[MKLDNN]Move mkldnn activation kernel to phi (#44365)

* move mkldnn activation kernel

* fix compile bugs

* fix compile bugs

* deal with conflict

* fix compile bugs

* fix windows compile bugs

* mkldnn unittest fix

* change mutable to alloc

* fix unittest bugs

* modify code according comment

cmake/operators.cmake

@@ -510,7 +510,7 @@ function(op_library TARGET)
   if(WITH_MKLDNN AND ${mkldnn_cc_srcs_len} GREATER 0)
     # Append first implemented MKLDNN activation operator
     if(${MKLDNN_FILE} STREQUAL "activation_mkldnn_op")
-      file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(relu, MKLDNN);\n")
+      file(APPEND ${pybind_file} "USE_OP_DEVICE_KERNEL(gelu, MKLDNN);\n")
     elseif(${MKLDNN_FILE} STREQUAL "conv_mkldnn_op")
       file(APPEND ${pybind_file}
            "USE_OP_DEVICE_KERNEL_WITH_CUSTOM_TYPE(conv2d, MKLDNN, FP32);\n")

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

@@ -20,17 +20,18 @@
 #include "paddle/fluid/framework/ir/mkldnn/mkldnn_inplace_pass.h"
 #include "paddle/fluid/framework/ir/pass_tester_helper.h"
 #include "paddle/fluid/framework/op_registry.h"
+#include "paddle/phi/core/kernel_registry.h"
 
 USE_OP_ITSELF(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
 USE_OP_ITSELF(elementwise_add);
 USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN);
 USE_OP_ITSELF(leaky_relu);
-USE_OP_DEVICE_KERNEL(leaky_relu, MKLDNN);
+PD_DECLARE_KERNEL(leaky_relu, OneDNN, ALL_LAYOUT);
 USE_OP_ITSELF(gelu);
 USE_OP_ITSELF(relu);
 USE_OP_ITSELF(tanh);
-USE_OP_DEVICE_KERNEL(tanh, MKLDNN);
+PD_DECLARE_KERNEL(tanh, OneDNN, ALL_LAYOUT);
 PD_DECLARE_ARG_MAPPING_FN(gelu);
 
 namespace paddle {

paddle/fluid/framework/operator.cc

@@ -2200,7 +2200,9 @@ Scope* OperatorWithKernel::PrepareData(
              (in_def->backend != phi::Backend::GPUDNN ||
               tensor_backend != phi::Backend::GPU) &&
              (in_def->backend != phi::Backend::KPS ||
-              tensor_backend != phi::Backend::XPU)) ||
+              tensor_backend != phi::Backend::XPU) &&
+             (in_def->backend != phi::Backend::ONEDNN ||
+              tensor_backend != phi::Backend::CPU)) ||
             tensor_in->place().GetType() == AllocationType::GPUPINNED) {
           new_expected_kernel_key = std::make_unique<OpKernelType>(
               expected_kernel_key.data_type_,

paddle/fluid/imperative/tests/test_prepare_op.cc

@@ -259,5 +259,5 @@ TEST(test_prepare_op, test_prepare_data_cpu_mkldnn) {
 USE_OP_ITSELF(split);
 USE_OP_ITSELF(relu);
 #ifdef PADDLE_WITH_MKLDNN
-USE_OP_DEVICE_KERNEL(relu, MKLDNN);
+PD_DECLARE_KERNEL(relu, OneDNN, ALL_LAYOUT);
 #endif

paddle/fluid/operators/mkldnn/activation_mkldnn_op.cc

@@ -196,100 +196,21 @@ struct SoftplusMKLDNNFunctor : public BaseActivationFunctor<T> {
   }
 };
 
-template <typename T>
-using ReluMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_relu>;
-
 template <typename T>
 using Relu6MKLDNNFunctor =
     MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_bounded_relu>;
 
-template <typename T>
-using SwishMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_swish>;
-
-template <typename T>
-using HardSwishMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_hardswish>;
-
-template <typename T>
-using MishMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_mish>;
-
-template <typename T>
-using SigmoidMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_logistic>;
-
-template <typename T>
-using TanhMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_tanh>;
-
-template <typename T>
-using SqrtMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_sqrt>;
-
 template <typename T>
 using AbsMKLDNNFunctor = MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_abs>;
 
-template <typename T>
-using EluMKLDNNFunctor = MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_elu>;
-
-template <typename T>
-using ExpMKLDNNFunctor = MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_exp>;
-
-template <typename T>
-using RoundMKLDNNFunctor =
-    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_round>;
-
-template <typename T>
-using ReluMKLDNNGradFunctor =
-    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_relu>;
-
 template <typename T>
 using Relu6MKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_bounded_relu>;
 
-template <typename T>
-using SwishMKLDNNGradFunctor =
-    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_swish>;
-
-template <typename T>
-using HardSwishMKLDNNGradFunctor =
-    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_hardswish>;
-
-template <typename T>
-using MishMKLDNNGradFunctor =
-    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_mish>;
-
-template <typename T>
-using SigmoidMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
-    T,
-    dnnl::algorithm::eltwise_logistic_use_dst_for_bwd>;
-
-template <typename T>
-using TanhMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
-    T,
-    dnnl::algorithm::eltwise_tanh_use_dst_for_bwd>;
-
-template <typename T>
-using SqrtMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
-    T,
-    dnnl::algorithm::eltwise_sqrt_use_dst_for_bwd>;
-
 template <typename T>
 using AbsMKLDNNGradFunctor =
     MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_abs>;
 
-template <typename T>
-using EluMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
-    T,
-    dnnl::algorithm::eltwise_elu_use_dst_for_bwd>;
-
-template <typename T>
-using ExpMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
-    T,
-    dnnl::algorithm::eltwise_exp_use_dst_for_bwd>;
-
 }  // namespace operators
 }  // namespace paddle
 
@@ -318,24 +239,11 @@ namespace ops = paddle::operators;
 
 #define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro)            \
   __macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);    \
-  __macro(elu, EluMKLDNNFunctor, EluMKLDNNGradUseOutFunctor);              \
-  __macro(exp, ExpMKLDNNFunctor, ExpMKLDNNGradUseOutFunctor);              \
   __macro(gelu, GeluMKLDNNFunctor, GeluMKLDNNGradFunctor); \
-  __macro(hard_swish, HardSwishMKLDNNFunctor, HardSwishMKLDNNGradFunctor); \
-  __macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor);           \
-  __macro(mish, MishMKLDNNFunctor, MishMKLDNNGradFunctor);                 \
-  __macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor);                 \
-  __macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor);              \
-  __macro(sigmoid, SigmoidMKLDNNFunctor, SigmoidMKLDNNGradUseOutFunctor);  \
-  __macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradUseOutFunctor);           \
-  __macro(swish, SwishMKLDNNFunctor, SwishMKLDNNGradFunctor);              \
-  __macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradUseOutFunctor);
+  __macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor);
 
 FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL);
 
-// round eltwise primitive doesn't support BF16, nor does it support grad
-REGISTER_ACTIVATION_MKLDNN_KERNEL_FWD_ONLY(round, RoundMKLDNNFunctor);
-
 namespace ops = paddle::operators;
 REGISTER_OP_KERNEL(
     softplus,

paddle/fluid/operators/mkldnn/test_mkldnn_caching.cc

@@ -25,13 +25,14 @@
 #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);
 USE_OP_ITSELF(elementwise_mul);
 USE_OP_DEVICE_KERNEL(elementwise_mul, MKLDNN);
 USE_OP_ITSELF(relu);
-USE_OP_DEVICE_KERNEL(relu, MKLDNN);
+PD_DECLARE_KERNEL(relu, OneDNN, ALL_LAYOUT);
 USE_OP_ITSELF(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
 USE_OP_ITSELF(conv2d);

paddle/fluid/operators/mkldnn/test_mkldnn_op_inplace.cc

@@ -30,7 +30,7 @@
 USE_OP_ITSELF(elementwise_add);
 USE_OP_DEVICE_KERNEL(elementwise_add, MKLDNN);
 USE_OP_ITSELF(relu);
-USE_OP_DEVICE_KERNEL(relu, MKLDNN);
+PD_DECLARE_KERNEL(relu, OneDNN, ALL_LAYOUT);
 USE_OP_ITSELF(softmax);
 USE_OP_DEVICE_KERNEL(softmax, MKLDNN);
 

paddle/fluid/operators/mkldnn/test_mkldnn_op_nhwc.cc

@@ -30,7 +30,7 @@
 USE_OP_ITSELF(pool2d);
 USE_OP_DEVICE_KERNEL(pool2d, MKLDNN);
 USE_OP_ITSELF(relu);
-USE_OP_DEVICE_KERNEL(relu, MKLDNN);
+PD_DECLARE_KERNEL(relu, OneDNN, ALL_LAYOUT);
 USE_OP_ITSELF(transpose);
 USE_OP_DEVICE_KERNEL(transpose, MKLDNN);
 USE_OP_ITSELF(shape);

paddle/fluid/platform/mkldnn_reuse.h

@@ -25,6 +25,7 @@ limitations under the License. */
 #include "paddle/fluid/operators/pool_op.h"
 #include "paddle/fluid/platform/mkldnn_helper.h"
 #include "paddle/fluid/platform/place.h"
+#include "paddle/phi/kernels/funcs/onednn/mkldnn_reuse.h"
 
 namespace paddle {
 namespace platform {
@@ -38,216 +39,8 @@ template <typename T,
           typename TForward,
           typename TBackward = mkldnn_dummy_primitive,
           typename TBackward_params = mkldnn_dummy_primitive>
-class MKLDNNHandlerNoCachingT {
- public:
-  MKLDNNHandlerNoCachingT(dnnl::engine engine, platform::Place cpu_place)
-      : engine_(engine), place_(cpu_place), fwd_pd_(nullptr), bwd_pd_(nullptr) {
-    platform::MKLDNNDeviceContext::tls().log_lib_version();
-  }
-
-  std::shared_ptr<TForward> AcquireForwardPrimitive() {
-    return std::make_shared<TForward>(*fwd_pd_);
-  }
-
-  std::shared_ptr<TBackward> AcquireBackwardPrimitive() {
-    return std::make_shared<TBackward>(*bwd_pd_);
-  }
-
-  std::shared_ptr<TBackward_params> AcquireBackwardWeightsPrimitive() {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_,
-        platform::errors::Unavailable("BWD_PD should be set when "
-                                      "getting BWD prim ."));
-    return std::make_shared<TBackward_params>(*bwd_w_pd_);
-  }
-
-  std::shared_ptr<dnnl::memory> AcquireSrcMemory(
-      const framework::Tensor* input) {
-    const T* input_data = input->data<T>();
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->src_desc(),
-                                            to_void_cast<T>(input_data));
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<dnnl::memory> AcquireDstMemory(framework::Tensor* output) {
-    T_out* ptr =
-        output->mutable_data<T_out>(place_, fwd_pd_->dst_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), ptr);
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<dnnl::memory> AcquireDstMemory(void) {
-    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc());
-  }
-
-  template <typename T_out = T>
-  std::shared_ptr<dnnl::memory> AcquireDstMemory(
-      const framework::Tensor* output) {
-    const T_out* output_data = output->data<T_out>();
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->dst_desc(),
-                                            to_void_cast<T_out>(output_data));
-  }
-
-  std::shared_ptr<dnnl::memory> AcquireDiffDstMemory(
-      const framework::Tensor* diffdst) {
-    const T* ptr = diffdst->data<T>();
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_dst_desc(),
-                                            to_void_cast<T>(ptr));
-  }
-
-  std::shared_ptr<dnnl::memory> AcquireDiffSrcMemory(
-      framework::Tensor* diffsrc) {
-    T* ptr =
-        diffsrc->mutable_data<T>(place_, bwd_pd_->diff_src_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_src_desc(), ptr);
-  }
-
-  // Buffer of given Tensor is used for oneDNN computation
-  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(
-      framework::Tensor* diff_weights) {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_,
-        platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
-    T* ptr = diff_weights->mutable_data<T>(
-        place_, bwd_w_pd_->diff_weights_desc().get_size());
-    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(),
-                                            ptr);
-  }
-
-  // Buffer is allocated by oneDNN to store computation results
-  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(void) {
-    PADDLE_ENFORCE_NOT_NULL(
-        bwd_w_pd_,
-        platform::errors::Unavailable(
-            "BWD_W_PD should be set when getting BWD grad of weights."));
-    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc());
-  }
-
- protected:
-  // If your primitive descriptor requires attributes, pass them as a
-  // first argument and paramters to descriptor constructor in the following
-  // arguments. Otherwise, all arguments will be forwarded to descriptor
-  // constructor, including the first one.
-  template <typename Arg, typename... Args>
-  void AcquireForwardPrimitiveDescriptor(Arg&& first_arg, Args&&... args) {
-    CreateForwardPrimitiveDescriptor(first_arg, std::forward<Args>(args)...);
-  }
-
-  // Using sfinae to specialise variadic function. Workaround for not having
-  // if constexpr in C++ 11.
-  template <class First, class... Args>
-  typename std::enable_if<std::is_same<typename std::decay<First>::type,
-                                       dnnl::primitive_attr>::value>::type
-  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
-    auto fwd_desc = typename TForward::desc(std::forward<Args>(args)...);
-    fwd_pd_ = std::make_shared<typename TForward::primitive_desc>(
-        fwd_desc, first, engine_);
-  }
-
-  template <class First, class... Args>
-  typename std::enable_if<!std::is_same<typename std::decay<First>::type,
-                                        dnnl::primitive_attr>::value>::type
-  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
-    auto fwd_desc = typename TForward::desc(std::forward<First>(first),
-                                            std::forward<Args>(args)...);
-    fwd_pd_ =
-        std::make_shared<typename TForward::primitive_desc>(fwd_desc, engine_);
-  }
-
-  template <typename... Args>
-  void AcquireBackwardPrimitiveDescriptor(Args&&... args) {
-    // fwd_pd_ is set during grad by calling
-    // AcquireForwardPrimitiveDescriptor
-    PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
-                            platform::errors::Unavailable(
-                                "Get MKLDNN Forward primitive %s failed."));
-    auto bwd_desc = typename TBackward::desc(std::forward<Args>(args)...);
-    bwd_pd_ = std::make_shared<typename TBackward::primitive_desc>(
-        bwd_desc, engine_, *fwd_pd_);
-  }
-
-  template <typename... Args>
-  void AcquireBackwardWeightsPrimitiveDescriptor(Args&&... args) {
-    // fwd_pd_ is set during grad by calling
-    // AcquireForwardPrimitiveDescriptor
-    PADDLE_ENFORCE_NOT_NULL(fwd_pd_,
-                            platform::errors::Unavailable(
-                                "Get MKLDNN Forward primitive %s failed."));
-    auto bwd_desc =
-        typename TBackward_params::desc(std::forward<Args>(args)...);
-    bwd_w_pd_ = std::make_shared<typename TBackward_params::primitive_desc>(
-        bwd_desc, engine_, *fwd_pd_);
-  }
-
-  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
-      dnnl::memory::desc md, void* ptr) {
-    return std::make_shared<dnnl::memory>(md, engine_, ptr);
-  }
-
-  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
-      dnnl::memory::desc md) {
-    return std::make_shared<dnnl::memory>(md, engine_);
-  }
-
-  void AcquireReorder(const std::shared_ptr<dnnl::memory>& user_memory_p,
-                      const std::shared_ptr<dnnl::memory>& target_memory_p) {
-    auto reorder_p =
-        std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
-
-    auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
-
-    platform::RecordEvent record_reorder("int_reorder",
-                                         platform::TracerEventType::UserDefined,
-                                         2,
-                                         platform::EventRole::kUniqueOp);
-    reorder_p->execute(
-        astream,
-        {{DNNL_ARG_FROM, *user_memory_p}, {DNNL_ARG_TO, *target_memory_p}});
-    astream.wait();
-  }
-
-  template <typename F = T>
-  std::shared_ptr<dnnl::memory> AcquireMemoryWithReorder(
-      const dnnl::memory::desc& user_md,
-      const dnnl::memory::desc& target_md,
-      void* ptr,
-      bool is_persistent = false,
-      std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {}) {
-    std::shared_ptr<dnnl::memory> target_memory_p;
-    if (custom_reorder_func) {
-      auto reordered_data =
-          custom_reorder_func(reinterpret_cast<const F*>(ptr));
-      ptr = reinterpret_cast<void*>(reordered_data.get());
-    }
-    auto user_memory_p = std::make_shared<dnnl::memory>(user_md, engine_, ptr);
-    if (user_md != target_md) {
-      target_memory_p = std::make_shared<dnnl::memory>(target_md, engine_);
-      auto reorder_p =
-          std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
-
-      auto& astream = platform::MKLDNNDeviceContext::tls().get_stream();
-      platform::RecordEvent record_reorder(
-          "int_reorder",
-          platform::TracerEventType::UserDefined,
-          2,
-          platform::EventRole::kUniqueOp);
-      reorder_p->execute(
-          astream,
-          {{DNNL_ARG_FROM, *user_memory_p}, {DNNL_ARG_TO, *target_memory_p}});
-      astream.wait();
-    } else {
-      target_memory_p = user_memory_p;
-    }
-    return target_memory_p;
-  }
-
-  dnnl::engine engine_;
-  platform::Place place_;
-  std::shared_ptr<typename TForward::primitive_desc> fwd_pd_;
-  std::shared_ptr<typename TBackward::primitive_desc> bwd_pd_;
-  std::shared_ptr<typename TBackward_params::primitive_desc> bwd_w_pd_;
-};
+using MKLDNNHandlerNoCachingT = phi::funcs::
+    MKLDNNHandlerNoCachingT<T, TForward, TBackward, TBackward_params>;
 
 template <typename T,
           typename TForward,

paddle/phi/kernels/CMakeLists.txt

@@ -89,17 +89,6 @@ copy_if_different(${kernel_declare_file} ${kernel_declare_file_final})
 file(GLOB kernel_h "*.h" "selected_rows/*.h" "sparse/*.h" "strings/*.h")
 file(GLOB kernel_impl_h "impl/*.h" "selected_rows/impl/*.h")
 file(GLOB kernel_primitive_h "primitive/*.h")
-file(
-  GLOB
-  kernel_cc
-  "*.cc"
-  "cpu/*.cc"
-  "selected_rows/*.cc"
-  "selected_rows/cpu/*.cc"
-  "sparse/*.cc"
-  "sparse/cpu/*.cc"
-  "strings/*.cc"
-  "strings/cpu/*.cc")
 
 file(
   GLOB
@@ -113,10 +102,34 @@ file(
   "strings/*.cu"
   "strings/gpu/*.cu")
 
-# file(GLOB kernel_cudnn "gpudnn/*.cu")
-# file(GLOB kernel_kps "kps/*.cu")
+if(WITH_MKLDNN)
+  file(
+    GLOB
+    kernel_cc
+    "*.cc"
+    "cpu/*.cc"
+    "selected_rows/*.cc"
+    "selected_rows/cpu/*.cc"
+    "sparse/*.cc"
+    "sparse/cpu/*.cc"
+    "strings/*.cc"
+    "strings/cpu/*.cc"
+    "onednn/*.cc")
+else()
+  file(
+    GLOB
+    kernel_cc
+    "*.cc"
+    "cpu/*.cc"
+    "selected_rows/*.cc"
+    "selected_rows/cpu/*.cc"
+    "sparse/*.cc"
+    "sparse/cpu/*.cc"
+    "strings/*.cc"
+    "strings/cpu/*.cc")
+endif()
+
 file(GLOB kernel_xpu "xpu/*.cc")
-file(GLOB kernel_onednn "onednn/*.cc")
 
 add_library(phi_cpu ${kernel_cc})
 kernel_declare("${kernel_cc}")
@@ -156,12 +169,4 @@ if(WITH_XPU)
   set(ADD_PHI_KERNELS ${ADD_PHI_KERNELS} phi_xpu)
 endif()
 
-if(WITH_MKLDNN)
-  add_library(phi_onednn ${kernel_onednn})
-  kernel_declare(${kernel_onednn})
-  set(COMMON_KERNEL_DEPS ${COMMON_KERNEL_DEPS} phi_backends)
-  target_link_libraries(phi_onednn ${COMMON_KERNEL_DEPS})
-  set(ADD_PHI_KERNELS ${ADD_PHI_KERNELS} phi_onednn)
-endif()
-
 set_property(GLOBAL PROPERTY PHI_KERNELS ${ADD_PHI_KERNELS})

paddle/phi/kernels/funcs/onednn/mkldnn_reuse.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 <algorithm>
+#include <memory>
+#include <sstream>
+#include <string>
+#include <utility>
+#include <vector>
+
+#include "paddle/fluid/framework/data_layout_transform.h"
+#include "paddle/fluid/platform/mkldnn_helper.h"
+#include "paddle/phi/backends/onednn/onednn_context.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/dense_tensor.h"
+
+namespace phi {
+namespace funcs {
+
+using user_function = std::function<std::shared_ptr<float>(const float*)>;
+using memory = dnnl::memory;
+using Place = phi::Place;
+
+template <typename T,
+          typename TForward,
+          typename TBackward = paddle::platform::mkldnn_dummy_primitive,
+          typename TBackward_params = paddle::platform::mkldnn_dummy_primitive>
+class MKLDNNHandlerNoCachingT {
+ public:
+  MKLDNNHandlerNoCachingT(dnnl::engine engine, Place cpu_place)
+      : engine_(engine), place_(cpu_place), fwd_pd_(nullptr), bwd_pd_(nullptr) {
+    phi::OneDNNContext::tls().log_lib_version();
+  }
+
+  std::shared_ptr<TForward> AcquireForwardPrimitive() {
+    return std::make_shared<TForward>(*fwd_pd_);
+  }
+
+  std::shared_ptr<TBackward> AcquireBackwardPrimitive() {
+    return std::make_shared<TBackward>(*bwd_pd_);
+  }
+
+  std::shared_ptr<TBackward_params> AcquireBackwardWeightsPrimitive() {
+    PADDLE_ENFORCE_NOT_NULL(
+        bwd_w_pd_,
+        phi::errors::Unavailable("BWD_PD should be set when "
+                                 "getting BWD prim ."));
+    return std::make_shared<TBackward_params>(*bwd_w_pd_);
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireSrcMemory(const DenseTensor* input) {
+    const T* input_data = input->data<T>();
+    return this->AcquireMemoryFromPrimitive(
+        fwd_pd_->src_desc(), paddle::platform::to_void_cast<T>(input_data));
+  }
+
+  template <typename T_out = T>
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(DenseTensor* output) {
+    T_out* ptr =
+        output->mutable_data<T_out>(place_, fwd_pd_->dst_desc().get_size());
+    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc(), ptr);
+  }
+
+  template <typename T_out = T>
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(void) {
+    return this->AcquireMemoryFromPrimitive(fwd_pd_->dst_desc());
+  }
+
+  template <typename T_out = T>
+  std::shared_ptr<dnnl::memory> AcquireDstMemory(const DenseTensor* output) {
+    const T_out* output_data = output->data<T_out>();
+    return this->AcquireMemoryFromPrimitive(
+        bwd_pd_->dst_desc(),
+        paddle::platform::to_void_cast<T_out>(output_data));
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireDiffDstMemory(
+      const DenseTensor* diffdst) {
+    const T* ptr = diffdst->data<T>();
+    return this->AcquireMemoryFromPrimitive(
+        bwd_pd_->diff_dst_desc(), paddle::platform::to_void_cast<T>(ptr));
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireDiffSrcMemory(DenseTensor* diffsrc) {
+    T* ptr =
+        diffsrc->mutable_data<T>(place_, bwd_pd_->diff_src_desc().get_size());
+    return this->AcquireMemoryFromPrimitive(bwd_pd_->diff_src_desc(), ptr);
+  }
+
+  // Buffer of given Tensor is used for oneDNN computation
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(
+      DenseTensor* diff_weights) {
+    PADDLE_ENFORCE_NOT_NULL(
+        bwd_w_pd_,
+        phi::errors::Unavailable(
+            "BWD_W_PD should be set when getting BWD grad of weights."));
+    T* ptr = diff_weights->mutable_data<T>(
+        place_, bwd_w_pd_->diff_weights_desc().get_size());
+    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc(),
+                                            ptr);
+  }
+
+  // Buffer is allocated by oneDNN to store computation results
+  std::shared_ptr<dnnl::memory> AcquireDiffWeightsMemory(void) {
+    PADDLE_ENFORCE_NOT_NULL(
+        bwd_w_pd_,
+        phi::errors::Unavailable(
+            "BWD_W_PD should be set when getting BWD grad of weights."));
+    return this->AcquireMemoryFromPrimitive(bwd_w_pd_->diff_weights_desc());
+  }
+
+ protected:
+  // If your primitive descriptor requires attributes, pass them as a
+  // first argument and paramters to descriptor constructor in the following
+  // arguments. Otherwise, all arguments will be forwarded to descriptor
+  // constructor, including the first one.
+  template <typename Arg, typename... Args>
+  void AcquireForwardPrimitiveDescriptor(Arg&& first_arg, Args&&... args) {
+    CreateForwardPrimitiveDescriptor(first_arg, std::forward<Args>(args)...);
+  }
+
+  // Using sfinae to specialise variadic function. Workaround for not having
+  // if constexpr in C++ 11.
+  template <class First, class... Args>
+  typename std::enable_if<std::is_same<typename std::decay<First>::type,
+                                       dnnl::primitive_attr>::value>::type
+  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
+    auto fwd_desc = typename TForward::desc(std::forward<Args>(args)...);
+    fwd_pd_ = std::make_shared<typename TForward::primitive_desc>(
+        fwd_desc, first, engine_);
+  }
+
+  template <class First, class... Args>
+  typename std::enable_if<!std::is_same<typename std::decay<First>::type,
+                                        dnnl::primitive_attr>::value>::type
+  CreateForwardPrimitiveDescriptor(First&& first, Args&&... args) {
+    auto fwd_desc = typename TForward::desc(std::forward<First>(first),
+                                            std::forward<Args>(args)...);
+    fwd_pd_ =
+        std::make_shared<typename TForward::primitive_desc>(fwd_desc, engine_);
+  }
+
+  template <typename... Args>
+  void AcquireBackwardPrimitiveDescriptor(Args&&... args) {
+    // fwd_pd_ is set during grad by calling
+    // AcquireForwardPrimitiveDescriptor
+    PADDLE_ENFORCE_NOT_NULL(
+        fwd_pd_,
+        phi::errors::Unavailable("Get MKLDNN Forward primitive %s failed."));
+    auto bwd_desc = typename TBackward::desc(std::forward<Args>(args)...);
+    bwd_pd_ = std::make_shared<typename TBackward::primitive_desc>(
+        bwd_desc, engine_, *fwd_pd_);
+  }
+
+  template <typename... Args>
+  void AcquireBackwardWeightsPrimitiveDescriptor(Args&&... args) {
+    // fwd_pd_ is set during grad by calling
+    // AcquireForwardPrimitiveDescriptor
+    PADDLE_ENFORCE_NOT_NULL(
+        fwd_pd_,
+        phi::errors::Unavailable("Get MKLDNN Forward primitive %s failed."));
+    auto bwd_desc =
+        typename TBackward_params::desc(std::forward<Args>(args)...);
+    bwd_w_pd_ = std::make_shared<typename TBackward_params::primitive_desc>(
+        bwd_desc, engine_, *fwd_pd_);
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md, void* ptr) {
+    return std::make_shared<dnnl::memory>(md, engine_, ptr);
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireMemoryFromPrimitive(
+      dnnl::memory::desc md) {
+    return std::make_shared<dnnl::memory>(md, engine_);
+  }
+
+  void AcquireReorder(const std::shared_ptr<dnnl::memory>& user_memory_p,
+                      const std::shared_ptr<dnnl::memory>& target_memory_p) {
+    auto reorder_p =
+        std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
+
+    auto& astream = phi::OneDNNContext::tls().get_stream();
+
+    paddle::platform::RecordEvent record_reorder(
+        "int_reorder",
+        paddle::platform::TracerEventType::UserDefined,
+        2,
+        paddle::platform::EventRole::kUniqueOp);
+    reorder_p->execute(
+        astream,
+        {{DNNL_ARG_FROM, *user_memory_p}, {DNNL_ARG_TO, *target_memory_p}});
+    astream.wait();
+  }
+
+  template <typename F = T>
+  std::shared_ptr<dnnl::memory> AcquireMemoryWithReorder(
+      const dnnl::memory::desc& user_md,
+      const dnnl::memory::desc& target_md,
+      void* ptr,
+      bool is_persistent = false,
+      std::function<std::shared_ptr<F>(const F*)> custom_reorder_func = {}) {
+    std::shared_ptr<dnnl::memory> target_memory_p;
+    if (custom_reorder_func) {
+      auto reordered_data =
+          custom_reorder_func(reinterpret_cast<const F*>(ptr));
+      ptr = reinterpret_cast<void*>(reordered_data.get());
+    }
+    auto user_memory_p = std::make_shared<dnnl::memory>(user_md, engine_, ptr);
+    if (user_md != target_md) {
+      target_memory_p = std::make_shared<dnnl::memory>(target_md, engine_);
+      auto reorder_p =
+          std::make_shared<dnnl::reorder>(*user_memory_p, *target_memory_p);
+
+      auto& astream = phi::OneDNNContext::tls().get_stream();
+      paddle::platform::RecordEvent record_reorder(
+          "int_reorder",
+          paddle::platform::TracerEventType::UserDefined,
+          2,
+          paddle::platform::EventRole::kUniqueOp);
+      reorder_p->execute(
+          astream,
+          {{DNNL_ARG_FROM, *user_memory_p}, {DNNL_ARG_TO, *target_memory_p}});
+      astream.wait();
+    } else {
+      target_memory_p = user_memory_p;
+    }
+    return target_memory_p;
+  }
+
+  dnnl::engine engine_;
+  Place place_;
+  std::shared_ptr<typename TForward::primitive_desc> fwd_pd_;
+  std::shared_ptr<typename TBackward::primitive_desc> bwd_pd_;
+  std::shared_ptr<typename TBackward_params::primitive_desc> bwd_w_pd_;
+};
+
+template <typename T>
+class ActivationMKLDNNHandler
+    : public MKLDNNHandlerNoCachingT<T,
+                                     dnnl::eltwise_forward,
+                                     dnnl::eltwise_backward> {
+ public:
+  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
+                          float alpha,
+                          float beta,
+                          const dnnl::engine engine,
+                          Place cpu_place,
+                          const DenseTensor* x)
+      : MKLDNNHandlerNoCachingT<T,
+                                dnnl::eltwise_forward,
+                                dnnl::eltwise_backward>(engine, cpu_place) {
+    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
+                                            algorithm,
+                                            x->mem_desc(),
+                                            alpha,
+                                            beta);
+  }
+
+  ActivationMKLDNNHandler(dnnl::algorithm algorithm,
+                          float alpha,
+                          float beta,
+                          const dnnl::engine engine,
+                          Place cpu_place,
+                          const DenseTensor* x,
+                          const DenseTensor* dout)
+      : MKLDNNHandlerNoCachingT<T,
+                                dnnl::eltwise_forward,
+                                dnnl::eltwise_backward>(engine, cpu_place) {
+    this->AcquireForwardPrimitiveDescriptor(dnnl::prop_kind::forward_training,
+                                            algorithm,
+                                            x->mem_desc(),
+                                            alpha,
+                                            beta);
+    this->AcquireBackwardPrimitiveDescriptor(
+        algorithm, dout->mem_desc(), x->mem_desc(), alpha, beta);
+  }
+
+  std::shared_ptr<dnnl::memory> AcquireBackwardSrcMemory(
+      const DenseTensor* input) {
+    const T* input_data = input->data<T>();
+    return this->AcquireMemoryFromPrimitive(
+        this->bwd_pd_->src_desc(),
+        paddle::platform::to_void_cast<T>(input_data));
+  }
+};
+
+}  // namespace funcs
+}  // namespace phi

paddle/phi/kernels/onednn/activation_grad_kernel.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/phi/kernels/activation_grad_kernel.h"
+
+#include "paddle/phi/backends/onednn/onednn_context.h"
+#include "paddle/phi/common/bfloat16.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/activation_functor.h"
+#include "paddle/phi/kernels/funcs/onednn/mkldnn_reuse.h"
+
+namespace phi {
+
+#define DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPX(name, functor_class) \
+  template <typename T, typename Context>                              \
+  void name##GradKernel(const Context& dev_ctx,                        \
+                        const DenseTensor& x,                          \
+                        const DenseTensor& dout,                       \
+                        DenseTensor* dx) {                             \
+    functor_class<T> functor;                                          \
+    functor(dev_ctx, x, dout, 0, 0, dx);                               \
+  }
+
+#define DEFINE_ONEDNN_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX( \
+    name, functor_class, attr)                             \
+  template <typename T, typename Context>                  \
+  void name##GradKernel(const Context& dev_ctx,            \
+                        const DenseTensor& x,              \
+                        const DenseTensor& dout,           \
+                        float attr,                        \
+                        DenseTensor* dx) {                 \
+    functor_class<T> functor;                              \
+    functor(dev_ctx, x, dout, attr, 0, dx);                \
+  }
+
+#define DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(name, functor_class) \
+  template <typename T, typename Context>                                \
+  void name##GradKernel(const Context& dev_ctx,                          \
+                        const DenseTensor& out,                          \
+                        const DenseTensor& dout,                         \
+                        DenseTensor* dx) {                               \
+    functor_class<T> functor;                                            \
+    functor(dev_ctx, out, dout, 0, 0, dx);                               \
+  }
+
+#define DEFINE_ONEDNN_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPOUT( \
+    name, functor_class, attr)                               \
+  template <typename T, typename Context>                    \
+  void name##GradKernel(const Context& dev_ctx,              \
+                        const DenseTensor& out,              \
+                        const DenseTensor& dout,             \
+                        float attr,                          \
+                        DenseTensor* dx) {                   \
+    functor_class<T> functor;                                \
+    functor(dev_ctx, out, dout, attr, 0, dx);                \
+  }
+
+template <typename T>
+void eltwise_grad(const OneDNNContext& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& dout,
+                  float alpha,
+                  float beta,
+                  DenseTensor* dx,
+                  dnnl::algorithm algorithm) {
+  const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+  funcs::ActivationMKLDNNHandler<T> handler(
+      algorithm, alpha, beta, mkldnn_engine, dev_ctx.GetPlace(), &x, &dout);
+
+  auto src_memory_p = handler.AcquireBackwardSrcMemory(&x);
+  auto diff_dst_memory_p = handler.AcquireDiffDstMemory(&dout);
+  auto diff_src_memory_p = handler.AcquireDiffSrcMemory(dx);
+  auto activation_backward_p = handler.AcquireBackwardPrimitive();
+
+  auto& astream = OneDNNContext::tls().get_stream();
+  activation_backward_p->execute(astream,
+                                 {{DNNL_ARG_SRC, *src_memory_p},
+                                  {DNNL_ARG_DIFF_DST, *diff_dst_memory_p},
+                                  {DNNL_ARG_DIFF_SRC, *diff_src_memory_p}});
+  astream.wait();
+
+  dx->set_mem_desc(diff_src_memory_p->get_desc());
+}
+
+template <typename T>
+void eltwise_grad_use_out(const OneDNNContext& dev_ctx,
+                          const DenseTensor& out,
+                          const DenseTensor& dout,
+                          float alpha,
+                          float beta,
+                          DenseTensor* dx,
+                          dnnl::algorithm algorithm) {
+  const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+  funcs::ActivationMKLDNNHandler<T> handler(
+      algorithm, alpha, beta, mkldnn_engine, dev_ctx.GetPlace(), &out, &dout);
+
+  auto dst_memory_p = handler.AcquireBackwardSrcMemory(&out);
+  auto diff_dst_memory_p = handler.AcquireDiffDstMemory(&dout);
+  auto diff_src_memory_p = handler.AcquireDiffSrcMemory(dx);
+  auto activation_backward_p = handler.AcquireBackwardPrimitive();
+
+  auto& astream = OneDNNContext::tls().get_stream();
+  activation_backward_p->execute(astream,
+                                 {{DNNL_ARG_DST, *dst_memory_p},
+                                  {DNNL_ARG_DIFF_DST, *diff_dst_memory_p},
+                                  {DNNL_ARG_DIFF_SRC, *diff_src_memory_p}});
+  astream.wait();
+
+  dx->set_mem_desc(diff_src_memory_p->get_desc());
+}
+
+template <typename T, dnnl::algorithm algorithm>
+struct MKLDNNActivationGradFunc : public funcs::BaseActivationFunctor<T> {
+  void operator()(const OneDNNContext& dev_ctx,
+                  const DenseTensor& x,
+                  const DenseTensor& dout,
+                  float alpha,
+                  float beta,
+                  DenseTensor* dx) const {
+    eltwise_grad<T>(dev_ctx, x, dout, alpha, beta, dx, algorithm);
+  }
+};
+
+template <typename T, dnnl::algorithm algorithm>
+struct MKLDNNActivationGradUseOutFunc : public funcs::BaseActivationFunctor<T> {
+  void operator()(const OneDNNContext& dev_ctx,
+                  const DenseTensor& out,
+                  const DenseTensor& dout,
+                  float alpha,
+                  float beta,
+                  DenseTensor* dx) const {
+    eltwise_grad_use_out<T>(dev_ctx, out, dout, alpha, beta, dx, algorithm);
+  }
+};
+
+template <typename T>
+using ReluMKLDNNGradFunctor =
+    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_relu>;
+
+template <typename T>
+using SwishMKLDNNGradFunctor =
+    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_swish>;
+
+template <typename T>
+using HardSwishMKLDNNGradFunctor =
+    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_hardswish>;
+
+template <typename T>
+using MishMKLDNNGradFunctor =
+    MKLDNNActivationGradFunc<T, dnnl::algorithm::eltwise_mish>;
+
+template <typename T>
+using SigmoidMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_logistic_use_dst_for_bwd>;
+
+template <typename T>
+using TanhMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_tanh_use_dst_for_bwd>;
+
+template <typename T>
+using SqrtMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_sqrt_use_dst_for_bwd>;
+
+template <typename T>
+using EluMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_elu_use_dst_for_bwd>;
+
+template <typename T>
+using ExpMKLDNNGradUseOutFunctor = MKLDNNActivationGradUseOutFunc<
+    T,
+    dnnl::algorithm::eltwise_exp_use_dst_for_bwd>;
+
+DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(Tanh, TanhMKLDNNGradUseOutFunctor);
+DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(Sqrt, SqrtMKLDNNGradUseOutFunctor);
+DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(Sigmoid,
+                                            SigmoidMKLDNNGradUseOutFunctor);
+DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(Exp, ExpMKLDNNGradUseOutFunctor);
+DEFINE_ONEDNN_ACTIVATION_GRAD_KERNEL_DEPOUT(Relu, ReluMKLDNNGradFunctor);
+
+DEFINE_ONEDNN_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(LeakyRelu,
+                                                  ReluMKLDNNGradFunctor,
+                                                  alpha);
+DEFINE_ONEDNN_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Mish,
+                                                  MishMKLDNNGradFunctor,
+                                                  threshold);
+DEFINE_ONEDNN_ACT_GRAD_KERNEL_WITH_ONE_ATTRS_DEPX(Swish,
+                                                  SwishMKLDNNGradFunctor,
+                                                  beta);
+template <typename T, typename Context>
+void HardSwishGradKernel(const Context& dev_ctx,
+                         const DenseTensor& x,
+                         const DenseTensor& dout,
+                         float threshold,
+                         float scale,
+                         float offset,
+                         DenseTensor* dx) {
+  HardSwishMKLDNNGradFunctor<T> functor;
+  functor(dev_ctx, x, dout, threshold, 0, dx);
+}
+
+template <typename T, typename Context>
+void EluGradKernel(const Context& dev_ctx,
+                   const DenseTensor& x,
+                   const DenseTensor& out,
+                   const DenseTensor& dout,
+                   float alpha,
+                   DenseTensor* dx) {
+  EluMKLDNNGradUseOutFunctor<T> functor;
+  functor(dev_ctx, out, dout, alpha, 0, dx);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(relu_grad,
+                   OneDNN,
+                   ALL_LAYOUT,
+                   phi::ReluGradKernel,
+                   float,
+                   phi::dtype::bfloat16) {}
+
+#define PD_REGISTER_ACTIVATION_GRAD_KERNEL(name, func) \
+  PD_REGISTER_KERNEL(                                  \
+      name, OneDNN, ALL_LAYOUT, phi::func, float, phi::dtype::bfloat16) {}
+
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(elu_grad, EluGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(exp_grad, ExpGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(hard_swish_grad, HardSwishGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(leaky_relu_grad, LeakyReluGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(mish_grad, MishGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(sigmoid_grad, SigmoidGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(sqrt_grad, SqrtGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(swish_grad, SwishGradKernel)
+PD_REGISTER_ACTIVATION_GRAD_KERNEL(tanh_grad, TanhGradKernel)

paddle/phi/kernels/onednn/activation_kernel.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/phi/kernels/activation_kernel.h"
+
+#include "paddle/phi/backends/onednn/onednn_context.h"
+#include "paddle/phi/common/bfloat16.h"
+#include "paddle/phi/common/place.h"
+#include "paddle/phi/core/kernel_registry.h"
+#include "paddle/phi/kernels/funcs/activation_functor.h"
+#include "paddle/phi/kernels/funcs/onednn/mkldnn_reuse.h"
+
+namespace phi {
+
+#define DEFINE_ONEDNN_ACTIVATION_KERNEL(name, functor_class)            \
+  template <typename T, typename Context>                               \
+  void name##Kernel(                                                    \
+      const Context& dev_ctx, const DenseTensor& x, DenseTensor* out) { \
+    functor_class<T> functor;                                           \
+    functor(dev_ctx, x, 0, 0, out);                                     \
+  }
+
+#define DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(name, functor_class, attr) \
+  template <typename T, typename Context>                                  \
+  void name##Kernel(const Context& dev_ctx,                                \
+                    const DenseTensor& x,                                  \
+                    float attr,                                            \
+                    DenseTensor* out) {                                    \
+    functor_class<T> functor;                                              \
+    functor(dev_ctx, x, attr, 0, out);                                     \
+  }
+
+template <typename T>
+void EltwiseForward(const OneDNNContext& dev_ctx,
+                    const DenseTensor& x,
+                    float alpha,
+                    float beta,
+                    DenseTensor* out,
+                    dnnl::algorithm algorithm) {
+  PADDLE_ENFORCE_EQ(paddle::platform::is_cpu_place(dev_ctx.GetPlace()),
+                    true,
+                    phi::errors::PreconditionNotMet(
+                        "Operator DNNL eletwise_forward must use ONEDNNPlace"));
+  const auto& mkldnn_engine = dev_ctx.GetEngine();
+
+  bool is_inplaced = x.IsSharedBufferWith(*out);
+
+  funcs::ActivationMKLDNNHandler<T> handler(
+      algorithm, alpha, beta, mkldnn_engine, dev_ctx.GetPlace(), &x);
+
+  auto src_memory_p = handler.AcquireSrcMemory(&x);
+  std::shared_ptr<dnnl::memory> dst_memory_p = nullptr;
+  if (is_inplaced) {
+    dst_memory_p = src_memory_p;
+    dev_ctx.template Alloc<T>(out);
+  } else {
+    dst_memory_p = handler.AcquireDstMemory(out);
+  }
+  auto activation_p = handler.AcquireForwardPrimitive();
+
+  auto& astream = OneDNNContext::tls().get_stream();
+  activation_p->execute(
+      astream, {{DNNL_ARG_FROM, *src_memory_p}, {DNNL_ARG_TO, *dst_memory_p}});
+  astream.wait();
+
+  out->set_mem_desc(dst_memory_p->get_desc());
+}
+
+template <typename T, dnnl::algorithm algorithm>
+struct MKLDNNActivationFunc : public funcs::BaseActivationFunctor<T> {
+  void operator()(const OneDNNContext& dev_ctx,
+                  const DenseTensor& x,
+                  float alpha,
+                  float beta,
+                  DenseTensor* out) const {
+    EltwiseForward<T>(dev_ctx, x, alpha, beta, out, algorithm);
+  }
+};
+
+template <typename T>
+using ReluMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_relu>;
+
+template <typename T>
+using SwishMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_swish>;
+
+template <typename T>
+using HardSwishMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_hardswish>;
+
+template <typename T>
+using MishMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_mish>;
+
+template <typename T>
+using SigmoidMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_logistic>;
+
+template <typename T>
+using TanhMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_tanh>;
+
+template <typename T>
+using SqrtMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_sqrt>;
+
+template <typename T>
+using EluMKLDNNFunctor = MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_elu>;
+
+template <typename T>
+using ExpMKLDNNFunctor = MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_exp>;
+
+template <typename T>
+using RoundMKLDNNFunctor =
+    MKLDNNActivationFunc<T, dnnl::algorithm::eltwise_round>;
+
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Relu, ReluMKLDNNFunctor)
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Tanh, TanhMKLDNNFunctor)
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Exp, ExpMKLDNNFunctor)
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Sqrt, SqrtMKLDNNFunctor)
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Sigmoid, SigmoidMKLDNNFunctor)
+// round eltwise primitive doesn't support BF16, nor does it support grad
+DEFINE_ONEDNN_ACTIVATION_KERNEL(Round, RoundMKLDNNFunctor)
+
+DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(LeakyRelu, ReluMKLDNNFunctor, alpha)
+DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Mish, MishMKLDNNFunctor, threshold)
+DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Elu, EluMKLDNNFunctor, alpha)
+DEFINE_ONEDNN_ACT_KERNEL_WITH_ONE_ATTRS(Swish, SwishMKLDNNFunctor, beta)
+
+template <typename T, typename Context>
+void HardSwishKernel(const Context& dev_ctx,
+                     const DenseTensor& x,
+                     float threshold,
+                     float scale,
+                     float offset,
+                     DenseTensor* out) {
+  HardSwishMKLDNNFunctor<T> functor;
+  functor(dev_ctx, x, threshold, 0, out);
+}
+
+}  // namespace phi
+
+PD_REGISTER_KERNEL(round, OneDNN, ALL_LAYOUT, phi::RoundKernel, float) {}
+
+#define PD_REGISTER_ACTIVATION_KERNEL(name, func) \
+  PD_REGISTER_KERNEL(                             \
+      name, OneDNN, ALL_LAYOUT, phi::func, float, phi::dtype::bfloat16) {}
+
+PD_REGISTER_ACTIVATION_KERNEL(elu, EluKernel)
+PD_REGISTER_ACTIVATION_KERNEL(exp, ExpKernel)
+PD_REGISTER_ACTIVATION_KERNEL(hard_swish, HardSwishKernel)
+PD_REGISTER_ACTIVATION_KERNEL(leaky_relu, LeakyReluKernel)
+PD_REGISTER_ACTIVATION_KERNEL(mish, MishKernel)
+PD_REGISTER_ACTIVATION_KERNEL(sigmoid, SigmoidKernel)
+PD_REGISTER_ACTIVATION_KERNEL(sqrt, SqrtKernel)
+PD_REGISTER_ACTIVATION_KERNEL(swish, SwishKernel)
+PD_REGISTER_ACTIVATION_KERNEL(tanh, TanhKernel)
+PD_REGISTER_ACTIVATION_KERNEL(relu, ReluKernel)