[cherry-pick] Implement Amp Layout AutoTune(41884) (#41964)

 cherry-pick #41884 

paddle/fluid/eager/tests/data_structure_tests/eager_tensor_test.cc

@@ -16,7 +16,9 @@
 #include "gtest/gtest.h"
 
 #include "paddle/fluid/eager/eager_tensor.h"
+#include "paddle/fluid/imperative/var_helper.h"
 #include "paddle/phi/api/lib/utils/allocator.h"
+#include "paddle/phi/common/layout.h"
 #include "paddle/phi/core/kernel_registry.h"
 
 PD_DECLARE_KERNEL(copy, CPU, ALL_LAYOUT);
@@ -206,3 +208,28 @@ TEST(EagerVariable, Constructor) {
 
   VLOG(6) << "Finish";
 }
+
+TEST(EagerVariable, DataLayout) {
+  paddle::experimental::Tensor tensor;
+  phi::DenseTensorMeta meta =
+      phi::DenseTensorMeta(phi::DataType::FLOAT32, phi::make_ddim({1, 1, 1, 1}),
+                           paddle::experimental::DataLayout::UNDEFINED);
+  std::shared_ptr<phi::DenseTensor> dt = std::make_shared<phi::DenseTensor>(
+      std::make_unique<paddle::experimental::DefaultAllocator>(
+          paddle::platform::CPUPlace())
+          .get(),
+      meta);
+  auto* dt_ptr = dt->mutable_data<float>(paddle::platform::CPUPlace());
+  dt_ptr[0] = 5.0f;
+  dt_ptr[1] = 5.0f;
+  dt_ptr[2] = 5.0f;
+  dt_ptr[3] = 5.0f;
+  tensor.set_impl(dt);
+  auto eager_var = std::make_shared<egr::EagerVariable>(tensor);
+  auto layout = paddle::imperative::GetDataLayout(eager_var);
+  CHECK_EQ(layout, paddle::experimental::DataLayout::UNDEFINED);
+  paddle::imperative::SetDataLayout(eager_var,
+                                    paddle::experimental::DataLayout::NCHW);
+  layout = paddle::imperative::GetDataLayout(eager_var);
+  CHECK_EQ(layout, paddle::experimental::DataLayout::NCHW);
+}

paddle/fluid/imperative/CMakeLists.txt

@@ -7,8 +7,13 @@ cc_library(prepared_operator SRCS prepared_operator.cc DEPS proto_desc operator
 ENDIF()
 cc_library(layer SRCS layer.cc DEPS prepared_operator math_function imperative_flag variable_helper op_registry var_helper phi_api)
 add_subdirectory(jit)
+if (WITH_GPU)
+cc_library(layout_autotune SRCS layout_autotune.cc DEPS op_info phi_gpu_info)
+else()
+cc_library(layout_autotune SRCS layout_autotune.cc DEPS op_info)
+endif()
 cc_library(amp SRCS amp_auto_cast.cc DEPS layer var_helper)
-cc_library(tracer SRCS tracer.cc DEPS layer engine program_desc_tracer amp denormal garbage_collector var_helper)
+cc_library(tracer SRCS tracer.cc DEPS layer engine program_desc_tracer amp denormal garbage_collector var_helper layout_autotune)
 cc_library(basic_engine SRCS basic_engine.cc DEPS layer gradient_accumulator switch_autotune)
 cc_library(engine SRCS basic_engine.cc partial_grad_engine.cc DEPS layer gradient_accumulator switch_autotune)
 cc_library(imperative_profiler SRCS profiler.cc DEPS flags)

paddle/fluid/imperative/layer.h

@@ -211,6 +211,14 @@ class VarBase {
 
   framework::proto::VarType::Type DataType() const { return var_->DataType(); }
 
+  void SetDataLayout(paddle::experimental::DataLayout data_layout) {
+    var_->SetDataLayout(data_layout);
+  }
+
+  paddle::experimental::DataLayout DataLayout() const {
+    return var_->DataLayout();
+  }
+
   size_t ElementSize() const { return framework::SizeOfType(var_->DataType()); }
 
   void SetForwardDataType(framework::proto::VarType::Type data_type) {

paddle/fluid/imperative/layout_autotune.cc

+// 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/imperative/layout_autotune.h"
+#include "paddle/fluid/framework/op_info.h"
+#include "paddle/fluid/imperative/layout_transformer.h"
+#include "paddle/phi/backends/gpu/gpu_info.h"
+#include "paddle/phi/core/enforce.h"
+#include "paddle/phi/core/errors.h"
+
+namespace paddle {
+namespace imperative {
+
+bool LayoutAutoTune::UseLayoutAutoTune() const {
+#if defined(PADDLE_WITH_CUDA)
+  if (!phi::backends::gpu::TensorCoreAvailable()) {
+    LOG(INFO) << "Layout AutoTuning is not available.";
+    return false;
+  } else {
+    return use_layout_autotune_;
+  }
+#else
+  return false;
+#endif
+}
+
+LayoutAutoTune::LayoutAutoTune() {
+  const auto& op_info = paddle::framework::OpInfoMap::Instance().map();
+  for (auto it = op_info.begin(); it != op_info.end(); it++) {
+    // only record forwrd operators
+    if (it->first.find("_grad") != std::string::npos) {
+      continue;
+    }
+
+    // some normalization operators such as instance_norm and layer_norm
+    // do not have data_format attr, but are layout sensitive.
+    if (it->first.find("norm") != std::string::npos) {
+      layout_agnostic_ops_.emplace(it->first);
+      continue;
+    }
+
+    auto* attr_checker = it->second.Checker();
+    if (attr_checker) {
+      auto attrs = attr_checker->GetDefaultAttrMap();
+      if (attrs.find("data_format") != attrs.end() ||
+          attrs.find("data_layout") != attrs.end()) {
+        VLOG(4) << "Heavily layout sensitive OP: " << it->first;
+        heavily_layout_sensitive_ops_.emplace(it->first);
+        continue;
+      }
+
+      // Attribute name is fuzzy matched, such as start and start_axis.
+      bool layout_agnostic = true;
+      for (auto& attr : attrs) {
+        auto attr_name = attr.first;
+        VLOG(6) << "OP: " << it->first << " Attr Name: " << attr_name;
+        if (attr_name.find("axis") != std::string::npos ||
+            attr_name.find("axes") != std::string::npos ||
+            attr_name.find("dim") != std::string::npos ||
+            attr_name.find("start") != std::string::npos ||
+            attr_name.find("end") != std::string::npos) {
+          VLOG(4) << "Lightly layout sensitive OP: " << it->first;
+          layout_agnostic = false;
+          lightly_layout_sensitive_ops_.emplace(it->first);
+          break;
+        }
+      }
+
+      if (layout_agnostic) {
+        VLOG(4) << "Layout agnostic_ops: " << it->first;
+        layout_agnostic_ops_.emplace(it->first);
+      }
+    }
+  }
+
+  VLOG(3) << "The number of layout agnostic OPs: "
+          << layout_agnostic_ops_.size() << ", heavily layout sensitive OPs: "
+          << heavily_layout_sensitive_ops_.size()
+          << ", lightly layout sensitive OPs: "
+          << lightly_layout_sensitive_ops_.size();
+}
+
+template <typename VarType>
+paddle::imperative::NameVarMap<VarType> AutoTuneLayout(
+    const std::string& op_type,
+    const paddle::imperative::NameVarMap<VarType>& ins,
+    const paddle::imperative::NameVarMap<VarType>& outs,
+    paddle::framework::AttributeMap* attrs,
+    const std::shared_ptr<imperative::Tracer>& tracer) {
+  if (!LayoutAutoTune::Instance().UseLayoutAutoTune()) {
+    return ins;
+  }
+
+  // When layout autotuning is enabled, the tuner will check the desired layout.
+  // (1) If the desired layout is undefined, and there is no convolutional
+  // layers, layout optimization is unnecessary. Otherwise, the desired layout
+  // will be set to the best layout only when these is a convolutional layer
+  // with
+  // NCHW-Layout and the TensorCore is available.
+  // (2) If the desired layout is defined, run the transposer.
+
+  if (LayoutAutoTune::Instance().GetDesiredLayout() == DataLayout::UNDEFINED) {
+    // Layout autotune only supports model with convolutional layers
+    if (op_type != "conv2d") {
+      return ins;
+    } else {
+      if (BOOST_GET_CONST(std::string, (*attrs)["data_format"]) == "NCHW") {
+        LayoutAutoTune::Instance().SetDesiredLayout(DataLayout::NHWC);
+        VLOG(3) << "Tune the layout from "
+                << BOOST_GET_CONST(std::string, (*attrs)["data_format"])
+                << " to " << paddle::framework::DataLayoutToString(
+                                 LayoutAutoTune::Instance().GetDesiredLayout());
+      } else {
+        LayoutAutoTune::Instance().DisableLayoutAutoTune();
+        return ins;
+      }
+    }
+  }
+
+  std::shared_ptr<LayoutTransformer<VarType>> transposer = nullptr;
+  if (op_type == "conv2d") {
+    transposer =
+        std::make_shared<HeavilyLayoutSensitiveOpTransformer<VarType>>(op_type);
+    transposer->SetArguments({"Input"}, {"Output"}, {"data_format"});
+  } else if (op_type == "batch_norm") {
+    transposer =
+        std::make_shared<HeavilyLayoutSensitiveOpTransformer<VarType>>(op_type);
+    transposer->SetArguments({"X"}, {"Y"}, {"data_layout"});
+  } else if (op_type == "pool2d") {
+    transposer =
+        std::make_shared<HeavilyLayoutSensitiveOpTransformer<VarType>>(op_type);
+    transposer->SetArguments({"X"}, {"Out"}, {"data_format"});
+  } else if (op_type == "transpose2") {
+    transposer = std::make_shared<TransposeOpTransformer<VarType>>(op_type);
+  } else if (op_type == "flatten_contiguous_range") {
+    transposer = std::make_shared<FlattenOpTransformer<VarType>>(op_type);
+  } else if (op_type.find("elementwise_") != std::string::npos) {
+    transposer = std::make_shared<ElementwiseOpTransformer<VarType>>(op_type);
+  } else if (LayoutAutoTune::Instance().IsLayoutAgnostic(op_type)) {
+    transposer = std::make_shared<LayoutTransformer<VarType>>(op_type);
+  } else if (LayoutAutoTune::Instance().IsLightlyLayoutSensitive(op_type)) {
+    transposer =
+        std::make_shared<LightlyLayoutSensitiveOpTransformer<VarType>>(op_type);
+  } else {
+    PADDLE_ENFORCE_NOT_NULL(
+        transposer, phi::errors::Unimplemented(
+                        "%s 's LayoutTransformer is unimplemented.", op_type));
+  }
+
+  return transposer->Apply(ins, outs, attrs, tracer);
+}
+template paddle::imperative::NameVarMap<VarBase> AutoTuneLayout<VarBase>(
+    const std::string& op_type,
+    const paddle::imperative::NameVarMap<VarBase>& ins,
+    const paddle::imperative::NameVarMap<VarBase>& outs,
+    paddle::framework::AttributeMap* attrs,
+    const std::shared_ptr<imperative::Tracer>& tracer);
+template paddle::imperative::NameVarMap<egr::EagerVariable>
+AutoTuneLayout<egr::EagerVariable>(
+    const std::string& op_type,
+    const paddle::imperative::NameVarMap<egr::EagerVariable>& ins,
+    const paddle::imperative::NameVarMap<egr::EagerVariable>& outs,
+    paddle::framework::AttributeMap* attrs,
+    const std::shared_ptr<imperative::Tracer>& tracer);
+
+}  // namespace imperative
+}  // namespace paddle

paddle/fluid/imperative/layout_autotune.h

+// 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.
+
+#pragma once
+#include <glog/logging.h>
+#include <memory>
+#include <unordered_set>
+#include "paddle/phi/common/layout.h"
+#include "paddle/phi/core/compat/type_defs.h"
+
+namespace paddle {
+namespace imperative {
+
+class Tracer;
+
+using DataLayout = paddle::experimental::DataLayout;
+
+class LayoutAutoTune {
+ public:
+  static LayoutAutoTune& Instance() {
+    static LayoutAutoTune layout_autoTune;
+    return layout_autoTune;
+  }
+
+  bool UseLayoutAutoTune() const;
+
+  void EnableLayoutAutoTune() { use_layout_autotune_ = true; }
+
+  void DisableLayoutAutoTune() { use_layout_autotune_ = false; }
+
+  bool IsLightlyLayoutSensitive(const std::string& op_type) const {
+    return lightly_layout_sensitive_ops_.count(op_type) != 0;
+  }
+
+  bool IsLayoutAgnostic(const std::string& op_type) const {
+    return layout_agnostic_ops_.count(op_type) != 0;
+  }
+
+  DataLayout GetDesiredLayout() const { return layout_; }
+
+  void SetDesiredLayout(const DataLayout& layout) { layout_ = layout; }
+
+ private:
+  LayoutAutoTune();
+
+  bool use_layout_autotune_{false};
+
+  std::unordered_set<std::string> layout_agnostic_ops_{};
+
+  std::unordered_set<std::string> heavily_layout_sensitive_ops_{};
+
+  std::unordered_set<std::string> lightly_layout_sensitive_ops_{};
+
+  DataLayout layout_{DataLayout::UNDEFINED};
+};
+
+template <typename VarType>
+paddle::imperative::NameVarMap<VarType> AutoTuneLayout(
+    const std::string& op_type,
+    const paddle::imperative::NameVarMap<VarType>& ins,
+    const paddle::imperative::NameVarMap<VarType>& outs,
+    paddle::framework::AttributeMap* attrs,
+    const std::shared_ptr<imperative::Tracer>& tracer);
+
+}  // namespace imperative
+}  // namespace paddle

paddle/fluid/imperative/layout_transformer.h

+// 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.
+
+#pragma once
+#include "paddle/fluid/imperative/layout_autotune.h"
+#include "paddle/fluid/imperative/tracer.h"
+#include "paddle/fluid/imperative/var_helper.h"
+#include "paddle/phi/core/enforce.h"
+#include "paddle/phi/core/errors.h"
+
+namespace paddle {
+namespace imperative {
+
+template <typename VarType>
+std::shared_ptr<VarType> TraceTransposeOp(
+    const std::shared_ptr<VarType>& var, const DataLayout layout,
+    const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+  std::vector<int> axis;
+  if (layout == DataLayout::NHWC) {
+    axis = {0, 2, 3, 1};
+  } else if (layout == DataLayout::NCHW) {
+    axis = {0, 3, 1, 2};
+  } else {
+    axis = {0, 1, 2, 3};
+  }
+  paddle::imperative::NameVarMap<VarType> ins = {{"X", {var}}};
+  auto out =
+      std::shared_ptr<VarType>(new VarType(tracer->GenerateUniqueName()));
+  auto x_shape =
+      std::shared_ptr<VarType>(new VarType(tracer->GenerateUniqueName()));
+  paddle::imperative::NameVarMap<VarType> outs = {{"Out", {out}},
+                                                  {"XShape", {x_shape}}};
+  paddle::framework::AttributeMap attrs = {{"axis", axis}};
+  tracer->TraceOp("transpose2", ins, outs, std::move(attrs));
+  paddle::imperative::SetDataLayout(out, layout);
+  VLOG(4) << "Transpose " << paddle::imperative::GetNameFromVar(var) << "["
+          << paddle::framework::DataLayoutToString(
+                 paddle::imperative::GetDataLayout(var))
+          << "]"
+          << " to " << paddle::imperative::GetNameFromVar(out) << "["
+          << paddle::framework::DataLayoutToString(
+                 paddle::imperative::GetDataLayout(out))
+          << "]";
+  return out;
+}
+
+template <typename VarType>
+class LayoutTransformer {
+ public:
+  explicit LayoutTransformer(const std::string& type) : type_(type) {}
+
+  virtual ~LayoutTransformer() {}
+
+  LayoutTransformer(const LayoutTransformer&) = delete;
+  LayoutTransformer& operator=(const LayoutTransformer&) = delete;
+
+  virtual paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    VLOG(3) << "Optimze Layout agnostic op: " << type_;
+    auto in_layout = DataLayout::UNDEFINED;
+    for (auto& pair : ins) {
+      for (auto& var : pair.second) {
+        // Once the any input is desired layout, we set in_layout is desired
+        // layout.
+        if (paddle::imperative::GetDataLayout(var) ==
+            LayoutAutoTune::Instance().GetDesiredLayout()) {
+          in_layout = LayoutAutoTune::Instance().GetDesiredLayout();
+          break;
+        }
+      }
+    }
+    SetVarsLayout(outs, in_layout);
+    return ins;
+  }
+
+  // Set inputs, outputs and attributes to be optimized for the transposer.
+  // Those may respectively be a subset of the corresponding original argument
+  // of the operator.
+  void SetArguments(const std::vector<std::string>& ins,
+                    const std::vector<std::string>& outs,
+                    const std::vector<std::string>& attrs) {
+    ins_ = ins;
+    outs_ = outs;
+    attrs_ = attrs;
+  }
+
+  // Set the variables's layout to the specified layout.
+  // If outs_ is not specified, it means all outputs of the operator
+  // will be considered. Otherwise, it only set layout for the specified output.
+  void SetVarsLayout(const paddle::imperative::NameVarMap<VarType>& outs,
+                     DataLayout layout) const {
+    if (outs_.empty()) {
+      for (auto& pair : outs) {
+        for (auto& var : pair.second) {
+          paddle::imperative::SetDataLayout(var, layout);
+        }
+      }
+    } else {
+      for (auto& name : outs_) {
+        auto out_vars = outs.at(name);
+        for (auto& var : out_vars) {
+          paddle::imperative::SetDataLayout(var, layout);
+        }
+      }
+    }
+  }
+
+  const std::vector<std::string>& Inputs() const { return ins_; }
+  const std::vector<std::string>& Outputs() const { return outs_; }
+  const std::vector<std::string>& Attributes() const { return attrs_; }
+
+  const std::string& Type() { return type_; }
+
+ protected:
+  std::string type_{};
+  std::vector<std::string> ins_{};
+  std::vector<std::string> outs_{};
+  std::vector<std::string> attrs_{};
+};
+
+template <typename VarType>
+class ElementwiseOpTransformer : public LayoutTransformer<VarType> {
+ public:
+  explicit ElementwiseOpTransformer(const std::string& type)
+      : LayoutTransformer<VarType>(type) {}
+
+  paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    // [Why we need the this?]
+    // The Elementwise Ops has a axis attr, it is to support broadcast.
+    // When bias_attr of Conv is not false, the elementwise_add will be
+    // appended, and the axis will be set to the channel dimension.
+
+    // If the axis is set to the channel dimension, the attr transformation
+    // is necessary. Otherwise, it will fall back to the
+    // LayoutTransformer::Apply.
+    auto desired_layout = LayoutAutoTune::Instance().GetDesiredLayout();
+    if (attrs->find("axis") != attrs->end() &&
+        BOOST_GET_CONST(int, (*attrs)["axis"]) != -1) {
+      VLOG(3) << "Optimze layout agnostic op " << this->Type();
+      if (desired_layout == DataLayout::NHWC) {
+        (*attrs)["axis"] = 3;
+      } else if (desired_layout == DataLayout::NCHW) {
+        (*attrs)["axis"] = 1;
+      } else {
+        PADDLE_ENFORCE_EQ(
+            desired_layout, DataLayout::UNDEFINED,
+            phi::errors::PreconditionNotMet("DataLayout is unsupport."));
+      }
+      this->SetVarsLayout(outs, desired_layout);
+      return ins;
+    } else {
+      return LayoutTransformer<VarType>::Apply(ins, outs, attrs, tracer);
+    }
+  }
+};
+
+/*
+ * Both functionality and performance are affected by data layout.
+ * Such as operators with data_format attribute.
+ */
+template <typename VarType>
+class HeavilyLayoutSensitiveOpTransformer : public LayoutTransformer<VarType> {
+ public:
+  explicit HeavilyLayoutSensitiveOpTransformer(const std::string& type)
+      : LayoutTransformer<VarType>(type) {}
+
+  paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    VLOG(3) << "Optimze heavily layout sensitive op " << this->Type();
+    paddle::imperative::NameVarMap<VarType> new_ins(ins);
+
+    // Step 1: Adjust the data_layout attr to the desired layout
+    auto desired_layout = LayoutAutoTune::Instance().GetDesiredLayout();
+    std::string desired_layout_str = paddle::framework::DataLayoutToString(
+        LayoutAutoTune::Instance().GetDesiredLayout());
+    if (attrs->find("data_format") != attrs->end() &&
+        BOOST_GET_CONST(std::string, (*attrs)["data_format"]) !=
+            desired_layout_str) {
+      VLOG(4) << "Origin layout attr: "
+              << BOOST_GET_CONST(std::string, (*attrs)["data_format"])
+              << ", Desired layout attr: " << desired_layout_str;
+      (*attrs)["data_format"] = desired_layout_str;
+    } else if (attrs->find("data_layout") != attrs->end() &&
+               BOOST_GET_CONST(std::string, (*attrs)["data_layout"]) !=
+                   desired_layout_str) {
+      VLOG(4) << "Origin layout attr: "
+              << BOOST_GET_CONST(std::string, (*attrs)["data_layout"])
+              << ", Desired layout attr: " << desired_layout_str;
+      (*attrs)["data_layout"] = desired_layout_str;
+    }
+
+    // Step 2: Transpose the specified input for Op and set the transposed var's
+    // layout.
+    for (auto& name : this->Inputs()) {
+      auto& in_vars = new_ins[name];
+      for (auto& var : in_vars) {
+        auto var_layout = paddle::imperative::GetDataLayout(var);
+        if (var_layout != desired_layout) {
+          var = TraceTransposeOp(var, DataLayout::NHWC, tracer);
+        }
+      }
+    }
+
+    // Step 3: Set the Op's layout sensitive outs var.
+    this->SetVarsLayout(outs, desired_layout);
+
+    return new_ins;
+  }
+};
+
+/*
+ * The functionality may be affected layout transformation before them.
+ * Such as operators with axis attribute.
+ */
+template <typename VarType>
+class LightlyLayoutSensitiveOpTransformer : public LayoutTransformer<VarType> {
+ public:
+  explicit LightlyLayoutSensitiveOpTransformer(const std::string& type)
+      : LayoutTransformer<VarType>(type) {}
+
+  paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    VLOG(3) << "Optimze lightly layout sensitive op " << this->Type();
+    paddle::imperative::NameVarMap<VarType> new_ins(ins);
+    // If input's layout is not tuned, transformation is unnecessary.
+    // If input's layout is already tuned, it will be transformed back to NCHW.
+    // TODO(zhangting): The op of this type should be adapted to the previous
+    // operator output data layout. Currently only a few operators are
+    // supported, and transposers need to be carefully designed to ensure that
+    // they do not cause exceptions.
+    for (auto& pair : new_ins) {
+      for (auto& var : pair.second) {
+        auto var_layout = paddle::imperative::GetDataLayout(var);
+        if (var_layout == LayoutAutoTune::Instance().GetDesiredLayout()) {
+          // Set layout to UNDEFINED so that TransposeOpTransformer do
+          // NHWC->NCHW transformation.
+          var = TraceTransposeOp(var, DataLayout::UNDEFINED, tracer);
+        }
+      }
+    }
+    return new_ins;
+  }
+};
+
+template <typename VarType>
+class TransposeOpTransformer
+    : public LightlyLayoutSensitiveOpTransformer<VarType> {
+ public:
+  explicit TransposeOpTransformer(const std::string& type)
+      : LightlyLayoutSensitiveOpTransformer<VarType>(type) {}
+
+  paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    VLOG(3) << "Optimze lightly layout sensitive op " << this->Type();
+    // When the input layout is the desired format, it means that there
+    // is a transpose layer in the network, it is better to transpose
+    // the result to the original format.
+    // Instead of actually inserting a transpose Op, we fuse the inserted
+    // transpose Op with the current transpose Op by transforming 'axis' attr.
+    auto& in_var = ins.at("X")[0];
+    auto var_layout = paddle::imperative::GetDataLayout(in_var);
+    if (var_layout == LayoutAutoTune::Instance().GetDesiredLayout()) {
+      auto axis = BOOST_GET_CONST(std::vector<int>, (*attrs)["axis"]);
+      // NHWC->NCHW, permutaion will be set as follows.
+      std::vector<int> perm = {0, 3, 1, 2};
+      // fuse the transpose Ops by transforming axis.
+      std::vector<int> fusion_axis = {perm[axis[0]], perm[axis[1]],
+                                      perm[axis[2]], perm[axis[3]]};
+      (*attrs)["axis"] = fusion_axis;
+    }
+    return ins;
+  }
+};
+
+template <typename VarType>
+class FlattenOpTransformer
+    : public LightlyLayoutSensitiveOpTransformer<VarType> {
+ public:
+  explicit FlattenOpTransformer(const std::string& type)
+      : LightlyLayoutSensitiveOpTransformer<VarType>(type) {}
+
+  paddle::imperative::NameVarMap<VarType> Apply(
+      const paddle::imperative::NameVarMap<VarType>& ins,
+      const paddle::imperative::NameVarMap<VarType>& outs,
+      paddle::framework::AttributeMap* attrs,
+      const std::shared_ptr<paddle::imperative::Tracer>& tracer) {
+    VLOG(3) << "Optimze lightly layout sensitive op " << this->Type();
+    // Flatten the C, H, W dimensions will not affect functionality.
+    // So transformation is unnecessary. But in other cases, it needs to
+    // fall back to the LightlyLayoutSensitiveOpTransformer.
+    auto start_axis = BOOST_GET_CONST(int, (*attrs)["start_axis"]);
+    auto stop_axis = BOOST_GET_CONST(int, (*attrs)["stop_axis"]);
+    if (paddle::imperative::GetDataLayout(ins.at("X")[0]) ==
+            LayoutAutoTune::Instance().GetDesiredLayout() &&
+        start_axis == 1 && stop_axis == 3) {
+      return ins;
+    } else {
+      return LightlyLayoutSensitiveOpTransformer<VarType>::Apply(ins, outs,
+                                                                 attrs, tracer);
+    }
+  }
+};
+
+}  // namespace imperative
+}  // namespace paddle

paddle/fluid/imperative/tracer.cc

@@ -19,6 +19,7 @@
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/imperative/amp_auto_cast.h"
 #include "paddle/fluid/imperative/execution_context.h"
+#include "paddle/fluid/imperative/layout_autotune.h"
 #include "paddle/fluid/imperative/op_base.h"
 #include "paddle/fluid/platform/denormal.h"
 #include "paddle/fluid/platform/device/device_wrapper.h"
@@ -222,16 +223,22 @@ void Tracer::TraceOpImpl(const std::string& type,
   NameVarMap<VarType> new_ins = ins;
   if (amp_level_ == AmpLevel::O1) {
     if (amp_dtype_ == phi::DataType::FLOAT16) {
+      const auto& tracer = imperative::GetCurrentTracer();
+      new_ins =
+          imperative::AutoTuneLayout<VarType>(type, ins, outs, &attrs, tracer);
       VLOG(5) << "Float16 Auto Mixed Precision O1 run operator: " << type;
-      new_ins = AutoCastInputs<VarType>(type, ins);
+      new_ins = AutoCastInputs<VarType>(type, new_ins);
     } else if (amp_dtype_ == phi::DataType::BFLOAT16) {
       VLOG(5) << "BFloat16 Auto Mixed Precision O1 run operator: " << type;
       new_ins = AutoCastBF16Inputs<VarType>(type, ins);
     }
   } else if (amp_level_ == AmpLevel::O2) {
     if (amp_dtype_ == phi::DataType::FLOAT16) {
+      const auto& tracer = imperative::GetCurrentTracer();
+      new_ins =
+          imperative::AutoTuneLayout<VarType>(type, ins, outs, &attrs, tracer);
       VLOG(5) << "Float16 Auto Mixed Precision O2 run operator: " << type;
-      new_ins = CastPureFp16Inputs<VarType>(type, ins);
+      new_ins = CastPureFp16Inputs<VarType>(type, new_ins);
     } else if (amp_dtype_ == phi::DataType::BFLOAT16) {
       VLOG(5) << "BFloat16 Auto Mixed Precision O2 run operator: " << type;
       new_ins = CastPureBf16Inputs<VarType>(type, ins);

paddle/fluid/imperative/var_helper.cc

@@ -190,6 +190,59 @@ template framework::proto::VarType::Type GetDataType<VarBase>(
 template framework::proto::VarType::Type GetDataType<VariableWrapper>(
     std::shared_ptr<VariableWrapper> var);
 
+/* GetDataLayout */
+template <typename VarType>
+paddle::experimental::DataLayout GetDataLayout(std::shared_ptr<VarType> var) {
+  return var->DataLayout();
+}
+template <>
+paddle::experimental::DataLayout GetDataLayout<egr::EagerVariable>(
+    std::shared_ptr<egr::EagerVariable> var) {
+  if (var->Var().IsType<framework::LoDTensor>()) {
+    return var->Var().Get<framework::LoDTensor>().layout();
+  } else {
+    PADDLE_THROW(paddle::platform::errors::PermissionDenied(
+        "Only support framework::LoDTensor, but got %s here, please checkout "
+        "var type of "
+        "tensor: %s",
+        paddle::framework::ToTypeName(framework::ToVarType(var->Var().Type())),
+        var->name()));
+  }
+}
+template paddle::experimental::DataLayout GetDataLayout<VarBase>(
+    std::shared_ptr<VarBase> var);
+template paddle::experimental::DataLayout GetDataLayout<VariableWrapper>(
+    std::shared_ptr<VariableWrapper> var);
+
+/* SetDataLayout */
+template <typename VarType>
+void SetDataLayout(std::shared_ptr<VarType> var,
+                   const paddle::experimental::DataLayout layout) {
+  var->SetDataLayout(layout);
+}
+template <>
+void SetDataLayout<egr::EagerVariable>(
+    std::shared_ptr<egr::EagerVariable> var,
+    const paddle::experimental::DataLayout layout) {
+  if (var->Var().IsType<framework::LoDTensor>()) {
+    var->MutableVar()->GetMutable<paddle::framework::LoDTensor>()->set_layout(
+        layout);
+  } else {
+    PADDLE_THROW(paddle::platform::errors::PermissionDenied(
+        "Only support framework::LoDTensor, but got %s here, please checkout "
+        "var type of "
+        "tensor: %s",
+        paddle::framework::ToTypeName(framework::ToVarType(var->Var().Type())),
+        var->name()));
+  }
+}
+template void SetDataLayout<VarBase>(
+    std::shared_ptr<VarBase> var,
+    const paddle::experimental::DataLayout layout);
+template void SetDataLayout<VariableWrapper>(
+    std::shared_ptr<VariableWrapper> var,
+    const paddle::experimental::DataLayout layout);
+
 /* CheckCachedKey */
 template <typename VarType>
 bool CheckCachedKey(std::shared_ptr<VarType> var,

paddle/fluid/imperative/var_helper.h

@@ -63,6 +63,13 @@ framework::proto::VarType::Type GetType(std::shared_ptr<VarType> var);
 template <typename VarType>
 framework::proto::VarType::Type GetDataType(std::shared_ptr<VarType> var);
 
+template <typename VarType>
+paddle::experimental::DataLayout GetDataLayout(std::shared_ptr<VarType> var);
+
+template <typename VarType>
+void SetDataLayout(std::shared_ptr<VarType> var,
+                   const paddle::experimental::DataLayout layout);
+
 template <typename VarType>
 const std::shared_ptr<VariableWrapper>& GetVariableWrapper(
     const std::shared_ptr<VarType>& var);

paddle/fluid/imperative/variable_wrapper.h

@@ -25,6 +25,7 @@
 #include "paddle/fluid/framework/variable.h"
 #include "paddle/fluid/imperative/hooks.h"
 #include "paddle/fluid/imperative/op_base.h"
+#include "paddle/phi/common/layout.h"
 
 namespace paddle {
 namespace imperative {
@@ -186,6 +187,12 @@ class VariableWrapper {
     return fwd_data_type_;
   }
 
+  paddle::experimental::DataLayout DataLayout() { return layout_; }
+
+  void SetDataLayout(const paddle::experimental::DataLayout layout) {
+    layout_ = layout;
+  }
+
   const platform::Place Place() const {
     const framework::Tensor* tensor = nullptr;
     auto place =
@@ -357,6 +364,10 @@ class VariableWrapper {
   // training
   // NOTE: Now no need to support remove void hook
   std::vector<std::shared_ptr<std::function<void()>>> void_hooks_;
+
+  // DataLayout for layoutAutotune
+  paddle::experimental::DataLayout layout_{
+      paddle::experimental::DataLayout::UNDEFINED};
 };
 
 }  // namespace imperative

paddle/fluid/pybind/pybind.cc

@@ -166,6 +166,7 @@ limitations under the License. */
 #endif
 
 #include "paddle/fluid/eager/api/utils/global_utils.h"
+#include "paddle/fluid/imperative/layout_autotune.h"
 #include "paddle/fluid/pybind/eager_utils.h"
 #include "paddle/phi/api/ext/op_meta_info.h"
 #include "paddle/phi/kernels/autotune/cache.h"
@@ -4448,6 +4449,20 @@ All parameter, weight, gradient are variables in Paddle.
     return res;
   });
 
+  m.def("enable_layout_autotune", [] {
+    return paddle::imperative::LayoutAutoTune::Instance()
+        .EnableLayoutAutoTune();
+  });
+
+  m.def("disable_layout_autotune", [] {
+    return paddle::imperative::LayoutAutoTune::Instance()
+        .DisableLayoutAutoTune();
+  });
+
+  m.def("use_layout_autotune", [] {
+    return paddle::imperative::LayoutAutoTune::Instance().UseLayoutAutoTune();
+  });
+
   BindFleetWrapper(&m);
   BindIO(&m);
 

python/paddle/fluid/tests/unittests/test_layout_autotune.py

+# 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.
+
+import paddle
+import unittest
+import numpy
+import paddle.nn.functional as F
+
+
+class SimpleNet(paddle.nn.Layer):
+    def __init__(self, data_format="NCHW", class_num=2):
+        super(SimpleNet, self).__init__()
+        self.conv = paddle.nn.Conv2D(3, 8, (3, 3))
+        self.bn = paddle.nn.BatchNorm(num_channels=8)
+        self.relu = paddle.nn.ReLU()
+        self.pool = paddle.nn.AvgPool2D(kernel_size=2, stride=2)
+        self.flatten = paddle.nn.Flatten()
+        self.fc = paddle.nn.Linear(392, class_num)
+
+    def forward(self, image):
+        conv_out = self.conv(image)
+        bn_out = self.bn(conv_out)
+        out = self.relu(bn_out)
+        out = self.pool(out)
+        out = self.flatten(out)
+        out = self.fc(out)
+        return conv_out, out
+
+
+class LayoutAutoTune(unittest.TestCase):
+    def use_autoune(self):
+        if paddle.is_compiled_with_cuda():
+            paddle.fluid.core.enable_layout_autotune()
+            return paddle.fluid.core.use_layout_autotune()
+        else:
+            paddle.fluid.core.disable_layout_autotune()
+            return paddle.fluid.core.use_layout_autotune()
+
+    def train(self, data_format):
+        model = SimpleNet(data_format="NCHW", class_num=2)
+        data = paddle.rand([1, 3, 16, 16])
+        if (data_format == "NHWC"):
+            data = paddle.rand([1, 16, 16, 3])
+        label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
+        optimizer = paddle.optimizer.SGD(learning_rate=0.0001,
+                                         parameters=model.parameters())
+        scaler = paddle.amp.GradScaler()
+        for i in range(2):
+            with paddle.amp.auto_cast(level="O2"):
+                conv_out, predict = model(data)
+                loss = F.cross_entropy(predict, label=label_data)
+                loss = loss.mean()
+
+            scaled = scaler.scale(loss)
+            scaled.backward()
+            scaler.minimize(optimizer, scaled)
+        return conv_out, predict
+
+    def test_enable_autotune(self):
+        if self.use_autoune():
+            conv_out, predict = self.train(data_format="NCHW")
+            self.assertEqual(conv_out.shape, [1, 14, 14, 8])
+            self.assertEqual(predict.shape, [1, 2])
+        else:
+            conv_out, predict = self.train(data_format="NCHW")
+            self.assertEqual(conv_out.shape, [1, 8, 14, 14])
+            self.assertEqual(predict.shape, [1, 2])
+
+    def test_transpose_op_transposer(self):
+        if not self.use_autoune():
+            return
+        conv = paddle.nn.Conv2D(3, 8, (3, 3))
+        data = paddle.rand([1, 3, 16, 14])
+        label_data = paddle.randint(0, 1, shape=[1, 1], dtype="int64")
+        optimizer = paddle.optimizer.SGD(learning_rate=0.0001,
+                                         parameters=conv.parameters())
+        scaler = paddle.amp.GradScaler()
+        with paddle.amp.auto_cast(level="O2"):
+            conv_out = conv(data)
+            # conv_out.shape = [1, 14, 12, 8] with NHWC
+            # layout tuner will transpose conv_out to 
+            # [1, 8, 14, 12] with NCHW before the following transpose op.
+            out = paddle.transpose(conv_out, perm=[0, 3, 1, 2])
+            loss = out.mean()
+        scaled = scaler.scale(loss)
+        scaled.backward()
+        scaler.minimize(optimizer, scaled)
+
+        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+        self.assertEqual(out.shape, [1, 12, 8, 14])
+
+    def test_flatten_op_transposer(self):
+        if not self.use_autoune():
+            return
+        paddle.fluid.core.enable_layout_autotune()
+        conv = paddle.nn.Conv2D(3, 8, (3, 3))
+        flatten = paddle.nn.Flatten(start_axis=1, stop_axis=2)
+        data = paddle.rand([1, 3, 16, 14])
+        with paddle.amp.auto_cast(level="O2"):
+            conv_out = conv(data)
+            # conv_out.shape = [1, 14, 12, 8] with NHWC
+            # layout tuner will transpose conv_out to
+            # [1, 8, 14, 12] with NCHW before the following flatten op
+            # because it flatten the C and H dimensions.
+            out = flatten(conv_out)
+
+        self.assertEqual(conv_out.shape, [1, 14, 12, 8])
+        self.assertEqual(out.shape, [1, 112, 12])
+
+
+if __name__ == '__main__':
+    unittest.main()