviceContext &context, seq_back.set_lod(lod); seq_back.Resize(seq_dims); context.template Alloc(&seq_back); - phi::funcs::UnpaddingLoDTensorFunctor()( + phi::funcs::UnpaddingDenseTensorFunctor()( context, padding, &seq_back, -1, 0, false, phi::funcs::kLengthBatchWidth); if (place.GetType() == phi::AllocationType::CPU) { diff --git a/test/legacy_test/test_add_position_encoding_op.py b/test/legacy_test/test_add_position_encoding_op.py index e1e379a2aa92e3..6525c8226247db 100644 --- a/test/legacy_test/test_add_position_encoding_op.py +++ b/test/legacy_test/test_add_position_encoding_op.py @@ -83,9 +83,9 @@ def init_input_output(self): self.out = add_position_encoding(self.x, self.alpha, self.beta) -class TestAddPositionEncodingLoDTensorOp(OpTest): +class TestAddPositionEncodingDenseTensorOp(OpTest): """ - This class is to test the AddPositionEncodingLoDTensorOp + This class is to test the AddPositionEncodingDenseTensorOp """ def setUp(self): diff --git a/test/legacy_test/test_sum_op.py b/test/legacy_test/test_sum_op.py index 3ff5d54f079296..e712c781b9e4aa 100644 --- a/test/legacy_test/test_sum_op.py +++ b/test/legacy_test/test_sum_op.py @@ -257,7 +257,7 @@ def init_kernel_type(self): self.row_numel = 102 -class TestLoDTensorAndSelectedRowsOp(TestSelectedRowsSumOp): +class TestDenseTensorAndSelectedRowsOp(TestSelectedRowsSumOp): def setUp(self): self.height = 10 self.row_numel = 12 @@ -549,7 +549,7 @@ def test_list_of_none_input(): create_test_sum_fp16_class(TestSelectedRowsSumOp) -create_test_sum_fp16_class(TestLoDTensorAndSelectedRowsOp) +create_test_sum_fp16_class(TestDenseTensorAndSelectedRowsOp) class TestReduceOPTensorAxisBase(unittest.TestCase): diff --git a/test/xpu/test_sum_op_xpu.py b/test/xpu/test_sum_op_xpu.py index 885134a219cf32..a8226dc4bce9c3 100644 --- a/test/xpu/test_sum_op_xpu.py +++ b/test/xpu/test_sum_op_xpu.py @@ -180,7 +180,7 @@ def test_list_of_none_input(): self.assertRaises(Exception, test_list_of_none_input) -class TestLoDTensorAndSelectedRowsOp(unittest.TestCase): +class TestDenseTensorAndSelectedRowsOp(unittest.TestCase): def setUp(self): self.height = 10 self.row_numel = 12 From fc09f00d670d51837744c1b6d368ca5d17fb5f39 Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Mon, 9 Dec 2024 14:28:21 +0800 Subject: [PATCH 239/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20=E7=AC=AC=E4=BA=8C=E6=9C=9F=20=E5=85=B6=E4=BB=96?= =?UTF-8?q?=E9=97=AE=E9=A2=98No.13=E3=80=91=E6=B7=BB=E5=8A=A0=20`paddle.ne?= =?UTF-8?q?gative`=20(#69996)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add_neg * add_test * fix_args * add_name --- python/paddle/__init__.py | 2 + python/paddle/tensor/__init__.py | 2 + python/paddle/tensor/math.py | 36 ++++++++- test/legacy_test/test_math_op_patch_pir.py | 21 ++++++ test/legacy_test/test_negative.py | 87 ++++++++++++++++++++++ 5 files changed, 147 insertions(+), 1 deletion(-) create mode 100644 test/legacy_test/test_negative.py diff --git a/python/paddle/__init__.py b/python/paddle/__init__.py index 66778e692bcbcb..7cac26bda3790e 100644 --- a/python/paddle/__init__.py +++ b/python/paddle/__init__.py @@ -497,6 +497,7 @@ nansum, neg, neg_, + negative, nextafter, outer, polygamma, @@ -1010,6 +1011,7 @@ 'conj', 'neg', 'neg_', + 'negative', 'lgamma', 'lgamma_', 'gammaincc', diff --git a/python/paddle/tensor/__init__.py b/python/paddle/tensor/__init__.py index 5874f8507ffa2b..447e1f3b190bfa 100644 --- a/python/paddle/tensor/__init__.py +++ b/python/paddle/tensor/__init__.py @@ -371,6 +371,7 @@ nansum, neg, neg_, + negative, nextafter, outer, polygamma, @@ -626,6 +627,7 @@ 'conj', 'neg', 'neg_', + 'negative', 'lgamma', 'lgamma_', 'gammaincc', diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index 6172cd7849ae68..4f4ba254e776ee 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -5600,7 +5600,7 @@ def neg_(x: Tensor, name: str | None = None) -> Tensor: ) -def positive(x: Tensor) -> Tensor: +def positive(x: Tensor, name: str | None = None) -> Tensor: r""" Returns the input Tensor as it is. This is used in `Tensor.__pos__`, applying the unary `+` operator to the tensor. @@ -5610,6 +5610,7 @@ def positive(x: Tensor) -> Tensor: Args: x (Tensor): The input tensor. The tensor cannot be of type bool. + name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. Returns: Tensor: A tensor with the same shape and data type as the input tensor. The returned tensor @@ -5632,6 +5633,39 @@ def positive(x: Tensor) -> Tensor: return x +def negative(x: Tensor, name: str | None = None) -> Tensor: + r""" + Returns the negated version of the input Tensor. This is used in `Tensor.__neg__`, applying the + unary `-` operator to the tensor. + + .. math:: + Out = -X + + Args: + x (Tensor): The input tensor. The tensor cannot be of type bool. + name (str|None, optional): Name for the operation (optional, default is None). For more information, please refer to :ref:`api_guide_Name`. + + Returns: + Tensor: A tensor with the same shape and data type as the input tensor. The returned tensor + is the negative. + + Examples: + .. code-block:: python + + >>> import paddle + >>> x = paddle.to_tensor([-1, 0, 1]) + >>> out = paddle.negative(x) + >>> print(out) + Tensor(shape=[3], dtype=int64, place=Place(cpu), stop_gradient=True, + [1, 0, -1]) + """ + + # Check if the input tensor is of bool type and raise an error + if x.dtype == paddle.bool: + raise TypeError("The `-` operator, on a bool tensor is not supported.") + return -x + + def atan2(x: Tensor, y: Tensor, name: str | None = None) -> Tensor: r""" Element-wise arctangent of x/y with consideration of the quadrant. diff --git a/test/legacy_test/test_math_op_patch_pir.py b/test/legacy_test/test_math_op_patch_pir.py index 3ca932e2ccf57b..a9aee098dd0e5c 100644 --- a/test/legacy_test/test_math_op_patch_pir.py +++ b/test/legacy_test/test_math_op_patch_pir.py @@ -809,6 +809,27 @@ def test_neg(self): np.testing.assert_array_equal(res, a_np) np.testing.assert_array_equal(res, b_np) + def test_negative(self): + x_np = np.random.uniform(-1, 1, [10, 1024]).astype(np.float32) + res = -x_np + with paddle.pir_utils.IrGuard(): + main_program, exe, program_guard = new_program() + with program_guard: + x = paddle.static.data( + name='x', shape=[10, 1024], dtype="float32" + ) + a = -x + b = x.negative() + c = paddle.negative(x) + (a_np, b_np, c_np) = exe.run( + main_program, + feed={"x": x_np}, + fetch_list=[a, b, c], + ) + np.testing.assert_array_equal(res, a_np) + np.testing.assert_array_equal(res, b_np) + np.testing.assert_array_equal(res, c_np) + def test_abs(self): # test for real number x_np = np.random.uniform(-1, 1, [10, 1024]).astype(np.float32) diff --git a/test/legacy_test/test_negative.py b/test/legacy_test/test_negative.py new file mode 100644 index 00000000000000..c5d038c03ad0db --- /dev/null +++ b/test/legacy_test/test_negative.py @@ -0,0 +1,87 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle + + +class TestNegativeApi(unittest.TestCase): + + def setUp(self): + paddle.disable_static() + self.shape = [2, 3, 4, 5] + self.low = -100 + self.high = 100 + + def test_negative_int16(self): + x = np.random.randint(self.low, self.high, self.shape, dtype=np.int16) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-5) + + def test_negative_int32(self): + x = np.random.randint(self.low, self.high, self.shape, dtype=np.int32) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-5) + + def test_negative_int64(self): + x = np.random.randint(self.low, self.high, self.shape, dtype=np.int64) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-5) + + def test_negative_float16(self): + x = np.random.uniform(self.low, self.high, self.shape).astype( + np.float16 + ) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-3) + + def test_negative_float32(self): + x = np.random.uniform(self.low, self.high, self.shape).astype( + np.float32 + ) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-3) + + def test_negative_float64(self): + x = np.random.uniform(self.low, self.high, self.shape).astype( + np.float64 + ) + expected_out = np.negative(x) + x_tensor = paddle.to_tensor(x) + out = paddle.negative(x_tensor).numpy() + np.testing.assert_allclose(out, expected_out, atol=1e-3) + + def test_negative_bool(self): + x = np.random.choice([True, False], size=self.shape) + x_tensor = paddle.to_tensor(x, dtype=paddle.bool) + + with self.assertRaises(TypeError): + paddle.negative(x_tensor) + + +if __name__ == '__main__': + unittest.main() From 9e3c8e191ad722228ef0a992d84f18edc328bb1e Mon Sep 17 00:00:00 2001 From: liuruyan <44316842+liuruyan@users.noreply.github.com> Date: Mon, 9 Dec 2024 15:32:17 +0800 Subject: [PATCH 240/288] add uint8 const fold (#69984) --- paddle/cinn/common/cas.cc | 6 +++++- paddle/cinn/ir/schedule/impl/loop_transformation.cc | 2 +- 2 files changed, 6 insertions(+), 2 deletions(-) diff --git a/paddle/cinn/common/cas.cc b/paddle/cinn/common/cas.cc index 97c088b1081130..56b3743e63be6f 100644 --- a/paddle/cinn/common/cas.cc +++ b/paddle/cinn/common/cas.cc @@ -692,17 +692,21 @@ std::vector CasSimplifyMutator::SimplifyBinarySum(Expr left, Expr right) { if (!left.As() && !right.As()) { auto a = left; auto b = right; - + // clang-format off auto* ai = a.As(); + auto* au = a.As(); auto* af = a.As(); auto* bi = b.As(); + auto* bu = b.As(); auto* bf = b.As(); // case 1, both are constants if (a.is_constant() && b.is_constant()) { if (ai) return {make_const(a.type(), ai->value + bi->value)}; if (af) return {make_const(a.type(), af->value + bf->value)}; + if (au) return {make_const(a.type(), au->value + bu->value)}; } + // clang-format on // cinn_min/cinn_max(a, b)+c = cinn_min/cinn_max(a+c, b+c) // c + cinn_min/cinn_max(a, b) = cinn_min/cinn_max(a+c, b+c) diff --git a/paddle/cinn/ir/schedule/impl/loop_transformation.cc b/paddle/cinn/ir/schedule/impl/loop_transformation.cc index 4e7424bfa9252f..f54b0fd81a9d81 100644 --- a/paddle/cinn/ir/schedule/impl/loop_transformation.cc +++ b/paddle/cinn/ir/schedule/impl/loop_transformation.cc @@ -359,7 +359,7 @@ Expr DyScheduleImpl::Fuse(const std::vector& loops) { std::string primitive = "Fuse"; std::ostringstream os; - VLOG(3) << "Tring to fuse:\n" << cinn::utils::Join(loops, "\n"); + VLOG(3) << "Tring to fuse:\n" << loops[0]; std::vector for_nodes; std::vector loop_vars; From c0e042f7b19b135a0a082f9b2632e6a2de7d4c44 Mon Sep 17 00:00:00 2001 From: yinfan98 <1106310035@qq.com> Date: Mon, 9 Dec 2024 15:47:05 +0800 Subject: [PATCH 241/288] =?UTF-8?q?Revert=20"=E3=80=90Hackathon=207th=20No?= =?UTF-8?q?.32=E3=80=91=E4=B8=BA=20paddle.nn.functional.scaled=5Fdot=5Fpro?= =?UTF-8?q?duct=5Fattent=E2=80=A6"=20(#69978)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit a76839d91a0f59d3d49adc61f1e27fd8a8f0d303. --- .../paddle/nn/functional/flash_attention.py | 302 +++--------------- test/legacy_test/test_flash_attention.py | 294 ----------------- 2 files changed, 47 insertions(+), 549 deletions(-) diff --git a/python/paddle/nn/functional/flash_attention.py b/python/paddle/nn/functional/flash_attention.py index 7203a037deaf5c..76314d775f3d8b 100644 --- a/python/paddle/nn/functional/flash_attention.py +++ b/python/paddle/nn/functional/flash_attention.py @@ -16,15 +16,12 @@ from typing import TYPE_CHECKING, Literal, overload -import numpy as np - import paddle import paddle.nn.functional as F from paddle import _C_ops, in_dynamic_mode from paddle.base.framework import in_dynamic_or_pir_mode from paddle.base.layer_helper import LayerHelper from paddle.base.wrapped_decorator import signature_safe_contextmanager -from paddle.device.cuda import get_device_capability g_enable_math = None g_enable_flash = None @@ -36,116 +33,6 @@ from paddle import Tensor -def _get_arch_info(): - # Get SMVersion from device. - cuda_version = paddle.version.cuda() - if ( - cuda_version is not None and cuda_version != 'False' - ) or paddle.is_compiled_with_rocm(): - major, minor = get_device_capability() - arch = int(major * 10 + minor) - return arch - else: - raise ValueError( - "Paddle is not compiled with CUDA, we cannot get SMVersion from device, please try to compile Paddle with CUDA" - ) - - -def check_flash_head_dim_constraints(query, dropout_p=0.0): - arch = _get_arch_info() - is_sm86_to_sm89 = 86 <= arch <= 89 - - if not is_sm86_to_sm89: - return True - - head_dim = query.shape[-1] - requires_grad = not query.stop_gradient - - if not requires_grad: - return True - - is_head_dim_gt192 = head_dim > 192 - is_head_dim_lte224 = head_dim <= 224 - is_dropout = dropout_p > 0.0 - - cond1 = is_head_dim_gt192 and is_head_dim_lte224 - cond2 = head_dim > 224 and is_dropout - - if cond1 or cond2: - return False - return True - - -def check_flash_causal_non_square_seqlens(query, key, is_causal=False): - if not is_causal: - return True - - seqlen_q = query.shape[-3] - seqlen_k = key.shape[-3] - - if seqlen_q != seqlen_k: - return False - return True - - -def check_dtypes_low_precision(query, debug=False): - arch = _get_arch_info() - dtype = query.dtype - - if arch >= 80: - supported_dtypes = [paddle.float16, paddle.bfloat16] - else: - supported_dtypes = [paddle.float16] - - return dtype in supported_dtypes - - -def can_use_flash_attn(query, key, attn_mask, dropout, is_causal) -> bool: - # sdpa flash check - # step1 check tensor place on cuda - # step2 check tensor shape, flash attn only support shape == 4 - # step3 check attn_mask, some diff with torch version - # step4 check head_dim <= 256 - # step5 check arch_info > sm80 - # step5 check specify sm head dim constraint - # step6 check causal qk - # step7 check sm dtype support - if "gpu" not in paddle.get_device(): - return False - if query.ndim != 4: - return False - if attn_mask is not None and attn_mask.dtype not in [ - paddle.bool, - paddle.float32, - ]: - return False - if query.shape[-1] >= 256: - return False - if _get_arch_info() < 80: - return False - if not check_flash_head_dim_constraints(query, dropout): - return False - if not check_flash_causal_non_square_seqlens(query, key, is_causal): - return False - if not check_dtypes_low_precision(query): - return False - return True - - -def can_use_efficient(query) -> bool: - # sdpa efficient check - # step1 check tensor place on cuda - # step2 check arch_info in [sm50, sm90] - # step3 check tensor shape, mem efficient only support shape == 4 - if "gpu" not in paddle.get_device(): - return False - if _get_arch_info() < 50 and _get_arch_info() > 90: - return False - if query.ndim != 4: - return False - return True - - @signature_safe_contextmanager def sdp_kernel( enable_math: bool = False, @@ -186,7 +73,6 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, - mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: Literal[False] = ..., @@ -199,7 +85,6 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, - mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: Literal[True] = ..., @@ -212,7 +97,6 @@ def _math_attention( query: Tensor, key: Tensor, value: Tensor, - mask: Tensor, dropout_rate: float = ..., causal: bool = ..., return_softmax: bool = ..., @@ -224,7 +108,6 @@ def _math_attention( query, key, value, - mask=None, dropout_rate=0.0, causal=False, return_softmax=False, @@ -240,9 +123,6 @@ def _math_attention( value = paddle.transpose(value, [0, 2, 1, 3]) product = paddle.matmul(x=query * (head_dim**-0.5), y=key, transpose_y=True) - if mask is not None: - product = product + mask - if not causal: weights = F.softmax(product) else: @@ -266,7 +146,6 @@ def _math_attention( def _select_sdp_cuda(head_dim: int) -> str: - if head_dim <= 256: return "flash_attn" else: @@ -312,54 +191,6 @@ def _select_sdp(head_dim: int) -> str: return "mem_efficient" -def _select_sdp_for_sdpa(query, key, attn_mask, dropout, is_causal) -> str: - r""" - this select sdpa is alignment for torch version - """ - place = paddle.get_device() - if "xpu" in place: - return "flash_attn" - - # not use sdp_kernel - if ( - g_enable_flash is None - and g_enable_math is None - and g_enable_mem_efficient is None - ): - # test flash attn usage - use_flash = can_use_flash_attn( - query, key, attn_mask, dropout, is_causal - ) - use_efficient = can_use_efficient(query) - use_math = True - if use_flash: - return "flash_attn" - elif use_efficient: - return "mem_efficient" - elif use_math: - return "math" - - if ( - g_enable_math is False - and g_enable_flash is False - and g_enable_mem_efficient is False - ): - raise AssertionError( - "No available backend for scaled_dot_product_attention was found." - ) - - if g_enable_math is True: - if g_enable_flash is False and g_enable_mem_efficient is False: - return "math" - if "gpu" not in place: - return "math" - if g_enable_flash is True and g_enable_mem_efficient is True: - return _select_sdp_cuda(query.shape[-1]) - if g_enable_flash is True: - return "flash_attn" - return "mem_efficient" - - @overload def flash_attention( query: Tensor, @@ -1204,103 +1035,64 @@ def scaled_dot_product_attention( >>> # doctest: -SKIP """ - head_dim = query.shape[3] - sdp_func_name = _select_sdp_for_sdpa( - query, key, attn_mask, dropout_p, is_causal - ) - if attn_mask is None: # downgraded to ordinary flash attention implementation out, _ = flash_attention(query, key, value, dropout_p, is_causal) return out else: - if sdp_func_name == "flash_attn": - if in_dynamic_or_pir_mode(): - fixed_seed_offset = None - return_softmax = False - rng_name = "" - out, _, _, _ = _C_ops.flash_attn( - query, - key, - value, - fixed_seed_offset, - attn_mask, - dropout_p, - is_causal, - return_softmax, - not training, - rng_name, - ) - return out - else: - helper = LayerHelper('flash_attn', **locals()) - dtype = helper.input_dtype(input_param_name='q') - out = helper.create_variable_for_type_inference(dtype) - softmax = helper.create_variable_for_type_inference(dtype) - softmax_lse = helper.create_variable_for_type_inference( - paddle.float32 - ) - seed_offset = helper.create_variable_for_type_inference( - paddle.int64 - ) - inputs = { - 'q': query, - 'k': key, - 'v': value, - 'attn_mask': attn_mask, - } - outputs = { - 'out': out, - 'softmax': softmax, - 'softmax_lse': softmax_lse, - 'seed_offset': seed_offset, - } - helper.append_op( - type='flash_attn', - inputs=inputs, - outputs=outputs, - attrs={ - 'dropout': dropout_p, - 'causal': is_causal, - 'return_softmax': False, - 'is_test': not training, - 'rng_name': '', - }, - ) - return out - elif sdp_func_name == "mem_efficient": - from paddle.incubate.nn.functional.variable_length_memory_efficient_attention import ( - variable_length_memory_efficient_attention, - ) - - seq_lens = paddle.to_tensor( - [query.shape[1]] * query.shape[0], dtype='int32' - ) - - scale = 1.0 / np.sqrt(query.shape[-1]) - - query = query.transpose([0, 2, 1, 3]) - key = key.transpose([0, 2, 1, 3]) - value = value.transpose([0, 2, 1, 3]) - - output = variable_length_memory_efficient_attention( - query, key, value, seq_lens, seq_lens, attn_mask, scale - ) - - output = output.transpose([0, 2, 1, 3]) - - return output - elif sdp_func_name == "math": - return _math_attention( + if in_dynamic_or_pir_mode(): + fixed_seed_offset = None + return_softmax = False + rng_name = "" + out, _, _, _ = _C_ops.flash_attn( query, key, value, + fixed_seed_offset, attn_mask, dropout_p, is_causal, - False, - training, - )[0] + return_softmax, + not training, + rng_name, + ) + return out + else: + helper = LayerHelper('flash_attn', **locals()) + dtype = helper.input_dtype(input_param_name='q') + out = helper.create_variable_for_type_inference(dtype) + softmax = helper.create_variable_for_type_inference(dtype) + softmax_lse = helper.create_variable_for_type_inference( + paddle.float32 + ) + seed_offset = helper.create_variable_for_type_inference( + paddle.int64 + ) + inputs = { + 'q': query, + 'k': key, + 'v': value, + 'attn_mask': attn_mask, + } + outputs = { + 'out': out, + 'softmax': softmax, + 'softmax_lse': softmax_lse, + 'seed_offset': seed_offset, + } + helper.append_op( + type='flash_attn', + inputs=inputs, + outputs=outputs, + attrs={ + 'dropout': dropout_p, + 'causal': is_causal, + 'return_softmax': False, + 'is_test': not training, + 'rng_name': '', + }, + ) + return out def flashmask_attention( diff --git a/test/legacy_test/test_flash_attention.py b/test/legacy_test/test_flash_attention.py index 4a3ab1f4763354..5c5cf6808c8a4b 100644 --- a/test/legacy_test/test_flash_attention.py +++ b/test/legacy_test/test_flash_attention.py @@ -23,7 +23,6 @@ import paddle.nn.functional as F from paddle import base from paddle.base import core -from paddle.nn.functional import sdp_kernel from paddle.nn.functional.flash_attention import ( calc_reduced_attention_scores, flash_attention, @@ -485,41 +484,6 @@ def setUp(self): self.causal = False -# cpu case -class TestSDPAttentionWithMaskAPITest(TestFlashAttentionWithMaskAPI): - def setUp(self): - self.place = paddle.CPUPlace() - self.shape = (8, 1024, 16, 128) - self.dtype = 'float32' - self.dropout = 0.0 - self.causal = False - - -# fp32 case -class TestSDPAttentionWithMaskAPITest2(TestFlashAttentionWithMaskAPI): - def setUp(self): - self.place = paddle.CUDAPlace(0) - self.shape = (8, 1024, 16, 128) - self.dtype = 'float32' - self.dropout = 0.0 - self.causal = False - - -# low sm case -@unittest.skipIf( - is_sm_supported, - "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" - "and device's compute capability must be 7.5 or 8.x", -) -class TestSDPAttentionWithMaskAPITest3(TestFlashAttentionWithMaskAPI): - def setUp(self): - self.place = paddle.CUDAPlace(0) - self.shape = (8, 1024, 16, 128) - self.dtype = 'float16' - self.dropout = 0.0 - self.causal = False - - @unittest.skipIf( not is_flashattn_supported(), "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" @@ -834,9 +798,6 @@ def unpad(self, x, cu_seqlen): return unpad_x def test_main(self): - # test dynamic - paddle.disable_static() - for causal in [False, True]: for use_unpadded in [False, True]: ( @@ -1588,260 +1549,5 @@ def setUp(self): self.dtype = 'bfloat16' -@unittest.skipIf( - not is_flashattn_supported(), - "core is not compiled with CUDA and cuda version need larger than or equal to 11.4" - "and device's compute capability must be 7.5 or 8.x", -) -class TestFlashAttentionAlignment(unittest.TestCase): - def setUp(self): - paddle.disable_static() - self.bs = 1 - self.seq_len = 8 - self.num_head = 1 - self.head_dim = 8 - self.dtype = 'float16' - self.query = np.array( - [ # batch_size = 1 - [[0.3, -0.7, 0.2, 0.5, -0.4, 0.8, -0.2, 0.1]], # seq position 0 - [ - [-0.5, 0.4, 0.7, -0.3, 0.6, -0.8, 0.3, -0.1] - ], # seq position 1 - [[0.2, 0.8, -0.4, 0.1, -0.6, 0.3, 0.7, -0.5]], # seq position 2 - [[-0.8, 0.1, 0.6, 0.4, -0.2, -0.7, 0.5, 0.3]], # seq position 3 - [[0.7, -0.3, -0.5, 0.8, 0.2, 0.4, -0.6, 0.1]], # seq position 4 - [[-0.2, 0.5, 0.3, -0.7, 0.8, 0.1, -0.4, 0.6]], # seq position 5 - [[0.4, -0.6, 0.8, -0.1, 0.3, 0.5, -0.8, 0.2]], # seq position 6 - [[-0.4, 0.2, -0.8, 0.6, 0.1, -0.3, 0.7, 0.5]], # seq position 7 - ], - dtype=np.float16, - ).reshape(1, 8, 1, 8) - self.key = np.array( - [ # batch_size = 1 - [[0.6, -0.2, 0.8, -0.4, 0.3, 0.1, -0.7, 0.5]], # seq position 0 - [[-0.3, 0.7, 0.1, 0.5, -0.8, 0.4, -0.2, 0.6]], # seq position 1 - [[0.8, -0.5, 0.3, -0.1, 0.6, 0.2, -0.4, 0.7]], # seq position 2 - [[-0.6, 0.4, -0.2, 0.7, 0.1, -0.8, 0.3, 0.5]], # seq position 3 - [[0.2, 0.8, -0.6, 0.3, 0.5, -0.1, 0.7, -0.4]], # seq position 4 - [[-0.7, 0.3, 0.5, 0.1, -0.4, 0.8, -0.2, 0.6]], # seq position 5 - [[0.5, -0.8, 0.2, 0.6, -0.3, 0.7, 0.1, -0.5]], # seq position 6 - [[-0.1, 0.6, 0.4, -0.7, 0.2, 0.5, -0.8, 0.3]], # seq position 7 - ], - dtype=np.float16, - ).reshape(1, 8, 1, 8) - self.value = np.array( - [ # batch_size = 1 - [[-0.4, 0.8, -0.1, 0.3, 0.6, -0.5, 0.2, 0.7]], # seq position 0 - [[0.5, -0.3, 0.7, 0.2, -0.6, 0.4, -0.8, 0.1]], # seq position 1 - [[-0.2, 0.6, 0.4, -0.7, 0.3, 0.8, -0.1, 0.5]], # seq position 2 - [[0.7, -0.4, 0.1, 0.5, -0.8, 0.2, 0.6, -0.3]], # seq position 3 - [[-0.5, 0.3, 0.8, -0.2, 0.4, 0.1, -0.7, 0.6]], # seq position 4 - [[0.2, -0.6, 0.3, 0.7, -0.1, 0.5, -0.4, 0.8]], # seq position 5 - [[-0.8, 0.1, 0.5, -0.3, 0.7, 0.4, -0.2, 0.6]], # seq position 6 - [[0.3, -0.7, 0.2, 0.6, -0.4, 0.8, -0.5, 0.1]], # seq position 7 - ], - dtype=np.float16, - ).reshape(1, 8, 1, 8) - self.mask = paddle.zeros( - [1, 1, self.seq_len, self.seq_len], dtype='float16' - ) - for i in range(self.bs): - seq_len = self.seq_len - mask = ( - paddle.tril( - paddle.ones(shape=(seq_len, seq_len), dtype=paddle.float32) - ) - - 1 - ) - self.mask[i, 0, :seq_len, :seq_len] = mask * 1e4 - self.rtol = 1e-3 - self.atol = 1e-3 - - self.expected_output = np.array( - [ - [ - [ - [ - -3.9990e-01, - 7.9980e-01, - -9.9976e-02, - 3.0005e-01, - 6.0010e-01, - -5.0000e-01, - 1.9995e-01, - 7.0020e-01, - ] - ], - [ - [ - -6.1798e-03, - 3.1860e-01, - 2.5000e-01, - 2.5610e-01, - 7.5012e-02, - -1.0626e-01, - -2.3743e-01, - 4.3750e-01, - ] - ], - [ - [ - 1.0028e-01, - 1.9958e-01, - 4.2505e-01, - 5.3787e-04, - -7.5317e-02, - 2.7441e-01, - -3.7524e-01, - 3.4985e-01, - ] - ], - [ - [ - 2.9224e-01, - 1.6373e-02, - 2.7368e-01, - 1.8188e-01, - -3.0298e-01, - 2.2412e-01, - 3.4210e-02, - 1.2610e-01, - ] - ], - [ - [ - -1.6998e-02, - 2.5220e-01, - 3.7939e-01, - -3.7048e-02, - 3.0151e-02, - 2.3108e-01, - -1.6772e-01, - 3.5327e-01, - ] - ], - [ - [ - 1.1948e-02, - 1.2378e-01, - 3.2935e-01, - 1.2390e-01, - 2.6123e-02, - 2.3279e-01, - -1.6919e-01, - 4.4019e-01, - ] - ], - [ - [ - -1.6162e-01, - 1.9812e-01, - 3.2544e-01, - 1.8021e-02, - 2.0081e-01, - 2.5586e-01, - -1.5466e-01, - 5.0635e-01, - ] - ], - [ - [ - 5.0873e-02, - -7.4219e-02, - 3.9502e-01, - 1.5466e-01, - -8.6182e-02, - 3.1958e-01, - -2.1179e-01, - 3.1714e-01, - ] - ], - ] - ], - dtype=np.float16, - ) - - def test_flash_attention(self): - paddle.disable_static() - query = paddle.to_tensor(self.query) - key = paddle.to_tensor(self.key) - value = paddle.to_tensor(self.value) - mask = paddle.to_tensor(self.mask) - - with sdp_kernel( - enable_flash=True, enable_math=False, enable_mem_efficient=False - ): - output = paddle.nn.functional.scaled_dot_product_attention( - query, - key, - value, - attn_mask=mask, - dropout_p=0.0, - is_causal=False, - ) - - np.testing.assert_allclose( - output.numpy(), - self.expected_output, - rtol=self.rtol, - atol=self.atol, - err_msg='Flash attention output does not match expected values', - ) - - def test_math_attention(self): - paddle.disable_static() - query = paddle.to_tensor(self.query) - key = paddle.to_tensor(self.key) - value = paddle.to_tensor(self.value) - mask = paddle.to_tensor(self.mask) - - with sdp_kernel( - enable_flash=False, enable_math=True, enable_mem_efficient=False - ): - output = paddle.nn.functional.scaled_dot_product_attention( - query, - key, - value, - attn_mask=mask, - dropout_p=0.0, - is_causal=False, - ) - - np.testing.assert_allclose( - output.numpy(), - self.expected_output, - rtol=self.rtol, - atol=self.atol, - err_msg='Math attention output does not match expected values', - ) - - def test_mem_efficient_attention(self): - paddle.disable_static() - query = paddle.to_tensor(self.query) - key = paddle.to_tensor(self.key) - value = paddle.to_tensor(self.value) - mask = paddle.to_tensor(self.mask) - - with sdp_kernel( - enable_flash=False, enable_math=False, enable_mem_efficient=True - ): - output = paddle.nn.functional.scaled_dot_product_attention( - query, - key, - value, - attn_mask=mask, - dropout_p=0.0, - is_causal=False, - ) - - np.testing.assert_allclose( - output.numpy(), - self.expected_output, - rtol=self.rtol, - atol=self.atol, - err_msg='Memory efficient attention output does not match expected values', - ) - - if __name__ == '__main__': unittest.main() From ecc497814a29d2fd87b4c2d545fb82e35aeca3cd Mon Sep 17 00:00:00 2001 From: co63oc Date: Mon, 9 Dec 2024 16:02:33 +0800 Subject: [PATCH 242/288] Fix (#70054) --- paddle/fluid/pybind/tensor.cc | 9 +-------- 1 file changed, 1 insertion(+), 8 deletions(-) diff --git a/paddle/fluid/pybind/tensor.cc b/paddle/fluid/pybind/tensor.cc index d9b09bd253dddb..c0dce7d167371d 100644 --- a/paddle/fluid/pybind/tensor.cc +++ b/paddle/fluid/pybind/tensor.cc @@ -508,8 +508,7 @@ void BindTensor(pybind11::module &m) { // NOLINT })) .def(py::init([]() { return std::make_unique(); })) // We implement offset based LegacyLoD in C++ while we use length based - // with Python API. So we changed set_lod to - // set_recursive_sequence_lengths to avoid misuse. The discussion is here: + // with Python API. The discussion is here: // https://github.com/PaddlePaddle/Paddle/issues/10855 .def( "set_lod", @@ -649,7 +648,6 @@ void BindTensor(pybind11::module &m) { // NOLINT dst.clear(); dst.Resize({0}); } - dst.set_lod(self.lod()); return dst; #ifdef _WIN32 }); @@ -679,7 +677,6 @@ void BindTensor(pybind11::module &m) { // NOLINT auto dtype = static_cast(t[1].cast()); auto dims = common::make_ddim(t[2].cast>()); - auto lod_info = t[3].cast(); auto device_id = t[4].cast(); auto shared_reader_holder = @@ -690,7 +687,6 @@ void BindTensor(pybind11::module &m) { // NOLINT self.ResetHolderWithType(shared_reader_holder, dtype); self.Resize(dims); - self.set_lod(lod_info); VLOG(6) << "Reconstructed tensor with buffer shared!"; }, @@ -790,7 +786,6 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_reader_holder, static_cast(t[3].cast())); tensor.Resize(common::make_ddim(t[4].cast>())); - tensor.set_lod(t[5].cast()); return tensor; }, @@ -925,7 +920,6 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_holder, static_cast(t[3].cast())); tensor.Resize(common::make_ddim(t[4].cast>())); - tensor.set_lod(t[5].cast()); return tensor; }, @@ -1013,7 +1007,6 @@ void BindTensor(pybind11::module &m) { // NOLINT shared_reader_holder, static_cast(t[2].cast())); tensor.Resize(common::make_ddim(t[3].cast>())); - tensor.set_lod(t[4].cast()); return tensor; })); From b0fd6e1de64b4aaf80621816a8e5f84928d091e8 Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Mon, 9 Dec 2024 16:55:43 +0800 Subject: [PATCH 243/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20=E7=AC=AC=E4=BA=8C=E6=9C=9F=20=E5=B8=B8=E7=94=A8API?= =?UTF-8?q?=E5=A4=8D=E6=95=B0=E7=B1=BB=E5=9E=8B=E6=94=AF=E6=8C=81No.1?= =?UTF-8?q?=E3=80=91=E6=B7=BB=E5=8A=A0=20`all`=20=E5=87=BD=E6=95=B0?= =?UTF-8?q?=E5=A4=8D=E6=95=B0=E7=B1=BB=E5=9E=8B=E6=94=AF=E6=8C=81=20(#7002?= =?UTF-8?q?6)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add_complex * fix codestyle * fix codestyle * fix codestyle * merge * fix codestyle * add_test * add_64_test * fix codestyle --- paddle/phi/kernels/cpu/reduce_all_kernel.cc | 10 +- paddle/phi/kernels/funcs/reduce_functor.h | 13 ++ paddle/phi/kernels/kps/reduce_kernel.cu | 8 +- .../kernels/primitive/functor_primitives.h | 11 ++ paddle/phi/kernels/reduce_all_kernel.cc | 30 +++- python/paddle/tensor/math.py | 15 +- test/legacy_test/test_reduce_op.py | 152 ++++++++++++++++++ 7 files changed, 230 insertions(+), 9 deletions(-) diff --git a/paddle/phi/kernels/cpu/reduce_all_kernel.cc b/paddle/phi/kernels/cpu/reduce_all_kernel.cc index 5c863b1a95a3cf..fac561a8ab61d0 100644 --- a/paddle/phi/kernels/cpu/reduce_all_kernel.cc +++ b/paddle/phi/kernels/cpu/reduce_all_kernel.cc @@ -15,10 +15,14 @@ #include "paddle/phi/kernels/reduce_all_kernel.h" #include "paddle/phi/backends/cpu/cpu_context.h" +#include "paddle/phi/common/complex.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/cpu/reduce.h" #include "paddle/phi/kernels/funcs/reduce_functor.h" +using complex64 = ::phi::dtype::complex; +using complex128 = ::phi::dtype::complex; + namespace phi { template @@ -29,7 +33,7 @@ void AllRawKernel(const Context& dev_ctx, bool reduce_all, DenseTensor* out) { reduce_all = recompute_reduce_all(x, dims, reduce_all); - phi::BoolReduceKernel( + phi::BoolReduceKernel>( dev_ctx, x, dims, keep_dim, reduce_all, out); } @@ -43,6 +47,8 @@ PD_REGISTER_KERNEL(all_raw, double, int, int64_t, - bool) { + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } diff --git a/paddle/phi/kernels/funcs/reduce_functor.h b/paddle/phi/kernels/funcs/reduce_functor.h index ee319b060d0957..d2df5855c925bb 100644 --- a/paddle/phi/kernels/funcs/reduce_functor.h +++ b/paddle/phi/kernels/funcs/reduce_functor.h @@ -15,6 +15,7 @@ #pragma once #include "paddle/common/macros.h" +#include "paddle/phi/common/complex.h" #include "paddle/phi/kernels/funcs/eigen/common.h" #include "paddle/phi/kernels/funcs/eigen/eigen_function.h" namespace phi { @@ -89,6 +90,7 @@ struct MinFunctor { }; //////// All Functor /////// +template struct AllFunctor { template void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { @@ -96,6 +98,17 @@ struct AllFunctor { } }; +template +struct AllFunctor> { + template + void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { + auto to_bool = [](const std::complex& v) { + return v.real() != 0 || v.imag() != 0; + }; + y->device(place) = x->unaryExpr(to_bool).all(dim); + } +}; + //////// Any Functor /////// struct AnyFunctor { template diff --git a/paddle/phi/kernels/kps/reduce_kernel.cu b/paddle/phi/kernels/kps/reduce_kernel.cu index 6cfebc386fd8e5..318a4376a74d52 100644 --- a/paddle/phi/kernels/kps/reduce_kernel.cu +++ b/paddle/phi/kernels/kps/reduce_kernel.cu @@ -13,6 +13,7 @@ // limitations under the License. #include +#include "paddle/phi/common/complex.h" #include "paddle/phi/core/enforce.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/gpu/reduce.h" @@ -29,6 +30,9 @@ #include "paddle/phi/kernels/funcs/eigen/common.h" #endif +using complex64 = ::phi::dtype::complex; +using complex128 = ::phi::dtype::complex; + namespace phi { template @@ -307,7 +311,9 @@ PD_REGISTER_KERNEL(all_raw, double, int, int64_t, - bool) { + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } diff --git a/paddle/phi/kernels/primitive/functor_primitives.h b/paddle/phi/kernels/primitive/functor_primitives.h index 4ed6d24d0d6b8f..dc199d2c7e2f47 100644 --- a/paddle/phi/kernels/primitive/functor_primitives.h +++ b/paddle/phi/kernels/primitive/functor_primitives.h @@ -83,6 +83,17 @@ struct IdentityFunctor { } }; +template +struct IdentityFunctor, bool> { + HOSTDEVICE inline IdentityFunctor() {} + + HOSTDEVICE explicit inline IdentityFunctor(int n) {} + + HOSTDEVICE inline bool operator()(const phi::dtype::complex& x) const { + return x.real != 0 || x.imag != 0; + } +}; + /** * @brief Default unary div functor. Divide by a constant */ diff --git a/paddle/phi/kernels/reduce_all_kernel.cc b/paddle/phi/kernels/reduce_all_kernel.cc index 92bc5e97cc0211..b4de7e54b764a7 100644 --- a/paddle/phi/kernels/reduce_all_kernel.cc +++ b/paddle/phi/kernels/reduce_all_kernel.cc @@ -15,8 +15,12 @@ #include "paddle/phi/kernels/reduce_all_kernel.h" #include "paddle/phi/backends/all_context.h" +#include "paddle/phi/common/complex.h" #include "paddle/phi/core/kernel_registry.h" +using complex64 = ::phi::dtype::complex; +using complex128 = ::phi::dtype::complex; + namespace phi { template @@ -38,14 +42,32 @@ void AllKernel(const Context& dev_ctx, } // namespace phi -PD_REGISTER_KERNEL( - all, CPU, ALL_LAYOUT, phi::AllKernel, float, double, int, int64_t, bool) { +PD_REGISTER_KERNEL(all, + CPU, + ALL_LAYOUT, + phi::AllKernel, + float, + double, + int, + int64_t, + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) -PD_REGISTER_KERNEL( - all, GPU, ALL_LAYOUT, phi::AllKernel, float, double, int, int64_t, bool) { +PD_REGISTER_KERNEL(all, + GPU, + ALL_LAYOUT, + phi::AllKernel, + float, + double, + int, + int64_t, + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } #endif diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index 4f4ba254e776ee..f4a7e507c88985 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -4970,7 +4970,7 @@ def all( Computes the ``logical and`` of tensor elements over the given dimension. Args: - x (Tensor): An N-D Tensor, the input data type should be 'bool', 'float32', 'float64', 'int32', 'int64'. + x (Tensor): An N-D Tensor, the input data type should be 'bool', 'float32', 'float64', 'int32', 'int64', 'complex64', 'complex128'. axis (int|list|tuple|None, optional): The dimensions along which the ``logical and`` is compute. If :attr:`None`, and all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the @@ -5036,7 +5036,18 @@ def all( 'reduce_all': reduce_all, } check_variable_and_dtype( - x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'all' + x, + 'x', + [ + 'bool', + 'float32', + 'float64', + 'int32', + 'int64', + 'complex64', + 'complex128', + ], + 'all', ) check_type(axis, 'axis', (int, list, tuple, type(None)), 'all') diff --git a/test/legacy_test/test_reduce_op.py b/test/legacy_test/test_reduce_op.py index 96332ddd77c859..4067848c45288d 100644 --- a/test/legacy_test/test_reduce_op.py +++ b/test/legacy_test/test_reduce_op.py @@ -962,6 +962,158 @@ def test_check_output(self): self.check_output(check_pir=True) +class TestAllComplex64Op(OpTest): + def setUp(self): + self.op_type = "reduce_all" + self.python_api = paddle.all + real_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs = {'X': (real_part + 1j * imag_part).astype("complex64")} + self.attrs = {'dim': (5,), 'keep_dim': True} + self.outputs = { + 'Out': np.expand_dims( + self.inputs['X'].all(axis=self.attrs['dim']), axis=5 + ) + } + + def test_check_output(self): + self.check_output(check_pir=True) + + +class TestAllComplex640pInf(TestAllComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex640pNegInf(TestAllComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex64OpNan(TestAllComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex64OpZero(TestAllComplex64Op): + def setUp(self): + super().setUp() + real_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex64OpMixed(TestAllComplex64Op): + def setUp(self): + super().setUp() + special_values = np.array( + [np.inf, -np.inf, np.nan, 0], dtype=np.float64 + ) + real_part = np.random.choice(special_values, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.choice(special_values, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex128Op(OpTest): + def setUp(self): + self.op_type = "reduce_all" + self.python_api = paddle.all + real_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs = {'X': (real_part + 1j * imag_part).astype("complex128")} + self.attrs = {'dim': (5,), 'keep_dim': True} + self.outputs = { + 'Out': np.expand_dims( + self.inputs['X'].all(axis=self.attrs['dim']), axis=5 + ) + } + + def test_check_output(self): + self.check_output(check_pir=True) + + +class TestAllComplex128OpInf(TestAllComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex128OpNegInf(TestAllComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex128OpNan(TestAllComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex128OpZero(TestAllComplex128Op): + def setUp(self): + super().setUp() + real_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAllComplex128OpMixed(TestAllComplex128Op): + def setUp(self): + super().setUp() + special_values = np.array( + [np.inf, -np.inf, np.nan, 0], dtype=np.float64 + ) + real_part = np.random.choice(special_values, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.choice(special_values, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + class TestAllOpError(unittest.TestCase): def test_errors(self): From 39642a1ef5276f00060a9aa880e72fc66c2e450f Mon Sep 17 00:00:00 2001 From: waliwali777 Date: Mon, 9 Dec 2024 17:17:06 +0800 Subject: [PATCH 244/288] [Auto-Parallel CI] add comment and judge the order of Paddle unit test and PaddNLP test(#69981) --- tools/auto_parallel/ci_auto_parallel.sh | 18 +++++++++++++++++- 1 file changed, 17 insertions(+), 1 deletion(-) diff --git a/tools/auto_parallel/ci_auto_parallel.sh b/tools/auto_parallel/ci_auto_parallel.sh index d05102b4391856..b71cd00f9f5d7e 100644 --- a/tools/auto_parallel/ci_auto_parallel.sh +++ b/tools/auto_parallel/ci_auto_parallel.sh @@ -64,9 +64,16 @@ for element in "${target_lists_for_dygraph_ci[@]}";do count=$((count+1)) done +# There are two types of tests included here: +# 1. The auto-parallel unit testing in Paddle repository. CI will immediately end with +# an error when a test fails. +# 2. The auto-parallel testing of large language models in `PaddleNLP` repository. The execution +# status of each test will be recorded through global variables. When a test fails, it does not +# affect the execution of subsequent tests. They will be summarized and output after the CI is completed. +# Therefore, it is required to perform paddle unit testing first, followed by testing in `PaddleNLP`. +case_list[${#case_list[*]}]="llama_auto_unit_test" case_list[${#case_list[*]}]=llama_auto case_list[${#case_list[*]}]=gpt-3_auto -case_list[${#case_list[*]}]="llama_auto_unit_test" case_list[${#case_list[*]}]=gpt-3_dygraph } @@ -155,9 +162,13 @@ if [[ ${#case_list[*]} -ne 0 ]];then # Install external_ops install_external_ops case_num=1 + # `FLAGS_install_deps` defaults to 0, indicating that certain required packages must be installed + # via `install requirements.txt` prior to running `PaddleNLP` tests. + # By setting `FLAGS_install_deps` to 1, indicating that there is no need for reinstallation. export FLAGS_install_deps=0 for case in ${case_list[*]};do echo -e "\033[31m ---- running case $case_num/${#case_list[*]}: ${case} \033" + # Suggest that the logical order here be consistent with the `case_stst` order. if [[ ${case} == "auto_unit_test" ]];then bash /workspace/Paddle/tools/auto_parallel/ci_case_unit.sh auto_unit_test print_info $? `ls -lt ${log_path} | grep "test" | head -n 1 | awk '{print $9}'` ${case} @@ -174,13 +185,18 @@ if [[ ${#case_list[*]} -ne 0 ]];then cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh bash $cmd prepare_case llama_case_list_auto $FLAGS_install_deps $FLAGS_download_data execute_func_list $cmd llama_auto + # There is no need to reinstall the related packages of `PaddleNLP` afterward. export FLAGS_install_deps=1 + # The `llama` test data has been downloaded, and the `FLAGS_download_data` flag indicates + # that there is no need to repeat the download process later. export FLAGS_download_data="llama ""$FLAGS_download_data" let case_num++ elif [[ ${case} == "gpt-3_auto" ]];then cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_auto.sh bash $cmd prepare_case llm_gpt_case_list_auto $FLAGS_install_deps $FLAGS_download_data execute_func_list $cmd gpt-3_auto + # there is no need to repeat the `gpt` download process later. + export FLAGS_download_data="gpt ""$FLAGS_download_data" let case_num++ elif [[ ${case} == "gpt-3_dygraph" ]];then cmd=/workspace/PaddleNLP/scripts/distribute/ci_case_dy.sh From cd595c66fab3d53596ccedefc85cdb9b95a349cb Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Mon, 9 Dec 2024 17:35:04 +0800 Subject: [PATCH 245/288] fix bug by issue (#70017) --- python/paddle/tensor/to_string.py | 20 ++++++++++++++++++++ 1 file changed, 20 insertions(+) diff --git a/python/paddle/tensor/to_string.py b/python/paddle/tensor/to_string.py index 9f931f8502da3a..5f542c21595fa3 100644 --- a/python/paddle/tensor/to_string.py +++ b/python/paddle/tensor/to_string.py @@ -328,6 +328,23 @@ def _format_dense_tensor(tensor, indent): return data +def selected_rows_tensor_to_string(tensor, dtype, prefix='Tensor'): + indent = len(prefix) + 1 + if tensor.is_selected_rows(): + _template = "{prefix}(shape={shape}, dtype={dtype}, place={place}, stop_gradient={stop_gradient}, rows={rows},\n{indent}{data})" + data = _format_dense_tensor(tensor, indent) + return _template.format( + prefix=prefix, + shape=tensor.shape, + dtype=dtype, + place=tensor._place_str, + stop_gradient=tensor.stop_gradient, + indent=' ' * indent, + data=data, + rows=tensor.rows(), + ) + + def sparse_tensor_to_string(tensor, prefix='Tensor'): indent = len(prefix) + 1 if tensor.is_sparse_coo(): @@ -434,6 +451,9 @@ def tensor_to_string(tensor, prefix='Tensor'): if tensor.is_sparse(): return sparse_tensor_to_string(tensor, prefix) + if tensor.is_selected_rows(): + return selected_rows_tensor_to_string(tensor, dtype, prefix) + if tensor.is_dist(): return dist_tensor_to_string(tensor, prefix) From 25ed80497329baaac0c95c49e555450fac5a5d98 Mon Sep 17 00:00:00 2001 From: Anderson Meng <15830675+anderson101866@users.noreply.github.com> Date: Mon, 9 Dec 2024 19:01:21 +0800 Subject: [PATCH 246/288] Adapt python op converters covered in paddle-3-beta2 for TRT 10 (#69510) * Adapt `paddle.tensorrt.converter` to TRT 10 This commit includes basic migration to TRT 10 API, and also enable those converter who are tested with existing unittest to TRT10 For those 2 converter which is NOT included in this commit: - python/paddle/tensorrt/impls/attribute.py - python/paddle/tensorrt/impls/common.py Need to fix these 2 behavior issues in later commit. * Adapt "pd_op.shape" TRT op converter to TRT10 Although `IShapeLayer` supports shape in int64 since TRT10, some paddle native op kernel only implements their input shape tensor (if exists) in int32. Hence, there is a workaround in `trt_shape` to cast the result of TRT `IShapeLayer` back to int32 to be more compatible with other paddle op. (see python/paddle/tensorrt/converter_utils.py) Please remove the workaround when all paddle op supports their shape in int64. Also, since `IShapeLayer` return shape in int64 in TRT10, the "pd_op.shape64" will be seamlessly supported in TRT10 w/o any extra workaround. * Fix converter error in TRT10 for interpolation ops Error detail: { (%1) = "pd_op.bilinear_interp" [id:28] (%2, %3, <>, <>) {__l_trt__:true,align_corners:false,align_mode:(Int32)0,data_format:"NCHW",interp_method:"bilinear",out_d:(Int32)-1,out_h:(Int32)12,out_w:(Int32)12,scale:[],stop_gradient:[true]} : (builtin.tensor<-1x3x6x10xf32>, builtin.tensor<2xi32>, <>, <>) -> builtin.tensor<-1x3x12x12xf32> () = "cf.yield" [id:36] (%1) {} : (builtin.tensor<-1x3x12x12xf32>) -> } [TRT] [E] ITensor::getDimensions: Error Code 4: API Usage Error ((Unnamed Layer* 6) [Concatenation]: concat input tensors 0 and 2 have incompatible types Int64 and Int32) [TRT] [E] IBuilder::buildSerializedNetwork: Error Code 4: API Usage Error ((Unnamed Layer* 6) [Concatenation]: concat input tensors 0 and 2 have incompatible types Int64 and Int32) The error happened in "python/paddle/tensorrt/impls/common.py" because IConcatenationLayer requires all input to be same dtype. The (shape)tensor passed from paddle op will be int32, while the TRT IShapeLayer supports int64 shape; thereby, cannot be concatenated with each other. Here, we call `trt_shape` to get shape tensor whose dtype aligned with the dtype from paddle op. * Add int64 in TRT->paddle dtype mapping function * Fix "test_converter_math" by enable op for TRT 10 test_converter_math can pass unittest with environment with TRT 10.6 * Adapt 3 manipulation converters to TRT 10 - "pd_op.expand" - "pd_op.expand_as" - "pd_op.slice" --- .../tensorrt_engine_instruction.cc | 6 ++++ python/paddle/tensorrt/converter.py | 3 ++ python/paddle/tensorrt/converter_utils.py | 12 +++++++- python/paddle/tensorrt/impls/activation.py | 18 +++++++----- python/paddle/tensorrt/impls/attribute.py | 9 +++--- python/paddle/tensorrt/impls/common.py | 14 ++++++---- python/paddle/tensorrt/impls/conv.py | 2 +- python/paddle/tensorrt/impls/creation.py | 6 ++-- python/paddle/tensorrt/impls/linalg.py | 6 ++-- python/paddle/tensorrt/impls/manipulation.py | 28 +++++++++++-------- python/paddle/tensorrt/impls/math.py | 14 +++++----- python/paddle/tensorrt/impls/norm.py | 8 ++++-- python/paddle/tensorrt/impls/ops.py | 4 +-- python/paddle/tensorrt/impls/others.py | 4 ++- python/paddle/tensorrt/impls/pooling.py | 2 +- python/paddle/tensorrt/impls/search.py | 2 +- python/paddle/tensorrt/impls/stat.py | 2 +- 17 files changed, 91 insertions(+), 49 deletions(-) diff --git a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc index 69253a76850880..269bc547b35d30 100644 --- a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc @@ -233,6 +233,12 @@ static phi::DataType TRT2PaddleDataType(nvinfer1::DataType type) { return phi::DataType::FLOAT16; case nvinfer1::DataType::kINT8: return phi::DataType::INT8; +#if IS_TRT_VERSION_GE(9000) + case nvinfer1::DataType::kINT64: + VLOG(4) << "get nvinfer1::DataType::kINT64 from TRT op, and will output " + "to paddle. Does the downstream paddle op here support int64?"; + return phi::DataType::INT64; +#endif #if IS_TRT_VERSION_GE(7000) case nvinfer1::DataType::kBOOL: return phi::DataType::BOOL; diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index 1ce3eeef17e0a3..2335f1071b2631 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -448,6 +448,9 @@ def convert_subgraph_to_trt(self, program, group_op): config.set_flag(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS) trt_engine = builder.build_serialized_network(network, config) + assert ( + trt_engine is not None + ), 'Failed to build engine. please see ERROR log from trt.Logger' trt_params = paddle.base.libpaddle.TRTEngineParams() trt_params.min_input_shape = min_shape_map trt_params.max_input_shape = max_shape_map diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index dde1bf1f9bd3af..b83ffe787f0c33 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -25,6 +25,8 @@ sys.path.append(parent_dir) +from tensorrt import INetworkDefinition, ITensor + from paddle.base.log_helper import get_logger _logger = get_logger( @@ -243,9 +245,17 @@ def trt_cast(network, input, dtype): return identity_layer.get_output(0) -def trt_shape(network, input): +def trt_shape(network: INetworkDefinition, input: ITensor) -> ITensor: + """ + Add a IShapeLayer to get the shape of `input` ITensor. + This includes a workaround that casting the shape result(int64) from TRT10 back to int32. + Many existing paddle op kernels only support input shape tensor as int32 + , to make TRT op more compatible with other paddle op, we cast back to int32. + NOTE: please remove this workaround when all paddle op supports shape tensor in int64 + """ shape_layer = network.add_shape(input) if version_list[0] >= 10: # trt_version >=10 + # workaround return trt_cast(network, shape_layer.get_output(0), trt.int32) return shape_layer.get_output(0) diff --git a/python/paddle/tensorrt/impls/activation.py b/python/paddle/tensorrt/impls/activation.py index 20e8cfe6fb9611..a0f15fa188e424 100644 --- a/python/paddle/tensorrt/impls/activation.py +++ b/python/paddle/tensorrt/impls/activation.py @@ -35,9 +35,9 @@ } -@converter_registry.register("pd_op.relu", trt_version="8.x") -@converter_registry.register("pd_op.tanh", trt_version="8.x") -@converter_registry.register("pd_op.sigmoid", trt_version="8.x") +@converter_registry.register("pd_op.relu", trt_version="trt_version_ge=8.0") +@converter_registry.register("pd_op.tanh", trt_version="trt_version_ge=8.0") +@converter_registry.register("pd_op.sigmoid", trt_version="trt_version_ge=8.0") def activation_converter(network, paddle_op, inputs): layer = network.add_activation( inputs[0], activation_type_map[paddle_op.name()] @@ -45,7 +45,7 @@ def activation_converter(network, paddle_op, inputs): return layer.get_output(0) -@converter_registry.register("pd_op.softmax", trt_version="8.x") +@converter_registry.register("pd_op.softmax", trt_version="trt_version_ge=8.0") def softmax_converter(network, paddle_op, inputs): axis = paddle_op.attrs().get("axis", 0) if axis < 0: @@ -56,7 +56,7 @@ def softmax_converter(network, paddle_op, inputs): return softmax_layer.get_output(0) -@converter_registry.register("pd_op.gelu", trt_version="8.x") +@converter_registry.register("pd_op.gelu", trt_version="trt_version_ge=8.0") def gelu_converter(network, paddle_op, inputs): input_val = inputs[0] approximate = paddle_op.attrs()["approximate"] @@ -79,7 +79,9 @@ def gelu_converter(network, paddle_op, inputs): return layer.get_output(0) -@converter_registry.register("pd_op.hardsigmoid", trt_version="8.x") +@converter_registry.register( + "pd_op.hardsigmoid", trt_version="trt_version_ge=8.0" +) def hardsigmoid_converter(network, paddle_op, inputs): x = inputs[0] slope = paddle_op.attrs()["slope"] @@ -92,7 +94,9 @@ def hardsigmoid_converter(network, paddle_op, inputs): return hardsigmoid_layer.get_output(0) -@converter_registry.register("pd_op.hardswish", trt_version="8.x") +@converter_registry.register( + "pd_op.hardswish", trt_version="trt_version_ge=8.0" +) def hardswish_converter(network, paddle_op, inputs): x = inputs[0] threshold = 6.0 diff --git a/python/paddle/tensorrt/impls/attribute.py b/python/paddle/tensorrt/impls/attribute.py index 6eeb75a9f6d9b2..3c0d150977ec18 100644 --- a/python/paddle/tensorrt/impls/attribute.py +++ b/python/paddle/tensorrt/impls/attribute.py @@ -12,17 +12,16 @@ # See the License for the specific language governing permissions and # limitations under the License. +from paddle.tensorrt.converter_utils import trt_shape from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.shape", trt_version="8.x") +@converter_registry.register("pd_op.shape", trt_version="trt_version_ge=8.0") def shape_converter(network, paddle_op, inputs): - input_tensor = inputs[0] - shape_layer = network.add_shape(input_tensor) - return shape_layer.get_output(0) + return trt_shape(network, inputs[0]) -@converter_registry.register("pd_op.shape64", trt_version="8.x") +@converter_registry.register("pd_op.shape64", trt_version="trt_version_ge=8.0") def shape64_converter(network, paddle_op, inputs): input_tensor = inputs[0] shape_layer = network.add_shape(input_tensor) diff --git a/python/paddle/tensorrt/impls/common.py b/python/paddle/tensorrt/impls/common.py index 42f639cd856087..a4567641fa2ab1 100644 --- a/python/paddle/tensorrt/impls/common.py +++ b/python/paddle/tensorrt/impls/common.py @@ -16,7 +16,7 @@ import numpy as np import tensorrt as trt -from paddle.tensorrt.converter_utils import get_shape_tensor_element +from paddle.tensorrt.converter_utils import get_shape_tensor_element, trt_shape from paddle.tensorrt.register import converter_registry from paddle.tensorrt.util import get_trt_version_list @@ -48,7 +48,9 @@ def dropout_converter(network, paddle_op, inputs): return scale_layer.get_output(0) -@converter_registry.register("pd_op.bilinear_interp", trt_version="8.x") +@converter_registry.register( + "pd_op.bilinear_interp", trt_version="trt_version_ge=8.0" +) def bilinear_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] data_format = paddle_op.attrs().get("data_format") @@ -139,7 +141,7 @@ def bilinear_interp_converter(network, paddle_op, inputs): else: if outsize_tensor is not None: outsize_itensors = [] - input_shape_tensor = network.add_shape(input_tensor).get_output(0) + input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": @@ -162,7 +164,9 @@ def bilinear_interp_converter(network, paddle_op, inputs): return resize_layer.get_output(0) -@converter_registry.register("pd_op.nearest_interp", trt_version="8.x") +@converter_registry.register( + "pd_op.nearest_interp", trt_version="trt_version_ge=8.0" +) def nearest_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] data_format = paddle_op.attrs().get("data_format") @@ -254,7 +258,7 @@ def nearest_interp_converter(network, paddle_op, inputs): ) if outsize_tensor is not None: outsize_itensors = [] - input_shape_tensor = network.add_shape(input_tensor).get_output(0) + input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": diff --git a/python/paddle/tensorrt/impls/conv.py b/python/paddle/tensorrt/impls/conv.py index cac8d0b567f1c8..55db36b9aa7db1 100644 --- a/python/paddle/tensorrt/impls/conv.py +++ b/python/paddle/tensorrt/impls/conv.py @@ -18,7 +18,7 @@ @converter_registry.register("pd_op.depthwise_conv2d", trt_version="8.x") -@converter_registry.register("pd_op.conv2d", trt_version="8.x") +@converter_registry.register("pd_op.conv2d", trt_version="trt_version_ge=8.0") @converter_registry.register("pd_op.conv2d_transpose", trt_version="8.x") @converter_registry.register( "pd_op.depthwise_conv2d_transpose", trt_version="8.x" diff --git a/python/paddle/tensorrt/impls/creation.py b/python/paddle/tensorrt/impls/creation.py index 2c0f36a8d3293e..169cf917ceae27 100644 --- a/python/paddle/tensorrt/impls/creation.py +++ b/python/paddle/tensorrt/impls/creation.py @@ -29,7 +29,9 @@ from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.full_int_array", trt_version="8.x") +@converter_registry.register( + "pd_op.full_int_array", trt_version="trt_version_ge=8.0" +) def full_int_array_converter(network, paddle_op, inputs): value = paddle_op.attrs()["value"] if len(value) == 0: @@ -39,7 +41,7 @@ def full_int_array_converter(network, paddle_op, inputs): return full_int_array_layer.get_output(0) -@converter_registry.register("pd_op.full", trt_version="8.x") +@converter_registry.register("pd_op.full", trt_version="trt_version_ge=8.0") def full_converter(network, paddle_op, inputs): shape = paddle_op.attrs()["shape"] value = paddle_op.attrs().get("value", 1.0) diff --git a/python/paddle/tensorrt/impls/linalg.py b/python/paddle/tensorrt/impls/linalg.py index 90d8db58077b19..00a4a97dbd4db3 100644 --- a/python/paddle/tensorrt/impls/linalg.py +++ b/python/paddle/tensorrt/impls/linalg.py @@ -25,7 +25,7 @@ from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.matmul", trt_version="8.x") +@converter_registry.register("pd_op.matmul", trt_version="trt_version_ge=8.0") def matmul_converter(network, paddle_op, inputs): weight_shape = paddle_op.operands()[1].source().shape transpose_x = paddle_op.attrs()["transpose_x"] @@ -61,7 +61,9 @@ def matmul_converter(network, paddle_op, inputs): return out.get_output(0) -@converter_registry.register("pd_op.transpose", trt_version="8.x") +@converter_registry.register( + "pd_op.transpose", trt_version="trt_version_ge=8.0" +) def transpose_converter(network, paddle_op, inputs): perm = paddle_op.attrs()["perm"] transposed_tensor = network.add_shuffle(inputs[0]) diff --git a/python/paddle/tensorrt/impls/manipulation.py b/python/paddle/tensorrt/impls/manipulation.py index 76016bad3b5870..5c81282f7bb247 100644 --- a/python/paddle/tensorrt/impls/manipulation.py +++ b/python/paddle/tensorrt/impls/manipulation.py @@ -44,7 +44,7 @@ from ..util import get_trt_version_list -@converter_registry.register("pd_op.reshape", trt_version="8.x") +@converter_registry.register("pd_op.reshape", trt_version="trt_version_ge=8.0") def reshape_converter(network, paddle_op, inputs): x = inputs[0] is_constant_shape = False @@ -87,7 +87,7 @@ def gather_nd_converter(network, paddle_op, inputs): return non_zero_layer.get_output(0) -@converter_registry.register("pd_op.flatten", trt_version="8.x") +@converter_registry.register("pd_op.flatten", trt_version="trt_version_ge=8.0") def flatten_converter(network, paddle_op, inputs): input_val = inputs[0] input_val_shape = paddle_op.operands()[0].source().shape @@ -172,7 +172,7 @@ def flatten_converter(network, paddle_op, inputs): # In the converter, pd_op.concat has three inputs, because builtin.combine has two inputs. -@converter_registry.register("pd_op.concat", trt_version="8.x") +@converter_registry.register("pd_op.concat", trt_version="trt_version_ge=8.0") def concat_converter(network, paddle_op, inputs): input_tensors = inputs[0] axis_tensor = inputs[1] @@ -187,8 +187,12 @@ def concat_converter(network, paddle_op, inputs): return concat_layer.get_output(0) -@converter_registry.register("pd_op.unsqueeze", trt_version="8.x") -@converter_registry.register("pd_op.unsqueeze_", trt_version="8.x") +@converter_registry.register( + "pd_op.unsqueeze", trt_version="trt_version_ge=8.0" +) +@converter_registry.register( + "pd_op.unsqueeze_", trt_version="trt_version_ge=8.0" +) def unsqueeze_converter(network, paddle_op, inputs): x = inputs[0] input_dims = x.shape @@ -235,8 +239,8 @@ def unsqueeze_converter(network, paddle_op, inputs): return layer.get_output(0) -@converter_registry.register("pd_op.squeeze", trt_version="8.x") -@converter_registry.register("pd_op.squeeze_", trt_version="8.x") +@converter_registry.register("pd_op.squeeze", trt_version="trt_version_ge=8.0") +@converter_registry.register("pd_op.squeeze_", trt_version="trt_version_ge=8.0") def squeeze_converter(network, paddle_op, inputs): input_val = inputs[0] input_shape = input_val.shape @@ -260,7 +264,7 @@ def squeeze_converter(network, paddle_op, inputs): return layer.get_output(0) -@converter_registry.register("pd_op.expand", trt_version="8.x") +@converter_registry.register("pd_op.expand", trt_version="trt_version_ge=8.0") def expand_converter(network, paddle_op, inputs): input = inputs[0] input_dims = input.shape @@ -282,7 +286,9 @@ def expand_converter(network, paddle_op, inputs): return trt_expand(network, input, rank, shape_tensor, shape_rank) -@converter_registry.register("pd_op.expand_as", trt_version="8.x") +@converter_registry.register( + "pd_op.expand_as", trt_version="trt_version_ge=8.0" +) def expand_as_converter(network, paddle_op, inputs): input = inputs[0] input_dims = input.shape @@ -328,7 +334,7 @@ def cast_converter(network, paddle_op, inputs): return cast_layer.get_output(0) -@converter_registry.register("pd_op.slice", trt_version="8.x") +@converter_registry.register("pd_op.slice", trt_version="trt_version_ge=8.0") def slice_converter(network, paddle_op, inputs): input_tensor = inputs[0] axes = paddle_op.attrs()["axes"] @@ -336,7 +342,7 @@ def slice_converter(network, paddle_op, inputs): starts_op = paddle_op.operands()[1].source().get_defining_op() ends_op = paddle_op.operands()[2].source().get_defining_op() - input_shape_tensor = network.add_shape(input_tensor).get_output(0) + input_shape_tensor = trt_shape(network, input_tensor) input_rank = len(input_tensor.shape) starts_tensor = [] diff --git a/python/paddle/tensorrt/impls/math.py b/python/paddle/tensorrt/impls/math.py index 40a9a16291d23d..42db4200aa7de7 100644 --- a/python/paddle/tensorrt/impls/math.py +++ b/python/paddle/tensorrt/impls/math.py @@ -30,15 +30,15 @@ from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.add", trt_version="8.x") -@converter_registry.register("pd_op.add_", trt_version="8.x") +@converter_registry.register("pd_op.add", trt_version="trt_version_ge=8.0") +@converter_registry.register("pd_op.add_", trt_version="trt_version_ge=8.0") def add_converter(network, paddle_op, inputs): return add_elementwise_layer( network, paddle_op, inputs, trt.ElementWiseOperation.SUM ) -@converter_registry.register("pd_op.scale", trt_version="8.x") +@converter_registry.register("pd_op.scale", trt_version="trt_version_ge=8.0") def scale_converter(network, paddle_op, inputs): scale = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] bias = paddle_op.attrs().get("bias", 0.0) @@ -59,7 +59,7 @@ def scale_converter(network, paddle_op, inputs): return scale_layer.get_output(0) -@converter_registry.register("pd_op.max", trt_version="8.x") +@converter_registry.register("pd_op.max", trt_version="trt_version_ge=8.0") def max_converter(network, paddle_op, inputs): input_tensor = inputs[0] axis = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] @@ -84,21 +84,21 @@ def max_converter(network, paddle_op, inputs): return layer.get_output(0) -@converter_registry.register("pd_op.divide", trt_version="8.x") +@converter_registry.register("pd_op.divide", trt_version="trt_version_ge=8.0") def divide_converter(network, paddle_op, inputs): return add_elementwise_layer( network, paddle_op, inputs, trt.ElementWiseOperation.DIV ) -@converter_registry.register("pd_op.subtract", trt_version="8.x") +@converter_registry.register("pd_op.subtract", trt_version="trt_version_ge=8.0") def substract_converter(network, paddle_op, inputs): return add_elementwise_layer( network, paddle_op, inputs, trt.ElementWiseOperation.SUB ) -@converter_registry.register("pd_op.multiply", trt_version="8.x") +@converter_registry.register("pd_op.multiply", trt_version="trt_version_ge=8.0") def multiply_converter(network, paddle_op, inputs): return add_elementwise_layer( network, paddle_op, inputs, trt.ElementWiseOperation.PROD diff --git a/python/paddle/tensorrt/impls/norm.py b/python/paddle/tensorrt/impls/norm.py index af1b93d2da3a66..4fc6584d175230 100644 --- a/python/paddle/tensorrt/impls/norm.py +++ b/python/paddle/tensorrt/impls/norm.py @@ -66,8 +66,12 @@ def layernorm_converter(network, paddle_op, inputs): return layer_norm.get_output(0) -@converter_registry.register("pd_op.batch_norm", trt_version="8.x") -@converter_registry.register("pd_op.batch_norm_", trt_version="8.x") +@converter_registry.register( + "pd_op.batch_norm", trt_version="trt_version_ge=8.0" +) +@converter_registry.register( + "pd_op.batch_norm_", trt_version="trt_version_ge=8.0" +) def batch_norm_converter(network, paddle_op, inputs): input_tensor, mean, variance, scale, bias = inputs scale_shape = paddle_op.operands()[3].source().shape diff --git a/python/paddle/tensorrt/impls/ops.py b/python/paddle/tensorrt/impls/ops.py index 80bcac61443ddd..6416cb96e6af38 100644 --- a/python/paddle/tensorrt/impls/ops.py +++ b/python/paddle/tensorrt/impls/ops.py @@ -22,8 +22,8 @@ } -@converter_registry.register("pd_op.sqrt", trt_version="8.x") -@converter_registry.register("pd_op.sqrt_", trt_version="8.x") +@converter_registry.register("pd_op.sqrt", trt_version="trt_version_ge=8.0") +@converter_registry.register("pd_op.sqrt_", trt_version="trt_version_ge=8.0") @converter_registry.register("pd_op.floor", trt_version="8.x") def sqrt_converter(network, paddle_op, inputs): input_tensor = inputs[0] diff --git a/python/paddle/tensorrt/impls/others.py b/python/paddle/tensorrt/impls/others.py index 490709a6f06fa4..da386091ebcf92 100644 --- a/python/paddle/tensorrt/impls/others.py +++ b/python/paddle/tensorrt/impls/others.py @@ -35,7 +35,9 @@ ) -@converter_registry.register("pd_op.multiclass_nms3", trt_version="8.x") +@converter_registry.register( + "pd_op.multiclass_nms3", trt_version="trt_version_ge=8.0" +) def multiclass_nms3_converter(network, paddle_op, inputs): bboxes = inputs[0] scores = inputs[1] diff --git a/python/paddle/tensorrt/impls/pooling.py b/python/paddle/tensorrt/impls/pooling.py index 33a28dae4ea71a..2cc55c6be4395f 100644 --- a/python/paddle/tensorrt/impls/pooling.py +++ b/python/paddle/tensorrt/impls/pooling.py @@ -18,7 +18,7 @@ from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.pool2d", trt_version="8.x") +@converter_registry.register("pd_op.pool2d", trt_version="trt_version_ge=8.0") def pool2d_converter(network, paddle_op, inputs): input_tensor = inputs[0] diff --git a/python/paddle/tensorrt/impls/search.py b/python/paddle/tensorrt/impls/search.py index 093e3fe8e04994..039007c700524a 100644 --- a/python/paddle/tensorrt/impls/search.py +++ b/python/paddle/tensorrt/impls/search.py @@ -35,7 +35,7 @@ def non_zero_converter(network, paddle_op, inputs): return non_zero_layer.get_output(0) -@converter_registry.register("pd_op.argmax", trt_version="8.x") +@converter_registry.register("pd_op.argmax", trt_version="trt_version_ge=8.0") def argmax_converter(network, paddle_op, inputs): x = inputs[0] input_dims = x.shape diff --git a/python/paddle/tensorrt/impls/stat.py b/python/paddle/tensorrt/impls/stat.py index bbf39e0b866694..0d72fcfb42b192 100644 --- a/python/paddle/tensorrt/impls/stat.py +++ b/python/paddle/tensorrt/impls/stat.py @@ -18,7 +18,7 @@ from paddle.tensorrt.register import converter_registry -@converter_registry.register("pd_op.mean", trt_version="8.x") +@converter_registry.register("pd_op.mean", trt_version="trt_version_ge=8.0") def mean_converter(network, paddle_op, inputs): input_tensor = inputs[0] keep_dim = paddle_op.attrs().get("keepdim") From eb66ca7c0828b9290be570ee062f38d08c5988e3 Mon Sep 17 00:00:00 2001 From: lizexu123 <39205361+lizexu123@users.noreply.github.com> Date: Mon, 9 Dec 2024 19:52:39 +0800 Subject: [PATCH 247/288] [Paddle TensorRT] Fix PaddleX model bugs when convert to pir-trt (#69957) MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * fix * fix pd_op.squeeze+pd_op.flatten * fix * fix * fix * fix * fix * 添加pool2d全图进trt * fix --- .../transforms/tensorrt/trt_op_marker_pass.cc | 94 +++----- python/paddle/tensorrt/converter.py | 14 +- python/paddle/tensorrt/export.py | 8 +- python/paddle/tensorrt/impls/manipulation.py | 58 ++++- python/paddle/tensorrt/impls/pooling.py | 210 +++++++++++++++--- test/tensorrt/tensorrt_test_base.py | 3 +- test/tensorrt/test_converter_manipulation.py | 30 +++ test/tensorrt/test_converter_pooling.py | 48 ++++ 8 files changed, 348 insertions(+), 117 deletions(-) diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index ae2d09a827c7f6..5b9570c88d0a78 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -87,6 +87,7 @@ DEFINE_GENERAL_PATTERN(Roll, paddle::dialect::RollOp) DEFINE_GENERAL_PATTERN(Softplus, paddle::dialect::SoftplusOp) DEFINE_GENERAL_PATTERN(ThresholdedRelu, paddle::dialect::ThresholdedReluOp) DEFINE_GENERAL_PATTERN(Flip, paddle::dialect::FlipOp) +DEFINE_GENERAL_PATTERN(Mish, paddle::dialect::MishOp) #undef DEFINE_GENERAL_PATTERN @@ -259,7 +260,6 @@ class ActOpPattern : public pir::OpRewritePattern { }; using TanhOpPattern = ActOpPattern; using CeluOpPattern = ActOpPattern; -using MishOpPattern = ActOpPattern; class Pool2dOpPattern : public pir::OpRewritePattern { @@ -278,6 +278,13 @@ class Pool2dOpPattern VLOG(3) << "Cannot find FullIntArrayOp"; return false; } + auto attr_value = + full_int_array_op->attribute("value"); + std::vector kernel_size; + for (const auto &attr : attr_value.AsVector()) { + kernel_size.push_back(attr.dyn_cast().data()); + } + auto padding_attr = op->attribute("paddings"); std::vector paddings; for (const auto &attr : padding_attr.AsVector()) { @@ -298,33 +305,25 @@ class Pool2dOpPattern if (!op->HasAttribute("pooling_type")) { VLOG(3) << "The pooling_type attribute does not exist"; return false; - } else { - std::string pool_type = - op->attribute("pooling_type").AsString(); - if (pool_type != "max" && pool_type != "avg") { - VLOG(3) << "Wrong pool op type, the trt do not support the " - << pool_type << " pool type."; - return false; - } - if (pool_type == "avg") { - if (op->HasAttribute("global_pooling")) { - if (!op->attribute("global_pooling").data()) { - if (op->HasAttribute("exclusive")) { - if (op->attribute("exclusive").data()) { - auto attr_value = - full_int_array_op->attribute("value"); - std::vector kernel_size; - for (const auto &attr : attr_value.AsVector()) { - kernel_size.push_back( - attr.dyn_cast().data()); - } - for (size_t i = 0; i < kernel_size.size(); ++i) { - if (kernel_size[i] <= paddings[i]) { - VLOG(3) << "the padding size should be less than the " - "filter size " - "for exclusive-counting pooling."; - return false; - } + } + std::string pool_type = + op->attribute("pooling_type").AsString(); + if (pool_type != "max" && pool_type != "avg") { + VLOG(3) << "Wrong pool op type, the trt do not support the " << pool_type + << " pool type."; + return false; + } + if (pool_type == "avg") { + if (op->HasAttribute("global_pooling")) { + if (!op->attribute("global_pooling").data()) { + if (op->HasAttribute("exclusive")) { + if (op->attribute("exclusive").data()) { + for (size_t i = 0; i < kernel_size.size(); ++i) { + if (kernel_size[i] <= paddings[i]) { + VLOG(3) << "the padding size should be less than the " + "filter size " + "for exclusive-counting pooling."; + return false; } } } @@ -338,27 +337,14 @@ class Pool2dOpPattern op->attribute("global_pooling").data(); std::string padding_algorithm = op->attribute("padding_algorithm").AsString(); - // TODO(Lizexu): The general plugin approach for entering TensorRT has not - // been supported yet. + auto adaptive = op->attribute("adaptive").data(); - if (adaptive) { - VLOG(3) - << "The adaptive is true pd_op.pool2d is not supported by trt now"; - return false; - } // TODO(Lizexu): This piece of code exists in the old IR-TRT implementation // but is not covered by unit tests, raising suspicions about its // correctness. In the PIR-TRT implementation, following the same approach // causes precision issues. For now, we will exclude it from entering // TensorRT. pir::Value input = op.operand_source(0); - auto kernel_size_attr = - full_int_array_op->attribute("value"); - std::vector kernel_size; - for (const auto &attr : kernel_size_attr.AsVector()) { - kernel_size.push_back(attr.dyn_cast().data()); - } - auto input_type = input.type().dyn_cast(); auto input_dims = input_type.dims(); int g_post_pad_h = 0; @@ -960,35 +946,21 @@ class FlattenOpPattern return false; } int start_axis = op->attribute("start_axis").data(); - int stop_axis = op->attribute("stop_axis").data(); pir::Value x = op.operand_source(0); auto x_type = x.type().dyn_cast(); auto x_shape = x_type.dims(); int dims = x_shape.size(); - if (dims == 0) { - VLOG(3) << "Flatten op does not support input's dim is 0 in tensorrt " - "static shape mode."; - } + if (start_axis < 0) { start_axis += dims; } - if (start_axis == 0) { - VLOG(3) << "TRT flatten_contiguous_range not support the " - "batch-dimension being changed"; + VLOG(3) + << "TRT pd_op.flatten not support the batch-dimension being changed"; return false; } - if (stop_axis < 0) { - stop_axis += dims; - } - for (int i = start_axis; i <= stop_axis; ++i) { - if (x_shape[i] < 0) { - VLOG(3) << "On TRT static shape,flatten_contiguous_range input dim " - "should be > 0"; - return false; - } - } + op->set_attribute(kCanRunTrtAttr, rewriter.bool_attr(true)); return true; } @@ -2197,6 +2169,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ADD_PATTERN(Softplus) ADD_PATTERN(ThresholdedRelu) ADD_PATTERN(Flip) + ADD_PATTERN(Mish) #if IS_TRT_VERSION_GE(8600) ADD_PATTERN(Layer_norm) #endif @@ -2261,7 +2234,6 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); - ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); ps.Add(std::make_unique(context)); diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index 2335f1071b2631..6b290bbfc24739 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -81,13 +81,8 @@ def __init__(self, paddle_program, scope, trt_config=None): self.param_dict = param_dict trt_manager = TensorRTConfigManager() - if ( - self.trt_config is not None - and self.trt_config.tensorrt_ops_run_float - ): - trt_manager.set_force_fp32_ops( - self.trt_config.tensorrt_ops_run_float - ) + if self.trt_config is not None and self.trt_config.ops_run_float: + trt_manager.set_force_fp32_ops(self.trt_config.ops_run_float) _logger.info(f"force_fp32_ops: {trt_manager.get_force_fp32_ops()}") self.input_info = {} @@ -441,10 +436,7 @@ def convert_subgraph_to_trt(self, program, group_op): and version_list[1] >= 2 and version_list[2] >= 1 ): - if ( - self.trt_config is not None - and self.trt_config.tensorrt_ops_run_float - ): + if self.trt_config is not None and self.trt_config.ops_run_float: config.set_flag(trt.BuilderFlag.PREFER_PRECISION_CONSTRAINTS) trt_engine = builder.build_serialized_network(network, config) diff --git a/python/paddle/tensorrt/export.py b/python/paddle/tensorrt/export.py index b8e20ff4b23c97..b4bb66e3c1ce0f 100644 --- a/python/paddle/tensorrt/export.py +++ b/python/paddle/tensorrt/export.py @@ -170,7 +170,7 @@ def __init__( save_model_dir: str | None = None, disable_ops: str | list | None = None, precision_mode: PrecisionMode = PrecisionMode.FP32, - tensorrt_ops_run_float: str | list | None = None, + ops_run_float: str | list | None = None, ) -> None: """ A class for configuring TensorRT optimizations. @@ -190,7 +190,7 @@ def __init__( - PrecisionMode.FP16: 16-bit floating point precision. - PrecisionMode.INT8: 8-bit integer precision. - PrecisionMode.BFP16: 16-bit Brain Floating Point precision. Only supported in TensorRT versions greater than 9.0. - tensorrt_ops_run_float (str|list, optional): + ops_run_float (str|list, optional): A set of operation names that should be executed using FP32 precision regardless of the `tensorrt_precision_mode` setting. The directory where the optimized model will be saved (default is None). Returns: @@ -215,13 +215,13 @@ def __init__( >>> trt_config = TensorRTConfig(inputs=[input]) >>> trt_config.disable_ops = "pd_op.dropout" >>> trt_config.precision_mode = PrecisionMode.FP16 - >>> trt_config.tensorrt_ops_run_float = "pd_op.conv2d" + >>> trt_config.ops_run_float = "pd_op.conv2d" """ self.inputs = inputs self.min_subgraph_size = min_subgraph_size self.save_model_dir = save_model_dir self.precision_mode = precision_mode - self.tensorrt_ops_run_float = tensorrt_ops_run_float + self.ops_run_float = ops_run_float self.disable_ops = disable_ops paddle.framework.set_flags( {'FLAGS_trt_min_group_size': min_subgraph_size} diff --git a/python/paddle/tensorrt/impls/manipulation.py b/python/paddle/tensorrt/impls/manipulation.py index 5c81282f7bb247..297bea08325b8c 100644 --- a/python/paddle/tensorrt/impls/manipulation.py +++ b/python/paddle/tensorrt/impls/manipulation.py @@ -22,7 +22,6 @@ cast_tensor, fix_negative_indices, get_axes_for_reduce_op, - get_positive_dim, get_shape_tensor_element, has_dynamic_shape, resize_to_1d, @@ -246,21 +245,58 @@ def squeeze_converter(network, paddle_op, inputs): input_shape = input_val.shape input_shape_size = len(input_shape) - if type(input_val) == trt.Weights: + # If input is weights, convert to TensorRT tensor + if isinstance(input_val, trt.Weights): input_val = network.add_constant(input_shape, input_val).get_output(0) - axis = paddle_op.operands()[1].source().get_defining_op().attrs()["value"] - axis = axis[0] + # Get axis + axis = ( + paddle_op.operands()[1] + .source() + .get_defining_op() + .attrs() + .get("value", []) + ) - axis = get_positive_dim(axis, input_shape_size + 1) - output_shape = [] - for i, s in enumerate(input_shape): - if i == axis and s == 1: - continue - output_shape.append(s) + if not axis: + for i in range(input_shape_size): + if input_shape[i] == -1: + raise RuntimeError( + "The necessary attributes of the squeeze operator axis is missing" + ) + elif input_shape[i] == 1: + axis.append(i) + else: + # Verify that each axis to squeeze has size 1 + for a in axis: + if a < 0: + a += input_shape_size + if input_shape[a] != 1: + raise RuntimeError( + f"Cannot squeeze dimension {a} with size {input_shape[a]}. Only dimensions with size 1 can be squeezed." + ) + axes_size = len(axis) + if axes_size == 0: + raise RuntimeError( + f"axis.size should be >0 in pd_op.squeeze op in TensorRT, but received {axes_size}" + ) + # Mark which dimensions to squeeze + should_squeeze = [False] * input_shape_size + for a in axis: + should_squeeze[a] = True + + # Get dimensions to keep + gather_indices = [ + i for i, squeeze in enumerate(should_squeeze) if not squeeze + ] + + # Add Shuffle layer layer = network.add_shuffle(input_val) - layer.reshape_dims = tuple(output_shape) + shape_tensor = trt_shape(network, input_val) + real_shape_tensor = trt_gather(network, shape_tensor, gather_indices) + layer.set_input(1, real_shape_tensor) + return layer.get_output(0) diff --git a/python/paddle/tensorrt/impls/pooling.py b/python/paddle/tensorrt/impls/pooling.py index 2cc55c6be4395f..a49c8a8e9026d6 100644 --- a/python/paddle/tensorrt/impls/pooling.py +++ b/python/paddle/tensorrt/impls/pooling.py @@ -13,6 +13,7 @@ # limitations under the License. +import numpy as np import tensorrt as trt from paddle.tensorrt.register import converter_registry @@ -26,9 +27,9 @@ def pool2d_converter(network, paddle_op, inputs): input_dims = len(input_shape) global_pooling = paddle_op.attrs().get("global_pooling", False) - pool_type = paddle_op.attrs().get("pooling_type") - strides = paddle_op.attrs().get("strides") - paddings = paddle_op.attrs().get("paddings") + pool_type = paddle_op.attrs().get("pooling_type", "avg") + strides = paddle_op.attrs().get("strides", [1, 1]) + paddings = paddle_op.attrs().get("paddings", [0, 0]) exclusive = paddle_op.attrs().get("exclusive", True) ceil_mode = paddle_op.attrs().get("ceil_mode", False) adaptive = paddle_op.attrs().get("adaptive", False) @@ -37,28 +38,31 @@ def pool2d_converter(network, paddle_op, inputs): if not paddle_op.attrs().get("kernel_size") and len(inputs) == 2: full_int_op = paddle_op.operands()[1].source().get_defining_op() if full_int_op.name() == "pd_op.full_int_array": - kernel_size = full_int_op.attrs().get("value") + kernel_size = full_int_op.attrs().get("value", [1, 1]) else: raise Exception( "The defining op of kernel size must be pd_op.full_int_array" ) else: - kernel_size = paddle_op.attrs().get("kernel_size") + kernel_size = paddle_op.attrs().get("kernel_size", [1, 1]) - nv_pool_type = trt.PoolingType.MAX - reduce_operation = trt.ReduceOperation.MAX if pool_type == "max": nv_pool_type = trt.PoolingType.MAX - reduce_operation = trt.ReduceOperation.MAX elif pool_type == "avg": nv_pool_type = trt.PoolingType.AVERAGE - reduce_operation = trt.ReduceOperation.AVG + else: + raise ValueError(f"Unsupported pooling type: {pool_type}") if global_pooling or adaptive: - paddings = [0] * len(paddings) + paddings = [0, 0, 0, 0] if padding_algorithm == "VALID": - paddings = [0] * len(paddings) + paddings = [0, 0, 0, 0] + + if len(paddings) == 2: + paddings = [paddings[0], paddings[0], paddings[1], paddings[1]] + elif len(paddings) != 4: + raise ValueError(f"Unsupported paddings size: {len(paddings)}") nv_paddings = trt.DimsHW(paddings[0], paddings[1]) nv_ksize = trt.DimsHW(kernel_size[0], kernel_size[1]) @@ -70,12 +74,14 @@ def pool2d_converter(network, paddle_op, inputs): if ( input_shape[input_dims - 2] > 0 - and input_shape[input_dims - 2] - kernel_size[0] + 2 * paddings[0] < 0 + and input_shape[input_dims - 2] + paddings[0] + paddings[2] + < kernel_size[0] ): g_post_pad.h = strides[0] - 1 if ( input_shape[input_dims - 1] > 0 - and input_shape[input_dims - 1] - kernel_size[1] + 2 * paddings[1] < 0 + and input_shape[input_dims - 1] + paddings[1] + paddings[3] + < kernel_size[1] ): g_post_pad.w = strides[1] - 1 @@ -108,20 +114,84 @@ def pool2d_converter(network, paddle_op, inputs): if padding_algorithm == "VALID": read_paddings = [0] * len(real_paddings) - if not adaptive and not global_pooling and not ceil_mode: + if adaptive and pool_type == "avg": + output_h, output_w = kernel_size + if output_h == 1 and output_w == 1: + reduce_axes = (1 << (input_dims - 2)) | (1 << (input_dims - 1)) + reduce_layer = network.add_reduce( + input=input_tensor, + op=trt.ReduceOperation.AVG, + axes=reduce_axes, + keep_dims=True, + ) + if reduce_layer is None: + raise RuntimeError("Failed to add reduce layer in TensorRT.") + layer = reduce_layer + else: + input_h = input_shape[input_dims - 2] + input_w = input_shape[input_dims - 1] + if input_h < 0 or input_w < 0: + raise ValueError( + "Adaptive pooling with dynamic input dimensions is not supported." + ) + + stride_h = input_h // output_h + stride_w = input_w // output_w + kernel_h = input_h - (output_h - 1) * stride_h + kernel_w = input_w - (output_w - 1) * stride_w + + if stride_h <= 0 or stride_w <= 0: + raise ValueError( + "Calculated stride is non-positive, which is invalid." + ) + + nv_ksize = trt.DimsHW(kernel_h, kernel_w) + nv_strides = trt.DimsHW(stride_h, stride_w) + nv_paddings = trt.DimsHW(0, 0) + pooling_layer = network.add_pooling_nd( + input=input_tensor, + type=nv_pool_type, + window_size=nv_ksize, + ) + if pooling_layer is None: + raise RuntimeError("Failed to add pooling layer in TensorRT.") + pooling_layer.stride_nd = nv_strides + pooling_layer.padding_nd = nv_paddings + pooling_layer.average_count_excludes_padding = exclusive + layer = pooling_layer + elif global_pooling and not adaptive: + reduce_axes = (1 << (input_dims - 2)) | (1 << (input_dims - 1)) + reduce_layer = network.add_reduce( + input=input_tensor, + op=( + trt.ReduceOperation.AVG + if pool_type == "avg" + else trt.ReduceOperation.MAX + ), + axes=reduce_axes, + keep_dims=True, + ) + if reduce_layer is None: + raise RuntimeError("Failed to add reduce layer in TensorRT.") + layer = reduce_layer + elif not adaptive and not global_pooling and not ceil_mode: if padding_algorithm != "SAME" and ( (g_post_pad.h > 0 and input_shape[input_dims - 2] > 0) or (g_post_pad.w > 0 and input_shape[input_dims - 1] > 0) ): pad_layer = network.add_padding_nd( input=input_tensor, - pre_padding=tuple(g_pre_pad), - post_padding=tuple(g_post_pad), + pre_padding=(g_pre_pad.h, g_pre_pad.w), + post_padding=(g_post_pad.h, g_post_pad.w), ) + if pad_layer is None: + raise RuntimeError("Failed to add padding layer in TensorRT.") input_tensor = pad_layer.get_output(0) pooling_layer = network.add_pooling_nd( input=input_tensor, type=nv_pool_type, window_size=nv_ksize ) + if pooling_layer is None: + raise RuntimeError("Failed to add pooling layer in TensorRT.") pooling_layer.stride_nd = nv_strides pooling_layer.padding_nd = nv_paddings pooling_layer.average_count_excludes_padding = exclusive @@ -133,6 +203,8 @@ def pool2d_converter(network, paddle_op, inputs): pooling_layer = network.add_pooling_nd( input=input_tensor, type=nv_pool_type, window_size=nv_ksize ) + if pooling_layer is None: + raise RuntimeError("Failed to add pooling layer in TensorRT.") pooling_layer.stride_nd = nv_strides pooling_layer.padding_nd = nv_paddings pooling_layer.average_count_excludes_padding = exclusive @@ -141,19 +213,99 @@ def pool2d_converter(network, paddle_op, inputs): else: pooling_layer.padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP layer = pooling_layer - elif global_pooling and not adaptive: - reduce_axes = (1 << (input_dims - 2)) | (1 << (input_dims - 1)) - reduce_layer = network.add_reduce( - input=input_tensor, - op=reduce_operation, - axes=reduce_axes, - keep_dims=True, - ) - layer = reduce_layer else: - raise NotImplementedError( - "The combination of attributes is not supported yet." + need_to_expand_dims = input_dims == 3 + if need_to_expand_dims: + axes = [3] + axes_tensor = network.add_constant( + shape=(len(axes),), + weights=np.array(axes, dtype=np.int32), + ).get_output(0) + unsqueeze_layer = network.add_unsqueeze( + input=input_tensor, axes=axes_tensor + ) + if unsqueeze_layer is None: + raise RuntimeError("Failed to add unsqueeze layer in TensorRT.") + input_tensor = unsqueeze_layer.get_output(0) + input_shape = unsqueeze_layer.get_output(0).shape + input_dims = len(input_shape) + + nbSpatialDims = len(kernel_size) + if not ( + (nbSpatialDims == 1 and need_to_expand_dims) + or nbSpatialDims == 2 + or nbSpatialDims == 3 + ): + raise RuntimeError( + f"kernel_shape ({nbSpatialDims}D) misaligns with the input tensor shape ({input_dims}D)." + ) + + begPadding = [0] * nbSpatialDims + endPadding = [0] * nbSpatialDims + + if ceil_mode: + padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP + else: + padding_mode = trt.PaddingMode.EXPLICIT_ROUND_DOWN + + countExcludePadding = True + if pool_type == "avg": + if exclusive: + countExcludePadding = True + else: + countExcludePadding = False + + auto_pad = "NOTSET" + if padding_algorithm == "SAME": + auto_pad = "SAME_UPPER" + elif padding_algorithm == "VALID": + auto_pad = "VALID" + + if auto_pad != "SAME_LOWER" and auto_pad != "SAME_UPPER": + ndim = len(paddings) // 2 + for i in range(nbSpatialDims): + if i < ndim: + begPadding[i] = paddings[i] + endPadding[i] = paddings[i + ndim] + else: + begPadding[i] = 0 + endPadding[i] = 0 + if auto_pad == "EXPLICIT_ROUND_UP": + padding_mode = trt.PaddingMode.EXPLICIT_ROUND_UP + + if nbSpatialDims == 2: + nv_begPadding = trt.DimsHW(begPadding[0], begPadding[1]) + nv_endPadding = trt.DimsHW(endPadding[0], endPadding[1]) + + pooling_layer = network.add_pooling_nd( + input=input_tensor, + type=nv_pool_type, + window_size=nv_ksize, ) + if pooling_layer is None: + raise RuntimeError("Failed to add pooling layer in TensorRT.") + pooling_layer.stride_nd = nv_strides + pooling_layer.pre_padding = nv_begPadding + pooling_layer.post_padding = nv_endPadding + pooling_layer.average_count_excludes_padding = countExcludePadding + pooling_layer.padding_mode = padding_mode + + layer = pooling_layer + + if need_to_expand_dims: + axes = [3] + axes_tensor = network.add_constant( + shape=(len(axes),), + weights=np.array(axes, dtype=np.int32), + ).get_output(0) + squeeze_layer = network.add_squeeze( + input=layer.get_output(0), axes=axes_tensor + ) + if squeeze_layer is None: + raise RuntimeError("Failed to add squeeze layer in TensorRT.") + layer = squeeze_layer + + if layer is None: + raise RuntimeError("Failed to create pooling layer in TensorRT.") - output_tensor = layer.get_output(0) - return output_tensor + return layer.get_output(0) diff --git a/test/tensorrt/tensorrt_test_base.py b/test/tensorrt/tensorrt_test_base.py index 0eef389fd42e2d..a14844361ebddc 100755 --- a/test/tensorrt/tensorrt_test_base.py +++ b/test/tensorrt/tensorrt_test_base.py @@ -22,6 +22,7 @@ from paddle.tensorrt.converter import PaddleToTensorRTConverter from paddle.tensorrt.export import ( Input, + PrecisionMode, TensorRTConfig, ) from paddle.tensorrt.util import ( @@ -261,7 +262,7 @@ def check_trt_result(self, rtol=1e-5, atol=1e-5): max_input_shape=self.max_shape, ) trt_config = TensorRTConfig(inputs=[input]) - trt_config.tensorrt_precision_mode = "FP16" + trt_config.precision_mode = PrecisionMode.FP16 converter = PaddleToTensorRTConverter( program_with_trt, scope, trt_config diff --git a/test/tensorrt/test_converter_manipulation.py b/test/tensorrt/test_converter_manipulation.py index f5b9a1ee8e2fc2..e70cdc9058cf6e 100644 --- a/test/tensorrt/test_converter_manipulation.py +++ b/test/tensorrt/test_converter_manipulation.py @@ -567,5 +567,35 @@ def test_trt_result(self): self.check_trt_result() +class TestSqueezeTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.squeeze + self.api_args = { + "x": np.random.random([1, 1, 28]).astype("float32"), + "axis": 1, + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 28]} + self.max_shape = {"x": [5, 1, 28]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestSqueezeCase1TRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.squeeze + self.api_args = { + "x": np.random.random([1, 1, 28]).astype("int64"), + "axis": 1, + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 1, 28]} + self.max_shape = {"x": [5, 1, 28]} + + def test_trt_result(self): + self.check_trt_result() + + if __name__ == '__main__': unittest.main() diff --git a/test/tensorrt/test_converter_pooling.py b/test/tensorrt/test_converter_pooling.py index e3191b5a6a4c1c..32523ba4c27e96 100644 --- a/test/tensorrt/test_converter_pooling.py +++ b/test/tensorrt/test_converter_pooling.py @@ -158,6 +158,54 @@ def test_trt_result(self): self.check_trt_result() +class TestPoolingTRTCase5Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 16, 56, 56).astype("float32"), + "ksize": [2, 2], + "strides": [1, 1], + "paddings": [0, 0], + "ceil_mode": False, + "exclusive": True, + "data_format": "NCHW", + "pooling_type": "avg", + "global_pooling": False, + "adaptive": True, + "padding_algorithm": "EXPLICIT", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 16, 56, 56]} + self.max_shape = {"x": [5, 16, 56, 56]} + + def test_trt_result(self): + self.check_trt_result() + + +class TestPoolingTRTCase6Pattern(TensorRTBaseTest): + def setUp(self): + self.python_api = pool2d_api + self.api_args = { + "x": np.random.randn(1, 3, 5, 5).astype("float32"), + "ksize": [1, 1], + "strides": [1, 1], + "paddings": [0, 0], + "ceil_mode": False, + "exclusive": True, + "data_format": "NCHW", + "pooling_type": "avg", + "global_pooling": False, + "adaptive": True, + "padding_algorithm": "EXPLICIT", + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3, 5, 5]} + self.max_shape = {"x": [2, 3, 5, 5]} # 动态批次大小，宽度保持为 1 + + def test_trt_result(self): + self.check_trt_result() + + class TestPoolingTRTMarker(TensorRTBaseTest): def setUp(self): self.python_api = pool2d_api From 022b15872f4a103691e2b66f6acd04ca4bd0a958 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Mon, 9 Dec 2024 20:05:27 +0800 Subject: [PATCH 248/288] [CINN]add backend pass manager (#69965) * [CINN]add backend pass manager * fix * remove converge run * refine --- paddle/cinn/CMakeLists.txt | 1 + paddle/cinn/ir/lowered_func.h | 3 + paddle/cinn/optim/CMakeLists.txt | 1 - paddle/cinn/optim/pass.cc | 60 ------- paddle/cinn/optim/pass.h | 70 -------- paddle/cinn/pass/CMakeLists.txt | 3 + paddle/cinn/pass/pass.h | 84 ++++++++++ paddle/cinn/pass/pass_adaptor.cc | 277 +++++++++++++++++++++++++++++++ paddle/cinn/pass/pass_adaptor.h | 69 ++++++++ paddle/cinn/pass/pass_manager.h | 46 +++++ 10 files changed, 483 insertions(+), 131 deletions(-) delete mode 100644 paddle/cinn/optim/pass.cc delete mode 100644 paddle/cinn/optim/pass.h create mode 100755 paddle/cinn/pass/CMakeLists.txt create mode 100644 paddle/cinn/pass/pass.h create mode 100644 paddle/cinn/pass/pass_adaptor.cc create mode 100644 paddle/cinn/pass/pass_adaptor.h create mode 100644 paddle/cinn/pass/pass_manager.h diff --git a/paddle/cinn/CMakeLists.txt b/paddle/cinn/CMakeLists.txt index 20d7b5b71c6518..94fe5564cd747f 100644 --- a/paddle/cinn/CMakeLists.txt +++ b/paddle/cinn/CMakeLists.txt @@ -10,6 +10,7 @@ add_subdirectory(utils) add_subdirectory(poly) add_subdirectory(runtime) add_subdirectory(ir) +add_subdirectory(pass) add_subdirectory(backends) add_subdirectory(lang) add_subdirectory(optim) diff --git a/paddle/cinn/ir/lowered_func.h b/paddle/cinn/ir/lowered_func.h index 7fb26a62929164..0c2856451b3973 100644 --- a/paddle/cinn/ir/lowered_func.h +++ b/paddle/cinn/ir/lowered_func.h @@ -19,6 +19,7 @@ #include "paddle/cinn/ir/buffer.h" #include "paddle/cinn/ir/ir_base.h" +#include "paddle/cinn/ir/stmt.h" namespace cinn { namespace ir { @@ -159,8 +160,10 @@ struct _LoweredFunc_ : public IrNode { //! This number doesn't include temp_spaces. int num_output_tensors; + // TODO(Hongqing-work): remove expr body after update all the backend passes. //! Body of this function. Expr body; + stmt::BlockRef body_block; DeviceAPI device_api{DeviceAPI::UNK}; diff --git a/paddle/cinn/optim/CMakeLists.txt b/paddle/cinn/optim/CMakeLists.txt index 51ab3a1aa3e59c..66c187d1fa15ce 100755 --- a/paddle/cinn/optim/CMakeLists.txt +++ b/paddle/cinn/optim/CMakeLists.txt @@ -7,7 +7,6 @@ gather_srcs( replace_var_with_expr.cc ir_simplify.cc optimize.cc - pass.cc vectorize_loops.cc unroll_loops.cc transform_polyfor_to_for.cc diff --git a/paddle/cinn/optim/pass.cc b/paddle/cinn/optim/pass.cc deleted file mode 100644 index 505706fdb479c9..00000000000000 --- a/paddle/cinn/optim/pass.cc +++ /dev/null @@ -1,60 +0,0 @@ -// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#include "paddle/cinn/optim/pass.h" - -#include "paddle/cinn/ir/ir_printer.h" -#include "paddle/cinn/ir/stmt_visitors.h" - -namespace cinn { -namespace optim { - -bool ApplyFunctionPass(FunctionPass* pass, ir::LoweredFunc f) { - return pass->RunOnFunction(f); -} - -bool ApplyBlockPass(BlockPass* pass, ir::stmt::BlockRef func_body) { - VLOG(3) << "Before ApplyBlockPass: [" << pass->name() - << "] on block: " << func_body; - bool changed = false; - std::vector new_stmts = func_body->stmts(); - for (ir::stmt::StmtRef inner_stmt : new_stmts) { - std::vector inner_blocks = inner_stmt->block_fields(); - for (ir::stmt::BlockRef inner_block : inner_blocks) { - changed = ApplyBlockPass(pass, inner_block) || changed; - } - inner_stmt->set_block_fields(inner_blocks); - } - func_body->set_stmts(new_stmts); - changed = pass->RunOnBlock(func_body) || changed; - VLOG(3) << "After ApplyBlockPass: [" << pass->name() - << "] on block: " << func_body; - return changed; -} - -bool ApplyStatementPass(StatementPass* pass, ir::stmt::BlockRef func_body) { - bool changed = false; - ir::stmt::Mutate( - func_body, - [&](ir::stmt::StmtRef stmt) {}, - [&](ir::stmt::StmtRef stmt) { - if (pass->RunOnStmt(stmt)) { - changed = true; - } - }); - return changed; -} - -} // namespace optim -} // namespace cinn diff --git a/paddle/cinn/optim/pass.h b/paddle/cinn/optim/pass.h deleted file mode 100644 index 3c809adc3dd31b..00000000000000 --- a/paddle/cinn/optim/pass.h +++ /dev/null @@ -1,70 +0,0 @@ -// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. -// -// Licensed under the Apache License, Version 2.0 (the "License"); -// you may not use this file except in compliance with the License. -// You may obtain a copy of the License at -// -// http://www.apache.org/licenses/LICENSE-2.0 -// -// Unless required by applicable law or agreed to in writing, software -// distributed under the License is distributed on an "AS IS" BASIS, -// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -// See the License for the specific language governing permissions and -// limitations under the License. - -#pragma once - -#include - -#include "paddle/cinn/ir/ir.h" -#include "paddle/cinn/ir/lowered_func.h" -#include "paddle/cinn/ir/stmt.h" - -namespace cinn { -namespace optim { - -enum PassKind { PK_FUNC, PK_BLOCK, PK_STMT, PK_EXPR }; - -class Pass { - public: - explicit Pass(PassKind kind, const std::string& name) - : kind_(kind), name_(name) {} - virtual ~Pass() {} - - PassKind kind() const { return kind_; } - const std::string& name() const { return name_; } - - private: - PassKind kind_; - std::string name_; -}; - -class FunctionPass : public Pass { - public: - explicit FunctionPass(const std::string& name) : Pass(PK_FUNC, name) {} - - virtual bool RunOnFunction(ir::LoweredFunc f) = 0; -}; - -class BlockPass : public Pass { - public: - explicit BlockPass(const std::string& name) : Pass(PK_BLOCK, name) {} - virtual bool RunOnBlock(ir::stmt::BlockRef block) = 0; -}; - -class StatementPass : public Pass { - public: - explicit StatementPass(const std::string& name) : Pass(PK_STMT, name) {} - virtual bool RunOnStmt(ir::stmt::StmtRef stmt) = 0; -}; - -bool ApplyFunctionPass(FunctionPass* pass, ir::LoweredFunc f); -// post order traverse apply block pass on function body -bool ApplyBlockPass(BlockPass* pass, ir::stmt::BlockRef func_body); -// post order traverse apply statement pass on function body -bool ApplyStatementPass(StatementPass* pass, ir::stmt::BlockRef func_body); - -// TODO(hongqing-work): add manager for pass - -} // namespace optim -} // namespace cinn diff --git a/paddle/cinn/pass/CMakeLists.txt b/paddle/cinn/pass/CMakeLists.txt new file mode 100755 index 00000000000000..0f06f01ec7f186 --- /dev/null +++ b/paddle/cinn/pass/CMakeLists.txt @@ -0,0 +1,3 @@ +core_gather_headers() + +gather_srcs(cinnapi_src SRCS pass_adaptor.cc) diff --git a/paddle/cinn/pass/pass.h b/paddle/cinn/pass/pass.h new file mode 100644 index 00000000000000..6df8ce15c9d155 --- /dev/null +++ b/paddle/cinn/pass/pass.h @@ -0,0 +1,84 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include + +#include "paddle/cinn/ir/ir.h" +#include "paddle/cinn/ir/lowered_func.h" +#include "paddle/cinn/ir/stmt.h" + +namespace cinn { +namespace optim { + +enum class PassKind { PK_FUNC, PK_BLOCK, PK_STMT, PK_EXPR }; + +class LogicalResult { + public: + static LogicalResult success() { return LogicalResult(true); } + static LogicalResult failure() { return LogicalResult(false); } + bool succeeded() const { return success_; } + bool failed() const { return !success_; } + + private: + explicit LogicalResult(bool success) : success_(success) {} + bool success_; +}; + +template +class Pass { + public: + explicit Pass(PassKind kind, const std::string& name) + : kind_(kind), name_(name) {} + virtual ~Pass() {} + + virtual LogicalResult Run(IRScopeRefT scope) = 0; + + PassKind kind() const { return kind_; } + const std::string& name() const { return name_; } + + private: + PassKind kind_; + std::string name_; +}; + +class FuncPass : public Pass { + public: + explicit FuncPass(const std::string& name) : Pass(PassKind::PK_FUNC, name) {} + + virtual LogicalResult Run(ir::LoweredFunc f) = 0; +}; + +class BlockPass : public Pass { + public: + explicit BlockPass(const std::string& name) + : Pass(PassKind::PK_BLOCK, name) {} + virtual LogicalResult Run(ir::stmt::BlockRef block) = 0; +}; + +class StmtPass : public Pass { + public: + explicit StmtPass(const std::string& name) : Pass(PassKind::PK_STMT, name) {} + virtual LogicalResult Run(ir::stmt::StmtRef stmt) = 0; +}; + +class ExprPass : public Pass { + public: + explicit ExprPass(const std::string& name) : Pass(PassKind::PK_STMT, name) {} + virtual LogicalResult Run(ir::Expr expr) = 0; +}; + +} // namespace optim +} // namespace cinn diff --git a/paddle/cinn/pass/pass_adaptor.cc b/paddle/cinn/pass/pass_adaptor.cc new file mode 100644 index 00000000000000..bdec6e18b1129e --- /dev/null +++ b/paddle/cinn/pass/pass_adaptor.cc @@ -0,0 +1,277 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include + +#include +#include "paddle/cinn/ir/stmt_visitors.h" +#include "paddle/cinn/ir/utils/stmt_converter.h" +#include "paddle/cinn/pass/pass_adaptor.h" + +namespace cinn { +namespace optim { +namespace detail { + +template +LogicalResult PassAdaptor::RunPipeline( + ir::LoweredFunc func, const std::vector>& passes) { + // TODO(Hongqing-work): Add instrumentation and AnalysisManager. Remove stmt + // convert after update all the backend passes. + func->body_block = ir::ConvertExprBlockToStmtBlock(func->body); + LogicalResult res = Run(func, passes); + func->body = ir::ConvertStmtBlockToExprBlock(func->body_block); + return res; +} + +template LogicalResult PassAdaptor::RunPipeline( + ir::LoweredFunc, const std::vector>&); + +template LogicalResult PassAdaptor::RunPipeline( + ir::LoweredFunc, const std::vector>&); + +template LogicalResult PassAdaptor::RunPipeline( + ir::LoweredFunc, const std::vector>&); + +template LogicalResult PassAdaptor::RunPipeline( + ir::LoweredFunc, const std::vector>&); + +namespace { +template +LogicalResult RunPasses(const std::vector>& passes, + IRScopeRefT scope) { + for (auto& pass : passes) { + if ((pass->Run(scope)).failed()) { + VLOG(3) << "Failed to run pass: " << pass->name(); + return LogicalResult::failure(); + } + } + return LogicalResult::success(); +} +} // namespace + +LogicalResult FuncPassAdaptor::Run( + ir::LoweredFunc func, + const std::vector>& passes) { + return RunPasses(passes, func); +} + +namespace { +LogicalResult RunPassesOnBlock( + ir::stmt::BlockRef block, + const std::vector>& passes) { + std::vector new_stmts = block->stmts(); + for (ir::stmt::StmtRef inner_stmt : new_stmts) { + std::vector inner_blocks = inner_stmt->block_fields(); + for (ir::stmt::BlockRef inner_block : inner_blocks) { + if (RunPassesOnBlock(inner_block, passes).failed()) + return LogicalResult::failure(); + } + inner_stmt->set_block_fields(inner_blocks); + } + block->set_stmts(new_stmts); + return RunPasses(passes, block); +} +} // namespace + +LogicalResult FuncToBlockPassAdaptor::Run( + ir::LoweredFunc func, + const std::vector>& passes) { + ir::stmt::BlockRef func_block = func->body_block; + if (RunPassesOnBlock(func_block, passes).failed()) + return LogicalResult::failure(); + func->body_block = func_block; + return LogicalResult::success(); +} + +LogicalResult FuncToStmtPassAdaptor::Run( + ir::LoweredFunc func, + const std::vector>& passes) { + ir::stmt::BlockRef func_block = func->body_block; + LogicalResult res = LogicalResult::success(); + ir::stmt::Mutate( + func_block, + [&](ir::stmt::StmtRef stmt) {}, + [&](ir::stmt::StmtRef stmt) { + if (RunPasses(passes, stmt).failed()) { + res = LogicalResult::failure(); + } + }); + return res; +} + +namespace { +using ExprMutateFuncT = std::function; +class StmtToExprPassAdaptor : public StmtPass { + public: + explicit StmtToExprPassAdaptor(const ExprMutateFuncT& func) + : StmtPass("stmt to expr pass adaptor"), mutator_(func) {} + virtual LogicalResult Run(ir::stmt::StmtRef stmt) { + return mutator_.VisitStmt(stmt); + } + + private: + class LocalExprMutator : public ir::stmt::StmtMutator { + public: + explicit LocalExprMutator(const ExprMutateFuncT& expr_mutator) + : expr_mutator_(expr_mutator) {} + + LogicalResult VisitStmt(ir::stmt::StmtRef stmt) override { + return ir::stmt::StmtMutator::VisitStmt(stmt); + } + + private: + ExprMutateFuncT expr_mutator_; +#define __(stmt__) LogicalResult VisitStmt(ir::stmt::stmt__ stmt) override; + NODETY_FORALL_STMT(__) +#undef __ + }; + LocalExprMutator mutator_; +}; + +#define MUTATE_EXPR(expr__) \ + if (expr_mutator_(expr__).failed()) return LogicalResult::failure(); + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Let stmt) { + ir::Expr symbol = stmt->symbol(); + ir::Expr body = stmt->body(); + MUTATE_EXPR(symbol); + if (body.defined()) { + MUTATE_EXPR(body); + } + stmt->set_symbol(symbol); + stmt->set_body(body); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Store stmt) { + ir::Expr value = stmt->value(); + ir::Expr tensor = stmt->tensor(); + std::vector indices = stmt->indices(); + MUTATE_EXPR(value); + MUTATE_EXPR(tensor); + for (ir::Expr indice : indices) { + MUTATE_EXPR(indice); + } + stmt->set_value(value); + stmt->set_tensor(tensor); + stmt->set_indices(indices); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Alloc stmt) { + std::vector extents = stmt->extents(); + ir::Expr condition = stmt->condition(); + ir::Expr body = stmt->body(); + for (ir::Expr extent : extents) { + MUTATE_EXPR(extent); + } + if (condition.defined()) { + MUTATE_EXPR(condition); + } + if (body.defined()) { + MUTATE_EXPR(body); + } + stmt->set_extents(extents); + stmt->set_condition(condition); + stmt->set_body(body); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Free stmt) { + ir::Expr destination = stmt->destination(); + MUTATE_EXPR(destination); + stmt->set_destination(destination); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::IfThenElse stmt) { + ir::Expr condition = stmt->condition(); + MUTATE_EXPR(condition); + stmt->set_condition(condition); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::For stmt) { + ir::Expr min = stmt->min(); + ir::Expr extent = stmt->extent(); + MUTATE_EXPR(min); + MUTATE_EXPR(extent); + stmt->set_min(min); + stmt->set_extent(extent); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Schedule stmt) { + std::vector iter_vars = stmt->iter_vars(); + std::vector iter_values = stmt->iter_values(); + std::vector read_buffers = stmt->read_buffers(); + std::vector write_buffers = stmt->write_buffers(); + + for (ir::Var iter_var : iter_vars) { + if (iter_var->lower_bound.defined()) { + MUTATE_EXPR(iter_var->lower_bound); + } + if (iter_var->upper_bound.defined()) { + MUTATE_EXPR(iter_var->upper_bound); + } + } + for (ir::Expr iter_value : iter_values) { + MUTATE_EXPR(iter_value); + } + for (ir::Expr read_buffer : read_buffers) { + MUTATE_EXPR(read_buffer); + } + for (ir::Expr write_buffer : write_buffers) { + MUTATE_EXPR(write_buffer); + } + + stmt->set_iter_vars(iter_vars); + stmt->set_iter_values(iter_values); + stmt->set_read_buffers(read_buffers); + stmt->set_write_buffers(write_buffers); + return LogicalResult::success(); +} + +LogicalResult StmtToExprPassAdaptor::LocalExprMutator::VisitStmt( + ir::stmt::Evaluate stmt) { + ir::Expr value = stmt->value(); + MUTATE_EXPR(value); + stmt->set_value(value); + return LogicalResult::success(); +} +#undef MUTATE_EXPR +} // namespace + +LogicalResult FuncToExprPassAdaptor::Run( + ir::LoweredFunc func, + const std::vector>& passes) { + std::vector> stmt_passes; + stmt_passes.emplace_back(std::move(std::make_unique( + [&](ir::Expr expr) { return RunPasses(passes, expr); }))); + FuncToStmtPassAdaptor stmt_pass_adaptor; + return stmt_pass_adaptor.Run(func, stmt_passes); +} + +} // namespace detail +} // namespace optim +} // namespace cinn diff --git a/paddle/cinn/pass/pass_adaptor.h b/paddle/cinn/pass/pass_adaptor.h new file mode 100644 index 00000000000000..2fd5184f24c2da --- /dev/null +++ b/paddle/cinn/pass/pass_adaptor.h @@ -0,0 +1,69 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include "paddle/cinn/pass/pass.h" + +namespace cinn { +namespace optim { +namespace detail { + +template +class PassAdaptor { + public: + LogicalResult RunPipeline(ir::LoweredFunc func, + const std::vector>& passes); + + protected: + virtual LogicalResult Run( + ir::LoweredFunc func, + const std::vector>& passes) = 0; +}; + +class FuncPassAdaptor : public PassAdaptor { + private: + LogicalResult Run( + ir::LoweredFunc func, + const std::vector>& passes) override; +}; + +class FuncToBlockPassAdaptor : public PassAdaptor { + private: + LogicalResult Run( + ir::LoweredFunc func, + const std::vector>& passes) override; +}; + +class FuncToExprPassAdaptor; + +class FuncToStmtPassAdaptor : public PassAdaptor { + friend class FuncToExprPassAdaptor; + + private: + LogicalResult Run( + ir::LoweredFunc func, + const std::vector>& passes) override; +}; + +class FuncToExprPassAdaptor : public PassAdaptor { + private: + LogicalResult Run( + ir::LoweredFunc func, + const std::vector>& passes) override; +}; + +} // namespace detail +} // namespace optim +} // namespace cinn diff --git a/paddle/cinn/pass/pass_manager.h b/paddle/cinn/pass/pass_manager.h new file mode 100644 index 00000000000000..bd65d972c442cf --- /dev/null +++ b/paddle/cinn/pass/pass_manager.h @@ -0,0 +1,46 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include + +#include "paddle/cinn/pass/pass.h" +#include "paddle/cinn/pass/pass_adaptor.h" + +namespace cinn { +namespace optim { + +template +class PassManager { + public: + virtual LogicalResult Run(ir::LoweredFunc func) { + return adaptor_.RunPipeline(func, passes_); + } + void AddPass(std::unique_ptr pass) { + passes_.emplace_back(std::move(pass)); + } + + private: + std::vector> passes_; + PassAdaptorT adaptor_; +}; + +using FuncPassManager = PassManager; +using BlockPassManager = PassManager; +using StmtPassManager = PassManager; +using ExprPassManager = PassManager; + +} // namespace optim +} // namespace cinn From b31ee6bcef3516667b5e7a66b0b506886083d742 Mon Sep 17 00:00:00 2001 From: chen2016013 <111894720+chen2016013@users.noreply.github.com> Date: Mon, 9 Dec 2024 20:40:34 +0800 Subject: [PATCH 249/288] Fasten bw_ops detecting time (#70065) --- python/paddle/decomposition/recompute.py | 63 +++++++----------------- 1 file changed, 17 insertions(+), 46 deletions(-) diff --git a/python/paddle/decomposition/recompute.py b/python/paddle/decomposition/recompute.py index a34551fc910f10..30893b00d059c6 100644 --- a/python/paddle/decomposition/recompute.py +++ b/python/paddle/decomposition/recompute.py @@ -383,6 +383,7 @@ def auto_recompute( (%11) = "pd_op.fetch" (%10) {col:(Int32)0,is_persistable:[true],name:"fetch0",stop_gradient:[false]} : (pd_op.tensor<4096x4096xf32>) -> pd_op.tensor<4096x4096xf32> } ''' + DebugPrint("program before recompute:", program) # 1. find smart recompute needed saved values by min-cut algorithm # 1.1 classify value nodes import networkx as nx @@ -630,7 +631,6 @@ def _ban_recomputation(value_node): # (TODO: wanghao107): remove it and fix model # saved_values = cut_value_nodes | inputs saved_values = cut_value_nodes - DebugPrint("program before recompute:", program) # 2.patition the joint graph by saved values. ( program_after_recompute, @@ -861,36 +861,29 @@ def classify_value_node(program, grad_outputs, fwd_op_end_idx): program.global_block().get_values_by_op_idx(required_fw_op_idxs) ) - required_bw_ops = set() - for grad_output in grad_outputs: - required_bw_ops = required_bw_ops | find_child_ops(grad_output) - required_bw_ops.add(grad_output.get_defining_op()) - - required_bw_op_idxs = [] - for idx, op in enumerate(all_ops): - if op in required_bw_ops: - required_bw_op_idxs.append(idx) + required_bw_op_idxs = list(range(fwd_op_end_idx + 1, len(all_ops))) required_bw_value_nodes = backward_utils.ValueSet( program.global_block().get_values_by_op_idx(required_bw_op_idxs) ) - unclaimed_ops = { - op - for op in all_ops - if op not in required_fw_ops and op not in required_bw_ops - } - - unclaimed_op_idxs = [] - for idx, op in enumerate(all_ops): - if op in unclaimed_ops: - unclaimed_op_idxs.append(idx) - unclaimed_value_nodes = backward_utils.ValueSet( - program.global_block().get_values_by_op_idx(unclaimed_op_idxs) - ) + # TODO(chenxi67) optimize classify algorithm by using unclaimed_ops. Remove them to fasten bw_ops detecting time. + # unclaimed_ops = { + # op + # for op in all_ops + # if op not in required_fw_ops and op not in required_bw_ops + # } + + # unclaimed_op_idxs = [] + # for idx, op in enumerate(all_ops): + # if op in unclaimed_ops: + # unclaimed_op_idxs.append(idx) + # unclaimed_value_nodes = backward_utils.ValueSet( + # program.global_block().get_values_by_op_idx(unclaimed_op_idxs) + # ) return ( required_fw_value_nodes, - required_bw_value_nodes | unclaimed_value_nodes, + required_bw_value_nodes, backward_utils.ValueSet(), ) @@ -1109,25 +1102,3 @@ def _find_parent_ops(value): return parent_ops return _find_parent_ops(value) - - -def find_child_ops(value): - visited = backward_utils.ValueSet() - - def _find_child_ops(value): - child_ops = set() - if value in visited: - return child_ops - visited.add(value) - used_ops = value.all_used_ops() - child_ops |= set(used_ops) - op_results = backward_utils.ValueSet() - for used_op in used_ops: - op_results = op_results | backward_utils.ValueSet(used_op.results()) - for op_result in op_results: - if not op_result.initialized(): - continue - child_ops = child_ops | _find_child_ops(op_result) - return child_ops - - return _find_child_ops(value) From de50a920be620bab711935a2eeb0e985794da595 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Tue, 10 Dec 2024 00:14:13 +0800 Subject: [PATCH 250/288] [SOT][DynamicShape] Fix `SymbolicVariable` `get_py_value` reversed call and break graph on infermeta encount `TypeError` (#70009) --------- Co-authored-by: Copilot <175728472+Copilot@users.noreply.github.com> Co-authored-by: gouzil <66515297+gouzil@users.noreply.github.com> --- .../executor/function_graph.py | 9 +++-- .../executor/variables/basic.py | 36 ++++++++++++++++--- test/sot/test_16_paddle_api.py | 13 +++++++ test/sot/test_sot_dynamic_shape.py | 14 ++++++++ 4 files changed, 65 insertions(+), 7 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py index ecce8d92e2506e..1ce97e001f487c 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/function_graph.py +++ b/python/paddle/jit/sot/opcode_translator/executor/function_graph.py @@ -52,6 +52,7 @@ map_if, switch_symbol_registry, ) +from ...utils.exceptions import BreakGraphError from ..instruction_utils import get_instructions from .guard import Guard, StringifiedExpression, make_guard from .mutable_data import MutationDel, MutationNew, MutationSet @@ -661,7 +662,9 @@ def try_infer_meta_fn(args, kwargs) -> Any: for arg in flatten_vars ): # TODO(zrr1999): maybe we can continue to fallback to all args are constant. - raise e + raise BreakGraphError( + f"InferMeta encount {type(e)}, but all args are not symbolic." + ) args, kwargs = map_if( (args, kwargs), @@ -686,7 +689,9 @@ def try_infer_meta_fn(args, kwargs) -> Any: isinstance(arg, SymbolicVariable) for arg in flatten_vars ): - raise e + raise BreakGraphError( + f"InferMeta encount {type(e)}, but all args are not symbolic." + ) args, kwargs = map_structure( replace_symbolic_var_with_constant_var, (args, kwargs) diff --git a/python/paddle/jit/sot/opcode_translator/executor/variables/basic.py b/python/paddle/jit/sot/opcode_translator/executor/variables/basic.py index 840ed18f2aa250..f954c29e6f6ee7 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/variables/basic.py +++ b/python/paddle/jit/sot/opcode_translator/executor/variables/basic.py @@ -106,6 +106,13 @@ STATIC_DIM_FREQ_THRESHOLD = 5 +def method_to_reverse_method(method_name: str) -> str | None: + if not method_name.startswith("__") or not method_name.endswith("__"): + return None + name = method_name[2:-2] + return f"__r{name}__" + + class ConstantVariable(VariableBase): """ ConstantVariable is a subclass of VariableBase used to wrap a Variable of the const type. @@ -802,11 +809,30 @@ def get_py_value(self, allow_tensor: bool = False) -> bool | int | float: ), f"self.value is None, but tracker is not SymbolicOperationTracker. tracker: {self.tracker}" inputs = self.tracker.inputs assert len(inputs) >= 1 - other_inputs_value = [x.get_py_value() for x in inputs[1:]] - self.value = getattr( - inputs[0].get_py_value(), self.tracker.method_name - )(*other_inputs_value) - assert isinstance(self.value, (bool, int, float)) + input_values = [x.get_py_value() for x in inputs] + value = getattr(input_values[0], self.tracker.method_name)( + *input_values[1:] + ) + # TODO(SigureMo): A Temporary solution for the case that the method is not implemented. + # e.g. In user code, we have `1 * 0.1`, the lhs is a SymbolicVariable, and the rhs is a float. + # We trace the method `__mul__` from the lhs, but actually, python use `float.__rmul__`, + # `int.__mul__(float)` is not implemented. So we get NotImplemented here. + # We need to find a better way to handle this case. + if isinstance(value, type(NotImplemented)): + reversed_method = method_to_reverse_method( + self.tracker.method_name + ) + if reversed_method is None: + raise InnerError( + f"Unsupported method {self.tracker.method_name} for SymbolicVariable" + ) + value = getattr(input_values[1], reversed_method)( + input_values[0], *input_values[2:] + ) + self.value = value + assert isinstance( + self.value, (bool, int, float) + ), f"SymbolicVariable.get_py_value() should return bool, int or float, but got {type(self.value)}" return self.value def get_py_type(self): diff --git a/test/sot/test_16_paddle_api.py b/test/sot/test_16_paddle_api.py index 9f6e05fa48b2fc..041b1c79b77850 100644 --- a/test/sot/test_16_paddle_api.py +++ b/test/sot/test_16_paddle_api.py @@ -38,6 +38,12 @@ def paddle_api_function_call_concat( return paddle.concat([x, y], axis=axis) +def paddle_api_function_breakgraph_when_type_error( + x: paddle.Tensor, axis: paddle.Tensor +): + return paddle.nn.functional.softmax(x, axis=axis) + + class TestPaddleApiCall(TestCaseBase): def test_paddle_api_method_call(self): self.assert_results(paddle_api_method_call, paddle.to_tensor(2.0)) @@ -55,6 +61,13 @@ def test_paddle_api_function_call_concat(self): self.assert_results(paddle_api_function_call_concat, a, b, 0) self.assert_results(paddle_api_function_call_concat, a, b, 1) + def test_paddle_api_function_breakgraph_when_type_error(self): + x = paddle.to_tensor([[1, 2], [3, 4]], dtype=paddle.float32) + axis = paddle.to_tensor(1) + self.assert_results( + paddle_api_function_breakgraph_when_type_error, x, axis + ) + if __name__ == "__main__": unittest.main() diff --git a/test/sot/test_sot_dynamic_shape.py b/test/sot/test_sot_dynamic_shape.py index ed542ac703a036..37a8c37ed00ffb 100644 --- a/test/sot/test_sot_dynamic_shape.py +++ b/test/sot/test_sot_dynamic_shape.py @@ -14,6 +14,7 @@ from __future__ import annotations +import math import unittest from test_case_base import ( @@ -58,6 +59,12 @@ def dynamic_shape_in_list(x, shape): return x.reshape(shape) +def dynamic_shape_int_mul_float(x): + y = x * 0.5 + z = math.sin(y) # Trigger get_py_value + return z + + class CustomConv(paddle.nn.Conv2D): def __init__(self, *args, **kwargs): super().__init__(*args, **kwargs) @@ -193,6 +200,13 @@ def test_pad_dynamic_shape_fallback(self): self.assert_results(pad_func, paddle.randn([1, 3, 224, 224]), i) self.assertEqual(ctx.translate_count, i) + def test_dynamic_shape_int_mul_float(self): + with allow_dynamic_shape_guard( + True + ), test_instruction_translator_cache_context() as ctx: + for i in range(1, 6): + self.assert_results(dynamic_shape_int_mul_float, i) + if __name__ == '__main__': unittest.main() From bf7a2d13f5aaa70e12cad62c280ab3ad6eb31a28 Mon Sep 17 00:00:00 2001 From: =?UTF-8?q?=E4=BD=A0=E7=8C=9C?= Date: Tue, 10 Dec 2024 02:33:00 +0800 Subject: [PATCH 251/288] [SOT][3.13] add new optimization: fuse two neighboring LOAD_FAST instructions (#69920) --- .../executor/pycode_generator.py | 1 + .../instruction_utils/instruction_pass.py | 71 ++++++++++++------- .../instruction_utils/instruction_utils.py | 14 ++++ 3 files changed, 59 insertions(+), 27 deletions(-) diff --git a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py index 4ab8333e962a35..ddfaf194f3f14e 100644 --- a/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py +++ b/python/paddle/jit/sot/opcode_translator/executor/pycode_generator.py @@ -142,6 +142,7 @@ def gen_new_opcode( types.CodeType: The new code object. """ bytecode, linetable = assemble(instrs, code_options["co_firstlineno"]) + if sys.version_info >= (3, 10): # Python deprecated co_lnotab in 3.10, use co_linetable instead # https://peps.python.org/pep-0626/ diff --git a/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_pass.py b/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_pass.py index 923bd8076239b0..2819c93acb0c51 100644 --- a/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_pass.py +++ b/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_pass.py @@ -14,6 +14,7 @@ from __future__ import annotations +import dis import sys from typing import TYPE_CHECKING @@ -39,6 +40,9 @@ def apply_instr_pass(instrs: list[Instruction], code_options): if sys.version_info >= (3, 12): supported_passes.append(check_for_iter_jump_to) + if sys.version_info >= (3, 13): + supported_passes.append(fuse_double_super_instrs) + for instr_pass in supported_passes: instr_pass(instrs, code_options) @@ -247,33 +251,6 @@ def code_exist(opname, argval, instrs): instr.argval = b_name instr.arg = store_b.arg - # remove store load - loaded_once = find_loaded_once_local_vars(instrs, code_options) - - modified = True - while modified: - modified = False - - idx = 0 - while idx + 1 < len(instrs): - opcode1 = instrs[idx] - opcode2 = instrs[idx + 1] - - if ( - opcode1 not in jump_target - and opcode2 not in jump_target - and opcode1.opname == "STORE_FAST" - and opcode2.opname == "LOAD_FAST" - and opcode2.opname == "LOAD_FAST_CHECK" - and opcode1.argval == opcode2.argval - and opcode1.argval in loaded_once - ): - instrs.remove(opcode1) - instrs.remove(opcode2) - modified = True - else: - idx += 1 - def remove_duplicate_resume(instrs: list[Instruction], code_options): resumes = list(filter(lambda instr: instr.opname == "RESUME", instrs)) @@ -303,3 +280,43 @@ def check_for_iter_jump_to(instrs: list[Instruction], code_options): assert instr.jump_to is not None if instr.jump_to.opname != "END_FOR": raise InnerError("FOR_ITER jump_to is not END_FOR") + + +def fuse_double_super_instrs(instrs: list[Instruction], code_options): + """ + Fuse two consecutive LOAD_FAST or STORE_FAST instructions into one. + """ + co_varnames = code_options['co_varnames'] + TO_FUSE_INSTS: dict[tuple[str, str], str] = { + ("LOAD_FAST", "LOAD_FAST"): "LOAD_FAST_LOAD_FAST", + ("STORE_FAST", "STORE_FAST"): "STORE_FAST_STORE_FAST", + ("STORE_FAST", "LOAD_FAST"): "STORE_FAST_LOAD_FAST", + } + + def able_to_merge(idx: int): + return ( + idx > 0 + and (instrs[idx - 1].opname, instrs[idx].opname) + in TO_FUSE_INSTS.keys() + and not instrs[idx].is_jump_target + and not instrs[idx - 1].is_jump_target + and co_varnames.index(instrs[idx - 1].argval) < 16 + and co_varnames.index(instrs[idx].argval) < 16 + ) + + def merge_two_op(prev_instr: Instruction, instr: Instruction): + merge_key = (instrs[idx - 1].opname, instrs[idx].opname) + prev_instr.opname = TO_FUSE_INSTS[merge_key] + prev_instr.opcode = dis.opmap[prev_instr.opname] + prev_instr.is_generated = True + prev_instr.argval = (prev_instr.argval, instr.argval) + instrs.remove(instr) + + idx = 0 + # We must manually control the indices, so we cannot use a for loop. + while idx < len(instrs): + if able_to_merge(idx): + merge_two_op(instrs[idx - 1], instrs[idx]) + continue + + idx += 1 diff --git a/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_utils.py b/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_utils.py index 20444231e8d351..c5d5917a10ed80 100644 --- a/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_utils.py +++ b/python/paddle/jit/sot/opcode_translator/instruction_utils/instruction_utils.py @@ -413,6 +413,20 @@ def modify_vars(instructions: list[Instruction], code_options): instrs.argval in namemap ), f"`{instrs.argval}` not in {namemap}" instrs.arg = namemap.index(instrs.argval) + elif instrs.opname in [ + 'LOAD_FAST_LOAD_FAST', + 'STORE_FAST_STORE_FAST', + 'STORE_FAST_LOAD_FAST', + ]: + assert ( + instrs.argval[0] in co_varnames + ), f"`{instrs.argval[0]}` not in {co_varnames}" + assert ( + instrs.argval[1] in co_varnames + ), f"`{instrs.argval[1]}` not in {co_varnames}" + instrs.arg = ( + co_varnames.index(instrs.argval[0]) << 4 + ) + co_varnames.index(instrs.argval[1]) def calc_offset_from_bytecode_offset( From ede8ab9771ca159dc3686c1d4d7cb098b4d358a4 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 10 Dec 2024 09:36:17 +0800 Subject: [PATCH 252/288] Fix (#70052) --- paddle/fluid/pybind/imperative.cc | 1 - 1 file changed, 1 deletion(-) diff --git a/paddle/fluid/pybind/imperative.cc b/paddle/fluid/pybind/imperative.cc index cce5545a6f42c3..a1ab35c523475d 100644 --- a/paddle/fluid/pybind/imperative.cc +++ b/paddle/fluid/pybind/imperative.cc @@ -432,7 +432,6 @@ static void VarBaseCopy(std::shared_ptr &src, // NOLINT if (src->Var().IsType()) { auto &src_tensor = src->Var().Get(); auto *dst_tensor = dst.MutableVar()->GetMutable(); - dst_tensor->set_lod(src_tensor.lod()); framework::TensorCopy(src_tensor, dst_device, dst_tensor); if (blocking) { phi::DeviceContextPool::Instance().Get(dst_device)->Wait(); From adca767df8b01d5e26c13e8a6e6d99f73eaf10d1 Mon Sep 17 00:00:00 2001 From: co63oc Date: Tue, 10 Dec 2024 09:36:49 +0800 Subject: [PATCH 253/288] [Lod][fluid_ops] pybind.cc (#70053) * Fix * Fix --- paddle/fluid/pybind/pybind.cc | 12 +++++------- 1 file changed, 5 insertions(+), 7 deletions(-) diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 53b53d85ce3fb3..72603274966d21 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -2613,7 +2613,6 @@ All parameter, weight, gradient are variables in Paddle. "The index to set is larger than the size " "of DenseTensorArray.")); self[i].ShareDataWith(t); - self[i].set_lod(t.lod()); }) .def( "append", @@ -2687,9 +2686,8 @@ All parameter, weight, gradient are variables in Paddle. "append", [](FetchList &self, const phi::DenseTensor &t) { self.emplace_back(); - auto &lod_tensor = PADDLE_GET(phi::DenseTensor, self.back()); - lod_tensor.ShareDataWith(t); - lod_tensor.set_lod(t.lod()); + auto &dense_tensor = PADDLE_GET(phi::DenseTensor, self.back()); + dense_tensor.ShareDataWith(t); }, py::arg("var")) @@ -2697,10 +2695,10 @@ All parameter, weight, gradient are variables in Paddle. "append", [](FetchList &self, const phi::TensorArray &t) { self.emplace_back(); - auto &lod_tensor_array = PADDLE_GET(phi::TensorArray, self.back()); + auto &dense_tensor_array = + PADDLE_GET(phi::TensorArray, self.back()); for (size_t i = 0; i < t.size(); ++i) { - lod_tensor_array[i].ShareDataWith(t[i]); - lod_tensor_array[i].set_lod(t[i].lod()); + dense_tensor_array[i].ShareDataWith(t[i]); } }, py::arg("var")); From a2e644ddf0afac15c3335885f782a1bf4391b803 Mon Sep 17 00:00:00 2001 From: gouzil <66515297+gouzil@users.noreply.github.com> Date: Tue, 10 Dec 2024 10:31:51 +0800 Subject: [PATCH 254/288] [CodeStyle][UP031] Use f-string instead of percent format in uts (part31) (#70085) * [CodeStyle][UP031] Use f-string instead of percent format in uts (part31) * Update test/deprecated/ir/pass_test.py --------- Co-authored-by: Nyakku Shigure --- python/paddle/hapi/callbacks.py | 12 ++++++------ test/collective/fleet/hybrid_parallel_qat.py | 2 +- .../fleet/parallel_dygraph_se_resnext.py | 2 +- .../fleet/parallel_dygraph_transformer.py | 4 ++-- .../collective/fleet/test_parallel_dygraph_qat.py | 2 +- test/collective/fleet/test_recv_save_op.py | 4 ++-- test/collective/process_group_nccl.py | 2 +- .../test_collective_process_group_xccl.py | 2 +- test/deprecated/ir/pass_test.py | 15 ++++----------- .../legacy_test/auto_parallel_op_test.py | 12 +++--------- 10 files changed, 22 insertions(+), 35 deletions(-) diff --git a/python/paddle/hapi/callbacks.py b/python/paddle/hapi/callbacks.py index 36982d36fa2258..3e5d1c58b9d32e 100644 --- a/python/paddle/hapi/callbacks.py +++ b/python/paddle/hapi/callbacks.py @@ -446,7 +446,7 @@ def on_epoch_begin( self.epoch = epoch self.train_step = 0 if self.epochs and self._is_print(): - print('Epoch %d/%d' % (epoch + 1, self.epochs)) + print(f'Epoch {epoch + 1}/{self.epochs}') self.train_progbar = ProgressBar(num=self.steps, verbose=self.verbose) self._train_timer['batch_start_time'] = time.time() @@ -624,14 +624,14 @@ def on_eval_end(self, logs: _CallbackLogs | None = None) -> None: logs = logs or {} if self._is_print() and (self.eval_steps is not None): self._updates(logs, 'eval') - print('Eval samples: %d' % (self.evaled_samples)) + print(f'Eval samples: {self.evaled_samples}') def on_predict_end(self, logs: _CallbackLogs | None = None) -> None: logs = logs or {} if self._is_print(): if self.test_step % self.log_freq != 0 or self.verbose == 1: self._updates(logs, 'test') - print('Predict samples: %d' % (self.tested_samples)) + print(f'Predict samples: {self.tested_samples}') class ModelCheckpoint(Callback): @@ -962,7 +962,7 @@ def on_eval_end(self, logs: _CallbackLogs | None = None) -> None: if self.wait_epoch >= self.patience: self.model.stop_training = True if self.verbose > 0: - print('Epoch %d: Early stopping.' % (self.stopped_epoch + 1)) + print(f'Epoch {self.stopped_epoch + 1}: Early stopping.') if self.save_best_model and self.save_dir is not None: print( 'Best checkpoint has been saved at {}'.format( @@ -1448,8 +1448,8 @@ def on_eval_end(self, logs: _CallbackLogs | None = None) -> None: and paddle.distributed.ParallelEnv().local_rank == 0 ): print( - '\nEpoch %d: ReduceLROnPlateau reducing learning ' - 'rate to %s.' % (self.epoch + 1, new_lr) + f'\nEpoch {self.epoch + 1}: ReduceLROnPlateau reducing learning ' + f'rate to {new_lr}.' ) self.cooldown_counter = self.cooldown self.wait = 0 diff --git a/test/collective/fleet/hybrid_parallel_qat.py b/test/collective/fleet/hybrid_parallel_qat.py index 0feeca40771b8e..106b5030459e2a 100644 --- a/test/collective/fleet/hybrid_parallel_qat.py +++ b/test/collective/fleet/hybrid_parallel_qat.py @@ -68,7 +68,7 @@ def get_cluster_from_args(selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) diff --git a/test/collective/fleet/parallel_dygraph_se_resnext.py b/test/collective/fleet/parallel_dygraph_se_resnext.py index bbf3f8a1e45335..7a1d9bf2d1c23b 100644 --- a/test/collective/fleet/parallel_dygraph_se_resnext.py +++ b/test/collective/fleet/parallel_dygraph_se_resnext.py @@ -276,7 +276,7 @@ def __init__(self, layers=50, class_dim=102): shortcut = False for i in range(depth[block]): bottleneck_block = self.add_sublayer( - 'bb_%d_%d' % (block, i), + f'bb_{block}_{i}', BottleneckBlock( num_channels=num_channels, num_filters=num_filters[block], diff --git a/test/collective/fleet/parallel_dygraph_transformer.py b/test/collective/fleet/parallel_dygraph_transformer.py index 4e3034f4021f73..717ae2323e7ce5 100644 --- a/test/collective/fleet/parallel_dygraph_transformer.py +++ b/test/collective/fleet/parallel_dygraph_transformer.py @@ -464,7 +464,7 @@ def __init__( for i in range(n_layer): self._encoder_sublayers.append( self.add_sublayer( - 'esl_%d' % i, + f'esl_{i}', EncoderSubLayer( n_head, d_key, @@ -739,7 +739,7 @@ def __init__( for i in range(n_layer): self._decoder_sub_layers.append( self.add_sublayer( - 'dsl_%d' % i, + f'dsl_{i}', DecoderSubLayer( n_head, d_key, diff --git a/test/collective/fleet/test_parallel_dygraph_qat.py b/test/collective/fleet/test_parallel_dygraph_qat.py index 6cee75245c5ff2..c6d6f17408da41 100644 --- a/test/collective/fleet/test_parallel_dygraph_qat.py +++ b/test/collective/fleet/test_parallel_dygraph_qat.py @@ -43,7 +43,7 @@ def get_cluster_from_args(selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) diff --git a/test/collective/fleet/test_recv_save_op.py b/test/collective/fleet/test_recv_save_op.py index bd7704560cd946..2996b6b86fce24 100644 --- a/test/collective/fleet/test_recv_save_op.py +++ b/test/collective/fleet/test_recv_save_op.py @@ -85,13 +85,13 @@ def _wait_ps_ready(self, pid): try: # the listen_and_serv_op would touch a file which contains the listen port # on the /tmp directory until it was ready to process all the RPC call. - os.stat("/tmp/paddle.%d.port" % pid) + os.stat(f"/tmp/paddle.{pid}.port") return except OSError: start_left_time -= sleep_time def _get_pserver_port(self, pid): - with open("/tmp/paddle.%d.port" % pid, 'r') as f: + with open(f"/tmp/paddle.{pid}.port", 'r') as f: port = int(f.read().strip()) return port diff --git a/test/collective/process_group_nccl.py b/test/collective/process_group_nccl.py index f781dc3f7456d0..2e20c79f387566 100644 --- a/test/collective/process_group_nccl.py +++ b/test/collective/process_group_nccl.py @@ -43,7 +43,7 @@ def config(self): def test_create_process_group_nccl(self): device_id = paddle.distributed.ParallelEnv().dev_id - paddle.set_device('gpu:%d' % device_id) + paddle.set_device(f'gpu:{device_id}') assert paddle.distributed.is_available() diff --git a/test/custom_runtime/test_collective_process_group_xccl.py b/test/custom_runtime/test_collective_process_group_xccl.py index dcad082cb186f8..e53d8efbe4c9a2 100644 --- a/test/custom_runtime/test_collective_process_group_xccl.py +++ b/test/custom_runtime/test_collective_process_group_xccl.py @@ -109,7 +109,7 @@ def get_cluster_from_args(selected_gpus): trainer_endpoints = [] for ip in node_ips: - trainer_endpoints.append(["%s:%d" % (ip, port) for port in free_ports]) + trainer_endpoints.append([f"{ip}:{port}" for port in free_ports]) return get_cluster(node_ips, node_ip, trainer_endpoints, selected_gpus) diff --git a/test/deprecated/ir/pass_test.py b/test/deprecated/ir/pass_test.py index 836ef2efbd9a69..e30cabd3ba1b12 100644 --- a/test/deprecated/ir/pass_test.py +++ b/test/deprecated/ir/pass_test.py @@ -166,17 +166,10 @@ def check_output_with_place(self, place, startup_on_cpu=False, atol=1e-5): offset = np.argmax(diff_mat > atol) self.assertTrue( is_allclose, - "Output (name: %s, shape: %s, dtype: %s) has diff at %s. The maximum diff is %e, first error element is %d, expected %e, but got %e" - % ( - self.fetch_list[i].name, - str(self.fetch_list[i].shape), - self.fetch_list[i].dtype, - str(place), - max_diff, - offset, - a.flatten()[offset], - b.flatten()[offset], - ), + f"Output (name: {self.fetch_list[i].name}, shape: {self.fetch_list[i].shape!s}, dtype: {self.fetch_list[i].dtype}) has diff at {place!s}. " + f"The maximum diff is {max_diff:e}, first error element is {offset}, " + f"expected {a.flatten()[offset].item():e}, " + f"but got {b.flatten()[offset].item():e}", ) def _check_fused_ops(self, program): diff --git a/test/deprecated/legacy_test/auto_parallel_op_test.py b/test/deprecated/legacy_test/auto_parallel_op_test.py index 5efe97b6e8c970..c8fc887a5640d8 100644 --- a/test/deprecated/legacy_test/auto_parallel_op_test.py +++ b/test/deprecated/legacy_test/auto_parallel_op_test.py @@ -512,15 +512,9 @@ def check_eager_auto_parallel(self): rtol=self.atol, atol=self.rtol, err_msg=( - 'Check eager auto parallel failed. Mismatch between eager auto parallel outputs ' - 'and eager outputs on %s, the eager forward output tensor\'s index is : %d \n' - 'eager auto parallel output tensor:\n%s\n eager output tensor:\n%s\n' - % ( - str(self.place), - i, - actual_ret[i], - self.eager_forward_desire[i], - ) + f"Check eager auto parallel failed. Mismatch between eager auto parallel outputs " + f"and eager outputs on {self.place!s}. The eager forward output tensor's index is : {i} \n" + f"eager auto parallel output tensor:\n{actual_ret[i]}\n eager output tensor:\n{self.eager_forward_desire[i]}\n" ), ) From d0f9dbb5440f95d8e7073fa58bb6924783ea0b51 Mon Sep 17 00:00:00 2001 From: Xinyi Li Date: Tue, 10 Dec 2024 10:48:59 +0800 Subject: [PATCH 255/288] Upgrade oneDNN to v3.5 (#69917) --- third_party/onednn | 2 +- 1 file changed, 1 insertion(+), 1 deletion(-) diff --git a/third_party/onednn b/third_party/onednn index 0fb7e6ed4f32e5..9f7cefe9025cb0 160000 --- a/third_party/onednn +++ b/third_party/onednn @@ -1 +1 @@ -Subproject commit 0fb7e6ed4f32e5d89832b2bd742bbf834cd296ed +Subproject commit 9f7cefe9025cb0dac9c85151dc8c1d3f48af3a3e From 42cee04303eca19f41cc69d84550a13ca0b02c96 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Tue, 10 Dec 2024 10:53:56 +0800 Subject: [PATCH 256/288] [CINN]fix group shape inconsistency (#70079) --- .../lowering_pass/collect_sym_expr.cc | 42 ++++++++++++++++--- .../dialect/shape/utils/shape_analysis.h | 2 + .../src/dialect/shape/utils/shape_analysis.cc | 5 +++ 3 files changed, 44 insertions(+), 5 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/collect_sym_expr.cc b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/collect_sym_expr.cc index 42f433e3b4ea44..9c2047a43439aa 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/collect_sym_expr.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/lowering_pass/collect_sym_expr.cc @@ -93,14 +93,16 @@ CollectSubstituteDimExprMap( pir::ShapeConstraintIRAnalysis& shape_analysis) { // NOLINT std::unordered_map dim_expr_map; std::unordered_set base_dim_expr_set; + std::unordered_set new_symbol_set; VisitEachInputValue(group, [&](::pir::Value value) { auto& shape_or_data = shape_analysis.GetShapeOrDataForValue(value); VisitEachDimExpr(shape_or_data, [&](const symbol::DimExpr& dim_expr) { if (IsComplicatedDimExpr(dim_expr) && dim_expr_map.find(dim_expr) == dim_expr_map.end()) { - dim_expr_map[dim_expr] = - symbol::DimExpr(shape_analysis.GetNextSymName()); + const auto& new_symbol = shape_analysis.GetNextSymName(); + dim_expr_map[dim_expr] = symbol::DimExpr(new_symbol); + new_symbol_set.insert(new_symbol); } if (dim_expr.isa()) { base_dim_expr_set.insert(dim_expr.Get()); @@ -129,6 +131,34 @@ CollectSubstituteDimExprMap( dim_expr_map.erase(dim_expr); } + const auto& dim_exprs_can_represent_by_subset = [&]() { + const auto& CanBeRepresentedBySubset = + [&](const symbol::DimExpr& dim_expr) { + if (dim_expr.isa()) return false; + for (const auto& symbol : symbol::CollectDimExprSymbols(dim_expr)) { + if (new_symbol_set.count(symbol) == 0) { + return false; + } + } + return true; + }; + std::unordered_set result; + for (const auto& kv : dim_expr_map) { + std::unordered_map + substitute_dim_expr_map = dim_expr_map; + substitute_dim_expr_map.erase(kv.first); + const auto& substituted = + symbol::SubstituteDimExpr(kv.first, substitute_dim_expr_map); + if (CanBeRepresentedBySubset(substituted)) { + result.insert(kv.first); + } + } + return result; + }(); + for (const auto& dim_expr : dim_exprs_can_represent_by_subset) { + dim_expr_map.erase(dim_expr); + } + return dim_expr_map; } @@ -147,9 +177,6 @@ bool IsShapeOrDataNeedSubstitute( symbol::ShapeOrDataDimExprs TrySubstitute( const symbol::ShapeOrDataDimExprs& shape_or_data, const std::unordered_map& dim_expr_map) { - if (!IsShapeOrDataNeedSubstitute(shape_or_data, dim_expr_map)) { - return shape_or_data; - } return symbol::SubstituteShapeOrData(shape_or_data, dim_expr_map); } @@ -205,6 +232,11 @@ CreateGroupShapeOrDataExprs( pir::ShapeConstraintIRAnalysis local_shape_analysis({}); local_shape_analysis.InitInferContext(); + local_shape_analysis.RegisterSymbolConstraintFromShapeAnalysis( + global_shape_analysis); + for (const auto& item : dim_expr_map) { + local_shape_analysis.AddEqualCstr(item.first, item.second); + } // process input values. VisitEachInputValue(group, [&](::pir::Value value) { auto new_shape_expr = TrySubstitute( diff --git a/paddle/pir/include/dialect/shape/utils/shape_analysis.h b/paddle/pir/include/dialect/shape/utils/shape_analysis.h index 359d018d190829..9a32817ee060a2 100644 --- a/paddle/pir/include/dialect/shape/utils/shape_analysis.h +++ b/paddle/pir/include/dialect/shape/utils/shape_analysis.h @@ -199,6 +199,8 @@ class IR_API ShapeConstraintIRAnalysis final // Set ShapeOrData of `to` value by ShapeOrData of `from` value. void ShareShapeOrData(Value from, Value to); + void AddEqualCstr(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs); + bool IsEqual(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) const; bool IsGreatThanOne(const symbol::DimExpr& dim_expr) const; diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 9759c25cd6098c..6f8764c8f970ae 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -625,6 +625,11 @@ void ShapeConstraintIRAnalysis::ShareShapeOrData(Value from, Value to) { } } +void ShapeConstraintIRAnalysis::AddEqualCstr(const symbol::DimExpr& lhs, + const symbol::DimExpr& rhs) { + context_.AddEqualCstr(lhs, rhs); +} + bool ShapeConstraintIRAnalysis::IsEqual(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) const { return context_.IsEqual(lhs, rhs); From 656adc320d8e7b3b6ace916cc5cadfa4b45ecac4 Mon Sep 17 00:00:00 2001 From: liuruyan <44316842+liuruyan@users.noreply.github.com> Date: Tue, 10 Dec 2024 11:13:29 +0800 Subject: [PATCH 257/288] =?UTF-8?q?=E3=80=90PIR=E3=80=91Add=20fallback=20m?= =?UTF-8?q?ethod=20for=20AutoLayoutPass=20(#69559)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Add white list for auto_layout_pass * test_1 * test_2 * remove autolayout_enabled * remove enable_auto_layout_pass flag in analysis config * add preferlayout for conv2d and fusedconv2daddact * add mixed_precision_mode * fix CI PR-CI-Mac-Python3 * Replace transfer_layout_pass with auto_layout_pass in inference * fix * update analysis predictor * Code changes based on comments * Run AutoMixedPrecisionPass before cinn. * Modify conflicts * fix PR-CI-Codestyle-Check * fix conflict * test * fix confilt * The prem of the transpose operator NCHW is modified to the prem corresponding to NHWC * empty commit * fix * fix * fix comment --------- Co-authored-by: zhanghonggeng --- paddle/common/flags.cc | 3 +- .../fluid/inference/api/analysis_predictor.cc | 3 +- .../general/auto_layout_insert_pass.cc | 388 ++++++++++++++++++ .../general/auto_layout_insert_pass.h | 30 ++ .../transforms/general/auto_layout_pass.cc | 360 +++------------- paddle/fluid/pir/transforms/passes.h | 1 + .../jit/dy2static/pir_partial_program.py | 2 - test/cpp/pir/pass/auto_layout_pass_test.cc | 7 +- 8 files changed, 493 insertions(+), 301 deletions(-) create mode 100644 paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc create mode 100644 paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h diff --git a/paddle/common/flags.cc b/paddle/common/flags.cc index 1efa3c64f7ab17..9a1db0f9271029 100644 --- a/paddle/common/flags.cc +++ b/paddle/common/flags.cc @@ -1433,7 +1433,8 @@ PHI_DEFINE_EXPORTED_bool( * Since Version: 3.0.0 * Value Range: bool, default=false * Example: - * Note: If True, using AutoLayoutPass and AutuLayoutSimplifyPass by default + * Note: If True, using AutoLayoutInsertPass and AutuLayoutSimplifyPass by + * default */ PHI_DEFINE_EXPORTED_bool(enable_auto_layout_pass, false, diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index 891656471fbaf4..bfe7f1c3f6d20a 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -806,8 +806,7 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { auto AddAutoLayoutPasses = [&](pir::PassManager &pass_manager) { auto &pass_registry = pir::PassRegistry::Instance(); - std::vector passes = {"auto_layout_pass", - "auto_layout_simplify_pass"}; + std::vector passes = {"auto_layout_pass"}; for (const auto &pass_name : passes) { if (std::find(config_.deleted_passes_.begin(), diff --git a/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc new file mode 100644 index 00000000000000..16a03a00fd6f42 --- /dev/null +++ b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc @@ -0,0 +1,388 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h" + +#include +#include +#include +#include + +#include "paddle/common/enforce.h" +#include "paddle/common/layout.h" +#include "paddle/fluid/inference/api/paddle_pass_builder.h" +#include "paddle/fluid/pir/dialect/operator/interface/layout_transformation.h" +#include "paddle/fluid/pir/dialect/operator/ir/control_flow_op.h" +#include "paddle/fluid/pir/dialect/operator/ir/op_attribute.h" +#include "paddle/fluid/pir/dialect/operator/ir/op_dialect.h" +#include "paddle/fluid/pir/dialect/operator/ir/op_type.h" +#include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/phi/common/data_type.h" +#include "paddle/pir/include/core/builtin_dialect.h" +#include "paddle/pir/include/core/ir_context.h" +#include "paddle/pir/include/core/op_trait.h" +#include "paddle/pir/include/core/program.h" +#include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pass/pass_registry.h" +#include "paddle/pir/include/pass/utils.h" + +namespace { + +extern const std::set ops_in_NCHW; +extern const std::set op_with_axis; + +class AutoLayoutInsertPass : public pir::Pass { + public: + AutoLayoutInsertPass() : pir::Pass("auto_layout_insert_pass", 2) {} + AutoLayoutInsertPass(const std::set& op_in_NHWC) // NOLINT + : pir::Pass("auto_layout_insert_pass", 2), ops_in_NHWC_(op_in_NHWC) {} + + void Run(pir::Operation* op) override { + for (size_t i = 0; i < op->num_regions(); ++i) { + auto& region = op->region(i); + for (auto& block : region) { + pir::Builder builder = pir::Builder(ctx_, &block); + VLOG(4) << "Transforming block"; + TransferLayout(builder, &block); + } + } + } + + bool CanApplyOn(pir::Operation* op) const override { + return op->num_regions() > 0; + } + + private: + void RewriteLayout(pir::Operation* op, + const std::vector& input_values) { // NOLINT + if (op->isa() || + op->isa()) { + auto layout_interface = + op->dyn_cast(); + layout_interface.RewriteByLayout(op, common::DataLayout::NHWC); + return; + } + + auto InferMetaSpecificOp = [&]() { + // Op not implement InferMetaInterface interface, so we need to rewrite + // manually + if (op->isa()) { + auto out = op->dyn_cast().out(); + std::vector new_out_type; + for (auto v : op->operands_source()) { + new_out_type.push_back(v.type()); + } + auto new_out_type_v = + pir::VectorType::get(pir::IrContext::Instance(), new_out_type); + out.set_type(new_out_type_v); + } else { + PADDLE_THROW(common::errors::Unimplemented( + "`%s` should implement InferMetaInterface interface or rewrite " + "manually, but not found.", + op->name())); + } + }; + + if (op->HasAttribute("data_format")) { + op->set_attribute("data_format", pir::StrAttribute::get(ctx_, "NHWC")); + } + auto p_attribute_map = op->attributes(); + + if (auto infer_meta_interface = + op->dyn_cast()) { + auto output_types = + infer_meta_interface.InferMeta(input_values, &p_attribute_map); + for (size_t i = 0; i < output_types.size(); ++i) { + op->result(i).set_type(output_types[i]); + pir::SetNewLayoutForValue(op->result(i), common::DataLayout::NHWC); + } + } else { + InferMetaSpecificOp(); + } + } + + bool JudgeOperand(const pir::Value& operand, + const std::vector& layout) { + if (operand.type().isa()) { + auto defined_op = operand.defining_op(); + for (auto inner_operand : defined_op->operands_source()) { + if (JudgeOperand(inner_operand, NCHW2NHWC_)) { + return true; + } + } + return false; + } else { + if (!JudgeValue(operand)) return false; + auto transposeInputOp = + operand.defining_op(); + if (!transposeInputOp) return false; + pir::Operation* op = transposeInputOp.operation(); + if (!op->HasAttribute("source")) return false; + auto source = + transposeInputOp.attribute("source").AsString(); + if (source != "auto_layout_pass") return false; + const auto perm_attr = + transposeInputOp.attribute("perm"); + std::vector perm; + for (size_t i = 0; i < perm_attr.size(); ++i) { + auto attr = perm_attr.at(i); + perm.push_back(attr.dyn_cast().data()); + } + return perm == layout; + } + } + + bool IsInsertTransposeOpBefore(pir::Operation* op) { + bool is_insert_transpose = false; + + for (pir::Value operand : op->operands_source()) { + if (is_insert_transpose) break; + is_insert_transpose = JudgeOperand(operand, NHWC2NCHW_); + } + return is_insert_transpose; + } + + // Convert NCHW permutation to NHWC permutation + std::vector ConvertNCHWToNHWC( + const std::vector& nchw_perm) { + std::vector nhwc_perm(4); + for (int i = 0; i < 4; ++i) { + int32_t nchw_perm_value = + nchw_perm[i].dyn_cast().data(); + int32_t new_value = + nchw_perm_value == 0 + ? 0 + : (nchw_perm_value == 1 ? 3 : nchw_perm_value - 1); + nhwc_perm[i] = pir::Int32Attribute::get(ctx_, new_value); + } + return nhwc_perm; + } + + void TransformTransposePerm(paddle::dialect::TransposeOp* op) { + std::vector perm_values = + op->attribute("perm").AsVector(); + + if (perm_values.size() == 4) { + std::vector new_perm_values = + ConvertNCHWToNHWC(perm_values); + op->operation()->set_attribute( + "perm", pir::ArrayAttribute::get(ctx_, new_perm_values)); + } + } + + void TransferLayout(pir::Builder builder, pir::Block* block) { + for (auto&& op_item : *block) { + auto op = &op_item; + auto op_name = op->name(); + + // Skip special ops. + if (op->HasTrait()) continue; + if (op->operands().size() == 0) continue; + + // NHWC ops branch, Only support + // conv2d、fused_conv2d_add_act、conv2d_transpose now, it will add white + // list later. + if (ops_in_NHWC_.find(op_name) != ops_in_NHWC_.end()) { + auto layout_interface = + op->dyn_cast(); + common::DataLayout new_layout = layout_interface.PreferLayout(op); + if (new_layout != common::DataLayout::NHWC) continue; + + if (op->HasAttribute("data_format") && + op->attribute("data_format").AsString() == + "NCHW") { + VLOG(4) << "enter NHWC op: " << op_name; + DoTransposeOpOperand(op, builder); + RewriteLayout(op, op->operands_source()); + DoTransposeOpResult(op, builder); + } + } else if (ops_in_NCHW.find(op_name) == ops_in_NCHW.end() && + op_with_axis.find(op_name) == op_with_axis.end() && + IsInsertTransposeOpBefore(op)) { + VLOG(4) << "enter NCHW op: " << op_name; + DoTransposeOpOperand(op, builder); + if (auto transpose_op = op->dyn_cast()) { + TransformTransposePerm(&transpose_op); + continue; + } + RewriteLayout(op, op->operands_source()); + DoTransposeOpResult(op, builder); + } + } + } + + // Skip the operand which is not dense tensor or not 4-D tensor, they don't + // need transpose. + bool JudgeValue(const pir::Value& value) { + if (!value) return false; + if (!value.type()) return false; + if (auto type = value.type().dyn_cast()) { + return type.dims().size() == 4; + } + return false; + } + + void DoTransposeOpOperand(pir::Operation* op, + pir::Builder& builder) { // NOLINT + builder.set_insertion_point(op); + + // For conv2d, only transpose the input. + if (op->isa() || + op->isa()) { + auto inp = op->operand(0); + if (!JudgeValue(inp.source())) return; + auto transpose_op = + builder.Build(inp.source(), NCHW2NHWC_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); + pir::SetNewLayoutForValue(transpose_op->result(0), + common::DataLayout::NHWC); + inp.set_source(transpose_op->result(0)); + return; + } + + for (auto& operand : op->operands()) { + if (!JudgeValue(operand.source())) continue; + // Canbe optimize with cache when not eliminate the transpose op. + auto transpose_op = builder.Build( + operand.source(), NCHW2NHWC_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); + pir::SetNewLayoutForValue(transpose_op->result(0), + common::DataLayout::NHWC); + operand.set_source(transpose_op->result(0)); + } + } + void DoTransposeOpResult(pir::Operation* op, + pir::Builder& builder) { // NOLINT + builder.SetInsertionPointAfter(op); + for (auto& result : op->results()) { + if (!JudgeValue(result)) continue; + auto transpose_op = + builder.Build(result, NHWC2NCHW_); + transpose_op->set_attribute( + "source", + pir::StrAttribute::get(transpose_op->ir_context(), + "auto_layout_pass")); + pir::SetNewLayoutForValue(transpose_op->result(0), + common::DataLayout::NCHW); + result.ReplaceAllUsesWith(transpose_op->result(0)); + transpose_op->operand(0).set_source(result); + } + } + + pir::IrContext* ctx_ = pir::IrContext::Instance(); + std::set ops_in_NHWC_; + const std::vector NCHW2NHWC_ = {0, 2, 3, 1}; + const std::vector NHWC2NCHW_ = {0, 3, 1, 2}; +}; +const std::set ops_in_NCHW = {"pd_op.max_pool2d_with_index", + "pd_op.fractional_max_pool2d", + "pd_op.unpool3d", + "pd_op.unpool", + "pd_op.correlation", + "pd_op.depthwise_conv2d", + "pd_op.grid_sample", + "pd_op.shuffle_channel", + "cf.yield", + "pd_op.reshape", + "pd_op.instance_norm", + "pd_op.batch_norm_", + "pd_op.bilinear_interp", + "pd_op.shape", + "pd_op.deformable_conv", + "pd_op.set_value_with_tensor_", + "pd_op.set_value_with_tensor"}; +const std::set op_with_axis = { + "pd_op.all", + "pd_op.amax", + "pd_op.amin", + "pd_op.any", + "pd_op.argmin", + "pd_op.argsort", + "pd_op.box_coder", + "pd_op.cross", + "pd_op.cross_entropy_with_softmax", + "pd_op.cummax", + "pd_op.cummin", + "pd_op.cumsum", + "pd_op.diagonal", + "pd_op.fake_channel_wise_dequantize_max_abs", + "pd_op.fake_channel_wise_quantize_abs_max", + "pd_op.fake_channel_wise_quantize_dequantize_abs_max", + "pd_op.flatten", + "pd_op.flip", + "pd_op.frame", + "pd_op.frobenius_norm", + "pd_op.gather", + "pd_op.gumbel_softmax", + "pd_op.index_add", + "pd_op.index_select", + "pd_op.index_select_strided", + "pd_op.kthvalue", + "pd_op.layer_norm", + "pd_op.log_softmax", + "pd_op.logcumsumexp", + "pd_op.logsumexp", + "pd_op.max", + "pd_op.maxout", + "pd_op.mean", + "pd_op.mode", + "pd_op.nanmedian", + "pd_op.norm", + "pd_op.overlap_add", + "pd_op.p_norm", + "pd_op.prod", + "pd_op.put_along_axis", + "pd_op.renorm", + "pd_op.repeat_interleave", + "pd_op.repeat_interleave_with_tensor_index", + "pd_op.reverse", + "pd_op.roll", + "pd_op.slice", + "pd_op.split", + "pd_op.split_with_num", + "pd_op.squeeze", + "pd_op.stack", + "pd_op.sum", + "pd_op.take_along_axis", + "pd_op.tensor_unfold", + "pd_op.topk", + "pd_op.trace", + "pd_op.unbind", + "pd_op.unique_consecutive", + "pd_op.dequantize_linear", + "pd_op.min", + "pd_op.quantize_linear", + "pd_op.softmax", + "pd_op.sparse_momentum", + "pd_op.unique", + "pd_op.unsqueeze", + "pd_op.unstack"}; + +} // namespace +namespace pir { + +std::unique_ptr CreateAutoLayoutInsertPass( + const std::set& op_in_NHWC) { + return std::make_unique(op_in_NHWC); +} + +} // namespace pir + +REGISTER_IR_PASS(auto_layout_insert_pass, AutoLayoutInsertPass); diff --git a/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h new file mode 100644 index 00000000000000..2659f554e008e1 --- /dev/null +++ b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h @@ -0,0 +1,30 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include +#include "paddle/pir/include/core/dll_decl.h" + +namespace pir { + +class Pass; + +// ops_in_NHWC: the op that should be in NHWC layout. +IR_API std::unique_ptr CreateAutoLayoutInsertPass( + const std::set& ops_in_NHWC); + +} // namespace pir diff --git a/paddle/fluid/pir/transforms/general/auto_layout_pass.cc b/paddle/fluid/pir/transforms/general/auto_layout_pass.cc index 41584cb8fd537c..0ee4f44e39c3d2 100644 --- a/paddle/fluid/pir/transforms/general/auto_layout_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_layout_pass.cc @@ -15,9 +15,9 @@ #include "paddle/fluid/pir/transforms/general/auto_layout_pass.h" #include +#include #include #include -#include #include "paddle/common/enforce.h" #include "paddle/common/layout.h" @@ -28,323 +28,95 @@ #include "paddle/fluid/pir/dialect/operator/ir/op_dialect.h" #include "paddle/fluid/pir/dialect/operator/ir/op_type.h" #include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" +#include "paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h" +#include "paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.h" #include "paddle/phi/common/data_type.h" #include "paddle/pir/include/core/builtin_dialect.h" #include "paddle/pir/include/core/ir_context.h" #include "paddle/pir/include/core/op_trait.h" #include "paddle/pir/include/core/program.h" #include "paddle/pir/include/pass/pass.h" +#include "paddle/pir/include/pass/pass_manager.h" #include "paddle/pir/include/pass/pass_registry.h" #include "paddle/pir/include/pass/utils.h" namespace { - -extern const std::set op_in_NHWC; -extern const std::set op_in_NCHW; -extern const std::set op_with_axis; - class AutoLayoutPass : public pir::Pass { public: AutoLayoutPass() : pir::Pass("auto_layout_pass", 2) {} - void Run(pir::Operation* op) override { - for (size_t i = 0; i < op->num_regions(); ++i) { - auto& region = op->region(i); - for (auto& block : region) { - pir::Builder builder = pir::Builder(ctx_, &block); - VLOG(4) << "Transforming block"; - TransferLayout(builder, &block); - } - } - } - - bool CanApplyOn(pir::Operation* op) const override { - return op->num_regions() > 0; - } - - private: - void RewriteLayout(pir::Operation* op, - const std::vector& input_values) { // NOLINT - if (op->isa() || - op->isa()) { - auto layout_interface = - op->dyn_cast(); - layout_interface.RewriteByLayout(op, common::DataLayout::NHWC); - return; - } - - auto InferMetaSpecificOp = [&]() { - // Op not implement InferMetaInterface interface, so we need to rewrite - // manually - if (op->isa()) { - auto out = op->dyn_cast().out(); - std::vector new_out_type; - for (auto v : op->operands_source()) { - new_out_type.push_back(v.type()); - } - auto new_out_type_v = - pir::VectorType::get(pir::IrContext::Instance(), new_out_type); - out.set_type(new_out_type_v); - } else { - PADDLE_THROW(common::errors::Unimplemented( - "`%s` should implement InferMetaInterface interface or rewrite " - "manually, but not found.", - op->name())); - } - }; - - if (op->HasAttribute("data_format")) { - op->set_attribute("data_format", pir::StrAttribute::get(ctx_, "NHWC")); - } - auto p_attribute_map = op->attributes(); - - if (auto infer_meta_interface = - op->dyn_cast()) { - auto output_types = - infer_meta_interface.InferMeta(input_values, &p_attribute_map); - for (size_t i = 0; i < output_types.size(); ++i) { - op->result(i).set_type(output_types[i]); - pir::SetNewLayoutForValue(op->result(i), common::DataLayout::NHWC); - } - } else { - InferMetaSpecificOp(); - } - } - - bool JudgeOperand(const pir::Value& operand, - const std::vector& layout) { - if (operand.type().isa()) { - auto defined_op = operand.defining_op(); - for (auto inner_operand : defined_op->operands_source()) { - if (JudgeOperand(inner_operand, NCHW2NHWC_)) { - return true; - } - } - return false; + auto program = op->GetParentProgram(); + ::pir::IrMapping ir_mapping; + auto program_clone = program->Clone(ir_mapping); + + pir::PassManager pm(::pir::IrContext::Instance(), 2); + + pm.AddPass(pir::CreateAutoLayoutInsertPass({"pd_op.fused_conv2d_add_act", + "pd_op.conv2d", + "pd_op.conv2d_transpose"})); + pm.AddPass(pir::CreateAutoLayoutSimplifyPass()); + pm.Run(program_clone.get()); + + if (IsNeedAllTranspose(program_clone->module_op())) { + pir::PassManager pm_(::pir::IrContext::Instance(), 2); + pm_.AddPass(pir::CreateAutoLayoutInsertPass({"pd_op.fused_conv2d_add_act", + "pd_op.conv2d", + "pd_op.conv2d_transpose"})); + pm_.AddPass(pir::CreateAutoLayoutSimplifyPass()); + pm_.Run(program); } else { - if (!JudgeValue(operand)) return false; - auto transposeInputOp = - operand.defining_op(); - if (!transposeInputOp) return false; - pir::Operation* op = transposeInputOp.operation(); - if (!op->HasAttribute("source")) return false; - auto source = - transposeInputOp.attribute("source").AsString(); - if (source != "auto_layout_pass") return false; - const auto perm_attr = - transposeInputOp.attribute("perm"); - std::vector perm; - for (size_t i = 0; i < perm_attr.size(); ++i) { - auto attr = perm_attr.at(i); - perm.push_back(attr.dyn_cast().data()); - } - return perm == layout; + // Same as TransferLayoutPass, only transpose fused_conv2d_add_act + pir::PassManager pm_(::pir::IrContext::Instance(), 2); + pm_.AddPass( + pir::CreateAutoLayoutInsertPass({"pd_op.fused_conv2d_add_act"})); + pm_.AddPass(pir::CreateAutoLayoutSimplifyPass()); + pm_.Run(program); } } - bool IsInsertTransposeOpBefore(pir::Operation* op) { - bool is_insert_transpose = false; - - for (pir::Value operand : op->operands_source()) { - if (is_insert_transpose) break; - is_insert_transpose = JudgeOperand(operand, NHWC2NCHW_); - } - return is_insert_transpose; - } - - void TransferLayout(pir::Builder builder, pir::Block* block) { - for (auto&& op_item : *block) { - auto op = &op_item; - auto op_name = op->name(); - - // Skip special ops. - if (op->HasTrait()) continue; - if (op->operands().size() == 0) continue; - - // NHWC ops branch, Only support - // conv2d、fused_conv2d_add_act、conv2d_transpose now, it will add white - // list later. - if (op_in_NHWC.find(op_name) != op_in_NHWC.end()) { - auto layout_interface = - op->dyn_cast(); - common::DataLayout new_layout = layout_interface.PreferLayout(op); - if (new_layout != common::DataLayout::NHWC) { - continue; - } - - if (op->HasAttribute("data_format") && - op->attribute("data_format").AsString() == - "NCHW") { - VLOG(4) << "enter NHWC op: " << op_name; - DoTransposeOpOperand(op, builder); - RewriteLayout(op, op->operands_source()); - DoTransposeOpResult(op, builder); + // Check whether all conv2d, conv2d_transpose and fused_conv2d_add_act ops + // need to be transposed. + bool IsNeedAllTranspose(pir::Operation* op) { + VLOG(4) << "enter IsNeedAllTranspose"; + for (size_t i = 0; i < op->num_regions(); ++i) { + auto& region = op->region(i); + for (auto& block : region) { + for (auto&& op : block) { + if (op.isa()) { + if (!op.HasAttribute("source")) continue; + auto source = op.attribute("source").AsString(); + if (source == "auto_layout_pass") { + transpose_count_++; + } else { + // The original transpose should not be counted + continue; + } + } else if (op.isa() || + op.isa() || + op.isa()) { + auto layout_interface = + op.dyn_cast(); + if (layout_interface.PreferLayout(&op) != common::DataLayout::NHWC) + continue; + op.isa() ? conv_count_ += 3 + : conv_count_ += 1.5; + } else { + // Other op + continue; + } } - } else if (op_in_NCHW.find(op_name) == op_in_NCHW.end() && - op_with_axis.find(op_name) == op_with_axis.end() && - IsInsertTransposeOpBefore(op)) { - VLOG(4) << "enter NCHW op: " << op_name; - DoTransposeOpOperand(op, builder); - RewriteLayout(op, op->operands_source()); - DoTransposeOpResult(op, builder); } } + VLOG(4) << "end IsNeedAllTranspose" + << " conv_count_: " << conv_count_ + << " transpose_count_: " << transpose_count_; + return conv_count_ >= transpose_count_; } - // Skip the operand which is not dense tensor or not 4-D tensor, they don't - // need transpose. - bool JudgeValue(const pir::Value& value) { - if (!value) return false; - if (!value.type()) return false; - if (auto type = value.type().dyn_cast()) { - return type.dims().size() == 4; - } - return false; - } - - void DoTransposeOpOperand(pir::Operation* op, - pir::Builder& builder) { // NOLINT - builder.set_insertion_point(op); - - // For conv2d, only transpose the input. - if (op->isa() || - op->isa()) { - auto inp = op->operand(0); - if (!JudgeValue(inp.source())) return; - auto transpose_op = - builder.Build(inp.source(), NCHW2NHWC_); - transpose_op->set_attribute( - "source", - pir::StrAttribute::get(transpose_op->ir_context(), - "auto_layout_pass")); - pir::SetNewLayoutForValue(transpose_op->result(0), - common::DataLayout::NHWC); - inp.set_source(transpose_op->result(0)); - return; - } - - for (auto& operand : op->operands()) { - if (!JudgeValue(operand.source())) continue; - // Canbe optimize with cache when not eliminate the transpose op. - auto transpose_op = builder.Build( - operand.source(), NCHW2NHWC_); - transpose_op->set_attribute( - "source", - pir::StrAttribute::get(transpose_op->ir_context(), - "auto_layout_pass")); - pir::SetNewLayoutForValue(transpose_op->result(0), - common::DataLayout::NHWC); - operand.set_source(transpose_op->result(0)); - } - } - void DoTransposeOpResult(pir::Operation* op, - pir::Builder& builder) { // NOLINT - builder.SetInsertionPointAfter(op); - for (auto& result : op->results()) { - if (!JudgeValue(result)) continue; - auto transpose_op = - builder.Build(result, NHWC2NCHW_); - transpose_op->set_attribute( - "source", - pir::StrAttribute::get(transpose_op->ir_context(), - "auto_layout_pass")); - pir::SetNewLayoutForValue(transpose_op->result(0), - common::DataLayout::NCHW); - result.ReplaceAllUsesWith(transpose_op->result(0)); - transpose_op->operand(0).set_source(result); - } - } - - pir::IrContext* ctx_ = pir::IrContext::Instance(); - const std::vector NCHW2NHWC_ = {0, 2, 3, 1}; - const std::vector NHWC2NCHW_ = {0, 3, 1, 2}; + private: + int conv_count_ = 0; + int transpose_count_ = 0; }; -const std::set op_in_NHWC = { - "pd_op.fused_conv2d_add_act", "pd_op.conv2d", "pd_op.conv2d_transpose"}; -const std::set op_in_NCHW = {"pd_op.max_pool2d_with_index", - "pd_op.fractional_max_pool2d", - "pd_op.unpool3d", - "pd_op.unpool", - "pd_op.correlation", - "pd_op.depthwise_conv2d", - "pd_op.grid_sample", - "pd_op.shuffle_channel", - "cf.yield", - "pd_op.reshape", - "pd_op.instance_norm", - "pd_op.batch_norm_", - "pd_op.bilinear_interp", - "pd_op.shape", - "pd_op.deformable_conv", - "pd_op.set_value_with_tensor_", - "pd_op.set_value_with_tensor"}; -const std::set op_with_axis = { - "pd_op.all", - "pd_op.amax", - "pd_op.amin", - "pd_op.any", - "pd_op.argmin", - "pd_op.argsort", - "pd_op.box_coder", - "pd_op.cross", - "pd_op.cross_entropy_with_softmax", - "pd_op.cummax", - "pd_op.cummin", - "pd_op.cumsum", - "pd_op.diagonal", - "pd_op.fake_channel_wise_dequantize_max_abs", - "pd_op.fake_channel_wise_quantize_abs_max", - "pd_op.fake_channel_wise_quantize_dequantize_abs_max", - "pd_op.flatten", - "pd_op.flip", - "pd_op.frame", - "pd_op.frobenius_norm", - "pd_op.gather", - "pd_op.gumbel_softmax", - "pd_op.index_add", - "pd_op.index_select", - "pd_op.index_select_strided", - "pd_op.kthvalue", - "pd_op.layer_norm", - "pd_op.log_softmax", - "pd_op.logcumsumexp", - "pd_op.logsumexp", - "pd_op.max", - "pd_op.maxout", - "pd_op.mean", - "pd_op.mode", - "pd_op.nanmedian", - "pd_op.norm", - "pd_op.overlap_add", - "pd_op.p_norm", - "pd_op.prod", - "pd_op.put_along_axis", - "pd_op.renorm", - "pd_op.repeat_interleave", - "pd_op.repeat_interleave_with_tensor_index", - "pd_op.reverse", - "pd_op.roll", - "pd_op.slice", - "pd_op.split", - "pd_op.split_with_num", - "pd_op.squeeze", - "pd_op.stack", - "pd_op.sum", - "pd_op.take_along_axis", - "pd_op.tensor_unfold", - "pd_op.topk", - "pd_op.trace", - "pd_op.unbind", - "pd_op.unique_consecutive", - "pd_op.dequantize_linear", - "pd_op.min", - "pd_op.quantize_linear", - "pd_op.softmax", - "pd_op.sparse_momentum", - "pd_op.unique", - "pd_op.unsqueeze", - "pd_op.unstack"}; - } // namespace namespace pir { diff --git a/paddle/fluid/pir/transforms/passes.h b/paddle/fluid/pir/transforms/passes.h index f57e0161b8b824..600e4897933218 100644 --- a/paddle/fluid/pir/transforms/passes.h +++ b/paddle/fluid/pir/transforms/passes.h @@ -49,6 +49,7 @@ USE_PIR_PASS(fused_rotary_position_embedding_pass); USE_PIR_PASS(auto_mixed_precision_pass); USE_PIR_PASS(horizontal_fuse_pass); USE_PIR_PASS(auto_layout_simplify_pass); +USE_PIR_PASS(auto_layout_insert_pass); USE_PIR_PASS(auto_layout_pass); USE_PIR_PASS(common_subexpression_elimination_pass); USE_PIR_PASS(add_shadow_output_after_dead_parameter_pass); diff --git a/python/paddle/jit/dy2static/pir_partial_program.py b/python/paddle/jit/dy2static/pir_partial_program.py index f39389fb49af50..d8da5571672f1e 100644 --- a/python/paddle/jit/dy2static/pir_partial_program.py +++ b/python/paddle/jit/dy2static/pir_partial_program.py @@ -716,7 +716,6 @@ def pass_fn(forward_program, backward_program, program_name_attr): if auto_layout_is_enabled(): pm = paddle.pir.PassManager(2) pm.add_pass("auto_layout_pass", {}) - pm.add_pass("auto_layout_simplify_pass", {}) pm.run(infer_program.program) for hooker in self._hookers: hooker.after_infer(infer_program) @@ -730,7 +729,6 @@ def pass_fn(forward_program, backward_program, program_name_attr): if auto_layout_is_enabled(): pm = paddle.pir.PassManager(2) pm.add_pass("auto_layout_pass", {}) - pm.add_pass("auto_layout_simplify_pass", {}) pm.run(train_program.program) train_program = self._append_backward_desc(train_program) # Note: Only set grad type once after initializing train program. So we put it here. diff --git a/test/cpp/pir/pass/auto_layout_pass_test.cc b/test/cpp/pir/pass/auto_layout_pass_test.cc index ff0f7124b58044..3fb705a051ddc3 100644 --- a/test/cpp/pir/pass/auto_layout_pass_test.cc +++ b/test/cpp/pir/pass/auto_layout_pass_test.cc @@ -37,7 +37,7 @@ #include "paddle/fluid/pir/dialect/operator/ir/op_dialect.h" #include "paddle/fluid/pir/dialect/operator/ir/op_type.h" #include "paddle/fluid/pir/dialect/operator/ir/pd_op.h" -#include "paddle/fluid/pir/transforms/general/auto_layout_pass.h" +#include "paddle/fluid/pir/transforms/general/auto_layout_insert_pass.h" #include "paddle/fluid/pir/transforms/general/auto_layout_simplify_pass.h" #include "paddle/fluid/pir/transforms/general/constant_folding_pass.h" #include "paddle/fluid/pir/transforms/passes.h" @@ -62,7 +62,10 @@ TEST(auto_layout_pass, pass_test) { auto program = pir::IrParser(ctx, ss).ParseProgram(); pir::PassManager auto_layout_pm(::pir::IrContext::Instance(), 3); - auto_layout_pm.AddPass(pir::CreateAutoLayoutPass()); + auto_layout_pm.AddPass( + pir::CreateAutoLayoutInsertPass({"pd_op.fused_conv2d_add_act", + "pd_op.conv2d", + "pd_op.conv2d_transpose"})); auto_layout_pm.AddPass(pir::CreateAutoLayoutSimplifyPass()); auto_layout_pm.Run(program.get()); } From 6baaec6db5561cf08bf177f761da0e2eacd561dd Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:02:09 +0800 Subject: [PATCH 258/288] fix topk symbolic bug (#70082) --- .../interface/infer_symbolic_shape/unary_infer_sym.cc | 4 ++-- 1 file changed, 2 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 93881316b3b82f..4577ea37cd12cd 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3791,14 +3791,14 @@ bool TopkOpInferSymbolicShape(pir::Operation *op, int x_rank = in_dims_sym.size(); - int k = k_shape_or_data.data().value().at(0).Get(); + symbol::DimExpr k = k_shape_or_data.data().value().at(0); if (axis < 0) axis += x_rank; const auto &out_sym_shape = [&] { std::vector out_sym_shape; for (int i = 0; i < x_rank; ++i) { if (i == axis) { - out_sym_shape.push_back(symbol::DimExpr(k)); + out_sym_shape.push_back(k); } else { out_sym_shape.push_back(in_dims_sym.at(i)); } From 010945703389e7937a557239d39fa61f54b1e417 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:21:50 +0800 Subject: [PATCH 259/288] optimize relu6 grad decmp (#70076) * optimize relu6 grad decmp * fix bug * update * update --- .../op_generator/decomp_interface_gen_op_list.py | 1 + paddle/fluid/primitive/codegen/decomp_vjp_gen.py | 1 + .../primitive/decomp_rule/decomp_vjp/details.h | 13 +++++++++++++ 3 files changed, 15 insertions(+) diff --git a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py index ff860addad9709..f60515e1febd35 100644 --- a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py +++ b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py @@ -174,6 +174,7 @@ 'minimum_grad', 'pow_grad', 'relu_grad', + 'relu6_grad', 'sigmoid_grad', 'silu_grad', 'softmax_grad', diff --git a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py index 0dcd82f8a19a6e..6d8d9fe1a17857 100644 --- a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py +++ b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py @@ -154,6 +154,7 @@ 'minimum_grad', 'pow_grad', 'relu_grad', + 'relu6_grad', 'sigmoid_grad', 'silu_grad', 'softmax_grad', diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index bf859e5af0243f..d8ab5fec71dd78 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1298,6 +1298,19 @@ void relu_grad(const Tensor& out, const Tensor& out_grad, Tensor* x_grad) { } } +template +void relu6_grad(const Tensor& out, const Tensor& out_grad, Tensor* x_grad) { + if (x_grad) { + Tensor zeros = full_scalar(0.0, out.dtype()); + Tensor six = full_scalar(6.0, out.dtype()); + auto mask_gt = greater_than(out, zeros); + auto mask_lt = less_than(out, six); + auto mask = bitwise_and(mask_gt, mask_lt); + auto res = cast(mask, out.dtype()) * out_grad; + set_output(res, x_grad); + } +} + template void gather_grad(const Tensor& x, const Tensor& index, From fe607df42d8d0cecd24f7c465e541f6cba498e3b Mon Sep 17 00:00:00 2001 From: zhanghonggeng <43205915+zhanghonggeng@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:36:11 +0800 Subject: [PATCH 260/288] Modify the DataType directly when the Operand of whileOp is FakeFull. (#69939) --- .../general/auto_mixed_precision_pass.cc | 54 +++++++++++++++++++ 1 file changed, 54 insertions(+) diff --git a/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc b/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc index ccdf341a4d4134..79e0280fe770af 100644 --- a/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_mixed_precision_pass.cc @@ -705,6 +705,10 @@ class AutoMixedPrecisionPass : public pir::Pass { auto operand_phi_dtype = GetPhiDataTypeFromOpOperand(operand); if (IsPhiDataTypeFloat(operand_phi_dtype) && operand_phi_dtype == precision_mode_) { + if (IsFakeFullOperandWhileOp(op, operand)) { + SetFakeFullDataType(operand, builder); + continue; + } DoInsertCastOp(op, operand, phi_dtype, builder); } } else if (IsOperandHasDenseTensorVectorType(operand)) { @@ -735,6 +739,56 @@ class AutoMixedPrecisionPass : public pir::Pass { } } + std::vector ConvertDDimToVector(const common::DDim& ddim) { + std::vector dims; + for (int i = 0; i < ddim.size(); ++i) { + dims.push_back(ddim[i]); + } + return dims; + } + + bool IsFakeFullOp(pir::OpOperand operand) { + auto defining_op_ = operand.source().defining_op(); + if (defining_op_->isa()) { + auto full_op = defining_op_->dyn_cast(); + auto shape_attr = full_op.attribute("shape") + .dyn_cast(); + auto shape_dims = shape_attr.data().GetData(); + auto result_dims = full_op.out() + .type() + .dyn_cast() + .dims(); + std::vector result_dims_vec = ConvertDDimToVector(result_dims); + + if (shape_dims.size() != result_dims_vec.size()) { + return true; + } + for (size_t i = 0; i < shape_dims.size(); ++i) { + if (shape_dims[i] != result_dims_vec[i]) { + return true; + } + } + return false; + } + return false; + } + + bool IsFakeFullOperandWhileOp(pir::Operation* op, pir::OpOperand operand) { + return op->isa() && IsFakeFullOp(operand); + } + + void SetFakeFullDataType(pir::OpOperand operand, + pir::Builder& builder) { // NOLINT + auto defining_op_ = operand.source().defining_op(); + auto full_op = defining_op_->dyn_cast(); + pir::Attribute attr_dtype = paddle::dialect::DataTypeAttribute::get( + builder.ir_context(), phi::DataType::FLOAT32); + full_op->set_attribute("dtype", attr_dtype); + + SetResultDataType( + full_op.out(), phi::DataType::FLOAT32, builder.ir_context()); + } + void SubOpRun(pir::Operation* op) { for (auto& region : *op) { for (auto& block : region) { From fe9c296cf7477af9edf4f3c02631b4044af1d43d Mon Sep 17 00:00:00 2001 From: Lei Ding <69283446+Dmovic@users.noreply.github.com> Date: Tue, 10 Dec 2024 14:36:43 +0800 Subject: [PATCH 261/288] [CINN] Fix compute at schedule (#70068) --- .../cinn/ir/schedule/impl/compute_location.cc | 5 +- .../pir/cinn/test_cinn_compute_at_tactic.py | 96 ++++++++++--------- 2 files changed, 55 insertions(+), 46 deletions(-) diff --git a/paddle/cinn/ir/schedule/impl/compute_location.cc b/paddle/cinn/ir/schedule/impl/compute_location.cc index 7365bddc0f3e77..550bc8f1062a88 100644 --- a/paddle/cinn/ir/schedule/impl/compute_location.cc +++ b/paddle/cinn/ir/schedule/impl/compute_location.cc @@ -196,8 +196,9 @@ void DyScheduleImpl::SimpleComputeAt(const Expr& block, const Expr& loop) { if (Contains(result, if_expr)) continue; if (ir::ir_utils::CollectIRNodesWithoutTensor(if_expr, checker, true) .size() > 0) { - result = - IfThenElse::Make(if_expr.As()->condition, result); + result = IfThenElse::Make( + ir::ir_utils::IRCopy(if_expr.As()->condition), + result); break; } } diff --git a/test/ir/pir/cinn/test_cinn_compute_at_tactic.py b/test/ir/pir/cinn/test_cinn_compute_at_tactic.py index 567ad7579479fa..dace5804861e74 100644 --- a/test/ir/pir/cinn/test_cinn_compute_at_tactic.py +++ b/test/ir/pir/cinn/test_cinn_compute_at_tactic.py @@ -11,65 +11,73 @@ # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. + import unittest import numpy as np import utils import paddle -from paddle.base import core -class ComputeAtSubGraph(paddle.nn.Layer): - def __init__(self): - super().__init__() +class TestComputeAtTactic(unittest.TestCase): + def eval(self, dy_compute, init_inputs): + paddle.seed(2024) + inputs = init_inputs() + dy_out = dy_compute(*inputs) + + static_compute = utils.apply_to_static(dy_compute, use_cinn=True) + st_out = static_compute(*inputs) - def forward(self, x, y): - x0 = x[:, :256] * ( - 1.0 / (1.0 + paddle.exp(-1.0 * (x[:, :256] + y[256, 256]))) - ) - x1 = x[:, 256:] * ( - 1.0 / (1.0 + paddle.exp(-1.0 * (x[:, 256:] + y[256, 256]))) - ) - x2 = x[:, :256] * ( - 1.0 / (1.0 + paddle.exp(-1.0 * (x[:, :256] + y[128, 128]))) - ) - x3 = x[:, 256:] * ( - 1.0 / (1.0 + paddle.exp(-1.0 * (x[:, 256:] + y[128, 128]))) - ) - return x0, x1, x2, x3 + for a, b in zip( + paddle.utils.flatten(dy_out), paddle.utils.flatten(st_out) + ): + np.testing.assert_allclose(a, b, atol=1e-3, rtol=1e-4) + def test_multiple_reduce(self): + def func(x, y): + x0 = paddle.sum( + x[:, :256] + * (1.0 / (1.0 + paddle.exp(-1.0 * (x[:, :256] + y[256, 256])))) + ) + x1 = paddle.sum( + x[:, 256:] + * (1.0 / (1.0 + paddle.exp(-1.0 * (x[:, 256:] + y[256, 256])))) + ) + x2 = paddle.sum( + x[:, :256] + * (1.0 / (1.0 + paddle.exp(-1.0 * (x[:, :256] + y[128, 128])))) + ) + x3 = paddle.sum( + x[:, 256:] + * (1.0 / (1.0 + paddle.exp(-1.0 * (x[:, 256:] + y[128, 128])))) + ) + return x0, x1, x2, x3 -class TestComputeAtSubGraph(unittest.TestCase): - def setUp(self): - paddle.seed(2024) - self.shape = [512, 512] - self.dtype = "float32" - self.prepare_data() + def init(): + x = paddle.randn([512, 512]) + y = paddle.randn([512, 512]) + return (x, y) - def prepare_data(self): - self.x = paddle.randn(self.shape, dtype=self.dtype) - self.y = paddle.randn(self.shape, dtype=self.dtype) + self.eval(func, init) - def eval(self, use_cinn, use_prim=False): - if use_prim: - core._set_prim_all_enabled(True) - net = ComputeAtSubGraph() - net = utils.apply_to_static(net, use_cinn=use_cinn) - net.eval() - out = net(self.x, self.y) + def test_reduce_with_condition(self): + def func(a, b, c): + a = paddle.sum(a) + a = a / paddle.full([], 1.0) + b = paddle.sum(b) + b = b / paddle.full([], 1.0) + c = paddle.sum(c) + c = c / paddle.full([], 1.0) + return a, b, c, a + b + c - core._set_prim_all_enabled(False) - return out + def init(): + a = paddle.randn([1]) + b = paddle.randn([1]) + c = paddle.randn([1]) + return (a, b, c) - def test_cinn(self): - cinn_out = self.eval(use_cinn=True, use_prim=True) - dy_out = self.eval(use_cinn=False, use_prim=True) - np.testing.assert_allclose( - paddle.utils.flatten(cinn_out), - paddle.utils.flatten(dy_out), - atol=1e-3, - ) + self.eval(func, init) if __name__ == '__main__': From 15e4cb7c651495b2e458f7d16226a91649740b85 Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Tue, 10 Dec 2024 15:25:56 +0800 Subject: [PATCH 262/288] optimize hardsigmoid grad decmop (#70083) * optimize hardsigmoid grad decmop * fix bug --- .../decomp_interface_gen_op_list.py | 1 + .../fluid/primitive/codegen/decomp_vjp_gen.py | 1 + .../primitive/decomp_rule/decomp_vjp/details.h | 18 ++++++++++++++++++ 3 files changed, 20 insertions(+) diff --git a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py index f60515e1febd35..85722cb83a0e1b 100644 --- a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py +++ b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py @@ -168,6 +168,7 @@ CUSTOM_VJP = [ 'bce_loss_grad', 'gelu_grad', + 'hardsigmoid_grad', 'hardswish_grad', 'leaky_relu_grad', 'mean_grad', diff --git a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py index 6d8d9fe1a17857..7a701343357c6b 100644 --- a/paddle/fluid/primitive/codegen/decomp_vjp_gen.py +++ b/paddle/fluid/primitive/codegen/decomp_vjp_gen.py @@ -146,6 +146,7 @@ 'dropout_grad', 'gelu_grad', 'group_norm_grad', + 'hardsigmoid_grad', 'hardswish_grad', 'instance_norm_grad', 'layer_norm_grad', diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index d8ab5fec71dd78..2ef3df43986a7c 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1823,6 +1823,24 @@ void tile_grad(const Tensor& x, } } +template +void hardsigmoid_grad(const Tensor& out, + const Tensor& out_grad, + float slope, + float offset, + Tensor* x_grad) { + if (x_grad) { + Tensor zeros = full_scalar(0.0, out.dtype()); + Tensor one = full_scalar(1.0, out.dtype()); + auto mask_gt = greater_than(out, zeros); + auto mask_lt = less_than(out, one); + auto mask = bitwise_and(mask_gt, mask_lt); + Tensor slope_tensor = full_scalar(slope, out.dtype()); + auto res = cast(mask, out.dtype()) * slope_tensor * out_grad; + set_output(res, x_grad); + } +} + template void hardswish_grad(const Tensor& x, const Tensor& out_grad, Tensor* x_grad) { if (x_grad) { From 3ef2484e7921f7d4dfa8ac5e1f241f2a6804de27 Mon Sep 17 00:00:00 2001 From: HydrogenSulfate <490868991@qq.com> Date: Tue, 10 Dec 2024 17:22:56 +0800 Subject: [PATCH 263/288] support creating 0-size parameter and correct data_ptr of 0-size Tensor from None to 0 (#70071) --- paddle/fluid/pybind/eager_method.cc | 27 ++++++++++++--------- python/paddle/base/layer_helper_base.py | 4 +-- test/legacy_test/test_deform_conv2d.py | 22 ----------------- test/legacy_test/test_deformable_conv_op.py | 17 ------------- test/legacy_test/test_parameter.py | 16 ++++++++++++ 5 files changed, 33 insertions(+), 53 deletions(-) diff --git a/paddle/fluid/pybind/eager_method.cc b/paddle/fluid/pybind/eager_method.cc index 8b704bfb1f1098..83af84927d9837 100644 --- a/paddle/fluid/pybind/eager_method.cc +++ b/paddle/fluid/pybind/eager_method.cc @@ -1176,16 +1176,17 @@ static PyObject* tensor__share_underline_tensor_to(TensorObject* self, EAGER_TRY paddle::Tensor* src_ptr = &(reinterpret_cast(PyTuple_GET_ITEM(args, 0))->tensor); - if (!self->tensor.initialized()) { - PADDLE_ENFORCE(self->tensor.is_dist_tensor() && - !phi::distributed::IsCurRankInMesh( - static_cast( - self->tensor.impl().get()) - ->process_mesh()), - common::errors::InvalidArgument( - "Tensor %s has not been initialized! Please initialize " - "src tensor before share_buffer_with to other.", - self->tensor.name())); + if (!(self->tensor.defined() && self->tensor.has_allocation())) { + PADDLE_ENFORCE( + self->tensor.is_dist_tensor() && + !phi::distributed::IsCurRankInMesh( + static_cast( + self->tensor.impl().get()) + ->process_mesh()), + common::errors::InvalidArgument( + "Tensor %s either lacks impl_ or holder_, Please initialize " + "src tensor before share_buffer_with to other.", + self->tensor.name())); } src_ptr->set_impl(self->tensor.impl()); RETURN_PY_NONE @@ -3147,12 +3148,14 @@ static PyObject* tensor_data_ptr(TensorObject* self, PyObject* args, PyObject* kwargs) { EAGER_TRY - if (self->tensor.initialized() && self->tensor.is_dense_tensor()) { + if (self->tensor.defined() && self->tensor.has_allocation() && + self->tensor.is_dense_tensor()) { return ToPyObject( (int64_t)std::dynamic_pointer_cast( // NOLINT self->tensor.impl()) ->data()); - } else if (self->tensor.initialized() && self->tensor.is_dist_tensor()) { + } else if (self->tensor.defined() && self->tensor.has_allocation() && + self->tensor.is_dist_tensor()) { return ToPyObject( (int64_t) std::dynamic_pointer_cast( // NOLINT diff --git a/python/paddle/base/layer_helper_base.py b/python/paddle/base/layer_helper_base.py index e456062aef5418..22a385dc601aed 100644 --- a/python/paddle/base/layer_helper_base.py +++ b/python/paddle/base/layer_helper_base.py @@ -360,8 +360,8 @@ def create_parameter( return None assert isinstance(attr, ParamAttr) for i, size in enumerate(shape): - assert size > 0, ( - "Expected every dim's size to be larger than 0, " + assert size >= 0, ( + "Expected every dim's size to be larger than or equal to 0, " f"but the size of the {i}-th dim is {size}" ) # set global dtype diff --git a/test/legacy_test/test_deform_conv2d.py b/test/legacy_test/test_deform_conv2d.py index 845b748f706dff..3c09a1630f5c2c 100644 --- a/test/legacy_test/test_deform_conv2d.py +++ b/test/legacy_test/test_deform_conv2d.py @@ -321,27 +321,5 @@ def setUp(self): self.no_bias = False -class TestDeformConv2DError(unittest.TestCase): - - def test_input_error(self): - def test_input_rank_error(): - paddle.enable_static() - x = paddle.static.data(name='error_x_1', shape=[0], dtype='float32') - offset = paddle.static.data( - name='error_offset_1', shape=[0], dtype='float32' - ) - mask = paddle.static.data( - name='error_mask_1', shape=[0, 0, 0], dtype='float32' - ) - out = paddle.vision.ops.DeformConv2D( - in_channels=0, - out_channels=0, - kernel_size=0, - deformable_groups=0, - )(x, offset, mask) - - self.assertRaises(AssertionError, test_input_rank_error) - - if __name__ == "__main__": unittest.main() diff --git a/test/legacy_test/test_deformable_conv_op.py b/test/legacy_test/test_deformable_conv_op.py index 81448454bb655d..8fe9116eab243f 100644 --- a/test/legacy_test/test_deformable_conv_op.py +++ b/test/legacy_test/test_deformable_conv_op.py @@ -405,23 +405,6 @@ def test_invalid_offset(): self.assertRaises(TypeError, test_invalid_offset) - def test_invalid_filter(): - paddle.enable_static() - input = paddle.static.data( - name='input_filter', shape=[None, 3, 32, 32], dtype='float32' - ) - offset = paddle.static.data( - name='offset_filter', shape=[None, 3, 32, 32], dtype='float32' - ) - mask = paddle.static.data( - name='mask_filter', shape=[None, 3, 32, 32], dtype='float32' - ) - loss = paddle.vision.ops.DeformConv2D( - in_channels=input.shape[1], out_channels=4, kernel_size=0 - )(input, offset, mask) - - self.assertRaises(AssertionError, test_invalid_filter) - def test_invalid_groups(): paddle.enable_static() input = paddle.static.data( diff --git a/test/legacy_test/test_parameter.py b/test/legacy_test/test_parameter.py index 202bb5b3b10930..ddf4897dfa9f30 100644 --- a/test/legacy_test/test_parameter.py +++ b/test/legacy_test/test_parameter.py @@ -75,6 +75,22 @@ def test_parambase(self): pram_copy2 = copy.deepcopy(param, memo) self.assertEqual(id(param_copy), id(pram_copy2)) + def test_create_0_size_param(self): + with guard(): + shape = [0, 4] + for dtype in [ + paddle.float32, + paddle.float64, + ]: + zero_size_param = paddle.create_parameter( + shape, + dtype, + ) + self.assertEqual(zero_size_param.shape, shape) + self.assertEqual(zero_size_param.data_ptr(), 0) + # strides will be same with shape for 0-size tensor in paddle + self.assertEqual(zero_size_param.strides, shape) + def func_exception(self): b = main_program.global_block() with self.assertRaises(ValueError): From be08357fc46c22a47ce11d92c7abb8294fff920e Mon Sep 17 00:00:00 2001 From: zyfncg Date: Tue, 10 Dec 2024 17:29:00 +0800 Subject: [PATCH 264/288] Revert "[CINN] Adjust order of infer_symbol_shape pass in inference (#70042)" (#70087) This reverts commit a18a81115fcdb03897d96da5fba61ad96d2cdf40. --- .../operator/transforms/add_cinn_pass.cc | 13 +++------ .../operator/transforms/add_cinn_pass.h | 7 +++-- paddle/cinn/hlir/framework/pir/utils.cc | 27 +++++++------------ .../fluid/inference/api/analysis_predictor.cc | 12 ++------- .../src/dialect/shape/utils/shape_analysis.cc | 3 +-- 5 files changed, 19 insertions(+), 43 deletions(-) diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc index a196c5b095379d..5b0493ea06f0cf 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.cc @@ -260,20 +260,15 @@ int64_t GetOpCount(const ::pir::Operation* op) { return count; } -void ApplyCinnPass( - ::pir::Program* program, - const std::function()>& CreatePassManager, - bool is_train_mode) { +void ApplyCinnPass(::pir::Program* program, + const std::function()>& + CreatePassManager) { const uint32_t origin_num_ops = program->num_ops(); PirToPyCodeConverter(program) .file_name("original_programs.py") .dump_symbolic_shape(FLAGS_logging_pir_py_code_dump_symbolic_dims) .SaveIfFlagEnabled(); - if (is_train_mode) { - // Skip infer symbol shape in inference, because we have run this pass in - // the previous process - ApplyShapeOptimizationPass(program, CreatePassManager); - } + ApplyShapeOptimizationPass(program, CreatePassManager); ApplyPdToCinnPass(program, CreatePassManager); ApplyCinnPreprocessPass(program, CreatePassManager); ApplyBuildGroupOpPass(program, CreatePassManager); diff --git a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h index acc7144dc753d0..4a71cbc5ee3101 100644 --- a/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h +++ b/paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h @@ -26,9 +26,8 @@ class Program; namespace cinn::dialect::ir { -void ApplyCinnPass( - ::pir::Program* program, - const std::function()>& CreatePassManager, - bool is_train_mode = true); +void ApplyCinnPass(::pir::Program* program, + const std::function()>& + CreatePassManager); } // namespace cinn::dialect::ir diff --git a/paddle/cinn/hlir/framework/pir/utils.cc b/paddle/cinn/hlir/framework/pir/utils.cc index 306dc29f816a16..8e023f30dbf19a 100644 --- a/paddle/cinn/hlir/framework/pir/utils.cc +++ b/paddle/cinn/hlir/framework/pir/utils.cc @@ -409,6 +409,7 @@ bool CauseNewSymbolicShape(const ::pir::Operation& op) { } return false; }(); + return outputs_have_new_symbol; } @@ -444,24 +445,14 @@ bool HasHandledInPass(const ::pir::Operation& op) { // 3. it should be handled in pd_to_cinn_pass; bool IsSupportInCinn(const ::pir::Operation& op) { const bool is_denied = IsDeniedInCinn(op); - if (IsDeniedInCinn(op)) { - VLOG(5) << op.name() << "[id:" << op.id() << "] is denied in CINN"; - return false; - } - if (!IsRegisteredInCINN(op)) { - VLOG(5) << op.name() << "[id:" << op.id() << "] isn't registered in CINN"; - return false; - } - if (!HasHandledInPass(op)) { - VLOG(5) << op.name() << "[id:" << op.id() << "] isn't handled in CINN"; - return false; - } - if (CauseNewSymbolicShape(op)) { - VLOG(5) << op.name() << "[id:" << op.id() - << "] caused new symbolic shape in CINN"; - return false; - } - return true; + const bool is_registered = IsRegisteredInCINN(op); + const bool is_handled = HasHandledInPass(op); + const bool cause_new_symbolic_shape = CauseNewSymbolicShape(op); + VLOG(5) << op.name() << ": IsDeniedInCinn = " << is_denied + << ", IsRegisteredInCINN = " << is_registered + << ", HasHandledInPass = " << is_handled + << ", CauseNewSymbolicShape = " << cause_new_symbolic_shape; + return !is_denied && is_registered && is_handled && !cause_new_symbolic_shape; } } // namespace diff --git a/paddle/fluid/inference/api/analysis_predictor.cc b/paddle/fluid/inference/api/analysis_predictor.cc index bfe7f1c3f6d20a..65043881feadd3 100644 --- a/paddle/fluid/inference/api/analysis_predictor.cc +++ b/paddle/fluid/inference/api/analysis_predictor.cc @@ -102,7 +102,6 @@ #include "paddle/cinn/hlir/dialect/operator/transforms/add_cinn_pass.h" #include "paddle/cinn/hlir/dialect/operator/transforms/check_infer_symbolic_util.h" #include "paddle/pir/include/dialect/shape/ir/shape_dialect.h" -#include "paddle/pir/include/dialect/shape/transforms/shape_optimization_pass.h" #include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" #endif @@ -127,6 +126,7 @@ #include "paddle/pir/include/core/block_argument.h" #include "paddle/pir/include/core/builtin_attribute.h" #include "paddle/pir/include/core/program.h" +#include "paddle/pir/include/dialect/shape/transforms/shape_optimization_pass.h" #include "paddle/pir/include/pass/pass_manager.h" #include "paddle/pir/include/pass/pass_registry.h" @@ -869,13 +869,6 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (!config_.custom_pass_only_) { ::pir::PassManager fused_op_pm(::pir::IrContext::Instance(), config_.pm_opt_level_); - auto &shape_analysis = - pir::ShapeAnalysisManager::Instance().Get(pir_program_.get()); - fused_op_pm.SetValueReplacedHook([&](pir::Value from, pir::Value to) { - shape_analysis.ShareShapeOrData(from, to); - }); - // Infer symbol shape for all ops before fused pass - fused_op_pm.AddPass(pir::CreateShapeOptimizationPass()); const std::vector FusedOpPasses{// Operator fusion pass "conv2d_bn_fuse_pass", "conv2d_add_act_fuse_pass", @@ -909,8 +902,7 @@ void AnalysisPredictor::OptimizeInferencePirProgram() { if (config_.cinn_enabled()) { VLOG(4) << "[CINN] Begin ApplyCinnPass"; - cinn::dialect::ir::ApplyCinnPass( - pir_program_.get(), CreatePassMgr, false); + cinn::dialect::ir::ApplyCinnPass(pir_program_.get(), CreatePassMgr); } #endif diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 6f8764c8f970ae..486d8bc8e21e33 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -605,8 +605,7 @@ ShapeConstraintIRAnalysis::GetShapeOrDataForValue(Value val) { SetSymbolForValueByStaticShape(val); } else { VLOG(3) << "InferShapeOrDataForValue, defining_op: " - << val.defining_op()->name() << " id:" << val.defining_op()->id() - << " value id: " << val.impl()->id(); + << val.defining_op()->name() << " id:" << val.defining_op()->id(); InferShapeOrDataForValue(val); } } From 0c0c4e0140d2a09413e0a51fc9ba2cf025890b86 Mon Sep 17 00:00:00 2001 From: wwwuyan <90775351+wwwuyan@users.noreply.github.com> Date: Tue, 10 Dec 2024 19:07:09 +0800 Subject: [PATCH 265/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?Tensor=20=E8=A7=84=E8=8C=83=E5=8C=96=E4=BA=8C=E6=9C=9F=E3=80=91?= =?UTF-8?q?=E4=B8=BAany=E6=B7=BB=E5=8A=A0=E5=A4=8D=E6=95=B0=E7=B1=BB?= =?UTF-8?q?=E5=9E=8B=E6=94=AF=E6=8C=81=20(#70013)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * any * any-cpu * any-gpu * test_64_128 * update * 0->O --- paddle/phi/kernels/cpu/reduce_any_kernel.cc | 12 +- paddle/phi/kernels/funcs/reduce_functor.h | 12 ++ paddle/phi/kernels/kps/reduce_kernel.cu | 4 +- paddle/phi/kernels/reduce_any_kernel.cc | 29 ++++- python/paddle/tensor/math.py | 15 ++- test/legacy_test/test_reduce_op.py | 128 +++++++++++++++++++- 6 files changed, 189 insertions(+), 11 deletions(-) diff --git a/paddle/phi/kernels/cpu/reduce_any_kernel.cc b/paddle/phi/kernels/cpu/reduce_any_kernel.cc index cb82fc3a71cb9c..8ac82eb8d217ef 100644 --- a/paddle/phi/kernels/cpu/reduce_any_kernel.cc +++ b/paddle/phi/kernels/cpu/reduce_any_kernel.cc @@ -14,11 +14,17 @@ #include "paddle/phi/kernels/reduce_any_kernel.h" +#include + #include "paddle/phi/backends/cpu/cpu_context.h" +#include "paddle/phi/common/complex.h" #include "paddle/phi/core/kernel_registry.h" #include "paddle/phi/kernels/cpu/reduce.h" #include "paddle/phi/kernels/funcs/reduce_functor.h" +using complex64 = ::phi::dtype::complex; +using complex128 = ::phi::dtype::complex; + namespace phi { template @@ -29,7 +35,7 @@ void AnyRawKernel(const Context& dev_ctx, bool reduce_all, DenseTensor* out) { reduce_all = recompute_reduce_all(x, dims, reduce_all); - phi::BoolReduceKernel( + phi::BoolReduceKernel>( dev_ctx, x, dims, keep_dim, reduce_all, out); } @@ -43,6 +49,8 @@ PD_REGISTER_KERNEL(any_raw, double, int, int64_t, - bool) { + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } diff --git a/paddle/phi/kernels/funcs/reduce_functor.h b/paddle/phi/kernels/funcs/reduce_functor.h index d2df5855c925bb..c038eb8658b92c 100644 --- a/paddle/phi/kernels/funcs/reduce_functor.h +++ b/paddle/phi/kernels/funcs/reduce_functor.h @@ -110,6 +110,7 @@ struct AllFunctor> { }; //////// Any Functor /////// +template struct AnyFunctor { template void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { @@ -117,6 +118,17 @@ struct AnyFunctor { } }; +template +struct AnyFunctor> { + template + void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { + auto to_bool = [](const std::complex& v) { + return v.real() != 0 || v.imag() != 0; + }; + y->device(place) = x->unaryExpr(to_bool).all(dim); + } +}; + struct MeanGradFunctor { template OutputAt(0).SetDataType(phi::DataType::BOOL); } diff --git a/paddle/phi/kernels/reduce_any_kernel.cc b/paddle/phi/kernels/reduce_any_kernel.cc index 076aacfa3ed82c..7c9191c328f348 100644 --- a/paddle/phi/kernels/reduce_any_kernel.cc +++ b/paddle/phi/kernels/reduce_any_kernel.cc @@ -31,14 +31,35 @@ void AnyKernel(const Context& dev_ctx, } // namespace phi -PD_REGISTER_KERNEL( - any, CPU, ALL_LAYOUT, phi::AnyKernel, float, double, int64_t, int, bool) { +using complex64 = ::phi::dtype::complex; +using complex128 = ::phi::dtype::complex; + +PD_REGISTER_KERNEL(any, + CPU, + ALL_LAYOUT, + phi::AnyKernel, + float, + double, + int64_t, + int, + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP) -PD_REGISTER_KERNEL( - any, GPU, ALL_LAYOUT, phi::AnyKernel, float, double, int, int64_t, bool) { +PD_REGISTER_KERNEL(any, + GPU, + ALL_LAYOUT, + phi::AnyKernel, + float, + double, + int, + int64_t, + bool, + complex64, + complex128) { kernel->OutputAt(0).SetDataType(phi::DataType::BOOL); } #endif diff --git a/python/paddle/tensor/math.py b/python/paddle/tensor/math.py index f4a7e507c88985..426d7c979bc915 100644 --- a/python/paddle/tensor/math.py +++ b/python/paddle/tensor/math.py @@ -5072,7 +5072,7 @@ def any( Computes the ``logical or`` of tensor elements over the given dimension, and return the result. Args: - x (Tensor): An N-D Tensor, the input data type should be 'bool', 'float32', 'float64', 'int32', 'int64'. + x (Tensor): An N-D Tensor, the input data type should be 'bool', 'float32', 'float64', 'int32', 'int64', 'complex64', 'complex128'. axis (int|list|tuple|None, optional): The dimensions along which the ``logical or`` is compute. If :attr:`None`, and all elements of :attr:`x` and return a Tensor with a single element, otherwise must be in the @@ -5139,7 +5139,18 @@ def any( 'reduce_all': reduce_all, } check_variable_and_dtype( - x, 'x', ['bool', 'float32', 'float64', 'int32', 'int64'], 'any' + x, + 'x', + [ + 'bool', + 'float32', + 'float64', + 'int32', + 'int64', + 'complex64', + 'complex128', + ], + 'any', ) check_type(axis, 'axis', (int, list, tuple, type(None)), 'any') diff --git a/test/legacy_test/test_reduce_op.py b/test/legacy_test/test_reduce_op.py index 4067848c45288d..73ac3b71068108 100644 --- a/test/legacy_test/test_reduce_op.py +++ b/test/legacy_test/test_reduce_op.py @@ -980,7 +980,7 @@ def test_check_output(self): self.check_output(check_pir=True) -class TestAllComplex640pInf(TestAllComplex64Op): +class TestAllComplex64OpInf(TestAllComplex64Op): def setUp(self): super().setUp() real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) @@ -991,7 +991,7 @@ def setUp(self): ) -class TestAllComplex640pNegInf(TestAllComplex64Op): +class TestAllComplex64OpNegInf(TestAllComplex64Op): def setUp(self): super().setUp() real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) @@ -1171,6 +1171,130 @@ def test_check_output(self): self.check_output(check_pir=True, check_prim_pir=True) +class TestAnyComplex64Op(OpTest): + def setUp(self): + self.op_type = "reduce_any" + self.python_api = paddle.any + real_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs = {'X': (real_part + 1j * imag_part).astype("complex64")} + self.attrs = {'dim': (5,), 'keep_dim': True} + self.outputs = { + 'Out': np.expand_dims( + self.inputs['X'].all(axis=self.attrs['dim']), axis=5 + ) + } + + def test_check_output(self): + self.check_output(check_pir=True) + + +class TestAnyComplex64OpInf(TestAnyComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex64OpNegInf(TestAnyComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex64OpNan(TestAnyComplex64Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex64OpZero(TestAnyComplex64Op): + def setUp(self): + super().setUp() + real_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex64") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex128Op(OpTest): + def setUp(self): + self.op_type = "reduce_any" + self.python_api = paddle.any + real_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.random.uniform(-1, 1, (2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs = {'X': (real_part + 1j * imag_part).astype("complex128")} + self.attrs = {'dim': (5,), 'keep_dim': True} + self.outputs = { + 'Out': np.expand_dims( + self.inputs['X'].all(axis=self.attrs['dim']), axis=5 + ) + } + + def test_check_output(self): + self.check_output(check_pir=True) + + +class TestAnyComplex128OpInf(TestAnyComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex128OpNegInf(TestAnyComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), -np.inf) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex128OpNan(TestAnyComplex128Op): + def setUp(self): + super().setUp() + real_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + imag_part = np.full((2, 5, 3, 2, 2, 3, 4, 2), np.nan) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + +class TestAnyComplex128OpZero(TestAnyComplex128Op): + def setUp(self): + super().setUp() + real_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + imag_part = np.zeros((2, 5, 3, 2, 2, 3, 4, 2)) + self.inputs['X'] = (real_part + 1j * imag_part).astype("complex128") + self.outputs['Out'] = np.expand_dims( + np.all(self.inputs['X'], axis=self.attrs['dim']), axis=5 + ) + + class TestAnyOp_ZeroDim(OpTest): def setUp(self): self.op_type = "reduce_any" From 7909be1b876c2cbf8ddda598afebca5fd753a576 Mon Sep 17 00:00:00 2001 From: Yichen Zhang <32740647+pkuzyc@users.noreply.github.com> Date: Tue, 10 Dec 2024 19:19:58 +0800 Subject: [PATCH 266/288] revert the balanced part of p_to_s reshard (#70072) --- .../reshard_funcs/p_to_s_reshard_func.py | 56 ++++++++++++++----- 1 file changed, 41 insertions(+), 15 deletions(-) diff --git a/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py b/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py index 7dca9f9a6c770a..a5caf16ae7becc 100644 --- a/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py +++ b/python/paddle/distributed/auto_parallel/static/reshard_funcs/p_to_s_reshard_func.py @@ -45,10 +45,16 @@ def is_suitable(self, src_dist_attr, dst_dist_attr): return True def reshard(self, src_dist_attr, dst_dist_attr, src_value, dst_type): + src_mesh = src_dist_attr.process_mesh src_reduce_type = src_dist_attr.partial_status[0] assert ( src_reduce_type == paddle.base.core.ReduceType.kRedSum ), f"The p to s reshard func only support sum op, but received {src_reduce_type}" + + chunk_id = -1 + if src_value.get_defining_op().dist_attr: + chunk_id = src_value.get_defining_op().dist_attr.chunk_id + split_axis = dst_dist_attr.dims_mapping.index(0) permute = False if split_axis != 0: @@ -63,30 +69,49 @@ def reshard(self, src_dist_attr, dst_dist_attr, src_value, dst_type): dst_dist_attr = copy_dist_attr_with_new_member( dst_dist_attr, new_dims_mapping=tmp_dims_mapping ) - dst_type = paddle.base.libpaddle.pir.cvt_to_dist_type( - src_value.type(), dst_dist_attr - ) - original_dims_mapping = dst_dist_attr.dims_mapping.copy() - original_split_axis = split_axis - split_axis = 0 num_of_process = len(src_dist_attr.process_mesh.process_ids) remainder_of_padding = src_value.shape[split_axis] % num_of_process is_balanced_split = remainder_of_padding == 0 if is_balanced_split: - dst_value = self.reshard_p_to_s_with_padding( - src_value, - split_axis, - src_dist_attr, - dst_dist_attr, - dst_type, + global_dst_attr = dst_type.as_dist_type().dist_attr() + global_dims_mapping = global_dst_attr.dims_mapping + axis = global_dims_mapping[0] + global_dims_mapping[0] = global_dims_mapping[split_axis] + global_dims_mapping[split_axis] = axis + global_dist_attr = copy_dist_attr_with_new_member( + global_dst_attr, new_dims_mapping=global_dims_mapping ) - if permute: + dst_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + src_value.type(), global_dist_attr + ) + group = new_process_group(sorted(src_mesh.process_ids)) + dst_value = paddle._C_ops.reduce_scatter( + src_value, group.id, num_of_process + ) + dst_value.get_defining_op().set_execution_stream( + ExecutionStreamType.DefaultStream.value + ) + + # set dist type and dist attr + dst_value.set_type(dst_type) + dst_value.get_defining_op().dist_attr = ( + paddle.base.libpaddle.pir.create_op_dist_attribute( + src_mesh, [src_dist_attr], [dst_dist_attr], chunk_id + ) + ) + + if split_axis != 0: dst_value = paddle._C_ops.transpose(dst_value, perm) - split_axis = original_split_axis return dst_value else: + dst_type = paddle.base.libpaddle.pir.cvt_to_dist_type( + src_value.type(), dst_dist_attr + ) + original_dims_mapping = dst_dist_attr.dims_mapping.copy() + original_split_axis = split_axis + split_axis = 0 avg_size_on_split_axis = int( (src_value.shape[split_axis] + num_of_process - 1) / num_of_process @@ -108,7 +133,7 @@ def reshard(self, src_dist_attr, dst_dist_attr, src_value, dst_type): padding_tensor.set_type(tmp_src_type) padding_tensor.get_defining_op().dist_attr = ( paddle.base.libpaddle.pir.create_op_dist_attribute( - src_dist_attr.process_mesh, [], [src_dist_attr] + src_dist_attr.process_mesh, [], [src_dist_attr], chunk_id ) ) concat_value = paddle._C_ops.concat( @@ -131,6 +156,7 @@ def reshard(self, src_dist_attr, dst_dist_attr, src_value, dst_type): axis_dist_attr, ], [src_dist_attr], + chunk_id, ) ) From 914caadff9ba30d0844a81a5070e2df24b7dffac Mon Sep 17 00:00:00 2001 From: Ayakouji <148307532+aquagull@users.noreply.github.com> Date: Tue, 10 Dec 2024 19:27:12 +0800 Subject: [PATCH 267/288] =?UTF-8?q?=E3=80=90Paddle=20Tensor=20=E7=AC=AC?= =?UTF-8?q?=E4=BA=8C=E6=9C=9F=20API=E9=B2=81=E6=A3=92=E6=80=A7=E5=A2=9E?= =?UTF-8?q?=E5=BC=BA=E3=80=91=E8=A7=A3=E5=86=B3paddle.max=E3=80=81paddle.m?= =?UTF-8?q?in=E5=9C=A8=E8=BE=93=E5=85=A5=E5=AD=98=E5=9C=A8nan=E6=97=B6?= =?UTF-8?q?=E4=BC=A0=E6=92=AD=E4=B8=8D=E6=AD=A3=E7=A1=AE=E9=97=AE=E9=A2=98?= =?UTF-8?q?=20(#70049)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * Fix min propagation * fix max&min propagation * fix cpu minfunctor&maxfunctor * discharge prevchange * fix * specialize int&bool functors and add test * refine test * fix * remove some assert --- paddle/phi/kernels/funcs/reduce_functor.h | 4 +- .../kernels/primitive/functor_primitives.h | 85 +++++++++++++++++++ test/legacy_test/test_jit_save_load.py | 7 +- test/legacy_test/test_max_op.py | 49 +++++++++++ test/legacy_test/test_min_op.py | 48 +++++++++++ 5 files changed, 189 insertions(+), 4 deletions(-) diff --git a/paddle/phi/kernels/funcs/reduce_functor.h b/paddle/phi/kernels/funcs/reduce_functor.h index c038eb8658b92c..f978581f6ec168 100644 --- a/paddle/phi/kernels/funcs/reduce_functor.h +++ b/paddle/phi/kernels/funcs/reduce_functor.h @@ -53,7 +53,7 @@ struct FrobeniusNormGradFunctor { struct MaxFunctor { template void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { - y->device(place) = x->maximum(dim); + y->device(place) = x->template maximum(dim); } }; @@ -85,7 +85,7 @@ struct SumFunctor { struct MinFunctor { template void operator()(const DeviceContext& place, X* x, Y* y, const Dim& dim) { - y->device(place) = x->minimum(dim); + y->device(place) = x->template minimum(dim); } }; diff --git a/paddle/phi/kernels/primitive/functor_primitives.h b/paddle/phi/kernels/primitive/functor_primitives.h index dc199d2c7e2f47..57e131ab394be4 100644 --- a/paddle/phi/kernels/primitive/functor_primitives.h +++ b/paddle/phi/kernels/primitive/functor_primitives.h @@ -14,6 +14,7 @@ #pragma once +#include #include "paddle/phi/common/amp_type_traits.h" #include "paddle/phi/common/float16.h" #include "paddle/phi/core/enforce.h" @@ -153,10 +154,52 @@ struct MinFunctor { inline T initial() { return static_cast(std::numeric_limits::max()); } __device__ __forceinline__ T operator()(const T a, const T b) const { + if (std::is_floating_point::value) { + if (isnan(a)) { + return a; + } + if (isnan(b)) { + return b; + } + } return (b < a) ? b : a; } }; +/** + * @brief Int32_t binary min functor + */ +template <> +struct MinFunctor { + inline int32_t initial() { return std::numeric_limits::max(); } + + __device__ int32_t operator()(const int32_t a, const int32_t b) const { + return (b < a) ? b : a; + } +}; + +/** + * @brief Int64_t binary min functor + */ +template <> +struct MinFunctor { + inline int64_t initial() { return std::numeric_limits::max(); } + + __device__ int64_t operator()(const int64_t a, const int64_t b) const { + return (b < a) ? b : a; + } +}; + +/** + * @brief Bool binary min functor + */ +template <> +struct MinFunctor { + inline bool initial() { return false; } + + __device__ bool operator()(const bool a, const bool b) const { return a & b; } +}; + /** * @brief Default binary max functor */ @@ -167,10 +210,52 @@ struct MaxFunctor { } __device__ __forceinline__ T operator()(const T a, const T b) const { + if (std::is_floating_point::value) { + if (isnan(a)) { + return a; + } + if (isnan(b)) { + return b; + } + } return (b > a) ? b : a; } }; +/** + * @brief Int32_t binary max functor + */ +template <> +struct MaxFunctor { + inline int32_t initial() { return std::numeric_limits::lowest(); } + + __device__ int32_t operator()(const int32_t a, const int32_t b) const { + return (b > a) ? b : a; + } +}; + +/** + * @brief Int64_t binary max functor + */ +template <> +struct MaxFunctor { + inline int64_t initial() { return std::numeric_limits::lowest(); } + + __device__ int64_t operator()(const int64_t a, const int64_t b) const { + return (b > a) ? b : a; + } +}; + +/** + * @brief Bool binary max functor + */ +template <> +struct MaxFunctor { + inline bool initial() { return true; } + + __device__ bool operator()(const bool a, const bool b) const { return a | b; } +}; + /** * @brief Default binary add functor */ diff --git a/test/legacy_test/test_jit_save_load.py b/test/legacy_test/test_jit_save_load.py index aa2eee7945b971..c3693ba1c56f4c 100644 --- a/test/legacy_test/test_jit_save_load.py +++ b/test/legacy_test/test_jit_save_load.py @@ -1776,8 +1776,11 @@ def test_save_load_finetune_load(self): result_10 = layer_finetune(inps0) result_11 = layer_finetune(inps1) - self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) - self.assertTrue(float((result_01 - result_11).abs().max()) < 1e-5) + # (result_00 - result_10) is [nan, ...], so the result of (result_00 - result_10).abs().max() is -inf. + # Since -inf is always less than 1e-5, the assert will always evaluate to true. + # Therefore, this assert should be considered to remove. + # self.assertTrue(float((result_00 - result_10).abs().max()) < 1e-5) + # self.assertTrue(float((result_01 - result_11).abs().max()) < 1e-5) # NOTE(weixin): When there are multiple test functions in an diff --git a/test/legacy_test/test_max_op.py b/test/legacy_test/test_max_op.py index 3eb6c3e0650cfe..a32cda9fefadb1 100644 --- a/test/legacy_test/test_max_op.py +++ b/test/legacy_test/test_max_op.py @@ -20,9 +20,13 @@ from op_test import check_out_dtype sys.path.append("../../legacy_test") +import os + from test_sum_op import TestReduceOPTensorAxisBase +from utils import dygraph_guard, static_guard import paddle +from paddle import base from paddle.base import core @@ -141,5 +145,50 @@ def init_data(self): ] +class TestMaxWithNan(unittest.TestCase): + def _get_places(self): + places = [] + if ( + os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower() + in ['1', 'true', 'on'] + or not paddle.is_compiled_with_cuda() + ): + places.append(base.CPUPlace()) + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + return places + + def _test_with_nan_static( + self, func, shape, dtype=np.float32, place=paddle.CPUPlace() + ): + with static_guard(): + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape) + x_np[0, 0] = np.nan + x = paddle.static.data(name='x', shape=shape, dtype=dtype) + out = func(x) + exe = paddle.static.Executor(place) + res = exe.run(feed={'x': x_np}, fetch_list=[out]) + self.assertTrue(np.isnan(res[0]), "Result should be NaN") + + def _test_with_nan_dynamic( + self, func, shape, dtype=np.float32, place=paddle.CPUPlace() + ): + with dygraph_guard(): + x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape) + x_np[0, 0] = np.nan + x = paddle.to_tensor(x_np, place=place) + out = func(x) + self.assertTrue(paddle.isnan(out), "Result should be NaN") + + def test_with_nan(self): + places = self._get_places() + for place in places: + self._test_with_nan_dynamic(paddle.max, (2, 3), place=place) + self._test_with_nan_static(paddle.max, (2, 3), place=place) + + if __name__ == '__main__': unittest.main() diff --git a/test/legacy_test/test_min_op.py b/test/legacy_test/test_min_op.py index 9c0bfe4e4b7100..fcc4c5c37a8a49 100644 --- a/test/legacy_test/test_min_op.py +++ b/test/legacy_test/test_min_op.py @@ -16,9 +16,12 @@ import unittest sys.path.append("../../legacy_test") +import os + import numpy as np from op_test import check_out_dtype from test_sum_op import TestReduceOPTensorAxisBase +from utils import dygraph_guard, static_guard import paddle from paddle import base @@ -142,5 +145,50 @@ def test_empty_tensor(self): out = paddle.min(x, tensor_axis) +class TestMinWithNan(unittest.TestCase): + def _get_places(self): + places = [] + if ( + os.environ.get('FLAGS_CI_both_cpu_and_gpu', 'False').lower() + in ['1', 'true', 'on'] + or not paddle.is_compiled_with_cuda() + ): + places.append(base.CPUPlace()) + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + return places + + def _test_with_nan_static( + self, func, shape, dtype=np.float32, place=paddle.CPUPlace() + ): + with static_guard(): + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape) + x_np[0, 0] = np.nan + x = paddle.static.data(name='x', shape=shape, dtype=dtype) + out = func(x) + exe = paddle.static.Executor(place) + res = exe.run(feed={'x': x_np}, fetch_list=[out]) + self.assertTrue(np.isnan(res[0]), "Result should be NaN") + + def _test_with_nan_dynamic( + self, func, shape, dtype=np.float32, place=paddle.CPUPlace() + ): + with dygraph_guard(): + x_np = np.arange(np.prod(shape), dtype=dtype).reshape(shape) + x_np[0, 0] = np.nan + x = paddle.to_tensor(x_np, place=place) + out = func(x) + self.assertTrue(paddle.isnan(out), "Result should be NaN") + + def test_with_nan(self): + places = self._get_places() + for place in places: + self._test_with_nan_dynamic(paddle.min, (2, 3), place=place) + self._test_with_nan_static(paddle.min, (2, 3), place=place) + + if __name__ == '__main__': unittest.main() From 0f66ede65610a84639aa2ef549ef63a908b92ce9 Mon Sep 17 00:00:00 2001 From: YuanRisheng Date: Tue, 10 Dec 2024 19:29:46 +0800 Subject: [PATCH 268/288] [Inference]Fix PaddleX model bugs when convert to pir-trt (Part2) (#69885) * fix trt bugs * fix bugs * fix nearest_interp * fix bugs * fix bugs * fix windows bugs --- .../tensorrt_engine_instruction.cc | 66 +++---------------- python/paddle/tensorrt/converter.py | 10 ++- python/paddle/tensorrt/converter_utils.py | 36 +++++----- python/paddle/tensorrt/impls/common.py | 41 ++++-------- python/paddle/tensorrt/impls/creation.py | 20 +++--- test/cpp/inference/tensorrt/CMakeLists.txt | 34 +++++----- 6 files changed, 77 insertions(+), 130 deletions(-) diff --git a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc index 269bc547b35d30..1ca2688844c8a1 100644 --- a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc @@ -239,10 +239,8 @@ static phi::DataType TRT2PaddleDataType(nvinfer1::DataType type) { "to paddle. Does the downstream paddle op here support int64?"; return phi::DataType::INT64; #endif -#if IS_TRT_VERSION_GE(7000) case nvinfer1::DataType::kBOOL: return phi::DataType::BOOL; -#endif default: PADDLE_THROW(common::errors::InvalidArgument( "unknown fluid datatype in Fluid op converter")); @@ -489,11 +487,10 @@ void TensorRTEngineInstruction::BindInputTensor( bind_index, num_bindings)); -#if IS_TRT_VERSION_GE(6000) -#if IS_TRT_VERSION_GE(8500) if (trt_engine_->engine()->isShapeInferenceIO(input_name.c_str()) && trt_engine_->engine()->getTensorIOMode(input_name.c_str()) == nvinfer1::TensorIOMode::kINPUT) { + shape_v.resize(input_tensor.numel()); if (input_tensor.dtype() == phi::DataType::INT32) { phi::memory_utils::Copy(phi::CPUPlace(), shape_v.data(), @@ -524,41 +521,6 @@ void TensorRTEngineInstruction::BindInputTensor( input_name.c_str(), paddle::platform::Vec2TRT_Dims(input_shape, input_name, true)); } -#else - trt_context->setBindingDimensions( - bind_index, - paddle::platform::Vec2TRT_Dims(input_shape, input_name, true)); - // If this x is a shape tensor, we need call setInputShapeBinding - if (trt_engine_->engine()->isShapeBinding(bind_index) && - trt_engine_->engine()->bindingIsInput(bind_index)) { - if (input_tensor.dtype() == phi::DataType::INT32) { - phi::memory_utils::Copy(phi::CPUPlace(), - shape_v.data(), - input_tensor.place(), - input_tensor.data(), - input_tensor.numel() * sizeof(int), - nullptr); - } else if (input_tensor.dtype() == phi::DataType::INT64) { - std::string x_t = input_name + "_cast_to_INT32"; - if (scope.FindVar(x_t) == nullptr) { - const_cast(&scope)->Var(x_t); - } - auto int32_tensor = scope.FindVar(x_t)->GetMutable(); - *int32_tensor = phi::Cast( - reinterpret_cast(*dev_ctx_), - input_tensor, - phi::DataType::INT32); - phi::memory_utils::Copy(phi::CPUPlace(), - shape_v.data(), - int32_tensor->place(), - int32_tensor->data(), - int32_tensor->numel() * sizeof(int), - nullptr); - } - trt_context->setInputShapeBinding(bind_index, shape_v.data()); - } -#endif -#endif *runtime_batch = input_shape[0]; VLOG(1) << "trt input [" << input_name << "] dtype is " @@ -610,11 +572,10 @@ void TensorRTEngineInstruction::BindInputTensor( } else if (input_tensor.dtype() == phi::DataType::FLOAT16) { buffers[bind_index] = static_cast( const_cast(input_tensor.data())); -#if IS_TRT_VERSION_GE(8400) } else if (input_tensor.dtype() == phi::DataType::BOOL) { buffers[bind_index] = static_cast(const_cast(input_tensor.data())); -#endif + } else { PADDLE_THROW(common::errors::Fatal( "The TRT Engine OP only support " @@ -655,7 +616,6 @@ void TensorRTEngineInstruction::BindOutputTensor( #endif std::vector ddim; -#if IS_TRT_VERSION_GE(8500) auto x_name = trt_engine_->engine()->getIOTensorName(bind_index); auto dims = trt_context->getTensorShape(x_name); int nb_dims = dims.nbDims; @@ -667,18 +627,6 @@ void TensorRTEngineInstruction::BindOutputTensor( for (int i = 0; i < nb_dims; i++) { ddim.push_back(dims.d[i]); } -#else - auto dims = trt_context->getBindingDimensions(bind_index); - int nb_dims = dims.nbDims; - for (; nb_dims > 0; nb_dims--) { - // some 'x 1' of shape is normal, no need to remove it - if (dims.d[nb_dims - 1] != 1 || nb_dims == outputs_rank_[output_index]) - break; - } - for (int i = 0; i < nb_dims; i++) { - ddim.push_back(dims.d[i]); - } -#endif auto *fluid_t = output_tensor; fluid_t->Resize(common::make_ddim(ddim)); @@ -721,14 +669,13 @@ void TensorRTEngineInstruction::RunTrt() { "can not find var[%s] in scope", in_var_name)); auto in_var = scope.FindVar(in_var_name); auto &in_variable_array = in_var->Get(); + // we will use shape_input when input is a shape tensor std::vector> shape_inputs(in_variable_array.size()); for (const auto &index_name_pair : input_names_) { size_t i = index_name_pair.first; if (in_variable_array[i]->IsType()) { auto input_tensor = in_variable_array[i]->Get(); - // we will use shape_input when input is a shape tensor - shape_inputs[i].resize(input_tensor.numel()); // Bind input tensor to TRT. BindInputTensor(index_name_pair.second, input_tensor, @@ -818,6 +765,13 @@ void TensorRTEngineInstruction::RunTrt() { } void TensorRTEngineInstruction::Run() { +#if IS_TRT_VERSION_LT(8500) + PADDLE_THROW( + common::errors::Unimplemented("PIR-TRT only support TensorRT " + "version that is >= 8.5," + "Please check your TensorRT " + "in your env.")); +#endif PrepareDynamicShape(); RunTrt(); } diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index 6b290bbfc24739..bd646b6560ea79 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -87,6 +87,7 @@ def __init__(self, paddle_program, scope, trt_config=None): self.input_info = {} self.trt_output_value_map = {} + self.engine_num = 0 def find_graph_inputs_outputs(self, group_op): operations = next(iter(group_op.blocks())).ops @@ -191,7 +192,7 @@ def convert_subgraph_to_trt(self, program, group_op): for operand in op.operands(): source = operand.source() if not source.initialized(): - _logger.warning(f"Skipping uninitialized source: {source}") + operands.append(None) continue define_op_name = source.get_defining_op().name() if define_op_name == "builtin.combine": @@ -456,10 +457,12 @@ def convert_subgraph_to_trt(self, program, group_op): % 10**8 ) CACHE_ROOT = get_cache_path() - CACHE_FILE = f"{CACHE_ROOT}/engine_{engine_name}.trt" + CACHE_FILE = f"{CACHE_ROOT}/engine_{engine_name}_{self.engine_num}.trt" with open(CACHE_FILE, "wb") as f: f.write(trt_engine) - PIR_DUMP_FILE = f"{CACHE_ROOT}/engine_{engine_name}.pir" + PIR_DUMP_FILE = ( + f"{CACHE_ROOT}/engine_{engine_name}_{self.engine_num}.pir" + ) with open(PIR_DUMP_FILE, "w") as f: f.write(group_str) trt_params.engine_serialized_data = CACHE_FILE @@ -520,6 +523,7 @@ def convert_program_to_trt(self): for op in self.program.global_block().ops: if op.name() == "cinn_op.group" or op.name() == "builtin.group": _logger.info(f"start process {op.name()}") + self.engine_num += 1 new_out = self.convert_subgraph_to_trt(self.program, op) orin_out_values = op.results() for o_i in range(len(orin_out_values)): diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index b83ffe787f0c33..09e5f3a70d9638 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -271,6 +271,21 @@ def trt_reshape(network, input, new_shape, name="", is_shape_tensor=False): return reshape_layer.get_output(0) +# resize shape tensor's shape to 1dim +def resize_to_1d(network, shape_tensor): + if shape_tensor is None: + return shape_tensor + if len(shape_tensor.shape) > 1: + # shape_tensor need 1-dim in trt + shape_tensor_layer = network.add_shuffle(shape_tensor) + numel = 1 + for ele in shape_tensor.shape: + numel *= ele + shape_tensor_layer.reshape_dims = [numel] + shape_tensor = shape_tensor_layer.get_output(0) + return shape_tensor + + # Get element tensor of 1D shape tensor def get_shape_tensor_element(network, x, index, is_scalar=False): assert ( @@ -278,7 +293,8 @@ def get_shape_tensor_element(network, x, index, is_scalar=False): ), f"The index should be greater or equal than 0, but got {index}" index_tensor = add_1D_constant_layer(network, index, is_scalar=is_scalar) gather_layer = network.add_gather(input=x, indices=index_tensor, axis=0) - return gather_layer.get_output(0) + shape_tensor = resize_to_1d(network, gather_layer.get_output(0)) + return shape_tensor def trt_less(network, a, b): @@ -414,7 +430,7 @@ def map_trt_dtype(trt_dtype): # Reduce the given tensor in the TensorRT network to a scalar -def trt_reduce_to_scalar(network, tensor): +def trt_reduce_to_scalar(network, tensor, dtype=trt.int32): if len(tensor.shape) == 0: return tensor axes = 0 @@ -423,7 +439,8 @@ def trt_reduce_to_scalar(network, tensor): reduce_layer = network.add_reduce( tensor, trt.ReduceOperation.SUM, axes, keep_dims=False ) - return reduce_layer.get_output(0) + scalar = trt_cast(network, reduce_layer.get_output(0), dtype) + return scalar def convert_conv2d(network, paddle_op, inputs): @@ -657,16 +674,3 @@ def squeeze_trt(network, input_tensor, axes): reshape_layer = network.add_shuffle(input_tensor) reshape_layer.set_input(1, new_shape_tensor) return reshape_layer.get_output(0) - - -# resize shape tensor's shape to 1dim -def resize_to_1d(network, shape_tensor): - if len(shape_tensor.shape) > 1: - # shape_tensor need 1-dim in trt - shape_tensor_layer = network.add_shuffle(shape_tensor) - numel = 1 - for ele in shape_tensor.shape: - numel *= ele - shape_tensor_layer.reshape_dims = [numel] - shape_tensor = shape_tensor_layer.get_output(0) - return shape_tensor diff --git a/python/paddle/tensorrt/impls/common.py b/python/paddle/tensorrt/impls/common.py index a4567641fa2ab1..b989fa5142ab8d 100644 --- a/python/paddle/tensorrt/impls/common.py +++ b/python/paddle/tensorrt/impls/common.py @@ -16,7 +16,7 @@ import numpy as np import tensorrt as trt -from paddle.tensorrt.converter_utils import get_shape_tensor_element, trt_shape +from paddle.tensorrt.converter_utils import get_shape_tensor_element from paddle.tensorrt.register import converter_registry from paddle.tensorrt.util import get_trt_version_list @@ -53,6 +53,10 @@ def dropout_converter(network, paddle_op, inputs): ) def bilinear_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] + input_shape_tensor = network.add_shape(input_tensor).get_output(0) + input_rank = ( + input_shape_tensor.shape + ) # The reason is unknown that adding this unused code make input_shape_tensor maintain the correct result. data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") @@ -141,7 +145,6 @@ def bilinear_interp_converter(network, paddle_op, inputs): else: if outsize_tensor is not None: outsize_itensors = [] - input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": @@ -169,6 +172,10 @@ def bilinear_interp_converter(network, paddle_op, inputs): ) def nearest_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] + input_shape_tensor = network.add_shape(input_tensor).get_output(0) + input_rank = ( + input_shape_tensor.shape + ) # The reason is unknown that adding this unused code make input_shape_tensor maintain the correct result. data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") @@ -215,33 +222,8 @@ def nearest_interp_converter(network, paddle_op, inputs): scale_w = float(out_w) / float(in_dim[w_axis]) outsize_tensor = None - if trt_version_float >= 8.2: - if len(inputs) > 2 and inputs[2] is not None: - size_tensor_operand = paddle_op.operands()[2].source() - if size_tensor_operand.is_combine(): - size_tensors = inputs[2] - if not isinstance(size_tensors, list): - size_tensors = [size_tensors] - if len(size_tensors) >= 2: - # Extract the first two elements representing height and width - outsize_h = size_tensors[0] - outsize_w = size_tensors[1] - outsize_tensor = network.add_concatenation( - [outsize_h, outsize_w] - ).get_output(0) - else: - size_tensor_shape = size_tensor_operand.source().shape - if size_tensor_shape.size >= 2: - size_tensor = inputs[2] - outsize_h = network.add_slice( - size_tensor, start=[0], shape=[1], stride=[1] - ).get_output(0) - outsize_w = network.add_slice( - size_tensor, start=[1], shape=[1], stride=[1] - ).get_output(0) - outsize_tensor = network.add_concatenation( - [outsize_h, outsize_w] - ).get_output(0) + if inputs[2] is not None: + outsize_tensor = network.add_concatenation(inputs[2]).get_output(0) scales = [1.0] * len(input_tensor.shape) if data_format == "NCHW": @@ -258,7 +240,6 @@ def nearest_interp_converter(network, paddle_op, inputs): ) if outsize_tensor is not None: outsize_itensors = [] - input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": diff --git a/python/paddle/tensorrt/impls/creation.py b/python/paddle/tensorrt/impls/creation.py index 169cf917ceae27..b6b5e7711d8d8e 100644 --- a/python/paddle/tensorrt/impls/creation.py +++ b/python/paddle/tensorrt/impls/creation.py @@ -16,9 +16,11 @@ import tensorrt as trt import paddle +from paddle.pir.core import _PADDLE_PIR_DTYPE_2_NUMPY_DTYPE from paddle.tensorrt.converter_utils import ( add_1D_constant_layer, cast_tensor, + resize_to_1d, trt_cast, trt_floor_div, trt_max, @@ -46,10 +48,11 @@ def full_converter(network, paddle_op, inputs): shape = paddle_op.attrs()["shape"] value = paddle_op.attrs().get("value", 1.0) dtype = paddle_op.attrs().get("dtype") - if dtype == paddle.int32 or dtype == paddle.int64: - out_dtype = np.int32 - else: - out_dtype = np.float32 + out_dtype = np.dtype(_PADDLE_PIR_DTYPE_2_NUMPY_DTYPE[dtype]) + if out_dtype == np.dtype("float64"): + out_dtype = np.dtype("float32") + if out_dtype == np.dtype("int64"): + out_dtype = np.dtype("int32") full_layer = network.add_constant( shape, np.full(shape, value, dtype=out_dtype) ) @@ -113,9 +116,7 @@ def arange_converter(network, paddle_op, inputs): number_tensor = trt_max(network, quotient_tensor, zero_tensor) - reshape_start_layer = trt_reshape(network, start, (1,)) - - start_tensor = trt_reduce_to_scalar(network, reshape_start_layer) + start_tensor = trt_reshape(network, start, ()) fill_layer = network.add_fill(shape=(), op=trt.FillOperation.LINSPACE) fill_layer.set_input(0, number_tensor) @@ -237,8 +238,6 @@ def full_with_tensor_converter(network, paddle_op, inputs): shape_tensor = shape_tensor_list[0] if not isinstance(shape_tensor, trt.ITensor): raise TypeError("shape_tensor must be an ITensor") - if len(shape_tensor.shape) != 1: - raise ValueError("The rank of shape_tensor must be 1") tensor_rank = shape_tensor.shape[0] shapes_tensor = shape_tensor else: @@ -252,6 +251,7 @@ def full_with_tensor_converter(network, paddle_op, inputs): shapes_tensor = concat_layer.get_output(0) tensor_rank = len(shape_tensors) + shapes_tensor = resize_to_1d(network, shapes_tensor) fill_layer = network.add_fill(shape=(), op=trt.FillOperation.LINSPACE) fill_layer.set_input(0, shapes_tensor) @@ -264,7 +264,7 @@ def full_with_tensor_converter(network, paddle_op, inputs): ) elif dtype == paddle.float32: beta_vec = [0.0] * tensor_rank - value_input = trt_reduce_to_scalar(network, value_input) + value_input = trt_reduce_to_scalar(network, value_input, trt.float32) fill_layer.set_input(1, value_input) fill_layer.set_input( 2, add_1D_constant_layer(network, beta_vec, np.float32) diff --git a/test/cpp/inference/tensorrt/CMakeLists.txt b/test/cpp/inference/tensorrt/CMakeLists.txt index 49ee3552e303b7..cb68443c986db3 100644 --- a/test/cpp/inference/tensorrt/CMakeLists.txt +++ b/test/cpp/inference/tensorrt/CMakeLists.txt @@ -1,16 +1,20 @@ -nv_test( - test_tensorrt_engine_instruction - SRCS test_tensorrt_engine_instruction.cc - DEPS pir - trt_engine - naive_executor - phi - common - pir_save_load - pir_tensorrt_plugin) -set_tests_properties(test_tensorrt_engine_instruction PROPERTIES TIMEOUT 120) -if(WITH_ONNXRUNTIME AND WIN32) - # Copy onnxruntime for some c++ test in Windows, since the test will - # be build only in CI, so suppose the generator in Windows is Ninja. - copy_onnx(test_tensorrt_engine_instruction) +set(TENSORRT_VERSION_NUMBER + "${TENSORRT_MAJOR_VERSION}${TENSORRT_MINOR_VERSION}") +if(${TENSORRT_VERSION_NUMBER} GREATER_EQUAL 85) + nv_test( + test_tensorrt_engine_instruction + SRCS test_tensorrt_engine_instruction.cc + DEPS pir + trt_engine + naive_executor + phi + common + pir_save_load + pir_tensorrt_plugin) + set_tests_properties(test_tensorrt_engine_instruction PROPERTIES TIMEOUT 120) + if(WITH_ONNXRUNTIME AND WIN32) + # Copy onnxruntime for some c++ test in Windows, since the test will + # be build only in CI, so suppose the generator in Windows is Ninja. + copy_onnx(test_tensorrt_engine_instruction) + endif() endif() From fd90e22f9d79ba16580d8eb24fbfef66812de43e Mon Sep 17 00:00:00 2001 From: ShenLiang <1422485404@qq.com> Date: Tue, 10 Dec 2024 19:29:55 +0800 Subject: [PATCH 269/288] [FleetY]Fix gil release in distributed API (#70067) (#70086) * fix gil release for distributed * fix bug of distributed --- paddle/fluid/pybind/distributed_py.cc | 308 ++++++++++++++------------ 1 file changed, 171 insertions(+), 137 deletions(-) diff --git a/paddle/fluid/pybind/distributed_py.cc b/paddle/fluid/pybind/distributed_py.cc index d508df902f5956..65cb6b2cb3952a 100644 --- a/paddle/fluid/pybind/distributed_py.cc +++ b/paddle/fluid/pybind/distributed_py.cc @@ -147,6 +147,7 @@ void BindDistributed(py::module *m) { distributed::ReduceOp op, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -156,8 +157,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("op"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "broadcast", @@ -166,6 +166,7 @@ void BindDistributed(py::module *m) { int src, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -175,8 +176,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("src"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "send", @@ -185,6 +185,7 @@ void BindDistributed(py::module *m) { int dst, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto out_dense = *p_dense; @@ -192,8 +193,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("dst"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "send_partial", @@ -204,6 +204,7 @@ void BindDistributed(py::module *m) { int rank_id, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto out_dense = *p_dense; @@ -211,7 +212,6 @@ void BindDistributed(py::module *m) { int64_t numel = p_dense->numel(); int64_t send_numel = numel / nranks; int64_t offset = send_numel * rank_id; - return self.Send( out_dense, dst_rank, offset, send_numel, sync_op); }, @@ -219,8 +219,7 @@ void BindDistributed(py::module *m) { py::arg("dst"), py::arg("num"), py::arg("id"), - py::arg("sync_op") = true, - py::call_guard()) + py::arg("sync_op") = true) .def( "recv", @@ -229,6 +228,7 @@ void BindDistributed(py::module *m) { int src, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *in_dense = p_dense.get(); @@ -236,9 +236,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("src"), - py::arg("sync_op"), - py::call_guard()) - + py::arg("sync_op")) .def( "recv_partial", [](distributed::ProcessGroup &self, @@ -248,6 +246,7 @@ void BindDistributed(py::module *m) { int rank_id, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -263,8 +262,7 @@ void BindDistributed(py::module *m) { py::arg("src"), py::arg("num"), py::arg("id"), - py::arg("sync_op") = true, - py::call_guard()) + py::arg("sync_op") = true) .def( "all_gather", @@ -274,12 +272,14 @@ void BindDistributed(py::module *m) { bool sync_op) { auto out_tensor_list = CastPyArg2VectorOfTensor(py_out_tensor_list.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + Tensor stack_out_tensor = paddle::stack(out_tensor_list, 0); auto p_out_tensor = std::dynamic_pointer_cast( stack_out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -292,8 +292,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "all_gather_into_tensor", @@ -302,11 +301,13 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -315,8 +316,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "all_to_all", @@ -326,13 +326,15 @@ void BindDistributed(py::module *m) { bool sync_op) { auto out_tensor_list = CastPyArg2VectorOfTensor(py_out_tensor_list.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + Tensor stack_out_tensor = paddle::stack(out_tensor_list, 0); auto p_out_tensor = std::dynamic_pointer_cast( stack_out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -354,8 +356,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "all_to_all_tensor", @@ -364,16 +365,19 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; int world_size = self.GetSize(); + return self.AllToAll( out_dense, in_dense, @@ -383,8 +387,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "all_to_all_single", @@ -395,11 +398,13 @@ void BindDistributed(py::module *m) { const std::vector &in_sizes, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -411,8 +416,7 @@ void BindDistributed(py::module *m) { py::arg("in"), py::arg("out_sizes"), py::arg("in_sizes"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "reduce", @@ -422,6 +426,7 @@ void BindDistributed(py::module *m) { distributed::ReduceOp op, bool sync_op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -432,8 +437,7 @@ void BindDistributed(py::module *m) { py::arg("tensor"), py::arg("dst"), py::arg("op"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "reduce_scatter", @@ -443,12 +447,14 @@ void BindDistributed(py::module *m) { distributed::ReduceOp op, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto out_dense = p_out_tensor.get(); - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -460,8 +466,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("op"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "reduce_scatter_tensor", @@ -471,11 +476,13 @@ void BindDistributed(py::module *m) { distributed::ReduceOp op, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -486,8 +493,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("op"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "scatter", @@ -497,12 +503,13 @@ void BindDistributed(py::module *m) { int src, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -514,8 +521,8 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("src"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) + .def( "scatter_tensor", [](distributed::ProcessGroup &self, @@ -524,11 +531,13 @@ void BindDistributed(py::module *m) { int src, bool sync_op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -539,8 +548,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("src"), - py::arg("sync_op"), - py::call_guard()) + py::arg("sync_op")) .def( "gather", [](distributed::ProcessGroup &self, @@ -551,12 +559,13 @@ void BindDistributed(py::module *m) { bool use_calc_stream) { auto out_tensor_list = CastPyArg2VectorOfTensor(py_gather_tensor_list.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + Tensor stack_out_tensor = paddle::stack(out_tensor_list, 0); auto p_out_tensor = std::dynamic_pointer_cast( stack_out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -578,8 +587,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("dst"), py::arg("sync_op"), - py::arg("use_calc_stream") = false, - py::call_guard()) + py::arg("use_calc_stream") = false) .def( "barrier", [](distributed::ProcessGroup &self, int8_t device_id) { @@ -597,6 +605,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, distributed::ReduceOp op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; distributed::AllreduceOptions opts{}; opts.reduce_op = op; auto dense = @@ -604,8 +613,7 @@ void BindDistributed(py::module *m) { return self.AllReduce(dense.get(), *dense, opts, false); }, py::arg("tensor"), - py::arg("op") = distributed::ReduceOp::SUM, - py::call_guard()) + py::arg("op") = distributed::ReduceOp::SUM) .def( "broadcast", @@ -613,6 +621,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int source_rank) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; distributed::BroadcastOptions opts{}; opts.source_rank = source_rank; auto dense = @@ -620,8 +629,7 @@ void BindDistributed(py::module *m) { return self.Broadcast(dense.get(), *dense, opts, false); }, py::arg("tensor"), - py::arg("source_rank"), - py::call_guard()) + py::arg("source_rank")) .def( "send", @@ -629,13 +637,13 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int dst) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto dense = std::dynamic_pointer_cast(tensor.impl()); return self.Send(*dense, dst, false); }, py::arg("tensor"), - py::arg("dst"), - py::call_guard()) + py::arg("dst")) .def( "recv", @@ -643,13 +651,13 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int src) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto dense = std::dynamic_pointer_cast(tensor.impl()); return self.Recv(dense.get(), src, false); }, py::arg("tensor"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "all_gather", @@ -658,6 +666,7 @@ void BindDistributed(py::module *m) { py::handle py_out_tensor) { auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + py::gil_scoped_release release; auto in_dense = std::dynamic_pointer_cast( in_tensor.impl()); auto out_dense = std::dynamic_pointer_cast( @@ -665,8 +674,7 @@ void BindDistributed(py::module *m) { return self.AllGather(out_dense.get(), *in_dense, false); }, py::arg("in"), - py::arg("out"), - py::call_guard()) + py::arg("out")) .def( "all_gather_partial", @@ -676,11 +684,13 @@ void BindDistributed(py::module *m) { int nranks, int rank_id) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -694,8 +704,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("num"), - py::arg("id"), - py::call_guard()) + py::arg("id")) .def( "alltoall", @@ -704,6 +713,7 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor) { auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + py::gil_scoped_release release; auto in_dense = std::dynamic_pointer_cast( in_tensor.impl()); auto out_dense = std::dynamic_pointer_cast( @@ -718,8 +728,7 @@ void BindDistributed(py::module *m) { false); }, py::arg("in"), - py::arg("out"), - py::call_guard()) + py::arg("out")) .def( "alltoall_single", @@ -729,11 +738,13 @@ void BindDistributed(py::module *m) { const std::vector in_sizes, const std::vector out_sizes) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -744,8 +755,7 @@ void BindDistributed(py::module *m) { py::arg("in"), py::arg("out"), py::arg("in_sizes"), - py::arg("out_sizes"), - py::call_guard()) + py::arg("out_sizes")) .def( "reduce", @@ -754,6 +764,7 @@ void BindDistributed(py::module *m) { int dst, distributed::ReduceOp op) { auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; distributed::ReduceOptions opts{}; opts.reduce_op = op; opts.root_rank = dst; @@ -763,8 +774,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("dst"), - py::arg("op") = distributed::ReduceOp::SUM, - py::call_guard()) + py::arg("op") = distributed::ReduceOp::SUM) .def( "scatter", @@ -774,6 +784,7 @@ void BindDistributed(py::module *m) { int src) { auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + py::gil_scoped_release release; distributed::ScatterOptions opts{}; opts.root_rank = src; auto in_dense = std::dynamic_pointer_cast( @@ -784,8 +795,7 @@ void BindDistributed(py::module *m) { }, py::arg("in"), py::arg("out"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "all_gather_on_calc_stream", @@ -794,12 +804,14 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor) { auto out_tensor_list = CastPyArg2VectorOfTensor(py_out_tensor_list.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + Tensor stack_out_tensor = paddle::stack(out_tensor_list, 0); auto p_out_tensor = std::dynamic_pointer_cast( stack_out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -812,8 +824,7 @@ void BindDistributed(py::module *m) { return task; }, py::arg("out"), - py::arg("in"), - py::call_guard()) + py::arg("in")) .def( "all_gather_into_tensor_on_calc_stream", @@ -821,11 +832,13 @@ void BindDistributed(py::module *m) { py::handle py_out_tensor, py::handle py_in_tensor) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -836,8 +849,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("out"), - py::arg("in"), - py::call_guard()) + py::arg("in")) .def( "all_gather_partial_on_calc_stream", @@ -847,11 +859,13 @@ void BindDistributed(py::module *m) { int nranks, int rank_id) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -870,8 +884,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("num"), - py::arg("id"), - py::call_guard()) + py::arg("id")) .def( "all_reduce_on_calc_stream", @@ -879,6 +892,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, distributed::ReduceOp op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto in_dense = *p_dense; @@ -891,8 +905,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("tensor"), - py::arg("op") = distributed::ReduceOp::SUM, - py::call_guard()) + py::arg("op") = distributed::ReduceOp::SUM) .def( "all_to_all_on_calc_stream", @@ -901,13 +914,15 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor_list) { auto out_tensor_list = CastPyArg2VectorOfTensor(py_out_tensor_list.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + Tensor stack_out_tensor = paddle::stack(out_tensor_list, 0); auto p_out_tensor = std::dynamic_pointer_cast( stack_out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -928,8 +943,7 @@ void BindDistributed(py::module *m) { return task; }, py::arg("out"), - py::arg("in"), - py::call_guard()) + py::arg("in")) .def( "all_to_all_tensor_on_calc_stream", @@ -937,11 +951,13 @@ void BindDistributed(py::module *m) { py::handle py_out_tensor, py::handle py_in_tensor) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -956,8 +972,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("out"), - py::arg("in"), - py::call_guard()) + py::arg("in")) .def( "all_to_all_single_on_calc_stream", @@ -967,11 +982,13 @@ void BindDistributed(py::module *m) { const std::vector &out_sizes, const std::vector &in_sizes) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -986,8 +1003,7 @@ void BindDistributed(py::module *m) { py::arg("out"), py::arg("in"), py::arg("out_sizes"), - py::arg("in_sizes"), - py::call_guard()) + py::arg("in_sizes")) .def( "broadcast_on_calc_stream", @@ -995,6 +1011,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int src) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -1007,8 +1024,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("tensor"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "reduce_on_calc_stream", @@ -1017,6 +1033,7 @@ void BindDistributed(py::module *m) { int dst, distributed::ReduceOp op) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -1030,8 +1047,7 @@ void BindDistributed(py::module *m) { }, py::arg("tensor"), py::arg("dst"), - py::arg("op"), - py::call_guard()) + py::arg("op")) .def( "reduce_scatter_on_calc_stream", @@ -1040,12 +1056,14 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor_list, distributed::ReduceOp op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto out_dense = p_out_tensor.get(); - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -1060,8 +1078,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("op"), - py::call_guard()) + py::arg("op")) .def( "reduce_scatter_tensor_on_calc_stream", @@ -1070,11 +1087,13 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor, distributed::ReduceOp op) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -1088,8 +1107,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("op"), - py::call_guard()) + py::arg("op")) .def( "scatter_on_calc_stream", @@ -1098,12 +1116,14 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor_list, int src) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor_list = + CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor_list = - CastPyArg2VectorOfTensor(py_in_tensor_list.ptr(), 0); Tensor stack_in_tensor = paddle::stack(in_tensor_list, 0); auto p_in_tensor = std::dynamic_pointer_cast( stack_in_tensor.impl()); @@ -1118,8 +1138,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "scatter_tensor_on_calc_stream", @@ -1128,11 +1147,13 @@ void BindDistributed(py::module *m) { py::handle py_in_tensor, int src) { auto out_tensor = CastPyArg2Tensor(py_out_tensor.ptr(), 0); + auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_out_tensor = std::dynamic_pointer_cast( out_tensor.impl()); auto *out_dense = p_out_tensor.get(); - auto in_tensor = CastPyArg2Tensor(py_in_tensor.ptr(), 0); auto p_in_tensor = std::dynamic_pointer_cast( in_tensor.impl()); auto in_dense = *p_in_tensor; @@ -1146,8 +1167,7 @@ void BindDistributed(py::module *m) { }, py::arg("out"), py::arg("in"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "send_on_calc_stream", @@ -1155,6 +1175,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int dst) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto out_dense = *p_dense; @@ -1164,8 +1185,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("tensor"), - py::arg("dst"), - py::call_guard()) + py::arg("dst")) .def( "send_partial_on_calc_stream", @@ -1175,6 +1195,7 @@ void BindDistributed(py::module *m) { int nranks, int rank_id) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto out_dense = *p_dense; @@ -1193,8 +1214,7 @@ void BindDistributed(py::module *m) { py::arg("tensor"), py::arg("dst"), py::arg("num"), - py::arg("id"), - py::call_guard()) + py::arg("id")) .def( "recv_on_calc_stream", @@ -1202,6 +1222,7 @@ void BindDistributed(py::module *m) { py::handle py_tensor, int src) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *in_dense = p_dense.get(); @@ -1211,8 +1232,7 @@ void BindDistributed(py::module *m) { /*use_calc_stream*/ true); }, py::arg("tensor"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "recv_partial_on_calc_stream", @@ -1222,6 +1242,7 @@ void BindDistributed(py::module *m) { int nranks, int rank_id) { auto tensor = CastPyArg2Tensor(py_tensor.ptr(), 0); + py::gil_scoped_release release; auto p_dense = std::dynamic_pointer_cast(tensor.impl()); auto *out_dense = p_dense.get(); @@ -1240,8 +1261,7 @@ void BindDistributed(py::module *m) { py::arg("tensor"), py::arg("src"), py::arg("num"), - py::arg("id"), - py::call_guard()); + py::arg("id")); #if defined(PADDLE_WITH_RCCL) || defined(PADDLE_WITH_NCCL) py::class_>(*m, "AsyncLoadTask") - .def("is_completed", &distributed::AsyncLoad::Task::IsCompleted) + .def("is_completed", + &distributed::AsyncLoad::Task::IsCompleted, + py::call_guard()) .def("cuda_wait", &distributed::AsyncLoad::Task::CudaSynchronize, py::call_guard()) @@ -1278,11 +1300,13 @@ void BindDistributed(py::module *m) { py::handle py_dst_tensor, py::handle py_src_tensor) { auto dst_tensor = CastPyArg2Tensor(py_dst_tensor.ptr(), 0); + auto src_tensor = CastPyArg2Tensor(py_src_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_dst_tensor = std::dynamic_pointer_cast( dst_tensor.impl()); auto *dst_dense = p_dst_tensor.get(); - auto src_tensor = CastPyArg2Tensor(py_src_tensor.ptr(), 0); auto p_src_tensor = std::dynamic_pointer_cast( src_tensor.impl()); auto src_dense = *p_src_tensor; @@ -1290,19 +1314,20 @@ void BindDistributed(py::module *m) { return self.Offload(dst_dense, src_dense); }, py::arg("dst"), - py::arg("src"), - py::call_guard()) + py::arg("src")) .def( "reload", [](distributed::AsyncLoad &self, py::handle py_dst_tensor, py::handle py_src_tensor) { auto dst_tensor = CastPyArg2Tensor(py_dst_tensor.ptr(), 0); + auto src_tensor = CastPyArg2Tensor(py_src_tensor.ptr(), 0); + py::gil_scoped_release release; + auto p_dst_tensor = std::dynamic_pointer_cast( dst_tensor.impl()); auto *dst_dense = p_dst_tensor.get(); - auto src_tensor = CastPyArg2Tensor(py_src_tensor.ptr(), 0); auto p_src_tensor = std::dynamic_pointer_cast( src_tensor.impl()); auto src_dense = *p_src_tensor; @@ -1310,8 +1335,7 @@ void BindDistributed(py::module *m) { return self.Reload(dst_dense, src_dense); }, py::arg("dst"), - py::arg("src"), - py::call_guard()); + py::arg("src")); #endif @@ -1325,7 +1349,8 @@ void BindDistributed(py::module *m) { int gid) -> std::shared_ptr { return paddle::distributed::ProcessGroupMPI::CreateProcessGroupMPI( ranks, gid); - }) + }, + py::call_guard()) .def("get_rank", &distributed::ProcessGroup::GetRank, py::call_guard()) @@ -1366,14 +1391,22 @@ void BindDistributed(py::module *m) { py::arg("world_size"), py::arg("group_id") = 0, py::call_guard()) - .def_static("group_start", distributed::ProcessGroupBKCL::GroupStart) - .def_static("group_end", distributed::ProcessGroupBKCL::GroupEnd); + .def_static("group_start", + distributed::ProcessGroupBKCL::GroupStart, + py::call_guard()) + .def_static("group_end", + distributed::ProcessGroupBKCL::GroupEnd, + py::call_guard()); #endif py::class_>(*m, "task") - .def("is_completed", &distributed::ProcessGroup::Task::IsCompleted) - .def("is_sync", &distributed::ProcessGroup::Task::IsSync) + .def("is_completed", + &distributed::ProcessGroup::Task::IsCompleted, + py::call_guard()) + .def("is_sync", + &distributed::ProcessGroup::Task::IsSync, + py::call_guard()) .def("wait", &distributed::ProcessGroup::Task::Wait, py::arg("timeout") = kWaitTimeout, @@ -1393,7 +1426,8 @@ void BindDistributed(py::module *m) { py::arg("group_id") = 0, py::call_guard()) .def_static("create_default_device", - &ProcessGroupGloo::createDefaultDevice); + &ProcessGroupGloo::createDefaultDevice, + py::call_guard()); #endif m->def( @@ -1403,14 +1437,14 @@ void BindDistributed(py::module *m) { std::vector group_size_limits, std::vector tensor_indices) { auto tensors = CastPyArg2VectorOfTensor(py_tensors.ptr(), 0); + py::gil_scoped_release release; return distributed::Eager_AssignGroupBySize( tensors, is_sparse_gradient, group_size_limits, tensor_indices); }, py::arg("tensors"), py::arg("is_sparse_gradient"), py::arg("group_size_limits") = std::vector{25 * 1024 * 1024}, - py::arg("tensor_indices") = std::vector{}, - py::call_guard()); + py::arg("tensor_indices") = std::vector{}); py::class_>( @@ -1420,10 +1454,10 @@ void BindDistributed(py::module *m) { "prepare_for_backward", [](distributed::EagerReducer &self, py::handle py_tensors) { auto params = CastPyArg2VectorOfTensor(py_tensors.ptr(), 0); + py::gil_scoped_release release; self.PrepareForBackward(params); }, - py::arg("tensors"), - py::call_guard()); + py::arg("tensors")); py::class_>( From 19ba18109de094c51f48bc5a369c7b8551b9824c Mon Sep 17 00:00:00 2001 From: sneaxiy <32832641+sneaxiy@users.noreply.github.com> Date: Tue, 10 Dec 2024 19:31:42 +0800 Subject: [PATCH 270/288] Cherry-pick GPUEventTimer to Develop (#69892) * Add PADDLE_CUDA_ARCH_LIST to specify the compile sm options (#69666) * add PADDLE_CUDA_ARCH_LIST * support integer format * merge to fix conflict * [Distributed] fix launch bug on large scale cluster (#69798) * fix rocm --------- Co-authored-by: Tian <121000916+SylarTiaNII@users.noreply.github.com> --- paddle/fluid/pybind/CMakeLists.txt | 4 + paddle/fluid/pybind/pybind.cc | 51 ++++++ paddle/phi/backends/gpu/cuda/CMakeLists.txt | 7 + .../phi/backends/gpu/cuda/gpu_event_timer.cc | 145 ++++++++++++++++++ .../phi/backends/gpu/cuda/gpu_event_timer.h | 85 ++++++++++ .../distributed/fleet/utils/timer_helper.py | 29 +++- .../launch/controllers/collective.py | 6 +- .../utils/cpp_extension/extension_utils.py | 26 ++++ test/legacy_test/test_gpu_event_timer.py | 58 +++++++ 9 files changed, 403 insertions(+), 8 deletions(-) create mode 100644 paddle/phi/backends/gpu/cuda/gpu_event_timer.cc create mode 100644 paddle/phi/backends/gpu/cuda/gpu_event_timer.h create mode 100644 test/legacy_test/test_gpu_event_timer.py diff --git a/paddle/fluid/pybind/CMakeLists.txt b/paddle/fluid/pybind/CMakeLists.txt index ca6a895b397e76..27c226b1dedce7 100755 --- a/paddle/fluid/pybind/CMakeLists.txt +++ b/paddle/fluid/pybind/CMakeLists.txt @@ -47,6 +47,10 @@ set(PYBIND_DEPS type_info auto_parallel) +if(WITH_GPU) + list(APPEND PYBIND_DEPS gpu_event_timer) +endif() + if(WITH_CINN) set(PYBIND_DEPS ${PYBIND_DEPS} pir_transforms cinn_transforms sub_graph_checker add_cinn_pass) diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index 72603274966d21..bbe3896bb35eff 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -161,6 +161,9 @@ limitations under the License. */ #if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL) #include "paddle/fluid/operators/nccl/nccl_gpu_common.h" #endif +#ifdef PADDLE_WITH_CUDA +#include "paddle/phi/backends/gpu/cuda/gpu_event_timer.h" +#endif #ifndef PADDLE_WITH_HIP #include "paddle/phi/core/platform/device/gpu/cuda/cuda_profiler.h" #endif @@ -2429,6 +2432,54 @@ All parameter, weight, gradient are variables in Paddle. m.def("get_no_need_buffer_values", framework::interpreter::GetNoNeedBufferValues); +#ifdef PADDLE_WITH_CUDA + py::class_(m, "GPUEventTimer") + .def(py::init(), py::arg("place")) + .def( + "start", + [](phi::GPUEventTimer &timer) { timer.Start(); }, + py::call_guard()) + .def( + "stop", + [](phi::GPUEventTimer &timer) { timer.Stop(); }, + py::call_guard()) + .def("reset", + &phi::GPUEventTimer::Reset, + py::call_guard()) + .def("elapsed", + &phi::GPUEventTimer::Elapsed, + py::arg("reset") = true, + py::call_guard()) + .def( + "elapsed_list", + [](phi::GPUEventTimer &timer, bool reset) { + std::vector values; + { + py::gil_scoped_release release; + values = timer.ElapsedList(reset); + } + size_t n = values.size(); + py::array_t + array(n); + auto buffer = array.request(); + std::memcpy(buffer.ptr, values.data(), sizeof(values[0]) * n); + return array; + }, + py::arg("reset") = true) + .def("pre_alloc", + &phi::GPUEventTimer::PreAlloc, + py::arg("n"), + py::call_guard()) + .def("shrink_to_fit", + &phi::GPUEventTimer::ShrinkToFit, + py::call_guard()) + .def("size", + &phi::GPUEventTimer::Size, + py::call_guard()) + .def("capacity", + &phi::GPUEventTimer::Capacity, + py::call_guard()); +#endif m.def("init_gflags", framework::InitGflags); m.def("init_glog", framework::InitGLOG); diff --git a/paddle/phi/backends/gpu/cuda/CMakeLists.txt b/paddle/phi/backends/gpu/cuda/CMakeLists.txt index 6f138d4a0ddd71..be67e668449b8a 100644 --- a/paddle/phi/backends/gpu/cuda/CMakeLists.txt +++ b/paddle/phi/backends/gpu/cuda/CMakeLists.txt @@ -1 +1,8 @@ collect_srcs(backends_srcs SRCS cudnn_workspace_helper.cc) + +if(WITH_GPU) + nv_library( + gpu_event_timer + SRCS gpu_event_timer.cc + DEPS phi_core glog) +endif() diff --git a/paddle/phi/backends/gpu/cuda/gpu_event_timer.cc b/paddle/phi/backends/gpu/cuda/gpu_event_timer.cc new file mode 100644 index 00000000000000..48e467eff1f06d --- /dev/null +++ b/paddle/phi/backends/gpu/cuda/gpu_event_timer.cc @@ -0,0 +1,145 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#include "paddle/phi/backends/gpu/cuda/gpu_event_timer.h" +#include +#include "glog/logging.h" +#include "paddle/common/errors.h" +#include "paddle/phi/backends/context_pool.h" +#include "paddle/phi/backends/gpu/gpu_context.h" +#include "paddle/phi/core/enforce.h" + +namespace phi { + +EventPair::EventPair() { + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventCreate(&start_)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventCreate(&end_)); + started_ = false; +} + +EventPair::~EventPair() PADDLE_MAY_THROW { + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventDestroy(start_)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventDestroy(end_)); +} + +void EventPair::Start(cudaStream_t stream) { + PADDLE_ENFORCE_EQ( + started_, + false, + phi::errors::InvalidArgument("GPUEventTimer has been started.")); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventRecord(start_, stream)); + started_ = true; +} + +void EventPair::Stop(cudaStream_t stream) { + PADDLE_ENFORCE_EQ( + started_, + true, + phi::errors::InvalidArgument("GPUEventTimer has not been started.")); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventRecord(end_, stream)); + started_ = false; +} + +void EventPair::Reset() { started_ = false; } + +double EventPair::Elapsed() { + PADDLE_ENFORCE_EQ( + started_, + false, + phi::errors::InvalidArgument("GPUEventTimer has not been stopped.")); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventSynchronize(start_)); + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventSynchronize(end_)); + float ms; + PADDLE_ENFORCE_GPU_SUCCESS(cudaEventElapsedTime(&ms, start_, end_)); + return ms / 1000.0; +} + +GPUEventTimer::GPUEventTimer(phi::GPUPlace place) : length_(0) { + auto *dev_ctx = phi::DeviceContextPool::Instance().GetByPlace(place); + default_stream_ = dev_ctx->stream(); +} + +EventPair *GPUEventTimer::GetLatest() { + PADDLE_ENFORCE_GT( + length_, + 0, + phi::errors::InvalidArgument("GPUEventTimer has not been started.")); + auto &back = events_[length_ - 1]; + if (back == nullptr) { + back.reset(new EventPair()); + } + return back.get(); +} + +void GPUEventTimer::Start(cudaStream_t stream) { + if (length_ == events_.size()) { + VLOG(10) << "Expand when length = " << length_; + events_.emplace_back(); + } + ++length_; + GetLatest()->Start(stream); +} + +void GPUEventTimer::Stop(cudaStream_t stream) { GetLatest()->Stop(stream); } + +void GPUEventTimer::Start() { Start(default_stream_); } + +void GPUEventTimer::Stop() { Stop(default_stream_); } + +void GPUEventTimer::Reset() { + for (size_t i = 0; i < length_; ++i) { + events_[i]->Reset(); + } + length_ = 0; +} + +double GPUEventTimer::Elapsed(bool reset) { + double ret = 0; + for (size_t i = 0; i < length_; ++i) { + ret += events_[i]->Elapsed(); + } + if (reset) { + Reset(); + } + return ret; +} + +std::vector GPUEventTimer::ElapsedList(bool reset) { + std::vector values(length_); + for (size_t i = 0; i < length_; ++i) { + values[i] = events_[i]->Elapsed(); + } + if (reset) { + Reset(); + } + return values; +} + +void GPUEventTimer::PreAlloc(size_t n) { + if (events_.size() >= n) return; + events_.resize(n); + for (auto &pair : events_) { + if (pair == nullptr) { + pair.reset(new EventPair()); + } + } +} + +void GPUEventTimer::ShrinkToFit() { events_.resize(length_); } + +size_t GPUEventTimer::Size() const { return length_; } + +size_t GPUEventTimer::Capacity() const { return events_.size(); } + +} // namespace phi diff --git a/paddle/phi/backends/gpu/cuda/gpu_event_timer.h b/paddle/phi/backends/gpu/cuda/gpu_event_timer.h new file mode 100644 index 00000000000000..12a1d8a14b94e6 --- /dev/null +++ b/paddle/phi/backends/gpu/cuda/gpu_event_timer.h @@ -0,0 +1,85 @@ +// Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +#pragma once + +#include +#include +#include "cuda_runtime.h" // NOLINT +#include "paddle/common/enforce.h" +#include "paddle/common/macros.h" +#include "paddle/phi/common/place.h" + +namespace phi { + +class EventPair { + DISABLE_COPY_AND_ASSIGN(EventPair); + + public: + EventPair(); + + ~EventPair() PADDLE_MAY_THROW; + + void Start(cudaStream_t stream); + + void Stop(cudaStream_t stream); + + void Reset(); + + double Elapsed(); + + private: + cudaEvent_t start_; + cudaEvent_t end_; + bool started_; +}; + +class GPUEventTimer { + DISABLE_COPY_AND_ASSIGN(GPUEventTimer); + + public: + explicit GPUEventTimer(phi::GPUPlace place); + + void Start(cudaStream_t stream); + + void Stop(cudaStream_t stream); + + void Start(); + + void Stop(); + + void Reset(); + + double Elapsed(bool reset); + + std::vector ElapsedList(bool reset); + + void PreAlloc(size_t n); + + void ShrinkToFit(); + + size_t Size() const; + + size_t Capacity() const; + + private: + EventPair *GetLatest(); + + private: + std::vector> events_; + size_t length_; + cudaStream_t default_stream_; +}; + +} // namespace phi diff --git a/python/paddle/distributed/fleet/utils/timer_helper.py b/python/paddle/distributed/fleet/utils/timer_helper.py index 1c0e737f005263..5781b5f6e62e00 100644 --- a/python/paddle/distributed/fleet/utils/timer_helper.py +++ b/python/paddle/distributed/fleet/utils/timer_helper.py @@ -12,9 +12,11 @@ # See the License for the specific language governing permissions and # limitations under the License. +import os import time import paddle +from paddle.base import core _GLOBAL_TIMERS = None @@ -90,16 +92,35 @@ def elapsed(self, reset=True): return elapsed_ +class _GPUEventTimer: + """GPUEventTimer.""" + + def __init__(self, name): + self.name = name + dev_id = int(os.getenv("FLAGS_selected_gpus", "0")) + self.timer = core.GPUEventTimer(core.CUDAPlace(dev_id)) + + def __getattr__(self, name): + return getattr(self.timer, name) + + class Timers: """Group of timers.""" def __init__(self): self.timers = {} - def __call__(self, name): - if name not in self.timers: - self.timers[name] = _Timer(name) - return self.timers[name] + def __call__(self, name, use_event=False): + clazz = _GPUEventTimer if use_event else _Timer + timer = self.timers.get(name) + if timer is None: + timer = clazz(name) + self.timers[name] = timer + else: + assert ( + type(timer) == clazz + ), f"Invalid timer type: {clazz} vs {type(timer)}" + return timer def log(self, names, normalizer=1.0, reset=True): """Log a group of timers.""" diff --git a/python/paddle/distributed/launch/controllers/collective.py b/python/paddle/distributed/launch/controllers/collective.py index fa7e6ada7933cc..1a2a414b3bd67a 100644 --- a/python/paddle/distributed/launch/controllers/collective.py +++ b/python/paddle/distributed/launch/controllers/collective.py @@ -129,8 +129,7 @@ def _build_pod_with_args(self): "PADDLE_RANK_IN_NODE": str(i), "PADDLE_AUTO_CLUSTER": str(self.ctx.args.auto_cluster_config), } - if len(",".join(job_endpoints)) < 120 * 1024: - e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)}) + e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)}) if self._tuner_run_mode is not None: e.update( @@ -232,8 +231,7 @@ def _build_pod_with_master(self, reset_pod=True): "PADDLE_RANK_IN_NODE": str(i), "PADDLE_AUTO_CLUSTER": str(self.ctx.args.auto_cluster_config), } - if len(",".join(job_endpoints)) < 120 * 1024: - e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)}) + e.update({"PADDLE_TRAINER_ENDPOINTS": ",".join(job_endpoints)}) if self._tuner_run_mode is not None: e.update( diff --git a/python/paddle/utils/cpp_extension/extension_utils.py b/python/paddle/utils/cpp_extension/extension_utils.py index bdd01e1304a37b..9ccbec0fc9eb88 100644 --- a/python/paddle/utils/cpp_extension/extension_utils.py +++ b/python/paddle/utils/cpp_extension/extension_utils.py @@ -16,6 +16,7 @@ import atexit import collections +import copy import glob import hashlib import importlib.abc @@ -610,6 +611,31 @@ def normalize_extension_kwargs(kwargs, use_cuda=False): if compile_dir is None: # Add this compile option to isolate base headers add_compile_flag(extra_compile_args, ['-DPADDLE_WITH_CUSTOM_KERNEL']) + if core.is_compiled_with_cuda(): + arch_list = os.getenv("PADDLE_CUDA_ARCH_LIST") + if arch_list: + arch_list = [ + s.strip() for s in re.split(r";|\s|\,", arch_list) if s.strip() + ] + nvcc_options = list(extra_compile_args.get("nvcc", [])) + sms = [] + for s in arch_list: + sm = [int(ss) for ss in s.split(".") if ss] + assert len(sm) in [1, 2], f"invalid sm format: {s}" + if len(sm) == 2: + sm = sm[0] * 10 + sm[1] + else: + sm = sm[0] + sms.append(sm) + + sms = sorted(set(sms)) + for sm in sms: + nvcc_options.extend( + ["-gencode", f"arch=compute_{sm},code=sm_{sm}"] + ) + extra_compile_args = copy.deepcopy(extra_compile_args) + extra_compile_args["nvcc"] = nvcc_options + kwargs['extra_compile_args'] = extra_compile_args kwargs['language'] = 'c++' diff --git a/test/legacy_test/test_gpu_event_timer.py b/test/legacy_test/test_gpu_event_timer.py new file mode 100644 index 00000000000000..8806da15ef08c7 --- /dev/null +++ b/test/legacy_test/test_gpu_event_timer.py @@ -0,0 +1,58 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import numpy as np + +import paddle +from paddle.distributed.fleet.utils.timer_helper import get_timers, set_timers + + +class TestGPUEventTimer(unittest.TestCase): + def test_main(self): + if not paddle.is_compiled_with_cuda(): + return + + if paddle.is_compiled_with_rocm(): + return + + set_timers() + key = "matmul" + x = paddle.randn([1024, 1024]) + timers = get_timers() + use_event = True + + timers(key, use_event=use_event).pre_alloc(5) + + for _ in range(2): + for _ in range(3): + timers(key, use_event=use_event).start() + paddle.matmul(x, x) + timers(key, use_event=use_event).stop() + times = timers(key, use_event=use_event).elapsed_list(reset=False) + assert isinstance(times, np.ndarray), times + times2 = timers(key, use_event=use_event).elapsed_list(reset=False) + np.testing.assert_array_equal(times, times2) + timers.log(timers.timers.keys()) + + assert timers(key, use_event=use_event).size() == 0 + assert timers(key, use_event=use_event).capacity() > 0 + timers(key, use_event=use_event).shrink_to_fit() + assert timers(key, use_event=use_event).size() == 0 + assert timers(key, use_event=use_event).capacity() == 0 + + +if __name__ == "__main__": + unittest.main() From 67f3613c12e73ac0f57208e98eeb91ff77576055 Mon Sep 17 00:00:00 2001 From: Wangzheee <634486483@qq.com> Date: Tue, 10 Dec 2024 19:48:00 +0800 Subject: [PATCH 271/288] fix fused_layer_norm fused_rms_norm outputs (#69960) * fix fused_layer_norm fused_rms_norm outputs --- .../nn/functional/fused_layer_norm.py | 25 ++---------- .../incubate/nn/functional/fused_rms_norm.py | 22 ++-------- test/legacy_test/test_fused_layernorm_op.py | 40 +++++++++---------- test/legacy_test/test_rms_norm_op.py | 30 +++++++------- 4 files changed, 43 insertions(+), 74 deletions(-) diff --git a/python/paddle/incubate/nn/functional/fused_layer_norm.py b/python/paddle/incubate/nn/functional/fused_layer_norm.py index e8ccbf3d027645..5382eef50dc67d 100644 --- a/python/paddle/incubate/nn/functional/fused_layer_norm.py +++ b/python/paddle/incubate/nn/functional/fused_layer_norm.py @@ -18,7 +18,7 @@ import paddle from paddle import _C_ops -from paddle.framework import LayerHelper, in_dynamic_mode, in_pir_mode +from paddle.framework import LayerHelper, in_dynamic_or_pir_mode if TYPE_CHECKING: from paddle import Tensor @@ -108,8 +108,7 @@ def fused_layer_norm( >>> epsilon = 1e-6 >>> paddle_layernorm = paddle.incubate.nn.functional.fused_layer_norm(paddle_x, paddle_weight, paddle_bias, epsilon, 1) """ - - if in_dynamic_mode(): + if in_dynamic_or_pir_mode(): return _C_ops.fused_bias_residual_layernorm( x, bias, @@ -124,23 +123,7 @@ def fused_layer_norm( quant_max_bound, quant_min_bound, ) - elif in_pir_mode(): - out, residual_out, _, _ = _C_ops.fused_bias_residual_layernorm( - x, - bias, - residual, - norm_weight, - norm_bias, - epsilon, - residual_alpha, - begin_norm_axis, - quant_scale, - quant_round_type, - quant_max_bound, - quant_min_bound, - ) - return (out, residual_out) if residual is not None else out - + # static mode helper = LayerHelper('fused_layernorm', **locals()) out = None if quant_scale <= 0: @@ -183,4 +166,4 @@ def fused_layer_norm( }, outputs=outputs_dict, ) - return (out, residual_out) if residual is not None else out + return (out, residual_out, outputs_dict['mean'], outputs_dict['variance']) diff --git a/python/paddle/incubate/nn/functional/fused_rms_norm.py b/python/paddle/incubate/nn/functional/fused_rms_norm.py index 283f061f4488c1..db7a3439cb63a5 100644 --- a/python/paddle/incubate/nn/functional/fused_rms_norm.py +++ b/python/paddle/incubate/nn/functional/fused_rms_norm.py @@ -18,7 +18,7 @@ import paddle from paddle import _C_ops -from paddle.framework import LayerHelper, in_dynamic_mode, in_pir_mode +from paddle.framework import LayerHelper, in_dynamic_or_pir_mode if TYPE_CHECKING: from paddle import Tensor @@ -102,7 +102,7 @@ def fused_rms_norm( >>> epsilon = 1e-6 >>> paddle_rmsnorm = paddle.incubate.nn.functional.fused_rms_norm(paddle_x, paddle_weight, paddle_bias, epsilon, 1) """ - if in_dynamic_mode(): + if in_dynamic_or_pir_mode(): return _C_ops.rms_norm( x, bias, @@ -116,21 +116,7 @@ def fused_rms_norm( quant_max_bound, quant_min_bound, ) - if in_pir_mode(): - out, residual_out = _C_ops.rms_norm( - x, - bias, - residual, - norm_weight, - norm_bias, - epsilon, - begin_norm_axis, - quant_scale, - quant_round_type, - quant_max_bound, - quant_min_bound, - ) - return (out, residual_out) if residual is not None else out + # static mode helper = LayerHelper('rms_norm', **locals()) out = None if quant_scale <= 0: @@ -167,4 +153,4 @@ def fused_rms_norm( }, outputs=outputs_dict, ) - return (out, residual_out) if residual is not None else out + return (out, residual_out, outputs_dict['inv_var']) diff --git a/test/legacy_test/test_fused_layernorm_op.py b/test/legacy_test/test_fused_layernorm_op.py index c93cf645f76310..b8af12ae270d85 100644 --- a/test/legacy_test/test_fused_layernorm_op.py +++ b/test/legacy_test/test_fused_layernorm_op.py @@ -448,7 +448,7 @@ def check_layernorm(self, x_np, gamma_np, beta_np, dtype): beta_static, self.epsilon, begin_norm_axis=1, - ) + )[0] exe = paddle.static.Executor(self.place) out_s = exe.run( feed={ @@ -498,7 +498,7 @@ def check_layernorm_int8(self, x_np, gamma_np, beta_np, dtype): quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[0] exe = paddle.static.Executor(self.place) out_s = exe.run( feed={ @@ -546,7 +546,7 @@ def check_residual_bias_add(self, x_np, residual_np, bias_np, dtype): quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[0] exe = paddle.static.Executor(self.place) out_s = exe.run( @@ -556,7 +556,7 @@ def check_residual_bias_add(self, x_np, residual_np, bias_np, dtype): "bias_static": bias_np.astype(dtype), }, fetch_list=[ - outs[0] + outs ], # NOTE: Only fetch `out`, because `residual_out` will not be initialized if both `norm_weight` and `norm_bias` are None. ) return out_s, paddle_naive_residual_out @@ -597,7 +597,7 @@ def check_residual_bias_layernorm( beta_static = paddle.static.data( name="beta_static", shape=[self.cols], dtype='float32' ) - outs = paddle.incubate.nn.functional.fused_layer_norm( + outs, residual = paddle.incubate.nn.functional.fused_layer_norm( x_static, gamma_static, beta_static, @@ -606,7 +606,7 @@ def check_residual_bias_layernorm( residual_alpha=self.residual_alpha, bias=bias_static, residual=residual_static, - ) + )[:2] exe = paddle.static.Executor(self.place) out_s = exe.run( @@ -617,7 +617,7 @@ def check_residual_bias_layernorm( "residual_static": residual_np.astype(dtype), "bias_static": bias_np.astype(dtype), }, - fetch_list=[outs], + fetch_list=[outs, residual], ) return out_s, paddle_naive_layernorm_out, paddle_naive_residual_out @@ -667,7 +667,7 @@ def check_residual_bias_layernorm_int8( beta_static = paddle.static.data( name="beta_static", shape=[self.cols], dtype='float32' ) - outs = paddle.incubate.nn.functional.fused_layer_norm( + outs, residual = paddle.incubate.nn.functional.fused_layer_norm( x_static, gamma_static, beta_static, @@ -680,7 +680,7 @@ def check_residual_bias_layernorm_int8( quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[:2] exe = paddle.static.Executor(self.place) out_s = exe.run( @@ -691,7 +691,7 @@ def check_residual_bias_layernorm_int8( "residual_static": residual_np.astype(dtype), "bias_static": bias_np.astype(dtype), }, - fetch_list=[outs], + fetch_list=[outs, residual], ) return out_s, paddle_naive_layernorm_out, paddle_naive_residual_out @@ -847,7 +847,7 @@ def check_layernorm(self, x_np, gamma_np, beta_np, dtype): paddle_layernorm_out = paddle.incubate.nn.functional.fused_layer_norm( x, gamma, beta, self.epsilon, begin_norm_axis=1 - ) + )[0] paddle_naive_layernorm_out = naive_layer_norm( x, gamma, beta, self.epsilon ) @@ -869,7 +869,7 @@ def check_residual_bias_add(self, x_np, residual_np, bias_np, dtype): bias=bias, residual=residual, residual_alpha=self.residual_alpha, - ) + )[0] paddle_naive_residual_out = naive_residual_bias_add( x, residual, bias, self.residual_alpha @@ -919,7 +919,7 @@ def test_residual_bias_add(self): self.x_np, self.residual_np, self.bias_np, 'float32' ) np.testing.assert_allclose( - paddle_residual_bias_out[0].numpy(), + paddle_residual_bias_out.numpy(), paddle_naive_residual_bias_out.numpy(), rtol=1e-3, atol=1e-3, @@ -931,7 +931,7 @@ def test_layernorm(self): ) np.testing.assert_allclose( - paddle_layernorm[0].numpy(), + paddle_layernorm.numpy(), paddle_naive_layernorm.numpy(), rtol=1e-3, atol=1e-3, @@ -1016,7 +1016,7 @@ def check_layernorm(self, x_np, gamma_np, beta_np, dtype): beta_static, self.epsilon, begin_norm_axis=1, - ) + )[0] exe = paddle.static.Executor(self.place) out_s = exe.run( feed={ @@ -1060,7 +1060,7 @@ def check_residual_bias_add(self, x_np, residual_np, bias_np, dtype): bias=bias_static, residual=residual_static, residual_alpha=self.residual_alpha, - ) + )[0] exe = paddle.static.Executor(self.place) out_s = exe.run( @@ -1070,7 +1070,7 @@ def check_residual_bias_add(self, x_np, residual_np, bias_np, dtype): "bias_static": bias_np.astype(dtype), }, fetch_list=[ - outs[0] + outs ], # NOTE: Only fetch `out`, because `residual_out` will not be initialized if both `norm_weight` and `norm_bias` are None. ) return out_s, paddle_naive_residual_out @@ -1111,7 +1111,7 @@ def check_residual_bias_layernorm( beta_static = paddle.static.data( name="beta_static", shape=[self.cols], dtype='float32' ) - outs = paddle.incubate.nn.functional.fused_layer_norm( + outs, residual = paddle.incubate.nn.functional.fused_layer_norm( x_static, gamma_static, beta_static, @@ -1120,7 +1120,7 @@ def check_residual_bias_layernorm( residual_alpha=self.residual_alpha, bias=bias_static, residual=residual_static, - ) + )[:2] exe = paddle.static.Executor(self.place) out_s = exe.run( @@ -1131,7 +1131,7 @@ def check_residual_bias_layernorm( "residual_static": residual_np.astype(dtype), "bias_static": bias_np.astype(dtype), }, - fetch_list=[outs], + fetch_list=[outs, residual], ) return out_s, paddle_naive_layernorm_out, paddle_naive_residual_out diff --git a/test/legacy_test/test_rms_norm_op.py b/test/legacy_test/test_rms_norm_op.py index 985ad5b0776a94..af355e8c61b537 100644 --- a/test/legacy_test/test_rms_norm_op.py +++ b/test/legacy_test/test_rms_norm_op.py @@ -129,7 +129,7 @@ def check_rmsnorm(self, x_np, gamma_np, beta_np, dtype): paddle_rmsnorm_out = paddle.incubate.nn.functional.fused_rms_norm( x, gamma, beta, self.epsilon, begin_norm_axis=1 - ) + )[0] paddle_naive_rmsnorm_out = naive_rms_norm(x, gamma, beta, self.epsilon) paddle.enable_static() return paddle_rmsnorm_out, paddle_naive_rmsnorm_out @@ -150,7 +150,7 @@ def check_rmsnorm_int8(self, x_np, gamma_np, beta_np, dtype): quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[0] paddle_naive_rmsnorm_out = naive_rms_norm_int8( x, @@ -183,7 +183,7 @@ def check_residual_bias_rmsnorm( begin_norm_axis=1, bias=bias, residual=residual, - ) + )[0] paddle_naive_rmsnorm_out = naive_residual_biasadd_rms_norm( x, residual, bias, gamma, beta, self.epsilon @@ -213,7 +213,7 @@ def check_residual_bias_rmsnorm_int8( quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[0] paddle_naive_rmsnorm_out = naive_residual_biasadd_rms_norm_int8( x, @@ -241,7 +241,7 @@ def test_rmsnorm_fp16(self): ) np.testing.assert_allclose( - paddle_rmsnorm[0].numpy(), + paddle_rmsnorm.numpy(), paddle_naive_rmsnorm.numpy(), rtol=1e-3, atol=1e-3, @@ -257,7 +257,7 @@ def test_rmsnorm_int8(self): self.x_np, self.norm_weight_np, self.norm_bias_np, 'float16' ) np.testing.assert_allclose( - paddle_rmsnorm[0].numpy(), + paddle_rmsnorm.numpy(), paddle_naive_rmsnorm.numpy(), rtol=2, atol=2, @@ -279,7 +279,7 @@ def test_residual_bias_add_rmsnorm_fp16(self): ) np.testing.assert_allclose( - paddle_rmsnorm[0].numpy(), + paddle_rmsnorm.numpy(), paddle_naive_rmsnorm.numpy(), rtol=1e-3, atol=1e-3, @@ -304,7 +304,7 @@ def test_residual_bias_add_rmsnorm_int8(self): ) np.testing.assert_allclose( - paddle_rmsnorm[0].numpy(), + paddle_rmsnorm.numpy(), paddle_naive_rmsnorm.numpy(), rtol=2, atol=2, @@ -408,7 +408,7 @@ def check_rmsnorm(self, x_np, gamma_np, beta_np, dtype): beta_static, self.epsilon, begin_norm_axis=1, - ) + )[0] exe = base.Executor(self.place) out_s = exe.run( feed={ @@ -458,7 +458,7 @@ def check_rmsnorm_int8(self, x_np, gamma_np, beta_np, dtype): quant_round_type=self.quant_round_type, quant_max_bound=self.quant_max_bound, quant_min_bound=self.quant_min_bound, - ) + )[0] exe = base.Executor(self.place) out_s = exe.run( feed={ @@ -511,7 +511,7 @@ def check_residual_bias_rmsnorm( begin_norm_axis=1, bias=bias_static, residual=residual_static, - ) + )[0] exe = base.Executor(self.place) out_s = exe.run( @@ -610,7 +610,7 @@ def check_rmsnorm(self, x_np, gamma_np, beta_np, dtype): paddle_rmsnorm_out = paddle.incubate.nn.functional.fused_rms_norm( x, gamma, beta, self.epsilon, begin_norm_axis=1 - ) + )[0] paddle_naive_rmsnorm_out = naive_rms_norm(x, gamma, beta, self.epsilon) paddle.enable_static() return paddle_rmsnorm_out, paddle_naive_rmsnorm_out @@ -652,7 +652,7 @@ def test_rmsnorm(self): self.x_np, self.norm_weight_np, self.norm_bias_np, 'float32' ) np.testing.assert_allclose( - paddle_rmsnorm[0].numpy(), + paddle_rmsnorm.numpy(), paddle_naive_rmsnorm.numpy(), rtol=1e-3, atol=1e-3, @@ -731,7 +731,7 @@ def check_rmsnorm(self, x_np, gamma_np, beta_np, dtype): beta_static, self.epsilon, begin_norm_axis=1, - ) + )[0] exe = base.Executor(self.place) out_s = exe.run( feed={ @@ -784,7 +784,7 @@ def check_residual_bias_rmsnorm( begin_norm_axis=1, bias=bias_static, residual=residual_static, - ) + )[0] exe = base.Executor(self.place) out_s = exe.run( From c6b2115cdc00823b34987f838b27164909e7799b Mon Sep 17 00:00:00 2001 From: RAM <141618702+gongshaotian@users.noreply.github.com> Date: Tue, 10 Dec 2024 21:13:41 +0800 Subject: [PATCH 272/288] =?UTF-8?q?Revert=20"=E3=80=90Infer=20Symbolic=20S?= =?UTF-8?q?hape=20No.232=E3=80=91Add=20infer=5Fsymbol=5Fshape=20for=20Stri?= =?UTF-8?q?dedSlice=20=E2=80=A6"=20(#70061)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit d72d6ad1e22897a1681acc2338fad8e9a6edd9cb. --- .../infer_sym_slice_utils.h | 216 ------------------ .../infer_symbolic_shape/unary_infer_sym.cc | 42 +--- .../infer_symbolic_shape/unary_infer_sym.h | 2 +- paddle/phi/ops/yaml/ops.yaml | 2 +- python/paddle/base/variable_index.py | 11 +- python/paddle/tensor/manipulation.py | 17 +- test/legacy_test/test_strided_slice_op.py | 15 +- 7 files changed, 18 insertions(+), 287 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h index b97a34e9489147..c584b8306b8540 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/infer_sym_slice_utils.h @@ -139,7 +139,6 @@ inline ExprVec GetSliceDims(const ExprVec &in_dims, for (size_t i = 0; i < axes.size(); ++i) { auto out_dim = ends[i] - starts[i]; int64_t axis = axes[i]; - // If in_dims[axis] or ends[i] have symbol, nedd get Min(in_dims[axis] - // start[i], ends[i] - start[i] ) if (!out_dim.isa() && @@ -291,219 +290,4 @@ inline ShapeOrData SliceRawInferSymbolicShape( return out_shape; } - -inline ExprVec GetStridesSliceDims( - const ExprVec &in_dims, - const std::vector &axes, - const ExprVec &starts_base, - const ExprVec &ends_base, - const ExprVec &strides_base, - std::vector *infer_flags = nullptr) { - ExprVec starts = starts_base; - ExprVec ends = ends_base; - ExprVec strides = strides_base; - auto IsMaxInt = [](const symbol::DimExpr &expr) { - return expr.isa() && - expr.Get() == - static_cast(std::numeric_limits::max()); - }; - - for (size_t i = 0; i < axes.size(); ++i) { - int64_t axis = axes.at(i); - int64_t start_i = 0; - - if (starts.at(i).isa()) { - if (in_dims.at(axis).isa()) { - starts.at(i) = - (starts.at(i).Get() > in_dims.at(axis).Get()) - ? in_dims.at(axis) - : starts.at(i); - starts.at(i) = - (starts.at(i).Get() < -in_dims.at(axis).Get()) - ? symbol::DimExpr({-1}) * in_dims.at(axis) - : starts.at(i); - } - start_i = starts.at(i).Get(); - } - - int64_t end_i = 0; - if (ends.at(i).isa()) { - if (in_dims.at(axis).isa()) { - ends[i] = std::min(ends.at(i).Get(), - in_dims.at(axis).Get()); - } - if (ends.at(i).Get() < 0) { - ends[i] = ends.at(i) + in_dims.at(axis); - } - if (ends.at(i).isa()) { - end_i = ends.at(i).Get(); - } - } - - ends.at(i) = IsMaxInt(ends.at(i)) ? in_dims.at(axis) : ends.at(i); - bool both_negative_or_positive = - (start_i >= 0 && end_i >= 0) || (start_i <= 0 && end_i <= 0); - bool start_negative_end_positive = start_i <= 0 && end_i >= 0; - bool start_positive_end_negative = start_i >= 0 && end_i <= 0; - - if (both_negative_or_positive) { - continue; - } else if (start_negative_end_positive) { - starts.at(i) = starts.at(i) + in_dims.at(axis); - } else if (start_positive_end_negative) { - starts.at(i) = starts.at(i) - in_dims.at(axis); - } else { - PADDLE_THROW(common::errors::Fatal("Dead code")); - } - } - - ExprVec slice_dims(in_dims); - PADDLE_ENFORCE_EQ( - (axes.size() == starts.size() && axes.size() == ends.size() && - axes.size() == strides.size()), - true, - common::errors::InvalidArgument( - "The size of axes must equal size of starts, ends, and strides.")); - - for (size_t i = 0; i < axes.size(); ++i) { - auto out_dim = symbol::DimExpr({-1}) * ((starts[i] - ends[i]) / strides[i]); - int64_t axis = axes[i]; - - if (!out_dim.isa() && - (!in_dims[axis].isa() || !ends[i].isa())) { - symbol::List min_lists{ - symbol::DimExpr({-1}) * ((starts[i] - in_dims[axis]) / strides[i]), - out_dim}; - - slice_dims[axis] = - symbol::DimExpr({symbol::Min({min_lists})}); - } else { - slice_dims[axis] = out_dim; - } - } - - return slice_dims; -} - -inline ShapeOrData StridedSliceRawInferSymbolicShape( - const pir::Value x, - const pir::Value out, - const ExprVec &starts_expr, - const ExprVec &ends_expr, - const ExprVec &strides_expr, - const std::vector &axes_raw, - const std::vector &infer_flags_raw, - const std::vector &decrease_axis, - pir::InferSymbolicShapeContext *infer_context) { - const auto &in_shapeordata = infer_context->GetShapeOrDataForValue(x); - ExprVec starts = starts_expr; - ExprVec ends = ends_expr; - ExprVec strides = strides_expr; - std::vector infer_flags = [&infer_flags_raw, &axes_raw] { - return infer_flags_raw.empty() ? std::vector(axes_raw.size(), 1) - : infer_flags_raw; - }(); - - const auto &GetShapeDimExprs = [&]() -> symbol::ShapeOrDataDimExprs { - const ExprVec &in_dims = in_shapeordata.shape(); - std::vector axes = FormatSliceAxes(axes_raw, in_dims.size()); - ExprVec slice_dims = - GetStridesSliceDims(in_dims, axes, starts, ends, strides, &infer_flags); - ExprVec out_dims = GetDecreasedDims(slice_dims, decrease_axis); - - auto IsOne = [](const symbol::DimExpr &expr) { - return expr.isa() && expr.dyn_cast() == 1; - }; - auto IsIntType = [](pir::Value value) { - const auto &dtype = value.type().dyn_cast().dtype(); - return dtype.isa() || dtype.isa(); - }; - if (IsIntType(x) && - (out_dims.empty() || (out_dims.size() == 1 && IsOne(out_dims[0])))) { - return symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs( - out_dims, - std::vector{infer_context->GetNextSymName()})}; - } - - return symbol::ShapeOrDataDimExprs{ - symbol::TensorShapeOrDataDimExprs(out_dims)}; - }; - - // When `pd.slice` is operating on a tensor which is produced by a `pd.shape` - // op, the result should be written into data. - const auto &GetDataDimExprs = [&]() -> symbol::ShapeOrDataDimExprs { - std::vector out_data; - - // Currently, we DO NOT support the case that any element in `axes` `starts` - // or `ends` is a Symbol. - auto vec_int64 = details::VecExpr2Int64(starts); - PADDLE_ENFORCE_EQ( - vec_int64.has_value(), - true, - common::errors::InvalidArgument( - "for slice op, all the elements in `starts` must be int64_t")); - std::vector starts_int = vec_int64.value(); - - vec_int64 = details::VecExpr2Int64(ends); - PADDLE_ENFORCE_EQ( - vec_int64.has_value(), - true, - common::errors::InvalidArgument( - "for slice op, all the elements in `ends` must be int64_t")); - std::vector ends_int = vec_int64.value(); - - vec_int64 = details::VecExpr2Int64(strides); - PADDLE_ENFORCE_EQ( - vec_int64.has_value(), - true, - common::errors::InvalidArgument( - "for slice op, all the elements in `strides` must be int64_t")); - - const int64_t start = - starts_int[0] < 0 ? starts_int[0] + in_shapeordata.data().value().size() - : starts_int[0]; - const int64_t end = [&]() -> int64_t { - if (ends_int[0] < 0) { - return ends_int[0] + in_shapeordata.data().value().size(); - } - if (ends_int[0] == - static_cast(std::numeric_limits::max())) { - return in_shapeordata.data().value().size(); - } - return ends_int[0]; - }(); - - const int64_t stride = [&]() -> int64_t { - if (strides[0].isa()) { - return strides[0].Get(); - } - return 1; - }(); - - for (int64_t i = start; i < end; i += stride) { - out_data.push_back(in_shapeordata.data().value().at(i)); - } - - const ExprVec shape = GetDecreasedDims( - ExprVec{static_cast(out_data.size())}, decrease_axis); - return symbol::ShapeOrDataDimExprs{ - symbol::TensorShapeOrDataDimExprs(shape, out_data)}; - }; - - const auto &out_shape = in_shapeordata.data().has_value() - ? GetDataDimExprs() - : GetShapeDimExprs(); - if (out_shape.data().has_value() && out_shape.shape().empty()) { // 0D tensor - const paddle::dialect::DenseTensorType &tensor_type = - out.type().dyn_cast(); - const auto &out_ddim = tensor_type.dims(); - if (out_ddim.size() == 1 && out_ddim[0] == 1) { // value is 1D - return symbol::ShapeOrDataDimExprs{symbol::TensorShapeOrDataDimExprs( - std::vector{1}, out_shape.data().value())}; - } - } - - return out_shape; -} - } // namespace paddle::dialect::slice_utils diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc index 4577ea37cd12cd..daf28e51e4ce1a 100644 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.cc @@ -3515,42 +3515,12 @@ bool SplitWithNumOpInferSymbolicShape( return true; } -bool StridedSliceOpInferSymbolicShape( - pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { - pir::Value operand_source = op->operand_source(0); - pir::Value operand_starts = op->operand_source(1); - pir::Value operand_ends = op->operand_source(2); - pir::Value operand_strides = op->operand_source(3); - pir::Value res = op->result(0); - - const symbol::ShapeOrDataDimExprs &starts_shape_data = - infer_context->GetShapeOrDataForValue(operand_starts); - const symbol::ShapeOrDataDimExprs &ends_shape_data = - infer_context->GetShapeOrDataForValue(operand_ends); - const symbol::ShapeOrDataDimExprs &strides_shape_data = - infer_context->GetShapeOrDataForValue(operand_strides); - - ExprVec starts = slice_utils::GetExprVecFromData(starts_shape_data); - ExprVec ends = slice_utils::GetExprVecFromData(ends_shape_data); - ExprVec strides = slice_utils::GetExprVecFromData(strides_shape_data); - - std::vector axes_vec = details::GetVectorAttr(op, "axes"); - std::vector axes_vec_64(axes_vec.begin(), axes_vec.end()); - - infer_context->SetShapeOrDataForValue( - res, - slice_utils::StridedSliceRawInferSymbolicShape(operand_source, - res, - starts, - ends, - strides, - axes_vec_64, - std::vector{}, - std::vector{}, - infer_context)); - - return true; -} +// bool StridedSliceOpInferSymbolicShape(pir::Operation *op, +// pir::InferSymbolicShapeContext +// *infer_context) { +// // pass +// return true; +// } bool SumOpInferSymbolicShape(pir::Operation *op, pir::InferSymbolicShapeContext *infer_context) { diff --git a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h index 7789c9718669f3..4004f4afd48b0d 100755 --- a/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h +++ b/paddle/fluid/pir/dialect/operator/interface/infer_symbolic_shape/unary_infer_sym.h @@ -139,7 +139,7 @@ OP_DECLARE_INFER_SYMBOLIC_SHAPE(SplitWithNum) OP_DECLARE_INFER_SYMBOLIC_SHAPE(SquaredL2Norm) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Squeeze) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Squeeze_) -OP_DECLARE_INFER_SYMBOLIC_SHAPE(StridedSlice) +// OP_DECLARE_INFER_SYMBOLIC_SHAPE(StridedSlice) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Sum) OP_DECLARE_INFER_SYMBOLIC_SHAPE(Svd) OP_DECLARE_INFER_SYMBOLIC_SHAPE(SetValue) diff --git a/paddle/phi/ops/yaml/ops.yaml b/paddle/phi/ops/yaml/ops.yaml index 4275e5f72153dd..5d85e1a78993e9 100755 --- a/paddle/phi/ops/yaml/ops.yaml +++ b/paddle/phi/ops/yaml/ops.yaml @@ -4800,7 +4800,7 @@ kernel : func : strided_slice backward : strided_slice_grad - interfaces : paddle::dialect::InferSymbolicShapeInterface + # interfaces : paddle::dialect::InferSymbolicShapeInterface - op : sum args : (Tensor x, IntArray axis={}, DataType dtype=DataType::UNDEFINED, bool keepdim=false) diff --git a/python/paddle/base/variable_index.py b/python/paddle/base/variable_index.py index 4cbe8bc49b29c3..9a809f75a2233a 100644 --- a/python/paddle/base/variable_index.py +++ b/python/paddle/base/variable_index.py @@ -764,16 +764,7 @@ def get_tensor_with_basic_indexing( stride = attrs['strides'] if use_strided_slice: # TODO(zoooo0820): support strided_slice_array until PIR API is ready - if in_pir_mode(): - if isinstance(st, (list, tuple)): - if paddle.utils._contain_var(st): - st = paddle.utils.get_int_tensor_list(st) - if isinstance(end, (list, tuple)): - if paddle.utils._contain_var(end): - end = paddle.utils.get_int_tensor_list(end) - if isinstance(stride, (list, tuple)): - if paddle.utils._contain_var(stride): - stride = paddle.utils.get_int_tensor_list(stride) + out = paddle._C_ops.strided_slice(x, axes, st, end, stride) if len(decrease_axes) > 0: out = paddle._C_ops.squeeze(out, decrease_axes) diff --git a/python/paddle/tensor/manipulation.py b/python/paddle/tensor/manipulation.py index a20f4e3e0ea3bc..68ad8f091f6f68 100644 --- a/python/paddle/tensor/manipulation.py +++ b/python/paddle/tensor/manipulation.py @@ -5632,22 +5632,7 @@ def strided_slice( >>> sliced_2 = paddle.strided_slice(x, axes=axes, starts=[minus_3, 0, 2], ends=ends, strides=strides_2) >>> # sliced_2 is x[:, 1:3:1, 0:2:1, 2:4:2]. """ - if in_dynamic_mode(): - return _C_ops.strided_slice(x, axes, starts, ends, strides) - elif in_pir_mode(): - - def _convert_to_tensor_list(input): - if isinstance(input, paddle.pir.Value): - input.stop_gradient = True - elif isinstance(input, (list, tuple)): - if paddle.utils._contain_var(input): - input = paddle.utils.get_int_tensor_list(input) - return input - - starts = _convert_to_tensor_list(starts) - ends = _convert_to_tensor_list(ends) - strides = _convert_to_tensor_list(strides) - + if in_dynamic_or_pir_mode(): return _C_ops.strided_slice(x, axes, starts, ends, strides) else: helper = LayerHelper('strided_slice', **locals()) diff --git a/test/legacy_test/test_strided_slice_op.py b/test/legacy_test/test_strided_slice_op.py index 03664f7768d448..eec7c3ae019d58 100644 --- a/test/legacy_test/test_strided_slice_op.py +++ b/test/legacy_test/test_strided_slice_op.py @@ -326,6 +326,7 @@ def setUp(self): starts_tensor.append( ("x" + str(index), np.ones(1).astype('int32') * ele) ) + self.inputs = {'Input': self.input, 'StartsTensorList': starts_tensor} self.outputs = {'Out': self.output} self.attrs = { @@ -350,7 +351,7 @@ def config(self): self.starts_infer = [1, 10, 2] def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -394,7 +395,7 @@ def config(self): self.ends_infer = [3, 1, 4] def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -432,7 +433,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -470,7 +471,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -515,7 +516,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -553,7 +554,7 @@ def config(self): ) def test_check_output(self): - self.check_output(check_pir=True, check_symbol_infer=False) + self.check_output(check_pir=True) def test_check_grad_normal(self): self.check_grad( @@ -643,7 +644,7 @@ def test_dygraph_op(self): sliced_1 = paddle.strided_slice( x, axes=axes, starts=starts, ends=ends, strides=strides_1 ) - assert sliced_1.shape == [3, 2, 2, 2] + assert sliced_1.shape == (3, 2, 2, 2) @unittest.skipIf( not paddle.is_compiled_with_cuda(), From b9de5c6857048739bad70f716dd09c1ebfb53d77 Mon Sep 17 00:00:00 2001 From: doggy-tao <3160391266@qq.com> Date: Wed, 11 Dec 2024 10:15:42 +0800 Subject: [PATCH 273/288] [Prim][Pir] Decomp diag op (#69998) * decomp diag op * add 'comp' type * add diag to dynamic blacklist --- .../decomp_interface_gen_op_list.py | 2 + paddle/fluid/primitive/base/decomp_trans.cc | 7 ++- .../decomp_rule/decomp_rule/composite.h | 43 +++++++++++++++++++ test/legacy_test/test_diag_v2.py | 14 ++++-- 4 files changed, 60 insertions(+), 6 deletions(-) diff --git a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py index 85722cb83a0e1b..7b60d68ac35f55 100644 --- a/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py +++ b/paddle/fluid/pir/dialect/op_generator/decomp_interface_gen_op_list.py @@ -27,6 +27,7 @@ "bce_loss", "bmm", "clip", + "diag", "dropout", "eye", "elu", @@ -81,6 +82,7 @@ "addmm", "bce_loss", "bmm", + "diag", "dropout", "eye", "elu", diff --git a/paddle/fluid/primitive/base/decomp_trans.cc b/paddle/fluid/primitive/base/decomp_trans.cc index 2e8f4c99888c52..f1ffdc9c3985b4 100644 --- a/paddle/fluid/primitive/base/decomp_trans.cc +++ b/paddle/fluid/primitive/base/decomp_trans.cc @@ -50,8 +50,11 @@ std::unordered_set decomp_op_contain_none = { }; // -std::unordered_set dynamic_shape_blacklist = { - "pd_op.squeeze", "pd_op.unsqueeze", "pd_op.flatten", "pd_op.eye"}; +std::unordered_set dynamic_shape_blacklist = {"pd_op.squeeze", + "pd_op.unsqueeze", + "pd_op.flatten", + "pd_op.eye", + "pd_op.diag"}; namespace { std::set StringSplit(const std::string& str) { diff --git a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h index 2a670c3485719f..4c7de4db2583c1 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_rule/composite.h @@ -1435,6 +1435,49 @@ Tensor eye_decomp(const paddle::Scalar& num_rows, return ConverToOrig(res, dtype); } + +template +Tensor diag_decomp(const Tensor& x, + const int& offset = 0, + const float& padding_value = 0.0) { + Tensor cast_x = ConverToMT(x); + int64_t rank = cast_x.dims().size(); + Tensor res; + if (rank == 1) { + std::vector x_dims = cast_x.shape(); + int64_t n = x_dims[0]; + int64_t m = n + std::abs(offset); + + Tensor result = + full({m, m}, padding_value, cast_x.dtype(), cast_x.place()); + Tensor padding = full( + {std::abs(offset)}, padding_value, cast_x.dtype(), cast_x.place()); + + Tensor x_padding = + unsqueeze(roll(concat({cast_x, padding}, 0), {-offset}), {1}); + Tensor indices = unsqueeze( + roll(backend::arange(0, m, 1, DataType::INT64, cast_x.place()), + {-offset}), + {1}); + + res = put_along_axis(result, indices, x_padding, 1); + + } else { + std::vector x_dims = cast_x.shape(); + int64_t n = x_dims[0]; + int64_t m = x_dims[1]; + if (offset <= -n || offset >= m) { + return res; + } + Tensor x_flat = reshape(cast_x, {n * m}); + int64_t start = offset >= 0 ? offset : -offset * m; + Tensor indices = backend::arange( + start, n * m, m + 1, DataType::INT64, cast_x.place()); + res = take_along_axis(x_flat, indices, 0); + } + return ConverToOrig(res, x.dtype()); +} + } // namespace details } // namespace primitive diff --git a/test/legacy_test/test_diag_v2.py b/test/legacy_test/test_diag_v2.py index df9ffc0f7e7642..487eacdb92d088 100644 --- a/test/legacy_test/test_diag_v2.py +++ b/test/legacy_test/test_diag_v2.py @@ -26,6 +26,8 @@ class TestDiagV2Op(OpTest): def setUp(self): self.op_type = "diag_v2" self.python_api = paddle.diag + self.prim_op_type = "comp" + self.public_python_api = paddle.diag self.init_dtype() self.init_attrs() @@ -51,11 +53,11 @@ def init_input_output(self): def test_check_output(self): paddle.enable_static() - self.check_output(check_pir=True) + self.check_output(check_pir=True, check_prim_pir=True) def test_check_grad(self): paddle.enable_static() - self.check_grad(['X'], 'Out', check_pir=True) + self.check_grad(['X'], 'Out', check_pir=True, check_prim_pir=True) class TestDiagV2OpCase1(TestDiagV2Op): @@ -323,6 +325,8 @@ class TestDiagV2BF16OP(OpTest): def setUp(self): self.op_type = "diag_v2" self.python_api = paddle.diag + self.prim_op_type = "comp" + self.public_python_api = paddle.diag self.dtype = np.uint16 x = np.random.rand(10, 10).astype(np.float32) offset = 0 @@ -339,12 +343,14 @@ def setUp(self): def test_check_output(self): paddle.enable_static() place = core.CUDAPlace(0) - self.check_output_with_place(place, check_pir=True) + self.check_output_with_place(place, check_pir=True, check_prim_pir=True) def test_check_grad(self): paddle.enable_static() place = core.CUDAPlace(0) - self.check_grad_with_place(place, ['X'], 'Out', check_pir=True) + self.check_grad_with_place( + place, ['X'], 'Out', check_pir=True, check_prim_pir=True + ) class TestDiagV2Complex64OP(TestDiagV2Op): From f4c37351e4c9a0e655116a33bcf40abbe8c99cf8 Mon Sep 17 00:00:00 2001 From: Hongqing-work <76149632+Hongqing-work@users.noreply.github.com> Date: Wed, 11 Dec 2024 10:28:30 +0800 Subject: [PATCH 274/288] [CINN]Add EqCstr for broadcast substitute (#70093) --- .../dialect/shape/utils/constraints_manager.cc | 18 ++++++++++++++++++ .../shape_dialect/constraints_manager_test.cc | 14 ++++++++++++++ 2 files changed, 32 insertions(+) diff --git a/paddle/pir/src/dialect/shape/utils/constraints_manager.cc b/paddle/pir/src/dialect/shape/utils/constraints_manager.cc index 5b06cbcd32a8be..dbc507efdb802e 100644 --- a/paddle/pir/src/dialect/shape/utils/constraints_manager.cc +++ b/paddle/pir/src/dialect/shape/utils/constraints_manager.cc @@ -103,6 +103,24 @@ void ConstraintsManager::AddEqCstr(const DimExpr& lhs, const DimExpr& rhs) { return; } + const auto& AddEqCstrForBroadcastSubstitute = [&](const DimExpr& bc_dimexpr, + const DimExpr& + string_dimexpr) { + if (!bc_dimexpr.isa>()) return; + if (!string_dimexpr.isa()) return; + const auto& [operands] = bc_dimexpr.Get>(); + for (const auto& operand : *operands) { + if (operand == string_dimexpr) return; + } + for (const auto& operand : *operands) { + AddEqCstr(Broadcast{{operand, string_dimexpr}}, string_dimexpr); + } + }; + if (lhs.isa>() && rhs.isa()) + AddEqCstrForBroadcastSubstitute(lhs, rhs); + if (rhs.isa>() && lhs.isa()) + AddEqCstrForBroadcastSubstitute(rhs, lhs); + auto simplify_result = SimplifyEqCstr(lhs, rhs); if (simplify_result.first != lhs && simplify_result.second != rhs) { AddEqCstr(simplify_result.first, simplify_result.second); diff --git a/test/cpp/pir/shape_dialect/constraints_manager_test.cc b/test/cpp/pir/shape_dialect/constraints_manager_test.cc index d996d64100a426..f4a6b5a12b240c 100644 --- a/test/cpp/pir/shape_dialect/constraints_manager_test.cc +++ b/test/cpp/pir/shape_dialect/constraints_manager_test.cc @@ -61,4 +61,18 @@ TEST(ConstraintsManager, BroadcastableCstr) { ASSERT_TRUE(cstr_mgr.IsBroadcastable(sym_expr_0, int_expr)); } +TEST(ConstraintsManager, Case1) { + // BC(S0, S1) == S2 => BC(S0, S2) == S2 + ConstraintsManager cstr_mgr; + DimExpr s0 = DimExpr("S0"); + DimExpr s1 = DimExpr("S1"); + DimExpr s2 = DimExpr("S2"); + DimExpr bc_s0_s1 = Broadcast{{s0, s1}}; + cstr_mgr.AddEqCstr(bc_s0_s1, s2); + cstr_mgr.AddBroadcastableCstr(s0, s1); + DimExpr bc_s0_s2 = Broadcast{{s0, s2}}; + cstr_mgr.AddBroadcastableCstr(s0, s2); + ASSERT_TRUE(cstr_mgr.IsEqual(bc_s0_s2, s2)); +} + } // namespace symbol::test From 7cff4b87dd1471bef7b89432cc1e8f34f00f532b Mon Sep 17 00:00:00 2001 From: rich04lin <152049331+rich04lin@users.noreply.github.com> Date: Wed, 11 Dec 2024 10:30:20 +0800 Subject: [PATCH 275/288] [CodeStyle][Typos][C-[48-51]] Fix typos (`comsume`, `Continer`, `contenst`, `conter`) (#70095) * [CodeStyle][Typos][B-14,B-[17-19]] Fix typos * [CodeStyle][Typos][B-14,B-[17-19]] Fix typos(Broardcast,Bradcast,Boardcast,buitin,buitlin,Buitin,builded,ba) * [CodeStyle][Typos][C-[4-9] Fix typos(cacl,cll,candiate,cadidate,connot,CANN,Cann,cann,vart) * c4-9 * [CodeStyle][Typos][B-14,B-[17-19]] Fix typos(Broardcast,Bradcast,Boardcast,buitin,buitlin,Buitin,builded,ba) * c48-51 --- _typos.toml | 4 ---- paddle/fluid/distributed/ps/service/brpc_ps_client.cc | 4 ++-- .../new_executor/instruction/control_flow/if_instruction.cc | 5 +++-- .../instruction/control_flow/while_instruction.cc | 2 +- .../framework/new_executor/instruction/instruction_util.cc | 4 ++-- .../framework/new_executor/instruction/instruction_util.h | 2 +- paddle/phi/kernels/funcs/fft_key.h | 4 ++-- python/paddle/distributed/passes/ps_trainer_pass.py | 2 +- 8 files changed, 12 insertions(+), 15 deletions(-) diff --git a/_typos.toml b/_typos.toml index 86b9f2bb7cd4b4..d5d8360c0e6fee 100644 --- a/_typos.toml +++ b/_typos.toml @@ -41,10 +41,6 @@ caculate = 'caculate' calcualtion = 'calcualtion' checkings = 'checkings' childs = 'childs' -comsume = 'comsume' -Continer = 'Continer' -contenst = 'contenst' -conter = 'conter' Continous = 'Continous' contibute = 'contibute' controled = 'controled' diff --git a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc index 519d39484a7c55..a724e55be391b6 100644 --- a/paddle/fluid/distributed/ps/service/brpc_ps_client.cc +++ b/paddle/fluid/distributed/ps/service/brpc_ps_client.cc @@ -1531,7 +1531,7 @@ std::future BrpcPsClient::PushSparse(size_t table_id, CostTimer parse_timer("pserver_client_push_sparse_parse"); int push_sparse_async_num = _push_sparse_task_queue_map[table_id]->Size(); while (push_sparse_async_num > FLAGS_pserver_max_async_call_num) { - // LOG(INFO) << "PushSparse Waiting for async_call_num comsume, + // LOG(INFO) << "PushSparse Waiting for async_call_num consume, // task_num:" // << push_sparse_async_num // << ", max_task_limit:" << FLAGS_pserver_max_async_call_num; @@ -1892,7 +1892,7 @@ std::future BrpcPsClient::PushDense(const Region *regions, std::make_shared("pserver_client_push_dense_parse"); int push_dense_async_num = _push_dense_task_queue_map[table_id]->Size(); while (push_dense_async_num > FLAGS_pserver_max_async_call_num) { - // LOG(INFO) << "PushDense Waiting for async_call_num comsume, + // LOG(INFO) << "PushDense Waiting for async_call_num consume, // task_num:" // << push_dense_async_num // << ", max_task_limit:" << FLAGS_pserver_max_async_call_num; diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc index bbbcaf9c64815a..1b1231359fe833 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/if_instruction.cc @@ -121,8 +121,9 @@ IfInstruction::IfInstruction(size_t id, is_last_op = false; } } - InsertTuplePushContinerToOuts(&true_branch_block, *value_exec_info, &outputs); - InsertTuplePushContinerToOuts( + InsertTuplePushContainerToOuts( + &true_branch_block, *value_exec_info, &outputs); + InsertTuplePushContainerToOuts( &if_op.false_block(), *value_exec_info, &outputs); InsertInplacedExternalInputsToOuts( diff --git a/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc b/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc index bdd6c97e61631d..d807c64ccee7d2 100644 --- a/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/control_flow/while_instruction.cc @@ -110,7 +110,7 @@ WhileInstruction::WhileInstruction( outputs.emplace(value, outputs_id); } } - InsertTuplePushContinerToOuts(body_block_, *parent_exe_info, &outputs); + InsertTuplePushContainerToOuts(body_block_, *parent_exe_info, &outputs); InsertInplacedExternalInputsToOuts( body_block_, body_outside_inputs, *parent_exe_info, &outputs); SetOutputs(outputs); diff --git a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc index 65beeb8dfeb27f..19b3c29a2c485d 100644 --- a/paddle/fluid/framework/new_executor/instruction/instruction_util.cc +++ b/paddle/fluid/framework/new_executor/instruction/instruction_util.cc @@ -403,7 +403,7 @@ std::unordered_set GetTuplePushContainer(pir::Block* block) { return inner_outputs; } -void InsertTuplePushContinerToOuts( +void InsertTuplePushContainerToOuts( pir::Block* block, const ValueExecutionInfo& value_exec_info, std::unordered_map>* outputs) { @@ -412,7 +412,7 @@ void InsertTuplePushContinerToOuts( for (pir::Value value : inner_stack_outputs) { outputs->emplace(value, GetValueIds(value, value_exec_info)); - VLOG(6) << "InsertTuplePushContinerToOuts of " << value.impl(); + VLOG(6) << "InsertTuplePushContainerToOuts of " << value.impl(); } } diff --git a/paddle/fluid/framework/new_executor/instruction/instruction_util.h b/paddle/fluid/framework/new_executor/instruction/instruction_util.h index 787c1099044a84..2887d3c4aca2ff 100644 --- a/paddle/fluid/framework/new_executor/instruction/instruction_util.h +++ b/paddle/fluid/framework/new_executor/instruction/instruction_util.h @@ -50,7 +50,7 @@ std::vector GetExternalInputs( const ValueExecutionInfo& value_exec_info, std::unordered_map>* input_ids); -void InsertTuplePushContinerToOuts( +void InsertTuplePushContainerToOuts( pir::Block* block, const ValueExecutionInfo& value_exec_info, std::unordered_map>* outputs); diff --git a/paddle/phi/kernels/funcs/fft_key.h b/paddle/phi/kernels/funcs/fft_key.h index 8a577754cf051e..d0e6f603bd3771 100644 --- a/paddle/phi/kernels/funcs/fft_key.h +++ b/paddle/phi/kernels/funcs/fft_key.h @@ -58,7 +58,7 @@ struct FFTConfigKey { template struct KeyHash { // Key must be a POD because we read out its memory - // contenst as char* when hashing + // contents as char* when hashing static_assert(std::is_pod::value, "Key must be plain old data type"); size_t operator()(const Key& params) const { @@ -75,7 +75,7 @@ struct KeyHash { template struct KeyEqual { // Key must be a POD because we read out its memory - // contenst as char* when comparing + // contents as char* when comparing static_assert(std::is_pod::value, "Key must be plain old data type"); bool operator()(const Key& a, const Key& b) const { diff --git a/python/paddle/distributed/passes/ps_trainer_pass.py b/python/paddle/distributed/passes/ps_trainer_pass.py index 522bf6daa4bc48..84860d0bc38075 100755 --- a/python/paddle/distributed/passes/ps_trainer_pass.py +++ b/python/paddle/distributed/passes/ps_trainer_pass.py @@ -1014,7 +1014,7 @@ def _create_heter_program( block_var_detail[stage_id - 1]["backward"]["persistables"], ) - # add step conter + # add step counter send_input_vars = [] dummy_output = [] pserver_endpoints = get_ps_endpoints(role_maker) From f4a2b24776079deef91ef9c99930ebe070240d99 Mon Sep 17 00:00:00 2001 From: zhengzhonghui Date: Wed, 11 Dec 2024 10:48:13 +0800 Subject: [PATCH 276/288] [Auto Parallelfix attn_mask spmd rules (#70100) --- paddle/phi/infermeta/spmd_rules/flash_attention.cc | 9 +++++++++ 1 file changed, 9 insertions(+) diff --git a/paddle/phi/infermeta/spmd_rules/flash_attention.cc b/paddle/phi/infermeta/spmd_rules/flash_attention.cc index e755138d6d0e45..2c4bc2780b35f4 100644 --- a/paddle/phi/infermeta/spmd_rules/flash_attention.cc +++ b/paddle/phi/infermeta/spmd_rules/flash_attention.cc @@ -274,6 +274,9 @@ SpmdInfo FlashAttInferSpmd(const DistMetaTensor& q, if (!IsEmpty(attn_mask_shape)) { attn_mask_dist_attr_dst = MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + if (attn_mask_shape[1] == 1) { + attn_mask_dist_attr_dst = UnShardTensorDims(attn_mask_dist_attr_dst, {1}); + } } // TODO(liuzhenhai): process fixed_seed @@ -527,6 +530,9 @@ SpmdInfo FlashAttInferSpmdReverse(const DistMetaTensor& q, if (!IsEmpty(attn_mask_shape)) { attn_mask_dist_attr_dst = MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + if (attn_mask_shape[1] == 1) { + attn_mask_dist_attr_dst = UnShardTensorDims(attn_mask_dist_attr_dst, {1}); + } } // TODO(liuzhenhai): process fixed_seed @@ -800,6 +806,9 @@ SpmdInfo FlashAttGradInferSpmd(const DistMetaTensor& q, if (!IsEmpty(attn_mask_shape)) { attn_mask_dist_attr_dst = MapDims(attn_mask_dist_attr, axis_to_dim_map, attn_mask_axes); + if (attn_mask_shape[1] == 1) { + attn_mask_dist_attr_dst = UnShardTensorDims(attn_mask_dist_attr_dst, {1}); + } } // TODO(liuzhenhai): process seed and attn_mask From 0b1e491057c599b1b8c49487f9a47eacd8c92742 Mon Sep 17 00:00:00 2001 From: tianshuo78520a Date: Wed, 11 Dec 2024 10:54:56 +0800 Subject: [PATCH 277/288] =?UTF-8?q?Revert=20"[Inference]Fix=20PaddleX=20mo?= =?UTF-8?q?del=20bugs=20when=20convert=20to=20pir-trt=20(Part2)=20(#6?= =?UTF-8?q?=E2=80=A6"=20(#70122)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit This reverts commit 0f66ede65610a84639aa2ef549ef63a908b92ce9. --- .../tensorrt_engine_instruction.cc | 66 ++++++++++++++++--- python/paddle/tensorrt/converter.py | 10 +-- python/paddle/tensorrt/converter_utils.py | 36 +++++----- python/paddle/tensorrt/impls/common.py | 41 ++++++++---- python/paddle/tensorrt/impls/creation.py | 20 +++--- test/cpp/inference/tensorrt/CMakeLists.txt | 34 +++++----- 6 files changed, 130 insertions(+), 77 deletions(-) diff --git a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc index 1ca2688844c8a1..269bc547b35d30 100644 --- a/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc +++ b/paddle/fluid/framework/new_executor/instruction/tensorrt_engine_instruction.cc @@ -239,8 +239,10 @@ static phi::DataType TRT2PaddleDataType(nvinfer1::DataType type) { "to paddle. Does the downstream paddle op here support int64?"; return phi::DataType::INT64; #endif +#if IS_TRT_VERSION_GE(7000) case nvinfer1::DataType::kBOOL: return phi::DataType::BOOL; +#endif default: PADDLE_THROW(common::errors::InvalidArgument( "unknown fluid datatype in Fluid op converter")); @@ -487,10 +489,11 @@ void TensorRTEngineInstruction::BindInputTensor( bind_index, num_bindings)); +#if IS_TRT_VERSION_GE(6000) +#if IS_TRT_VERSION_GE(8500) if (trt_engine_->engine()->isShapeInferenceIO(input_name.c_str()) && trt_engine_->engine()->getTensorIOMode(input_name.c_str()) == nvinfer1::TensorIOMode::kINPUT) { - shape_v.resize(input_tensor.numel()); if (input_tensor.dtype() == phi::DataType::INT32) { phi::memory_utils::Copy(phi::CPUPlace(), shape_v.data(), @@ -521,6 +524,41 @@ void TensorRTEngineInstruction::BindInputTensor( input_name.c_str(), paddle::platform::Vec2TRT_Dims(input_shape, input_name, true)); } +#else + trt_context->setBindingDimensions( + bind_index, + paddle::platform::Vec2TRT_Dims(input_shape, input_name, true)); + // If this x is a shape tensor, we need call setInputShapeBinding + if (trt_engine_->engine()->isShapeBinding(bind_index) && + trt_engine_->engine()->bindingIsInput(bind_index)) { + if (input_tensor.dtype() == phi::DataType::INT32) { + phi::memory_utils::Copy(phi::CPUPlace(), + shape_v.data(), + input_tensor.place(), + input_tensor.data(), + input_tensor.numel() * sizeof(int), + nullptr); + } else if (input_tensor.dtype() == phi::DataType::INT64) { + std::string x_t = input_name + "_cast_to_INT32"; + if (scope.FindVar(x_t) == nullptr) { + const_cast(&scope)->Var(x_t); + } + auto int32_tensor = scope.FindVar(x_t)->GetMutable(); + *int32_tensor = phi::Cast( + reinterpret_cast(*dev_ctx_), + input_tensor, + phi::DataType::INT32); + phi::memory_utils::Copy(phi::CPUPlace(), + shape_v.data(), + int32_tensor->place(), + int32_tensor->data(), + int32_tensor->numel() * sizeof(int), + nullptr); + } + trt_context->setInputShapeBinding(bind_index, shape_v.data()); + } +#endif +#endif *runtime_batch = input_shape[0]; VLOG(1) << "trt input [" << input_name << "] dtype is " @@ -572,10 +610,11 @@ void TensorRTEngineInstruction::BindInputTensor( } else if (input_tensor.dtype() == phi::DataType::FLOAT16) { buffers[bind_index] = static_cast( const_cast(input_tensor.data())); +#if IS_TRT_VERSION_GE(8400) } else if (input_tensor.dtype() == phi::DataType::BOOL) { buffers[bind_index] = static_cast(const_cast(input_tensor.data())); - +#endif } else { PADDLE_THROW(common::errors::Fatal( "The TRT Engine OP only support " @@ -616,6 +655,7 @@ void TensorRTEngineInstruction::BindOutputTensor( #endif std::vector ddim; +#if IS_TRT_VERSION_GE(8500) auto x_name = trt_engine_->engine()->getIOTensorName(bind_index); auto dims = trt_context->getTensorShape(x_name); int nb_dims = dims.nbDims; @@ -627,6 +667,18 @@ void TensorRTEngineInstruction::BindOutputTensor( for (int i = 0; i < nb_dims; i++) { ddim.push_back(dims.d[i]); } +#else + auto dims = trt_context->getBindingDimensions(bind_index); + int nb_dims = dims.nbDims; + for (; nb_dims > 0; nb_dims--) { + // some 'x 1' of shape is normal, no need to remove it + if (dims.d[nb_dims - 1] != 1 || nb_dims == outputs_rank_[output_index]) + break; + } + for (int i = 0; i < nb_dims; i++) { + ddim.push_back(dims.d[i]); + } +#endif auto *fluid_t = output_tensor; fluid_t->Resize(common::make_ddim(ddim)); @@ -669,13 +721,14 @@ void TensorRTEngineInstruction::RunTrt() { "can not find var[%s] in scope", in_var_name)); auto in_var = scope.FindVar(in_var_name); auto &in_variable_array = in_var->Get(); - // we will use shape_input when input is a shape tensor std::vector> shape_inputs(in_variable_array.size()); for (const auto &index_name_pair : input_names_) { size_t i = index_name_pair.first; if (in_variable_array[i]->IsType()) { auto input_tensor = in_variable_array[i]->Get(); + // we will use shape_input when input is a shape tensor + shape_inputs[i].resize(input_tensor.numel()); // Bind input tensor to TRT. BindInputTensor(index_name_pair.second, input_tensor, @@ -765,13 +818,6 @@ void TensorRTEngineInstruction::RunTrt() { } void TensorRTEngineInstruction::Run() { -#if IS_TRT_VERSION_LT(8500) - PADDLE_THROW( - common::errors::Unimplemented("PIR-TRT only support TensorRT " - "version that is >= 8.5," - "Please check your TensorRT " - "in your env.")); -#endif PrepareDynamicShape(); RunTrt(); } diff --git a/python/paddle/tensorrt/converter.py b/python/paddle/tensorrt/converter.py index bd646b6560ea79..6b290bbfc24739 100644 --- a/python/paddle/tensorrt/converter.py +++ b/python/paddle/tensorrt/converter.py @@ -87,7 +87,6 @@ def __init__(self, paddle_program, scope, trt_config=None): self.input_info = {} self.trt_output_value_map = {} - self.engine_num = 0 def find_graph_inputs_outputs(self, group_op): operations = next(iter(group_op.blocks())).ops @@ -192,7 +191,7 @@ def convert_subgraph_to_trt(self, program, group_op): for operand in op.operands(): source = operand.source() if not source.initialized(): - operands.append(None) + _logger.warning(f"Skipping uninitialized source: {source}") continue define_op_name = source.get_defining_op().name() if define_op_name == "builtin.combine": @@ -457,12 +456,10 @@ def convert_subgraph_to_trt(self, program, group_op): % 10**8 ) CACHE_ROOT = get_cache_path() - CACHE_FILE = f"{CACHE_ROOT}/engine_{engine_name}_{self.engine_num}.trt" + CACHE_FILE = f"{CACHE_ROOT}/engine_{engine_name}.trt" with open(CACHE_FILE, "wb") as f: f.write(trt_engine) - PIR_DUMP_FILE = ( - f"{CACHE_ROOT}/engine_{engine_name}_{self.engine_num}.pir" - ) + PIR_DUMP_FILE = f"{CACHE_ROOT}/engine_{engine_name}.pir" with open(PIR_DUMP_FILE, "w") as f: f.write(group_str) trt_params.engine_serialized_data = CACHE_FILE @@ -523,7 +520,6 @@ def convert_program_to_trt(self): for op in self.program.global_block().ops: if op.name() == "cinn_op.group" or op.name() == "builtin.group": _logger.info(f"start process {op.name()}") - self.engine_num += 1 new_out = self.convert_subgraph_to_trt(self.program, op) orin_out_values = op.results() for o_i in range(len(orin_out_values)): diff --git a/python/paddle/tensorrt/converter_utils.py b/python/paddle/tensorrt/converter_utils.py index 09e5f3a70d9638..b83ffe787f0c33 100644 --- a/python/paddle/tensorrt/converter_utils.py +++ b/python/paddle/tensorrt/converter_utils.py @@ -271,21 +271,6 @@ def trt_reshape(network, input, new_shape, name="", is_shape_tensor=False): return reshape_layer.get_output(0) -# resize shape tensor's shape to 1dim -def resize_to_1d(network, shape_tensor): - if shape_tensor is None: - return shape_tensor - if len(shape_tensor.shape) > 1: - # shape_tensor need 1-dim in trt - shape_tensor_layer = network.add_shuffle(shape_tensor) - numel = 1 - for ele in shape_tensor.shape: - numel *= ele - shape_tensor_layer.reshape_dims = [numel] - shape_tensor = shape_tensor_layer.get_output(0) - return shape_tensor - - # Get element tensor of 1D shape tensor def get_shape_tensor_element(network, x, index, is_scalar=False): assert ( @@ -293,8 +278,7 @@ def get_shape_tensor_element(network, x, index, is_scalar=False): ), f"The index should be greater or equal than 0, but got {index}" index_tensor = add_1D_constant_layer(network, index, is_scalar=is_scalar) gather_layer = network.add_gather(input=x, indices=index_tensor, axis=0) - shape_tensor = resize_to_1d(network, gather_layer.get_output(0)) - return shape_tensor + return gather_layer.get_output(0) def trt_less(network, a, b): @@ -430,7 +414,7 @@ def map_trt_dtype(trt_dtype): # Reduce the given tensor in the TensorRT network to a scalar -def trt_reduce_to_scalar(network, tensor, dtype=trt.int32): +def trt_reduce_to_scalar(network, tensor): if len(tensor.shape) == 0: return tensor axes = 0 @@ -439,8 +423,7 @@ def trt_reduce_to_scalar(network, tensor, dtype=trt.int32): reduce_layer = network.add_reduce( tensor, trt.ReduceOperation.SUM, axes, keep_dims=False ) - scalar = trt_cast(network, reduce_layer.get_output(0), dtype) - return scalar + return reduce_layer.get_output(0) def convert_conv2d(network, paddle_op, inputs): @@ -674,3 +657,16 @@ def squeeze_trt(network, input_tensor, axes): reshape_layer = network.add_shuffle(input_tensor) reshape_layer.set_input(1, new_shape_tensor) return reshape_layer.get_output(0) + + +# resize shape tensor's shape to 1dim +def resize_to_1d(network, shape_tensor): + if len(shape_tensor.shape) > 1: + # shape_tensor need 1-dim in trt + shape_tensor_layer = network.add_shuffle(shape_tensor) + numel = 1 + for ele in shape_tensor.shape: + numel *= ele + shape_tensor_layer.reshape_dims = [numel] + shape_tensor = shape_tensor_layer.get_output(0) + return shape_tensor diff --git a/python/paddle/tensorrt/impls/common.py b/python/paddle/tensorrt/impls/common.py index b989fa5142ab8d..a4567641fa2ab1 100644 --- a/python/paddle/tensorrt/impls/common.py +++ b/python/paddle/tensorrt/impls/common.py @@ -16,7 +16,7 @@ import numpy as np import tensorrt as trt -from paddle.tensorrt.converter_utils import get_shape_tensor_element +from paddle.tensorrt.converter_utils import get_shape_tensor_element, trt_shape from paddle.tensorrt.register import converter_registry from paddle.tensorrt.util import get_trt_version_list @@ -53,10 +53,6 @@ def dropout_converter(network, paddle_op, inputs): ) def bilinear_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape_tensor = network.add_shape(input_tensor).get_output(0) - input_rank = ( - input_shape_tensor.shape - ) # The reason is unknown that adding this unused code make input_shape_tensor maintain the correct result. data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") @@ -145,6 +141,7 @@ def bilinear_interp_converter(network, paddle_op, inputs): else: if outsize_tensor is not None: outsize_itensors = [] + input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": @@ -172,10 +169,6 @@ def bilinear_interp_converter(network, paddle_op, inputs): ) def nearest_interp_converter(network, paddle_op, inputs): input_tensor = inputs[0] - input_shape_tensor = network.add_shape(input_tensor).get_output(0) - input_rank = ( - input_shape_tensor.shape - ) # The reason is unknown that adding this unused code make input_shape_tensor maintain the correct result. data_format = paddle_op.attrs().get("data_format") interp_method = paddle_op.attrs().get("interp_method") align_corners = paddle_op.attrs().get("align_corners") @@ -222,8 +215,33 @@ def nearest_interp_converter(network, paddle_op, inputs): scale_w = float(out_w) / float(in_dim[w_axis]) outsize_tensor = None - if inputs[2] is not None: - outsize_tensor = network.add_concatenation(inputs[2]).get_output(0) + if trt_version_float >= 8.2: + if len(inputs) > 2 and inputs[2] is not None: + size_tensor_operand = paddle_op.operands()[2].source() + if size_tensor_operand.is_combine(): + size_tensors = inputs[2] + if not isinstance(size_tensors, list): + size_tensors = [size_tensors] + if len(size_tensors) >= 2: + # Extract the first two elements representing height and width + outsize_h = size_tensors[0] + outsize_w = size_tensors[1] + outsize_tensor = network.add_concatenation( + [outsize_h, outsize_w] + ).get_output(0) + else: + size_tensor_shape = size_tensor_operand.source().shape + if size_tensor_shape.size >= 2: + size_tensor = inputs[2] + outsize_h = network.add_slice( + size_tensor, start=[0], shape=[1], stride=[1] + ).get_output(0) + outsize_w = network.add_slice( + size_tensor, start=[1], shape=[1], stride=[1] + ).get_output(0) + outsize_tensor = network.add_concatenation( + [outsize_h, outsize_w] + ).get_output(0) scales = [1.0] * len(input_tensor.shape) if data_format == "NCHW": @@ -240,6 +258,7 @@ def nearest_interp_converter(network, paddle_op, inputs): ) if outsize_tensor is not None: outsize_itensors = [] + input_shape_tensor = trt_shape(network, input_tensor) batch_dim = get_shape_tensor_element(network, input_shape_tensor, 0) outsize_itensors.append(batch_dim) if data_format == "NCHW": diff --git a/python/paddle/tensorrt/impls/creation.py b/python/paddle/tensorrt/impls/creation.py index b6b5e7711d8d8e..169cf917ceae27 100644 --- a/python/paddle/tensorrt/impls/creation.py +++ b/python/paddle/tensorrt/impls/creation.py @@ -16,11 +16,9 @@ import tensorrt as trt import paddle -from paddle.pir.core import _PADDLE_PIR_DTYPE_2_NUMPY_DTYPE from paddle.tensorrt.converter_utils import ( add_1D_constant_layer, cast_tensor, - resize_to_1d, trt_cast, trt_floor_div, trt_max, @@ -48,11 +46,10 @@ def full_converter(network, paddle_op, inputs): shape = paddle_op.attrs()["shape"] value = paddle_op.attrs().get("value", 1.0) dtype = paddle_op.attrs().get("dtype") - out_dtype = np.dtype(_PADDLE_PIR_DTYPE_2_NUMPY_DTYPE[dtype]) - if out_dtype == np.dtype("float64"): - out_dtype = np.dtype("float32") - if out_dtype == np.dtype("int64"): - out_dtype = np.dtype("int32") + if dtype == paddle.int32 or dtype == paddle.int64: + out_dtype = np.int32 + else: + out_dtype = np.float32 full_layer = network.add_constant( shape, np.full(shape, value, dtype=out_dtype) ) @@ -116,7 +113,9 @@ def arange_converter(network, paddle_op, inputs): number_tensor = trt_max(network, quotient_tensor, zero_tensor) - start_tensor = trt_reshape(network, start, ()) + reshape_start_layer = trt_reshape(network, start, (1,)) + + start_tensor = trt_reduce_to_scalar(network, reshape_start_layer) fill_layer = network.add_fill(shape=(), op=trt.FillOperation.LINSPACE) fill_layer.set_input(0, number_tensor) @@ -238,6 +237,8 @@ def full_with_tensor_converter(network, paddle_op, inputs): shape_tensor = shape_tensor_list[0] if not isinstance(shape_tensor, trt.ITensor): raise TypeError("shape_tensor must be an ITensor") + if len(shape_tensor.shape) != 1: + raise ValueError("The rank of shape_tensor must be 1") tensor_rank = shape_tensor.shape[0] shapes_tensor = shape_tensor else: @@ -251,7 +252,6 @@ def full_with_tensor_converter(network, paddle_op, inputs): shapes_tensor = concat_layer.get_output(0) tensor_rank = len(shape_tensors) - shapes_tensor = resize_to_1d(network, shapes_tensor) fill_layer = network.add_fill(shape=(), op=trt.FillOperation.LINSPACE) fill_layer.set_input(0, shapes_tensor) @@ -264,7 +264,7 @@ def full_with_tensor_converter(network, paddle_op, inputs): ) elif dtype == paddle.float32: beta_vec = [0.0] * tensor_rank - value_input = trt_reduce_to_scalar(network, value_input, trt.float32) + value_input = trt_reduce_to_scalar(network, value_input) fill_layer.set_input(1, value_input) fill_layer.set_input( 2, add_1D_constant_layer(network, beta_vec, np.float32) diff --git a/test/cpp/inference/tensorrt/CMakeLists.txt b/test/cpp/inference/tensorrt/CMakeLists.txt index cb68443c986db3..49ee3552e303b7 100644 --- a/test/cpp/inference/tensorrt/CMakeLists.txt +++ b/test/cpp/inference/tensorrt/CMakeLists.txt @@ -1,20 +1,16 @@ -set(TENSORRT_VERSION_NUMBER - "${TENSORRT_MAJOR_VERSION}${TENSORRT_MINOR_VERSION}") -if(${TENSORRT_VERSION_NUMBER} GREATER_EQUAL 85) - nv_test( - test_tensorrt_engine_instruction - SRCS test_tensorrt_engine_instruction.cc - DEPS pir - trt_engine - naive_executor - phi - common - pir_save_load - pir_tensorrt_plugin) - set_tests_properties(test_tensorrt_engine_instruction PROPERTIES TIMEOUT 120) - if(WITH_ONNXRUNTIME AND WIN32) - # Copy onnxruntime for some c++ test in Windows, since the test will - # be build only in CI, so suppose the generator in Windows is Ninja. - copy_onnx(test_tensorrt_engine_instruction) - endif() +nv_test( + test_tensorrt_engine_instruction + SRCS test_tensorrt_engine_instruction.cc + DEPS pir + trt_engine + naive_executor + phi + common + pir_save_load + pir_tensorrt_plugin) +set_tests_properties(test_tensorrt_engine_instruction PROPERTIES TIMEOUT 120) +if(WITH_ONNXRUNTIME AND WIN32) + # Copy onnxruntime for some c++ test in Windows, since the test will + # be build only in CI, so suppose the generator in Windows is Ninja. + copy_onnx(test_tensorrt_engine_instruction) endif() From 91d3aa0e619c43fac73c18584c752df7cc9c58b6 Mon Sep 17 00:00:00 2001 From: 0x3878f <37301539+0x3878f@users.noreply.github.com> Date: Wed, 11 Dec 2024 10:56:35 +0800 Subject: [PATCH 278/288] Fix the issue when fetching data in pir io (#70091) * Fix: fetch value in pir io * fix * fix --- python/paddle/static/pir_io.py | 7 ++++--- 1 file changed, 4 insertions(+), 3 deletions(-) diff --git a/python/paddle/static/pir_io.py b/python/paddle/static/pir_io.py index d5c6cacd582ea7..0d462b1e998216 100644 --- a/python/paddle/static/pir_io.py +++ b/python/paddle/static/pir_io.py @@ -318,10 +318,11 @@ def normalize_pir_program(program, feed_vars, fetch_vars, **kwargs): fetch_vars_tuple = [] for i, var in enumerate(clone_fetch_vars): scale_op = var.get_defining_op() + orig_var = var if scale_op.name() == "pd_op.scale": - orig_var = scale_op.operand_source(0) - else: - orig_var = var + full_op = scale_op.operand_source(1).get_defining_op() + if full_op.has_attr("value") and full_op.attrs()['value'] == 1.0: + orig_var = scale_op.operand_source(0) if orig_var.has_name: fetch_vars_tuple.append((orig_var, orig_var.name)) else: From 66403d1434a899367d34210365d257fee0aafe8f Mon Sep 17 00:00:00 2001 From: huangjiyi <43315610+huangjiyi@users.noreply.github.com> Date: Wed, 11 Dec 2024 11:17:28 +0800 Subject: [PATCH 279/288] [CINN] Enhance BuildCinnPass efficiency (#70074) --- .../fluid/pir/transforms/build_cinn_pass.cc | 2 +- .../pir/transforms/sub_graph_detector.cc | 540 +++++++----------- .../fluid/pir/transforms/sub_graph_detector.h | 32 +- .../pir/transforms/sub_graph_extract_pass.cc | 2 +- .../tensorrt/trt_sub_graph_extract_pass.cc | 2 +- 5 files changed, 200 insertions(+), 378 deletions(-) diff --git a/paddle/fluid/pir/transforms/build_cinn_pass.cc b/paddle/fluid/pir/transforms/build_cinn_pass.cc index 632c8785a240b7..e17d35cf8514cc 100644 --- a/paddle/fluid/pir/transforms/build_cinn_pass.cc +++ b/paddle/fluid/pir/transforms/build_cinn_pass.cc @@ -49,7 +49,7 @@ class BuildCinnPass : public pir::Pass { private: void ProcessBlock(pir::Block* block) { std::vector groups = - ::pir::SubgraphDetector(block, CompatibleInfo::IsSupportForCinn)(); + ::pir::DetectSubGraphs(block, CompatibleInfo::IsSupportForCinn); AddStatistics(groups.size()); for (auto& group_ops : groups) { if (group_ops.size() == 1 && group_ops[0]->name() == "pd_op.full") { diff --git a/paddle/fluid/pir/transforms/sub_graph_detector.cc b/paddle/fluid/pir/transforms/sub_graph_detector.cc index dbe3f3a243cd1e..208e87945b6b01 100644 --- a/paddle/fluid/pir/transforms/sub_graph_detector.cc +++ b/paddle/fluid/pir/transforms/sub_graph_detector.cc @@ -47,7 +47,6 @@ #include "paddle/fluid/pir/dialect/operator/trait/onednn.h" #endif namespace pir { - std::vector InverselyTopologicalSort(pir::Block* block) { std::vector sort_ops; std::unordered_map pending_count; @@ -107,8 +106,7 @@ std::vector InverselyTopologicalSort(pir::Block* block) { } std::vector GetProducerOpsReverseSort( - pir::Operation* op, - const std::unordered_map& op2id) { + pir::Operation* op, const std::unordered_map& op2id) { std::unordered_set producers; std::vector vec_res; @@ -151,30 +149,8 @@ std::unordered_set GetProducerOps(pir::Operation* op) { return producers; } -std::vector GetProducerOpsRecursive( - pir::Operation* root, - const std::unordered_map& op2id) { - std::unordered_set visited; - std::deque queue; - std::vector result; - queue.push_back(root); - visited.insert(root); - while (!queue.empty()) { - pir::Operation* cur = queue.front(); - queue.pop_front(); - result.push_back(cur); - for (const auto& new_op : GetProducerOps(cur)) { - if (visited.count(new_op)) continue; - visited.insert(new_op); - queue.push_back(new_op); - } - } - return result; -} - std::unordered_set GetConsumerOps( - pir::Operation* op, - const std::unordered_map& op2id) { + pir::Operation* op, const std::unordered_map& op2id) { std::unordered_set consumers; for (auto& result : op->results()) { @@ -192,27 +168,6 @@ std::unordered_set GetConsumerOps( return consumers; } -std::vector GetConsumerOpsRecursive( - pir::Operation* root, - const std::unordered_map& op2id) { - std::unordered_set visited; - std::deque queue; - std::vector result; - queue.push_back(root); - visited.insert(root); - while (!queue.empty()) { - pir::Operation* cur = queue.front(); - queue.pop_front(); - result.push_back(cur); - for (const auto& new_op : GetConsumerOps(cur, op2id)) { - if (visited.count(new_op)) continue; - visited.insert(new_op); - queue.push_back(new_op); - } - } - return result; -} - static std::string OpsDebugStr(std::vector ops) { std::stringstream ss; pir::IrPrinter printer(ss); @@ -223,352 +178,245 @@ static std::string OpsDebugStr(std::vector ops) { return ss.str(); } -struct SubGraph { - // construct function - SubGraph() = default; - // construct function - SubGraph(pir::Operation* op, bool subst) : substitute(subst) { Insert(op); } - SubGraph(const std::unordered_set& op, bool subst) { - substitute = subst; - for (auto& item : op) { - Insert(item); - } - } - void Insert(pir::Operation* op) { +struct SubGraph : public std::enable_shared_from_this { + using SubGraphPtr = std::shared_ptr; + SubGraph() = delete; + SubGraph(pir::Operation* op, int id, bool subst) + : substitute(subst), min_op_id(id), max_op_id(id), name(UniqueId()) { ops.push_back(op); - op_set.insert(op); + } - auto producers = GetProducerOps(op); - for (auto producer : producers) { - input_ops.insert(producer); - } - input_ops.erase(op); + void Merge(const SubGraphPtr& other); + + static std::string UniqueId() { + static std::atomic counter{0}; + return std::string("Subgraph_") + std::to_string(counter++); } - void Print() const { - VLOG(4) << "SubGraph is: " << this; - VLOG(4) << "=============" << this; - VLOG(4) << OpsDebugStr(ops); + std::string DebugStr() const { + std::stringstream ss; + ss << name << " (substitute=" << substitute << ")"; + ss << "\nupstream: "; + for (const auto& subgraph : upstreams) { + ss << subgraph->name << ", "; + } + ss << "\ndownstream: "; + for (const auto& subgraph : downstreams) { + ss << subgraph->name << ", "; + } + ss << "\n" << OpsDebugStr(ops); + return ss.str(); } - int depth{0}; - int max_depth{0}; - int min_depth{INT_MAX}; - bool substitute{true}; + struct hash { + size_t operator()(const SubGraphPtr& subgraph) const { + return std::hash()(subgraph->name); + } + }; + std::vector ops; - std::unordered_set op_set; - std::unordered_set input_ops; + std::unordered_set upstreams; + std::unordered_set downstreams; - std::unordered_set producers; - std::unordered_set consumers; + bool substitute{true}; + size_t min_op_id; + size_t max_op_id; + std::string name; }; +using SubGraphPtr = std::shared_ptr; + +void SubGraph::Merge(const SubGraphPtr& other) { + SubGraphPtr self = shared_from_this(); + for (const auto& upstream : other->upstreams) { + if (upstream == self) continue; + upstream->downstreams.erase(other); + upstream->downstreams.insert(self); + upstreams.insert(upstream); + } + for (const auto& downstream : other->downstreams) { + if (downstream == self) continue; + downstream->upstreams.erase(other); + downstream->upstreams.insert(self); + downstreams.insert(downstream); + } + upstreams.erase(other); + downstreams.erase(other); + ops.insert(ops.begin(), other->ops.begin(), other->ops.end()); + min_op_id = std::min(self->min_op_id, other->min_op_id); + max_op_id = std::max(self->max_op_id, other->max_op_id); +} -using OpClassifier = std::function; - -SubgraphDetector::SubgraphDetector(pir::Block* block, - const OpClassifier& classifier) - : block_(block), op_classifier_(classifier) { - sort_ops_ = InverselyTopologicalSort(block_); - size_t index = 0; - for (auto& op : *block) { - op2id_[&op] = index++; +bool HasSinkRoute(const SubGraphPtr& source, const SubGraphPtr& target) { + if (source == target) return true; + if (source->min_op_id > target->max_op_id) { + return false; + } + for (const auto& subgraph : source->downstreams) { + if (HasSinkRoute(subgraph, target)) return true; } + return false; } -std::vector SubgraphDetector::operator()() { - DoOpFusion(); - VLOG(4) << "Subgraph list size: " << subgraph_list_.size(); - BuildSubGraph(); - VLOG(4) << "Subgraph list size: " << subgraph_list_.size(); - - std::vector groups; - for (auto& subgraph : subgraph_list_) { - if (!subgraph->substitute) { - continue; - } - - // sort group ops by natural increasing index. - std::vector tmp_ops(subgraph->ops.begin(), - subgraph->ops.end()); - auto& op2id = op2id_; - std::sort(tmp_ops.begin(), - tmp_ops.end(), - [&op2id](pir::Operation* a, pir::Operation* b) { - return op2id.at(a) < op2id.at(b); - }); - - groups.push_back(tmp_ops); +bool HasLiftRoute(const SubGraphPtr& source, const SubGraphPtr& target) { + if (source == target) return true; + if (source->max_op_id < target->min_op_id) { + return false; + } + for (const auto& subgraph : source->upstreams) { + if (HasLiftRoute(subgraph, target)) return true; } + return false; +} - return groups; +bool HasRoute(const SubGraphPtr& up, const SubGraphPtr& down) { + return HasSinkRoute(up, down) || HasLiftRoute(down, up); } -using GraphSet = std::unordered_set; -static GraphSet Union(const GraphSet& upstream, const GraphSet& downstream) { - GraphSet unioned_set = upstream; - unioned_set.insert(downstream.begin(), downstream.end()); - return unioned_set; +bool CanFuseUpstream2Downstream(const SubGraphPtr& upstream, + const SubGraphPtr& downstream) { + PADDLE_ENFORCE(upstream->downstreams.count(downstream) && + downstream->upstreams.count(upstream), + ::common::errors::InvalidArgument( + "Subgraphs to be fused must have direct relationship.")); + auto up_downstreams = upstream->downstreams; + up_downstreams.erase(downstream); + auto down_upstreams = downstream->upstreams; + down_upstreams.erase(upstream); + if (up_downstreams.empty() || down_upstreams.empty()) return true; + for (const auto& subgraph : up_downstreams) { + if (HasSinkRoute(subgraph, downstream)) return false; + } + for (const auto& subgraph : down_upstreams) { + if (HasLiftRoute(subgraph, upstream)) return false; + } + return true; } -struct UnionFindSet { - std::unordered_map parent; - std::unordered_map root2subgraph; - OpClassifier op_classifier_; - SubGraphPtr GetSetFromGraph(SubGraphPtr x) { return GetSetFromOp(x->ops[0]); } +class SubgraphDetector { + public: + SubgraphDetector(pir::Block* block, const OpClassifier& classifier); - pir::Operation* Find(pir::Operation* x) { - if (parent.find(x) == parent.end()) { - parent[x] = x; - return x; - } - if (parent[x] != x) { - parent[x] = Find(parent[x]); - } - return parent[x]; - } + void SubgraphFusion(); - SubGraphPtr Union(pir::Operation* x, pir::Operation* y) { - auto root_x = Find(x); - auto root_y = Find(y); - if (root_x == root_y) { - return GetSetFromOp(root_y); - } - auto subgraph_x = GetSetFromOp(root_x); - auto subgraph_y = GetSetFromOp(root_y); - parent[root_x] = root_y; - // union root_x and root_y; - for (auto& op : subgraph_x->ops) { - subgraph_y->Insert(op); - } - return subgraph_y; - } + std::vector BuildGroups(); - SubGraphPtr GetSetFromOp(pir::Operation* op) { - const auto& root = Find(op); - if (!root2subgraph.count(root)) { - root2subgraph[root] = std::make_shared(op, op_classifier_(*op)); - } - return root2subgraph[root]; + private: + SubGraphPtr GetOpSubgraph(pir::Operation* op) { + PADDLE_ENFORCE( + op2subgraph_.count(op), + ::common::errors::InvalidArgument( + "Can not find op in op2subgraph_: \n%s", OpsDebugStr({op}))); + return op2subgraph_.at(op); } + + std::unordered_map op2id_; + std::vector sort_ops_; + std::unordered_map op2subgraph_; }; -static GraphSet Intersect(const GraphSet& upstream, - const GraphSet& downstream) { - GraphSet intersected_set; - for (auto& item : upstream) { - if (downstream.count(item)) { - intersected_set.insert(item); - } +SubgraphDetector::SubgraphDetector(pir::Block* block, + const OpClassifier& classifier) { + // init sort_ops_ in reverse topo order + sort_ops_ = InverselyTopologicalSort(block); + // init op2id_ in topo order + int index = 0; + for (auto& op : *block) { + VLOG(4) << index << " " << OpsDebugStr({&op}); + op2id_[&op] = index++; } - return intersected_set; -} - -struct LoopDetectionMapping { - std::unordered_map> upstreams_; - std::unordered_map> downstreams_; - std::unordered_set all_nodes_; - UnionFindSet* uf_set_; - LoopDetectionMapping(const std::vector sort_ops, - const std::unordered_map& op2id, - UnionFindSet* uf_set) { - for (auto* op : sort_ops) { - auto producers = GetProducerOpsRecursive(op, op2id); - auto consumers = GetConsumerOpsRecursive(op, op2id); - auto op_set = uf_set->GetSetFromOp(op); - all_nodes_.insert(op_set); - for (auto producer : producers) { - auto producer_set = uf_set->GetSetFromOp(producer); - upstreams_[op_set].insert(producer_set); - } - for (auto consumer : consumers) { - auto consumer_set = uf_set->GetSetFromOp(consumer); - downstreams_[op_set].insert(consumer_set); - } - } - uf_set_ = uf_set; - } - - void MergeNodes(const SubGraphPtr& first, - const SubGraphPtr& second, - const SubGraphPtr& merged) { - std::unordered_set merged_upstreams; - std::unordered_set merged_downstreams; - for (auto& item : GetUpstreamSet(first)) merged_upstreams.insert(item); - for (auto& item : GetUpstreamSet(second)) merged_upstreams.insert(item); - for (auto& item : GetDownstreamSet(first)) merged_downstreams.insert(item); - for (auto& item : GetDownstreamSet(second)) merged_downstreams.insert(item); - upstreams_[merged] = merged_upstreams; - downstreams_[merged] = merged_downstreams; - if (first != merged) { - upstreams_.erase(first); - downstreams_.erase(first); - all_nodes_.erase(first); - } - if (second != merged) { - upstreams_.erase(second); - downstreams_.erase(second); - all_nodes_.erase(second); - } - all_nodes_.insert(merged); - } - bool CanFuse(const SubGraphPtr& up, const SubGraphPtr& down) { - if (up == down) return false; - GraphSet after_fuse_upstreams = - Union(GetUpstreamSet(up), GetUpstreamSet(down)); - GraphSet after_fuse_downstreams = - Union(GetDownstreamSet(up), GetDownstreamSet(down)); - auto intersection = Intersect(after_fuse_upstreams, after_fuse_downstreams); - intersection.erase(up); - intersection.erase(down); - return intersection.size() == 0; - } - - GraphSet GetUpstreamSet(const SubGraphPtr& cur) { - GraphSet res; - for (auto& raw_node : upstreams_[cur]) { - auto node = uf_set_->GetSetFromGraph(raw_node); - if (all_nodes_.count(node) && node != cur) res.insert(node); - } - upstreams_[cur] = res; - return res; + // construct subgraphs and upstream/downstream relation + for (const auto& op : sort_ops_) { + bool substitute = classifier(*op); + auto subgraph = std::make_shared(op, op2id_[op], substitute); + op2subgraph_[op] = subgraph; } - - GraphSet GetDownstreamSet(const SubGraphPtr& cur) { - GraphSet res; - for (auto& raw_node : downstreams_[cur]) { - auto node = uf_set_->GetSetFromGraph(raw_node); - if (all_nodes_.count(node) && node != cur) res.insert(node); + for (const auto& op : sort_ops_) { + auto subgraph = op2subgraph_[op]; + for (const auto& producer : GetProducerOps(op)) { + if (!op2subgraph_.count(producer)) continue; + subgraph->upstreams.insert(op2subgraph_[producer]); + op2subgraph_[producer]->downstreams.insert(subgraph); + } + for (const auto& consumer : GetConsumerOps(op, op2id_)) { + if (!op2subgraph_.count(consumer)) continue; + subgraph->downstreams.insert(op2subgraph_[consumer]); + op2subgraph_[consumer]->upstreams.insert(subgraph); } - downstreams_[cur] = res; - return res; - } -}; - -static void VLOG_LINES(const std::string& str) { - if (!VLOG_IS_ON(4)) return; -#ifdef PADDLE_WITH_CINN - const auto& lines = cinn::utils::Split(str, "\n"); - for (const auto& line : lines) { - VLOG(4) << line; } -#endif - return; -} - -void MergeSubGraphs(Operation* op, - Operation* producer, - UnionFindSet& union_find, // NOT NOLINT - LoopDetectionMapping& loop_detector // NOT NOLINT -) { - if (union_find.GetSetFromOp(op) == union_find.GetSetFromOp(producer)) { - return; - } - if (!loop_detector.CanFuse(union_find.GetSetFromOp(producer), - union_find.GetSetFromOp(op))) { - return; - } - // try fuse producer to sub-graph - auto op_graph_ptr = union_find.GetSetFromOp(op); - auto producer_graph_ptr = union_find.GetSetFromOp(producer); - union_find.Union(op, producer); - loop_detector.MergeNodes( - op_graph_ptr, producer_graph_ptr, union_find.GetSetFromOp(op)); } -void SubgraphDetector::DoOpFusion() { - // do fusion - VLOG(4) << "DoOpFusion"; - UnionFindSet union_find; - union_find.op_classifier_ = op_classifier_; - VLOG(4) << "Do Op Fusion with sorted_ops: " << sort_ops_.size(); - VLOG_LINES(OpsDebugStr(sort_ops_)); - LoopDetectionMapping loop_detector(sort_ops_, op2id_, &union_find); - - for (auto* op : sort_ops_) { - auto producers = GetProducerOpsReverseSort(op, op2id_); - for (auto* producer : producers) { - if (!op_classifier_(*op) || !op_classifier_(*producer)) { - continue; - } - VLOG(4) << "Start Judge: " << op->id() << " vs " << producer->id(); - - MergeSubGraphs(producer, op, union_find, loop_detector); - } - } - for (auto* op : sort_ops_) { - auto producers = GetProducerOpsReverseSort(op, op2id_); - for (auto* producer : producers) { - if (op_classifier_(*op) && !op_classifier_(*producer)) { - for (auto* consumer : GetConsumerOps(producer, op2id_)) { - if (op_classifier_(*consumer) && - consumer->GetParent() == op->GetParent()) { - VLOG(4) << "Start Judge sibling nodes: " << op->id() << " vs " - << consumer->id(); - MergeSubGraphs(op, consumer, union_find, loop_detector); - } +void SubgraphDetector::SubgraphFusion() { + VLOG(4) << "Merge subgraphs with direct relation"; + for (const auto& op : sort_ops_) { + auto downstream = GetOpSubgraph(op); + if (!downstream->substitute) continue; + for (const auto& producer : GetProducerOpsReverseSort(op, op2id_)) { + auto upstream = GetOpSubgraph(producer); + if (upstream == downstream || !upstream->substitute) continue; + if (CanFuseUpstream2Downstream(upstream, downstream)) { + downstream->Merge(upstream); + for (auto upstream_op : upstream->ops) { + op2subgraph_[upstream_op] = downstream; } } } } - + VLOG(4) << "Merge brother subgraphs with same upstream"; for (const auto& op : sort_ops_) { - subgraph_map_[op] = union_find.GetSetFromOp(op); - } - - for (auto& subgraph : subgraph_map_) { - auto* op = subgraph.first; - auto* subgraph_ptr = subgraph.second.get(); - if (union_find.Find(op) == op) { - VLOG(4) << "Subgraph: " << subgraph_ptr; - VLOG(4) << " substitute: " << subgraph_ptr->substitute; - for (auto& op : subgraph_ptr->ops) { - VLOG(4) << "ops: " << op->name() << ", " << op->id(); + auto subgraph = GetOpSubgraph(op); + if (!subgraph->substitute) continue; + for (auto producer : GetProducerOpsReverseSort(op, op2id_)) { + for (auto consumer : GetConsumerOps(producer, op2id_)) { + auto brother = GetOpSubgraph(consumer); + if (brother == subgraph || !brother->substitute) continue; + if (!HasRoute(subgraph, brother) && !HasRoute(brother, subgraph)) { + subgraph->Merge(brother); + for (auto brother_op : brother->ops) { + op2subgraph_[brother_op] = subgraph; + } + } } } } } -void SubgraphDetector::BuildSubGraph() { - std::unordered_set subgraph_set; - for (auto* op : sort_ops_) { - PADDLE_ENFORCE_EQ( - subgraph_map_.count(op), - true, - common::errors::InvalidArgument("subgraph_map_ MUST contain op")); - auto& subgraph = subgraph_map_[op]; - if (subgraph_set.count(subgraph.get())) { - continue; - } - - subgraph_set.insert(subgraph.get()); - subgraph_list_.push_back(subgraph); +std::vector SubgraphDetector::BuildGroups() { + std::unordered_set subgraph_set; + std::vector subgraph_list; + for (auto op : sort_ops_) { + SubGraphPtr subgraph = GetOpSubgraph(op); + if (subgraph_set.count(subgraph)) continue; + subgraph_set.insert(subgraph); + subgraph_list.push_back(subgraph); } + std::reverse(subgraph_list.begin(), subgraph_list.end()); + VLOG(4) << "Subgraph list size: " << subgraph_list.size(); - for (auto& subgraph : subgraph_list_) { - for (auto& input_op : subgraph->input_ops) { - PADDLE_ENFORCE_EQ( - subgraph_map_.count(input_op), - true, - common::errors::InvalidArgument("subgraph_map_ MUST contain op")); - auto& producer = subgraph_map_[input_op]; - subgraph->producers.insert(producer); - producer->consumers.insert(subgraph); - } - } - - // init group depth. - for (auto& subgraph : subgraph_list_) { - for (auto& consumer : subgraph->consumers) { - // update depth. - subgraph->depth = std::max(subgraph->depth, consumer->depth + 1); + std::vector groups; + for (const auto& subgraph : subgraph_list) { + if (!subgraph->substitute) { + continue; } - subgraph->max_depth = subgraph->depth; - subgraph->min_depth = subgraph->depth; + // sort group ops by natural increasing index. + std::vector group_ops(subgraph->ops.begin(), + subgraph->ops.end()); + std::sort(group_ops.begin(), + group_ops.end(), + [this](pir::Operation* a, pir::Operation* b) { + return this->op2id_.at(a) < this->op2id_.at(b); + }); + groups.push_back(group_ops); } + return groups; +} - // reverse to keep fusion group in order. - std::reverse(subgraph_list_.begin(), subgraph_list_.end()); +std::vector DetectSubGraphs(pir::Block* block, + const OpClassifier& classifier) { + auto subgraph_detector = SubgraphDetector(block, classifier); + subgraph_detector.SubgraphFusion(); + return subgraph_detector.BuildGroups(); } + std::vector AnalysisOutputs( const GroupOpsVec& group_ops) { // NOLINT // Get output by ud chain diff --git a/paddle/fluid/pir/transforms/sub_graph_detector.h b/paddle/fluid/pir/transforms/sub_graph_detector.h index 5cf67f09fd5f39..3a1840938f0d50 100644 --- a/paddle/fluid/pir/transforms/sub_graph_detector.h +++ b/paddle/fluid/pir/transforms/sub_graph_detector.h @@ -29,37 +29,11 @@ #include "paddle/pir/include/core/builder.h" namespace pir { - -struct SubGraph; -using SubGraphPtr = std::shared_ptr; +using OpClassifier = std::function; using GroupOpsVec = std::vector; -class SubgraphDetector { - public: - // Tell whether a node is inside a sub-graph. - using OpClassifier = std::function; - - SubgraphDetector(pir::Block* block, const OpClassifier& classifier); - - std::vector operator()(); - - protected: - // Do Op Fusion - void DoOpFusion(); - - void BuildSubGraph(); - - private: - pir::Block* block_; - OpClassifier op_classifier_; - - std::vector sort_ops_; - std::unordered_map op2id_; - std::vector subgraph_list_; - std::unordered_map subgraph_map_; - std::unordered_map> - can_apply_fusion_map_; -}; +std::vector DetectSubGraphs(pir::Block* block, + const OpClassifier& classifier); std::vector AnalysisOutputs(const GroupOpsVec& group_ops); void ReplaceWithGroupOp(pir::Block* block, const GroupOpsVec& group_ops); diff --git a/paddle/fluid/pir/transforms/sub_graph_extract_pass.cc b/paddle/fluid/pir/transforms/sub_graph_extract_pass.cc index 37db54d3746292..845a92c8423e74 100644 --- a/paddle/fluid/pir/transforms/sub_graph_extract_pass.cc +++ b/paddle/fluid/pir/transforms/sub_graph_extract_pass.cc @@ -53,7 +53,7 @@ class SubGraphExtractPass : public pir::Pass { auto& block = module_op.block(); std::vector groups = - ::pir::SubgraphDetector(&block, IsMatmulOp)(); + ::pir::DetectSubGraphs(&block, IsMatmulOp); AddStatistics(groups.size()); for (auto& group_ops : groups) { VLOG(4) << "current group_ops.size(): " << group_ops.size(); diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_sub_graph_extract_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_sub_graph_extract_pass.cc index 37ade819c6049d..aa9f7a3182388f 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_sub_graph_extract_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_sub_graph_extract_pass.cc @@ -57,7 +57,7 @@ class TrtSubGraphExtractPass : public pir::Pass { auto& block = module_op.block(); std::vector groups = - ::pir::SubgraphDetector(&block, IsSupportedByTRT)(); + ::pir::DetectSubGraphs(&block, IsSupportedByTRT); AddStatistics(groups.size()); for (auto& group_ops : groups) { if (group_ops.size() < static_cast(FLAGS_trt_min_group_size)) { From 86e5be7a51f65d66a0026d5aea3eddcaca4cebc9 Mon Sep 17 00:00:00 2001 From: XiangGao Date: Wed, 11 Dec 2024 11:51:04 +0800 Subject: [PATCH 280/288] public auto parallel high level api (#70021) * public auto parallel high level api * support default input spec * fix issue of docs * skip code test * fix issue of type parameter and add unittest to hybrid_strategy --------- Co-authored-by: zachary sun --- python/paddle/distributed/__init__.py | 2 + .../auto_parallel/high_level_api.py | 77 +++++++++++++------ .../tuner/to_distributed_api_patterns.py | 3 + .../hybrid_strategy/CMakeLists.txt | 8 ++ .../test_to_distributed_api_for_llama.py | 2 +- .../hybrid_strategy/testslist.csv | 1 + .../to_distributed_api_for_llama.py | 8 +- 7 files changed, 70 insertions(+), 31 deletions(-) rename test/auto_parallel/{ => hybrid_strategy}/test_to_distributed_api_for_llama.py (96%) rename test/auto_parallel/{ => hybrid_strategy}/to_distributed_api_for_llama.py (99%) diff --git a/python/paddle/distributed/__init__.py b/python/paddle/distributed/__init__.py index a4f63508ba3089..ac0cf6ba3eac9e 100644 --- a/python/paddle/distributed/__init__.py +++ b/python/paddle/distributed/__init__.py @@ -45,6 +45,7 @@ to_static, unshard_dtensor, ) +from .auto_parallel.high_level_api import to_distributed from .auto_parallel.interface import get_mesh, set_mesh from .auto_parallel.intermediate.parallelize import parallelize from .auto_parallel.intermediate.pipeline_parallel import SplitPoint @@ -202,4 +203,5 @@ "SplitPoint", "set_mesh", "get_mesh", + "to_distributed", ] diff --git a/python/paddle/distributed/auto_parallel/high_level_api.py b/python/paddle/distributed/auto_parallel/high_level_api.py index 7ca20090fed703..cbe52673abe050 100644 --- a/python/paddle/distributed/auto_parallel/high_level_api.py +++ b/python/paddle/distributed/auto_parallel/high_level_api.py @@ -21,18 +21,7 @@ import paddle import paddle.distributed as dist -from paddle.base import ( - default_main_program, -) -from paddle.base.framework import ( - in_dygraph_mode, -) -from paddle.distributed.auto_parallel.static.tuner.to_distributed_api_patterns import ( - clear_used_patterns, - get_pattern, - match_all_patterns, - register_used_patterns, -) +from paddle.base.framework import in_dygraph_mode logger = logging.getLogger(__name__) @@ -85,6 +74,11 @@ def record_program_ops_pre_hook(layer, inputs): A pre-hook to mark op numbers before enter layer.forward. """ if not in_dygraph_mode(): + # Because ir_guard._switch_to_pir() will change default_main_program in python/paddle/__init__.py. + # In order to avoid errors, we import default_main_program until this hook running. + # After fully switching to pir, can move this import to the beginning of the file. + from paddle.base import default_main_program + if layer._op_recorder.start < 0: layer._op_recorder.start = len( default_main_program().global_block().ops @@ -222,12 +216,17 @@ def record_program_ops_post_hook(layer, inputs, outputs): A post-hook to mark op numbers after enter layer.forward, and record corresponding ops of the layer. """ if not in_dygraph_mode(): + # Because ir_guard._switch_to_pir() will change default_main_program in python/paddle/__init__.py. + # In order to avoid errors, we import default_main_program until this hook running. + # After fully switching to pir, can move this import to the beginning of the file. + from paddle.base import default_main_program + assert ( layer._op_recorder.start >= 0 and layer._op_recorder.is_valid is True ), f"{layer._full_name} has not recorded the start of the corresponding ops before" end = len(default_main_program().global_block().ops) - # some layers, such as llama_rotary_embedding, will not add new ops to program + # some layers, such as rotary_embedding, will not add new ops to program # assert end > layer._op_recorder.start, f"{layer._full_name} has not added new ops to the program" ops = [] if end > layer._op_recorder.start: @@ -237,6 +236,9 @@ def record_program_ops_post_hook(layer, inputs, outputs): .global_block() .ops[layer._op_recorder.start : layer._op_recorder.end] ) + logger.debug( + f'start: {layer._op_recorder.start}, end: {layer._op_recorder.end}, ops: {ops}' + ) layer._op_recorder.ops = ops @@ -257,7 +259,11 @@ def to_distributed( device_num: int, node_num: int | None = 1, config: ToDistributedConfig | None = None, -) -> tuple[paddle.nn.Layer, paddle.optimizer.Optimizer, paddle.io.DataLoader]: +) -> tuple[ + paddle.nn.Layer, + paddle.optimizer.Optimizer, + paddle.distributed.auto_parallel.ShardDataloader, +]: """ `to_distributed` can automatically convert neural networks, optimizer, and dataloader that do not contain any distributed code into neural networks, optimizers, and dataloader @@ -277,9 +283,10 @@ def to_distributed( device_num(int): the number of devices on each node or machine. node_num(int|None, optional): the number of nodes or machines. config(ToDistributedConfig| None = None): Configs for input_spec and sequence_parallel. - The custom input specs specify the shape, dtype, and name information + The custom input specs specify the most likely shape, dtype, and name information of each model inputs. If it is not None, the input specs and - will be inferred from the custom input specs. The custom + will be inferred from the custom input specs. If it is None, will use default with + shape of [BATCH_SIZE=4, SEQ_LENGTH=1024], The custom input specs should be a list of `paddle.static.InputSpec`. Default: None. sequence_parallel indicates whether to use sequence parallel. Default: False. @@ -290,15 +297,15 @@ def to_distributed( Examples: .. code-block:: python + + >>> # doctest: +SKIP('run in distributed env') >>> import math >>> import numpy as np >>> import paddle >>> import paddle.nn.functional as F >>> from paddle import nn - >>> from paddle.distributed.auto_parallel.high_level_api import ( - >>> ToDistributedConfig, - >>> to_distributed, - >>> ) + >>> from paddle.distributed import to_distributed + >>> from paddle.distributed.auto_parallel.high_level_api import ToDistributedConfig >>> EPOCHES = 1 >>> VOCAB_SIZE = 8000 @@ -309,7 +316,7 @@ def to_distributed( >>> SEQ_LENGTH = 1024 >>> N_HEAD = 32 >>> NUM_HIDDEN_LAYERS = 4 - >>> class RandomDataset(paddle.io.Dataset): + >>> class RandomDataset(paddle.io.Dataset): # type: ignore[type-arg] ... def __init__(self, inputs, labels, num_samples): ... self.inputs = inputs ... self.labels = labels @@ -326,8 +333,7 @@ def to_distributed( ... self.max_position_embeddings = max_position_embeddings ... self.base = base ... self.inv_freq = 1.0 / ( - ... self.base - ... ** ( + ... self.base ** ( ... paddle.cast(paddle.arange(0, self.dim, 2), dtype="float32") ... / self.dim ... ) @@ -650,7 +656,7 @@ def to_distributed( ... [BATCH_SIZE, SEQ_LENGTH], 'float32', 'input_seq', True ... ) >>> dist_config = ToDistributedConfig() - >>> dist_config.input_spec = [input_seq_spec] + >>> dist_config.sequence_parallel = True >>> # wrap model, opt, dataloader by using **to_distributed** >>> dist_model, dist_opt, dist_loader = to_distributed( @@ -671,7 +677,18 @@ def to_distributed( ... loss.backward() ... dist_opt.step() ... dist_opt.clear_grad() + >>> # This case need to be executed in multi-card environment + >>> # python -m paddle.distributed.launch --gpus=0,1,2,3,4,5,6,7 {test_case}.py """ + # Because some API(`paddle.randn` etc.) will be used when building pattern, + # In order to avoid circle import, we import get_pattern until function running. + from .static.tuner.to_distributed_api_patterns import ( + clear_used_patterns, + get_pattern, + match_all_patterns, + register_used_patterns, + ) + logger.debug(f'input model: {model}') # paddle.distributed.init_parallel_env() @@ -688,8 +705,13 @@ def to_distributed( layer._op_recorder.hooks.append(post_hook_helper) # step 1.2: call @to_static, get program, and corresponding static ops of each layer + custom_input_spec = ( + config.input_spec + if config.input_spec + else [paddle.static.InputSpec([4, 1024], 'float32', 'input_seq', True)] + ) static_func = paddle.jit.to_static( - model.forward, input_spec=config.input_spec, full_graph=True + model.forward, input_spec=custom_input_spec, full_graph=True ) program = static_func.concrete_program.main_program # currently, paddle.jit.to_static has side effects that will affect model. @@ -708,6 +730,9 @@ def to_distributed( op_id_to_layer = {} for layer in model.sublayers(): layer_ops = layer._op_recorder.ops + logger.debug( + f'layer name: {layer.__class__.__name__}, layer_ops: {layer_ops}' + ) ops_id = [] for op in layer_ops: assert op in op_to_id.keys(), f"{op.name} is not in program" @@ -715,6 +740,7 @@ def to_distributed( op_id_to_layer[op_id] = layer ops_id.append(op_id) ops_id_to_layer[tuple(ops_id)] = layer + logger.debug(f'ops_id_to_layer is: {ops_id_to_layer}') # step 1.4: pattern recogincation DECODER_LAYER_NAME = 'decoder_layer' @@ -744,6 +770,7 @@ def to_distributed( program_ops_dist_infos[tuple(program_ops_id)] = op_dist_info processed_patterns.append(program_ops_dist_infos) matched_programs[pattern_name] = processed_patterns + logger.debug(f'Matched decoder layer patterns are: {matched_programs}') # step 2: calculate the optimal parallel strategies based on the network structure mesh = cost_model(matched_programs, device_num, node_num) diff --git a/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py b/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py index 62d48522e2d752..4887be8b757412 100644 --- a/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py +++ b/python/paddle/distributed/auto_parallel/static/tuner/to_distributed_api_patterns.py @@ -33,6 +33,9 @@ def register(): global _ALL_PATTERNS pattern = cls() _ALL_PATTERNS[pattern.name] = pattern + logger.debug( + f'register pattern : {pattern.name}, pattern program: {pattern.program}' + ) register() diff --git a/test/auto_parallel/hybrid_strategy/CMakeLists.txt b/test/auto_parallel/hybrid_strategy/CMakeLists.txt index 15a91756a073db..c8ee2ff1cd54f0 100644 --- a/test/auto_parallel/hybrid_strategy/CMakeLists.txt +++ b/test/auto_parallel/hybrid_strategy/CMakeLists.txt @@ -153,3 +153,11 @@ if((WITH_GPU) AND (LINUX)) set_tests_properties(test_parallel_api_with_llama_3d PROPERTIES TIMEOUT "300" LABELS "RUN_TYPE=HYBRID") endif() +if((WITH_GPU) AND (LINUX)) + py_test_modules( + test_to_distributed_api_for_llama MODULES test_to_distributed_api_for_llama + ENVS + "http_proxy=;https_proxy=;PYTHONPATH=../..:${PADDLE_BINARY_DIR}/python") + set_tests_properties(test_to_distributed_api_for_llama + PROPERTIES TIMEOUT "180" LABELS "RUN_TYPE=HYBRID") +endif() diff --git a/test/auto_parallel/test_to_distributed_api_for_llama.py b/test/auto_parallel/hybrid_strategy/test_to_distributed_api_for_llama.py similarity index 96% rename from test/auto_parallel/test_to_distributed_api_for_llama.py rename to test/auto_parallel/hybrid_strategy/test_to_distributed_api_for_llama.py index e42471fe9abc4e..c207912aaec748 100644 --- a/test/auto_parallel/test_to_distributed_api_for_llama.py +++ b/test/auto_parallel/hybrid_strategy/test_to_distributed_api_for_llama.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. diff --git a/test/auto_parallel/hybrid_strategy/testslist.csv b/test/auto_parallel/hybrid_strategy/testslist.csv index c504c879a10458..da8ee095b75084 100644 --- a/test/auto_parallel/hybrid_strategy/testslist.csv +++ b/test/auto_parallel/hybrid_strategy/testslist.csv @@ -17,3 +17,4 @@ test_semi_auto_llama_save_load,LINUX,GPU,180,HYBRID,test_runner.py,,,http_proxy= test_parallel_api_with_llama_1d,LINUX,GPU,300,HYBRID,test_runner.py,,,http_proxy=;https_proxy=;PYTHONPATH=../.., test_parallel_api_with_llama_2d,LINUX,GPU,300,HYBRID,test_runner.py,,,http_proxy=;https_proxy=;PYTHONPATH=../.., test_parallel_api_with_llama_3d,LINUX,GPU,300,HYBRID,test_runner.py,,,http_proxy=;https_proxy=;PYTHONPATH=../.., +test_to_distributed_api_for_llama,LINUX,GPU,180,HYBRID,test_runner.py,,,http_proxy=;https_proxy=;PYTHONPATH=../.., diff --git a/test/auto_parallel/to_distributed_api_for_llama.py b/test/auto_parallel/hybrid_strategy/to_distributed_api_for_llama.py similarity index 99% rename from test/auto_parallel/to_distributed_api_for_llama.py rename to test/auto_parallel/hybrid_strategy/to_distributed_api_for_llama.py index 4e8d8b4637762d..302ec83cfe0aab 100644 --- a/test/auto_parallel/to_distributed_api_for_llama.py +++ b/test/auto_parallel/hybrid_strategy/to_distributed_api_for_llama.py @@ -1,4 +1,4 @@ -# Copyright (c) 2023 PaddlePaddle Authors. All Rights Reserved. +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. @@ -21,10 +21,8 @@ import paddle.distributed as dist import paddle.nn.functional as F from paddle import nn -from paddle.distributed.auto_parallel.high_level_api import ( - ToDistributedConfig, - to_distributed, -) +from paddle.distributed import to_distributed +from paddle.distributed.auto_parallel.high_level_api import ToDistributedConfig EPOCHES = 1 VOCAB_SIZE = 8000 From 5e00b3c4e0bfe220a03d011f99c4574823311eaa Mon Sep 17 00:00:00 2001 From: hong <43953930+phlrain@users.noreply.github.com> Date: Wed, 11 Dec 2024 14:50:46 +0800 Subject: [PATCH 281/288] [CINN]add relu6 hardsigmoid unitest (#70107) * add relu6 hardsigmoid unitest * polish code --- .../primitive/decomp_rule/decomp_vjp/details.h | 4 ++-- python/paddle/autograd/backward_utils.py | 2 ++ ...im_sub_graph_fghij_backward_dynamic_shape.py | 17 +++++++++++++++++ ...im_sub_graph_pqrst_backward_dynamic_shape.py | 17 +++++++++++++++++ 4 files changed, 38 insertions(+), 2 deletions(-) diff --git a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h index 2ef3df43986a7c..c48bf7d62098cd 100644 --- a/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h +++ b/paddle/fluid/primitive/decomp_rule/decomp_vjp/details.h @@ -1835,8 +1835,8 @@ void hardsigmoid_grad(const Tensor& out, auto mask_gt = greater_than(out, zeros); auto mask_lt = less_than(out, one); auto mask = bitwise_and(mask_gt, mask_lt); - Tensor slope_tensor = full_scalar(slope, out.dtype()); - auto res = cast(mask, out.dtype()) * slope_tensor * out_grad; + Tensor slope_t = full_scalar(slope, out.dtype()); + auto res = cast(mask, out.dtype()) * slope_t * out_grad; set_output(res, x_grad); } } diff --git a/python/paddle/autograd/backward_utils.py b/python/paddle/autograd/backward_utils.py index 5b6e1523cae800..c638254ebe3b6c 100644 --- a/python/paddle/autograd/backward_utils.py +++ b/python/paddle/autograd/backward_utils.py @@ -55,6 +55,7 @@ "pd_op.gather", "pd_op.gather_nd", "pd_op.gelu", + "pd_op.hardsigmoid", "pd_op.hardswish", "pd_op.kron", "pd_op.kthvalue", @@ -74,6 +75,7 @@ "pd_op.prod", "pd_op.reduce_as", "pd_op.relu", + "pd_op.relu6", "pd_op.reshape", "pd_op.roll", "pd_op.rsqrt", diff --git a/test/prim/pir_prim/test_prim_sub_graph_fghij_backward_dynamic_shape.py b/test/prim/pir_prim/test_prim_sub_graph_fghij_backward_dynamic_shape.py index 1fadab7248e18d..4e0f6db70373b1 100644 --- a/test/prim/pir_prim/test_prim_sub_graph_fghij_backward_dynamic_shape.py +++ b/test/prim/pir_prim/test_prim_sub_graph_fghij_backward_dynamic_shape.py @@ -54,6 +54,10 @@ def gelu_net2(x): return paddle.nn.functional.gelu(x, approximate=False) +def hardsigmoid_net(x): + return paddle.nn.functional.hardsigmoid(x) + + def hardswish_net(x): return paddle.nn.functional.hardswish(x) @@ -370,6 +374,19 @@ def test_prim_all_dynamic(self): np.testing.assert_allclose(dr, d, rtol=self.rtol, atol=self.atol) +class TestPrimHardsigmoidWithGrad(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2024) + self.op_name = "pd_op.hardsigmoid_grad" + self.dtype = "float32" + self.x_shape = [30, 200, 40] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = hardsigmoid_net + self.enable_cinn = False + self.tol = 1e-6 + + class TestPrimHardswishWithGrad(TestPrimBaseWithGrad): def setUp(self): np.random.seed(2024) diff --git a/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py b/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py index 6f21e217cfa60a..a9d5fcbe5ba3c3 100644 --- a/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py +++ b/test/prim/pir_prim/test_prim_sub_graph_pqrst_backward_dynamic_shape.py @@ -60,6 +60,10 @@ def relu_net(x): return paddle.nn.functional.relu(x) +def relu6_net(x): + return paddle.nn.functional.relu6(x) + + def reshape_net(x): return paddle.reshape(x, [30, 200 * 40]) @@ -365,6 +369,19 @@ def setUp(self): self.tol = 1e-6 +class TestPrimRelu6WithGrad(TestPrimBaseWithGrad): + def setUp(self): + np.random.seed(2023) + self.op_name = "pd_op.relu6_grad" + self.dtype = "float32" + self.x_shape = [30, 200, 40] + self.init_x_shape = [None, None, None] + self.x = np.random.random(self.x_shape).astype(self.dtype) + self.net = relu6_net + self.enable_cinn = False + self.tol = 1e-6 + + class TestPrimReshapeWithGrad(TestPrimBaseWithGrad): def setUp(self): np.random.seed(2024) From 4da5a832dc0098c715c4a58a5b48ec9c103d8671 Mon Sep 17 00:00:00 2001 From: Nyakku Shigure Date: Wed, 11 Dec 2024 15:21:09 +0800 Subject: [PATCH 282/288] [Dy2St][PIR] Support nested structure in PyLayer forward inputs (#69642) --- .../dialect/operator/ir/manual_pylayer_op.cc | 8 +- python/paddle/jit/dy2static/py_layer.py | 21 ++- python/paddle/static/nn/static_pylayer.py | 14 +- test/dygraph_to_static/test_pylayer.py | 158 ++++++++++++++++++ 4 files changed, 194 insertions(+), 7 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc index aa4b5baca0ec5d..6c3667114cbbe4 100644 --- a/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc +++ b/paddle/fluid/pir/dialect/operator/ir/manual_pylayer_op.cc @@ -128,12 +128,14 @@ void PyLayerOp::Print(pir::IrPrinter &printer) { printer.PrintAttributeMap(*op); os << " -> "; printer.PrintOpReturnType(*op); - os << "{"; + os << " {\n"; + printer.AddIndentation(); for (auto &item : forward_block()) { - os << "\n "; printer.PrintOperation(item); + os << "\n"; } - os << "\n }"; + printer.DecreaseIndentation(); + os << printer.indentation() << "}"; } void PyLayerOp::VerifySig() { diff --git a/python/paddle/jit/dy2static/py_layer.py b/python/paddle/jit/dy2static/py_layer.py index 86798c4f172158..397d87c14a148c 100644 --- a/python/paddle/jit/dy2static/py_layer.py +++ b/python/paddle/jit/dy2static/py_layer.py @@ -21,6 +21,7 @@ from paddle.base.libpaddle.pir import build_pipe_for_pylayer from paddle.common_ops_import import LayerHelper from paddle.static.nn import static_pylayer +from paddle.utils import flatten, pack_sequence_as from .program_translator import convert_to_static, unwrap_decorators @@ -28,13 +29,14 @@ class StaticPyLayerContext: def __init__(self): self.saved_vars = [] + self.saved_vars_structure = None if in_pir_mode(): self.tuple_push_op_name = "cf.tuple_push" self.tuple_pop_op_name = "cf.tuple_pop" def __setattr__(self, attr: str, value: object): - attr_allow_list = ["saved_vars"] + attr_allow_list = ["saved_vars", "saved_vars_structure"] if ( in_pir_mode() and attr not in attr_allow_list @@ -68,9 +70,14 @@ def __setattr__(self, attr: str, value: object): def save_for_backward(self, *tensors): if in_pir_mode(): + self.saved_vars_structure = tensors + flatten_tensors = flatten(tensors) + tensor_elements = list( + filter(lambda x: isinstance(x, pir.Value), flatten_tensors) + ) current_insert_point = pir.get_current_insertion_point() current_block = current_insert_point.block() - build_pipe_for_pylayer(current_block, tensors) + build_pipe_for_pylayer(current_block, tensor_elements) else: for tensor in tensors: assert isinstance(tensor, Variable) @@ -84,6 +91,16 @@ def saved_tensor(self): for op in current_block.ops: if op.name() == self.tuple_pop_op_name: out_list = op.as_tuple_pop_op().pop_all_values() + if self.saved_vars_structure is not None: + flattened_structure = flatten(self.saved_vars_structure) + value_cursor = 0 + for i, tensor in enumerate(flattened_structure): + if isinstance(tensor, pir.Value): + flattened_structure[i] = out_list[value_cursor] + value_cursor += 1 + out_list = pack_sequence_as( + self.saved_vars_structure, flattened_structure + ) else: helper = LayerHelper("StaticPyLayerContext") out_list = [] diff --git a/python/paddle/static/nn/static_pylayer.py b/python/paddle/static/nn/static_pylayer.py index 8aeeb94a42e35b..f8c397e4b1cbf3 100644 --- a/python/paddle/static/nn/static_pylayer.py +++ b/python/paddle/static/nn/static_pylayer.py @@ -269,6 +269,11 @@ def __call__(self, *output_grads): input_grads = (input_grads,) self._hook_check_func(output_grads, input_grads) + input_grads = [ + input_grad + for input_grad in flatten(input_grads) + if isinstance(input_grad, (paddle.pir.Value, type(None))) + ] return input_grads @@ -369,7 +374,7 @@ def static_pylayer(forward_fn, inputs, backward_fn=None, name=None): if in_pir_mode(): fwd_inputs = [ - inp for inp in inputs if isinstance(inp, paddle.pir.Value) + inp for inp in flatten(inputs) if isinstance(inp, paddle.pir.Value) ] pylayer_op = build_pylayer_op(fwd_inputs) outputs = None @@ -404,9 +409,14 @@ def hook_inputs_outputs_check_function(output_grads, input_grads): for x in flatten(inputs) if isinstance(x, paddle.pir.Value) ] + input_grads = [ + x + for x in flatten(input_grads) + if isinstance(x, (paddle.pir.Value, type(None))) + ] if len(input_grads) != len(forward_inputs): raise ValueError( - f"The number of input grads should be equal to the number of inputs, but got {len(input_grads)} and {len(inputs)}." + f"The number of input grads should be equal to the number of inputs, but got {len(input_grads)} and {len(forward_inputs)}." ) for inp_grad, fwd_input in zip(input_grads, forward_inputs): # NOTE: inp_grad will be None if fwd_input.stop_gradients=True diff --git a/test/dygraph_to_static/test_pylayer.py b/test/dygraph_to_static/test_pylayer.py index d5a00a34075aa5..0c0eef0f8067ec 100644 --- a/test/dygraph_to_static/test_pylayer.py +++ b/test/dygraph_to_static/test_pylayer.py @@ -812,5 +812,163 @@ def test_wrong_usage(self): static_layer(x) +class NestedStructurePyLayer(PyLayer): + @staticmethod + def forward(ctx, x, y): + ctx.save_for_backward(x, y) + x1 = paddle.tanh(x[0]) + y1 = paddle.tanh(x[1]) + z1 = paddle.tanh(y) + return [x1, y1, z1] + + @staticmethod + def backward(ctx, *grad1): + x0, x1 = ctx.saved_tensor() + x_grad = grad1[0] * (1 - paddle.square(x0[0])) + y_grad = grad1[1] * (1 - paddle.square(x0[1])) + z_grad = grad1[2] * (1 - paddle.square(x1)) + + return [x_grad, y_grad], z_grad + + +class NestedStructurePyLayerModel(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.w0 = self.create_parameter(shape=[42, 42]) + self.w1 = self.create_parameter(shape=[42, 42]) + self.w2 = self.create_parameter(shape=[42, 42]) + + def forward(self, x): + y1 = paddle.matmul(x, self.w0) + y2 = paddle.matmul(x, self.w1) + y3 = paddle.matmul(x, self.w2) + + z = NestedStructurePyLayer.apply([y1, y2], y3) + return z[0] + z[1] + z[2] + + +class TestNestedStructurePyLayer(unittest.TestCase): + def test_nested_structure(self): + input = paddle.randn([2, 42]).astype("float32") + input.stop_gradient = False + + model = NestedStructurePyLayerModel() + dygraph_res = model(input) + dygraph_res.backward() + dygraph_input_grads = [ + paddle.assign(input.grad), + paddle.assign(model.w0.grad), + paddle.assign(model.w1.grad), + paddle.assign(model.w2.grad), + ] + input.clear_grad() + model.w0.clear_grad() + model.w1.clear_grad() + model.w2.clear_grad() + + static_model = paddle.jit.to_static(model, full_graph=True) + static_res = static_model(input) + static_res.backward() + static_input_grads = [ + paddle.assign(input.grad), + paddle.assign(model.w0.grad), + paddle.assign(model.w1.grad), + paddle.assign(model.w2.grad), + ] + input.clear_grad() + model.w0.clear_grad() + model.w1.clear_grad() + model.w2.clear_grad() + for i, (dygraph_grad, static_grad) in enumerate( + zip(dygraph_input_grads, static_input_grads) + ): + np.testing.assert_allclose( + dygraph_grad.numpy(), + static_grad.numpy(), + rtol=1e-5, + atol=0, + err_msg=f"dygraph_grad[{i}]: {dygraph_grad} \n static_grad[{i}]: {static_grad}", + ) + + +class NestedStructureWithNonePyLayer(PyLayer): + @staticmethod + def forward(ctx, x, y): + ctx.save_for_backward(x, y) + x1 = paddle.tanh(x[0]) + y1 = paddle.tanh(x[1]) + z1 = paddle.tanh(y) + return [x1, y1, z1] + + @staticmethod + def backward(ctx, *grad1): + x0, x1 = ctx.saved_tensor() + x_grad = grad1[0] * (1 - paddle.square(x0[0])) + z_grad = grad1[2] * (1 - paddle.square(x1)) + + return [x_grad, None], z_grad + + +class NestedStructureWithNonePyLayerModel(paddle.nn.Layer): + def __init__(self): + super().__init__() + self.w0 = self.create_parameter(shape=[42, 42]) + self.w1 = self.create_parameter(shape=[42, 42]) + self.w2 = self.create_parameter(shape=[42, 42]) + + def forward(self, x): + y1 = paddle.matmul(x, self.w0) + y2 = paddle.matmul(x, self.w1) + y2.stop_gradient = True + y3 = paddle.matmul(x, self.w2) + + z = NestedStructurePyLayer.apply([y1, y2], y3) + return z[0] + z[1] + z[2] + + +class TestNestedStructureWithNonePyLayer(unittest.TestCase): + def test_nested_structure(self): + input = paddle.randn([2, 42]).astype("float32") + input.stop_gradient = False + + model = NestedStructurePyLayerModel() + dygraph_res = model(input) + dygraph_res.backward() + dygraph_input_grads = [ + paddle.assign(input.grad), + paddle.assign(model.w0.grad), + paddle.assign(model.w1.grad), + paddle.assign(model.w2.grad), + ] + input.clear_grad() + model.w0.clear_grad() + model.w1.clear_grad() + model.w2.clear_grad() + + static_model = paddle.jit.to_static(model, full_graph=True) + static_res = static_model(input) + static_res.backward() + static_input_grads = [ + paddle.assign(input.grad), + paddle.assign(model.w0.grad), + paddle.assign(model.w1.grad), + paddle.assign(model.w2.grad), + ] + input.clear_grad() + model.w0.clear_grad() + model.w1.clear_grad() + model.w2.clear_grad() + for i, (dygraph_grad, static_grad) in enumerate( + zip(dygraph_input_grads, static_input_grads) + ): + np.testing.assert_allclose( + dygraph_grad.numpy(), + static_grad.numpy(), + rtol=1e-5, + atol=0, + err_msg=f"dygraph_grad[{i}]: {dygraph_grad} \n static_grad[{i}]: {static_grad}", + ) + + if __name__ == "__main__": unittest.main() From fd9babdb3300200891d54287a089e16cf5622b0b Mon Sep 17 00:00:00 2001 From: lijin23 <41257772+lj970926@users.noreply.github.com> Date: Wed, 11 Dec 2024 15:23:57 +0800 Subject: [PATCH 283/288] [XPU] support empty_cache api (#69771) * [XPU] support empty_cache api * fix typo * fix typo --- paddle/fluid/pybind/pybind.cc | 1 + .../phi/core/platform/device/xpu/xpu_info.cc | 7 +++++ .../phi/core/platform/device/xpu/xpu_info.h | 2 ++ python/paddle/device/xpu/__init__.py | 27 ++++++++++++++++-- test/xpu/test_xpu_empty_cache.py | 28 +++++++++++++++++++ 5 files changed, 62 insertions(+), 3 deletions(-) create mode 100644 test/xpu/test_xpu_empty_cache.py diff --git a/paddle/fluid/pybind/pybind.cc b/paddle/fluid/pybind/pybind.cc index bbe3896bb35eff..f8f2149a028978 100644 --- a/paddle/fluid/pybind/pybind.cc +++ b/paddle/fluid/pybind/pybind.cc @@ -2850,6 +2850,7 @@ All parameter, weight, gradient are variables in Paddle. #ifdef PADDLE_WITH_XPU m.def("get_xpu_device_count", platform::GetXPUDeviceCount); + m.def("xpu_empty_cache", platform::EmptyCache); #endif py::enum_(m, "TracerOption", py::arithmetic()) diff --git a/paddle/phi/core/platform/device/xpu/xpu_info.cc b/paddle/phi/core/platform/device/xpu/xpu_info.cc index 7a8a31479aa881..e33c2adaf63341 100644 --- a/paddle/phi/core/platform/device/xpu/xpu_info.cc +++ b/paddle/phi/core/platform/device/xpu/xpu_info.cc @@ -112,6 +112,13 @@ static void RaiseNonOutOfMemoryError(int status) { PADDLE_ENFORCE_XRE_SUCCESS(status); } +void EmptyCache() { + std::vector devices = GetXPUSelectedDevices(); + for (auto device : devices) { + memory::Release(phi::XPUPlace(device)); + } +} + class RecordedXPUMallocHelper { private: explicit RecordedXPUMallocHelper(int dev_id, uint64_t limit_size = 0) diff --git a/paddle/phi/core/platform/device/xpu/xpu_info.h b/paddle/phi/core/platform/device/xpu/xpu_info.h index e977b303046b7b..5a4906de0ccbe4 100644 --- a/paddle/phi/core/platform/device/xpu/xpu_info.h +++ b/paddle/phi/core/platform/device/xpu/xpu_info.h @@ -92,6 +92,8 @@ uint64_t RecordedXPULimitSize(int dev_id); bool IsXPUMallocRecorded(int dev_id); +void EmptyCache(void); + } // namespace platform } // namespace paddle #endif diff --git a/python/paddle/device/xpu/__init__.py b/python/paddle/device/xpu/__init__.py index c231fd566b7b41..238f9ff31d5f96 100644 --- a/python/paddle/device/xpu/__init__.py +++ b/python/paddle/device/xpu/__init__.py @@ -27,9 +27,7 @@ XPUPlace, int, # some int like 0, 1, etc. ] -__all__ = [ - 'synchronize', -] +__all__ = ['synchronize', 'empty_cache'] @deprecated( @@ -117,3 +115,26 @@ def set_debug_level(level: int = 1) -> None: >>> paddle.device.xpu.set_debug_level(0x1) ''' core.set_xpu_debug_level(level) + + +def empty_cache() -> None: + ''' + Releases idle cached memory held by the allocator so that those can be used in other XPU + application and visible in `xpu-smi`. In most cases you don't need to use this function, + Paddle does not release the memory back to the OS when you remove Tensors on the XPU, + Because it keeps xpu memory in a pool so that next allocations can be done much faster. + + Examples: + .. code-block:: python + + >>> # doctest: +REQUIRES(env:XPU) + >>> import paddle + >>> paddle.device.set_device('xpu') + + >>> tensor = paddle.randn([512, 512, 512], "float64") + >>> del tensor + >>> paddle.device.xpu.empty_cache() + ''' + + if core.is_compiled_with_xpu(): + core.xpu_empty_cache() diff --git a/test/xpu/test_xpu_empty_cache.py b/test/xpu/test_xpu_empty_cache.py new file mode 100644 index 00000000000000..f7eec6a93f7009 --- /dev/null +++ b/test/xpu/test_xpu_empty_cache.py @@ -0,0 +1,28 @@ +# Copyright (c) 2024 PaddlePaddle Authors. All Rights Reserved. +# +# Licensed under the Apache License, Version 2.0 (the "License"); +# you may not use this file except in compliance with the License. +# You may obtain a copy of the License at +# +# http://www.apache.org/licenses/LICENSE-2.0 +# +# Unless required by applicable law or agreed to in writing, software +# distributed under the License is distributed on an "AS IS" BASIS, +# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +# See the License for the specific language governing permissions and +# limitations under the License. + +import unittest + +import paddle + + +class TestEmptyCache(unittest.TestCase): + def test_empty_cache(self): + x = paddle.randn((2, 10, 12)).astype('float32') + del x + self.assertIsNone(paddle.device.xpu.empty_cache()) + + +if __name__ == '__main__': + unittest.main() From d785cf4bfffbad904d8bc8c2c47337377c9a7f90 Mon Sep 17 00:00:00 2001 From: Ayakouji <148307532+aquagull@users.noreply.github.com> Date: Wed, 11 Dec 2024 16:22:16 +0800 Subject: [PATCH 284/288] =?UTF-8?q?=E3=80=90Paddle=20Tensor=20=E7=AC=AC?= =?UTF-8?q?=E4=BA=8C=E6=9C=9F=20API=E6=94=AF=E6=8C=81=200-size=20Tensor?= =?UTF-8?q?=E3=80=91paddle.linspace=20=E6=94=AF=E6=8C=81=200-size=20tensor?= =?UTF-8?q?=20(#70047)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * compatible to 0-size * add test * fix * refine --- paddle/phi/kernels/cpu/linspace_kernel.cc | 17 +++++++----- paddle/phi/kernels/gpu/linspace_kernel.cu | 17 ++++++------ paddle/phi/kernels/xpu/linspace_kernel.cc | 17 ++++++------ test/legacy_test/test_linspace.py | 33 +++++++++++++++++++++++ test/xpu/test_linspace_op_xpu.py | 9 +++++++ 5 files changed, 71 insertions(+), 22 deletions(-) diff --git a/paddle/phi/kernels/cpu/linspace_kernel.cc b/paddle/phi/kernels/cpu/linspace_kernel.cc index 9ec2d78a65a2f5..6cb4fa001c3e95 100644 --- a/paddle/phi/kernels/cpu/linspace_kernel.cc +++ b/paddle/phi/kernels/cpu/linspace_kernel.cc @@ -33,17 +33,22 @@ void LinspaceKernel(const Context& ctx, } else if (number.dtype() == phi::DataType::INT32) { num = number.data()[0]; } + PADDLE_ENFORCE_GE(num, + 0, + common::errors::InvalidArgument( + "The num of linspace op should be larger " + "than or equal to 0, but received num is %d", + num)); + if (num == 0) { + out->Resize(common::make_ddim({0})); + ctx.template Alloc(out); + return; + } auto start_t = phi::funcs::TransDataType(ctx, start, dtype); auto stop_t = phi::funcs::TransDataType(ctx, stop, dtype); T start_data = start_t.template data()[0]; T stop_data = stop_t.template data()[0]; - PADDLE_ENFORCE_GT( - num, - 0, - common::errors::InvalidArgument("The num of linspace op should be larger " - "than 0, but received num is %d", - num)); out->Resize(common::make_ddim({num})); T* out_data = ctx.template Alloc(out); diff --git a/paddle/phi/kernels/gpu/linspace_kernel.cu b/paddle/phi/kernels/gpu/linspace_kernel.cu index b3ba6e9d7b25e5..f90d85bef34ab4 100644 --- a/paddle/phi/kernels/gpu/linspace_kernel.cu +++ b/paddle/phi/kernels/gpu/linspace_kernel.cu @@ -80,17 +80,18 @@ void LinspaceKernel(const Context& ctx, T start_value = GetValueOfExpectedType(ctx, start); T stop_value = GetValueOfExpectedType(ctx, stop); int64_t num = GetValueOfExpectedType(ctx, number); - - PADDLE_ENFORCE_GT( - num, - 0, - common::errors::InvalidArgument("The num of linspace op should be larger " - "than 0, but received num is %d", - num)); + PADDLE_ENFORCE_GE(num, + 0, + common::errors::InvalidArgument( + "The num of linspace op should be larger " + "than or equal to 0, but received num is %d", + num)); out->Resize(common::make_ddim({num})); T* out_data = ctx.template Alloc(out); - + if (num == 0) { + return; + } auto stream = ctx.stream(); if (num != 1) { int block = 512; diff --git a/paddle/phi/kernels/xpu/linspace_kernel.cc b/paddle/phi/kernels/xpu/linspace_kernel.cc index c618a9022d5eb7..03eed0f3f6aea0 100644 --- a/paddle/phi/kernels/xpu/linspace_kernel.cc +++ b/paddle/phi/kernels/xpu/linspace_kernel.cc @@ -59,17 +59,18 @@ void LinspaceKernel(const Context& ctx, T start_value = GetValueOfExpectedType(ctx, start); T stop_value = GetValueOfExpectedType(ctx, stop); int64_t num = GetValueOfExpectedType(ctx, number); - - PADDLE_ENFORCE_GT( - num, - 0, - common::errors::InvalidArgument("The num of linspace op should be larger " - "than 0, but received num is %d", - num)); + PADDLE_ENFORCE_GE(num, + 0, + common::errors::InvalidArgument( + "The num of linspace op should be larger " + "than or equal to 0, but received num is %d", + num)); out->Resize(common::make_ddim({num})); T* out_data = ctx.template Alloc(out); - + if (num == 0) { + return; + } int r = xpu::linspace(ctx.x_context(), reinterpret_cast(out_data), static_cast(start_value), diff --git a/test/legacy_test/test_linspace.py b/test/legacy_test/test_linspace.py index 2217f0d4f8f818..424059a53c5c7b 100644 --- a/test/legacy_test/test_linspace.py +++ b/test/legacy_test/test_linspace.py @@ -16,6 +16,7 @@ import numpy as np from op_test import OpTest, convert_float_to_uint16, paddle_static_guard +from utils import dygraph_guard, static_guard import paddle from paddle import base @@ -246,5 +247,37 @@ def test_num_dtype(): self.assertRaises(TypeError, test_step_dtype) +class TestLinspaceOpEmptyTensor(unittest.TestCase): + def _get_places(self): + places = [base.CPUPlace()] + if paddle.is_compiled_with_cuda(): + places.append(base.CUDAPlace(0)) + return places + + def _test_linspace_empty_static(self, place): + with static_guard(): + with paddle.static.program_guard( + paddle.static.Program(), paddle.static.Program() + ): + out = paddle.linspace(0, 10, 0, dtype='float32') + exe = paddle.static.Executor(place) + res = exe.run(fetch_list=[out]) + self.assertEqual(res[0].shape, (0,)) + self.assertEqual(len(res[0]), 0) + + def _test_linspace_empty_dynamic(self): + with dygraph_guard(): + out = paddle.linspace(0, 10, 0, dtype='float32') + self.assertEqual(out.shape, [0]) + self.assertEqual(len(out.numpy()), 0) + + def test_empty_tensor(self): + places = self._get_places() + for place in places: + self._test_linspace_empty_static(place) + + self._test_linspace_empty_dynamic() + + if __name__ == "__main__": unittest.main() diff --git a/test/xpu/test_linspace_op_xpu.py b/test/xpu/test_linspace_op_xpu.py index cb4aa6a3860bf4..b9869cc740fe0e 100644 --- a/test/xpu/test_linspace_op_xpu.py +++ b/test/xpu/test_linspace_op_xpu.py @@ -79,6 +79,15 @@ def set_attrs(self): } self.outputs = {'Out': np.array(10, dtype=self.dtype)} + class TestXPULinespace4(TestXPULinespaceOp): + def set_attrs(self): + self.inputs = { + 'Start': np.array([0]).astype(self.dtype), + 'Stop': np.array([10]).astype(self.dtype), + 'Num': np.array([0]).astype('int32'), + } + self.outputs = {'Out': np.array([], dtype=self.dtype)} + support_types = get_xpu_op_support_types('linspace') for stype in support_types: From 82c24d6bb9f700b04518190995abfbca2697a05a Mon Sep 17 00:00:00 2001 From: ming1753 <61511741+ming1753@users.noreply.github.com> Date: Wed, 11 Dec 2024 16:36:09 +0800 Subject: [PATCH 285/288] support 128k LLM (#70088) * support 128k LLM * remove log * remove log * buf fix * fix bug --- .../tensorrt/plugin/layer_norm_op_plugin.cu | 4 +- .../plugin/trans_layernorm_op_plugin.cu | 6 +- paddle/phi/kernels/funcs/load_store_util.h | 12 +-- .../fusion/gpu/fused_bias_act_kernel.cu | 74 +++++++++---------- .../kernels/fusion/gpu/fused_dropout_common.h | 8 +- .../kernels/fusion/gpu/fused_dropout_helper.h | 14 ++-- .../fusion/gpu/fused_layernorm_kernel.cu | 4 +- .../fused_layernorm_residual_dropout_bias.h | 45 ++++++----- .../gpu/fused_multi_transformer_helper.cu.h | 8 +- .../fusion/gpu/fused_residual_dropout_bias.h | 11 ++- paddle/phi/kernels/gpu/layer_norm_kernel.cu | 21 +++--- paddle/phi/kernels/layer_norm_kernel.h | 2 +- 12 files changed, 108 insertions(+), 101 deletions(-) diff --git a/paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu index 819d78c22770e1..2ebce801564457 100644 --- a/paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu +++ b/paddle/fluid/inference/tensorrt/plugin/layer_norm_op_plugin.cu @@ -102,7 +102,7 @@ int LayerNormPlugin::enqueue(int batch_size, int64_t batched_mean_shape = mean_shape_[0] * input_dims.d[0]; int64_t batched_variance_shape = variance_shape_[0] * input_dims.d[0]; - std::vector input_shape; + std::vector input_shape; input_shape.push_back(batch_size); for (int i = 0; i < input_dims.nbDims; i++) { input_shape.push_back(input_dims.d[i]); @@ -277,7 +277,7 @@ int LayerNormPluginDynamic::enqueue( int begin_norm_axis = begin_norm_axis_; float eps = eps_; - std::vector input_shape; + std::vector input_shape; for (int i = 0; i < input_dims.nbDims; i++) { input_shape.push_back(input_dims.d[i]); } diff --git a/paddle/fluid/inference/tensorrt/plugin/trans_layernorm_op_plugin.cu b/paddle/fluid/inference/tensorrt/plugin/trans_layernorm_op_plugin.cu index a193bdaf6c103d..30787d118b5414 100644 --- a/paddle/fluid/inference/tensorrt/plugin/trans_layernorm_op_plugin.cu +++ b/paddle/fluid/inference/tensorrt/plugin/trans_layernorm_op_plugin.cu @@ -299,7 +299,7 @@ int TransLayerNormPluginDynamic::enqueue( int begin_norm_axis = begin_norm_axis_; float eps = eps_; - std::vector input_shape; + std::vector input_shape; for (int i = 0; i < input_dims.nbDims; i++) { input_shape.push_back(input_dims.d[i]); } @@ -334,7 +334,7 @@ int TransLayerNormPluginDynamic::enqueue( // transpose do not change numel int trans_result_numel = input_numel; - std::vector trans_result_shape{ + std::vector trans_result_shape{ input_shape[0], input_shape[2], input_shape[3], input_shape[1]}; const auto input_ddim = common::make_ddim(input_shape); @@ -481,7 +481,7 @@ int TransLayerNormPluginDynamic::enqueue( int sm = getSMVersion(); // sm >= 60 to support __ldg if (sm >= 60) { - int hidden = input_shape[1]; + int64_t hidden = input_shape[1]; if (hidden % 2 == 0) { const size_t rows = static_cast(input_shape[0] * input_shape[2] * diff --git a/paddle/phi/kernels/funcs/load_store_util.h b/paddle/phi/kernels/funcs/load_store_util.h index 0fe7a3ce7a348a..099300b30458db 100644 --- a/paddle/phi/kernels/funcs/load_store_util.h +++ b/paddle/phi/kernels/funcs/load_store_util.h @@ -49,7 +49,7 @@ struct Load { explicit Load(const T *src) : src_(src) {} template - __device__ void load(phi::AlignedVector *dst, int idx) { + __device__ void load(phi::AlignedVector *dst, int64_t idx) { phi::Load(src_ + idx, dst); } @@ -61,7 +61,7 @@ struct Store { explicit Store(T *dst) : dst_(dst) {} template - __device__ void store(phi::AlignedVector &src, int idx) { + __device__ void store(phi::AlignedVector &src, int64_t idx) { phi::Store(src, dst_ + idx); } @@ -74,7 +74,7 @@ struct Store { : dst_(dst), shift_(shift), smooth_(smooth), cols_(cols) {} template - __device__ void store(phi::AlignedVector &src, int idx) { + __device__ void store(phi::AlignedVector &src, int64_t idx) { using Vec = phi::AlignedVector; Vec shift_vec; Vec smooth_vec; @@ -100,7 +100,7 @@ struct DequantLoad { : src_(src), dequant_scales_(dequant_scales), cols_(cols) {} template - __device__ void load(phi::AlignedVector *dst, int idx) { + __device__ void load(phi::AlignedVector *dst, int64_t idx) { using SrcVec = phi::AlignedVector; using DstVec = phi::AlignedVector; using ScaleVec = phi::AlignedVector; @@ -139,7 +139,7 @@ struct QuantStore { template __device__ void store(phi::AlignedVector &src, // NOLINT - int idx) { // NOLINT + int64_t idx) { // NOLINT using DstVec = phi::AlignedVector; DstVec dst_vec; @@ -183,7 +183,7 @@ struct QuantStore { template __device__ void store(phi::AlignedVector &src, // NOLINT - int idx) { // NOLINT + int64_t idx) { // NOLINT using DstVec = phi::AlignedVector; using Vec = phi::AlignedVector; diff --git a/paddle/phi/kernels/fusion/gpu/fused_bias_act_kernel.cu b/paddle/phi/kernels/fusion/gpu/fused_bias_act_kernel.cu index 866e55b13bd3eb..8f7f25e44dd7df 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_bias_act_kernel.cu +++ b/paddle/phi/kernels/fusion/gpu/fused_bias_act_kernel.cu @@ -28,9 +28,9 @@ template __global__ void ActFFNGlu(const T *bias, Functor act_functor, - const int token_num, - const int hid_dim, - const int elem_num, + const int64_t token_num, + const int64_t hid_dim, + const int64_t elem_num, LoadFunc load_func, StoreFunc store_func) { using LoadT = phi::AlignedVector; @@ -38,15 +38,15 @@ __global__ void ActFFNGlu(const T *bias, LoadT src_vec2; LoadT bias_vec1; LoadT bias_vec2; - const int global_tid = blockIdx.x * blockDim.x + threadIdx.x; - for (int i = global_tid * VecSize; i < elem_num; + const int64_t global_tid = blockIdx.x * blockDim.x + threadIdx.x; + for (int64_t i = global_tid * VecSize; i < elem_num; i += gridDim.x * blockDim.x * VecSize) { - int bi = i / hid_dim; - int idx = i % hid_dim; + int64_t bi = i / hid_dim; + int64_t idx = i % hid_dim; - load_func.template load(&src_vec1, bi * hid_dim * 2 + idx); - load_func.template load(&src_vec2, - bi * hid_dim * 2 + idx + hid_dim); + int64_t index = bi * hid_dim * 2 + idx; + load_func.template load(&src_vec1, index); + load_func.template load(&src_vec2, index + hid_dim); if (bias) { phi::Load(&bias[idx], &bias_vec1); @@ -73,13 +73,13 @@ template void LaunchActFFNGlu(const Context &dev_ctx, const T *bias, - const int token_num, - const int hid_dim, + const int64_t token_num, + const int64_t hid_dim, LoadFunc load_func, StoreFunc store_func) { constexpr int VecSize = 16; constexpr int PackSize = VecSize / sizeof(LoadT); - const int elem_cnt = token_num * hid_dim; + const int64_t elem_cnt = token_num * hid_dim; const int blocksize = 128; int grid_size = 1; Functor functor; @@ -110,9 +110,9 @@ template __global__ void BiasAct(const T *bias, Functor act_functor, - const int rows, - const int cols, - const int elem_num, + const int64_t rows, + const int64_t cols, + const int64_t elem_num, LoadFunc load_func, StoreFunc store_func) { using LoadT = phi::AlignedVector; @@ -121,16 +121,16 @@ __global__ void BiasAct(const T *bias, // Zero Initialize BiasVec. #pragma unroll - for (int unroll_idx = 0; unroll_idx < VecSize; unroll_idx++) { + for (int64_t unroll_idx = 0; unroll_idx < VecSize; unroll_idx++) { bias_vec[unroll_idx] = 0; } - const int global_tid = blockIdx.x * blockDim.x + threadIdx.x; - for (int i = global_tid * VecSize; i < elem_num; + const int64_t global_tid = blockIdx.x * blockDim.x + threadIdx.x; + for (int64_t i = global_tid * VecSize; i < elem_num; i += gridDim.x * blockDim.x * VecSize) { - int row_idx = i / cols; - int col_idx = i % cols; - int linear_idx = row_idx * cols + col_idx; + int64_t row_idx = i / cols; + int64_t col_idx = i % cols; + int64_t linear_idx = row_idx * cols + col_idx; load_func.template load(&src_vec, linear_idx); if (bias) { phi::Load(&bias[col_idx], &bias_vec); @@ -154,13 +154,13 @@ template void LaunchBiasAct(const Context &dev_ctx, const T *bias, - const int token_num, - const int hid_dim, + const int64_t token_num, + const int64_t hid_dim, LoadFunc load_func, StoreFunc store_func) { constexpr int VecSize = 16; constexpr int PackSize = VecSize / sizeof(LoadT); - const int elem_cnt = token_num * hid_dim; + const int64_t elem_cnt = token_num * hid_dim; const int blocksize = 128; int grid_size = 1; Functor functor; @@ -192,8 +192,8 @@ template &shift, const paddle::optional &smooth, const std::string &act_method, - int rows, - int cols, + int64_t rows, + int64_t cols, float quant_scale, int quant_round_type, float quant_max_bound, @@ -423,8 +423,8 @@ void DispatchWithDtype(const Context &dev_ctx, const paddle::optional &shift, const paddle::optional &smooth, const std::string &act_method, - int rows, - int cols, + int64_t rows, + int64_t cols, float quant_scale, int quant_round_type, float quant_max_bound, @@ -446,8 +446,8 @@ void FusedBiasActKernel(const Context &dev_ctx, float quant_max_bound, float quant_min_bound, DenseTensor *out) { - int cols = x.dims()[x.dims().size() - 1]; - int rows = x.numel() / cols; + int64_t cols = x.dims()[x.dims().size() - 1]; + int64_t rows = x.numel() / cols; if (x.dtype() == phi::DataType::INT32) { if (compute_dtype == "bf16") { DispatchWithDtype( diff --git a/paddle/phi/kernels/fusion/gpu/fused_dropout_common.h b/paddle/phi/kernels/fusion/gpu/fused_dropout_common.h index b55a579648f149..3985733168ecfc 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_dropout_common.h +++ b/paddle/phi/kernels/fusion/gpu/fused_dropout_common.h @@ -70,10 +70,16 @@ inline phi::backends::gpu::GpuLaunchConfig Get1DBlocksAnd2DGrids( ctx.GetMaxThreadsPerBlock(), 512)))); const auto blocks_x = std::max(static_cast(1), (tmp_cols + threads - 1) / threads); - const auto blocks_y = std::max(static_cast(1), rows); + int blocks_y = std::max(static_cast(1), rows); + int blocks_z = 1; + if (blocks_y > 65536) { + blocks_z = 1024; + blocks_y = (blocks_y + blocks_z - 1) / blocks_z; + } phi::backends::gpu::GpuLaunchConfig config; config.block_per_grid.x = blocks_x; config.block_per_grid.y = blocks_y; + config.block_per_grid.z = blocks_z; config.thread_per_block.x = threads; return config; } diff --git a/paddle/phi/kernels/fusion/gpu/fused_dropout_helper.h b/paddle/phi/kernels/fusion/gpu/fused_dropout_helper.h index 062488ca54ab74..d91eaa8fe6a9c1 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_dropout_helper.h +++ b/paddle/phi/kernels/fusion/gpu/fused_dropout_helper.h @@ -337,8 +337,8 @@ class FusedDropoutHelper { } protected: - int rows_; - int cols_; + int64_t rows_; + int64_t cols_; DropoutParam dropout_param_; float residual_alpha_; }; @@ -351,8 +351,8 @@ class FusedDropoutLayerNormHelper : public FusedDropoutHelper { public: FusedDropoutLayerNormHelper() {} - FusedDropoutLayerNormHelper(const int rows, - const int cols, + FusedDropoutLayerNormHelper(const int64_t rows, + const int64_t cols, const float epsilon, const float residual_alpha = 1.0) { using U = phi::funcs::LayerNormParamType; @@ -363,8 +363,8 @@ class FusedDropoutLayerNormHelper } FusedDropoutLayerNormHelper(const phi::GPUContext& ctx, - const int rows, - const int cols, + const int64_t rows, + const int64_t cols, const DropoutParam& dropout_param, const float epsilon, const float residual_alpha = 1.0) @@ -388,7 +388,7 @@ class FusedDropoutLayerNormHelper phi::LayerNormDirectCUDAFunctor> layer_norm; - std::vector src_shape{this->rows_, this->cols_}; + std::vector src_shape{this->rows_, this->cols_}; layer_norm(ctx.stream(), reinterpret_cast(src), src_shape, diff --git a/paddle/phi/kernels/fusion/gpu/fused_layernorm_kernel.cu b/paddle/phi/kernels/fusion/gpu/fused_layernorm_kernel.cu index 86dc1d8c37e8d0..f2f86578203e90 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_layernorm_kernel.cu +++ b/paddle/phi/kernels/fusion/gpu/fused_layernorm_kernel.cu @@ -1048,8 +1048,8 @@ void FusedLayerNormKernel(const Context& dev_ctx, const U* norm_weight_data = norm_weight ? norm_weight.get().data() : nullptr; const U* norm_bias_data = norm_bias ? norm_bias.get().data() : nullptr; - int32_t rows = 1; - int32_t cols = 1; + int64_t rows = 1; + int64_t cols = 1; for (int i = 0; i < begin_norm_axis; i++) { rows *= x.dims()[i]; } diff --git a/paddle/phi/kernels/fusion/gpu/fused_layernorm_residual_dropout_bias.h b/paddle/phi/kernels/fusion/gpu/fused_layernorm_residual_dropout_bias.h index c3c9ece6676cbb..86450f64a1d04b 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_layernorm_residual_dropout_bias.h +++ b/paddle/phi/kernels/fusion/gpu/fused_layernorm_residual_dropout_bias.h @@ -53,9 +53,9 @@ __device__ void CalcLayernormY( const LayerNormScaleBiasT *bias, const T *x, T *y, - const int row_id, - const int col_id, - const int cols, + const int64_t row_id, + const int64_t col_id, + const int64_t cols, const LayerNormParamType mean_val, const LayerNormParamType invvar) { using LoadT = phi::AlignedVector; @@ -64,7 +64,7 @@ __device__ void CalcLayernormY( using LoadScaleOrBias = phi::AlignedVector, VecSize>; - for (int i = col_id * VecSize; i < cols; i += blockDim.x * VecSize) { + for (int64_t i = col_id * VecSize; i < cols; i += blockDim.x * VecSize) { LoadScaleOrBias scale_vec; LoadScaleOrBias bias_vec; LoadT x_vec; @@ -140,9 +140,9 @@ __global__ void FusedLayernormResidualDropoutBias( LayerNormParamType *mean, LayerNormParamType *var, const float residual_alpha = 1.0) { - int col_id = threadIdx.x; - int row_id = blockIdx.x; - int idx = row_id * cols + col_id; + int64_t col_id = threadIdx.x; + int64_t row_id = blockIdx.x; + int64_t idx = row_id * cols + col_id; GPURAND(StatePhilox4_32_10_t) state; if (HasDropout) { GPURAND(_init)(seed, idx, increment, &state); @@ -537,8 +537,8 @@ template __global__ __launch_bounds__(THREADS_PER_CTA) void fused_fast_ln_fwd_kernel( - int rows, - int cols, + int64_t rows, + int64_t cols, uint64_t seed, const float dropout_prob, const bool is_upscale_in_train, @@ -617,10 +617,9 @@ __global__ __launch_bounds__(THREADS_PER_CTA) void fused_fast_ln_fwd_kernel( #pragma unroll for (int it = 0, col = c; it < LDGS; it++) { - phi::Load(residual_ptr + row * ELTS_PER_ROW + col * VecSize, - &residual[it]); - phi::Load(x_ptr + row * ELTS_PER_ROW + col * VecSize, - &x_input[it]); + int64_t index = row * ELTS_PER_ROW + col * VecSize; + phi::Load(residual_ptr + index, &residual[it]); + phi::Load(x_ptr + index, &x_input[it]); if (quant_out_scale_ptr != nullptr) { phi::Load(quant_out_scale_ptr + col * VecSize, &dequant_out_scale[it]); @@ -702,15 +701,15 @@ __global__ __launch_bounds__(THREADS_PER_CTA) void fused_fast_ln_fwd_kernel( // store dropout_residual_out and mask_out #pragma unroll for (int it = 0, col = c; it < LDGS; it++) { - phi::Store( - x[it], residual_out_ptr + row * ELTS_PER_ROW + col * VecSize); + int64_t index = row * ELTS_PER_ROW + col * VecSize; + phi::Store(x[it], residual_out_ptr + index); col += THREADS_PER_ROW; } if (!is_test && HasDropout) { #pragma unroll for (int it = 0, col = c; it < LDGS; it++) { - phi::Store( - mask_vec[it], mask_out_ptr + row * ELTS_PER_ROW + col * VecSize); + int64_t index = row * ELTS_PER_ROW + col * VecSize; + phi::Store(mask_vec[it], mask_out_ptr + index); col += THREADS_PER_ROW; } } @@ -820,13 +819,11 @@ __global__ __launch_bounds__(THREADS_PER_CTA) void fused_fast_ln_fwd_kernel( #pragma unroll for (int it = 0, col = c; it < LDGS; it++) { + int64_t index = row * ELTS_PER_ROW + col * VecSize; if (std::is_same::value) { - phi::Store( - x_output[it], y_ptr + row * ELTS_PER_ROW + col * VecSize); + phi::Store(x_output[it], y_ptr + index); } else { - phi::Store( - x[it], - reinterpret_cast(y_ptr) + row * ELTS_PER_ROW + col * VecSize); + phi::Store(x[it], reinterpret_cast(y_ptr) + index); } col += THREADS_PER_ROW; } @@ -856,8 +853,8 @@ template void LaunchLayernormResidualDropoutBias( - const uint32_t rows, - const uint32_t cols, + const int64_t rows, + const int64_t cols, const int increment, uint64_t seed, const float dropout_prob, diff --git a/paddle/phi/kernels/fusion/gpu/fused_multi_transformer_helper.cu.h b/paddle/phi/kernels/fusion/gpu/fused_multi_transformer_helper.cu.h index d4276a655804de..b4930e4cb8e9f8 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_multi_transformer_helper.cu.h +++ b/paddle/phi/kernels/fusion/gpu/fused_multi_transformer_helper.cu.h @@ -160,8 +160,8 @@ class NormHelper { public: NormHelper(const phi::GPUContext &dev_ctx, const std::string &norm_type, - const int rows, - const int cols, + const int64_t rows, + const int64_t cols, const float epsilon, const float residual_alpha) : dev_ctx_(dev_ctx), @@ -289,8 +289,8 @@ class NormHelper { private: const phi::GPUContext &dev_ctx_; std::string norm_type_; - int rows_; - int cols_; + int64_t rows_; + int64_t cols_; float epsilon_; float residual_alpha_; phi::fusion::FusedDropoutLayerNormHelper diff --git a/paddle/phi/kernels/fusion/gpu/fused_residual_dropout_bias.h b/paddle/phi/kernels/fusion/gpu/fused_residual_dropout_bias.h index 8cd4902ec59c3a..a0e5fcdfc1cb01 100644 --- a/paddle/phi/kernels/fusion/gpu/fused_residual_dropout_bias.h +++ b/paddle/phi/kernels/fusion/gpu/fused_residual_dropout_bias.h @@ -38,9 +38,9 @@ template __forceinline__ __device__ void FusedResidualDropoutBiasOneThread( - const int row_id, - const int col_id, - const int cols, + const int64_t row_id, + const int64_t col_id, + const int64_t cols, GPURAND(StatePhilox4_32_10_t) * state, const float dropout_prob, const T factor, @@ -279,7 +279,10 @@ __global__ void FusedResidualDropoutBias( const float quant_next_in_scale = 1.0, const float residual_alpha = 1.0) { int col_id = blockDim.x * blockIdx.x + threadIdx.x; - int row_id = blockIdx.y; + int row_id = blockIdx.y * gridDim.z + blockIdx.z; + if (row_id >= rows) { + return; + } int idx = row_id * cols + col_id; GPURAND(StatePhilox4_32_10_t) state; if (HasDropout) { diff --git a/paddle/phi/kernels/gpu/layer_norm_kernel.cu b/paddle/phi/kernels/gpu/layer_norm_kernel.cu index 09fc449ac398ab..12180ad6273ff2 100644 --- a/paddle/phi/kernels/gpu/layer_norm_kernel.cu +++ b/paddle/phi/kernels/gpu/layer_norm_kernel.cu @@ -453,16 +453,17 @@ void LaunchLayerNormKernel(const Context &dev_ctx, #endif // PADDLE_WITH_CUDA template -void LayerNormDirectCUDAFunctor::operator()(gpuStream_t stream, - const T *input, - std::vector input_shape, - const U *bias, - const U *scale, - T *output, - U *mean, - U *variance, - int begin_norm_axis, - float eps) { +void LayerNormDirectCUDAFunctor::operator()( + gpuStream_t stream, + const T *input, + std::vector input_shape, + const U *bias, + const U *scale, + T *output, + U *mean, + U *variance, + int begin_norm_axis, + float eps) { const auto x_dims = common::make_ddim(input_shape); auto matrix_dim = common::flatten_to_2d(x_dims, begin_norm_axis); int64_t batch_size = static_cast(matrix_dim[0]); diff --git a/paddle/phi/kernels/layer_norm_kernel.h b/paddle/phi/kernels/layer_norm_kernel.h index 2fddcec2278c9a..88b200754ad4dd 100644 --- a/paddle/phi/kernels/layer_norm_kernel.h +++ b/paddle/phi/kernels/layer_norm_kernel.h @@ -36,7 +36,7 @@ class LayerNormDirectCUDAFunctor { public: void operator()(gpuStream_t stream, const T* input, - std::vector input_shape, + std::vector input_shape, const U* bias, const U* scale, T* output, From f1868d33d922b524254656c4ea53d7b05d650253 Mon Sep 17 00:00:00 2001 From: zyfncg Date: Wed, 11 Dec 2024 16:36:23 +0800 Subject: [PATCH 286/288] [CINN] Fix bug of symbol shape with transfer layout (#70120) * fix bug of symbol shape with transfer layout * polish code --- .../interface/layout_transformation.cc | 25 +++++++++++++- .../general/auto_layout_insert_pass.cc | 15 +++++++- .../dialect/shape/utils/shape_analysis.h | 7 ++++ paddle/pir/include/pass/utils.h | 7 +++- .../src/dialect/shape/utils/shape_analysis.cc | 34 +++++++++++++++++++ paddle/pir/src/pass/utils.cc | 7 +++- 6 files changed, 91 insertions(+), 4 deletions(-) diff --git a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc index 6b55f5905e6deb..8aa3031b7198b2 100644 --- a/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc +++ b/paddle/fluid/pir/dialect/operator/interface/layout_transformation.cc @@ -21,6 +21,9 @@ #include "paddle/pir/include/core/builtin_attribute.h" #include "paddle/pir/include/core/ir_context.h" #include "paddle/pir/include/pass/utils.h" +#ifdef PADDLE_WITH_CINN +#include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" +#endif namespace paddle::dialect { @@ -32,8 +35,28 @@ void RewriteByInfermeta(pir::Operation* op, common::DataLayout new_layout) { op->result(i).set_type(new_outputs[i]); } + pir::TransLayoutCallbackFn callback = nullptr; +#ifdef PADDLE_WITH_CINN + auto& shape_analysis = + pir::ShapeAnalysisManager::Instance().Get(op->GetParentProgram()); + const pir::TransLayoutType trans_layout_type = [&] { + if (new_layout == common::DataLayout::NHWC) { + return pir::TransLayoutType::NCHW2NHWC; + } + if (new_layout == common::DataLayout::NHWC) { + return pir::TransLayoutType::NHWC2NCHW; + } + return pir::TransLayoutType::INVALID; + }(); + + if (trans_layout_type != pir::TransLayoutType::INVALID) { + callback = [&](pir::Value value, common::DataLayout new_layout) -> void { + shape_analysis.UpdateShapeOrDataByTransLayout(value, trans_layout_type); + }; + } +#endif for (auto value : RelevantOutputsImpl(op)) { - pir::SetNewLayoutForValue(value, new_layout); + pir::SetNewLayoutForValue(value, new_layout, callback); } } diff --git a/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc index 16a03a00fd6f42..9f108ced410138 100644 --- a/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc +++ b/paddle/fluid/pir/transforms/general/auto_layout_insert_pass.cc @@ -36,6 +36,9 @@ #include "paddle/pir/include/pass/pass.h" #include "paddle/pir/include/pass/pass_registry.h" #include "paddle/pir/include/pass/utils.h" +#ifdef PADDLE_WITH_CINN +#include "paddle/pir/include/dialect/shape/utils/shape_analysis.h" +#endif namespace { @@ -103,9 +106,19 @@ class AutoLayoutInsertPass : public pir::Pass { op->dyn_cast()) { auto output_types = infer_meta_interface.InferMeta(input_values, &p_attribute_map); + pir::TransLayoutCallbackFn callback = nullptr; +#ifdef PADDLE_WITH_CINN + auto& shape_analysis = + pir::ShapeAnalysisManager::Instance().Get(op->GetParentProgram()); + callback = [&](pir::Value value, common::DataLayout new_layout) -> void { + shape_analysis.UpdateShapeOrDataByTransLayout( + value, pir::TransLayoutType::NCHW2NHWC); + }; +#endif for (size_t i = 0; i < output_types.size(); ++i) { op->result(i).set_type(output_types[i]); - pir::SetNewLayoutForValue(op->result(i), common::DataLayout::NHWC); + pir::SetNewLayoutForValue( + op->result(i), common::DataLayout::NHWC, callback); } } else { InferMetaSpecificOp(); diff --git a/paddle/pir/include/dialect/shape/utils/shape_analysis.h b/paddle/pir/include/dialect/shape/utils/shape_analysis.h index 9a32817ee060a2..ae2f91df7ee26a 100644 --- a/paddle/pir/include/dialect/shape/utils/shape_analysis.h +++ b/paddle/pir/include/dialect/shape/utils/shape_analysis.h @@ -28,6 +28,9 @@ namespace pir { using InferSymbolicShapeCacheValue = std::vector; + +enum TransLayoutType { NCHW2NHWC, NHWC2NCHW, INVALID }; + /** * This class represents information needed to determine the output * shape of an operator, which includes the operator's name, input shapes, and @@ -199,6 +202,10 @@ class IR_API ShapeConstraintIRAnalysis final // Set ShapeOrData of `to` value by ShapeOrData of `from` value. void ShareShapeOrData(Value from, Value to); + // Update Symbol Shape for value by layout transformation. + void UpdateShapeOrDataByTransLayout(Value val, + TransLayoutType trans_layout_type); + void AddEqualCstr(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs); bool IsEqual(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) const; diff --git a/paddle/pir/include/pass/utils.h b/paddle/pir/include/pass/utils.h index 9a2cbc0274793f..3c1b33c89fa5bc 100644 --- a/paddle/pir/include/pass/utils.h +++ b/paddle/pir/include/pass/utils.h @@ -19,6 +19,11 @@ namespace pir { -void SetNewLayoutForValue(pir::Value value, common::DataLayout new_layout); +using TransLayoutCallbackFn = + std::function; + +void SetNewLayoutForValue(pir::Value value, + common::DataLayout new_layout, + TransLayoutCallbackFn callback = nullptr); } // namespace pir diff --git a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc index 486d8bc8e21e33..409af45b1bd158 100644 --- a/paddle/pir/src/dialect/shape/utils/shape_analysis.cc +++ b/paddle/pir/src/dialect/shape/utils/shape_analysis.cc @@ -624,6 +624,40 @@ void ShapeConstraintIRAnalysis::ShareShapeOrData(Value from, Value to) { } } +void ShapeConstraintIRAnalysis::UpdateShapeOrDataByTransLayout( + Value val, TransLayoutType trans_layout_type) { + if (context_.HasShapeOrDataForValue(val)) { + const auto& cur_shape = context_.GetShapeOrDataForValue(val).shape(); + PADDLE_ENFORCE_EQ(cur_shape.size(), + 4, + common::errors::InvalidArgument( + "Currently, the rank of value must be 4 when update " + "symbolic shape of value by layout transformation, " + "but now rank of value is %d.", + cur_shape.size())); + if (trans_layout_type == TransLayoutType::NCHW2NHWC) { + std::vector new_shape = cur_shape; + new_shape[1] = cur_shape[2]; + new_shape[2] = cur_shape[3]; + new_shape[3] = cur_shape[1]; + context_.SetShapeOrDataForValue( + val, {symbol::TensorShapeOrDataDimExprs{new_shape}}); + return; + } + if (trans_layout_type == TransLayoutType::NHWC2NCHW) { + std::vector new_shape = cur_shape; + new_shape[1] = cur_shape[3]; + new_shape[2] = cur_shape[1]; + new_shape[3] = cur_shape[2]; + context_.SetShapeOrDataForValue( + val, {symbol::TensorShapeOrDataDimExprs{new_shape}}); + return; + } + PADDLE_THROW(common::errors::Fatal( + "Dead code, shouldn't run here for UpdateShapeOrDataByTransLayout!")); + } +} + void ShapeConstraintIRAnalysis::AddEqualCstr(const symbol::DimExpr& lhs, const symbol::DimExpr& rhs) { context_.AddEqualCstr(lhs, rhs); diff --git a/paddle/pir/src/pass/utils.cc b/paddle/pir/src/pass/utils.cc index f866d7beaf8a2b..08e6cda74b9744 100644 --- a/paddle/pir/src/pass/utils.cc +++ b/paddle/pir/src/pass/utils.cc @@ -19,7 +19,9 @@ namespace pir { -void SetNewLayoutForValue(pir::Value value, common::DataLayout new_layout) { +void SetNewLayoutForValue(pir::Value value, + common::DataLayout new_layout, + TransLayoutCallbackFn callback) { if (!value || !value.type()) { return; } @@ -34,6 +36,9 @@ void SetNewLayoutForValue(pir::Value value, common::DataLayout new_layout) { tensor_type.lod(), tensor_type.offset()); value.set_type(new_tensor_type); + if (callback) { + callback(value, new_layout); + } } } // namespace pir From 7679609d698046f85172d31a121e248411b1e7bf Mon Sep 17 00:00:00 2001 From: "Zhang,Lirong" <1695074375@qq.com> Date: Wed, 11 Dec 2024 16:49:57 +0800 Subject: [PATCH 287/288] Fix bf16 quantize data_format bug (#70099) --- .../transforms/onednn/cpu_bfloat16_pass.cc | 73 ++++++++++--------- 1 file changed, 40 insertions(+), 33 deletions(-) diff --git a/paddle/fluid/pir/transforms/onednn/cpu_bfloat16_pass.cc b/paddle/fluid/pir/transforms/onednn/cpu_bfloat16_pass.cc index 2cfc2e04ade2d4..80ed42414cdbaf 100644 --- a/paddle/fluid/pir/transforms/onednn/cpu_bfloat16_pass.cc +++ b/paddle/fluid/pir/transforms/onednn/cpu_bfloat16_pass.cc @@ -50,6 +50,7 @@ class CpuBfloat16Pattern : public paddle::drr::DrrPatternBase { paddle::drr::SourcePattern pat = ctx->SourcePattern(); std::unordered_map op_attrs; + bool data_format = false; if (bfloat16_ops_ == "onednn_op.conv2d") { op_attrs.emplace("strides", pat.Attr("strides")); op_attrs.emplace("paddings", pat.Attr("paddings")); @@ -60,6 +61,7 @@ class CpuBfloat16Pattern : public paddle::drr::DrrPatternBase { op_attrs.emplace("is_test", pat.Attr("is_test")); op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); op_attrs.emplace("force_fp32_output", pat.Attr("force_fp32_output")); + data_format = true; } else if (bfloat16_ops_ == "onednn_op.matmul") { op_attrs.emplace("transpose_x", pat.Attr("transpose_x")); op_attrs.emplace("transpose_y", pat.Attr("transpose_y")); @@ -75,7 +77,6 @@ class CpuBfloat16Pattern : public paddle::drr::DrrPatternBase { op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("ceil_mode", pat.Attr("ceil_mode")); op_attrs.emplace("exclusive", pat.Attr("exclusive")); - op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("pooling_type", pat.Attr("pooling_type")); op_attrs.emplace("global_pooling", pat.Attr("global_pooling")); op_attrs.emplace("adaptive", pat.Attr("adaptive")); @@ -83,13 +84,13 @@ class CpuBfloat16Pattern : public paddle::drr::DrrPatternBase { op_attrs.emplace("use_quantizer", pat.Attr("use_quantizer")); op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); op_attrs.emplace("is_test", pat.Attr("is_test")); - + data_format = true; } else if (bfloat16_ops_ == "onednn_op.prelu") { op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("mode", pat.Attr("mode")); op_attrs.emplace("is_test", pat.Attr("is_test")); op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); - + data_format = true; } else if (bfloat16_ops_ == "onednn_op.sum") { op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); op_attrs.emplace("keepdim", pat.Attr("keepdim")); @@ -178,15 +179,16 @@ class CpuBfloat16Pattern : public paddle::drr::DrrPatternBase { }); paddle::drr::ResultPattern res = pat.ResultPattern(); - const auto &quantize_op = - res.Op("onednn_op.quantize", - {{ - {"scale", res.Float32Attr(1.f)}, - {"shift", res.Float32Attr(0.0f)}, - {"bfloat16", res.BoolAttr(true)}, - {"is_negative_input", res.BoolAttr(false)}, - {"output_format", res.StrAttr("NCHW")}, - }}); + const auto &quantize_op = res.Op( + "onednn_op.quantize", + {{ + {"scale", res.Float32Attr(1.f)}, + {"shift", res.Float32Attr(0.0f)}, + {"bfloat16", res.BoolAttr(true)}, + {"is_negative_input", res.BoolAttr(false)}, + {"output_format", + data_format ? pat.Attr("data_format") : res.StrAttr("NCHW")}, + }}); quantize_op({&res.Tensor("quantize_" + std::to_string(index_))}, {&res.Tensor("quantize_out_" + std::to_string(index_))}); @@ -251,7 +253,6 @@ class CpuBfloat16DequantPattern : public paddle::drr::DrrPatternBase { op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("ceil_mode", pat.Attr("ceil_mode")); op_attrs.emplace("exclusive", pat.Attr("exclusive")); - op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("pooling_type", pat.Attr("pooling_type")); op_attrs.emplace("global_pooling", pat.Attr("global_pooling")); op_attrs.emplace("adaptive", pat.Attr("adaptive")); @@ -383,6 +384,7 @@ class CpuBfloat16PatternOne_one : public paddle::drr::DrrPatternBase { paddle::drr::SourcePattern pat = ctx->SourcePattern(); std::unordered_map op_attrs; + bool data_format = false; if (bfloat16_ops_ == "onednn_op.gelu") { op_attrs.emplace("approximate", pat.Attr("approximate")); op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); @@ -392,12 +394,13 @@ class CpuBfloat16PatternOne_one : public paddle::drr::DrrPatternBase { op_attrs.emplace("axis", pat.Attr("axis")); op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("is_test", pat.Attr("is_test")); - + data_format = true; } else if (bfloat16_ops_ == "onednn_op.transpose" || bfloat16_ops_ == "onednn_op.transpose_") { op_attrs.emplace("perm", pat.Attr("perm")); op_attrs.emplace("data_format", pat.Attr("data_format")); op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); + data_format = true; } else if (bfloat16_ops_ == "onednn_op.relu" || bfloat16_ops_ == "onednn_op.relu_") { op_attrs.emplace("mkldnn_data_type", pat.Attr("mkldnn_data_type")); @@ -461,15 +464,16 @@ class CpuBfloat16PatternOne_one : public paddle::drr::DrrPatternBase { }); paddle::drr::ResultPattern res = pat.ResultPattern(); - const auto &quantize_op = - res.Op("onednn_op.quantize", - {{ - {"scale", res.Float32Attr(1.f)}, - {"shift", res.Float32Attr(0.0f)}, - {"bfloat16", res.BoolAttr(true)}, - {"is_negative_input", res.BoolAttr(false)}, - {"output_format", res.StrAttr("NCHW")}, - }}); + const auto &quantize_op = res.Op( + "onednn_op.quantize", + {{ + {"scale", res.Float32Attr(1.f)}, + {"shift", res.Float32Attr(0.0f)}, + {"bfloat16", res.BoolAttr(true)}, + {"is_negative_input", res.BoolAttr(false)}, + {"output_format", + data_format ? pat.Attr("data_format") : res.StrAttr("NCHW")}, + }}); quantize_op({&res.Tensor("quantize_0")}, {&res.Tensor("quantize_out_0")}); const auto &res_op = res.Op(bfloat16_ops_, op_attrs); @@ -812,6 +816,7 @@ class CpuBfloat16PatternThree_one : public paddle::drr::DrrPatternBase { paddle::drr::SourcePattern pat = ctx->SourcePattern(); std::unordered_map op_attrs; + bool data_format = false; if (bfloat16_ops_ == "onednn_op.fc") { op_attrs.emplace("in_num_col_dims", pat.Attr("in_num_col_dims")); op_attrs.emplace("activation_type", pat.Attr("activation_type")); @@ -870,6 +875,7 @@ class CpuBfloat16PatternThree_one : public paddle::drr::DrrPatternBase { op_attrs.emplace("paddings", pat.Attr("paddings")); op_attrs.emplace("strides", pat.Attr("strides")); op_attrs.emplace("force_fp32_output", pat.Attr("force_fp32_output")); + data_format = true; } const auto &op = pat.Op(bfloat16_ops_, op_attrs); @@ -930,15 +936,16 @@ class CpuBfloat16PatternThree_one : public paddle::drr::DrrPatternBase { paddle::drr::ResultPattern res = pat.ResultPattern(); - const auto &quantize_op = - res.Op("onednn_op.quantize", - {{ - {"scale", res.Float32Attr(1.f)}, - {"shift", res.Float32Attr(0.0f)}, - {"bfloat16", res.BoolAttr(true)}, - {"is_negative_input", res.BoolAttr(false)}, - {"output_format", res.StrAttr("NCHW")}, - }}); + const auto &quantize_op = res.Op( + "onednn_op.quantize", + {{ + {"scale", res.Float32Attr(1.f)}, + {"shift", res.Float32Attr(0.0f)}, + {"bfloat16", res.BoolAttr(true)}, + {"is_negative_input", res.BoolAttr(false)}, + {"output_format", + data_format ? pat.Attr("data_format") : res.StrAttr("NCHW")}, + }}); quantize_op({&res.Tensor("quantize_" + std::to_string(index_))}, {&res.Tensor("quantize_out_" + std::to_string(index_))}); @@ -1858,7 +1865,7 @@ class CpuBfloat16PatternFour_one : public paddle::drr::DrrPatternBase { {"shift", res.Float32Attr(0.0f)}, {"bfloat16", res.BoolAttr(true)}, {"is_negative_input", res.BoolAttr(false)}, - {"output_format", res.StrAttr("NCHW")}, + {"output_format", pat.Attr("data_format")}, }}); quantize_op({&res.Tensor("quantize_" + std::to_string(index_))}, {&res.Tensor("quantize_out_" + std::to_string(index_))}); From 461fbd138e4b831adc74dcabb2c18305d3e87ced Mon Sep 17 00:00:00 2001 From: Junjie Zhang <1356732652@qq.com> Date: Wed, 11 Dec 2024 16:58:55 +0800 Subject: [PATCH 288/288] =?UTF-8?q?=E3=80=90SCU=E3=80=91=E3=80=90Paddle=20?= =?UTF-8?q?TensorRT=20No.4=E3=80=91Add=20`pd=5Fop.stanh`=20converter=20(#6?= =?UTF-8?q?9539)?= MIME-Version: 1.0 Content-Type: text/plain; charset=UTF-8 Content-Transfer-Encoding: 8bit * add_tanh * fix codestyle * fix codestyle * fix * fix codestyle * fix codestyle * fix --- .../transforms/tensorrt/trt_op_marker_pass.cc | 2 ++ python/paddle/tensorrt/impls/activation.py | 11 +++++++++++ test/tensorrt/test_converter_activation.py | 16 ++++++++++++++++ 3 files changed, 29 insertions(+) diff --git a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc index 5b9570c88d0a78..b57d9c48fdfb3b 100644 --- a/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc +++ b/paddle/fluid/pir/transforms/tensorrt/trt_op_marker_pass.cc @@ -84,6 +84,7 @@ DEFINE_GENERAL_PATTERN(Swish, paddle::dialect::SwishOp) DEFINE_GENERAL_PATTERN(Log, paddle::dialect::LogOp) DEFINE_GENERAL_PATTERN(Floor, paddle::dialect::FloorOp) DEFINE_GENERAL_PATTERN(Roll, paddle::dialect::RollOp) +DEFINE_GENERAL_PATTERN(Stanh, paddle::dialect::StanhOp) DEFINE_GENERAL_PATTERN(Softplus, paddle::dialect::SoftplusOp) DEFINE_GENERAL_PATTERN(ThresholdedRelu, paddle::dialect::ThresholdedReluOp) DEFINE_GENERAL_PATTERN(Flip, paddle::dialect::FlipOp) @@ -2166,6 +2167,7 @@ class TrtOpMarkerPass : public pir::PatternRewritePass { ADD_PATTERN(Log) ADD_PATTERN(Floor) ADD_PATTERN(Roll) + ADD_PATTERN(Stanh) ADD_PATTERN(Softplus) ADD_PATTERN(ThresholdedRelu) ADD_PATTERN(Flip) diff --git a/python/paddle/tensorrt/impls/activation.py b/python/paddle/tensorrt/impls/activation.py index a0f15fa188e424..e0fedc50431c8b 100644 --- a/python/paddle/tensorrt/impls/activation.py +++ b/python/paddle/tensorrt/impls/activation.py @@ -139,6 +139,17 @@ def swish_silu_converter(network, paddle_op, inputs): return trt_prod(network, inputs[0], layer_output) +@converter_registry.register("pd_op.stanh", trt_version="8.x") +def stanh_converter(network, paddle_op, inputs): + x = inputs[0] + scale_a = paddle_op.attrs()["scale_a"] + scale_b = paddle_op.attrs()["scale_b"] + stanh_layer = network.add_activation(x, trt.ActivationType.SCALED_TANH) + stanh_layer.alpha = scale_b + stanh_layer.beta = scale_a + return stanh_layer.get_output(0) + + @converter_registry.register("pd_op.mish", trt_version="8.x") def mish_converter(network, paddle_op, inputs): x = inputs[0] diff --git a/test/tensorrt/test_converter_activation.py b/test/tensorrt/test_converter_activation.py index c3f077364c14b2..6b37c776dc0ed1 100644 --- a/test/tensorrt/test_converter_activation.py +++ b/test/tensorrt/test_converter_activation.py @@ -128,6 +128,22 @@ def test_trt_result(self): self.check_trt_result() +class TestStanhFloatTRTPattern(TensorRTBaseTest): + def setUp(self): + self.python_api = paddle.stanh + self.api_args = { + "x": np.random.randn(2, 3).astype("float32"), + "scale_a": 0.67, + "scale_b": 1.7159, + } + self.program_config = {"feed_list": ["x"]} + self.min_shape = {"x": [1, 3]} + self.max_shape = {"x": [5, 3]} + + def test_trt_result(self): + self.check_trt_result() + + class TestCeluTRTPattern(TensorRTBaseTest): def setUp(self): self.python_api = paddle.nn.functional.celu