Merge pull request #9081 from kbinias/kbinias/mkldnn-activations
MKLDNN Relu Tanh Sqrt Abs activations addedhelinwang-patch-1
Tao Luo
7 years ago
committed by
GitHub
commit
cb3bbbd5c6
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/* 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 "mkldnn.hpp"
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#include "mkldnn_activation_op.h"
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#include "paddle/fluid/operators/activation_op.h"
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namespace paddle {
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namespace operators {
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using paddle::framework::Tensor;
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using paddle::platform::MKLDNNDeviceContext;
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namespace {
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template <typename T, typename ExecContext>
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void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm,
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const T alpha = 0, 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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// get buffers
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const auto *src = ctx.template Input<Tensor>("X");
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const auto *src_data = src->template data<T>();
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auto *dst = ctx.template Output<Tensor>("Out");
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const T *dst_data = dst->template mutable_data<T>(ctx.GetPlace());
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// get memory dim
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PADDLE_ENFORCE(src->dims().size() == 4,
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"Input dim must be with 4, i.e. NCHW");
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std::vector<int> src_tz = framework::vectorize2int(src->dims());
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// create memory description
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// TODO(kbinias-intel): support more formats
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auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
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mkldnn::memory::format::nchw);
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// create memory primitives
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auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src_data);
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auto dst_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)dst_data);
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auto forward_desc = mkldnn::eltwise_forward::desc(
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mkldnn::prop_kind::forward_training, algorithm, data_md, alpha, beta);
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// save prim desc into global device context to be referred in backward path
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const std::string key = ctx.op().Output("Out");
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const std::string key_eltwise_pd = key + "@eltwise_pd";
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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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dev_ctx.SetBlob(key_eltwise_pd, forward_pd);
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auto eltwise = mkldnn::eltwise_forward(*forward_pd, src_memory, dst_memory);
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// push primitive to stream and wait until it's executed
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std::vector<mkldnn::primitive> pipeline = {eltwise};
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mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
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}
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template <typename T, typename ExecContext>
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void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm,
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const T alpha = 0, 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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// get buffers
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const auto *x = ctx.template Input<Tensor>("X");
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const auto *src = x->template data<T>();
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auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out"));
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const auto *diff_dst = dout->template data<T>();
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auto *dx =
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ctx.template Output<framework::Tensor>(framework::GradVarName("X"));
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const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace());
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// get memory dim
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std::vector<int> src_tz = framework::vectorize2int(x->dims());
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// create memory description
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auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32,
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mkldnn::memory::format::nchw);
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// create memory primitives
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auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src);
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auto diff_src_memory =
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mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_src);
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auto diff_dst_memory =
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mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_dst);
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auto backward_desc =
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mkldnn::eltwise_backward::desc(algorithm, data_md, data_md, alpha, beta);
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// retrieve eltwise primitive desc from device context
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const std::string key = ctx.op().Input("Out");
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const std::string key_eltwise_pd = key + "@eltwise_pd";
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const std::shared_ptr<void> forward_pd = dev_ctx.GetBlob(key_eltwise_pd);
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PADDLE_ENFORCE(forward_pd != nullptr,
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"Fail to find eltwise_pd in device context");
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auto *p_forward_pd =
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static_cast<mkldnn::eltwise_forward::primitive_desc *>(forward_pd.get());
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auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc(
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backward_desc, mkldnn_engine, *p_forward_pd);
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auto eltwise_bwd = mkldnn::eltwise_backward(eltwise_bwd_prim_desc, src_memory,
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diff_dst_memory, diff_src_memory);
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// push primitive to stream and wait until it's executed
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std::vector<mkldnn::primitive> pipeline = {eltwise_bwd};
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mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait();
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}
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} // anonymous namespace
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template <typename T, mkldnn::algorithm algorithm>
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struct MKLDNNActivationFunc : public BaseActivationFunctor<T> {
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template <typename ExecContext>
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void operator()(const ExecContext &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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template <typename ExecContext>
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void operator()(const ExecContext &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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@ -0,0 +1,111 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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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#pragma once
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/detail/safe_ref.h"
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#ifdef PADDLE_WITH_MKLDNN
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#include "paddle/fluid/platform/mkldnn_helper.h"
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#endif
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namespace paddle {
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namespace operators {
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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& context) const override {
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PADDLE_ENFORCE(context.Input<framework::Tensor>("X") != nullptr,
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"Cannot get input tensor X, variable name = %s",
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context.op().Input("X"));
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PADDLE_ENFORCE(context.Output<framework::Tensor>("Out") != nullptr,
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"Cannot find output tensor Out, variable name = %s",
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context.op().Output("Out"));
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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 = context.Attr<float>(attr.first);
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}
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functor(context);
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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& context) const override {
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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 = context.Attr<float>(attr.first);
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}
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functor(context);
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}
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};
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namespace {
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framework::OpKernelType GetKernelType(
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const framework::ExecutionContext& ctx,
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const framework::OperatorWithKernel& oper) {
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framework::LibraryType library{framework::LibraryType::kPlain};
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#ifdef PADDLE_WITH_MKLDNN
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if (library == framework::LibraryType::kPlain &&
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platform::CanMKLDNNBeUsed(ctx)) {
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library = framework::LibraryType::kMKLDNN;
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}
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#endif
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framework::DataLayout layout = framework::DataLayout::kAnyLayout;
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::Tensor>("X")->type()),
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ctx.GetPlace(), layout, library);
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}
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} // anonymous namespace
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class ActivationWithMKLDNNOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return GetKernelType(ctx, *this);
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}
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};
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class ActivationWithMKLDNNOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("Out"));
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return GetKernelType(ctx, *this);
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}
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};
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} // namespace operators
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} // namespace paddle
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