[hybrid performance] Grad fuse for gradient merge under pipeline mode (…

…#35004)

paddle/fluid/framework/distributed_strategy.proto

@@ -200,6 +200,7 @@ message DistributedStrategy {
   optional int32 fuse_grad_size_in_num = 31 [ default = 8 ];
   optional bool calc_comm_same_stream = 32 [ default = false ];
   optional bool asp = 33 [ default = false ];
+  optional bool fuse_grad_merge = 34 [ default = false ];
 
   optional RecomputeConfig recompute_configs = 101;
   optional AMPConfig amp_configs = 102;

paddle/fluid/operators/coalesce_tensor_op.cc

@@ -20,10 +20,49 @@
 #include "paddle/fluid/framework/var_type.h"
 #include "paddle/fluid/operators/math/math_function.h"
 #include "paddle/fluid/platform/device_memory_aligment.h"
+#ifdef PADDLE_WITH_ASCEND_CL
+#include "paddle/fluid/operators/npu_op_runner.h"
+#endif
 
 namespace paddle {
 namespace operators {
 
+template <typename DeviceContext>
+struct FillConstantVisitor {
+  FillConstantVisitor(const DeviceContext &dev_ctx,
+                      framework::LoDTensor *tensor, const float value)
+      : dev_ctx_(dev_ctx), tensor_(tensor), value_(value) {}
+
+  template <typename T>
+  void apply(typename std::enable_if<std::is_same<T, int8_t>::value ||
+                                     std::is_same<T, int16_t>::value>::type * =
+                 nullptr) const {
+    PADDLE_THROW(platform::errors::InvalidArgument(
+        "Not support data type for set_constant attr"));
+  }
+
+  template <typename T>
+  void apply(typename std::enable_if<!(std::is_same<T, int8_t>::value ||
+                                       std::is_same<T, int16_t>::value)>::type
+                 * = nullptr) const {
+#ifdef PADDLE_WITH_ASCEND_CL
+    if (platform::is_npu_place(dev_ctx_.GetPlace())) {
+      FillNpuTensorWithConstant<T>(tensor_, static_cast<T>(value_));
+    } else {
+      math::SetConstant<DeviceContext, T> set_constant;
+      set_constant(dev_ctx_, tensor_, static_cast<T>(value_));
+    }
+#else
+    math::SetConstant<DeviceContext, T> set_constant;
+    set_constant(dev_ctx_, tensor_, static_cast<T>(value_));
+#endif
+  }
+
+  const DeviceContext &dev_ctx_;
+  framework::LoDTensor *tensor_;
+  float value_;
+};
+
 template <typename DeviceContext, typename T>
 class CoalesceTensorOpKernel : public framework::OpKernel<T> {
  public:
@@ -70,6 +109,7 @@ class CoalesceTensorOpKernel : public framework::OpKernel<T> {
     auto in_tensors = context.MultiInput<framework::LoDTensor>("Input");
     bool use_align = context.Attr<bool>("use_align");
     auto align_size = context.Attr<int>("align_size");
+    auto size_of_dtype = context.Attr<int>("user_defined_size_of_dtype");
 
     if (context.Attr<bool>("check_name")) {
       for (size_t i = 0; i < in_var_names.size(); ++i) {
@@ -94,7 +134,9 @@ class CoalesceTensorOpKernel : public framework::OpKernel<T> {
     size_t numel = 0;
     auto dtype = static_cast<framework::proto::VarType::Type>(
         context.Attr<int>("dtype"));
-    size_t size_of_dtype = framework::SizeOfType(dtype);
+    if (size_of_dtype == -1) {
+      size_of_dtype = framework::SizeOfType(dtype);
+    }
     GetMemSizeAndDtype(in_tensors, in_var_names, &numel, size_of_dtype,
                        context.GetPlace(), use_align, align_size);
 
@@ -121,10 +163,9 @@ class CoalesceTensorOpKernel : public framework::OpKernel<T> {
                       : len;
       }
     } else if (context.Attr<bool>("set_constant")) {
-      // TODO(Liu yuang) ADD NPU SET_CONSTANT FUNCTION.
-      math::SetConstant<DeviceContext, T> set_constant;
-      set_constant(dev_ctx, fused_tensor,
-                   static_cast<T>(context.Attr<float>("constant")));
+      framework::VisitDataType(
+          dtype, FillConstantVisitor<DeviceContext>(
+                     dev_ctx, fused_tensor, context.Attr<float>("constant")));
     } else if (context.Attr<bool>("persist_output")) {
       for (size_t i = 0; i < out_var_names.size(); ++i) {
         size_t len = static_cast<size_t>(out_tensors[i]->numel());
@@ -227,10 +268,13 @@ class CoalesceTensorOp : public framework::OperatorWithKernel {
     }
     auto use_align = ctx->Attrs().Get<bool>("use_align");
     auto align_size = ctx->Attrs().Get<int>("align_size");
+    auto size_of_dtype = ctx->Attrs().Get<int>("user_defined_size_of_dtype");
 
     auto dtype = static_cast<framework::proto::VarType::Type>(
         ctx->Attrs().Get<int>("dtype"));
-    size_t size_of_dtype = framework::SizeOfType(dtype);
+    if (size_of_dtype == -1) {
+      size_of_dtype = framework::SizeOfType(dtype);
+    }
 
     auto alignment = [](size_t size, size_t align_size) {
       size_t remaining = size % align_size;
@@ -308,6 +352,15 @@ class CoalesceTensorOpMaker : public framework::OpProtoAndCheckerMaker {
         .SetDefault(true);
     AddAttr<int>("align_size", "The alignment size when use_align is True")
         .SetDefault(-1);
+    AddAttr<int>("user_defined_size_of_dtype",
+                 "The user defined size of dtype. This is used to coalesce "
+                 "grad vars and merged_grad vars at the same time. For some "
+                 "strategy, the dtype of fused_grad_vars and the dtype of "
+                 "fused_grad_merged_vars are not identical, which will cause "
+                 "the shape of these two coalesced vars are different. To "
+                 "make sure the shape of these two vars are identical with "
+                 "each other, this attr is added.")
+        .SetDefault(-1);
     AddComment(R"DOC(
 CoalesceTensor Operator.
 

python/paddle/distributed/fleet/base/distributed_strategy.py

@@ -967,6 +967,28 @@ def _calc_comm_same_stream(self, same):
                 "WARNING: calc_comm_same_stream should have value of boolean type"
             )
 
+    @property
+    def fuse_grad_merge(self):
+        """
+        Set whether fuse the grad for gradient merge.
+        Note: this flag will only effect the gradient merge under pipeline mode
+        The default value for the fuse_grad_merge is False
+        Examples:
+          .. code-block:: python
+            import paddle.distributed.fleet as fleet
+            strategy = fleet.DistributedStrategy()
+            strategy.fuse_param_grad = True
+        """
+        return self.strategy.fuse_grad_merge
+
+    @fuse_grad_merge.setter
+    @is_strict_auto
+    def fuse_grad_merge(self, fuse_grad_merge):
+        if isinstance(fuse_grad_merge, bool):
+            self.strategy.fuse_grad_merge = fuse_grad_merge
+        else:
+            print("WARNING: fuse_grad_merge should have value of boolean type")
+
     @property
     def fuse_grad_size_in_num(self):
         """

python/paddle/distributed/fleet/meta_optimizers/sharding/offload_helper.py

@@ -122,6 +122,9 @@ def remove_param(input_name):
         for idx, op in enumerate(block.ops):
             if is_optimizer_op(op):
                 break
+            # TODO (Yuang Liu): tmp solution for fuse_grad_merge + optimize_cast
+            if not offload and op.type == 'coalesce_tensor':
+                continue
             for input_name in op.desc.input_arg_names():
                 if input_name not in param_to_idx:
                     continue

python/paddle/distributed/fleet/meta_optimizers/sharding/utils.py

@@ -341,7 +341,11 @@ def insert_allreduce_ops(block,
     if len(allreduce_vars) == 0:
         return
 
-    if user_defined_strategy and user_defined_strategy.fuse_all_reduce_ops:
+    if user_defined_strategy and \
+            user_defined_strategy.fuse_all_reduce_ops and \
+            not user_defined_strategy.fuse_grad_merge:
+        # If fuse_grad_merge is enable, the grad vars have already been fused during
+        # gradient merge pass, therefore, those vars are not need to be fused here
         insert_fused_allreduce_ops(block, insert_idx, ring_id, allreduce_vars,
                                    op_role, use_calc_stream,
                                    user_defined_strategy.fuse_grad_size_in_MB)

python/paddle/distributed/fleet/meta_optimizers/sharding_optimizer.py

@@ -319,7 +319,9 @@ def _insert_allreduce_for_pp(self):
                     main_block._remove_op(idx)
 
         accumulated_grad_names = self._pp_optimizer._accumulate_gradients(
-            main_block, fp16_allreduce=fp16_allreduce)
+            main_block,
+            fp16_allreduce=fp16_allreduce,
+            user_defined_strategy=strategy)
 
         len_of_ops = len(main_block.ops)
         first_optimize_op_index = get_first_optimize_op_idx(main_block)