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350 lines
13 KiB
350 lines
13 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_helper.h"
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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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namespace {
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std::string gethash(const mkldnn::memory::dims &operand_dims,
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const mkldnn::algorithm algorithm) {
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auto dim2str = [](const mkldnn::memory::dims &operand_dims) {
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std::string dstr = "";
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for (size_t i = 0; i < operand_dims.size(); ++i) {
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dstr += std::to_string(operand_dims[i]) + "-";
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}
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return dstr;
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};
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return dim2str(operand_dims) + std::to_string(algorithm);
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}
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} // namespace
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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(x->layout() == DataLayout::kMKLDNN &&
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x->format() != memory::format::format_undef,
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"Wrong layout/format set for Input x tensor");
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Functor functor;
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auto attrs = functor.GetAttrs();
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for (auto &attr : attrs) {
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*attr.second = ctx.Attr<float>(attr.first);
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}
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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(diff_y->layout() == DataLayout::kMKLDNN &&
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diff_y->format() != memory::format::format_undef,
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"Wrong layout/format set for Input OutGrad tensor");
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Functor functor;
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auto attrs = functor.GetAttrs();
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for (auto &attr : attrs) {
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*attr.second = ctx.Attr<float>(attr.first);
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}
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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, const T alpha = 0,
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const T beta = 0) {
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PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()),
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"It must use CPUPlace.");
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auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
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const auto &mkldnn_engine = dev_ctx.GetEngine();
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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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const T *x_data = x->data<T>();
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T *y_data = y->mutable_data<T>(ctx.GetPlace());
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PADDLE_ENFORCE(x->dims().size() == 2 || x->dims().size() == 4,
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"Input dim must be with 2 or 4");
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std::vector<int> src_tz = framework::vectorize2int(x->dims());
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auto src_format =
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src_tz.size() == 2 ? mkldnn::memory::format::nc : x->format();
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const std::string key = gethash(src_tz, algorithm);
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const std::string key_src_data =
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key + ctx.op().Output("Out") + "@eltwise_fwd_src_data";
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const std::string key_src_layout =
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key + ctx.op().Output("Out") + "@eltwise_fwd_src_layout";
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const std::string key_with_layout = key + std::to_string(src_format);
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const std::string key_src_mem = key_with_layout + "@eltwise_fwd_src_mem";
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const std::string key_dst_mem = key_with_layout + "@eltwise_fwd_dst_mem";
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const std::string key_fwd = key_with_layout + "@eltwise_fwd";
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const std::string key_fwd_pd = key_with_layout + "@eltwise_fwd_pd";
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// save input data and layout to be referred in backward path
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auto p_src_data = std::make_shared<const T *>(x_data);
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dev_ctx.SetBlob(key_src_data, p_src_data);
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auto p_src_layout = std::make_shared<memory::format>(src_format);
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dev_ctx.SetBlob(key_src_layout, p_src_layout);
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auto p_fwd = std::static_pointer_cast<mkldnn::eltwise_forward>(
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dev_ctx.GetBlob(key_fwd));
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std::shared_ptr<memory> dst_memory;
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if (p_fwd == nullptr) {
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// create mkldnn memory for input X
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auto src_md = platform::MKLDNNMemDesc(
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src_tz, platform::MKLDNNGetDataType<T>(), src_format);
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auto src_memory = std::shared_ptr<memory>(
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new memory({src_md, mkldnn_engine}, to_void_cast(x_data)));
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// save src_memory to be referred in backward path
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dev_ctx.SetBlob(key_src_mem, src_memory);
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// create primitive descriptor for activation forward and save it
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auto forward_desc = mkldnn::eltwise_forward::desc(
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mkldnn::prop_kind::forward_training, algorithm,
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src_memory->get_primitive_desc().desc(), alpha, beta);
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auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>(
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forward_desc, mkldnn_engine);
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// save prim desc into global device context to be referred in backward path
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dev_ctx.SetBlob(key_fwd_pd, forward_pd);
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// create mkldnn memory for output y
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dst_memory =
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std::make_shared<memory>(forward_pd->dst_primitive_desc(), y_data);
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dev_ctx.SetBlob(key_dst_mem, dst_memory);
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// create activation primitive
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p_fwd = std::make_shared<mkldnn::eltwise_forward>(*forward_pd, *src_memory,
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*dst_memory);
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dev_ctx.SetBlob(key_fwd, p_fwd);
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} else {
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// primitives already exist
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auto src_memory =
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std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
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PADDLE_ENFORCE(src_memory != nullptr,
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"Fail to find eltwise src_memory in device context.");
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dst_memory =
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std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_dst_mem));
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PADDLE_ENFORCE(dst_memory != nullptr,
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"Fail to find eltwise dst_memory in device context.");
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src_memory->set_data_handle(platform::to_void_cast(x_data));
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dst_memory->set_data_handle(y_data);
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}
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// push primitive to stream and wait until it's executed
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std::vector<primitive> pipeline;
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pipeline.push_back(*p_fwd);
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stream(stream::kind::eager).submit(pipeline).wait();
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y->set_layout(DataLayout::kMKLDNN);
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y->set_format(GetMKLDNNFormat(*dst_memory));
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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, const T alpha = 0,
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const T beta = 0) {
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auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>();
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const auto &mkldnn_engine = dev_ctx.GetEngine();
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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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const T *diff_y_data = diff_y->data<T>();
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T *diff_x_data = diff_x->mutable_data<T>(ctx.GetPlace());
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std::vector<int> diff_dst_tz = framework::vectorize2int(diff_y->dims());
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auto diff_y_format =
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diff_dst_tz.size() == 2 ? mkldnn::memory::format::nc : diff_y->format();
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const std::string key = gethash(diff_dst_tz, algorithm);
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const std::string key_src_data =
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key + ctx.op().Input("Out") + "@eltwise_fwd_src_data";
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const std::string key_src_layout =
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key + ctx.op().Input("Out") + "@eltwise_fwd_src_layout";
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const auto p_src_layout =
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std::static_pointer_cast<memory::format>(dev_ctx.GetBlob(key_src_layout));
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const std::string key_src_mem =
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key + std::to_string(*p_src_layout) + "@eltwise_fwd_src_mem";
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const std::string key_fwd_pd =
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key + std::to_string(*p_src_layout) + "@eltwise_fwd_pd";
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const std::string key_with_layouts =
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key + std::to_string(*p_src_layout) + "-" + std::to_string(diff_y_format);
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const std::string key_diff_src_mem =
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key_with_layouts + "@eltwise_diff_src_mem";
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const std::string key_diff_dst_mem =
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key_with_layouts + "@eltwise_diff_dst_mem";
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const std::string key_grad = key_with_layouts + "@eltwise_grad";
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const auto p_src_data =
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std::static_pointer_cast<T *>(dev_ctx.GetBlob(key_src_data));
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auto src_memory =
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std::static_pointer_cast<mkldnn::memory>(dev_ctx.GetBlob(key_src_mem));
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PADDLE_ENFORCE(src_memory != nullptr,
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"Fail to find src_memory in device context");
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src_memory->set_data_handle(*p_src_data.get());
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std::shared_ptr<memory> diff_src_memory;
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auto p_grad = std::static_pointer_cast<mkldnn::eltwise_backward>(
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dev_ctx.GetBlob(key_grad));
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if (p_grad == nullptr) {
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// create mkldnn memory for input diff_y
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auto diff_dst_md = platform::MKLDNNMemDesc(
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diff_dst_tz, platform::MKLDNNGetDataType<T>(), diff_y_format);
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auto diff_dst_memory = std::shared_ptr<memory>(
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new memory({diff_dst_md, mkldnn_engine}, to_void_cast(diff_y_data)));
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dev_ctx.SetBlob(key_diff_dst_mem, diff_dst_memory);
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// retrieve eltwise primitive desc from device context
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auto forward_pd =
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std::static_pointer_cast<mkldnn::eltwise_forward::primitive_desc>(
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dev_ctx.GetBlob(key_fwd_pd));
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PADDLE_ENFORCE(forward_pd != nullptr,
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"Fail to find eltwise_fwd_pd in device context");
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// ceate primitive descriptor for activation backward
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auto backward_desc = mkldnn::eltwise_backward::desc(
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algorithm, diff_dst_memory->get_primitive_desc().desc(),
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src_memory->get_primitive_desc().desc(), alpha, beta);
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auto backward_pd = mkldnn::eltwise_backward::primitive_desc(
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backward_desc, mkldnn_engine, *forward_pd);
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// create mkldnn memory for output diff_src
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diff_src_memory = std::make_shared<memory>(
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backward_pd.diff_src_primitive_desc(), diff_x_data);
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dev_ctx.SetBlob(key_diff_src_mem, diff_src_memory);
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// create activation backward primitive
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p_grad = std::make_shared<mkldnn::eltwise_backward>(
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backward_pd, *src_memory, *diff_dst_memory, *diff_src_memory);
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dev_ctx.SetBlob(key_grad, p_grad);
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} else {
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// primitives already exist
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diff_src_memory = std::static_pointer_cast<mkldnn::memory>(
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dev_ctx.GetBlob(key_diff_src_mem));
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auto diff_dst_memory = std::static_pointer_cast<mkldnn::memory>(
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dev_ctx.GetBlob(key_diff_dst_mem));
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diff_src_memory->set_data_handle(
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platform::to_void_reinterpret_cast(diff_x_data));
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diff_dst_memory->set_data_handle(
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platform::to_void_reinterpret_cast(diff_y_data));
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}
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// push primitive to stream and wait until it's executed
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std::vector<primitive> pipeline;
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pipeline.push_back(*p_grad);
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stream(stream::kind::eager).submit(pipeline).wait();
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diff_x->set_layout(DataLayout::kMKLDNN);
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diff_x->set_format(GetMKLDNNFormat(*diff_src_memory));
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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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using ReluMKLDNNFunctor =
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MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_relu>;
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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 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 FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \
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__macro(relu, ReluMKLDNNFunctor, ReluMKLDNNGradFunctor); \
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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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