Merge branch 'develop' of https://github.com/PaddlePaddle/Paddle into move_embedding_to_phi

cmake/external/cinn.cmake

@@ -26,7 +26,7 @@ add_definitions(-w)
 ######################################
 include(ExternalProject)
 set(CINN_PREFIX_DIR ${THIRD_PARTY_PATH}/CINN)
-set(CINN_GIT_TAG release/v0.1)
+set(CINN_GIT_TAG 56879b637e2c4db19091eedad03d7cc674e092a2)
 set(CINN_OPTIONAL_ARGS -DPY_VERSION=${PY_VERSION}
                        -DWITH_CUDA=${WITH_GPU}
                        -DWITH_CUDNN=${WITH_GPU}

cmake/external/llvm.cmake

@@ -99,7 +99,8 @@ endfunction()
 
 function(mlir_add_rewriter td_base)
   set(LLVM_TARGET_DEFINITIONS ${td_base}.td)
-  mlir_tablegen(${td_base}.cpp.inc -gen-rewriters "-I${CMAKE_SOURCE_DIR}/infrt/dialect/pass")
+  set(LLVM_TARGET_DEPENDS  ${LLVM_TARGET_DEPENDS} ${CMAKE_SOURCE_DIR}/paddle/infrt/dialect/infrt/ir/infrt_base.td)
+  mlir_tablegen(${td_base}.cpp.inc -gen-rewriters)
   add_public_tablegen_target(MLIR${td_base}IncGen)
   add_dependencies(mlir-headers MLIR${td_base}IncGen)
 endfunction()

paddle/fluid/distributed/collective/CMakeLists.txt

 cc_library(processgroup SRCS ProcessGroup.cc DEPS phi phi_api eager_api)
+cc_library(eager_reducer SRCS reducer.cc DEPS eager_api processgroup phi phi_api)
+
 if (WITH_DISTRIBUTE)
   cc_library(processgroup_gloo SRCS ProcessGroupGloo.cc DEPS phi phi_api eager_api gloo_wrapper)
 endif()
-cc_library(eager_reducer SRCS reducer.cc DEPS eager_api processgroup)
 
 if(WITH_NCCL)
     cc_library(processgroup_nccl SRCS ProcessGroupNCCL.cc DEPS place cuda_stream enforce collective_helper device_context phi phi_api eager_api)

paddle/fluid/distributed/collective/ProcessGroupNCCL.cc

@@ -88,8 +88,8 @@ void SyncDefaultStream(
   for (size_t i = 0; i < places.size(); ++i) {
     auto* default_ctx = static_cast<platform::CUDADeviceContext*>(
         platform::DeviceContextPool::Instance().Get(places[i]));
-    ncclEvents[i].Record(*dev_ctx[i]);
-    ncclEvents[i].Block(*default_ctx);
+    ncclEvents[i].Record(*default_ctx);
+    ncclEvents[i].Block(*dev_ctx[i]);
   }
 }
 

paddle/fluid/distributed/collective/reducer.cc

@@ -13,7 +13,6 @@
 // limitations under the License.
 
 #include "paddle/fluid/distributed/collective/reducer.h"
-#include "paddle/phi/common/data_type.h"
 
 namespace paddle {
 namespace distributed {
@@ -127,5 +126,430 @@ std::vector<std::vector<size_t>> Eager_AssignGroupBySize(
   return res;
 }
 
+template <typename DeviceContext, typename T>
+static void ConcatTensorsForAllReduce(
+    const DeviceContext &context,
+    const std::vector<phi::DenseTensor> &dense_tensors_,
+    Tensor *p_dense_contents) {
+  operators::math::ConcatFunctor<DeviceContext, T> concat_functor_;
+  concat_functor_(
+      context, dense_tensors_, 0,
+      std::dynamic_pointer_cast<phi::DenseTensor>(p_dense_contents->impl())
+          .get());
+}
+
+template <typename DeviceContext, typename T>
+static void SplitTensorsForAllReduce(
+    const DeviceContext &context, Tensor *p_dense_contents,
+    std::vector<phi::DenseTensor> *p_dense_tensors) {
+  auto *in =
+      std::dynamic_pointer_cast<phi::DenseTensor>(p_dense_contents->impl())
+          .get();
+  std::vector<phi::DenseTensor *> outs;
+  std::vector<const phi::DenseTensor *> shape_refer;
+
+  outs.reserve(p_dense_tensors->size());
+  shape_refer.reserve(p_dense_tensors->size());
+
+  for (auto &tensor : *p_dense_tensors) {
+    outs.emplace_back(&tensor);
+    shape_refer.emplace_back(&tensor);
+  }
+
+  operators::math::SplitFunctor<DeviceContext, T> split_functor_;
+  split_functor_(context, *in, shape_refer, 0, &outs);
+}
+
+// context is used to select the stream for concat
+template <typename DeviceContext>
+static void ConcatTensorsWithType(
+    const DeviceContext &context,
+    const std::vector<phi::DenseTensor> &dense_tensors_,
+    Tensor *p_dense_contents, phi::DataType type) {
+  switch (type) {
+    case phi::DataType::FLOAT16:
+      ConcatTensorsForAllReduce<DeviceContext, platform::float16>(
+          context, dense_tensors_, p_dense_contents);
+      break;
+    case phi::DataType::FLOAT32:
+      ConcatTensorsForAllReduce<DeviceContext, float>(context, dense_tensors_,
+                                                      p_dense_contents);
+      break;
+    case phi::DataType::FLOAT64:
+      ConcatTensorsForAllReduce<DeviceContext, double>(context, dense_tensors_,
+                                                       p_dense_contents);
+      break;
+    default:
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Data type (%s) is not supported when it concats tensors for "
+          "allreduce.",
+          type));
+  }
+}
+
+// context is used to select the stream for split
+template <typename DeviceContext>
+static void SplitTensorsWithType(const DeviceContext &context,
+                                 Tensor *p_dense_contents,
+                                 std::vector<phi::DenseTensor> *p_dense_tensors,
+                                 phi::DataType type) {
+  switch (type) {
+    case phi::DataType::FLOAT16:
+      SplitTensorsForAllReduce<DeviceContext, platform::float16>(
+          context, p_dense_contents, p_dense_tensors);
+      break;
+    case phi::DataType::FLOAT32:
+      SplitTensorsForAllReduce<DeviceContext, float>(context, p_dense_contents,
+                                                     p_dense_tensors);
+      break;
+    case phi::DataType::FLOAT64:
+      SplitTensorsForAllReduce<DeviceContext, double>(context, p_dense_contents,
+                                                      p_dense_tensors);
+      break;
+    default:
+      PADDLE_THROW(platform::errors::Unimplemented(
+          "Data type (%s) is not supported when it splits tensors for "
+          "allreduce.",
+          type));
+  }
+}
+
+void EagerGroup::ConcatTensors(const platform::Place &place) {
+  if (platform::is_gpu_place(place)) {
+#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
+    auto *default_ctx = static_cast<platform::CUDADeviceContext *>(
+        platform::DeviceContextPool::Instance().Get(place));
+    ConcatTensorsWithType(*default_ctx, dense_tensors_, &dense_contents_,
+                          dtype_);
+#else
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't concat grad tensors since it's not compiled with NCCL,"
+        "Please recompile or reinstall Paddle with NCCL support."));
+#endif
+  } else if (platform::is_cpu_place(place)) {
+    auto *default_ctx = static_cast<platform::CPUDeviceContext *>(
+        platform::DeviceContextPool::Instance().Get(place));
+    ConcatTensorsWithType(*default_ctx, dense_tensors_, &dense_contents_,
+                          dtype_);
+  } else {
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Concat grad tensor not supported on place (%s)", place));
+  }
+}
+
+void EagerGroup::SplitTensors(const platform::Place &place) {
+  if (platform::is_gpu_place(place)) {
+#if defined(PADDLE_WITH_NCCL) || defined(PADDLE_WITH_RCCL)
+    auto *default_ctx = static_cast<platform::CUDADeviceContext *>(
+        platform::DeviceContextPool::Instance().Get(place));
+    SplitTensorsWithType(*default_ctx, &dense_contents_, &dense_tensors_,
+                         dtype_);
+#else
+    PADDLE_THROW(platform::errors::PermissionDenied(
+        "Paddle can't split grad tensor since it's not compiled with NCCL,"
+        "Please recompile or reinstall Paddle with NCCL support."));
+#endif
+  } else if (platform::is_cpu_place(place)) {
+    auto *default_ctx = static_cast<platform::CPUDeviceContext *>(
+        platform::DeviceContextPool::Instance().Get(place));
+    SplitTensorsWithType(*default_ctx, &dense_contents_, &dense_tensors_,
+                         dtype_);
+  } else {
+    PADDLE_THROW(platform::errors::Unimplemented(
+        "Split grad tensor not supported on place (%s)", place));
+  }
+}
+
+EagerReducer::EagerReducer(
+    const std::vector<Tensor> tensors,
+    const std::vector<std::vector<size_t>> &group_indices,
+    const std::vector<bool> &is_sparse_gradient,
+    std::shared_ptr<distributed::ProcessGroup> process_group,
+    const std::vector<size_t> &group_size_limits, bool find_unused_parameters)
+    : tensors_(tensors),
+      group_indices_(group_indices),
+      is_sparse_gradient_(is_sparse_gradient),
+      process_group_(process_group),
+      group_size_limits_(group_size_limits),
+      find_unused_vars_each_step_(find_unused_parameters) {
+  VLOG(3) << "Start construct the Reducer ...";
+
+  nranks_ = process_group_->GetSize();
+
+  // initialize groups
+  InitializeGroups(group_indices);
+
+  for (size_t global_var_index = 0; global_var_index < tensors_.size();
+       ++global_var_index) {
+    auto tensor = tensors_[global_var_index];
+    auto reduce_hook = [=](void) -> void {
+      this->AddDistHook(global_var_index);
+    };
+
+    const auto &grad_node = GetGradNodeFromTensor(&tensor);
+
+    PADDLE_ENFORCE(
+        grad_node.get() != nullptr,
+        paddle::platform::errors::Fatal("Detected NULL grad_node,"
+                                        "Leaf tensor should have had grad_node "
+                                        "with type: GradNodeAccumulation"));
+    const auto &accumulation_grad_node =
+        std::dynamic_pointer_cast<egr::GradNodeAccumulation>(grad_node);
+    accumulation_grad_node->RegisterReduceHook(
+        std::make_shared<egr::CppTensorVoidHook>(reduce_hook));
+  }
+
+  vars_marked_ready_.resize(tensors_.size(), false);
+  local_used_vars_.resize(tensors_.size(), 0);
+}
+
+std::shared_ptr<egr::GradNodeBase> EagerReducer::GetGradNodeFromTensor(
+    Tensor *tensor) {
+  auto *autograd_meta = tensor->get_autograd_meta();
+  const auto &grad_node =
+      static_cast<egr::AutogradMeta *>(autograd_meta)->GetMutableGradNode();
+  return grad_node;
+}
+
+void EagerReducer::InitializeGroups(
+    const std::vector<std::vector<size_t>> &group_indices) {
+  VLOG(3) << "Start initialize groups ..";
+
+  // clear the group
+  groups_.clear();
+  groups_.reserve(group_indices.size());
+
+  variable_locators_.clear();
+  variable_locators_.resize(tensors_.size());
+
+  auto group_nums = group_indices.size();
+  for (size_t group_index = 0; group_index < group_nums; ++group_index) {
+    const auto &tensor_indices_ = group_indices[group_index];
+    PADDLE_ENFORCE_GT(
+        tensor_indices_.size(), 0,
+        platform::errors::PreconditionNotMet(
+            "The number of group[%d]'s elements is 0.", group_index));
+
+    EagerGroup group;
+
+    // It's just for check the sparse or dense
+    auto first_var = tensors_[tensor_indices_.front()];
+    if (tensor_indices_.size() == 1 &&
+        is_sparse_gradient_[tensor_indices_.front()]) {
+      // process the sparse gradient. one sparse, one group
+      group.dtype_ = first_var.dtype();
+    } else {
+      // process the dense gradient.
+      InitializeDenseGroups(tensor_indices_, &group);
+      experimental::Backend backend;
+      switch (inner_place_.GetType()) {
+        case phi::AllocationType::GPU:
+          backend = experimental::Backend::GPU;
+          break;
+        case phi::AllocationType::CPU:
+          backend = experimental::Backend::CPU;
+          break;
+        default:
+          PADDLE_THROW(platform::errors::Unimplemented(
+              "Place type (%s) is not supported. ", inner_place_));
+          break;
+      }
+      group.dense_contents_ = paddle::experimental::empty(
+          ScalarArray({group.all_length_}), group.dtype_, backend);
+    }
+
+    // map tensors to this group by VariableLocator
+    size_t inside_group_index = 0;
+    for (const auto var_index : tensor_indices_) {
+      TensorLocator tensor_locator;
+      tensor_locator.group_index = group_index;
+      tensor_locator.inside_group_index = inside_group_index++;
+      variable_locators_[var_index] = tensor_locator;
+    }
+    group.tensor_indices_ = std::move(tensor_indices_);
+    groups_.emplace_back(std::move(group));
+
+    VLOG(3) << "The Group[" << group_index << "]:" << groups_.back();
+  }
+}
+
+void EagerReducer::InitializeDenseGroups(
+    const std::vector<size_t> &tensor_indices_, EagerGroup *p_group) {
+  VLOG(3) << "InitializeDenseGroups.";
+  int64_t all_length = 0;
+  for (size_t index = 0; index < tensor_indices_.size(); ++index) {
+    auto tensor_index = tensor_indices_[index];
+    auto &tensor = tensors_[tensor_index];
+    auto &tensor_name = tensor.name();
+
+    PADDLE_ENFORCE_EQ(tensor.is_initialized(), true,
+                      platform::errors::PreconditionNotMet(
+                          "Tensor %s is not initialized.", tensor_name));
+    const auto size = tensor.numel();
+    PADDLE_ENFORCE_GT(
+        size, 0, platform::errors::PreconditionNotMet(
+                     "The number of tensor %s's elements is 0.", tensor_name));
+    all_length += size;
+
+    p_group->length_.push_back(size);
+
+    // for concat operator
+    p_group->origin_shapes_.push_back(ScalarArray(tensor.shape()));
+    p_group->dense_tensors_.push_back(phi::DenseTensor());
+
+    const auto &dtype = tensor.dtype();
+    const auto &place = tensor.place();
+    const auto &inner_place = tensor.impl()->place();
+    if (index > 0) {
+      PADDLE_ENFORCE_EQ(dtype, p_group->dtype_,
+                        platform::errors::PreconditionNotMet(
+                            "Tensor %s has unexpected dtype.", tensor_name));
+      PADDLE_ENFORCE_EQ(place, place_,
+                        platform::errors::PreconditionNotMet(
+                            "Tensor %s has different place. Expected place is "
+                            "%s, but actual place is %s",
+                            tensor_name, inner_place_, inner_place));
+    } else {
+      p_group->dtype_ = dtype;
+      place_ = place;
+      inner_place_ = inner_place;
+    }
+  }
+  p_group->all_length_ = all_length;
+}
+
+void EagerReducer::PrepareForBackward(const std::vector<Tensor> &outputs) {
+  VLOG(3) << "after forward, then reset count for backward.";
+  grad_need_hooks_ = true;
+  next_group_ = 0;
+  std::for_each(groups_.begin(), groups_.end(), [](EagerGroup &group) {
+    group.pending_ = group.tensor_indices_.size();
+  });
+
+  // reinitialize vars_marked_ready_ for next iteration
+  vars_marked_ready_.clear();
+  vars_marked_ready_.resize(tensors_.size(), false);
+}
+
+void EagerReducer::AddDistHook(size_t var_index) {
+  PADDLE_ENFORCE_LT(var_index, variable_locators_.size(),
+                    platform::errors::OutOfRange(
+                        "Out of bounds variable index. it must be less"
+                        "than %d, but it is %d",
+                        variable_locators_.size(), var_index));
+
+  // gradient synchronization is not required when grad_need_hooks_ is false.
+  if (!grad_need_hooks_) {
+    return;
+  }
+
+  auto &tensor = tensors_[var_index];
+  const auto &grad_node = GetGradNodeFromTensor(&tensor);
+
+  VLOG(3) << "Var[" << var_index << "] [" << (*grad_node).name()
+          << "] arrived and triggered disthook";
+
+  local_used_vars_[var_index] = 1;
+
+  MarkVarReady(var_index, true);
+}
+
+void EagerReducer::MarkVarReady(const size_t var_index,
+                                const bool is_used_var) {
+  const auto &var_locator = variable_locators_[var_index];
+  const auto group_index = var_locator.group_index;
+  const auto inside_group_index = var_locator.inside_group_index;
+
+  auto &group = groups_[group_index];
+  auto &group_tensor = group.dense_tensors_[inside_group_index];
+  auto *autograd_meta = tensors_[var_index].get_autograd_meta();
+  auto &grad_tensor = static_cast<egr::AutogradMeta *>(autograd_meta)->Grad();
+
+  group_tensor
+      .ShareDataWith(
+          *(std::dynamic_pointer_cast<phi::DenseTensor>(grad_tensor.impl())))
+      .Resize({grad_tensor.numel()});
+
+  vars_marked_ready_[var_index] = true;
+
+  if (--group.pending_ == 0) {
+    // can start allreduce
+    MarkGroupReady(group_index);
+  }
+}
+
+void EagerReducer::MarkGroupReady(size_t group_index) {
+  VLOG(3) << "Group[" << group_index << "] is ready";
+
+  PADDLE_ENFORCE_GE(
+      group_index, next_group_,
+      platform::errors::PreconditionNotMet(
+          "The index of the incoming group must be greater "
+          "than or equal to the previously synchronized group index, "
+          "expect it to greater than or equal to %d, but got %d.",
+          next_group_, group_index));
+
+  if (group_index > next_group_) {
+    VLOG(3) << "It will adjust the order of group in next batch automatically";
+    return;
+  }
+
+  for (; next_group_ < groups_.size() && groups_[next_group_].pending_ == 0;
+       ++next_group_) {
+    UNUSED auto &group = groups_[next_group_];
+    FusedAllReduceSchedule(&group, next_group_);
+  }
+}
+
+void EagerReducer::FusedAllReduceSchedule(EagerGroup *group,
+                                          const int curr_group_index) {
+  // The overall timeline: concat > div_nranks > allreduce > split
+  distributed::AllreduceOptions opts;
+  opts.reduce_op = ReduceOp::SUM;
+
+  VLOG(3) << "group [" << curr_group_index << "] start fused_allreduce.";
+
+  // concat tensors
+  group->ConcatTensors(inner_place_);
+
+  // div nranks
+  double scaling = 1.0 / nranks_;
+  paddle::experimental::scale_(group->dense_contents_, scaling, 0.0, false);
+
+  // all_reduce
+  std::vector<Tensor> reduce_tensors = {group->dense_contents_};
+  tasks_.push_back(process_group_->AllReduce(reduce_tensors, opts));
+
+  if (tasks_.size() == groups_.size()) {
+    for (size_t index = 0; index < tasks_.size(); index++) {
+      auto &task = tasks_.back();
+      task->Synchronize();
+      tasks_.pop_back();
+    }
+    for (size_t index = 0; index < groups_.size(); index++) {
+      auto &group = groups_[index];
+      group.SplitTensors(inner_place_);
+    }
+  }
+}
+
+std::ostream &operator<<(std::ostream &out, const EagerGroup &group) {
+  const auto &tensors_ = group.tensor_indices_;
+  out << "numel: " << group.all_length_ << " ;var number: " << tensors_.size()
+      << "\n";
+  auto begin = tensors_.begin();
+  auto end = tensors_.end();
+  out << "[";
+  for (int i = 0; begin != end && i < 100; ++i, ++begin) {
+    if (i > 0) out << ' ';
+    out << *begin;
+  }
+  if (begin != end) {
+    out << " ...";
+  }
+  out << "]\n";
+  return out;
+}
+
 }  //  namespace distributed
 }  //  namespace paddle

paddle/fluid/distributed/collective/reducer.h

@@ -17,16 +17,109 @@
 #include <map>
 #include <vector>
 #include "paddle/fluid/distributed/collective/ProcessGroup.h"
+#include "paddle/fluid/eager/accumulation/accumulation_node.h"
+#include "paddle/fluid/eager/api/utils/hook_utils.h"
 #include "paddle/fluid/eager/api/utils/tensor_utils.h"
+#include "paddle/fluid/eager/autograd_meta.h"
+#include "paddle/fluid/eager/utils.h"
+#include "paddle/fluid/operators/math/concat_and_split.h"
+#include "paddle/fluid/platform/device/gpu/gpu_info.h"
+#include "paddle/phi/api/include/api.h"
+#include "paddle/phi/api/include/tensor.h"
+#include "paddle/phi/api/lib/ext_compat_utils.h"
+#include "paddle/phi/common/data_type.h"
 
 namespace paddle {
 namespace distributed {
 using Tensor = paddle::experimental::Tensor;
+using Scalar = paddle::experimental::ScalarBase<paddle::experimental::Tensor>;
+using ScalarArray =
+    paddle::experimental::ScalarArrayBase<paddle::experimental::Tensor>;
 
 std::vector<std::vector<size_t>> Eager_AssignGroupBySize(
-    const std::vector<Tensor>, const std::vector<bool>& is_sparse_gradient,
-    const std::vector<size_t>& group_size_limits,
-    const std::vector<int64_t>& tensor_indices = {});
+    const std::vector<Tensor>, const std::vector<bool> &is_sparse_gradient,
+    const std::vector<size_t> &group_size_limits,
+    const std::vector<int64_t> &tensor_indices = {});
+
+class EagerGroup {
+ public:
+  Tensor dense_contents_;
+
+  // for concat kernel
+  std::vector<phi::DenseTensor> dense_tensors_;
+  std::vector<int64_t> length_;
+  int64_t all_length_{0};
+  std::vector<ScalarArray> origin_shapes_;
+
+  // Global indices of participating tensors in the group
+  std::vector<size_t> tensor_indices_;
+
+  // Number of params that haven't been ready. When it is 0, it means
+  // the group is ready.
+  size_t pending_ = -1;
+
+  // external message of group
+  phi::DataType dtype_;
+
+  // context is used to select the stream for concat
+  void ConcatTensors(const platform::Place &);
+
+  // context is used to select the stream for split
+  void SplitTensors(const platform::Place &);
+
+  friend std::ostream &operator<<(std::ostream &, const EagerGroup &);
+};
+
+struct TensorLocator {
+  // record the index in groups_
+  size_t group_index;
+  size_t inside_group_index;
+};
+
+class EagerReducer {
+ public:
+  explicit EagerReducer(
+      const std::vector<Tensor> tensors,
+      const std::vector<std::vector<size_t>> &group_indices,
+      const std::vector<bool> &is_sparse_gradient,
+      std::shared_ptr<distributed::ProcessGroup> process_group,
+      const std::vector<size_t> &group_size_limits,
+      bool find_unused_parameters);
+
+  virtual ~EagerReducer() {}
+
+  std::shared_ptr<egr::GradNodeBase> GetGradNodeFromTensor(Tensor *tensor);
+
+  void InitializeGroups(const std::vector<std::vector<size_t>> &group_indices);
+  void InitializeDenseGroups(const std::vector<size_t> &tensor_indices_,
+                             EagerGroup *p_group);
+  void PrepareForBackward(const std::vector<Tensor> &outputs);
+  void AddDistHook(size_t var_index);
+  void MarkVarReady(const size_t var_index, const bool is_used_var);
+  void MarkGroupReady(const size_t group_index);
+  void FusedAllReduceSchedule(EagerGroup *group, const int curr_group_index);
+
+ private:
+  std::vector<Tensor> tensors_;
+  std::vector<std::vector<size_t>> group_indices_;
+  std::vector<bool> is_sparse_gradient_;
+  std::shared_ptr<distributed::ProcessGroup> process_group_;
+  std::vector<size_t> group_size_limits_;
+  bool find_unused_vars_each_step_;
+
+  std::vector<EagerGroup> groups_;
+  std::vector<TensorLocator> variable_locators_;
+  PlaceType place_;
+  platform::Place inner_place_;
+  size_t next_group_ = 0;
+  int64_t nranks_ = -1;
+  std::vector<std::shared_ptr<paddle::distributed::ProcessGroup::Task>> tasks_;
+
+  bool grad_need_hooks_{false};
+
+  std::vector<bool> vars_marked_ready_;
+  std::vector<int> local_used_vars_;
+};
 
 }  //  namespace distributed
 }  //  namespace paddle

paddle/fluid/distributed/ps/table/depends/initializers.h

@@ -23,7 +23,6 @@
 #include "gflags/gflags.h"
 
 #include "paddle/fluid/framework/generator.h"
-
 #include "paddle/fluid/operators/truncated_gaussian_random_op.h"
 
 namespace paddle {
@@ -118,9 +117,13 @@ class TruncatedGaussianInitializer : public Initializer {
     seed_ = static_cast<unsigned int>(std::stoi(attrs[1]));
     mean_ = std::stof(attrs[2]);
     std_ = std::stof(attrs[3]);
-
-    std::uniform_real_distribution<float> dist_(
-        std::numeric_limits<float>::min(), 1.0);
+    auto normal_cdf = [](float x) {
+      return (1.0 + std::erf(x / std::sqrt(2.0))) / 2.0;
+    };
+    float a_normal_cdf = normal_cdf((-2.0 - mean_) / std_);
+    float b_normal_cdf = normal_cdf((2.0 - mean_) / std_);
+    std::uniform_real_distribution<float> dist_(2.0 * a_normal_cdf - 1.0,
+                                                2.0 * b_normal_cdf - 1.0);
     random_engine_ = framework::GetCPURandomEngine(seed_);
   }
 

paddle/fluid/eager/CMakeLists.txt

-set(eager_deps phi_api hook_utils tensor_utils utils global_utils backward phi_tensor tracer layer autograd_meta grad_node_info grad_tensor_holder accumulation_node)
+set(eager_deps phi_api hook_utils tensor_utils utils global_utils backward phi_tensor tracer layer autograd_meta grad_node_info grad_tensor_holder accumulation_node custom_operator_node)
+
 set(fluid_deps tracer layer proto_desc operator op_registry variable_helper memcpy)
 set(generated_deps final_dygraph_function final_dygraph_node dygraph_function dygraph_node)
 
@@ -9,6 +10,8 @@ endif()
 
 add_subdirectory(api)
 add_subdirectory(accumulation)
+add_subdirectory(custom_operator)
+
 
 cc_library(grad_node_info SRCS grad_node_info.cc DEPS phi_api phi_tensor)
 cc_library(grad_tensor_holder SRCS grad_tensor_holder.cc DEPS grad_node_info gradient_accumulator)

paddle/fluid/eager/api/utils/global_utils.h

@@ -18,7 +18,7 @@
 #include <atomic>
 #include <memory>
 #include "paddle/fluid/imperative/tracer.h"
-
+#include "paddle/phi/api/ext/op_meta_info.h"
 namespace egr {
 
 class UniqueNameGenerator {
@@ -70,6 +70,21 @@ class Controller {
 
   void SetInEagerMode(bool in_eager_mode) { in_eager_mode_ = in_eager_mode; }
 
+  const std::unordered_map<std::string, std::vector<paddle::OpMetaInfo>>&
+  GetOpMetaInfoMap() {
+    return op_meta_info_map_;
+  }
+
+  void MergeOpMetaInfoMap(const std::unordered_map<
+                          std::string, std::vector<paddle::OpMetaInfo>>& map) {
+    op_meta_info_map_.insert(map.begin(), map.end());
+  }
+
+  std::unordered_map<std::string, std::vector<std::unordered_map<int, int>>>&
+  GetCustomEdgesSlotMap() {
+    return custom_edges_slot_map_;
+  }
+
  private:
   Controller() = default;
   static Controller* controller_;
@@ -77,6 +92,11 @@ class Controller {
       new paddle::imperative::Tracer()};
   // TODO(jiabin): remove when we don't need imperative.
   bool in_eager_mode_{false};
+  std::unordered_map<std::string, std::vector<paddle::OpMetaInfo>>
+      op_meta_info_map_;
+  /* op_type : {{grad_outputs}, {grad_inputs}, {input}, {output}, {attrs}}*/
+  std::unordered_map<std::string, std::vector<std::unordered_map<int, int>>>
+      custom_edges_slot_map_;
   DISABLE_COPY_AND_ASSIGN(Controller);
 };
 

paddle/fluid/eager/auto_code_generator/eager_generator.cc

@@ -1553,9 +1553,23 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
   core_ops_returns_info[op_type] = return_contents;
 
   // [Generation] ComputeRequireGrad -> GradNodeCreation
+
   if (!bwd_info.GenerateForwardOnly()) {
     std::string grad_node_creation_body_str =
         GenerateGradNodeCreationContent(fwd_info, bwd_info);
+
+    // Add event record
+    std::string event_name = op_type + " node_creation";
+    const char* NODE_CREATION_TEMPLATE =
+        "{\n"
+        "   paddle::platform::RecordEvent node_creation_record_event(\"%s\", "
+        "paddle::platform::TracerEventType::Operator, 1);\n"
+        "   %s\n"
+        "}";
+
+    grad_node_creation_body_str = paddle::string::Sprintf(
+        NODE_CREATION_TEMPLATE, event_name, grad_node_creation_body_str);
+
     generated_function_body += grad_node_creation_body_str;
     generated_function_body += "\n";
 
@@ -1614,10 +1628,20 @@ static std::pair<std::string, std::string> GenerateForwardFunctionContents(
     if ((*iter) == ',') dygraph_function_args_str.erase(iter);
   }
 
-  const char* FWD_FUNCTION_TEMPLATE = "%s %s(%s) {\n\n%s\n}\n\n";
+  const char* DYGRAPH_FUNCTION_EVENT_RECORD_FUNCTION_TEMPLATE =
+      "paddle::platform::RecordEvent dygraph_entrance_record_event(\"%s\", "
+      "paddle::platform::TracerEventType::Operator, 1);";
+  std::string event_name = op_type + " dygraph";
+  std::string fwd_record_event_str = paddle::string::Sprintf(
+      DYGRAPH_FUNCTION_EVENT_RECORD_FUNCTION_TEMPLATE, event_name);
+  const char* FWD_FUNCTION_TEMPLATE =
+      "%s %s(%s) {\n\n"
+      " %s\n"
+      " %s\n"
+      "}\n\n";
   std::string fwd_function_str = paddle::string::Sprintf(
       FWD_FUNCTION_TEMPLATE, function_proto_return_type_str, function_name,
-      dygraph_function_args_str, generated_function_body);
+      dygraph_function_args_str, fwd_record_event_str, generated_function_body);
 
   // [Generation] Generate forward functions header
   const char* FWD_HEADER_TEMPLATE = "%s %s(%s);\n";
@@ -2240,8 +2264,9 @@ static void GenerateForwardDygraphFile(const std::string& forward_cc_path,
       "\"paddle/fluid/eager/api/generated/fluid_generated/"
       "dygraph_forward_api.h\"\n"
       "#include "
-      "\"paddle/fluid/eager/api/generated/fluid_generated/nodes/nodes.h\"\n\n"
-      "#include \"paddle/fluid/eager/api/utils/global_utils.h\"\n";
+      "\"paddle/fluid/eager/api/generated/fluid_generated/nodes/nodes.h\"\n"
+      "#include \"paddle/fluid/eager/api/utils/global_utils.h\"\n"
+      "#include \"paddle/fluid/platform/profiler/event_tracing.h\"\n\n";
   std::string forward_cc_include_str =
       paddle::string::Sprintf(FORWARD_INCLUDE_TEMPLATE);
   std::ofstream forward_cc_stream(forward_cc_path, std::ios::out);

paddle/fluid/eager/auto_code_generator/final_state_generator/eager_gen.py

@@ -148,6 +148,12 @@ def ReadBwdFile(filepath):
 ######################
 ###  Yaml Parsers  ###
 ######################
+def RemoveSpecialSymbolsInName(string):
+    # Remove any name after '@'
+    ret = string.split("@")[0]
+    return ret
+
+
 def IntermediateValidationCheck(intermediate_outputs, forward_returns_list):
     # intermediate_outputs : [name0, name1, ...]
     # forward_returns_list : [[ret_name, type, orig_pos], ...]
@@ -166,15 +172,19 @@ def IntermediateValidationCheck(intermediate_outputs, forward_returns_list):
 
 def ParseDispensable(string):
     # string: "X, Y"
+    string = RemoveSpecialSymbolsInName(string)
     return [v.strip() for v in string.split(",")]
 
 
 def ParseIntermediate(string):
+    string = RemoveSpecialSymbolsInName(string)
     return [v.strip() for v in string.split(",")]
 
 
 def ParseNoNeedBuffer(string):
     # string: "x, y"
+    string = RemoveSpecialSymbolsInName(string)
+
     no_need_buffer_set = set()
     for name in string.split(","):
         no_need_buffer_set.add(name.strip())
@@ -204,6 +214,8 @@ def ParseYamlArgs(string):
 
         assert arg_type in yaml_types_mapping.keys()
         arg_type = yaml_types_mapping[arg_type]
+
+        arg_name = RemoveSpecialSymbolsInName(arg_name)
         if "Tensor" in arg_type:
             assert default_value is None
             inputs_list.append([arg_name, arg_type, i])
@@ -239,6 +251,7 @@ def ParseYamlReturns(string):
         ret_type = yaml_types_mapping[ret_type]
 
         assert "Tensor" in ret_type
+        ret_name = RemoveSpecialSymbolsInName(ret_name)
         returns_list.append([ret_name, ret_type, i])
 
     return returns_list
@@ -910,8 +923,20 @@ def GenerateForwardDefinition(fwd_api_name, bwd_api_name,
         backward_fwd_input_map, backward_grad_input_map,
         backward_grad_output_map, backward_attrs_list, optional_inputs)
 
+    node_event_name = fwd_api_name + " node_creation"
+    NODE_CREATION_TEMPLATE = """{{\n
+           paddle::platform::RecordEvent node_creation_record_event(\"{}\", paddle::platform::TracerEventType::Operator, 1);\n
+           {}\n
+        }}"""
+    node_creation_str = NODE_CREATION_TEMPLATE.format(node_event_name,
+                                                      node_creation_str)
+
+    dygraph_event_str = f"paddle::platform::RecordEvent dygraph_entrance_record_event(\"{fwd_api_name} dygraph\", paddle::platform::TracerEventType::Operator, 1);"
+
     FORWARD_FUNCTION_TEMPLATE = """
 {} {}({}) {{
+    {}
+
     // Forward API Call
     {}
     
@@ -925,7 +950,7 @@ def GenerateForwardDefinition(fwd_api_name, bwd_api_name,
     forward_function_name = GetForwardFunctionName(fwd_api_name)
     forward_function_str = FORWARD_FUNCTION_TEMPLATE.format(
         returns_type_str, forward_function_name, inputs_args_definition_str,
-        forward_call_str, node_creation_str, returns_str)
+        dygraph_event_str, forward_call_str, node_creation_str, returns_str)
     forward_function_declaration_str = f"{returns_type_str} {forward_function_name}({inputs_args_declaration_str});"
 
     return forward_function_str, forward_function_declaration_str
@@ -1052,6 +1077,8 @@ def GenerateForwardCCFile(filepath, forward_definition_str):
 
 #include "paddle/phi/api/include/sparse_api.h"
 #include "paddle/fluid/eager/api/utils/global_utils.h"
+#include "paddle/fluid/platform/profiler/event_tracing.h"
+
 """
 
     file_contents += GenerateCoreOpInfoDefinition()

paddle/fluid/eager/auto_code_generator/final_state_generator/python_c_gen.py

@@ -94,9 +94,13 @@ def GeneratePythonCFunction(fwd_api_name, forward_inputs_position_map,
         dygraph_function_call_list[pos] = f"{name}"
     dygraph_function_call_str = ",".join(dygraph_function_call_list)
 
+    pythonc_event_str = f"paddle::platform::RecordEvent pythonc_record_event(\"{fwd_api_name} pybind_imperative_func\", paddle::platform::TracerEventType::Operator, 1);"
+
     PYTHON_C_FUNCTION_TEMPLATE = """
 static PyObject * eager_final_state_api_{}(PyObject *self, PyObject *args, PyObject *kwargs)
 {{
+  {}
+
   PyThreadState *tstate = nullptr;
   try
   {{
@@ -136,8 +140,8 @@ static PyObject * eager_final_state_api_{}(PyObject *self, PyObject *args, PyObj
         fwd_function_name = namespace_str + GetForwardFunctionName(fwd_api_name)
 
     python_c_function_str = PYTHON_C_FUNCTION_TEMPLATE.format(
-        fwd_api_name, fwd_api_name, get_eager_tensor_str, parse_attributes_str,
-        fwd_function_name, dygraph_function_call_str)
+        fwd_api_name, pythonc_event_str, fwd_api_name, get_eager_tensor_str,
+        parse_attributes_str, fwd_function_name, dygraph_function_call_str)
 
     python_c_function_reg_str = f"{{\"final_state_{fwd_api_name}\", (PyCFunction)(void(*)(void)) {namespace_str}eager_final_state_api_{fwd_api_name}, METH_VARARGS | METH_KEYWORDS, \"C++ interface function for {fwd_api_name} in dygraph.\"}}\n"
 
@@ -231,6 +235,7 @@ def GeneratePythonCWrappers(python_c_function_str, python_c_function_reg_str):
 #include  "paddle/fluid/pybind/op_function_common.h"
 #include  "paddle/fluid/eager/api/generated/eager_generated/forwards/dygraph_functions.h"
 #include  "paddle/fluid/pybind/exception.h"
+#include  "paddle/fluid/platform/profiler/event_tracing.h"
 #include  <Python.h>
 
 namespace paddle {{

paddle/fluid/eager/backward.cc

@@ -19,6 +19,8 @@
 #include "paddle/fluid/eager/grad_node_info.h"
 #include "paddle/fluid/eager/grad_tensor_holder.h"
 #include "paddle/fluid/eager/utils.h"
+#include "paddle/fluid/platform/profiler.h"
+#include "paddle/fluid/platform/profiler/event_tracing.h"
 
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/errors.h"
@@ -77,6 +79,9 @@ std::unordered_map<GradNodeBase*, int> getInDegreeMap(
 void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
                  const std::vector<paddle::experimental::Tensor>& grad_tensors,
                  bool retain_graph) {
+  paddle::platform::RecordEvent backward_record_event(
+      "backward", paddle::platform::TracerEventType::Operator, 1);
+
   VLOG(6) << "Start Backward";
   // *Gradient Hook should happen at node-level
   // *Inplace version check should perform at node-level
@@ -112,7 +117,8 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
 
     // Prepare GradTensorHolder
     if (!node_input_buffers_dict.count(grad_node)) {
-      VLOG(6) << "Create Value for grad input tensor " << i;
+      VLOG(6) << "Create Value for grad input tensor " << i
+              << " of grad node: " << grad_node->name();
       node_input_buffers_dict[grad_node] =
           std::make_unique<GradTensorHolder>(grad_node->InputMeta());
     }
@@ -158,19 +164,27 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
   VLOG(6) << "Run Backward";
   while (!queue.empty()) {
     GradNodeBase* node = queue.front();
-    queue.pop();
 
+    paddle::platform::RecordEvent node_record_event(
+        std::string(typeid(*node).name()) + " grad_node",
+        paddle::platform::TracerEventType::Operator, 1);
+
+    if (queue.size() > 1 && node_in_degree_map[node] != 0) {
+      queue.pop();
+      continue;
+    }
+    queue.pop();
     // Run node: This is where Hook happens
     PADDLE_ENFORCE(
         node_input_buffers_dict.count(node),
         paddle::platform::errors::Fatal(
-            "Unable to find next node in the InputBuufer"
+            "Unable to find next node in the GradTensorHolder \n"
             "Trying to run Node without configuring its GradTensorHolder"));
 
     std::unique_ptr<GradTensorHolder> node_input_buffer =
         std::move(node_input_buffers_dict[node]);
 
-    VLOG(6) << "Run Backward Kernel with input_buffer";
+    VLOG(6) << "Run Backward Kernel with GradTensorHolder";
     // Run Pre Backward Node and get outputs
     std::vector<std::vector<paddle::experimental::Tensor>> grad_output_tensors =
         (*node)(node_input_buffer->Buffers());
@@ -215,9 +229,8 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
 
         if ((!grad_output_tensor.defined() ||
              !grad_output_tensor.initialized())) {
-          VLOG(6)
-              << "We get grad_output_tensor with slot: " << i << ", rank: " << j
-              << " as uninitialized or undefined in both tensor and variable";
+          VLOG(6) << "We get grad_output_tensor with slot: " << i
+                  << ", rank: " << j << " as uninitialized or undefined tensor";
         }
         VLOG(6) << "Get Edge and grad_output_tensor with slot: " << i
                 << ", rank: " << j
@@ -228,6 +241,8 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
           const auto& input_meta = next_node->InputMeta();
           auto grad_tensor_holder =
               std::make_unique<GradTensorHolder>(input_meta);
+          VLOG(6) << "Construct GradTensorHolder for grad node: "
+                  << next_node->name();
           node_input_buffers_dict[next_node] = std::move(grad_tensor_holder);
         }
         VLOG(6) << "Sum grad inputs for edge slot: " << edge_rank.first
@@ -237,10 +252,12 @@ void RunBackward(const std::vector<paddle::experimental::Tensor>& tensors,
 
         // Update queue
         node_in_degree_map[next_node]--;
-        PADDLE_ENFORCE(node_in_degree_map[next_node] >= 0,
-                       paddle::platform::errors::Fatal(
-                           "Detected in-degree value smaller than zero."
-                           "Node's in-degree cannot be negative"));
+        PADDLE_ENFORCE(
+            node_in_degree_map[next_node] >= 0,
+            paddle::platform::errors::Fatal(
+                "Detected in-degree value smaller than zero. For Node: %s"
+                "Node's in-degree cannot be negative",
+                next_node->name()));
         if (node_in_degree_map[next_node] == 0) {
           queue.emplace(std::move(next_node));
         }

paddle/fluid/eager/custom_operator/CMakeLists.txt

+cc_library(custom_operator_node SRCS custom_operator_node.cc DEPS phi_tensor phi_api grad_node_info custom_operator op_meta_info)

paddle/fluid/eager/custom_operator/custom_operator_node.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/eager/custom_operator/custom_operator_node.h"
+#include "paddle/fluid/framework/custom_operator.h"
+#include "paddle/fluid/framework/op_meta_info_helper.h"
+#include "paddle/phi/api/ext/op_meta_info.h"
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace egr {
+std::vector<std::vector<paddle::experimental::Tensor>> RunCustomOpNode::
+operator()(
+    const std::vector<std::vector<paddle::experimental::Tensor>>& grads) {
+  paddle::CustomOpKernelContext ctx;
+  auto grad_inputs_name = paddle::framework::OpMetaInfoHelper::GetInputs(
+      egr::Controller::Instance().GetOpMetaInfoMap().at(op_type_)[1]);
+  auto grad_outputs_names = paddle::framework::OpMetaInfoHelper::GetOutputs(
+      egr::Controller::Instance().GetOpMetaInfoMap().at(op_type_)[1]);
+  auto map = egr::Controller::Instance().GetCustomEdgesSlotMap().at(op_type_);
+  auto kernel_map = egr::Controller::Instance().GetOpMetaInfoMap();
+
+  std::vector<std::vector<paddle::experimental::Tensor>> tmp_ins(
+      grad_inputs_name.size());
+  VLOG(7) << " Prepare Backward inputs of grads with size: " << grads.size()
+          << ", whose grad_inputs_name size is: " << grad_inputs_name.size();
+  for (size_t i = 0; i < grads.size(); i++) {
+    if (map[1].find(i) != map[1].end()) {
+      VLOG(7) << "Insert grad: " << i << " to grad_inputs: " << map[1][i];
+      tmp_ins[map[1][i]] = grads[i];
+    }
+  }
+
+  for (auto it : fwd_outs) {
+    VLOG(7) << "Insert fwd_outs to grad_inputs: " << it.first;
+    tmp_ins[it.first] = RunCustomOpNode::Recover(&(it.second));
+  }
+
+  for (auto it : fwd_ins) {
+    VLOG(7) << "Insert fwd_ins to grad_inputs: " << it.first;
+    tmp_ins[it.first] = RunCustomOpNode::Recover(&(it.second));
+  }
+
+  VLOG(6) << "Prepare Grad inputs";
+  for (const auto& in : tmp_ins) {
+    ctx.EmplaceBackInputs(in);
+  }
+  VLOG(6) << "Prepare Grad attrs";
+  ctx.EmplaceBackAttrs(attrs_);
+  std::vector<std::vector<paddle::experimental::Tensor>> outs(
+      GetEdges().size());
+  std::vector<std::vector<paddle::experimental::Tensor>> tmp_outs(
+      grad_outputs_names.size());
+  VLOG(6) << "Prepare Grad outputs for size: " << grad_outputs_names.size();
+  for (size_t i = 0; i < GetEdges().size(); i++) {
+    if (map[0].find(i) != map[0].end()) {
+      VLOG(7) << "Insert grad outputs: " << i
+              << " with size: " << GetEdges()[i].size()
+              << " to tmp_outputs: " << map[0][i];
+      for (size_t j = 0; j < GetEdges()[i].size(); j++) {
+        outs[i].emplace_back(/* init it incase of copy nullptr of shared_ptr */
+                             std::make_shared<phi::DenseTensor>(
+                                 phi::DataType::UNDEFINED),
+                             egr::Controller::Instance().GenerateUniqueName(
+                                 "custom_tmp_grad"));
+      }
+      tmp_outs[map[0][i]] = outs[i];
+    }
+  }
+  for (size_t i = 0; i < tmp_outs.size(); i++) {
+    VLOG(7) << "Prepare grad outputs size: " << tmp_outs[i].size();
+    ctx.EmplaceBackOutputs(tmp_outs[i]);
+  }
+  VLOG(7) << "Run Kernel of Grad Custom Op: " << op_type_;
+
+  (*paddle::framework::OpMetaInfoHelper::GetKernelFn(
+      kernel_map.at(op_type_)[1]))(&ctx);
+  return outs;
+}
+}  // namespace egr

paddle/fluid/eager/custom_operator/custom_operator_node.h

+// 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.
+
+#pragma once
+
+#include "paddle/fluid/eager/autograd_meta.h"
+#include "paddle/fluid/eager/grad_node_info.h"
+#include "paddle/fluid/eager/hooks.h"
+#include "paddle/fluid/eager/tensor_wrapper.h"
+#include "paddle/fluid/framework/custom_operator.h"
+#include "paddle/utils/any.h"
+
+namespace egr {
+class RunCustomOpNode : public GradNodeBase {
+ public:
+  // Constructor: configure fwd input tensors to grad node
+  explicit RunCustomOpNode(size_t bwd_in_slot_num, size_t bwd_out_slot_num,
+                           const std::string& op_type)
+      : GradNodeBase(bwd_in_slot_num, bwd_out_slot_num), op_type_(op_type) {
+    VLOG(6) << "Construct RunCustomOpNode for op: " << op_type;
+  }
+
+  ~RunCustomOpNode() override {
+    VLOG(6) << "Destruct RunCustomOpNode for op: " << op_type_;
+  }
+
+  // Functor: perform backward computations
+  virtual std::vector<std::vector<paddle::experimental::Tensor>> operator()(
+      const std::vector<std::vector<paddle::experimental::Tensor>>& grads)
+      override;
+
+  std::string name() {
+    return paddle::string::Sprintf("RunCustomOpNode: %s_grad", op_type_);
+  }
+
+  static std::vector<egr::TensorWrapper> ConstructTensorWrapper(
+      const std::vector<paddle::experimental::Tensor>& fwd_var) {
+    std::vector<egr::TensorWrapper> res;
+    for (auto const& var : fwd_var) {
+      res.emplace_back(var);
+    }
+    return res;
+  }
+
+  static std::vector<paddle::experimental::Tensor> Recover(
+      std::vector<egr::TensorWrapper>* fwd_var) {
+    std::vector<paddle::experimental::Tensor> res;
+    for (size_t i = 0; i < fwd_var->size(); i++) {
+      res.emplace_back(fwd_var->at(i).recover(nullptr));
+    }
+    return res;
+  }
+
+  void SetAttrs(const std::vector<paddle::any>& attr) { attrs_ = attr; }
+
+ public:
+  std::unordered_map<int, std::vector<egr::TensorWrapper>> fwd_outs;
+  std::unordered_map<int, std::vector<egr::TensorWrapper>> fwd_ins;
+  std::unordered_map<int, int> grads2grad_in_map;
+
+ private:
+  std::vector<paddle::any> attrs_;
+  std::string op_type_{""};
+};
+
+}  // namespace egr

paddle/fluid/eager/grad_node_info.cc

@@ -25,7 +25,7 @@
 #include "glog/logging.h"
 
 /**
- * Implementation of GradNodeBase, Edge and InputBuffer.
+ * Implementation of GradNodeBase, Edge and GradTensorHolder.
 **/
 namespace egr {
 

paddle/fluid/eager/tests/performance_tests/benchmark_eager_cpu.cc

@@ -40,6 +40,8 @@ PD_DECLARE_KERNEL(matmul, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(matmul_grad, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum_grad, CPU, ALL_LAYOUT);
 
 using namespace egr;            // NOLINT
 using namespace egr_utils_api;  // NOLINT

paddle/fluid/eager/tests/performance_tests/benchmark_eager_cuda.cc

@@ -44,6 +44,8 @@ PD_DECLARE_KERNEL(matmul, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(matmul_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum_grad, GPU, ALL_LAYOUT);
 
 TEST(Benchmark, EagerScaleCUDA) {
   eager_test::InitEnv(paddle::platform::CUDAPlace());

paddle/fluid/eager/tests/performance_tests/benchmark_fluid_cpu.cc

@@ -41,6 +41,8 @@ PD_DECLARE_KERNEL(matmul, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(matmul_grad, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum, CPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum_grad, CPU, ALL_LAYOUT);
 
 namespace paddle {
 namespace imperative {

paddle/fluid/eager/tests/performance_tests/benchmark_fluid_cuda.cc

@@ -43,6 +43,8 @@ PD_DECLARE_KERNEL(matmul, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(matmul_grad, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add, GPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum_grad, GPU, ALL_LAYOUT);
 
 namespace paddle {
 namespace imperative {

paddle/fluid/eager/to_static/run_program_op_func.h

@@ -57,6 +57,7 @@ inline void run_program_dygraph_function(
     auto grad_node = std::make_shared<GradNodeRunProgram>(1, 2);
 
     grad_node->SetFwdOutNames(out_names);
+    grad_node->SetOut(out);
     // Set Attributes
     grad_node->SetAttrMap(attrs);
     // Set TensorWrappers

paddle/fluid/eager/to_static/run_program_op_node.h

@@ -260,9 +260,9 @@ inline void RunProgramAPI(
   }
   VLOG(2) << "The number of sub scopes after forward: "
           << out_scope_vec->front()->kids().size();
-  // #ifdef PADDLE_WITH_MKLDNN
-  //     if (FLAGS_use_mkldnn) paddle::platform::DontClearMKLDNNCache(place);
-  // #endif
+#ifdef PADDLE_WITH_MKLDNN
+  if (FLAGS_use_mkldnn) paddle::platform::DontClearMKLDNNCache(place);
+#endif
 }
 
 inline void RunProgramGradAPI(
@@ -357,7 +357,7 @@ inline void RunProgramGradAPI(
   details::ShareTensorsFromScope(params_grad, *global_block, &scope);
 
   // Step5. drop current scope
-  // global_inner_scope->DeleteScope(&scope);
+  global_inner_scope->DeleteScope(&scope);
   VLOG(2) << "The number of sub scopes after backward: "
           << global_inner_scope->kids().size();
 }
@@ -400,6 +400,10 @@ class GradNodeRunProgram : public egr::GradNodeBase {
         paddle::platform::errors::InvalidArgument(
             "The grads[0].size() and fwd_out_names_.size() should be equal."));
     for (size_t i = 0; i < fwd_out_names_.size(); ++i) {
+      auto &out_grad = egr::EagerUtils::unsafe_autograd_meta(*out_[i])->Grad();
+      const_cast<paddle::experimental::Tensor &>(out_grad).set_impl(
+          grads[0][i].impl());
+
       const_cast<paddle::experimental::Tensor &>(grads[0][i])
           .set_name(fwd_out_names_[i] + "@GRAD");
     }
@@ -432,6 +436,10 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     fwd_out_names_ = out_names;
   }
 
+  void SetOut(const std::vector<paddle::experimental::Tensor *> &out) {
+    out_ = out;
+  }
+
  protected:
   void ConstructGradTensors(
       const std::vector<paddle::experimental::Tensor> &fwd_tensors,
@@ -440,7 +448,11 @@ class GradNodeRunProgram : public egr::GradNodeBase {
     // such as: name, tensor type(DenseTensor or SelectedRows).
     VLOG(3) << "fwd_tensors.size(): " << fwd_tensors.size();
     for (auto &fwd_t : fwd_tensors) {
-      grad_tensors->emplace_back(fwd_t.impl());
+      if (phi::DenseTensor::classof(fwd_t.impl().get())) {
+        grad_tensors->emplace_back(std::make_shared<phi::DenseTensor>());
+      } else if (phi::SelectedRows::classof(fwd_t.impl().get())) {
+        grad_tensors->emplace_back(std::make_shared<phi::SelectedRows>());
+      }
       auto &grad_t = grad_tensors->back();
       grad_t.set_name(fwd_t.name() + "@GRAD");
     }
@@ -462,6 +474,7 @@ class GradNodeRunProgram : public egr::GradNodeBase {
   std::vector<paddle::framework::Scope *> step_scope_;
 
   std::vector<std::string> fwd_out_names_;
+  std::vector<paddle::experimental::Tensor *> out_;
 
   // Attribute Map
   paddle::framework::AttributeMap attrs_;

paddle/fluid/framework/CMakeLists.txt

@@ -440,6 +440,7 @@ message(STATUS "branch: ${PADDLE_BRANCH}")
 configure_file(commit.h.in commit.h)
 
 cc_library(custom_operator SRCS custom_operator.cc DEPS tensor attribute framework_proto op_registry operator dynamic_loader string_helper phi_tensor op_meta_info phi_api)
+
 #cc_binary(test_executor SRCS test_executor.cc DEPS executor op_registry ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS} )
 #cc_binary(new_executor SRCS new_exec_test.cc DEPS operator op_registry executor ${GLOB_OP_LIB} ${GLOB_OPERATOR_DEPS} profiler)
 

paddle/fluid/framework/custom_operator.cc

@@ -25,6 +25,7 @@ limitations under the License. */
 #include <utility>
 #include <vector>
 
+#include "paddle/fluid/eager/api/utils/global_utils.h"
 #include "paddle/fluid/framework/attribute.h"
 #include "paddle/fluid/framework/convert_utils.h"
 #include "paddle/fluid/framework/op_meta_info_helper.h"
@@ -946,15 +947,16 @@ void RegisterOperatorWithMetaInfoMap(
 ////////////////////// User APIs ///////////////////////
 
 // load op api
-void LoadOpMetaInfoAndRegisterOp(const std::string& dso_name) {
+const std::unordered_map<std::string, std::vector<OpMetaInfo>>&
+LoadOpMetaInfoAndRegisterOp(const std::string& dso_name) {
   void* handle = paddle::platform::dynload::GetOpDsoHandle(dso_name);
   VLOG(3) << "load custom_op lib: " << dso_name;
   typedef OpMetaInfoMap& get_op_meta_info_map_t();
   auto* get_op_meta_info_map =
       detail::DynLoad<get_op_meta_info_map_t>(handle, "PD_GetOpMetaInfoMap");
   auto& op_meta_info_map = get_op_meta_info_map();
-
   RegisterOperatorWithMetaInfoMap(op_meta_info_map, handle);
+  return op_meta_info_map.GetMap();
 }
 
 }  // namespace framework

paddle/fluid/framework/custom_operator.h

@@ -20,9 +20,9 @@ limitations under the License. */
 
 namespace paddle {
 namespace framework {
-
 // Load custom op api: register op after user compiled
-void LoadOpMetaInfoAndRegisterOp(const std::string& dso_name);
+const std::unordered_map<std::string, std::vector<OpMetaInfo>>&
+LoadOpMetaInfoAndRegisterOp(const std::string& dso_name);
 
 // Register custom op api: register op directly
 void RegisterOperatorWithMetaInfoMap(
@@ -31,6 +31,5 @@ void RegisterOperatorWithMetaInfoMap(
 // Interface for selective register custom op.
 void RegisterOperatorWithMetaInfo(const std::vector<OpMetaInfo>& op_meta_infos,
                                   void* dso_handle = nullptr);
-
 }  // namespace framework
 }  // namespace paddle

paddle/fluid/framework/infershape_utils.cc

@@ -249,13 +249,13 @@ class CompatMetaTensor : public phi::MetaTensor {
   }
 
   void share_meta(const MetaTensor& meta_tensor) override {
+    share_dims(meta_tensor);
     set_dtype(meta_tensor.dtype());
     // VarDesc doesn't contains layout, so we cannot share layout
     // set_layout(meta_tensor.layout());
 
-    // special case 1: share lod of LoDTensor
+    // special case: share lod of LoDTensor
     share_lod(meta_tensor);
-    share_dims(meta_tensor);
   }
 
  private:
@@ -297,7 +297,8 @@ phi::InferMetaContext BuildInferMetaContext(InferShapeContext* ctx,
   VLOG(3) << "BuildInferMetaContext: op kernel signature - " << signature;
 
   // 2. build infermeta context
-  phi::InferMetaContext infer_meta_context(ctx->IsRuntime());
+  phi::InferMetaContext infer_meta_context(
+      {ctx->IsRuntime(), ctx->IsRunMKLDNNKernel()});
 
   auto& input_names = std::get<0>(signature.args);
   auto& attr_names = std::get<1>(signature.args);
@@ -499,8 +500,22 @@ phi::InferMetaContext BuildInferMetaContext(InferShapeContext* ctx,
             "Unsupported attribute type is received when call "
             "InferShapeFunctor."));
       }
-    } else {
-      // do nothing
+    } else if (ctx->HasInput(attr_name)) {
+      // convert from data
+      if (attr_defs[i].type_index == std::type_index(typeid(int32_t))) {
+        if (ctx->IsRuntime()) {
+          const auto& infershape_inputs = ctx->GetInputVarPtrs(attr_name);
+          auto var_temp = BOOST_GET_CONST(Variable*, infershape_inputs[i]);
+          auto val = experimental::MakePhiScalarFromVar(*var_temp);
+          int32_t val_int = val.template to<int32_t>();
+          infer_meta_context.EmplaceBackAttr(val_int);
+        } else {
+          infer_meta_context.EmplaceBackAttr(-1);
+        }
+      } else {
+        PADDLE_THROW(platform::errors::Unimplemented(
+            "Get value from variable only support int yet"));
+      }
     }
   }
 

paddle/fluid/framework/ir/CMakeLists.txt

@@ -126,6 +126,7 @@ if(WITH_MKLDNN)
     pass_library(interpolate_mkldnn_pass inference DIR mkldnn)
     pass_library(softplus_activation_mkldnn_fuse_pass inference DIR mkldnn)
     pass_library(fc_act_mkldnn_fuse_pass inference DIR mkldnn)
+    pass_library(elt_act_mkldnn_fuse_pass inference DIR mkldnn)
     pass_library(cpu_quantize_placement_pass base DIR mkldnn)
     pass_library(cpu_quantize_pass inference DIR mkldnn)
     pass_library(cpu_quantize_squash_pass inference DIR mkldnn)

paddle/fluid/framework/ir/graph_pattern_detector.cc

@@ -918,6 +918,36 @@ PDNode *patterns::ConvActivation::operator()(
   return activation_out_var;
 }
 
+PDNode *patterns::ElementwiseActivation::operator()(
+    paddle::framework::ir::PDNode *elementwise_a,
+    const std::string &elementwise_type, const std::string &activation_type) {
+  // Create Operators
+  elementwise_a->assert_is_op_input(elementwise_type, "X");
+  auto *elementwise_op =
+      pattern->NewNode(elementwise_repr())->assert_is_op(elementwise_type);
+  auto *activation_op =
+      pattern->NewNode(activation_repr())->assert_is_op(activation_type);
+  // Create variables
+  auto *elementwise_b = pattern->NewNode(elementwise_b_repr())
+                            ->AsInput()
+                            ->assert_is_op_input(elementwise_type, "Y");
+  // intermediate variable, will be removed in the IR after fuse.
+  auto *elementwise_out_var =
+      pattern->NewNode(elementwise_out_repr())
+          ->AsIntermediate()
+          ->assert_is_only_output_of_op(elementwise_type)
+          ->assert_is_op_input(activation_type);
+  // output
+  auto *activation_out_var = pattern->NewNode(activation_out_repr())
+                                 ->AsOutput()
+                                 ->assert_is_op_output(activation_type);
+
+  elementwise_op->LinksFrom({elementwise_a, elementwise_b})
+      .LinksTo({elementwise_out_var});
+  activation_op->LinksFrom({elementwise_out_var}).LinksTo({activation_out_var});
+  return activation_out_var;
+}
+
 PDNode *patterns::SeqConvEltAddRelu::operator()(
     paddle::framework::ir::PDNode *seqconv_input) {
   // Create Operators

paddle/fluid/framework/ir/graph_pattern_detector.h

@@ -487,6 +487,28 @@ struct ConvActivation : public PatternBase {
   PATTERN_DECL_NODE(activation_out);
 };
 
+// Elementwise with Activation
+// op: elementwise + activation
+// named nodes:
+// elementwise_a, elementwise_b,
+// elementwise_out, elementwise,
+// activation_out, activation
+struct ElementwiseActivation : public PatternBase {
+  ElementwiseActivation(PDPattern* pattern, const std::string& name_scope)
+      : PatternBase(pattern, name_scope, "elementwise_add_activation") {}
+
+  PDNode* operator()(PDNode* elementwise_a, const std::string& elementwise_type,
+                     const std::string& activation_type);
+
+  // declare operator node's name
+  PATTERN_DECL_NODE(elementwise);
+  PATTERN_DECL_NODE(activation);
+  // declare variable node's name
+  PATTERN_DECL_NODE(elementwise_b);
+  PATTERN_DECL_NODE(elementwise_out);
+  PATTERN_DECL_NODE(activation_out);
+};
+
 // SEQCONV with Elementwise_Add ReLU
 // op: seqconv + elementwise_add + relu
 // named nodes:

paddle/fluid/framework/ir/mkldnn/elt_act_mkldnn_fuse_pass.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/framework/ir/mkldnn/elt_act_mkldnn_fuse_pass.h"
+#include "paddle/fluid/framework/ir/graph_pattern_detector.h"
+#include "paddle/fluid/framework/op_version_registry.h"
+#include "paddle/fluid/platform/enforce.h"
+#include "paddle/fluid/string/pretty_log.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+using string::PrettyLogDetail;
+
+void ElementwiseActivationOneDNNPass::ApplyImpl(Graph *graph) const {
+  std::vector<std::string> act_types = {
+      "relu", "tanh", "leaky_relu", "swish", "hardswish", "sqrt",
+      "abs",  "clip", "gelu",       "relu6", "sigmoid"};
+  std::vector<std::string> elt_types = {"elementwise_add", "elementwise_sub",
+                                        "elementwise_mul"};
+
+  for (const auto &elt_type : elt_types)
+    for (const auto &act_type : act_types) {
+      std::unordered_map<std::string, std::string> attr_map;
+
+      if (act_type == "swish")
+        attr_map.emplace("beta", "activation_alpha");
+      else if (act_type == "relu6")
+        attr_map.emplace("threshold", "activation_alpha");
+      else if (act_type == "clip") {
+        attr_map.emplace("min", "activation_alpha");
+        attr_map.emplace("max", "activation_beta");
+      } else {
+        attr_map.emplace("alpha", "activation_alpha");
+        attr_map.emplace("beta", "activation_beta");
+      }
+      FuseElementwiseAct(graph, elt_type, act_type, attr_map);
+    }
+}
+
+void ElementwiseActivationOneDNNPass::FuseElementwiseAct(
+    Graph *graph, const std::string &elt_type, const std::string &act_type,
+    const std::unordered_map<std::string, std::string> &attr_map) const {
+  PADDLE_ENFORCE_NOT_NULL(
+      graph, platform::errors::InvalidArgument("Graph cannot be nullptr."));
+  FusePassBase::Init("elementwise_act", graph);
+
+  GraphPatternDetector gpd;
+  auto *elementwise_input = gpd.mutable_pattern()
+                                ->NewNode(elt_type + "_act/elementwise_input")
+                                ->AsInput()
+                                ->assert_is_op_input(elt_type, "X");
+  patterns::ElementwiseActivation elementwise_act_pattern(gpd.mutable_pattern(),
+                                                          elt_type + "_act");
+  elementwise_act_pattern(elementwise_input, elt_type, act_type);
+
+  int found_elementwise_activation_count = 0;
+  auto handler = [&](const GraphPatternDetector::subgraph_t &subgraph,
+                     Graph *g) {
+    VLOG(4) << "Fuse " << elt_type << " with activation op.";
+    // Elementwise output
+    GET_IR_NODE_FROM_SUBGRAPH(elementwise_out, elementwise_out,
+                              elementwise_act_pattern);
+    // ACT output
+    GET_IR_NODE_FROM_SUBGRAPH(activation_out, activation_out,
+                              elementwise_act_pattern);
+    // ops
+    GET_IR_NODE_FROM_SUBGRAPH(elementwise, elementwise,
+                              elementwise_act_pattern);
+    GET_IR_NODE_FROM_SUBGRAPH(activation, activation, elementwise_act_pattern);
+
+    auto *elementwise_op = elementwise->Op();
+
+    if (elementwise_op->HasAttr("use_mkldnn")) {
+      const std::string wo_elt_type =
+          "The " + elt_type;  // Workaround for PP error message checking.
+      PADDLE_ENFORCE_EQ(
+          BOOST_GET_CONST(bool, elementwise_op->GetAttr("use_mkldnn")), true,
+          platform::errors::PreconditionNotMet(
+              wo_elt_type + "+Act fusion may happen only when oneDNN library "
+                            "is used."));
+    }
+
+    auto *activation_op = activation->Op();
+    for (const auto &attr : attr_map) {
+      if (activation_op->HasAttr(attr.first)) {
+        elementwise_op->SetAttr(attr.second,
+                                activation_op->GetAttr(attr.first));
+      }
+    }
+
+    if (act_type == "gelu" && activation_op->HasAttr("approximate") &&
+        BOOST_GET_CONST(bool, activation_op->GetAttr("approximate")))
+      elementwise_op->SetAttr("activation_type", std::string("gelu_tanh"));
+    else
+      elementwise_op->SetAttr("activation_type", act_type);
+
+    elementwise_op->SetOutput("Out", {activation_out->Name()});
+
+    IR_OP_VAR_LINK(elementwise, activation_out);
+    GraphSafeRemoveNodes(g, {activation, elementwise_out});
+    found_elementwise_activation_count++;
+  };
+
+  gpd(graph, handler);
+  AddStatis(found_elementwise_activation_count);
+  PrettyLogDetail("---    fused %d %s with %s activation",
+                  found_elementwise_activation_count, elt_type, act_type);
+}
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle
+
+REGISTER_PASS(elt_act_mkldnn_fuse_pass,
+              paddle::framework::ir::ElementwiseActivationOneDNNPass);
+REGISTER_PASS_CAPABILITY(elt_act_mkldnn_fuse_pass)
+    .AddCombination(
+        paddle::framework::compatible::OpVersionComparatorCombination()
+            .LE("elementwise_add", 1)
+            .LE("elementwise_sub", 1)
+            .LE("elementwise_mul", 1)
+            .LE("relu", 0)
+            .LE("tanh", 0)
+            .LE("leaky_relu", 1)
+            .LE("swish", 0)
+            .LE("hard_swish", 0)
+            .LE("sqrt", 0)
+            .LE("abs", 0)
+            .LE("clip", 1)
+            .LE("gelu", 0)
+            .LE("relu6", 0)
+            .LE("sigmoid", 0));

paddle/fluid/framework/ir/mkldnn/elt_act_mkldnn_fuse_pass.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 <string>
+
+#include "paddle/fluid/framework/ir/fuse_pass_base.h"
+#include "paddle/fluid/framework/ir/graph.h"
+
+namespace paddle {
+namespace framework {
+namespace ir {
+
+/*
+ * \brief   Fuse the Elementwise and activation operators into single
+ * OneDNN's Elementwise with post-op.
+ */
+class ElementwiseActivationOneDNNPass : public FusePassBase {
+ public:
+  virtual ~ElementwiseActivationOneDNNPass() {}
+
+ protected:
+  void ApplyImpl(Graph *graph) const override;
+
+  void FuseElementwiseAct(
+      Graph *graph, const std::string &elt_types, const std::string &act_types,
+      const std::unordered_map<std::string, std::string> &attr_map) const;
+};
+
+}  // namespace ir
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass_tester.cc

@@ -25,11 +25,11 @@ USE_OP_ITSELF(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
 USE_OP_ITSELF(elementwise_add);
 USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN);
-USE_OP(leaky_relu);
+USE_OP_ITSELF(leaky_relu);
 USE_OP_DEVICE_KERNEL(leaky_relu, MKLDNN);
 USE_OP(gelu);
 USE_OP_ITSELF(relu);
-USE_OP(tanh);
+USE_OP_ITSELF(tanh);
 USE_OP_DEVICE_KERNEL(tanh, MKLDNN);
 
 namespace paddle {

paddle/fluid/framework/new_executor/standalone_executor_test.cc

@@ -32,7 +32,7 @@ USE_OP(concat);
 USE_OP(matmul);
 USE_OP_ITSELF(elementwise_add);
 USE_OP(sigmoid);
-USE_OP(tanh);
+USE_OP_ITSELF(tanh);
 USE_OP(elementwise_mul);
 USE_OP(softmax_with_cross_entropy);
 USE_OP_ITSELF(reduce_mean);
@@ -48,7 +48,7 @@ USE_OP(transpose2_grad);
 USE_OP(concat_grad);
 USE_OP_ITSELF(elementwise_mul_grad);
 USE_OP(sigmoid_grad);
-USE_OP(tanh_grad);
+USE_OP_ITSELF(tanh_grad);
 USE_OP(sum);
 USE_OP(slice_grad);
 USE_OP(lookup_table_grad);

paddle/fluid/framework/operator.cc

@@ -2250,41 +2250,62 @@ void OperatorWithKernel::BuildPhiKernelContext(
       }
     } else {
       // TODO(chenweihang): support other attrs later
-      auto& attr = Attrs().at(attr_names[i]);
+      auto attr_it = attrs_.find(attr_names[i]);
       if (attr_defs[i].type_index == std::type_index(typeid(int))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(int, attr));
+        if (attr_it == attrs_.end()) {
+          auto in_it = ctx.inputs.find(attr_names[i]);
+          if (in_it != ctx.inputs.end()) {
+            // get data from input
+            auto val = experimental::MakePhiScalarFromVar(*(in_it->second[0]));
+            int32_t val_int = val.template to<int32_t>();
+            pt_kernel_context->EmplaceBackAttr(val_int);
+          } else {
+            PADDLE_THROW(platform::errors::NotFound(
+                "can not find attribute `%s` both in attribute and input ",
+                attr_names[i]));
+          }
+        } else {
+          pt_kernel_context->EmplaceBackAttr(
+              BOOST_GET_CONST(int, attr_it->second));
+        }
       } else if (attr_defs[i].type_index == std::type_index(typeid(float))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(float, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(float, attr_it->second));
       } else if (attr_defs[i].type_index == std::type_index(typeid(bool))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(bool, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(bool, attr_it->second));
       } else if (attr_defs[i].type_index == std::type_index(typeid(int64_t))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(int64_t, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(int64_t, attr_it->second));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::string))) {
-        pt_kernel_context->EmplaceBackAttr(BOOST_GET_CONST(std::string, attr));
+        pt_kernel_context->EmplaceBackAttr(
+            BOOST_GET_CONST(std::string, attr_it->second));
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(phi::DataType))) {
         auto data_type = paddle::framework::TransToPhiDataType(
             static_cast<framework::proto::VarType::Type>(
-                BOOST_GET_CONST(int, attr)));
+                BOOST_GET_CONST(int, attr_it->second)));
         pt_kernel_context->EmplaceBackAttr(data_type);
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::vector<int64_t>))) {
-        if (std::type_index(attr.type()) ==
+        if (std::type_index(attr_it->second.type()) ==
             std::type_index(typeid(std::vector<int64_t>))) {
           pt_kernel_context->EmplaceBackAttr(
-              BOOST_GET_CONST(std::vector<int64_t>, attr));
-        } else if (std::type_index(attr.type()) ==
+              BOOST_GET_CONST(std::vector<int64_t>, attr_it->second));
+        } else if (std::type_index(attr_it->second.type()) ==
                    std::type_index(typeid(std::vector<int>))) {
           // Emplace Back Attr according to the type of Phi_Kernel args.
-          const auto& vector_int_attr = BOOST_GET_CONST(std::vector<int>, attr);
+          const auto& vector_int_attr =
+              BOOST_GET_CONST(std::vector<int>, attr_it->second);
           const std::vector<int64_t> vector_int64_attr(vector_int_attr.begin(),
                                                        vector_int_attr.end());
           pt_kernel_context->EmplaceBackAttr(vector_int64_attr);
         }
       } else if (attr_defs[i].type_index ==
                  std::type_index(typeid(std::vector<int32_t>))) {
-        const auto& vector_int_attr = BOOST_GET_CONST(std::vector<int>, attr);
+        const auto& vector_int_attr =
+            BOOST_GET_CONST(std::vector<int>, attr_it->second);
         pt_kernel_context->EmplaceBackAttr(vector_int_attr);
       } else {
         PADDLE_THROW(platform::errors::Unimplemented(

paddle/fluid/framework/paddle2cinn/cinn_lib_test.cc

@@ -45,8 +45,8 @@ Program CreateAddProgram() {
   NetBuilder builder("net_builder");
   auto a = builder.CreateInput(Float(32), {M, N});
   auto b = builder.CreateInput(Float(32), {M, N});
-  auto c = builder.add(a, b);
-  auto d = builder.add(a, c);
+  auto c = builder.Add(a, b);
+  auto d = builder.Add(a, c);
   auto program = builder.Build();
 
   return program;
@@ -116,8 +116,8 @@ TEST(net_build, program_execute_fc) {
   auto w = builder.CreateInput(Float(32), {N, K}, "W");  // weight
   auto b = builder.CreateInput(Float(32), {N}, "B");     // bias
 
-  auto mul_out = builder.mul(a, w, 2, 1);
-  auto add_out = builder.add(mul_out, b);
+  auto mul_out = builder.Mul(a, w, 2, 1);
+  auto add_out = builder.Add(mul_out, b);
   auto program = builder.Build();
 
 #ifdef PADDLE_WITH_CUDA

paddle/fluid/imperative/basic_engine.cc

@@ -389,6 +389,9 @@ static void PerformBackwardInplace(const std::string& op_type,
 }
 
 void BasicEngine::Execute() {
+  platform::RecordEvent backward_record_event(
+      "backward", platform::TracerEventType::Operator, 1);
+
   if (init_nodes_.empty()) {
     return;
   }
@@ -412,7 +415,7 @@ void BasicEngine::Execute() {
 
     for (auto& cur_op : *shared_cur_node) {
       platform::RecordEvent op_type_record_event(
-          cur_op.Type(), platform::TracerEventType::Operator, 1);
+          cur_op.Type() + " grad_node", platform::TracerEventType::Operator, 1);
 
       ++op_num;
 

paddle/fluid/imperative/prepared_operator.h

@@ -264,14 +264,23 @@ void BuildDygraphPhiKernelContext(
 
     size_t start_idx = (i == 0 ? 0 : kernel_ctx->InputRangeAt(i - 1).second);
 
-    if ((it == ins.end()) &&
-        (input_defs[i].type_index ==
-         std::type_index(typeid(paddle::optional<const phi::DenseTensor&>)))) {
-      kernel_ctx->EmplaceBackInputWithoutSetRange(nullptr);
-      auto end_idx = start_idx + 1;
-      kernel_ctx->AssignInputRange(std::make_pair(start_idx, end_idx), i);
-      continue;
+    if (it == ins.end()) {
+      if (LIKELY(input_defs[i].type_index ==
+                 std::type_index(
+                     typeid(paddle::optional<const phi::DenseTensor&>)))) {
+        kernel_ctx->EmplaceBackInputWithoutSetRange(nullptr);
+        auto end_idx = start_idx + 1;
+        kernel_ctx->AssignInputRange(std::make_pair(start_idx, end_idx), i);
+        continue;
+      } else {
+        PADDLE_THROW(phi::errors::NotFound(
+            "Can not find input variable '%s' for %s OP, please check whether "
+            "the name setting in OpArgumentMapping is consistent with that in "
+            "OpMaker.",
+            input_names[i], pt_kernel_signature.name));
+      }
     }
+
     auto ins_vector = it->second;
     size_t end_idx = start_idx + ins_vector.size();
 
@@ -410,6 +419,17 @@ void BuildDygraphPhiKernelContext(
             experimental::MakePhiScalarFromVar(ins_vector[0]->Var())));
       }
 
+    } else if (ins.find(attr_names[i]) != ins.end()) {
+      // deal tensor attr here
+      auto& ins_vector = ins.at(attr_names[i]);
+      auto tensor_attr =
+          experimental::MakePhiScalarFromVar(ins_vector[0]->Var());
+      if (attr_defs[i].type_index == std::type_index(typeid(int))) {
+        int val = tensor_attr.template to<int>();
+        kernel_ctx->EmplaceBackAttr(val);
+      } else {
+        PADDLE_THROW(platform::errors::Unimplemented("only support int here"));
+      }
     } else if (attr_defs[i].type_index ==
                std::type_index(typeid(std::vector<phi::Scalar>))) {
       auto& attr = GetAttr(attrs, default_attrs, attr_names[i]);
@@ -466,6 +486,7 @@ void BuildDygraphPhiKernelContext(
       }
     } else {
       // TODO(chenweihang): support other attrs later
+
       auto& attr = GetAttr(attrs, default_attrs, attr_names[i]);
       if (attr_defs[i].type_index == std::type_index(typeid(int))) {
         kernel_ctx->EmplaceBackAttr(BOOST_GET_CONST(int, attr));

paddle/fluid/imperative/tests/test_tracer.cc

@@ -34,6 +34,7 @@ PD_DECLARE_KERNEL(add, CPU, ALL_LAYOUT);
 PD_DECLARE_KERNEL(add_grad, CPU, ALL_LAYOUT);
 #if defined(PADDLE_WITH_CUDA) || defined(PADDLE_WITH_HIP)
 PD_DECLARE_KERNEL(add_grad, GPU, ALL_LAYOUT);
+PD_DECLARE_KERNEL(sum_grad, GPU, ALL_LAYOUT);
 #endif
 
 namespace imperative = paddle::imperative;

paddle/fluid/imperative/tracer.cc

@@ -177,7 +177,7 @@ void Tracer::TraceOp(const std::string& type, const NameVarMap<VarType>& ins,
                      paddle::framework::AttributeMap* passed_default_attrs_,
                      bool use_default_attr_map) {
   platform::RecordEvent op_type_record_event(
-      type, platform::TracerEventType::Operator, 1);
+      type + " trace_op", platform::TracerEventType::Operator, 1);
   platform::ScopedFlushDenormal flush;
   VLOG(1) << "Trace Op: " << type;
   if (FLAGS_use_mkldnn) {
@@ -297,19 +297,24 @@ void Tracer::TraceOp(const std::string& type, const NameVarMap<VarType>& ins,
     program_desc_tracer_->InsertOp(type, new_ins, outs, attrs);
   }
 
-  if (ComputeRequiredGrad(new_ins, outs, trace_backward)) {
-    PADDLE_ENFORCE_EQ(
-        passed_default_attrs_, nullptr,
-        paddle::platform::errors::PermissionDenied(
-            "We expect passed_default_attrs_ is nullptr while "
-            "use_default_attr_map is true, however we got not null "
-            "passed_default_attrs_. Please check your usage of trace_op. "));
-    CreateGradOpNode(*op, new_ins, outs, attrs, default_attrs, place,
-                     inplace_map);
-  } else {
-    VLOG(3) << "No Grad to track for Op: " << type;
+  {
+    platform::RecordEvent node_creation_record_event(
+        type + " node_creation", platform::TracerEventType::Operator, 1);
+
+    if (ComputeRequiredGrad(new_ins, outs, trace_backward)) {
+      PADDLE_ENFORCE_EQ(
+          passed_default_attrs_, nullptr,
+          paddle::platform::errors::PermissionDenied(
+              "We expect passed_default_attrs_ is nullptr while "
+              "use_default_attr_map is true, however we got not null "
+              "passed_default_attrs_. Please check your usage of trace_op. "));
+      CreateGradOpNode(*op, new_ins, outs, attrs, default_attrs, place,
+                       inplace_map);
+    } else {
+      VLOG(3) << "No Grad to track for Op: " << type;
+    }
+    VLOG(6) << "Finish Trace Op: " << type;
   }
-  VLOG(6) << "Finish Trace Op: " << type;
 }
 
 template void Tracer::TraceOp<VarBase>(

paddle/fluid/inference/api/paddle_pass_builder.cc

@@ -262,6 +262,7 @@ void CpuPassStrategy::EnableMKLDNN() {
              //  "fc_act_mkldnn_fuse_pass",
              "batch_norm_act_fuse_pass",              //
              "softplus_activation_mkldnn_fuse_pass",  //
+             "elt_act_mkldnn_fuse_pass",              //
              // TODO(intel): Please fix the bug on windows.
              // https://github.com/PaddlePaddle/Paddle/issues/29710
              // "mkldnn_inplace_pass",  // This pass should be activated after

paddle/fluid/inference/tensorrt/convert/pool2d_op.cc

@@ -328,5 +328,5 @@ class Pool2dOpConverter : public OpConverter {
 }  // namespace inference
 }  // namespace paddle
 
-USE_OP(pool2d);
+USE_OP_ITSELF(pool2d);
 REGISTER_TRT_OP_CONVERTER(pool2d, Pool2dOpConverter);

paddle/fluid/inference/tensorrt/convert/pool3d_op.cc

@@ -224,5 +224,5 @@ class Pool3dOpConverter : public OpConverter {
 }  // namespace inference
 }  // namespace paddle
 
-USE_OP(pool3d);
+USE_OP_ITSELF(pool3d);
 REGISTER_TRT_OP_CONVERTER(pool3d, Pool3dOpConverter);

paddle/fluid/inference/tensorrt/convert/test_activation_op.cc

@@ -54,5 +54,5 @@ TEST(Relu6OpConverter, main) { test_activation("relu6"); }
 
 USE_OP_ITSELF(relu);
 USE_OP(sigmoid);
-USE_OP(tanh);
+USE_OP_ITSELF(tanh);
 USE_OP(relu6);

paddle/fluid/inference/tensorrt/convert/test_leaky_relu_op.cc

@@ -45,4 +45,4 @@ TEST(leaky_relu_op, test_leaky_relu) {
 }  // namespace paddle
 
 // USE_OP(leaky_relu);
-USE_OP(leaky_relu);
+USE_OP_ITSELF(leaky_relu);

paddle/fluid/inference/tensorrt/convert/test_pool2d_op.cc

@@ -71,4 +71,4 @@ TEST(Pool2dOpConverter, avg_ceil_test) { test_pool2d(false, true, "avg"); }
 }  // namespace inference
 }  // namespace paddle
 
-USE_OP(pool2d);
+USE_OP_ITSELF(pool2d);

paddle/fluid/inference/tensorrt/plugin/pool3d_op_plugin.cu

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/tensorrt/plugin/pool3d_op_plugin.h"
-#include "paddle/fluid/operators/math/pooling.h"
+#include "paddle/phi/kernels/funcs/pooling.h"
 
 namespace paddle {
 namespace inference {
@@ -108,16 +108,14 @@ int Pool3DPlugin::enqueue(int batchSize, const void *const *inputs,
   output_shape.insert(output_shape.begin(), batchSize);
 
   if (pool3d_type_ == Pool3DType::max) {
-    paddle::operators::math::MaxPool<float> pool_process;
-    paddle::operators::math::Pool3dDirectCUDAFunctor<
-        paddle::operators::math::MaxPool<float>, float>
+    phi::funcs::MaxPool<float> pool_process;
+    phi::funcs::Pool3dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
         pool3d_forward;
     pool3d_forward(idata, input_shape, output_shape, ksize_, strides_,
                    paddings_, true, adaptive_, odatas[0], stream, pool_process);
   } else if (pool3d_type_ == Pool3DType::avg) {
-    paddle::operators::math::AvgPool<float> pool_process;
-    paddle::operators::math::Pool3dDirectCUDAFunctor<
-        paddle::operators::math::AvgPool<float>, float>
+    phi::funcs::AvgPool<float> pool_process;
+    phi::funcs::Pool3dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
         pool3d_forward;
     pool3d_forward(idata, input_shape, output_shape, ksize_, strides_,
                    paddings_, true, adaptive_, odatas[0], stream, pool_process);
@@ -351,16 +349,14 @@ int Pool3DPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
   }
 
   if (pool3d_type_ == "max") {
-    paddle::operators::math::MaxPool<float> pool_process;
-    paddle::operators::math::Pool3dDirectCUDAFunctor<
-        paddle::operators::math::MaxPool<float>, float>
+    phi::funcs::MaxPool<float> pool_process;
+    phi::funcs::Pool3dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
         pool3d_forward;
     pool3d_forward(input, input_shape, output_shape, ksize, strides_, paddings,
                    true, adaptive_, output, stream, pool_process);
   } else if (pool3d_type_ == "avg") {
-    paddle::operators::math::AvgPool<float> pool_process;
-    paddle::operators::math::Pool3dDirectCUDAFunctor<
-        paddle::operators::math::AvgPool<float>, float>
+    phi::funcs::AvgPool<float> pool_process;
+    phi::funcs::Pool3dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
         pool3d_forward;
     pool3d_forward(input, input_shape, output_shape, ksize, strides_, paddings,
                    true, adaptive_, output, stream, pool_process);

paddle/fluid/inference/tensorrt/plugin/pool_op_plugin.cu

@@ -13,7 +13,7 @@
 // limitations under the License.
 
 #include "paddle/fluid/inference/tensorrt/plugin/pool_op_plugin.h"
-#include "paddle/fluid/operators/math/pooling.h"
+#include "paddle/phi/kernels/funcs/pooling.h"
 
 namespace paddle {
 namespace inference {
@@ -84,16 +84,14 @@ int PoolPlugin::enqueue(int batchSize, const void *const *inputs,
   output_shape.insert(output_shape.begin(), batchSize);
 
   if (pool_type_ == PoolType::max) {
-    paddle::operators::math::MaxPool<float> pool_process;
-    paddle::operators::math::Pool2dDirectCUDAFunctor<
-        paddle::operators::math::MaxPool<float>, float>
+    phi::funcs::MaxPool<float> pool_process;
+    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
         pool2d_forward;
     pool2d_forward(idata, input_shape, output_shape, ksize_, strides_,
                    paddings_, true, false, odatas[0], stream, pool_process);
   } else if (pool_type_ == PoolType::avg) {
-    paddle::operators::math::AvgPool<float> pool_process;
-    paddle::operators::math::Pool2dDirectCUDAFunctor<
-        paddle::operators::math::AvgPool<float>, float>
+    phi::funcs::AvgPool<float> pool_process;
+    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
         pool2d_forward;
     pool2d_forward(idata, input_shape, output_shape, ksize_, strides_,
                    paddings_, exclusive_, adaptive_, odatas[0], stream,
@@ -292,16 +290,14 @@ int PoolPluginDynamic::enqueue(const nvinfer1::PluginTensorDesc *input_desc,
   }
 
   if (pool_type_ == "max") {
-    paddle::operators::math::MaxPool<float> pool_process;
-    paddle::operators::math::Pool2dDirectCUDAFunctor<
-        paddle::operators::math::MaxPool<float>, float>
+    phi::funcs::MaxPool<float> pool_process;
+    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::MaxPool<float>, float>
         pool2d_forward;
     pool2d_forward(input, input_shape, output_shape, ksize, strides_, paddings,
                    true, false, output, stream, pool_process);
   } else if (pool_type_ == "avg") {
-    paddle::operators::math::AvgPool<float> pool_process;
-    paddle::operators::math::Pool2dDirectCUDAFunctor<
-        paddle::operators::math::AvgPool<float>, float>
+    phi::funcs::AvgPool<float> pool_process;
+    phi::funcs::Pool2dDirectCUDAFunctor<phi::funcs::AvgPool<float>, float>
         pool2d_forward;
     pool2d_forward(input, input_shape, output_shape, ksize, strides_, paddings,
                    exclusive_, adaptive_, output, stream, pool_process);

paddle/fluid/memory/allocation/CMakeLists.txt

@@ -131,4 +131,7 @@ cc_library(virtual_memory_auto_growth_best_fit_allocator SRCS virtual_memory_aut
 if(NOT WIN32)
   cc_library(mmap_allocator SRCS mmap_allocator.cc DEPS allocator)
   cc_test(mmap_allocator_test SRCS mmap_allocator_test.cc DEPS mmap_allocator allocator)
+  if (WITH_GPU)
+    cc_library(cuda_ipc_allocator SRCS cuda_ipc_allocator.cc DEPS allocator)
+  endif()
 endif(NOT WIN32)

paddle/fluid/memory/allocation/cuda_ipc_allocator.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.
+
+#ifndef _WIN32
+
+#include "paddle/fluid/memory/allocation/cuda_ipc_allocator.h"
+#include "paddle/fluid/platform/cuda_device_guard.h"
+
+#include <fcntl.h>
+#include <stdlib.h>
+#include <sys/mman.h>
+#include <random>
+#include <string>
+
+#include "glog/logging.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace memory {
+namespace allocation {
+
+namespace {
+std::mutex ipc_mutex_;
+std::unordered_map<std::string, std::weak_ptr<void>> ipc_handle_to_baseptr_;
+}  // namespace
+
+std::shared_ptr<void> GetIpcBasePtr(std::string handle) {
+  std::lock_guard<std::mutex> lock(ipc_mutex_);
+
+  auto iter = ipc_handle_to_baseptr_.find(handle);
+  if (iter != ipc_handle_to_baseptr_.end()) {
+    auto baseptr = iter->second.lock();
+    if (baseptr) return baseptr;
+  }
+  // The IpcMemHandle can only open once for the same handle,
+  // so here we cache it here.
+  void *baseptr = nullptr;
+  auto ipc_handle =
+      reinterpret_cast<const cudaIpcMemHandle_t *>(handle.c_str());
+  PADDLE_ENFORCE_GPU_SUCCESS(cudaIpcOpenMemHandle(
+      &baseptr, *ipc_handle, cudaIpcMemLazyEnablePeerAccess));
+  // Close ipc handle on the same device.
+  int device_id = platform::GetCurrentDeviceId();
+  // Add deleter to close ipc handle.
+  auto sp = std::shared_ptr<void>(baseptr, [handle, device_id](void *ptr) {
+    platform::CUDADeviceGuard guard(device_id);
+    std::lock_guard<std::mutex> lock(ipc_mutex_);
+    PADDLE_ENFORCE_GPU_SUCCESS(cudaIpcCloseMemHandle(ptr));
+    ipc_handle_to_baseptr_.erase(handle);
+    VLOG(6) << "cudaIpcCloseMemHandle for ptr:"
+            << "\t" << ptr;
+  });
+  std::weak_ptr<void> wp = sp;
+  ipc_handle_to_baseptr_.insert(iter, {handle, wp});
+
+  return sp;
+}
+
+CudaIpcAllocation::~CudaIpcAllocation() {
+  shared_ptr_.reset();
+  VLOG(6) << "tensor deleted cudaIpcCloseMemHandle for ptr:"
+          << "\t" << this->ptr();
+}
+
+}  // namespace allocation
+}  // namespace memory
+}  // namespace paddle
+
+#endif

paddle/infrt/dialect/pd_types.h → paddle/fluid/memory/allocation/cuda_ipc_allocator.h

-// Copyright (c) 2021 PaddlePaddle Authors. All Rights Reserved.
+// 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.
@@ -12,45 +12,45 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-// This file defines the types used in PaddlePaddle MLIR dialect.
-// We borrowed much ideas from tensorflow mlir dialect (tf_types.h in
-// tensorflow).
-
+#ifndef _WIN32
 #pragma once
 
-#include <mlir/IR/Diagnostics.h>
-#include <mlir/IR/Location.h>
-#include <mlir/IR/Operation.h>
-#include <mlir/IR/TypeUtilities.h>
-#include <mlir/IR/Types.h>
+#include <memory>
+#include <mutex>  // NOLINT
+#include <string>
+#include <unordered_set>
+#include <utility>
+
+#include "paddle/fluid/memory/allocation/allocator.h"
+#include "paddle/fluid/platform/cuda_device_guard.h"
+#include "paddle/fluid/platform/device/gpu/gpu_info.h"
+#include "paddle/fluid/platform/enforce.h"
+
+namespace paddle {
+namespace memory {
+namespace allocation {
 
-namespace mlir {
-namespace PD {
+std::shared_ptr<void> GetIpcBasePtr(std::string handle);
 
-class PaddleType : public Type {
+class CudaIpcAllocation : public Allocation {
  public:
-  using Type::Type;
+  explicit CudaIpcAllocation(void *ptr, size_t size, int device_id,
+                             std::shared_ptr<void> shared_ptr)
+      : Allocation(ptr, size, platform::CUDAPlace(device_id)),
+        device_id_(std::move(device_id)),
+        shared_ptr_(std::move(shared_ptr)) {}
 
-  static bool classof(Type type);
-};
+  inline const int &device_id() const { return device_id_; }
 
-namespace detail {
+  ~CudaIpcAllocation() override;
 
-template <typename Derived>
-class PaddleTypeImpl : public Type::TypeBase<Derived, PaddleType, TypeStorage> {
- public:
-  using Base = typename Type::TypeBase<Derived, PaddleType, TypeStorage>;
-  using PDBase = PaddleTypeImpl<Derived>;
-  using Base::Base;
+ private:
+  int device_id_;
+  std::shared_ptr<void> shared_ptr_;
 };
 
-}  // namespace detail
-
-#define HANDLE_PD_TYPE(pdtype, enumerant, name)                      \
-  class pdtype##Type : public detail::PaddleTypeImpl<pdtype##Type> { \
-   public:                                                           \
-    using PDBase::PDBase;                                            \
-  };
+}  // namespace allocation
+}  // namespace memory
+}  // namespace paddle
 
-}  // namespace PD
-}  // namespace mlir
+#endif

paddle/fluid/memory/allocation/mmap_allocator.cc

@@ -29,6 +29,155 @@ namespace paddle {
 namespace memory {
 namespace allocation {
 
+std::string GetIPCName() {
+  static std::random_device rd;
+  std::string handle = "/paddle_";
+#ifdef _WIN32
+  handle += std::to_string(GetCurrentProcessId());
+#else
+  handle += std::to_string(getpid());
+#endif
+  handle += "_";
+  handle += std::to_string(rd());
+  return handle;
+}
+
+struct CountInfo {
+  std::atomic<int> refcount;
+};
+
+void AllocateMemoryMap(std::string filename, int flags, size_t size,
+                       void **map_ptr_, int *fd_) {
+  // TODO(@ZHUI): support win32
+  int file_flags = 0;
+  int fd = -1;
+  if (flags & MAPPED_SHAREDMEM) {
+    file_flags = O_RDWR | O_CREAT;
+  } else {
+    file_flags = O_RDONLY;
+  }
+  if (flags & MAPPED_EXCLUSIVE) {
+    file_flags |= O_EXCL;
+  }
+  if (flags & MAPPED_NOCREATE) {
+    file_flags &= ~O_CREAT;
+  }
+
+  if (!(flags & MAPPED_FROMFD)) {
+    if (flags & MAPPED_SHAREDMEM) {
+      fd = shm_open(filename.c_str(), file_flags, (mode_t)0600);
+      PADDLE_ENFORCE_NE(
+          fd, -1,
+          platform::errors::Unavailable(
+              "File descriptor %s open failed, unable in read-write mode",
+              filename.c_str()));
+      VLOG(6) << "shm_open: " << filename;
+    }
+  } else {
+    fd = -1;
+  }
+
+  PADDLE_ENFORCE_EQ(ftruncate(fd, size), 0,
+                    platform::errors::Unavailable(
+                        "Fruncate a file to a specified length failed!"));
+
+  if (flags & MAPPED_SHAREDMEM) {
+    *map_ptr_ = mmap(nullptr, size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
+  } else {
+    *map_ptr_ = mmap(nullptr, size, PROT_READ | PROT_WRITE, MAP_PRIVATE, fd, 0);
+  }
+
+  PADDLE_ENFORCE_NE(*map_ptr_, MAP_FAILED,
+                    platform::errors::Unavailable(
+                        "Memory map failed when create shared memory."));
+
+  if (flags & MAPPED_KEEPFD) {
+    *fd_ = fd;
+  } else {
+    PADDLE_ENFORCE_NE(::close(fd), -1,
+                      platform::errors::Unavailable(
+                          "Error closing memory maped file <", filename, ">"));
+
+    *fd_ = -1;
+  }
+}
+
+std::shared_ptr<RefcountedMemoryMapAllocation>
+AllocateRefcountedMemoryMapAllocation(std::string filename, int flags,
+                                      size_t size) {
+  int fd = -1;
+  void *base_ptr = nullptr;
+  AllocateMemoryMap(filename, flags, size + mmap_alignment, &base_ptr, &fd);
+  void *aliged_base_ptr =
+      static_cast<void *>(static_cast<char *>(base_ptr) + mmap_alignment);
+  return std::make_shared<RefcountedMemoryMapAllocation>(aliged_base_ptr, size,
+                                                         filename, flags, fd);
+}
+
+RefcountedMemoryMapAllocation::RefcountedMemoryMapAllocation(
+    void *ptr, size_t size, std::string ipc_name, int fd, int flags)
+    : MemoryMapAllocation(ptr, size, ipc_name, fd, flags) {
+  // must reset base ptr first.
+  resetBaseptr();
+  initializeRefercount();
+}
+
+void MemoryMapAllocation::close() {
+  if (closed_) {
+    return;
+  }
+  closed_ = true;
+}
+
+MemoryMapAllocation::~MemoryMapAllocation() { close(); }
+
+void RefcountedMemoryMapAllocation::incref() {
+  CountInfo *info = static_cast<CountInfo *>(map_ptr_);
+  ++info->refcount;
+}
+
+int RefcountedMemoryMapAllocation::decref() {
+  CountInfo *info = static_cast<CountInfo *>(map_ptr_);
+  return --info->refcount == 0;
+}
+
+void RefcountedMemoryMapAllocation::resetBaseptr() {
+  map_ptr_ =
+      static_cast<void *>(static_cast<char *>(map_ptr_) - mmap_alignment);
+  map_size_ = map_size_ + mmap_alignment;
+}
+
+void RefcountedMemoryMapAllocation::initializeRefercount() {
+  CountInfo *info = reinterpret_cast<CountInfo *>(map_ptr_);
+
+  if (flags_ & MAPPED_EXCLUSIVE) {
+    new (&info->refcount) std::atomic<int>(1);
+  } else {
+    info->refcount++;
+  }
+}
+
+void RefcountedMemoryMapAllocation::close() {
+  if (closed_) {
+    return;
+  }
+  closed_ = true;
+  void *data = map_ptr_;
+  CountInfo *info = reinterpret_cast<CountInfo *>(data);
+  if (--info->refcount == 0) {
+    PADDLE_ENFORCE_NE(
+        shm_unlink(ipc_name_.c_str()), -1,
+        platform::errors::Unavailable(
+            "could not unlink the shared memory file ", ipc_name_));
+    VLOG(6) << "shm_unlink file: " << ipc_name_;
+  }
+
+  PADDLE_ENFORCE_NE(
+      munmap(map_ptr_, map_size_), -1,
+      platform::errors::Unavailable("could not unmap the shared memory file: ",
+                                    strerror(errno), " (", errno, ")"));
+}
+
 MemoryMapWriterAllocation::~MemoryMapWriterAllocation() {
   PADDLE_ENFORCE_NE(
       munmap(this->ptr(), this->size()), -1,
@@ -44,30 +193,30 @@ MemoryMapReaderAllocation::~MemoryMapReaderAllocation() {
   /* Here we do not pay attention to the result of shm_unlink,
      because the memory mapped file may have been cleared due to the
      MemoryMapFdSet::Clear() */
+
+  // Code of DataLoader subprocess:
+  //
+  //    core._array_to_share_memory_tensor(b)
+  //    out_queue.put((idx, tensor_list, structure))
+  //    core._remove_tensor_list_mmap_fds(tensor_list)
+
+  /* If the tensor in already in the send queue, the tensor will be
+   * deconstructed by the function. If the tensor not send yet, it
+   * will be cleared by MemoryMapFdSet::Clear().
+   * If the `_remove_tensor_list_mmap_fds` have be interrupted, the
+   * tensor will be cleared by both methods.
+   * */
+
   shm_unlink(this->ipc_name().c_str());
   MemoryMapFdSet::Instance().Remove(this->ipc_name());
   VLOG(3) << "~MemoryMapReaderAllocation: " << this->ipc_name();
 }
 
-std::string GetIPCName() {
-  static std::random_device rd;
-  std::string handle = "/paddle_";
-#ifdef _WIN32
-  handle += std::to_string(GetCurrentProcessId());
-#else
-  handle += std::to_string(getpid());
-#endif
-  handle += "_";
-  handle += std::to_string(rd());
-  return handle;
-}
-
 std::shared_ptr<MemoryMapWriterAllocation> AllocateMemoryMapWriterAllocation(
     size_t size) {
   const std::string &ipc_name = GetIPCName();
   int flags = O_RDWR | O_CREAT;
-
-  int fd = shm_open(ipc_name.c_str(), flags, 0644);
+  int fd = shm_open(ipc_name.c_str(), flags, 0600);
   PADDLE_ENFORCE_NE(
       fd, -1, platform::errors::Unavailable("File descriptor %s open failed",
                                             ipc_name.c_str()));
@@ -86,12 +235,14 @@ std::shared_ptr<MemoryMapWriterAllocation> AllocateMemoryMapWriterAllocation(
 
 std::shared_ptr<MemoryMapReaderAllocation> RebuildMemoryMapReaderAllocation(
     const std::string &ipc_name, size_t size) {
-  int fd = shm_open(ipc_name.c_str(), O_RDONLY, 0644);
+  int flags = O_RDWR | O_CREAT;
+  flags &= ~O_CREAT;
+
+  int fd = shm_open(ipc_name.c_str(), flags, 0600);
   PADDLE_ENFORCE_NE(
       fd, -1, platform::errors::Unavailable("File descriptor %s open failed",
                                             ipc_name.c_str()));
-
-  void *ptr = mmap(NULL, size, PROT_READ, MAP_SHARED, fd, 0);
+  void *ptr = mmap(nullptr, size, PROT_READ | PROT_WRITE, MAP_SHARED, fd, 0);
   PADDLE_ENFORCE_NE(ptr, MAP_FAILED,
                     platform::errors::Unavailable(
                         "Memory map failed when rebuild shared memory."));

paddle/fluid/memory/allocation/mmap_allocator.h

@@ -16,8 +16,9 @@
 
 #ifndef _WIN32
 
+#include <atomic>
 #include <memory>
-#include <mutex>  // NOLINT
+#include <mutex>
 #include <string>
 #include <unordered_set>
 #include <utility>
@@ -28,6 +29,72 @@ namespace paddle {
 namespace memory {
 namespace allocation {
 
+std::string GetIPCName();
+
+static constexpr int64_t mmap_alignment = 64;
+
+enum MappedModes {
+  MAPPED_SHAREDMEM = 1,
+  MAPPED_EXCLUSIVE = 2,
+  MAPPED_NOCREATE = 4,
+  MAPPED_KEEPFD = 8,
+  MAPPED_FROMFD = 16,
+  MAPPED_UNLINK = 32
+};
+
+class MemoryMapAllocation : public Allocation {
+ public:
+  explicit MemoryMapAllocation(void *ptr, size_t size, std::string ipc_name)
+      : Allocation(ptr, size, platform::CPUPlace()),
+        ipc_name_(std::move(ipc_name)),
+        map_ptr_(ptr),
+        map_size_(size) {}
+  explicit MemoryMapAllocation(void *ptr, size_t size, std::string ipc_name,
+                               int flags, int fd)
+      : Allocation(ptr, size, platform::CPUPlace()),
+        ipc_name_(std::move(ipc_name)),
+        fd_(fd),
+        flags_(flags),
+        map_ptr_(ptr),
+        map_size_(size) {}
+
+  inline const std::string &ipc_name() const { return ipc_name_; }
+
+  virtual void close();
+
+  ~MemoryMapAllocation() override;
+
+ protected:
+  std::string ipc_name_;
+  int fd_ = -1;
+  int flags_ = 0;
+  void *map_ptr_ = nullptr;
+  size_t map_size_ = 0;
+  bool closed_ = false;
+};
+
+class RefcountedMemoryMapAllocation : public MemoryMapAllocation {
+ public:
+  RefcountedMemoryMapAllocation(void *ptr, size_t size, std::string ipc_name,
+                                int flags, int fd);
+
+  void incref();
+  int decref();
+  void close() override;
+  virtual ~RefcountedMemoryMapAllocation() { close(); }
+
+ protected:
+  void initializeRefercount();
+  void resetBaseptr();
+};
+
+void AllocateMemoryMap(std::string filename, int flags, size_t size,
+                       void **base_ptr_, int *fd_);
+
+std::shared_ptr<RefcountedMemoryMapAllocation>
+AllocateRefcountedMemoryMapAllocation(std::string filename, int flags,
+                                      size_t size);
+
 class MemoryMapWriterAllocation : public Allocation {
  public:
   explicit MemoryMapWriterAllocation(void *ptr, size_t size,

paddle/fluid/operators/activation_op.cc

@@ -1482,6 +1482,9 @@ REGISTER_ACTIVATION_OP(cosh, Cosh, CoshFunctor, CoshGradFunctor);
 REGISTER_ACTIVATION_OP(asinh, Asinh, AsinhFunctor, AsinhGradFunctor);
 REGISTER_ACTIVATION_OP(acosh, Acosh, AcoshFunctor, AcoshGradFunctor);
 REGISTER_ACTIVATION_OP(atanh, Atanh, AtanhFunctor, AtanhGradFunctor);
+REGISTER_ACTIVATION_OP(brelu, BRelu, BReluFunctor, BReluGradFunctor);
+REGISTER_ACTIVATION_OP(thresholded_relu, ThresholdedRelu,
+                       ThresholdedReluFunctor, ThresholdedReluGradFunctor);
 
 /* ==========================    sigmoid register  =============================
  */
@@ -1567,23 +1570,6 @@ REGISTER_OPERATOR(
     ops::ActivationOpTripleGrad<ops::TanhTripleGradFunctor<float>::FwdDeps()>,
     ops::ActivationTripleGradOpInplaceInferer);
 
-REGISTER_ACTIVATION_CPU_KERNEL(tanh, Tanh, TanhFunctor, TanhGradFunctor);
-REGISTER_OP_CPU_KERNEL(
-    tanh_grad_grad, ops::TanhDoubleGradKernel<plat::CPUDeviceContext,
-                                              ops::TanhGradGradFunctor<float>>,
-    ops::TanhDoubleGradKernel<plat::CPUDeviceContext,
-                              ops::TanhGradGradFunctor<double>>,
-    ops::TanhDoubleGradKernel<plat::CPUDeviceContext,
-                              ops::TanhGradGradFunctor<plat::float16>>);
-// Register TripleGrad Kernel
-REGISTER_OP_CPU_KERNEL(
-    tanh_triple_grad,
-    ops::TanhTripeGradKernel<plat::CPUDeviceContext,
-                             ops::TanhTripleGradFunctor<float>>,
-    ops::TanhTripeGradKernel<plat::CPUDeviceContext,
-                             ops::TanhTripleGradFunctor<double>>,
-    ops::TanhTripeGradKernel<plat::CPUDeviceContext,
-                             ops::TanhTripleGradFunctor<plat::float16>>);
 /* ========================================================================== */
 
 /* ==========================    relu register  ============================= */
@@ -1623,16 +1609,6 @@ REGISTER_OPERATOR(
     ops::ActivationOpDoubleGrad2<ops::LeakyReluGradFunctor<float>::FwdDeps()>,
     ops::ActivationDoubleGradOpInplaceInferer);
 
-REGISTER_ACTIVATION_CPU_KERNEL(leaky_relu, LeakyRelu, LeakyReluFunctor,
-                               LeakyReluGradFunctor);
-REGISTER_OP_CPU_KERNEL(
-    leaky_relu_grad_grad,
-    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
-                                    ops::LeakyReluGradGradFunctor<float>>,
-    ops::ActivationDoubleGradKernel<plat::CPUDeviceContext,
-                                    ops::LeakyReluGradGradFunctor<double>>,
-    ops::ActivationDoubleGradKernel<
-        plat::CPUDeviceContext, ops::LeakyReluGradGradFunctor<plat::float16>>);
 /* ========================================================================== */
 
 /* ========================    elu  register     ============================ */

paddle/fluid/operators/activation_op.h

@@ -253,6 +253,14 @@ struct SigmoidFunctor : public BaseActivationFunctor<T> {
   template <typename T>                               \
   using name##GradFunctor = phi::funcs::name##GradFunctor<T>;
 
+#define USE_PHI_DOUBLE_GRAD_FUNCTOR(name) \
+  template <typename T>                   \
+  using name##GradGradFunctor = phi::funcs::name##GradGradFunctor<T>;
+
+#define USE_PHI_TRIPLE_GRAD_FUNCTOR(name) \
+  template <typename T>                   \
+  using name##TripleGradFunctor = phi::funcs::name##TripleGradFunctor<T>;
+
 USE_PHI_FUNCTOR(Cos)
 USE_PHI_FUNCTOR(Tan)
 USE_PHI_FUNCTOR(Acos)
@@ -264,6 +272,13 @@ USE_PHI_FUNCTOR(Cosh)
 USE_PHI_FUNCTOR(Asinh)
 USE_PHI_FUNCTOR(Acosh)
 USE_PHI_FUNCTOR(Atanh)
+USE_PHI_FUNCTOR(Tanh)
+USE_PHI_DOUBLE_GRAD_FUNCTOR(Tanh)
+USE_PHI_TRIPLE_GRAD_FUNCTOR(Tanh)
+USE_PHI_FUNCTOR(BRelu)
+USE_PHI_FUNCTOR(ThresholdedRelu)
+USE_PHI_FUNCTOR(LeakyRelu)
+USE_PHI_DOUBLE_GRAD_FUNCTOR(LeakyRelu)
 
 template <typename T>
 struct SigmoidGradFunctor : public BaseActivationFunctor<T> {
@@ -497,117 +512,6 @@ using ReluGradGradFunctor = phi::funcs::ReluGradGradFunctor<T>;
 template <typename T>
 using ReluCUDAFunctor = phi::funcs::ReluCUDAFunctor<T>;
 
-// tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
-template <typename T>
-struct TanhFunctor : public BaseActivationFunctor<T> {
-  template <typename Device, typename X, typename Out>
-  void operator()(Device d, X x, Out out) const {
-    out.device(d) = x.tanh();
-  }
-};
-
-template <typename T>
-struct TanhGradFunctor : public BaseActivationFunctor<T> {
-  template <typename Device, typename X, typename Out, typename dOut,
-            typename dX>
-  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    dx.device(d) = dout * (static_cast<T>(1) - out * out);
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() {
-    return ActBwdOpFwdDeps::kDepOut;
-  }
-};
-
-template <typename T>
-struct TanhGradGradFunctor : public BaseActivationFunctor<T> {
-  template <typename Device>
-  void operator()(const Device& dev, const framework::Tensor* Out,
-                  const framework::Tensor* ddX, const framework::Tensor* dOut,
-                  framework::Tensor* dOutNew, framework::Tensor* ddOut) const {
-    auto* d = dev.eigen_device();
-    auto ddx = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(ddX, "Input", "DDX", "TanhGradGrad"));
-    auto out = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(Out, "Input", "Out", "TanhGradGrad"));
-    // tanh grad grad : ddout = (1 - out^2) * ddx, dout = - (dout_old * 2 * out
-    // * ddx)
-    if (dOutNew) {
-      auto dout = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(dOut, "Input", "DOut", "TanhGradGrad"));
-      auto dout_new = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(dOutNew, "Output", "DOutNew", "TanhGradGrad"));
-      dout_new.device(*d) =
-          static_cast<T>(-1) * dout * static_cast<T>(2) * out * ddx;
-    }
-    if (ddOut) {
-      auto ddout = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(ddOut, "Output", "DDOut", "TanhGradGrad"));
-      ddout.device(*d) = (static_cast<T>(1) - out * out) * ddx;
-    }
-  }
-  static constexpr ActBwdOpFwdDeps FwdDeps() {
-    return ActBwdOpFwdDeps::kDepOut;
-  }
-};
-/*
-    Out
-    DOut                            D_Dout
-    DDx     -> TanhTripleGrad ->    D_DDx
-    D_DDout                         d_OutNew
-    D_Dout_new
-
-    D_Dout = (-2) * Out * DDx * D_Dout_new
-    D_DDx = (1-Out^2)*D_DDout + (-2) * Out * DOut * D_Dout_new
-    D_OutNew = (-2) * Out * DDx * D_DDout + (-2) * DOut * DDx * D_Dout_new
-
-    Out, DDX, DOut, D_DDOut, D_DOut_New   // input
-    D_OutNew, D_DOut, D_DDx               // output
-*/
-template <typename T>
-struct TanhTripleGradFunctor : public BaseActivationFunctor<T> {
-  template <typename Device>
-  void operator()(const Device& dev, const framework::Tensor* Out,
-                  const framework::Tensor* ddX, const framework::Tensor* dOut,
-                  const framework::Tensor* d_DDOut,
-                  const framework::Tensor* d_dOut_New,
-                  framework::Tensor* d_d_Out, framework::Tensor* d_Out_New,
-                  framework::Tensor* d_DDx) const {
-    auto* d = dev.eigen_device();
-    auto ddx = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(ddX, "Input", "DDX", "TanhTripleGrad"));
-    auto out = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(Out, "Input", "Out", "TanhTripleGrad"));
-    auto dout = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(dOut, "Input", "DOut", "TanhTripleGrad"));
-    auto d_ddOut = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(d_DDOut, "Input", "D_DDOut", "TanhTripleGrad"));
-    auto d_dOutNew = framework::EigenVector<T>::Flatten(
-        GET_DATA_SAFELY(d_dOut_New, "Input", "D_DOut_New", "TanhTripleGrad"));
-
-    if (d_Out_New) {
-      auto d_OutNew = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(d_Out_New, "Output", "D_OutNew", "TanhTripleGrad"));
-      d_OutNew.device(*d) = (static_cast<T>(-2) * out * ddx * d_ddOut) -
-                            (static_cast<T>(2) * dout * ddx * d_dOutNew);
-    }
-    if (d_d_Out) {
-      auto d_dOut = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(d_d_Out, "Output", "D_DOut", "TanhTripleGrad"));
-      d_dOut.device(*d) = static_cast<T>(-2) * out * ddx * d_dOutNew;
-    }
-    if (d_DDx) {
-      auto d_ddx = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(d_DDx, "Output", "D_DDx", "TanhTripleGrad"));
-      d_ddx.device(*d) = (static_cast<T>(1) - (out * out)) * d_ddOut -
-                         static_cast<T>(2) * out * dout * d_dOutNew;
-    }
-  }
-  static constexpr ActBwdOpFwdDeps FwdDeps() {
-    return ActBwdOpFwdDeps::kDepOut;
-  }
-};
-
 // tanhshrink(x) = x - tanh(x)
 // where tanh(x) = (exp(x) - exp(-x)) / (exp(x) + exp(-x))
 template <typename T>
@@ -909,42 +813,6 @@ struct SquareGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-template <typename T>
-struct BReluFunctor : public BaseActivationFunctor<T> {
-  float t_min;
-  float t_max;
-
-  // NOTE: Explicit hides the `BaseActivationFunctor<T>::GetAttrs`
-  // not polymorphism for speed.
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"t_min", &t_min}, {"t_max", &t_max}};
-  }
-
-  template <typename Device, typename X, typename Out>
-  void operator()(Device d, X x, Out out) const {
-    out.device(d) =
-        x.cwiseMax(static_cast<T>(t_min)).cwiseMin(static_cast<T>(t_max));
-  }
-};
-
-template <typename T>
-struct BReluGradFunctor : public BaseActivationFunctor<T> {
-  float t_min;
-  float t_max;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"t_min", &t_min}, {"t_max", &t_max}};
-  }
-  template <typename Device, typename X, typename Out, typename dOut,
-            typename dX>
-  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    dx.device(d) = dout *
-                   ((x > static_cast<T>(t_min)) * (x < static_cast<T>(t_max)))
-                       .template cast<T>();
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 // relu6(x) = min(max(0, x), 6)
 template <typename T>
 struct Relu6Functor : public BaseActivationFunctor<T> {
@@ -1168,41 +1036,6 @@ struct SoftReluGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
-template <typename T>
-struct LeakyReluFunctor : public BaseActivationFunctor<T> {
-  float alpha;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"alpha", &alpha}};
-  }
-
-  template <typename Device, typename X, typename Out>
-  void operator()(Device d, X x, Out out) const {
-    if (alpha < 1.f) {
-      out.device(d) = x.cwiseMax(static_cast<T>(alpha) * x);
-    } else {
-      out.device(d) = x.cwiseMin(static_cast<T>(alpha) * x);
-    }
-  }
-};
-
-template <typename T>
-struct LeakyReluGradFunctor : public BaseActivationFunctor<T> {
-  float alpha;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"alpha", &alpha}};
-  }
-  template <typename Device, typename X, typename Out, typename dOut,
-            typename dX>
-  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    auto temp1 =
-        static_cast<T>(alpha) * (x < static_cast<T>(0)).template cast<T>();
-    auto temp2 = (x >= static_cast<T>(0)).template cast<T>();
-    dx.device(d) = dout * (temp1 + temp2).template cast<T>();
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct ELUFunctor : public BaseActivationFunctor<T> {
   float alpha;
@@ -1430,37 +1263,6 @@ struct STanhGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-template <typename T>
-struct ThresholdedReluFunctor : public BaseActivationFunctor<T> {
-  float threshold;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"threshold", &threshold}};
-  }
-
-  template <typename Device, typename X, typename Out>
-  void operator()(Device d, X x, Out out) const {
-    auto th = static_cast<T>(threshold);
-    out.device(d) = (x > th).template cast<T>() * x;
-  }
-};
-
-template <typename T>
-struct ThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
-  float threshold;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"threshold", &threshold}};
-  }
-
-  template <typename Device, typename X, typename Out, typename dOut,
-            typename dX>
-  void operator()(Device d, X x, Out out, dOut dout, dX dx) const {
-    auto th = static_cast<T>(threshold);
-    dx.device(d) = dout * (x > th).template cast<T>();
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct HardSigmoidFunctor : public BaseActivationFunctor<T> {
   float slope;
@@ -1531,121 +1333,6 @@ struct SwishGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-/*
- * in arguments: x, out, ddx
- * out arguments: ddout, dout, dx
- */
-template <ActBwdOpFwdDeps kDepValue>
-inline void ExtractActivationDoubleGradTensor(
-    const framework::ExecutionContext& ctx, const framework::Tensor** X,
-    const framework::Tensor** Out, const framework::Tensor** ddX,
-    framework::Tensor** dX, framework::Tensor** dOut,
-    framework::Tensor** ddOut) {
-  auto ddx_var = ctx.InputVar("DDX");
-  auto ddo_var = ctx.OutputVar("DDOut");
-  PADDLE_ENFORCE_NOT_NULL(
-      ddx_var, platform::errors::NotFound(
-                   "Cannot get input Variable Out, variable name = %s",
-                   ctx.InputName("DDX")));
-  if (CanBeUsedBySelectedRows.count(ctx.Type())) {
-    *ddX = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*ddx_var);
-    if (ddo_var) {
-      *ddOut = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
-          ddo_var);
-    }
-  } else {
-    *ddX = ctx.Input<framework::Tensor>("DDX");
-    if (ddo_var) {
-      *ddOut = ctx.Output<framework::Tensor>("DDOut");
-    }
-  }
-  PADDLE_ENFORCE_NOT_NULL(
-      *ddX,
-      platform::errors::NotFound(
-          "Cannot get the tensor from the Variable Output, variable name = %s",
-          ctx.OutputName("DDX")));
-
-  if (static_cast<int>(kDepValue) & static_cast<int>(ActBwdOpFwdDeps::kDepX)) {
-    auto x_var = ctx.InputVar("X");
-    PADDLE_ENFORCE_NOT_NULL(
-        x_var, platform::errors::NotFound(
-                   "Cannot get input Variable Out, variable name = %s",
-                   ctx.InputName("X")));
-    auto dx_var = ctx.OutputVar("DX");
-    if (CanBeUsedBySelectedRows.count(ctx.Type())) {
-      *X = paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*x_var);
-      if (dx_var) {
-        *dX = paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
-            dx_var);
-      }
-    } else {
-      *X = ctx.Input<framework::Tensor>("X");
-      if (dx_var) {
-        *dX = ctx.Output<framework::Tensor>("DX");
-      }
-    }
-  } else {
-    VLOG(10) << "Inplace activation of Op: " << ctx.Type();
-    *X = *ddX;
-  }
-  if (static_cast<int>(kDepValue) &
-      static_cast<int>(ActBwdOpFwdDeps::kDepOut)) {
-    auto out_var = ctx.InputVar("Out");
-    PADDLE_ENFORCE_NOT_NULL(
-        out_var,
-        platform::errors::NotFound(
-            "Cannot get the tensor from the Variable Out, variable name = %s",
-            ctx.InputName("Out")));
-    auto dout_var = ctx.OutputVar("DOut");
-    if (CanBeUsedBySelectedRows.count(ctx.Type())) {
-      *Out =
-          paddle::framework::GetLoDTensorOrSelectedRowsValueFromVar(*out_var);
-      if (dout_var) {
-        *dOut =
-            paddle::framework::GetMutableLoDTensorOrSelectedRowsValueFromVar(
-                dout_var);
-      }
-    } else {
-      *Out = ctx.Input<framework::Tensor>("Out");
-      if (dout_var) {
-        *dOut = ctx.Output<framework::Tensor>("DOut");
-      }
-    }
-  } else {
-    VLOG(10) << "Inplace activation of Op: " << ctx.Type();
-    *Out = *ddX;
-  }
-}
-
-template <typename DeviceContext, typename Functor>
-class ActivationDoubleGradKernel
-    : public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
- public:
-  using T = typename Functor::ELEMENT_TYPE;
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    const framework::Tensor *X, *Out, *ddX;
-    X = Out = ddX = nullptr;
-    framework::Tensor *ddOut, *dOut, *dX;
-    ddOut = dOut = dX = nullptr;
-
-    ExtractActivationDoubleGradTensor<Functor::FwdDeps()>(ctx, &X, &Out, &ddX,
-                                                          &dX, &dOut, &ddOut);
-
-    if (ddOut) ddOut->mutable_data<T>(ctx.GetPlace());
-    if (dOut) dOut->mutable_data<T>(ctx.GetPlace());
-    if (dX) dX->mutable_data<T>(Out->dims(), ctx.GetPlace());
-
-    auto& place = ctx.template device_context<DeviceContext>();
-
-    Functor functor;
-    auto attrs = functor.GetAttrs();
-    for (auto& attr : attrs) {
-      *attr.second = ctx.Attr<float>(attr.first);
-    }
-    functor(place, X, Out, ddX, ddOut, dOut, dX);
-  }
-};
-
 template <typename T>
 struct AbsGradGradFunctor : public BaseActivationFunctor<T> {
   template <typename Device>
@@ -1667,35 +1354,6 @@ struct AbsGradGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-template <typename T>
-struct LeakyReluGradGradFunctor : public BaseActivationFunctor<T> {
-  float alpha;
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"alpha", &alpha}};
-  }
-  template <typename Device>
-  void operator()(const Device& dev, const framework::Tensor* X,
-                  const framework::Tensor* Out, const framework::Tensor* ddX,
-                  framework::Tensor* ddOut, framework::Tensor* dOut,
-                  framework::Tensor* dX) const {
-    if (ddOut) {
-      auto* d = dev.eigen_device();
-      auto ddx = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(ddX, "Input", "DDX", "LeakyReluGradGrad"));
-      auto x = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(X, "Input", "X", "LeakyReluGradGrad"));
-      auto ddout = framework::EigenVector<T>::Flatten(
-          GET_DATA_SAFELY(ddOut, "Output", "DOut", "LeakyReluGradGrad"));
-      ddout.device(*d) =
-          ddx *
-          ((x > static_cast<T>(0)).template cast<T>() +
-           static_cast<T>(alpha) * (x <= static_cast<T>(0)).template cast<T>())
-              .template cast<T>();
-    }
-  }
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct ELUGradGradFunctor : public BaseActivationFunctor<T> {
   float alpha;
@@ -2504,7 +2162,6 @@ struct LogGradGradFunctor : public BaseActivationFunctor<T> {
   __macro(log1p, Log1p, Log1pFunctor, Log1pGradFunctor);                      \
   __macro(log2, Log2, Log2Functor, Log2GradFunctor);                          \
   __macro(log10, Log10, Log10Functor, Log10GradFunctor);                      \
-  __macro(brelu, BRelu, BReluFunctor, BReluGradFunctor);                      \
   __macro(soft_relu, SoftRelu, SoftReluFunctor, SoftReluGradFunctor);         \
   __macro(stanh, STanh, STanhFunctor, STanhGradFunctor);                      \
   __macro(softplus, Softplus, SoftplusFunctor, SoftplusGradFunctor);          \
@@ -2515,7 +2172,5 @@ struct LogGradGradFunctor : public BaseActivationFunctor<T> {
   __macro(hard_sigmoid, HardSigmoid, HardSigmoidFunctor,                      \
           HardSigmoidGradFunctor);                                            \
   __macro(swish, Swish, SwishFunctor, SwishGradFunctor);                      \
-  __macro(thresholded_relu, ThresholdedRelu, ThresholdedReluFunctor,          \
-          ThresholdedReluGradFunctor);                                        \
   __macro(mish, Mish, MishFunctor, MishGradFunctor);                          \
   __macro(hard_swish, HardSwish, HardSwishFunctor, HardSwishGradFunctor);

paddle/fluid/operators/activation_op.kps

@@ -18,38 +18,6 @@ limitations under the License. */
 namespace paddle {
 namespace operators {
 
-template <typename T>
-struct CudaLeakyReluFunctor : public BaseActivationFunctor<T> {
-  T zero = static_cast<T>(0.0f);
-  float alpha;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"alpha", &alpha}};
-  }
-
-  // leakyrelu(x) = x > 0 ? x : alpha * x
-  __device__ __forceinline__ T operator()(const T x) const {
-    return x > zero ? x : static_cast<T>(alpha) * x;
-  }
-};
-
-template <typename T>
-struct CudaLeakyReluGradFunctor : public BaseActivationFunctor<T> {
-  T zero = static_cast<T>(0.0f);
-  float alpha;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"alpha", &alpha}};
-  }
-
-  // dx = dout * (x > 0 ? 1 : alpha)
-  __device__ __forceinline__ T operator()(const T dout, const T x) const {
-    return x > zero ? dout : static_cast<T>(alpha) * dout;
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct CudaSigmoidFunctor : public BaseActivationFunctor<T> {
   using MPType = typename details::MPTypeTrait<T>::Type;
@@ -224,31 +192,6 @@ struct CudaZeroGradFunctor : public BaseActivationFunctor<T> {
   }
 };
 
-template <typename T>
-struct CudaTanhFunctor : public BaseActivationFunctor<T> {
-  using MPType = typename details::MPTypeTrait<T>::Type;
-
-  // tanh(x) = tanh(x)
-  __device__ __forceinline__ T operator()(const T arg_x) const {
-    MPType x = static_cast<MPType>(arg_x);
-    return static_cast<T>(tanh(x));
-  }
-};
-
-template <typename T>
-struct CudaTanhGradFunctor : public BaseActivationFunctor<T> {
-  T one = static_cast<T>(1.0f);
-
-  // dx = dout * (1 - out^2)
-  __device__ __forceinline__ T operator()(const T dout, const T out) const {
-    return dout * (one - out * out);
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() {
-    return ActBwdOpFwdDeps::kDepOut;
-  }
-};
-
 template <typename T>
 struct CudaReciprocalFunctor : public BaseActivationFunctor<T> {
   T one = static_cast<T>(1.0f);
@@ -476,45 +419,6 @@ struct CudaLog10GradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-template <typename T>
-struct CudaBReluFunctor : public BaseActivationFunctor<T> {
-  float t_min;
-  float t_max;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"t_min", &t_min}, {"t_max", &t_max}};
-  }
-
-  // brelu(x) = min(max(x, t_min), t_max)
-  __device__ __forceinline__ T operator()(const T x) const {
-    T t_min_cast = static_cast<T>(t_min);
-    T t_max_cast = static_cast<T>(t_max);
-    T temp_max = x > t_min_cast ? x : t_min_cast;
-    T temp_min = temp_max < t_max_cast ? temp_max : t_max_cast;
-    return temp_min;
-  }
-};
-
-template <typename T>
-struct CudaBReluGradFunctor : public BaseActivationFunctor<T> {
-  T zero = static_cast<T>(0.0f);
-  float t_min;
-  float t_max;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"t_min", &t_min}, {"t_max", &t_max}};
-  }
-
-  // dx = (x > t_min && x < t_max) ? dout : 0
-  __device__ __forceinline__ T operator()(const T dout, const T x) const {
-    T t_min_cast = static_cast<T>(t_min);
-    T t_max_cast = static_cast<T>(t_max);
-    return (x > t_min_cast && x < t_max_cast) ? dout : zero;
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct CudaSoftReluFunctor : public BaseActivationFunctor<T> {
   using MPType = typename details::MPTypeTrait<T>::Type;
@@ -907,38 +811,6 @@ struct CudaMishGradFunctor : public BaseActivationFunctor<T> {
   static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
 };
 
-template <typename T>
-struct CudaThresholdedReluFunctor : public BaseActivationFunctor<T> {
-  T zero = static_cast<T>(0.0f);
-  float threshold;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"threshold", &threshold}};
-  }
-
-  // thresholded_relu(x) = x > threshold ? x : 0
-  __device__ __forceinline__ T operator()(const T x) const {
-    return x > static_cast<T>(threshold) ? x : zero;
-  }
-};
-
-template <typename T>
-struct CudaThresholdedReluGradFunctor : public BaseActivationFunctor<T> {
-  T zero = static_cast<T>(0.0f);
-  float threshold;
-
-  typename BaseActivationFunctor<T>::AttrPair GetAttrs() {
-    return {{"threshold", &threshold}};
-  }
-
-  // dx = x > threshold ? dout : 0
-  __device__ __forceinline__ T operator()(const T dout, const T x) const {
-    return x > static_cast<T>(threshold) ? dout : zero;
-  }
-
-  static constexpr ActBwdOpFwdDeps FwdDeps() { return ActBwdOpFwdDeps::kDepX; }
-};
-
 template <typename T>
 struct CudaHardSwishFunctor : public BaseActivationFunctor<T> {
   T zero = static_cast<T>(0.0f);
@@ -1212,6 +1084,22 @@ class ActivationGradCudaKernel
   }
 };
 
+USE_PHI_FUNCTOR(CudaCos)
+USE_PHI_FUNCTOR(CudaTan)
+USE_PHI_FUNCTOR(CudaAcos)
+USE_PHI_FUNCTOR(CudaSin)
+USE_PHI_FUNCTOR(CudaAsin)
+USE_PHI_FUNCTOR(CudaAtan)
+USE_PHI_FUNCTOR(CudaSinh)
+USE_PHI_FUNCTOR(CudaCosh)
+USE_PHI_FUNCTOR(CudaAsinh)
+USE_PHI_FUNCTOR(CudaAcosh)
+USE_PHI_FUNCTOR(CudaAtanh)
+USE_PHI_FUNCTOR(CudaTanh)
+USE_PHI_FUNCTOR(CudaBRelu)
+USE_PHI_FUNCTOR(CudaLeakyRelu)
+USE_PHI_FUNCTOR(CudaThresholdedRelu)
+
 }  // namespace operators
 }  // namespace paddle
 
@@ -1270,20 +1158,6 @@ namespace plat = paddle::platform;
       ops::ActivationGradCudaKernel<plat::CUDADeviceContext,                   \
                                     ops::grad_functor<plat::bfloat16>>);
 
-/* ======================== leaky relu register  ============================ */
-REGISTER_ACTIVATION_CUDA_KERNEL(leaky_relu, LeakyRelu, CudaLeakyReluFunctor,
-                                CudaLeakyReluGradFunctor);
-
-REGISTER_OP_CUDA_KERNEL(
-    leaky_relu_grad_grad,
-    ops::ActivationDoubleGradKernel<plat::CUDADeviceContext,
-                                    ops::LeakyReluGradGradFunctor<float>>,
-    ops::ActivationDoubleGradKernel<plat::CUDADeviceContext,
-                                    ops::LeakyReluGradGradFunctor<double>>,
-    ops::ActivationDoubleGradKernel<
-        plat::CUDADeviceContext, ops::LeakyReluGradGradFunctor<plat::float16>>);
-/* ========================================================================== */
-
 /* ======================== elu register  ============================ */
 REGISTER_OP_CUDA_KERNEL(
     elu, ops::ActivationCudaKernel<paddle::platform::CUDADeviceContext,
@@ -1348,29 +1222,6 @@ REGISTER_OP_CUDA_KERNEL(
         ops::SigmoidTripleGradFunctor<plat::bfloat16>>);
 /* ========================================================================== */
 
-/* ===========================    tanh register  ============================ */
-REGISTER_ACTIVATION_CUDA_KERNEL(tanh, Tanh, CudaTanhFunctor,
-                                CudaTanhGradFunctor);
-
-REGISTER_OP_CUDA_KERNEL(
-    tanh_grad_grad,
-    ops::TanhDoubleGradKernel<paddle::platform::CUDADeviceContext,
-                              ops::TanhGradGradFunctor<float>>,
-    ops::TanhDoubleGradKernel<paddle::platform::CUDADeviceContext,
-                              ops::TanhGradGradFunctor<double>>,
-    ops::TanhDoubleGradKernel<plat::CUDADeviceContext,
-                              ops::TanhGradGradFunctor<plat::float16>>);
-
-REGISTER_OP_CUDA_KERNEL(
-    tanh_triple_grad,
-    ops::TanhTripeGradKernel<paddle::platform::CUDADeviceContext,
-                             ops::TanhTripleGradFunctor<float>>,
-    ops::TanhTripeGradKernel<paddle::platform::CUDADeviceContext,
-                             ops::TanhTripleGradFunctor<double>>,
-    ops::TanhTripeGradKernel<plat::CUDADeviceContext,
-                             ops::TanhTripleGradFunctor<plat::float16>>);
-/* ========================================================================== */
-
 /* ===========================   sqrt register  ============================= */
 REGISTER_ACTIVATION_CUDA_KERNEL(sqrt, Sqrt, CudaSqrtFunctor,
                                 CudaSqrtGradFunctor);
@@ -1521,7 +1372,6 @@ REGISTER_OP_CUDA_KERNEL(
   __macro(log1p, Log1p, CudaLog1pFunctor, CudaLog1pGradFunctor);              \
   __macro(log2, Log2, CudaLog2Functor, CudaLog2GradFunctor);                  \
   __macro(log10, Log10, CudaLog10Functor, CudaLog10GradFunctor);              \
-  __macro(brelu, BRelu, CudaBReluFunctor, CudaBReluGradFunctor);              \
   __macro(soft_relu, SoftRelu, CudaSoftReluFunctor, CudaSoftReluGradFunctor); \
   __macro(stanh, STanh, CudaSTanhFunctor, CudaSTanhGradFunctor);              \
   __macro(softplus, Softplus, CudaSoftplusFunctor, CudaSoftplusGradFunctor);  \
@@ -1535,8 +1385,6 @@ REGISTER_OP_CUDA_KERNEL(
           CudaHardSigmoidGradFunctor);                                        \
   __macro(swish, Swish, CudaSwishFunctor, CudaSwishGradFunctor);              \
   __macro(mish, Mish, CudaMishFunctor, CudaMishGradFunctor);                  \
-  __macro(thresholded_relu, ThresholdedRelu, CudaThresholdedReluFunctor,      \
-          CudaThresholdedReluGradFunctor);                                    \
   __macro(hard_swish, HardSwish, CudaHardSwishFunctor,                        \
           CudaHardSwishGradFunctor);
 FOR_EACH_ACTIVATION_CUDA_OP(REGISTER_ACTIVATION_CUDA_KERNEL)

paddle/fluid/operators/allclose_op.cc

@@ -12,52 +12,20 @@
 // See the License for the specific language governing permissions and
 // limitations under the License.
 
-#include "paddle/fluid/operators/allclose_op.h"
 #include <cmath>
 #include <string>
+
+#include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/op_version_registry.h"
 #include "paddle/fluid/framework/operator.h"
 #include "paddle/fluid/platform/enforce.h"
+#include "paddle/phi/core/infermeta_utils.h"
+#include "paddle/phi/infermeta/binary.h"
 
 namespace paddle {
 namespace operators {
 
-template <typename T>
-struct GetTensorValue<platform::CPUDeviceContext, T> {
-  T operator()(const platform::CPUDeviceContext& dev_ctx,
-               const framework::Tensor& tensor) const {
-    return *(tensor.data<T>());
-  }
-};
-
-template <typename T>
-struct AllcloseFunctor<platform::CPUDeviceContext, T> {
-  void operator()(const platform::CPUDeviceContext& ctx,
-                  const framework::Tensor& in, const framework::Tensor& other,
-                  const double rtol, const double atol, bool equal_nan,
-                  framework::Tensor* output) {
-    auto* in_a = in.data<T>();
-    auto* in_b = other.data<T>();
-    auto* out_data = output->mutable_data<bool>(ctx.GetPlace());
-    auto num = in.numel();
-    *out_data = true;
-    for (int i = 0; i < num; i++) {
-      const T a = in_a[i], b = in_b[i];
-      bool val;
-      if (std::isnan(a) || std::isnan(b)) {
-        val = equal_nan && std::isnan(a) == std::isnan(b);
-      } else {
-        T left = (a > b ? a - b : b - a);
-        T right = atol + (b > 0 ? rtol * b : (-rtol) * b);
-        T diff = (left > right ? left - right : right - left);
-        val = a == b || left <= right || diff <= 1e-15;
-      }
-      *out_data &= val;
-    }
-  }
-};
-
 class AllcloseOpMaker : public framework::OpProtoAndCheckerMaker {
  public:
   void Make() override {
@@ -96,40 +64,6 @@ class AllcloseOp : public framework::OperatorWithKernel {
  public:
   using framework::OperatorWithKernel::OperatorWithKernel;
 
-  void InferShape(framework::InferShapeContext* ctx) const override {
-    OP_INOUT_CHECK(ctx->HasInput("Input"), "Input", "Input", "Allclose");
-    OP_INOUT_CHECK(ctx->HasInput("Other"), "Input", "Other", "Allclose");
-    OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "Allclose");
-
-    auto input_dim = ctx->GetInputDim("Input");
-    auto other_dim = ctx->GetInputDim("Other");
-    PADDLE_ENFORCE_EQ(input_dim.size(), other_dim.size(),
-                      platform::errors::PreconditionNotMet(
-                          "Input(Input) and Input(Other) must have the same "
-                          "dimension size."));
-    int n = input_dim.size();
-    bool is_runtime = ctx->IsRuntime();
-    for (int i = 0; i < n; i++) {
-      if (is_runtime) {
-        PADDLE_ENFORCE_EQ(input_dim[i], other_dim[i],
-                          platform::errors::PreconditionNotMet(
-                              "The value at dim %d of Input(Input) is not "
-                              "equal to the Input(Other): %ld != %ld.",
-                              i, input_dim[i], other_dim[i]));
-      } else {
-        if (!(input_dim[i] < 0 || other_dim[i] < 0)) {
-          PADDLE_ENFORCE_EQ(input_dim[i], other_dim[i],
-                            platform::errors::PreconditionNotMet(
-                                "The value at dim %d of Input(Input) is not "
-                                "equal to the Input(Other): %ld != %ld.",
-                                i, input_dim[i], other_dim[i]));
-        }
-      }
-    }
-
-    ctx->SetOutputDim("Out", phi::make_ddim({1}));
-  }
-
  protected:
   framework::OpKernelType GetExpectedKernelType(
       const framework::ExecutionContext& ctx) const override {
@@ -152,13 +86,13 @@ class AllcloseOpVarTypeInference : public framework::VarTypeInference {
 namespace ops = paddle::operators;
 using CPU = paddle::platform::CPUDeviceContext;
 
+DECLARE_INFER_SHAPE_FUNCTOR(allclose, AllcloseInferShapeFunctor,
+                            PD_INFER_META(phi::AllValueCompareInferMeta));
 REGISTER_OPERATOR(
     allclose, ops::AllcloseOp, ops::AllcloseOpMaker,
     paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
     paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    ops::AllcloseOpVarTypeInference);
-REGISTER_OP_CPU_KERNEL(allclose, ops::AllcloseKernel<CPU, float>,
-                       ops::AllcloseKernel<CPU, double>);
+    ops::AllcloseOpVarTypeInference, AllcloseInferShapeFunctor);
 
 /* ==========================  register checkpoint ===========================*/
 REGISTER_OP_VERSION(allclose)

paddle/fluid/operators/allclose_op.h

-// 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.
-
-#pragma once
-
-#include <string>
-#include "paddle/fluid/framework/data_type.h"
-#include "paddle/fluid/framework/eigen.h"
-#include "paddle/fluid/framework/operator.h"
-#include "paddle/fluid/platform/place.h"
-
-namespace paddle {
-namespace operators {
-using Tensor = framework::Tensor;
-
-template <typename DeviceContext, typename T>
-struct GetTensorValue {
-  T operator()(const platform::DeviceContext& ctx,
-               const framework::Tensor& tensor) const;
-};
-
-template <typename DeviceContext, typename T>
-struct AllcloseFunctor {
-  void operator()(const DeviceContext& ctx, const framework::Tensor& in,
-                  const framework::Tensor& other, const float rtol,
-                  const float atol, bool equal_nan, framework::Tensor* output);
-};
-
-template <typename DeviceContext, typename T>
-class AllcloseKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    // get attrs
-    bool equal_nan = ctx.Attr<bool>("equal_nan");
-    // get input/output
-    const auto* input = ctx.Input<Tensor>("Input");
-    const auto* other = ctx.Input<Tensor>("Other");
-    auto* out = ctx.Output<Tensor>("Out");
-
-    double rtol_v = std::stod(ctx.Attr<std::string>("rtol"));
-    double atol_v = std::stod(ctx.Attr<std::string>("atol"));
-
-    auto& dev_ctx = ctx.template device_context<DeviceContext>();
-    GetTensorValue<DeviceContext, double> get_tensor_value;
-    if (ctx.HasInput("Rtol")) {
-      const auto* rtol = ctx.Input<Tensor>("Rtol");
-      PADDLE_ENFORCE_EQ(
-          rtol->numel(), 1,
-          platform::errors::InvalidArgument(
-              "Input(Rtol) size must be 1, but get %d.", rtol->numel()));
-      PADDLE_ENFORCE_EQ(
-          framework::TransToProtoVarType(rtol->dtype()),
-          framework::proto::VarType::FP64,
-          platform::errors::InvalidArgument(
-              "Input(Rtol) type must be double, but get %s.",
-              framework::DataTypeToString(
-                  framework::TransToProtoVarType(rtol->dtype()))));
-      rtol_v = get_tensor_value(dev_ctx, *rtol);
-    }
-    if (ctx.HasInput("Atol")) {
-      const auto* atol = ctx.Input<Tensor>("Atol");
-      PADDLE_ENFORCE_EQ(
-          atol->numel(), 1,
-          platform::errors::InvalidArgument(
-              "Input(Atol) size must be 1, but get %d", atol->numel()));
-      PADDLE_ENFORCE_EQ(
-          framework::TransToProtoVarType(atol->dtype()),
-          framework::proto::VarType::FP64,
-          platform::errors::InvalidArgument(
-              "Input(Atol) type must be double, but get %s",
-              framework::DataTypeToString(
-                  framework::TransToProtoVarType(atol->dtype()))));
-      atol_v = get_tensor_value(dev_ctx, *atol);
-    }
-
-    AllcloseFunctor<DeviceContext, T>()(dev_ctx, *input, *other, rtol_v, atol_v,
-                                        equal_nan, out);
-  }
-};
-
-}  // namespace operators
-}  // namespace paddle

paddle/fluid/operators/amp/check_finite_and_unscale_op_mlu.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/operators/amp/check_finite_and_unscale_op.h"
+#include "paddle/fluid/operators/mlu/mlu_baseop.h"
+
+namespace paddle {
+namespace operators {
+
+using Tensor = framework::Tensor;
+
+template <typename T>
+class CheckFiniteAndUnscaleMLUKernel : public framework::OpKernel<T> {
+ public:
+  void Compute(const framework::ExecutionContext& ctx) const {
+    auto& dev_ctx = ctx.template device_context<platform::MLUDeviceContext>();
+    const auto xs = ctx.MultiInput<framework::Tensor>("X");
+    const auto* scale = ctx.Input<framework::Tensor>("Scale");
+    auto outs = ctx.MultiOutput<framework::Tensor>("Out");
+    auto* found_inf = ctx.Output<framework::Tensor>("FoundInfinite");
+
+    found_inf->mutable_data<bool>(dev_ctx.GetPlace());
+
+    MLUCnnlTensorDesc scale_desc(*scale);
+    MLUCnnlTensorDesc found_inf_desc(*found_inf, CNNL_LAYOUT_ARRAY,
+                                     ToCnnlDataType<bool>());
+
+    for (size_t i = 0; i < xs.size(); ++i) {
+      const auto* x = xs[i];
+      auto* out = outs[i];
+      out->mutable_data<T>(ctx.GetPlace());
+
+      // check is_finite or is_nan
+      Tensor is_finite(found_inf->type());
+      if (i != 0) {
+        is_finite.Resize(phi::make_ddim({1}));
+        is_finite.mutable_data<bool>(ctx.GetPlace());
+      } else {
+        is_finite.ShareDataWith(*found_inf);
+      }
+
+      MLUCnnlTensorDesc x_desc(*x);
+
+      MLUCnnl::IsNanInf(ctx, x_desc.get(), GetBasePtr(x),
+                        GetBasePtr(&is_finite));
+
+      // save is_finite by logical_and op after checking every input
+      if (i != 0) {
+        MLUCnnlTensorDesc is_finite_desc(is_finite, CNNL_LAYOUT_ARRAY,
+                                         ToCnnlDataType<bool>());
+        MLUCnnl::Logic(ctx, CNNL_LOGIC_OP_OR, found_inf_desc.get(),
+                       GetBasePtr(found_inf), is_finite_desc.get(),
+                       GetBasePtr(&is_finite), found_inf_desc.get(),
+                       GetBasePtr(found_inf));
+      }
+
+      // The normal logic is :
+      // out = in, if found_inf = true
+      // out = in/scale, if found_inf = false
+      // But when found_inf is true, the data of Out should not be used.
+      // So, on MLU, we always compute out with in/scale.
+      MLUCnnlTensorDesc out_desc(*out);
+      MLUCnnl::Div(ctx, CNNL_COMPUTATION_HIGH_PRECISION, x_desc.get(),
+                   GetBasePtr(x), scale_desc.get(), GetBasePtr(scale),
+                   out_desc.get(), GetBasePtr(out));
+    }
+  }
+};
+
+}  // namespace operators
+}  // namespace paddle
+
+namespace ops = paddle::operators;
+namespace plat = paddle::platform;
+REGISTER_OP_MLU_KERNEL(check_finite_and_unscale,
+                       ops::CheckFiniteAndUnscaleMLUKernel<float>,
+                       ops::CheckFiniteAndUnscaleMLUKernel<plat::float16>);

paddle/fluid/operators/controlflow/compare_op_mlu.cc

@@ -11,7 +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. */
 
-#include "paddle/fluid/operators/controlflow/compare_op.h"
+#include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/operators/mlu/mlu_baseop.h"
 
 namespace paddle {

paddle/fluid/operators/diag_v2_op.cc

@@ -12,8 +12,6 @@ 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 <algorithm>
-
 #include "paddle/fluid/framework/infershape_utils.h"
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/phi/infermeta/unary.h"
@@ -58,15 +56,56 @@ class DiagV2OpMaker : public framework::OpProtoAndCheckerMaker {
   }
 };
 
+class DiagV2GradOp : public framework::OperatorWithKernel {
+ public:
+  using framework::OperatorWithKernel::OperatorWithKernel;
+
+  void InferShape(framework::InferShapeContext *ctx) const override {
+    OP_INOUT_CHECK(ctx->HasInput("X"), "X", "X", "DiagV2Grad");
+    OP_INOUT_CHECK(ctx->HasOutput(framework::GradVarName("X")), "Output",
+                   framework::GradVarName("X"), "DiagV2Grad");
+
+    ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
+  }
+
+ protected:
+  framework::OpKernelType GetExpectedKernelType(
+      const framework::ExecutionContext &ctx) const override {
+    return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
+                                       ctx, framework::GradVarName("Out")),
+                                   ctx.GetPlace());
+  }
+};
+
+template <typename T>
+class DiagV2GradOpMaker : public framework::SingleGradOpMaker<T> {
+ public:
+  using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
+
+ protected:
+  void Apply(GradOpPtr<T> grad_op) const override {
+    grad_op->SetType("diag_v2_grad");
+    grad_op->SetInput("X", this->Input("X"));
+    grad_op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
+    grad_op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
+    grad_op->SetAttrMap(this->Attrs());
+  }
+};
+
+DECLARE_NO_NEED_BUFFER_VARS_INFERER(DiagGradV2NoNeedBufferVarsInferer, "X");
+
 }  // namespace operators
 }  // namespace paddle
 
 namespace ops = paddle::operators;
+
 DECLARE_INFER_SHAPE_FUNCTOR(diag_v2, DiagInferShapeFunctor,
                             PD_INFER_META(phi::DiagInferMeta));
 
-REGISTER_OPERATOR(
-    diag_v2, ops::DiagV2Op, ops::DiagV2OpMaker,
-    paddle::framework::EmptyGradOpMaker<paddle::framework::OpDesc>,
-    paddle::framework::EmptyGradOpMaker<paddle::imperative::OpBase>,
-    DiagInferShapeFunctor);
+REGISTER_OPERATOR(diag_v2, ops::DiagV2Op, ops::DiagV2OpMaker,
+                  ops::DiagV2GradOpMaker<paddle::framework::OpDesc>,
+                  ops::DiagV2GradOpMaker<paddle::imperative::OpBase>,
+                  DiagInferShapeFunctor);
+
+REGISTER_OPERATOR(diag_v2_grad, ops::DiagV2GradOp,
+                  ops::DiagGradV2NoNeedBufferVarsInferer);

paddle/fluid/operators/elementwise/elementwise_functor.h

 /* 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.
@@ -90,86 +87,6 @@ struct MinFunctor {
 template <typename T>
 using Complex = paddle::platform::complex<T>;
 
-// Fmax
-template <typename T>
-struct FMaxFunctor {
-  inline HOSTDEVICE T operator()(const T a, const T b) const {
-    return std::fmax(a, b);
-  }
-};
-
-template <>
-struct FMaxFunctor<paddle::platform::float16> {
-  inline HOSTDEVICE paddle::platform::float16 operator()(
-      const paddle::platform::float16 a,
-      const paddle::platform::float16 b) const {
-    float float_a = static_cast<float>(a);
-    float float_b = static_cast<float>(b);
-    auto result = std::fmax(float_a, float_b);
-    return static_cast<paddle::platform::float16>(result);
-  }
-};
-
-template <>
-struct FMaxFunctor<int> {
-  inline HOSTDEVICE int operator()(const int a, const int b) const {
-    float float_a = static_cast<float>(a);
-    float float_b = static_cast<float>(b);
-    auto result = std::fmax(float_a, float_b);
-    return std::lrint(result);
-  }
-};
-
-template <>
-struct FMaxFunctor<int64_t> {
-  inline HOSTDEVICE int64_t operator()(const int64_t a, const int64_t b) const {
-    double double_a = static_cast<double>(a);
-    double double_b = static_cast<double>(b);
-    auto result = std::fmax(double_a, double_b);
-    return std::llrint(result);
-  }
-};
-
-// Fmin
-template <typename T>
-struct FMinFunctor {
-  inline HOSTDEVICE T operator()(const T a, const T b) const {
-    return std::fmin(a, b);
-  }
-};
-
-template <>
-struct FMinFunctor<paddle::platform::float16> {
-  inline HOSTDEVICE paddle::platform::float16 operator()(
-      const paddle::platform::float16 a,
-      const paddle::platform::float16 b) const {
-    float float_a = static_cast<float>(a);
-    float float_b = static_cast<float>(b);
-    auto result = std::fmin(float_a, float_b);
-    return static_cast<paddle::platform::float16>(result);
-  }
-};
-
-template <>
-struct FMinFunctor<int> {
-  inline HOSTDEVICE int operator()(const int a, const int b) const {
-    float float_a = static_cast<float>(a);
-    float float_b = static_cast<float>(b);
-    auto result = std::fmin(float_a, float_b);
-    return std::lrint(result);
-  }
-};
-
-template <>
-struct FMinFunctor<int64_t> {
-  inline HOSTDEVICE int64_t operator()(const int64_t a, const int64_t b) const {
-    double double_a = static_cast<double>(a);
-    double double_b = static_cast<double>(b);
-    auto result = std::fmin(double_a, double_b);
-    return std::llrint(result);
-  }
-};
-
 template <typename T>
 struct MinGradXFunctor {
   inline HOSTDEVICE T operator()(const T x, const T y, const T dout) const {

paddle/fluid/operators/elementwise/elementwise_max_op.cc

@@ -151,21 +151,3 @@ REGISTER_OPERATOR(elementwise_fmax, ops::ElementwiseOp,
                   ops::ElementwiseFMaxGradOpMaker<paddle::imperative::OpBase>);
 
 REGISTER_OPERATOR(elementwise_fmax_grad, ops::ElementwiseOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    elementwise_fmax,
-    ops::ElementwiseFMaxKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CPUDeviceContext,
-                               paddle::platform::float16>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CPUDeviceContext, int64_t>);
-REGISTER_OP_CPU_KERNEL(
-    elementwise_fmax_grad,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CPUDeviceContext,
-                                   paddle::platform::float16>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CPUDeviceContext,
-                                   int64_t>);

paddle/fluid/operators/elementwise/elementwise_max_op.cu

@@ -86,21 +86,3 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseMaxGradKernel<paddle::platform::CUDADeviceContext,
                                   int64_t>);
-
-REGISTER_OP_CUDA_KERNEL(
-    elementwise_fmax,
-    ops::ElementwiseFMaxKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CUDADeviceContext,
-                               paddle::platform::float16>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseFMaxKernel<paddle::platform::CUDADeviceContext, int64_t>);
-REGISTER_OP_CUDA_KERNEL(
-    elementwise_fmax_grad,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CUDADeviceContext,
-                                   paddle::platform::float16>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseFMaxGradKernel<paddle::platform::CUDADeviceContext,
-                                   int64_t>);

paddle/fluid/operators/elementwise/elementwise_max_op.h

@@ -35,21 +35,6 @@ class ElementwiseMaxKernel : public framework::OpKernel<T> {
   }
 };
 
-template <typename DeviceContext, typename T>
-class ElementwiseFMaxKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* x = ctx.Input<framework::LoDTensor>("X");
-    auto* y = ctx.Input<framework::LoDTensor>("Y");
-    auto* z = ctx.Output<framework::LoDTensor>("Out");
-
-    z->mutable_data<T>(ctx.GetPlace());
-    int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<FMaxFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
-                                                           FMaxFunctor<T>(), z);
-  }
-};
-
 template <typename T>
 struct MaxGradDx {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
@@ -104,88 +89,5 @@ class ElementwiseMaxGradKernel : public ElemwiseGradKernel<T> {
   }
 };
 
-template <typename T>
-struct FMaxGradDx {
-  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * static_cast<T>((x >= y) || isnan(y));
-  }
-};
-
-template <>
-struct FMaxGradDx<paddle::platform::float16> {
-  HOSTDEVICE paddle::platform::float16 operator()(
-      paddle::platform::float16 x, paddle::platform::float16 y,
-      paddle::platform::float16 out, paddle::platform::float16 dout) const {
-    return dout * static_cast<paddle::platform::float16>(
-                      (x >= y) || paddle::platform::isnan(y));
-  }
-};
-
-template <>
-struct FMaxGradDx<int> {
-  HOSTDEVICE int operator()(int x, int y, int out, int dout) const {
-    return dout * static_cast<int>((x >= y));
-  }
-};
-
-template <>
-struct FMaxGradDx<int64_t> {
-  HOSTDEVICE int64_t operator()(int64_t x, int64_t y, int64_t out,
-                                int64_t dout) const {
-    return dout * static_cast<int64_t>((x >= y));
-  }
-};
-
-template <typename T>
-struct FMaxGradDy {
-  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * static_cast<T>(!((x >= y) || isnan(y)));
-  }
-};
-
-template <>
-struct FMaxGradDy<paddle::platform::float16> {
-  HOSTDEVICE paddle::platform::float16 operator()(
-      paddle::platform::float16 x, paddle::platform::float16 y,
-      paddle::platform::float16 out, paddle::platform::float16 dout) const {
-    return dout * static_cast<paddle::platform::float16>(
-                      !((x >= y) || paddle::platform::isnan(y)));
-  }
-};
-
-template <>
-struct FMaxGradDy<int64_t> {
-  HOSTDEVICE int64_t operator()(int64_t x, int64_t y, int64_t out,
-                                int64_t dout) const {
-    return dout * static_cast<int64_t>(!((x >= y)));
-  }
-};
-
-template <>
-struct FMaxGradDy<int> {
-  HOSTDEVICE int operator()(int x, int y, int out, int dout) const {
-    return dout * static_cast<int>(!((x >= y)));
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ElementwiseFMaxGradKernel : public ElemwiseGradKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    ElemwiseGradKernel<T>::Compute(ctx);
-    using Tensor = framework::Tensor;
-
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-    auto* out = dout;  // Fake out, not used
-    int axis = ctx.Attr<int>("axis");
-    ElemwiseGradCompute<DeviceContext, T, FMaxGradDx<T>, FMaxGradDy<T>>(
-        ctx, *x, *y, *out, *dout, axis, dx, dy, FMaxGradDx<T>(),
-        FMaxGradDy<T>());
-  }
-};
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/elementwise_min_op.cc

@@ -147,21 +147,3 @@ REGISTER_OPERATOR(elementwise_fmin, ops::ElementwiseOp,
                   ops::ElementwiseFMinGradOpMaker<paddle::imperative::OpBase>);
 
 REGISTER_OPERATOR(elementwise_fmin_grad, ops::ElementwiseOpGrad);
-
-REGISTER_OP_CPU_KERNEL(
-    elementwise_fmin,
-    ops::ElementwiseFMinKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::ElementwiseFMinKernel<paddle::platform::CPUDeviceContext,
-                               paddle::platform::float16>,
-    ops::ElementwiseFMinKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::ElementwiseFMinKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::ElementwiseFMinKernel<paddle::platform::CPUDeviceContext, int64_t>);
-REGISTER_OP_CPU_KERNEL(
-    elementwise_fmin_grad,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CPUDeviceContext,
-                                   paddle::platform::float16>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CPUDeviceContext,
-                                   int64_t>);

paddle/fluid/operators/elementwise/elementwise_min_op.cu

@@ -82,21 +82,3 @@ REGISTER_OP_CUDA_KERNEL(
     ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext, int>,
     ops::ElementwiseMinGradKernel<paddle::platform::CUDADeviceContext,
                                   int64_t>);
-
-REGISTER_OP_CUDA_KERNEL(
-    elementwise_fmin,
-    ops::ElementwiseFMinKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseFMinKernel<paddle::platform::CUDADeviceContext,
-                               paddle::platform::float16>,
-    ops::ElementwiseFMinKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseFMinKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseFMinKernel<paddle::platform::CUDADeviceContext, int64_t>);
-REGISTER_OP_CUDA_KERNEL(
-    elementwise_fmin_grad,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CUDADeviceContext,
-                                   paddle::platform::float16>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::ElementwiseFMinGradKernel<paddle::platform::CUDADeviceContext,
-                                   int64_t>);

paddle/fluid/operators/elementwise/elementwise_min_op.h

@@ -35,21 +35,6 @@ class ElementwiseMinKernel : public framework::OpKernel<T> {
   }
 };
 
-template <typename DeviceContext, typename T>
-class ElementwiseFMinKernel : public framework::OpKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    auto* x = ctx.Input<framework::LoDTensor>("X");
-    auto* y = ctx.Input<framework::LoDTensor>("Y");
-    auto* z = ctx.Output<framework::LoDTensor>("Out");
-
-    z->mutable_data<T>(ctx.GetPlace());
-    int axis = ctx.Attr<int>("axis");
-    ElementwiseComputeEx<FMinFunctor<T>, DeviceContext, T>(ctx, x, y, axis,
-                                                           FMinFunctor<T>(), z);
-  }
-};
-
 template <typename T>
 struct MinGradDx {
   HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
@@ -124,89 +109,5 @@ class ElementwiseMinGradKernel : public ElemwiseGradKernel<T> {
     ElementwiseMinGrad<DeviceContext, T>(ctx, x, y, out, dout, dx, dy);
   }
 };
-
-template <typename T>
-struct FMinGradDx {
-  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * static_cast<T>((x <= y) || isnan(y));
-  }
-};
-
-template <>
-struct FMinGradDx<paddle::platform::float16> {
-  HOSTDEVICE paddle::platform::float16 operator()(
-      paddle::platform::float16 x, paddle::platform::float16 y,
-      paddle::platform::float16 out, paddle::platform::float16 dout) const {
-    return dout * static_cast<paddle::platform::float16>(
-                      (x <= y) || paddle::platform::isnan(y));
-  }
-};
-
-template <>
-struct FMinGradDx<int> {
-  HOSTDEVICE int operator()(int x, int y, int out, int dout) const {
-    return dout * static_cast<int>((x <= y));
-  }
-};
-
-template <>
-struct FMinGradDx<int64_t> {
-  HOSTDEVICE int64_t operator()(int64_t x, int64_t y, int64_t out,
-                                int64_t dout) const {
-    return dout * static_cast<int64_t>((x <= y));
-  }
-};
-
-template <typename T>
-struct FMinGradDy {
-  HOSTDEVICE T operator()(T x, T y, T out, T dout) const {
-    return dout * static_cast<T>(!((x <= y) || isnan(y)));
-  }
-};
-
-template <>
-struct FMinGradDy<paddle::platform::float16> {
-  HOSTDEVICE paddle::platform::float16 operator()(
-      paddle::platform::float16 x, paddle::platform::float16 y,
-      paddle::platform::float16 out, paddle::platform::float16 dout) const {
-    return dout * static_cast<paddle::platform::float16>(
-                      !((x <= y) || paddle::platform::isnan(y)));
-  }
-};
-
-template <>
-struct FMinGradDy<int> {
-  HOSTDEVICE int operator()(int x, int y, int out, int dout) const {
-    return dout * static_cast<int>(!((x <= y)));
-  }
-};
-
-template <>
-struct FMinGradDy<int64_t> {
-  HOSTDEVICE int64_t operator()(int64_t x, int64_t y, int64_t out,
-                                int64_t dout) const {
-    return dout * static_cast<int64_t>(!((x <= y)));
-  }
-};
-
-template <typename DeviceContext, typename T>
-class ElementwiseFMinGradKernel : public ElemwiseGradKernel<T> {
- public:
-  void Compute(const framework::ExecutionContext& ctx) const override {
-    ElemwiseGradKernel<T>::Compute(ctx);
-    using Tensor = framework::Tensor;
-
-    auto* x = ctx.Input<Tensor>("X");
-    auto* y = ctx.Input<Tensor>("Y");
-    auto* dout = ctx.Input<Tensor>(framework::GradVarName("Out"));
-    auto* dx = ctx.Output<Tensor>(framework::GradVarName("X"));
-    auto* dy = ctx.Output<Tensor>(framework::GradVarName("Y"));
-    auto* out = dout;  // Fake out, not used
-    int axis = ctx.Attr<int>("axis");
-    ElemwiseGradCompute<DeviceContext, T, FMinGradDx<T>, FMinGradDy<T>>(
-        ctx, *x, *y, *out, *dout, axis, dx, dy, FMinGradDx<T>(),
-        FMinGradDy<T>());
-  }
-};
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/elementwise/mkldnn/elementwise_mkldnn_op.h

@@ -32,6 +32,45 @@ using dnnl::stream;
 
 template <typename T, dnnl::algorithm BINARY_OP>
 class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
+ private:
+  dnnl::post_ops get_post_ops(const framework::ExecutionContext& ctx) const {
+    dnnl::post_ops post_operations;
+    if (ctx.HasAttr("activation_type")) {
+      const float scale = ctx.HasAttr("activation_scale")
+                              ? ctx.Attr<float>("activation_scale")
+                              : 1.0f;
+      const float alpha = ctx.HasAttr("activation_alpha")
+                              ? ctx.Attr<float>("activation_alpha")
+                              : 0.0f;
+      const float beta = ctx.HasAttr("activation_beta")
+                             ? ctx.Attr<float>("activation_beta")
+                             : 0.0f;
+
+      static std::unordered_map<std::string, dnnl::algorithm> algo_map = {
+          {"relu", dnnl::algorithm::eltwise_relu},
+          {"tanh", dnnl::algorithm::eltwise_tanh},
+          {"leaky_relu", dnnl::algorithm::eltwise_relu},
+          {"swish", dnnl::algorithm::eltwise_swish},
+          {"hardswish", dnnl::algorithm::eltwise_hardswish},
+          {"sqrt", dnnl::algorithm::eltwise_sqrt},
+          {"abs", dnnl::algorithm::eltwise_abs},
+          {"clip", dnnl::algorithm::eltwise_clip},
+          {"gelu", dnnl::algorithm::eltwise_gelu_erf},
+          {"gelu_tanh", dnnl::algorithm::eltwise_gelu_tanh},
+          {"relu6", dnnl::algorithm::eltwise_bounded_relu},
+          {"sigmoid", dnnl::algorithm::eltwise_logistic}};
+
+      const auto& activation_type =
+          algo_map.find(ctx.Attr<std::string>("activation_type"));
+
+      if (activation_type != algo_map.end()) {
+        post_operations.append_eltwise(scale, activation_type->second, alpha,
+                                       beta);
+      }
+    }
+    return post_operations;
+  }
+
  public:
   void Compute(const framework::ExecutionContext& ctx) const override {
     const auto& dev_ctx =
@@ -47,9 +86,9 @@ class EltwiseMKLDNNKernel : public framework::OpKernel<T> {
     float scale_o = ctx.Attr<float>("Scale_out");
     int axis = ctx.Attr<int>("axis");
 
-    platform::BinaryMKLDNNHandler<T> handler(BINARY_OP, axis, mkldnn_engine,
-                                             ctx.GetPlace(), x, y, z, scale_x,
-                                             scale_y, scale_o);
+    platform::BinaryMKLDNNHandler<T> handler(
+        BINARY_OP, axis, mkldnn_engine, ctx.GetPlace(), x, y, z, scale_x,
+        scale_y, scale_o, get_post_ops(ctx));
 
     const auto src_x_memory = handler.AcquireSrcMemory(x);
     const auto src_y_memory = handler.AcquireSecondSrcMemory(y);

paddle/fluid/operators/filter_by_instag_op.cu

@@ -96,30 +96,6 @@ __global__ void filter_copy_fuse_kernel(
 
   if (N < ins_end) ins_end = N;
 
-  /*
-    if (!x1_lods_filled) {
-      for (int p = ins_start; p < ins_end; p++) {
-        x1_lods_data[p] = p;
-      }
-      if (idx == 0) {
-        x1_lods_data[N] = N;
-      }
-    }
-
-    if (!x2_lods_filled) {
-      for (int p = ins_start; p < ins_end; p++) {
-        x2_lods_data[p] = p;
-      }
-      if (idx == 0) {
-        x2_lods_data[N] = N;
-      }
-    }
-
-    if (!x1_lods_filled || !x2_lods_filled) {
-      b.sync();
-    }
-  */
-
   int flag_data[5];
   int prefix_sum_data[5];
   int prefix_sum_data2[5];
@@ -173,8 +149,6 @@ __global__ void filter_copy_fuse_kernel(
     local_addr = prefix_sum_data[ins_end - 1 - ins_start];
     sum_addr = local_addr;
 
-    // flag
-    // local_flag = 0;
     for (int p = ins_start; p < ins_end; p++) {
       local_flag += flag_data[p - ins_start];
     }
@@ -188,7 +162,6 @@ __global__ void filter_copy_fuse_kernel(
     sum_out_lods = local_out_lods;
   }
 
-  // 32 threads
   for (int i = 1; i < warp_thread_num; i *= 2) {
     int temp_addr = g.shfl_up(sum_addr, i);
     int temp_flag = g.shfl_up(sum_flag, i);
@@ -266,27 +239,16 @@ __global__ void filter_copy_fuse_kernel(
 
   if (ins_start < ins_end) {
     int out_lods_idx = p_flag + 1;
-
-    // ins_start = 1
-    // BUG fix
     for (int p = ins_start; p < ins_end; p++) {
       if (flag_data[p - ins_start] == 1) {
-        // batch_len = 2
-        // batch_len = 4
         size_t batch_len = x1_lods_data[p + 1] - x1_lods_data[p];
-        // t = 0
-        // t = 1
         int t = out_lods_idx - 1;
-        // out_lods_data[0] = 0;
         int previous;
-
         if (out_lods_idx == p_flag + 1) {
-          // out_lods_data[t] = p_out_lods;
           previous = p_out_lods;
         } else {
           previous = out_lods_data[t];
         }
-
         map_data[t * 3] = (int64_t)previous;
         map_data[t * 3 + 1] = x1_lods_data[p];
         map_lods_data[t] = t;
@@ -300,7 +262,6 @@ __global__ void filter_copy_fuse_kernel(
     if (sum_out_lods4 > 1) {
       int out_data_num = sum_out_lods4 - 1;
       int out_start = ins_start;
-
       if (out_start < out_data_num) {
         int out_end = ins_end >= out_data_num ? out_data_num : ins_end;
         for (int p = out_start; p < out_end; p++) {
@@ -314,11 +275,8 @@ __global__ void filter_copy_fuse_kernel(
       if (flag_data[p - ins_start] == 1) {
         auto output_start_idx = prefix_sum_data2[p - ins_start];
         T* dst = out_data + output_start_idx * x1_embed_size;
-
         const T* src_start = x1_data + x1_lods_data[p] * x1_embed_size;
         const T* src_end = x1_data + x1_lods_data[p + 1] * x1_embed_size;
-
-        // optimized
         for (const T *j = src_start; j != src_end; dst++, j++) {
           *dst = *j;
         }
@@ -338,12 +296,10 @@ __global__ void copy_grad_kernel(const size_t N, const int ins_per_thread,
   int idx = blockIdx.x * blockDim.x + threadIdx.x;
   int ins_start = idx * ins_per_thread;
   int ins_end = (idx + 1) * ins_per_thread;
-
   if (ins_start >= N) {
     return;
   }
   if (ins_end > N) ins_end = N;
-
   for (int p = ins_start; p < ins_end; p++) {
     T* dst = x1_grad_data + map_data[p * 3 + 1] * x1_embed_size;
     const T* src_start = out_grad_data + map_data[p * 3] * x1_embed_size;
@@ -394,21 +350,17 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
     const Tensor* x3 = context.Input<Tensor>("Filter_tag");
     const int64_t* x3_data = x3->data<int64_t>();
 
-    // int x2_lods_filled = 1;
-
     Vector<size_t> x2_lods;
-    // Vector, in GPU
     if (x2->lod().size() != 0) {  // lod_level = 1
       x2_lods = x2->lod()[0];
-      // x2_lods_filled = 1;
-
     } else {  // lod_level = 0
       const size_t x2_lods_size = x2->dims()[0];
+      const size_t instag_per_num = x2->dims()[1];
       // x2_lods.resize(x2->dims()[0] + 1);
       // move to cuda
       x2_lods.push_back(0);
       for (size_t i = 0; i < x2_lods_size; i++) {
-        x2_lods.push_back(i + 1);
+        x2_lods.push_back(x2_lods.back() + instag_per_num);
       }
     }
 
@@ -417,13 +369,8 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
 
     size_t* x2_lods_data = mixv_x2_lods.CUDAMutableData(gpu_place);
 
-    // Vector, in GPU
-    // int x1_lods_filled = 1;
     Vector<size_t> x1_lods;
-
     if (!is_x1_lod) {
-      // move to cuda
-      // x1_lods.resize(x1->dims()[0] + 1);
       x1_lods.push_back(0);
       for (int i = 0; i < x1->dims()[0]; i++) {
         x1_lods.push_back(i + 1);
@@ -432,7 +379,6 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
       // x1_lods = context.Input<LoDTensor>("Ins")->lod()[0];
       // new: lod_level=0 => lod() return {}
       if (x1->lod().size() != 0) {  // lod_level = 1
-        // x1_lods_filled = 1;
         x1_lods = x1->lod()[0];
       } else {  // lod_level = 0
         // x1_lods.resize(x1->dims()[0] + 1);
@@ -458,10 +404,6 @@ class FilterByInstagGPUKernel : public framework::OpKernel<T> {
     LoDTensor* loss_weight = context.Output<LoDTensor>("LossWeight");
 
     int out_first = x1_lods.back();
-    // int out_first = x1->dims()[0];
-    // if (x1_lods_filled) {
-    //  out_first = x1_lods.back();
-    // }
 
     out->Resize(phi::make_ddim({(int64_t)out_first, (int64_t)x1_embed_size}));
     map->Resize(phi::make_ddim({(int64_t)x2_lods_size, 3}));

paddle/fluid/operators/flatten_op.h

@@ -16,7 +16,6 @@ limitations under the License. */
 #include <vector>
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/fluid/framework/phi_utils.h"
-#include "paddle/fluid/operators/math/pooling.h"
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/phi/kernels/empty_kernel.h"
 #include "paddle/phi/kernels/flatten_grad_kernel.h"

paddle/fluid/operators/gather_nd_op.cc

@@ -16,7 +16,6 @@ limitations under the License. */
 #include "paddle/fluid/framework/op_registry.h"
 #include "paddle/phi/infermeta/backward.h"
 #include "paddle/phi/infermeta/binary.h"
-#include "paddle/phi/infermeta/ternary.h"
 
 namespace paddle {
 namespace operators {

paddle/fluid/operators/gather_op_npu.cc

@@ -17,7 +17,6 @@ limitations under the License. */
 #include <string>
 #include <vector>
 #include "paddle/fluid/framework/tensor_util.h"
-#include "paddle/fluid/operators/kron_op.h"
 #include "paddle/fluid/platform/device/npu/npu_info.h"
 #include "paddle/fluid/platform/device/npu/npu_op_runner.h"
 

paddle/fluid/operators/group_norm_op.cc

@@ -167,9 +167,11 @@ class GroupNormGradOp : public framework::OperatorWithKernel {
 
   void InferShape(framework::InferShapeContext *ctx) const override {
     // check input
+    OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "GroupNormGrad");
     OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "GroupNormGrad");
     OP_INOUT_CHECK(ctx->HasInput("Variance"), "Input", "Variance",
                    "GroupNormGrad");
+    OP_INOUT_CHECK(ctx->HasInput("Mean"), "Input", "Mean", "GroupNormGrad");
     OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Y")), "Input",
                    framework::GradVarName("Y"), "GroupNormGrad");
 
@@ -216,10 +218,12 @@ class GroupNormGradMaker : public framework::SingleGradOpMaker<T> {
 
   void Apply(GradOpPtr<T> op) const override {
     op->SetType("group_norm_grad");
+    op->SetInput("X", this->Input("X"));
     op->SetInput("Scale", this->Input("Scale"));
     op->SetInput("Bias", this->Input("Bias"));
     op->SetInput(framework::GradVarName("Y"), this->OutputGrad("Y"));
     op->SetInput("Y", this->Output("Y"));
+    op->SetInput("Mean", this->Output("Mean"));
     op->SetInput("Variance", this->Output("Variance"));
 
     op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));

paddle/fluid/operators/group_norm_op.cu

@@ -81,46 +81,74 @@ __global__ void GroupNormForwardGetMeanAndVar(const T* x, int N, int C, int W,
   CudaAtomicAddWithWarp(&var[bid * groups + gid], x_var);
 }
 
-template <typename T, typename AccT, int VecSize>
-__device__ __forceinline__ void ThreadReduce(const T* input, int size,
-                                             const int offset, AccT* mean,
-                                             AccT* var) {
+template <typename T, typename AccT, int VecSize, int Num>
+__device__ __forceinline__ void ThreadReduce(phi::Array<const T*, Num> arrs,
+                                             int size, const int offset,
+                                             AccT* out_mean, AccT* out_var) {
+  const T* x = arrs[0];
+  const T* y;
+  if (Num == 2) {
+    y = arrs[1];
+  }
   using VecT = kps::details::VectorType<T, VecSize>;
   int tid = threadIdx.x;
   if (offset > 0) {
-    input -= offset;
+    x -= offset;
+    if (Num == 2) {
+      y -= offset;
+    }
     size += offset;
     if (tid >= offset) {
-      AccT temp = input[tid];
-      *mean += temp;
-      *var += temp * temp;
+      if (Num == 1) {
+        *out_mean += x[tid];
+        *out_var += x[tid] * x[tid];
+      } else if (Num == 2) {
+        *out_mean += y[tid];
+        *out_var += y[tid] * x[tid];
+      }
     }
     size -= blockDim.x;
-    input += blockDim.x;
+    x += blockDim.x;
+    if (Num == 2) {
+      y += blockDim.x;
+    }
   }
   int remain = size % (VecSize * blockDim.x);
 
-  T ins[VecSize];
-  VecT* ins_vec = reinterpret_cast<VecT*>(&ins);
+  T ins_x[VecSize];
+  T ins_y[VecSize];
+  VecT* ins_vec_x = reinterpret_cast<VecT*>(&ins_x);
+  VecT* ins_vec_y = reinterpret_cast<VecT*>(&ins_y);
 
   // vector part
   for (; VecSize * tid < (size - remain); tid += blockDim.x) {
-    *ins_vec = reinterpret_cast<const VecT*>(input)[tid];
+    *ins_vec_x = reinterpret_cast<const VecT*>(x)[tid];
+    if (Num == 2) {
+      *ins_vec_y = reinterpret_cast<const VecT*>(y)[tid];
+    }
 
 #pragma unroll
     for (int i = 0; i < VecSize; ++i) {
-      AccT temp = ins[i];
-      *mean += temp;
-      *var += temp * temp;
+      if (Num == 1) {
+        *out_mean += ins_x[i];
+        *out_var += ins_x[i] * ins_x[i];
+      } else if (Num == 2) {
+        *out_mean += ins_y[i];
+        *out_var += ins_y[i] * ins_x[i];
+      }
     }
   }
 
   // scalar part
   tid = size - remain + threadIdx.x;
   for (; tid < size; tid += blockDim.x) {
-    AccT temp = input[tid];
-    *mean += temp;
-    *var += temp * temp;
+    if (Num == 1) {
+      *out_mean += x[tid];
+      *out_var += x[tid] * x[tid];
+    } else if (Num == 2) {
+      *out_mean += y[tid];
+      *out_var += y[tid] * x[tid];
+    }
   }
 }
 
@@ -148,7 +176,10 @@ __global__ void VectorizedGetMeanAndVarNCHW(const T* x, T* mean, T* var,
   AccT x_var = static_cast<AccT>(0);
   const int input_offset = ((uint64_t)x) % ALIGN_BYTES / sizeof(T);
   x += i * size;
-  ThreadReduce<T, AccT, VecSize>(x, size, input_offset, &x_mean, &x_var);
+  phi::Array<const T*, 1> ins;
+  ins[0] = x;
+  ThreadReduce<T, AccT, VecSize, 1>(ins, size, input_offset, &x_mean, &x_var);
+
   x_mean = kps::details::BlockXReduce<AccT, kps::AddFunctor<AccT>>(
       x_mean, kps::AddFunctor<AccT>());
   x_var = kps::details::BlockXReduce<AccT, kps::AddFunctor<AccT>>(
@@ -310,10 +341,12 @@ class GroupNormKernel<platform::CUDADeviceContext, T>
 };
 
 template <typename T, int flags>
-__global__ void GroupNormBackwardGetMeanAndVar(
-    const T* x, const T* scale, const T* bias, const T* d_y, int N, int C,
-    int W, int imsize, int groups, int group_size, T epsilon, T* d_mean,
-    T* d_var, T* d_scale, T* d_bias, const DataLayout data_layout) {
+__global__ void GroupNormBackwardGetMeanAndVar(const T* x, const T* scale,
+                                               const T* bias, const T* d_y,
+                                               int N, int C, int W, int imsize,
+                                               int groups, int group_size,
+                                               T epsilon, T* d_mean, T* d_var,
+                                               T* d_scale, T* d_bias) {
   int gid = blockIdx.y;
   int cid = blockIdx.x;
   int bid = blockIdx.z;
@@ -329,15 +362,11 @@ __global__ void GroupNormBackwardGetMeanAndVar(
 
   for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
     T val, dval;
-    if (data_layout == DataLayout::kNCHW) {
-      val = x[(bid * C + ccid) * imsize + imid] - x_bias;
-      dval = d_y[(bid * C + ccid) * imsize + imid];
-    } else {
-      int hid = imid / W;
-      int wid = imid % W;
-      val = x[(bid * H + hid) * W * C + wid * C + ccid] - x_bias;
-      dval = d_y[(bid * H + hid) * W * C + wid * C + ccid];
-    }
+
+    int hid = imid / W;
+    int wid = imid % W;
+    val = x[(bid * H + hid) * W * C + wid * C + ccid] - x_bias;
+    dval = d_y[(bid * H + hid) * W * C + wid * C + ccid];
 
     d_var_data += val * dval;
     d_mean_data += dval * x_scale;
@@ -357,8 +386,7 @@ __global__ void GroupNormBackward(const T* x, const T* d_y, const T* scale,
                                   const T* bias, const T* var, const T* d_mean,
                                   const T* d_var, int N, int C, int W,
                                   int imsize, int groups, int group_size,
-                                  T epsilon, T* d_x,
-                                  const DataLayout data_layout) {
+                                  T epsilon, T* d_x) {
   int gid = blockIdx.y;
   int cid = blockIdx.x;
   int bid = blockIdx.z;
@@ -379,26 +407,142 @@ __global__ void GroupNormBackward(const T* x, const T* d_y, const T* scale,
   if (x_scale != 0) x_scale_inv = 1.0 / x_scale;
 
   for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
-    if (data_layout == DataLayout::kNCHW) {
-      T tmp = x[(bid * C + ccid) * imsize + imid];
-      T v_y = (tmp - x_bias) * x_scale_inv;
-      T dly = d_y[(bid * C + ccid) * imsize + imid];
-      d_x[(bid * C + ccid) * imsize + imid] =
-          x_var_inv *
-          (dly * x_scale - number_inv * d_x_var * v_y - number_inv * d_x_mean);
-    } else {
-      int hid = imid / W;
-      int wid = imid % W;
-      T tmp = x[(bid * H + hid) * W * C + wid * C + ccid];
-      T v_y = (tmp - x_bias) * x_scale_inv;
-      T dly = d_y[(bid * H + hid) * W * C + wid * C + ccid];
-      d_x[(bid * H + hid) * W * C + wid * C + ccid] =
-          x_var_inv *
-          (dly * x_scale - number_inv * d_x_var * v_y - number_inv * d_x_mean);
+    int hid = imid / W;
+    int wid = imid % W;
+    T tmp = x[(bid * H + hid) * W * C + wid * C + ccid];
+    T v_y = (tmp - x_bias) * x_scale_inv;
+    T dly = d_y[(bid * H + hid) * W * C + wid * C + ccid];
+    d_x[(bid * H + hid) * W * C + wid * C + ccid] =
+        x_var_inv *
+        (dly * x_scale - number_inv * d_x_var * v_y - number_inv * d_x_mean);
+  }
+}
+
+template <typename T, typename AccT, int VecSize>
+__global__ void VectorizedGetDsDbCUDAKernel(int imsize, const T* x, const T* dy,
+                                            T* ds, T* db) {
+  int i = blockIdx.x;
+  AccT ds_sum = static_cast<AccT>(0);
+  AccT db_sum = static_cast<AccT>(0);
+  const int input_offset = ((uint64_t)x) % ALIGN_BYTES / sizeof(T);
+  x += i * imsize;
+
+  phi::Array<const T*, 2> ins;
+  ins[0] = x;
+  ins[1] = dy;
+  ThreadReduce<T, AccT, VecSize, 2>(ins, imsize, input_offset, &db_sum,
+                                    &ds_sum);
+
+  ds_sum = kps::details::BlockXReduce<AccT, kps::AddFunctor<AccT>>(
+      ds_sum, kps::AddFunctor<AccT>());
+  db_sum = kps::details::BlockXReduce<AccT, kps::AddFunctor<AccT>>(
+      db_sum, kps::AddFunctor<AccT>());
+  __syncthreads();
+  if (threadIdx.x == 0) {
+    ds[i] = ds_sum;
+    db[i] = db_sum;
+  }
+}
+
+template <typename T>
+__global__ void ScalarGetDsDbCUDAKernel(int imsize, const T* x, const T* dy,
+                                        T* ds, T* db) {
+  const int nc = blockIdx.x;
+  T ds_sum = 0;
+  T db_sum = 0;
+  for (int i = threadIdx.x; i < imsize; i += blockDim.x) {
+    const int index = nc * imsize + i;
+    ds_sum += dy[index] * x[index];
+    db_sum += dy[index];
+  }
+  CudaAtomicAddWithWarp(&ds[nc], ds_sum);
+  CudaAtomicAddWithWarp(&db[nc], db_sum);
+}
+
+template <typename T>
+__global__ void GetScaleBiasGradientCUDAKernel(int N, int C, int group,
+                                               T epsilon, const T* mean,
+                                               const T* var, const T* ds,
+                                               const T* db, T* d_scale,
+                                               T* d_bias) {
+  const int c = blockIdx.x * blockDim.x + threadIdx.x;
+  if (c < C) {
+    const int G = group;
+    const int D = C / G;
+    T sum1 = 0;
+    T sum2 = 0;
+    for (int n = 0; n < N; ++n) {
+      const int nc = n * C + c;
+      const int ng = n * G + c / D;
+      sum1 += (d_scale == nullptr)
+                  ? T(0)
+                  : ((ds[nc] - db[nc] * static_cast<T>(mean[ng])) *
+                     static_cast<T>(rsqrt(var[ng] + epsilon)));
+      sum2 += (d_bias == nullptr) ? T(0) : db[nc];
+    }
+    if (d_scale != nullptr) {
+      d_scale[c] = sum1;
+    }
+    if (d_bias != nullptr) {
+      d_bias[c] = sum2;
     }
   }
 }
 
+template <typename T, int BlockDim>
+__global__ void GetBackwardParamsCUDAKernel(int imsize, int groups,
+                                            int group_size, T epsilon,
+                                            const T* mean, const T* var,
+                                            const T* scale, const T* ds,
+                                            const T* db, T* p1, T* p2, T* p3) {
+  const int n = blockIdx.x;
+  const int g = blockIdx.y;
+  const int ng = n * groups + g;
+  T sum1 = 0;
+  T sum2 = 0;
+  T var_inv = rsqrt(var[ng] + epsilon);
+  for (int64_t i = threadIdx.x; i < group_size; i += blockDim.x) {
+    const int64_t index = ng * group_size + i;
+    const int64_t c = g * group_size + i;
+    const T scale_v = scale == nullptr ? T(1) : static_cast<T>(scale[c]);
+    sum1 += ds[index] * scale_v;
+    sum2 += db[index] * scale_v;
+    const T scale_c = scale == nullptr ? T(0) : static_cast<T>(scale[c]);
+    p1[index] = scale_c * var_inv;
+  }
+
+  typedef cub::BlockReduce<T, BlockDim> BlockReduce;
+  __shared__ typename BlockReduce::TempStorage ds_storage;
+  __shared__ typename BlockReduce::TempStorage db_storage;
+  sum1 = BlockReduce(ds_storage).Reduce(sum1, cub::Sum());
+  sum2 = BlockReduce(db_storage).Reduce(sum2, cub::Sum());
+
+  if (threadIdx.x == 0) {
+    const T s = T(1) / static_cast<T>(group_size * imsize);
+    const T x = (sum2 * static_cast<T>(mean[ng]) - sum1) *
+                static_cast<T>(var_inv) * static_cast<T>(var_inv) *
+                static_cast<T>(var_inv) * s;
+    p2[ng] = x;
+    p3[ng] = -x * static_cast<T>(mean[ng]) - sum2 * static_cast<T>(var_inv) * s;
+  }
+}
+
+template <typename T>
+__global__ void GetXGradientCUDAKernel(int imsize, int C, int group_size,
+                                       int groups, T* p1, T* p2, T* p3,
+                                       const T* x, const T* dy, T* dx) {
+  int cid = blockIdx.x;
+  int gid = blockIdx.y;
+  int bid = blockIdx.z;
+  int ccid = bid * C + gid * group_size + cid;
+  int ng = bid * groups + gid;
+  int nc = gid * group_size + cid;
+  for (int imid = threadIdx.x; imid < imsize; imid += blockDim.x) {
+    int index = (bid * C + nc) * imsize + imid;
+    dx[index] = p1[ccid] * dy[index] + p2[ng] * x[index] + p3[ng];
+  }
+}
+
 template <typename T>
 class GroupNormGradKernel<platform::CUDADeviceContext, T>
     : public framework::OpKernel<T> {
@@ -408,7 +552,9 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
     const DataLayout data_layout =
         framework::StringToDataLayout(data_layout_str);
     const float epsilon = ctx.Attr<float>("epsilon");
-    auto* x = ctx.Input<Tensor>("Y");
+    auto* x = ctx.Input<Tensor>("X");
+    auto* y = ctx.Input<Tensor>("Y");
+    auto* mean = ctx.Input<Tensor>("Mean");
     auto* var = ctx.Input<Tensor>("Variance");
     auto* scale = ctx.Input<Tensor>("Scale");
     auto* bias = ctx.Input<Tensor>("Bias");
@@ -433,31 +579,27 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
     phi::funcs::SetConstant<platform::CUDADeviceContext, T> set_zero;
     auto& dev_ctx = ctx.template device_context<platform::CUDADeviceContext>();
 
-    Tensor temp_var;
-    temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
-    set_zero(dev_ctx, &temp_var, static_cast<T>(0));
-    T* temp_var_data = temp_var.data<T>();
-
-    Tensor temp_mean;
-    temp_mean.mutable_data<T>(var->dims(), ctx.GetPlace());
-    set_zero(dev_ctx, &temp_mean, static_cast<T>(0));
-    T* temp_mean_data = temp_mean.data<T>();
+    Tensor ds, db;
+    ds.mutable_data<T>({x_dims[0], C}, ctx.GetPlace());
+    db.mutable_data<T>({x_dims[0], C}, ctx.GetPlace());
+    T* ds_data = ds.data<T>();
+    T* db_data = db.data<T>();
 
+    auto* y_data = y->data<T>();
     auto* x_data = x->data<T>();
     T* d_x_data = nullptr;
     if (d_x) d_x_data = d_x->data<T>();
-    auto* y_data = d_y->data<T>();
+    auto* dy_data = d_y->data<T>();
     auto* var_data = var->data<T>();
+    auto* mean_data = mean->data<T>();
     T* d_scale_data = nullptr;
     if (d_scale) {
       d_scale->mutable_data<T>(ctx.GetPlace());
-      set_zero(dev_ctx, d_scale, static_cast<T>(0));
       d_scale_data = d_scale->data<T>();
     }
     T* d_bias_data = nullptr;
     if (d_bias) {
       d_bias->mutable_data<T>(ctx.GetPlace());
-      set_zero(dev_ctx, d_bias, static_cast<T>(0));
       d_bias_data = d_bias->data<T>();
     }
 
@@ -479,22 +621,103 @@ class GroupNormGradKernel<platform::CUDADeviceContext, T>
 
 #ifdef __HIPCC__
     int block_size = std::max(std::min(256, imsize), 64);
+    const int block_dims = 256;
 #else
     int block_size = std::min(1024, imsize);
+    const int block_dims = 1024;
 #endif
     dim3 grid(group_size, groups, x_dims[0]);
     dim3 threads(block_size, 1, 1);
     int flags =
         (scale_data != nullptr) * kHasScale + (bias_data != nullptr) * kHasBias;
-    UNROLL_ALL_CASES(flags, GroupNormBackwardGetMeanAndVar, x_data, scale_data,
-                     bias_data, y_data, x_dims[0], C, W, imsize, groups,
-                     group_size, epsilon, temp_mean_data, temp_var_data,
-                     d_scale_data, d_bias_data, data_layout);
-    if (d_x_data != nullptr) {
-      UNROLL_ALL_CASES(flags, GroupNormBackward, x_data, y_data, scale_data,
-                       bias_data, var_data, temp_mean_data, temp_var_data,
-                       x_dims[0], C, W, imsize, groups, group_size, epsilon,
-                       d_x_data, data_layout);
+    if (data_layout == DataLayout::kNCHW) {
+      using AccT = typename details::MPTypeTrait<T>::Type;
+      constexpr int vec_size = sizeof(float4) / sizeof(T);
+      const int max_num_threads = 1024;
+      int max_block_size = std::min(imsize / vec_size, max_num_threads);
+      int block_size_nchw = 1;
+      while (block_size_nchw < max_block_size) {
+        block_size_nchw *= 2;
+      }
+      block_size_nchw = std::max(block_size_nchw, kps::details::kWarpSize);
+      dim3 blocks(block_size_nchw);
+      if (imsize < vec_size) {
+        if (d_scale) {
+          set_zero(dev_ctx, d_scale, static_cast<T>(0));
+        }
+        if (d_bias) {
+          set_zero(dev_ctx, d_bias, static_cast<T>(0));
+        }
+        ScalarGetDsDbCUDAKernel<
+            T><<<x_dims[0] * C, blocks, 0, dev_ctx.stream()>>>(
+            imsize, x_data, dy_data, ds_data, db_data);
+      } else {
+        VectorizedGetDsDbCUDAKernel<
+            T, AccT, vec_size><<<x_dims[0] * C, blocks, 0, dev_ctx.stream()>>>(
+            imsize, x_data, dy_data, ds_data, db_data);
+      }
+
+      if (d_scale || d_bias) {
+        const int block = 256;
+        GetScaleBiasGradientCUDAKernel<
+            T><<<(C + block - 1) / block, block, 0, dev_ctx.stream()>>>(
+            x_dims[0], C, groups, epsilon, mean_data, var_data, ds_data,
+            db_data, d_scale_data, d_bias_data);
+      }
+
+      if (d_x_data != nullptr) {
+        // p1 * dy + p2 * x + p3,
+        // p1, p2, p3 represent the reverse calculation of temporary variables
+        // p1 = scale * var_inv
+        // p2 = (db * scale * mean - ds * scale) * pow(var_inv, 3) * (1/n)
+        // p3 = -p2 * mean[ng] - db * scale * var_inv * (1/n);
+        Tensor p1, p2, p3;
+        p1.mutable_data<T>({x_dims[0] * C}, ctx.GetPlace());
+        p2.mutable_data<T>({x_dims[0], groups}, ctx.GetPlace());
+        p3.mutable_data<T>({x_dims[0], groups}, ctx.GetPlace());
+        T* p1_data = p1.data<T>();
+        T* p2_data = p2.data<T>();
+        T* p3_data = p3.data<T>();
+
+        GetBackwardParamsCUDAKernel<T, block_dims><<<
+            dim3(x_dims[0], groups), block_dims, 0, dev_ctx.stream()>>>(
+            imsize, groups, group_size, epsilon, mean_data, var_data,
+            scale_data, ds_data, db_data, p1_data, p2_data, p3_data);
+        GetXGradientCUDAKernel<T><<<grid, threads, 0, dev_ctx.stream()>>>(
+            imsize, C, group_size, groups, p1_data, p2_data, p3_data, x_data,
+            dy_data, d_x_data);
+      }
+
+    } else {
+      if (d_scale) {
+        set_zero(dev_ctx, d_scale, static_cast<T>(0));
+      }
+      if (d_bias) {
+        set_zero(dev_ctx, d_bias, static_cast<T>(0));
+      }
+
+      Tensor temp_var;
+      temp_var.mutable_data<T>(var->dims(), ctx.GetPlace());
+      set_zero(dev_ctx, &temp_var, static_cast<T>(0));
+      T* temp_var_data = temp_var.data<T>();
+
+      Tensor temp_mean;
+      temp_mean.mutable_data<T>(var->dims(), ctx.GetPlace());
+      set_zero(dev_ctx, &temp_mean, static_cast<T>(0));
+      T* temp_mean_data = temp_mean.data<T>();
+
+      int flags = (scale_data != nullptr) * kHasScale +
+                  (bias_data != nullptr) * kHasBias;
+      UNROLL_ALL_CASES(flags, GroupNormBackwardGetMeanAndVar, y_data,
+                       scale_data, bias_data, dy_data, x_dims[0], C, W, imsize,
+                       groups, group_size, epsilon, temp_mean_data,
+                       temp_var_data, d_scale_data, d_bias_data);
+      if (d_x_data != nullptr) {
+        UNROLL_ALL_CASES(flags, GroupNormBackward, y_data, dy_data, scale_data,
+                         bias_data, var_data, temp_mean_data, temp_var_data,
+                         x_dims[0], C, W, imsize, groups, group_size, epsilon,
+                         d_x_data);
+      }
     }
   }
 };

paddle/fluid/operators/interpolate_v2_op.cu

@@ -61,13 +61,13 @@ inline platform::GpuLaunchConfig GetGpuLaunchConfig3D(
 
 template <typename T>
 __forceinline__ __device__ void PreCalculatorForLinearInterpInputIndex(
-    int* in_img_idx, int* w_id, T* w1lambda, T* w2lambda, T src_w,
-    const int in_img_w) {
-  src_w = (src_w > 0) ? src_w : 0.f;
-  *in_img_idx = static_cast<int>(src_w);
-  *w_id = (*in_img_idx < in_img_w - 1) ? 1 : 0;
-  *w1lambda = src_w - *in_img_idx;
-  *w2lambda = 1.f - *w1lambda;
+    int* in_img_idx, int* x_id, T* lambda1, T* lambda2, T src_x,
+    const int in_img_x) {
+  src_x = (src_x > 0) ? src_x : 0.f;
+  *in_img_idx = static_cast<int>(src_x);
+  *x_id = (*in_img_idx < in_img_x - 1) ? 1 : 0;
+  *lambda1 = src_x - *in_img_idx;
+  *lambda2 = 1.f - *lambda1;
 }
 
 struct FastDivModForInterpolate {
@@ -670,83 +670,102 @@ __global__ void KeBilinearInterpBwShareMemory(
   }
 }
 
+__device__ __forceinline__ int GetInputIndex(const size_t nc, const int height,
+                                             const int width, const int h,
+                                             const int w) {
+  return (nc * height + h) * width + w;
+}
+
+template <typename T>
+__global__ void KeBilinearInterpNCHWBw(T* in, const int in_h, const int in_w,
+                                       const int out_h, const int out_w,
+                                       const int n, const int num_channels,
+                                       float ratio_h, float ratio_w,
+                                       const T* __restrict__ out,
+                                       const T align_type_value) {
+  int index = threadIdx.x + blockDim.x * blockIdx.x;
+  int stride = blockDim.x * gridDim.x;
+  int num_out = n * num_channels * out_h * out_w;
+  int num_in = n * num_channels * in_h * in_w;
+
+  for (; index < num_out; index += stride) {
+    int index_tmp = index;
+    int w2 = index_tmp % out_w;
+    index_tmp /= out_w;
+    int h2 = index_tmp % out_h;
+    int nc = index_tmp / out_h;
+
+    int h1, y_id;
+    T h1lambda, h0lambda;
+    T src_y = ratio_h * (h2 + align_type_value) - align_type_value;
+
+    PreCalculatorForLinearInterpInputIndex(&h1, &y_id, &h1lambda, &h0lambda,
+                                           src_y, in_h);
+    int w1, x_id;
+    T w1lambda, w0lambda;
+    T src_x = ratio_w * (w2 + align_type_value) - align_type_value;
+    PreCalculatorForLinearInterpInputIndex(&w1, &x_id, &w1lambda, &w0lambda,
+                                           src_x, in_w);
+
+    T d2val = out[index];
+
+    platform::CudaAtomicAdd(in + GetInputIndex(nc, in_h, in_w, h1, w1),
+                            h0lambda * w0lambda * d2val);
+    platform::CudaAtomicAdd(in + GetInputIndex(nc, in_h, in_w, h1, w1 + x_id),
+                            h0lambda * w1lambda * d2val);
+    platform::CudaAtomicAdd(in + GetInputIndex(nc, in_h, in_w, h1 + y_id, w1),
+                            h1lambda * w0lambda * d2val);
+    platform::CudaAtomicAdd(
+        in + GetInputIndex(nc, in_h, in_w, h1 + y_id, w1 + x_id),
+        h1lambda * w1lambda * d2val);
+  }
+}
+
 template <typename T>
 __global__ void KeBilinearInterpBw(T* in, const int in_h, const int in_w,
                                    const T* __restrict__ out, const int out_h,
                                    const int out_w, const int n,
-                                   const int num_channels, float ratio_h,
-                                   float ratio_w, const T align_type_value,
-                                   bool is_nchw) {
+                                   const int out_chw, const int num_channels,
+                                   float ratio_h, float ratio_w,
+                                   const T align_type_value,
+                                   FastDivModForInterpolate divmods) {
   int tid = blockIdx.x * blockDim.x + threadIdx.x;
   int stride = blockDim.x * gridDim.x;
   int in_chw = in_h * in_w * num_channels;
-  int out_chw = num_channels * out_h * out_w;
   int nthreads = n * out_chw;
 
-  if (is_nchw) {
-    for (; tid < nthreads; tid += stride) {
-      int out_id_h = tid / out_chw;
-      int out_id_w = tid % out_chw;
-      const int in_img_size = in_h * in_w;
-      const int out_img_size = out_h * out_w;
-      T value = out[out_id_h * out_chw + out_id_w];
-
-      int channel_id = out_id_w / out_img_size;
-      int out_img_idy = (out_id_w % out_img_size) / out_w;
-      int out_img_idx = tid % out_w;
-      int in_img_idx, in_img_idy, w_id, h_id;
-      T w1lambda, h1lambda, w2lambda, h2lambda;
-
-      T src_w = ratio_w * (out_img_idx + align_type_value) - align_type_value;
-      T src_h = ratio_h * (out_img_idy + align_type_value) - align_type_value;
-
-      PreCalculatorForLinearInterpInputIndex(&in_img_idx, &w_id, &w1lambda,
-                                             &w2lambda, src_w, in_w);
-      PreCalculatorForLinearInterpInputIndex(&in_img_idy, &h_id, &h1lambda,
-                                             &h2lambda, src_h, in_h);
-
-      T* in_pos = &in[out_id_h * in_chw + channel_id * in_img_size +
-                      in_img_idy * in_w + in_img_idx];
-      platform::CudaAtomicAdd(&in_pos[0], h2lambda * w2lambda * value);
-      platform::CudaAtomicAdd(&in_pos[w_id], h2lambda * w1lambda * value);
-      platform::CudaAtomicAdd(&in_pos[h_id * in_w],
-                              h1lambda * w2lambda * value);
-      platform::CudaAtomicAdd(&in_pos[h_id * in_w + w_id],
-                              h1lambda * w1lambda * value);
-    }
-  } else {
-    for (; tid < nthreads; tid += stride) {
-      int out_id_h = tid / out_chw;
-      int out_id_w = tid % out_chw;
-      const int in_img_size = in_h * in_w;
-      const int out_img_size = out_h * out_w;
-      T value = out[out_id_h * out_chw + out_id_w];
-
-      int out_img_idy = out_id_w / (out_w * num_channels);
-      int out_img_idx = out_id_w % (out_w * num_channels) / num_channels;
-      int channel_id = tid % num_channels;
-
-      int in_img_idx, in_img_idy, w_id, h_id;
-      T w1lambda, h1lambda, w2lambda, h2lambda;
-      T src_w = ratio_w * (out_img_idx + align_type_value) - align_type_value;
-      T src_h = ratio_h * (out_img_idy + align_type_value) - align_type_value;
-
-      PreCalculatorForLinearInterpInputIndex(&in_img_idx, &w_id, &w1lambda,
-                                             &w2lambda, src_w, in_w);
-      PreCalculatorForLinearInterpInputIndex(&in_img_idy, &h_id, &h1lambda,
-                                             &h2lambda, src_h, in_h);
-
-      T* in_pos = &in[out_id_h * in_chw + in_img_idy * in_w * num_channels +
-                      in_img_idx * num_channels + channel_id];
-      platform::CudaAtomicAdd(&in_pos[0], h2lambda * w2lambda * value);
-      platform::CudaAtomicAdd(&in_pos[w_id * num_channels],
-                              h2lambda * w1lambda * value);
-      platform::CudaAtomicAdd(&in_pos[h_id * in_w * num_channels],
-                              h1lambda * w2lambda * value);
-      platform::CudaAtomicAdd(
-          &in_pos[h_id * in_w * num_channels + w_id * num_channels],
-          h1lambda * w1lambda * value);
-    }
+  for (; tid < nthreads; tid += stride) {
+    auto out_id_divmod = divmods.output_w_div.Divmod(tid);
+    int out_id_h = out_id_divmod.val[0];
+    int out_id_w = out_id_divmod.val[1];
+
+    int channel_id = divmods.channels_div.Divmod(tid).val[1];
+    auto outimg_id_divmod = divmods.output_wc_div.Divmod(out_id_w);
+    int out_img_idy = outimg_id_divmod.val[0];
+    int out_img_idx =
+        divmods.channels_div.Divmod(outimg_id_divmod.val[1]).val[0];
+
+    int in_img_idx, in_img_idy, w_id, h_id;
+    T w1lambda, h1lambda, w2lambda, h2lambda;
+    T src_w = ratio_w * (out_img_idx + align_type_value) - align_type_value;
+    T src_h = ratio_h * (out_img_idy + align_type_value) - align_type_value;
+
+    PreCalculatorForLinearInterpInputIndex(&in_img_idx, &w_id, &w1lambda,
+                                           &w2lambda, src_w, in_w);
+    PreCalculatorForLinearInterpInputIndex(&in_img_idy, &h_id, &h1lambda,
+                                           &h2lambda, src_h, in_h);
+
+    T value = out[tid];
+    T* in_pos = &in[out_id_h * in_chw + in_img_idy * in_w * num_channels +
+                    in_img_idx * num_channels + channel_id];
+    platform::CudaAtomicAdd(&in_pos[0], h2lambda * w2lambda * value);
+    platform::CudaAtomicAdd(&in_pos[w_id * num_channels],
+                            h2lambda * w1lambda * value);
+    platform::CudaAtomicAdd(&in_pos[h_id * in_w * num_channels],
+                            h1lambda * w2lambda * value);
+    platform::CudaAtomicAdd(
+        &in_pos[h_id * in_w * num_channels + w_id * num_channels],
+        h1lambda * w1lambda * value);
   }
 }
 
@@ -1907,11 +1926,23 @@ static void Interpolate2DCUDABwd(const framework::ExecutionContext& ctx,
                ctx.cuda_device_context().stream()>>>(
           input_grad_data, in_h, in_w, output_grad_data, out_h, out_w, n, c,
           ratio_h, ratio_w, align_type_value, is_nchw);
+    } else if (!optimize_flag & is_nchw) {
+      //
+      const int num_kernels = n * c * out_h * out_w;
+      const int num_threads =
+          std::min(ctx.cuda_device_context().GetMaxThreadsPerBlock(), 1024);
+      KeBilinearInterpNCHWBw<
+          T><<<platform::DivUp(num_kernels, num_threads), num_threads, 0,
+               ctx.cuda_device_context().stream()>>>(
+          input_grad_data, in_h, in_w, out_h, out_w, n, c, ratio_h, ratio_w,
+          output_grad_data, align_type_value);
     } else {
+      int64_t cw = c * out_w;
+      auto interp_divmods = FastDivModForInterpolate(c, out_chw, cw);
       KeBilinearInterpBw<T><<<config.block_per_grid, config.thread_per_block, 0,
                               ctx.cuda_device_context().stream()>>>(
-          input_grad_data, in_h, in_w, output_grad_data, out_h, out_w, n, c,
-          ratio_h, ratio_w, align_type_value, is_nchw);
+          input_grad_data, in_h, in_w, output_grad_data, out_h, out_w, n,
+          out_chw, c, ratio_h, ratio_w, align_type_value, interp_divmods);
     }
   } else if ("bicubic" == interp_method) {
 #ifdef __HIPCC__

paddle/fluid/operators/kron_op.cc

@@ -17,9 +17,7 @@ limitations under the License. */
 #include <unordered_map>
 #include <vector>
 
-#include "paddle/fluid/operators/kron_op.h"
-#include "paddle/fluid/platform/complex.h"
-#include "paddle/fluid/platform/float16.h"
+#include "paddle/fluid/framework/op_registry.h"
 
 namespace paddle {
 namespace operators {
@@ -178,27 +176,4 @@ namespace ops = paddle::operators;
 REGISTER_OPERATOR(kron, ops::KronOp, ops::KronOpMaker,
                   ops::KronGradOpMaker<paddle::framework::OpDesc>,
                   ops::KronGradOpMaker<paddle::imperative::OpBase>);
-REGISTER_OP_CPU_KERNEL(
-    kron, ops::KronKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext,
-                    paddle::platform::float16>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext, int64_t>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext,
-                    paddle::platform::complex<float>>,
-    ops::KronKernel<paddle::platform::CPUDeviceContext,
-                    paddle::platform::complex<double>>);
-
 REGISTER_OPERATOR(kron_grad, ops::KronGradOp);
-REGISTER_OP_CPU_KERNEL(
-    kron_grad, ops::KronGradKernel<paddle::platform::CPUDeviceContext, float>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext, double>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext,
-                        paddle::platform::float16>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext, int>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext, int64_t>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext,
-                        paddle::platform::complex<float>>,
-    ops::KronGradKernel<paddle::platform::CPUDeviceContext,
-                        paddle::platform::complex<double>>);

paddle/fluid/operators/kron_op.cu

-/* 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. */
-
-#include "paddle/fluid/operators/kron_op.h"
-#include "paddle/fluid/platform/complex.h"
-#include "paddle/fluid/platform/float16.h"
-
-namespace ops = paddle::operators;
-REGISTER_OP_CUDA_KERNEL(
-    kron, ops::KronKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext,
-                    paddle::platform::float16>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext, int64_t>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext,
-                    paddle::platform::complex<float>>,
-    ops::KronKernel<paddle::platform::CUDADeviceContext,
-                    paddle::platform::complex<double>>);
-
-REGISTER_OP_CUDA_KERNEL(
-    kron_grad, ops::KronGradKernel<paddle::platform::CUDADeviceContext, float>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext, double>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext,
-                        paddle::platform::float16>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext, int>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext, int64_t>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext,
-                        paddle::platform::complex<float>>,
-    ops::KronGradKernel<paddle::platform::CUDADeviceContext,
-                        paddle::platform::complex<double>>);

paddle/fluid/operators/lstsq_op.cu

@@ -17,9 +17,11 @@
 
 #include <string>
 #include <vector>
+#include "paddle/fluid/framework/phi_utils.h"
 #include "paddle/fluid/operators/lstsq_op.h"
 #include "paddle/fluid/operators/qr_op.h"
 #include "paddle/fluid/platform/dynload/cusolver.h"
+#include "paddle/phi/kernels/triangular_solve_kernel.h"
 
 namespace paddle {
 namespace operators {
@@ -70,6 +72,10 @@ class LstsqCUDAKernel : public framework::OpKernel<T> {
     Tensor tau = dito.Fill(tau_dims_vec, 0);
     auto tau_data = tau.mutable_data<T>(context.GetPlace());
 
+    using Context =
+        typename framework::ConvertToPhiContext<DeviceContext>::TYPE;
+    auto& phi_dev_ctx = static_cast<const Context&>(dev_ctx);
+
     if (m >= n) {
       Tensor tmp_x = dito.Transpose(new_x);
       Tensor tmp_y = dito.Transpose(new_y);
@@ -93,8 +99,9 @@ class LstsqCUDAKernel : public framework::OpKernel<T> {
       Tensor slice_y = dito.Slice(trans_y, {-2}, {0}, {min_mn});
 
       // Step 3, solve R X = Y
-      triangular_solve<DeviceContext, T>(dev_ctx, res_r, slice_y, solution,
-                                         true, false, false);
+      phi::TriangularSolveKernel<T, Context>(phi_dev_ctx, res_r, slice_y, true,
+                                             false, false, solution);
+
     } else {
       auto x_data = new_x.mutable_data<T>(context.GetPlace());
       auto y_data = new_y.mutable_data<T>(context.GetPlace());
@@ -105,8 +112,8 @@ class LstsqCUDAKernel : public framework::OpKernel<T> {
 
       // Step 2, solve R^H Z = Y
       Tensor trans_r = dito.Transpose(new_x);
-      triangular_solve<DeviceContext, T>(dev_ctx, trans_r, new_y, solution,
-                                         true, true, false);
+      phi::TriangularSolveKernel<T, Context>(phi_dev_ctx, trans_r, new_y, true,
+                                             true, false, solution);
 
       // Step 3, X <- Q Z
       BatchedOrgqr<DeviceContext, T>(dev_ctx, batch_count, n, n, min_mn, x_data,

paddle/fluid/operators/lstsq_op.h

@@ -22,7 +22,6 @@
 #include "paddle/fluid/operators/math/matrix_solve.h"
 #include "paddle/fluid/operators/svd_helper.h"
 #include "paddle/fluid/operators/transpose_op.h"
-#include "paddle/fluid/operators/triangular_solve_op.h"
 #include "paddle/fluid/platform/for_range.h"
 #include "paddle/phi/kernels/funcs/complex_functors.h"
 #include "paddle/phi/kernels/funcs/lapack/lapack_function.h"

paddle/fluid/operators/math/CMakeLists.txt

@@ -20,7 +20,6 @@ math_library(sampler DEPS generator)
 
 # math_library(math_function DEPS blas dense_tensor tensor)
 math_library(maxouting)
-math_library(pooling)
 
 if(WITH_MKLDNN)
     math_library(selected_rows_functor DEPS selected_rows_utils math_function blas mkldnn_axpy_handler)

paddle/fluid/operators/math/matrix_solve.h

@@ -117,14 +117,6 @@ class MatrixSolveFunctor {
                   const framework::Tensor& b, framework::Tensor* out);
 };
 
-template <typename DeviceContext, typename T>
-class TriangularSolveFunctor {
- public:
-  void operator()(const DeviceContext& context, const framework::Tensor* a,
-                  framework::Tensor* b, bool left, bool upper, bool transpose,
-                  bool unitriangular);
-};
-
 }  // namespace math
 }  // namespace operators
 }  // namespace paddle

paddle/fluid/operators/mkldnn/test_mkldnn_op_inplace.cc

@@ -24,6 +24,7 @@
 #include "paddle/fluid/platform/device_context.h"
 #include "paddle/fluid/platform/enforce.h"
 #include "paddle/fluid/platform/place.h"
+#include "paddle/phi/core/kernel_registry.h"
 
 USE_OP_ITSELF(elementwise_add);
 USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN);
@@ -32,6 +33,8 @@ USE_OP_DEVICE_KERNEL(relu, MKLDNN);
 USE_OP_ITSELF(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
 
+PD_DECLARE_KERNEL(softmax, CPU, ALL_LAYOUT);
+
 namespace paddle {
 namespace operators {