mv fused_bias_dropout_residual_ln to fluid manual dir

paddle/fluid/eager/api/manual/fluid_manual/dygraph_forward_api.h

@@ -107,3 +107,16 @@ paddle::experimental::Tensor fused_gemm_epilogue_dygraph_function(
     const paddle::experimental::Tensor& Y,
     const paddle::experimental::Tensor& Bias,
     const paddle::framework::AttributeMap& attr_map);
+
+std::tuple<paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor>
+fused_bias_dropout_residual_layer_norm_dygraph_function(
+    const paddle::experimental::Tensor& X,
+    const paddle::experimental::Tensor& Residual,
+    const paddle::experimental::Tensor& Bias,
+    const paddle::experimental::Tensor& LnScale,
+    const paddle::experimental::Tensor& LnBias,
+    const paddle::framework::AttributeMap& attr_map);

paddle/fluid/eager/api/manual/fluid_manual/forwards/CMakeLists.txt

@@ -3,4 +3,5 @@ set(fluid_manual_functions
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_feedforward_fwd_func.cc
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_attention_fwd_func.cc
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_gemm_epilogue_fwd_func.cc
+    ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_bias_dropout_residual_layer_norm_fwd_func.cc
     PARENT_SCOPE)

paddle/fluid/eager/api/manual/fluid_manual/forwards/fused_bias_dropout_residual_layer_norm_fwd_func.cc

@@ -0,0 +1,230 @@
+// 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 "paddle/fluid/eager/accumulation/accumulation_node.h"
+#include "paddle/fluid/eager/amp_auto_cast.h"
+#include "paddle/fluid/eager/amp_utils.h"
+#include "paddle/fluid/eager/api/manual/fluid_manual/dygraph_forward_api.h"
+#include "paddle/fluid/eager/api/manual/fluid_manual/nodes/nodes.h"
+#include "paddle/fluid/eager/api/utils/global_utils.h"
+#include "paddle/fluid/platform/profiler/event_tracing.h"
+
+std::tuple<paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor,
+           paddle::experimental::Tensor>
+fused_bias_dropout_residual_layer_norm_dygraph_function(
+    const paddle::experimental::Tensor& X,
+    const paddle::experimental::Tensor& Residual,
+    const paddle::experimental::Tensor& Bias,
+    const paddle::experimental::Tensor& LnScale,
+    const paddle::experimental::Tensor& LnBias,
+    const paddle::framework::AttributeMap& attr_map) {
+  paddle::platform::RecordEvent dygraph_entrance_record_event(
+      "fused_bias_dropout_residual_layer_norm dygraph",
+      paddle::platform::TracerEventType::Operator,
+      1);
+  VLOG(3) << "Running Eager Forward Op: fused_bias_dropout_residual_layer_norm";
+  // Dygraph Forward Pass
+
+  if (egr::Controller::Instance().GetAMPLevel() !=
+      paddle::imperative::AmpLevel::O0) {
+    VLOG(5) << "Check and Prepare For AMP";
+
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         egr::kSlotSmallVectorSize>
+        amp_tensors_vector = {{X}, {Residual}};
+    if (Bias.initialized()) amp_tensors_vector.push_back({Bias});
+    if (LnScale.initialized()) amp_tensors_vector.push_back({LnScale});
+    if (LnBias.initialized()) amp_tensors_vector.push_back({LnBias});
+
+    auto amp_dst_dtype = egr::GetAmpDestDtype(
+        "fused_bias_dropout_residual_layer_norm", amp_tensors_vector);
+
+    auto NEW_X = egr::AmpAutoCast(
+        "X", X, amp_dst_dtype, "fused_bias_dropout_residual_layer_norm");
+    auto NEW_Residual =
+        egr::AmpAutoCast("Residual",
+                         Residual,
+                         amp_dst_dtype,
+                         "fused_bias_dropout_residual_layer_norm");
+    auto NEW_Bias =
+        ((Bias.initialized())
+             ? egr::AmpAutoCast("Bias",
+                                Bias,
+                                amp_dst_dtype,
+                                "fused_bias_dropout_residual_layer_norm")
+             : Bias);
+    auto NEW_LnScale =
+        ((LnScale.initialized())
+             ? egr::AmpAutoCast("LnScale",
+                                LnScale,
+                                amp_dst_dtype,
+                                "fused_bias_dropout_residual_layer_norm")
+             : LnScale);
+    auto NEW_LnBias =
+        ((LnBias.initialized())
+             ? egr::AmpAutoCast("LnBias",
+                                LnBias,
+                                amp_dst_dtype,
+                                "fused_bias_dropout_residual_layer_norm")
+             : LnBias);
+
+    {
+      paddle::imperative::AutoCastGuard guard(
+          egr::Controller::Instance().GetCurrentTracer(),
+          paddle::imperative::AmpLevel::O0);
+      return fused_bias_dropout_residual_layer_norm_dygraph_function(
+          NEW_X, NEW_Residual, NEW_Bias, NEW_LnScale, NEW_LnBias, attr_map);
+    }
+  }
+
+  std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> ins =
+      {{"X", egr::EagerUtils::TrySyncToVars(X)},
+       {"Residual", egr::EagerUtils::TrySyncToVars(Residual)}};
+  if (Bias.initialized()) ins["Bias"] = egr::EagerUtils::TrySyncToVars(Bias);
+  if (LnScale.initialized())
+    ins["LnScale"] = egr::EagerUtils::TrySyncToVars(LnScale);
+  if (LnBias.initialized())
+    ins["LnBias"] = egr::EagerUtils::TrySyncToVars(LnBias);
+
+  std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> outs =
+      {{"BiasDropoutResidualOut",
+        {std::make_shared<egr::EagerVariable>(
+            egr::Controller::Instance().GenerateUniqueName())}},
+       {"DropoutMaskOut",
+        {std::make_shared<egr::EagerVariable>(
+            egr::Controller::Instance().GenerateUniqueName())}},
+       {"LnMean",
+        {std::make_shared<egr::EagerVariable>(
+            egr::Controller::Instance().GenerateUniqueName())}},
+       {"LnVariance",
+        {std::make_shared<egr::EagerVariable>(
+            egr::Controller::Instance().GenerateUniqueName())}},
+       {"Y",
+        {std::make_shared<egr::EagerVariable>(
+            egr::Controller::Instance().GenerateUniqueName())}}};
+
+  // Prepare Autograd Meta
+  egr::AutogradMeta* p_autograd_X = egr::EagerUtils::nullable_autograd_meta(X);
+  egr::AutogradMeta* p_autograd_Residual =
+      egr::EagerUtils::nullable_autograd_meta(Residual);
+  egr::AutogradMeta* p_autograd_Bias =
+      egr::EagerUtils::nullable_autograd_meta(Bias);
+  egr::AutogradMeta* p_autograd_LnScale =
+      egr::EagerUtils::nullable_autograd_meta(LnScale);
+  egr::AutogradMeta* p_autograd_LnBias =
+      egr::EagerUtils::nullable_autograd_meta(LnBias);
+
+  bool trace_backward = egr::Controller::Instance().HasGrad();
+
+  bool require_any_grad =
+      egr::EagerUtils::ComputeRequireGrad(trace_backward,
+                                          p_autograd_X,
+                                          p_autograd_Residual,
+                                          p_autograd_Bias,
+                                          p_autograd_LnScale,
+                                          p_autograd_LnBias);
+
+  paddle::framework::AttributeMap attrs = attr_map;
+  paddle::framework::AttributeMap default_attrs;
+  egr::Controller::Instance().GetCurrentTracer()->TraceOp(
+      "fused_bias_dropout_residual_layer_norm",
+      ins,
+      outs,
+      attrs,
+      egr::Controller::Instance().GetExpectedPlace(),
+      &default_attrs,
+      true,
+      {});
+
+  paddle::experimental::Tensor BiasDropoutResidualOut;
+  egr::EagerUtils::GetOutput(outs["BiasDropoutResidualOut"][0],
+                             &BiasDropoutResidualOut);
+  paddle::experimental::Tensor DropoutMaskOut;
+  egr::EagerUtils::GetOutput(outs["DropoutMaskOut"][0], &DropoutMaskOut);
+  paddle::experimental::Tensor LnMean;
+  egr::EagerUtils::GetOutput(outs["LnMean"][0], &LnMean);
+  paddle::experimental::Tensor LnVariance;
+  egr::EagerUtils::GetOutput(outs["LnVariance"][0], &LnVariance);
+  paddle::experimental::Tensor Y;
+  egr::EagerUtils::GetOutput(outs["Y"][0], &Y);
+
+  {
+    paddle::platform::RecordEvent node_creation_record_event(
+        "fused_bias_dropout_residual_layer_norm node_creation",
+        paddle::platform::TracerEventType::OperatorInner,
+        1);
+    egr::AutogradMeta* p_autograd_BiasDropoutResidualOut =
+        egr::EagerUtils::autograd_meta(&BiasDropoutResidualOut);
+    egr::AutogradMeta* p_autograd_DropoutMaskOut =
+        egr::EagerUtils::autograd_meta(&DropoutMaskOut);
+    egr::AutogradMeta* p_autograd_LnMean =
+        egr::EagerUtils::autograd_meta(&LnMean);
+    egr::AutogradMeta* p_autograd_LnVariance =
+        egr::EagerUtils::autograd_meta(&LnVariance);
+    egr::AutogradMeta* p_autograd_Y = egr::EagerUtils::autograd_meta(&Y);
+    if (require_any_grad) {
+      VLOG(6) << " Construct Grad for fused_bias_dropout_residual_layer_norm ";
+      egr::EagerUtils::PassStopGradient(false,
+                                        p_autograd_BiasDropoutResidualOut,
+                                        p_autograd_DropoutMaskOut,
+                                        p_autograd_LnMean,
+                                        p_autograd_LnVariance,
+                                        p_autograd_Y);
+      // Create GradOpNode
+      auto grad_node =
+          std::shared_ptr<fused_bias_dropout_residual_layer_normGradNodeCompat>(
+              new fused_bias_dropout_residual_layer_normGradNodeCompat(5, 5));
+
+      // Set Attributes
+      grad_node->SetAttrMap(std::move(attrs));
+      grad_node->SetDefaultAttrMap(std::move(default_attrs));
+
+      // Set Tensor Wrappers
+      grad_node->SetTensorWrapperBias(Bias);
+      grad_node->SetTensorWrapperBiasDropoutResidualOut(BiasDropoutResidualOut);
+      grad_node->SetTensorWrapperDropoutMaskOut(DropoutMaskOut);
+      grad_node->SetTensorWrapperLnBias(LnBias);
+      grad_node->SetTensorWrapperLnMean(LnMean);
+      grad_node->SetTensorWrapperLnScale(LnScale);
+      grad_node->SetTensorWrapperLnVariance(LnVariance);
+      grad_node->SetTensorWrapperResidual(Residual);
+      grad_node->SetTensorWrapperX(X);
+
+      grad_node->SetGradOutMeta(X, 0);
+      grad_node->SetGradOutMeta(Residual, 1);
+      grad_node->SetGradOutMeta(Bias, 2);
+      grad_node->SetGradOutMeta(LnScale, 3);
+      grad_node->SetGradOutMeta(LnBias, 4);
+
+      egr::EagerUtils::SetOutRankWithSlot(p_autograd_BiasDropoutResidualOut, 0);
+      grad_node->SetGradInMeta(BiasDropoutResidualOut, 0);
+      egr::EagerUtils::SetOutRankWithSlot(p_autograd_DropoutMaskOut, 1);
+      grad_node->SetGradInMeta(DropoutMaskOut, 1);
+      egr::EagerUtils::SetOutRankWithSlot(p_autograd_LnMean, 2);
+      grad_node->SetGradInMeta(LnMean, 2);
+      egr::EagerUtils::SetOutRankWithSlot(p_autograd_LnVariance, 3);
+      grad_node->SetGradInMeta(LnVariance, 3);
+      egr::EagerUtils::SetOutRankWithSlot(p_autograd_Y, 4);
+      egr::EagerUtils::SetHistory(p_autograd_Y, grad_node);
+      grad_node->SetGradInMeta(Y, 4);
+      egr::EagerUtils::CheckAndRetainGrad(Y);
+    }
+  }
+
+  return std::make_tuple(
+      BiasDropoutResidualOut, DropoutMaskOut, LnMean, LnVariance, Y);
+}

paddle/fluid/eager/api/manual/fluid_manual/nodes/CMakeLists.txt

@@ -3,4 +3,5 @@ set(fluid_manual_nodes
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_feedforward_node.cc
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_attention_node.cc
     ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_gemm_epilogue_node.cc
+    ${PADDLE_SOURCE_DIR}/paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_bias_dropout_residual_layer_norm_node.cc
     PARENT_SCOPE)

paddle/fluid/eager/api/manual/fluid_manual/nodes/fused_bias_dropout_residual_layer_norm_node.cc

@@ -0,0 +1,135 @@
+// 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::experimental::Tensor>,
+                     egr::kSlotSmallVectorSize>
+fused_bias_dropout_residual_layer_normGradNodeCompat::operator()(
+    paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                         egr::kSlotSmallVectorSize>& grads,
+    bool create_graph,
+    bool is_new_grad) {
+  const auto& out_metas = OutputMeta();
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       egr::kSlotSmallVectorSize>
+      outputs(5);
+  VLOG(3) << "Running Eager Backward Node: "
+             "fused_bias_dropout_residual_layer_normGradNodeCompat";
+  paddle::small_vector<std::vector<paddle::experimental::Tensor>,
+                       egr::kSlotSmallVectorSize>
+      hooked_grads0 = fused_bias_dropout_residual_layer_normGradNodeCompat::
+          ApplyGradientHooks(grads);
+  std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> ins0 =
+      {{"BiasDropoutResidualOut",
+        egr::EagerUtils::TrySyncToVars(egr::EagerUtils::RecoverTensorWrapper(
+            &this->BiasDropoutResidualOut_))},
+       {"DropoutMaskOut",
+        egr::EagerUtils::TrySyncToVars(
+            egr::EagerUtils::RecoverTensorWrapper(&this->DropoutMaskOut_))},
+       {"LnMean",
+        egr::EagerUtils::TrySyncToVars(
+            egr::EagerUtils::RecoverTensorWrapper(&this->LnMean_))},
+       {"LnVariance",
+        egr::EagerUtils::TrySyncToVars(
+            egr::EagerUtils::RecoverTensorWrapper(&this->LnVariance_))},
+       {"Residual",
+        egr::EagerUtils::TrySyncToVars(
+            egr::EagerUtils::RecoverTensorWrapper(&this->Residual_))},
+       {"X",
+        egr::EagerUtils::TrySyncToVars(
+            egr::EagerUtils::RecoverTensorWrapper(&this->X_))},
+       {"Y@GRAD", egr::EagerUtils::TrySyncToVars(hooked_grads0[4])}};
+
+  auto Bias = egr::EagerUtils::RecoverTensorWrapper(&this->Bias_);
+
+  if (Bias.defined()) ins0["Bias"] = egr::EagerUtils::TrySyncToVars(Bias);
+
+  auto LnBias = egr::EagerUtils::RecoverTensorWrapper(&this->LnBias_);
+  if (LnBias.defined()) ins0["LnBias"] = egr::EagerUtils::TrySyncToVars(LnBias);
+  auto LnScale = egr::EagerUtils::RecoverTensorWrapper(&this->LnScale_);
+  if (LnScale.defined())
+    ins0["LnScale"] = egr::EagerUtils::TrySyncToVars(LnScale);
+  std::map<std::string, std::vector<std::shared_ptr<egr::EagerVariable>>> outs0;
+
+  if ((!out_metas[0].empty()) && (!(out_metas[0][0].IsStopGradient()))) {
+    outs0.insert({"BiasDropoutResidualOut@GRAD",
+                  egr::EagerUtils::TrySyncToVars(hooked_grads0[0])});
+  }
+  if ((!out_metas[1].empty()) && (!(out_metas[1][0].IsStopGradient()))) {
+    outs0.insert({"Residual@GRAD",
+                  {std::make_shared<egr::EagerVariable>(
+                      egr::Controller::Instance().GenerateUniqueName())}});
+  }
+  if ((!out_metas[0].empty()) && (!(out_metas[0][0].IsStopGradient()))) {
+    outs0.insert({"X@GRAD",
+                  {std::make_shared<egr::EagerVariable>(
+                      egr::Controller::Instance().GenerateUniqueName())}});
+  }
+
+  if (Bias.defined() && (!out_metas[2].empty()) &&
+      (!out_metas[2][0].IsStopGradient()))
+    outs0["Bias@GRAD"] = {std::make_shared<egr::EagerVariable>(
+        egr::Controller::Instance().GenerateUniqueName())};
+  if (LnBias.defined() && (!out_metas[4].empty()) &&
+      (!out_metas[4][0].IsStopGradient()))
+    outs0["LnBias@GRAD"] = {std::make_shared<egr::EagerVariable>(
+        egr::Controller::Instance().GenerateUniqueName())};
+  if (LnScale.defined() && (!out_metas[3].empty()) &&
+      (!out_metas[3][0].IsStopGradient()))
+    outs0["LnScale@GRAD"] = {std::make_shared<egr::EagerVariable>(
+        egr::Controller::Instance().GenerateUniqueName())};
+  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_bias_dropout_residual_layer_norm_grad",
+      ins0,
+      outs0,
+      attrs_map0,
+      egr::Controller::Instance().GetExpectedPlace(),
+      &this->default_attr_map_,
+      false,
+      {});
+
+  if (outs0.find("Bias@GRAD") != outs0.end()) {
+    outputs[2] = egr::EagerUtils::GetOutputs(outs0["Bias@GRAD"]);
+  }
+
+  if (outs0.find("LnBias@GRAD") != outs0.end()) {
+    outputs[4] = egr::EagerUtils::GetOutputs(outs0["LnBias@GRAD"]);
+  }
+
+  if (outs0.find("LnScale@GRAD") != outs0.end()) {
+    outputs[3] = egr::EagerUtils::GetOutputs(outs0["LnScale@GRAD"]);
+  }
+
+  if (outs0.find("Residual@GRAD") != outs0.end()) {
+    outputs[1] = egr::EagerUtils::GetOutputs(outs0["Residual@GRAD"]);
+  }
+
+  if (outs0.find("X@GRAD") != outs0.end()) {
+    outputs[0] = egr::EagerUtils::GetOutputs(outs0["X@GRAD"]);
+  }
+
+  if (NeedComplexToRealConversion()) HandleComplexGradToRealGrad(&outputs);
+  return outputs;
+}