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300 lines
11 KiB
300 lines
11 KiB
/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/activation_op.h"
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#include "paddle/fluid/platform/mkldnn_reuse.h"
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namespace paddle {
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namespace framework {
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class Tensor;
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} // namespace framework
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namespace platform {
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class MKLDNNDeviceContext;
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} // namespace platform
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} // namespace paddle
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namespace paddle {
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namespace operators {
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using framework::DataLayout;
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using framework::Tensor;
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using mkldnn::memory;
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using mkldnn::primitive;
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using mkldnn::stream;
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using platform::GetMKLDNNFormat;
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using platform::MKLDNNDeviceContext;
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using platform::to_void_cast;
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template <typename Functor>
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class MKLDNNActivationKernel
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: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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const auto *x = ctx.Input<Tensor>("X");
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PADDLE_ENFORCE_EQ(
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x->layout(), DataLayout::kMKLDNN,
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platform::errors::InvalidArgument("Wrong layout set for X tensor"));
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PADDLE_ENFORCE_NE(
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x->format(), MKLDNNMemoryFormat::undef,
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platform::errors::InvalidArgument("Wrong format set for X tensor"));
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Functor functor;
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functor(ctx);
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}
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};
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template <typename Functor>
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class MKLDNNActivationGradKernel
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: public framework::OpKernel<typename Functor::ELEMENT_TYPE> {
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
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PADDLE_ENFORCE_EQ(diff_y->layout(), DataLayout::kMKLDNN,
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platform::errors::InvalidArgument(
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"Wrong layout set for Input OutGrad tensor"));
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PADDLE_ENFORCE_NE(diff_y->format(), MKLDNNMemoryFormat::undef,
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platform::errors::InvalidArgument(
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"Wrong format set for Input OutGrad tensor"));
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Functor functor;
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functor(ctx);
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}
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};
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template <typename T>
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void eltwise_forward(const framework::ExecutionContext &ctx,
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mkldnn::algorithm algorithm) {
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PADDLE_ENFORCE_EQ(platform::is_cpu_place(ctx.GetPlace()), true,
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paddle::platform::errors::PreconditionNotMet(
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"Operator DNNL eletwise_forward must use CPUPlace"));
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auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
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const auto *x = ctx.Input<Tensor>("X");
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auto *y = ctx.Output<Tensor>("Out");
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float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
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float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
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// paddle uses beta but mkldnn uses alpha for swish
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if (algorithm == mkldnn::algorithm::eltwise_swish) {
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std::swap(alpha, beta);
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} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
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alpha = ctx.Attr<float>("threshold");
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}
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PADDLE_ENFORCE(
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x->dims().size() >= 1 || x->dims().size() <= 6,
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platform::errors::Unimplemented("Input dimension size can be 1, 2, 3, 4, "
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"5, or 6, but now the dimension size is",
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x->dims().size()));
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auto src_tz = framework::vectorize<int64_t>(x->dims());
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auto src_format = src_tz.size() == 2 ? MKLDNNMemoryFormat::nc : x->format();
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platform::ActivationMKLDNNHandler<T> handler(
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src_tz, algorithm, alpha, beta, src_format, dev_ctx, ctx.GetPlace(),
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ctx.InputName("X"));
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auto src_memory_p = handler.AcquireSrcMemory(x);
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auto dst_memory_p =
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x->IsSharedBufferWith(*y) ? src_memory_p : handler.AcquireDstMemory(y);
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auto activation_p = handler.AcquireForwardPrimitive();
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mkldnn::stream astream(dev_ctx.GetEngine());
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activation_p->execute(astream, {{MKLDNN_ARG_FROM, *src_memory_p},
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{MKLDNN_ARG_TO, *dst_memory_p}});
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astream.wait();
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y->set_layout(DataLayout::kMKLDNN);
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y->set_format(GetMKLDNNFormat(*dst_memory_p));
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}
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template <typename T>
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void eltwise_grad(const framework::ExecutionContext &ctx,
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mkldnn::algorithm algorithm) {
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auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
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const auto *x = ctx.Input<Tensor>("X");
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const auto *diff_y = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto *diff_x = ctx.Output<Tensor>(framework::GradVarName("X"));
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float alpha = ctx.HasAttr("alpha") ? ctx.Attr<float>("alpha") : 0;
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float beta = ctx.HasAttr("beta") ? ctx.Attr<float>("beta") : 0;
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// paddle uses beta but mkldnn uses alpha for swish
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if (algorithm == mkldnn::algorithm::eltwise_swish) {
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std::swap(alpha, beta);
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} else if (algorithm == dnnl::algorithm::eltwise_bounded_relu) {
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alpha = ctx.Attr<float>("threshold");
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}
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auto diff_dst_tz = framework::vectorize<int64_t>(diff_y->dims());
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// diff_dst and src dims should be the same
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auto src_format =
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diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : x->format();
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auto diff_y_format =
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diff_dst_tz.size() == 2 ? MKLDNNMemoryFormat::nc : diff_y->format();
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platform::ActivationMKLDNNHandler<T> handler(
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diff_dst_tz, algorithm, alpha, beta, src_format, diff_y_format, dev_ctx,
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ctx.GetPlace(), ctx.InputName("X"));
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auto src_memory_p = handler.AcquireBackwardSrcMemory(x);
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auto diff_dst_memory_p = handler.AcquireDiffDstMemory(diff_y);
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auto diff_src_memory_p = handler.AcquireDiffSrcMemory(diff_x);
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auto activation_backward_p = handler.AcquireBackwardPrimitive();
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mkldnn::stream astream(dev_ctx.GetEngine());
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activation_backward_p->execute(astream,
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{{MKLDNN_ARG_SRC, *src_memory_p},
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{MKLDNN_ARG_DIFF_DST, *diff_dst_memory_p},
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{MKLDNN_ARG_DIFF_SRC, *diff_src_memory_p}});
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astream.wait();
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diff_x->set_layout(DataLayout::kMKLDNN);
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diff_x->set_format(GetMKLDNNFormat(*diff_src_memory_p));
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}
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template <typename T, mkldnn::algorithm algorithm>
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struct MKLDNNActivationFunc : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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eltwise_forward<T>(ctx, algorithm);
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}
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};
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template <typename T, mkldnn::algorithm algorithm>
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struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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eltwise_grad<T>(ctx, algorithm);
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}
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};
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template <typename T>
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struct GeluMKLDNNFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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const bool approximate = ctx.Attr<bool>("approximate");
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if (approximate) {
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eltwise_forward<T>(ctx, mkldnn::algorithm::eltwise_gelu_tanh);
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} else {
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eltwise_forward<T>(ctx, mkldnn::algorithm::eltwise_gelu_erf);
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}
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}
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};
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template <typename T>
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struct GeluMKLDNNGradFunctor : public BaseActivationFunctor<T> {
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void operator()(const framework::ExecutionContext &ctx) const {
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const bool approximate = ctx.Attr<bool>("approximate");
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if (approximate) {
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eltwise_grad<T>(ctx, mkldnn::algorithm::eltwise_gelu_tanh);
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} else {
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eltwise_grad<T>(ctx, mkldnn::algorithm::eltwise_gelu_erf);
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}
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}
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};
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template <typename T>
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using ReluMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_relu>;
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template <typename T>
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using Relu6MKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_bounded_relu>;
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template <typename T>
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using SwishMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_swish>;
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template <typename T>
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using SigmoidMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_logistic>;
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template <typename T>
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using TanhMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_tanh>;
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template <typename T>
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using SqrtMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_sqrt>;
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template <typename T>
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using AbsMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_abs>;
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template <typename T>
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using ReluMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_relu>;
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template <typename T>
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using Relu6MKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_bounded_relu>;
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template <typename T>
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using SwishMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_swish>;
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template <typename T>
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using SigmoidMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_logistic>;
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template <typename T>
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using TanhMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_tanh>;
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template <typename T>
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using SqrtMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_sqrt>;
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template <typename T>
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using AbsMKLDNNGradFunctor =
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MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_abs>;
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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#define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor) \
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REGISTER_OP_KERNEL(act_type, MKLDNN, ::paddle::platform::CPUPlace, \
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ops::MKLDNNActivationKernel<ops::functor<float>>); \
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REGISTER_OP_KERNEL( \
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act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \
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ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);
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#define REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(act_type, functor, \
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grad_functor) \
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REGISTER_OP_KERNEL( \
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act_type, MKLDNN, ::paddle::platform::CPUPlace, \
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ops::MKLDNNActivationKernel<ops::functor<float>>, \
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ops::MKLDNNActivationKernel<ops::functor<paddle::platform::bfloat16>>); \
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REGISTER_OP_KERNEL( \
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act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \
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ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>);
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#define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \
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__macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
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__macro(relu6, Relu6MKLDNNFunctor, Relu6MKLDNNGradFunctor); \
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__macro(leaky_relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
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__macro(swish, SwishMKLDNNFunctor, SwishMKLDNNGradFunctor); \
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__macro(sigmoid, SigmoidMKLDNNFunctor, SigmoidMKLDNNGradFunctor); \
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__macro(tanh, TanhMKLDNNFunctor, TanhMKLDNNGradFunctor); \
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__macro(sqrt, SqrtMKLDNNFunctor, SqrtMKLDNNGradFunctor); \
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__macro(abs, AbsMKLDNNFunctor, AbsMKLDNNGradFunctor);
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FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL);
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REGISTER_ACTIVATION_MKLDNN_BF16_KERNEL(gelu, GeluMKLDNNFunctor,
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GeluMKLDNNGradFunctor);
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