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paddle/fluid/eager/api/manual/fluid_manual/forwards/CMakeLists.txt
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paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_gate_attention_fwd_func.cc
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paddle/fluid/eager/api/manual/fluid_manual/nodes/CMakeLists.txt
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paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_gate_attention_node.cc
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| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,242 @@ | ||
| // Copyright (c) 2022 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 "glog/logging.h" | ||
| #include "paddle/fluid/eager/api/manual/fluid_manual/nodes/nodes.h" | ||
| #include "paddle/fluid/eager/api/utils/global_utils.h" | ||
| #include "paddle/fluid/eager/utils.h" | ||
| #include "paddle/fluid/framework/op_registry.h" | ||
| #include "paddle/fluid/imperative/tracer.h" | ||
| #include "paddle/phi/api/all.h" | ||
|
|
||
| paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize> | ||
| fused_gate_attentionGradNodeCompat::operator()( | ||
| paddle::small_vector<std::vector<paddle::Tensor>, | ||
| egr::kSlotSmallVectorSize>& grads, | ||
| bool create_graph, | ||
| bool is_new_grad) { | ||
| VLOG(3) << "Running Eager Backward Node: fused_gate_attentionGradNodeCompat"; | ||
|
|
||
| const auto& out_metas = OutputMeta(); | ||
| paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize> | ||
| outputs(12); | ||
| paddle::small_vector<std::vector<paddle::Tensor>, egr::kSlotSmallVectorSize> | ||
| hooked_grads0 = | ||
| fused_gate_attentionGradNodeCompat::ApplyGradientHooks(grads); | ||
|
|
||
| bool merge_qkv = true; | ||
| if (attr_map_.count("merge_qkv")) { | ||
| merge_qkv = PADDLE_GET_CONST(bool, attr_map_.at("merge_qkv")); | ||
| } | ||
|
|
||
| bool has_gating = true; | ||
| if (attr_map_.count("has_gating")) { | ||
| has_gating = PADDLE_GET_CONST(bool, attr_map_.at("has_gating")); | ||
| } | ||
|
|
||
| bool use_flash_attn = false; | ||
| if (attr_map_.count("use_flash_attn")) { | ||
| use_flash_attn = PADDLE_GET_CONST(bool, attr_map_.at("use_flash_attn")); | ||
| } | ||
|
|
||
| std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> ins0 = | ||
| {{"FMHAOut", | ||
| egr::EagerUtils::TrySyncToVars( | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->FMHAOut_))}, | ||
| {"Out@GRAD", egr::EagerUtils::TrySyncToVars(hooked_grads0[7])}, | ||
| {"OutLinearBias", | ||
| egr::EagerUtils::TrySyncToVars( | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->OutLinearBias_))}, | ||
| {"OutLinearWeight", | ||
| egr::EagerUtils::TrySyncToVars( | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->OutLinearWeight_))}, | ||
| {"Query", | ||
| egr::EagerUtils::TrySyncToVars( | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->Query_))}, | ||
| {"SoftmaxOut", | ||
| egr::EagerUtils::TrySyncToVars( | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->SoftmaxOut_))}}; | ||
| std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> outs0; | ||
|
|
||
| if ((!out_metas[11].empty()) && (!(out_metas[11][0].IsStopGradient()))) { | ||
| outs0.insert({"OutLinearBias@GRAD", | ||
| {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}}); | ||
| } | ||
| if ((!out_metas[10].empty()) && (!(out_metas[10][0].IsStopGradient()))) { | ||
| outs0.insert({"OutLinearWeight@GRAD", | ||
| {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}}); | ||
| } | ||
| if ((!out_metas[0].empty()) && (!(out_metas[0][0].IsStopGradient()))) { | ||
| outs0.insert({"Query@GRAD", | ||
| {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}}); | ||
| } | ||
|
|
||
| if (merge_qkv) { | ||
| auto QKVTransposeOut = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->QKVTransposeOut_); | ||
| if (QKVTransposeOut.defined()) | ||
| ins0["QKVTransposeOut"] = egr::EagerUtils::TrySyncToVars(QKVTransposeOut); | ||
| auto QKVWeight = egr::EagerUtils::RecoverTensorWrapper(&this->QKVWeight_); | ||
| if (QKVWeight.defined()) | ||
| ins0["QKVWeight"] = egr::EagerUtils::TrySyncToVars(QKVWeight); | ||
| if (QKVWeight.defined() && (!out_metas[5].empty()) && | ||
| (!out_metas[5][0].IsStopGradient())) | ||
| outs0["QKVWeight@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| } else { | ||
| auto Key = egr::EagerUtils::RecoverTensorWrapper(&this->Key_); | ||
| if (Key.defined()) ins0["Key"] = egr::EagerUtils::TrySyncToVars(Key); | ||
| auto QueryWeight = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->QueryWeight_); | ||
| if (QueryWeight.defined()) | ||
| ins0["QueryWeight"] = egr::EagerUtils::TrySyncToVars(QueryWeight); | ||
| auto KeyWeight = egr::EagerUtils::RecoverTensorWrapper(&this->KeyWeight_); | ||
| if (KeyWeight.defined()) | ||
| ins0["KeyWeight"] = egr::EagerUtils::TrySyncToVars(KeyWeight); | ||
| auto ValueWeight = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->ValueWeight_); | ||
| if (ValueWeight.defined()) | ||
| ins0["ValueWeight"] = egr::EagerUtils::TrySyncToVars(ValueWeight); | ||
| auto QueryTransposeOut = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->QueryTransposeOut_); | ||
| if (QueryTransposeOut.defined()) | ||
| ins0["QueryTransposeOut"] = | ||
| egr::EagerUtils::TrySyncToVars(QueryTransposeOut); | ||
| auto KeyTransposeOut = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->KeyTransposeOut_); | ||
| if (KeyTransposeOut.defined()) | ||
| ins0["KeyTransposeOut"] = egr::EagerUtils::TrySyncToVars(KeyTransposeOut); | ||
| auto ValueTransposeOut = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->ValueTransposeOut_); | ||
| if (ValueTransposeOut.defined()) | ||
| ins0["ValueTransposeOut"] = | ||
| egr::EagerUtils::TrySyncToVars(ValueTransposeOut); | ||
|
|
||
| if (Key.defined() && (!out_metas[1].empty()) && | ||
| (!out_metas[1][0].IsStopGradient())) | ||
| outs0["Key@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| if (QueryWeight.defined() && (!out_metas[2].empty()) && | ||
| (!out_metas[2][0].IsStopGradient())) | ||
| outs0["QueryWeight@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| if (KeyWeight.defined() && (!out_metas[3].empty()) && | ||
| (!out_metas[3][0].IsStopGradient())) | ||
| outs0["KeyWeight@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| if (ValueWeight.defined() && (!out_metas[4].empty()) && | ||
| (!out_metas[4][0].IsStopGradient())) | ||
| outs0["ValueWeight@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| } | ||
|
|
||
| if (has_gating) { | ||
| auto GateBias = egr::EagerUtils::RecoverTensorWrapper(&this->GateBias_); | ||
| if (GateBias.defined()) | ||
| ins0["GateBias"] = egr::EagerUtils::TrySyncToVars(GateBias); | ||
| auto GateWeight = egr::EagerUtils::RecoverTensorWrapper(&this->GateWeight_); | ||
| if (GateWeight.defined()) | ||
| ins0["GateWeight"] = egr::EagerUtils::TrySyncToVars(GateWeight); | ||
| auto GateOut = egr::EagerUtils::RecoverTensorWrapper(&this->GateOut_); | ||
| if (GateOut.defined()) | ||
| ins0["GateOut"] = egr::EagerUtils::TrySyncToVars(GateOut); | ||
| if (GateBias.defined() && (!out_metas[9].empty()) && | ||
| (!out_metas[9][0].IsStopGradient())) | ||
| outs0["GateBias@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| if (GateWeight.defined() && (!out_metas[8].empty()) && | ||
| (!out_metas[8][0].IsStopGradient())) | ||
| outs0["GateWeight@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| } | ||
|
|
||
| auto NonbatchedBias = | ||
| egr::EagerUtils::RecoverTensorWrapper(&this->NonbatchedBias_); | ||
| if (NonbatchedBias.defined()) { | ||
| ins0["NonbatchedBias"] = egr::EagerUtils::TrySyncToVars(NonbatchedBias); | ||
| if ((!out_metas[6].empty()) && (!out_metas[6][0].IsStopGradient())) | ||
| outs0["NonbatchedBias@GRAD"] = {std::make_shared<egr::EagerVariable>( | ||
| egr::Controller::Instance().GenerateUniqueName())}; | ||
| } | ||
|
|
||
| if (use_flash_attn) { | ||
| auto SrcMask = egr::EagerUtils::RecoverTensorWrapper(&this->SrcMask_); | ||
| ins0["SrcMask"] = egr::EagerUtils::TrySyncToVars(SrcMask); | ||
| auto SoftmaxLse = egr::EagerUtils::RecoverTensorWrapper(&this->SoftmaxLse_); | ||
| ins0["SoftmaxLse"] = egr::EagerUtils::TrySyncToVars(SoftmaxLse); | ||
| } | ||
|
|
||
| auto& attrs_map0 = this->attr_map_; | ||
| // Pass the entire attribute map to TraceOp | ||
| // The underlying kernel will pickup whatever attribute they need at runtime | ||
| egr::Controller::Instance().GetCurrentTracer()->TraceOp( | ||
| "fused_gate_attention_grad", | ||
| ins0, | ||
| outs0, | ||
| attrs_map0, | ||
| egr::Controller::Instance().GetExpectedPlace(), | ||
| &this->default_attr_map_, | ||
| false, | ||
| {}); | ||
|
|
||
| if (outs0.find("Query@GRAD") != outs0.end()) { | ||
| outputs[0] = egr::EagerUtils::GetOutputs(outs0["Query@GRAD"]); | ||
| } | ||
| if (outs0.find("OutLinearBias@GRAD") != outs0.end()) { | ||
| outputs[11] = egr::EagerUtils::GetOutputs(outs0["OutLinearBias@GRAD"]); | ||
| } | ||
| if (outs0.find("OutLinearWeight@GRAD") != outs0.end()) { | ||
| outputs[10] = egr::EagerUtils::GetOutputs(outs0["OutLinearWeight@GRAD"]); | ||
| } | ||
|
|
||
| if (merge_qkv) { | ||
| if (outs0.find("QKVWeight@GRAD") != outs0.end()) { | ||
| outputs[5] = egr::EagerUtils::GetOutputs(outs0["QKVWeight@GRAD"]); | ||
| } | ||
| } else { | ||
| if (outs0.find("Key@GRAD") != outs0.end()) { | ||
| outputs[1] = egr::EagerUtils::GetOutputs(outs0["Key@GRAD"]); | ||
| } | ||
| if (outs0.find("QueryWeight@GRAD") != outs0.end()) { | ||
| outputs[2] = egr::EagerUtils::GetOutputs(outs0["QueryWeight@GRAD"]); | ||
| } | ||
| if (outs0.find("KeyWeight@GRAD") != outs0.end()) { | ||
| outputs[3] = egr::EagerUtils::GetOutputs(outs0["KeyWeight@GRAD"]); | ||
| } | ||
| if (outs0.find("ValueWeight@GRAD") != outs0.end()) { | ||
| outputs[4] = egr::EagerUtils::GetOutputs(outs0["ValueWeight@GRAD"]); | ||
| } | ||
| } | ||
|
|
||
| if (has_gating) { | ||
| if (outs0.find("GateBias@GRAD") != outs0.end()) { | ||
| outputs[9] = egr::EagerUtils::GetOutputs(outs0["GateBias@GRAD"]); | ||
| } | ||
| if (outs0.find("GateWeight@GRAD") != outs0.end()) { | ||
| outputs[8] = egr::EagerUtils::GetOutputs(outs0["GateWeight@GRAD"]); | ||
| } | ||
| } | ||
|
|
||
| if (NonbatchedBias.defined()) { | ||
| if (outs0.find("NonbatchedBias@GRAD") != outs0.end()) { | ||
| outputs[6] = egr::EagerUtils::GetOutputs(outs0["NonbatchedBias@GRAD"]); | ||
| } | ||
| } | ||
|
|
||
| if (NeedComplexToRealConversion()) HandleComplexGradToRealGrad(&outputs); | ||
| return outputs; | ||
| } |
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