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Merge pull request #9081 from kbinias/kbinias/mkldnn-activations
MKLDNN Relu Tanh Sqrt Abs activations added
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| /* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserve. | ||
| 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. */ | ||
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| #include "mkldnn.hpp" | ||
| #include "mkldnn_activation_op.h" | ||
| #include "paddle/fluid/operators/activation_op.h" | ||
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| namespace paddle { | ||
| namespace operators { | ||
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| using paddle::framework::Tensor; | ||
| using paddle::platform::MKLDNNDeviceContext; | ||
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| namespace { | ||
| template <typename T, typename ExecContext> | ||
| void eltwise_forward(const ExecContext &ctx, mkldnn::algorithm algorithm, | ||
| const T alpha = 0, const T beta = 0) { | ||
| PADDLE_ENFORCE(paddle::platform::is_cpu_place(ctx.GetPlace()), | ||
| "It must use CPUPlace."); | ||
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| auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
| const auto &mkldnn_engine = dev_ctx.GetEngine(); | ||
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| // get buffers | ||
| const auto *src = ctx.template Input<Tensor>("X"); | ||
| const auto *src_data = src->template data<T>(); | ||
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| auto *dst = ctx.template Output<Tensor>("Out"); | ||
| const T *dst_data = dst->template mutable_data<T>(ctx.GetPlace()); | ||
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| // get memory dim | ||
| PADDLE_ENFORCE(src->dims().size() == 4, | ||
| "Input dim must be with 4, i.e. NCHW"); | ||
| std::vector<int> src_tz = framework::vectorize2int(src->dims()); | ||
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| // create memory description | ||
| // TODO(kbinias-intel): support more formats | ||
| auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32, | ||
| mkldnn::memory::format::nchw); | ||
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| // create memory primitives | ||
| auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src_data); | ||
| auto dst_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)dst_data); | ||
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| auto forward_desc = mkldnn::eltwise_forward::desc( | ||
| 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 | ||
| const std::string key = ctx.op().Output("Out"); | ||
| const std::string key_eltwise_pd = key + "@eltwise_pd"; | ||
| auto forward_pd = std::make_shared<mkldnn::eltwise_forward::primitive_desc>( | ||
| forward_desc, mkldnn_engine); | ||
| 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 | ||
| std::vector<mkldnn::primitive> pipeline = {eltwise}; | ||
| mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
| } | ||
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| template <typename T, typename ExecContext> | ||
| void eltwise_grad(const ExecContext &ctx, mkldnn::algorithm algorithm, | ||
| const T alpha = 0, const T beta = 0) { | ||
| auto &dev_ctx = ctx.template device_context<MKLDNNDeviceContext>(); | ||
| const auto &mkldnn_engine = dev_ctx.GetEngine(); | ||
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| // get buffers | ||
| const auto *x = ctx.template Input<Tensor>("X"); | ||
| const auto *src = x->template data<T>(); | ||
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| auto *dout = ctx.template Input<Tensor>(framework::GradVarName("Out")); | ||
| const auto *diff_dst = dout->template data<T>(); | ||
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| auto *dx = | ||
| ctx.template Output<framework::Tensor>(framework::GradVarName("X")); | ||
| const T *diff_src = dx->template mutable_data<T>(ctx.GetPlace()); | ||
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| // get memory dim | ||
| std::vector<int> src_tz = framework::vectorize2int(x->dims()); | ||
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| // create memory description | ||
| auto data_md = platform::MKLDNNMemDesc(src_tz, mkldnn::memory::f32, | ||
| mkldnn::memory::format::nchw); | ||
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| // create memory primitives | ||
| auto src_memory = mkldnn::memory({data_md, mkldnn_engine}, (void *)src); | ||
| auto diff_src_memory = | ||
| mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_src); | ||
| auto diff_dst_memory = | ||
| mkldnn::memory({data_md, mkldnn_engine}, (void *)diff_dst); | ||
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| auto backward_desc = | ||
| mkldnn::eltwise_backward::desc(algorithm, data_md, data_md, alpha, beta); | ||
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| // retrieve eltwise primitive desc from device context | ||
| const std::string key = ctx.op().Input("Out"); | ||
| const std::string key_eltwise_pd = key + "@eltwise_pd"; | ||
| const std::shared_ptr<void> forward_pd = dev_ctx.GetBlob(key_eltwise_pd); | ||
| PADDLE_ENFORCE(forward_pd != nullptr, | ||
| "Fail to find eltwise_pd in device context"); | ||
| auto *p_forward_pd = | ||
| static_cast<mkldnn::eltwise_forward::primitive_desc *>(forward_pd.get()); | ||
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| auto eltwise_bwd_prim_desc = mkldnn::eltwise_backward::primitive_desc( | ||
| backward_desc, mkldnn_engine, *p_forward_pd); | ||
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| auto eltwise_bwd = mkldnn::eltwise_backward(eltwise_bwd_prim_desc, src_memory, | ||
| diff_dst_memory, diff_src_memory); | ||
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| // push primitive to stream and wait until it's executed | ||
| std::vector<mkldnn::primitive> pipeline = {eltwise_bwd}; | ||
| mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
| } | ||
| } // anonymous namespace | ||
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| template <typename T, mkldnn::algorithm algorithm> | ||
| struct MKLDNNActivationFunc : public BaseActivationFunctor<T> { | ||
| template <typename ExecContext> | ||
| void operator()(const ExecContext &ctx) const { | ||
| eltwise_forward<T>(ctx, algorithm); | ||
| } | ||
| }; | ||
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| template <typename T, mkldnn::algorithm algorithm> | ||
| struct MKLDNNActivationGradFunc : public BaseActivationFunctor<T> { | ||
| template <typename ExecContext> | ||
| void operator()(const ExecContext &ctx) const { | ||
| eltwise_grad<T>(ctx, algorithm); | ||
| } | ||
| }; | ||
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| template <typename T> | ||
| using ReluMkldnnFunctor = | ||
| MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_relu>; | ||
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| template <typename T> | ||
| using TanhMkldnnFunctor = | ||
| MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_tanh>; | ||
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| template <typename T> | ||
| using SqrtMkldnnFunctor = | ||
| MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_sqrt>; | ||
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| template <typename T> | ||
| using AbsMkldnnFunctor = | ||
| MKLDNNActivationFunc<T, mkldnn::algorithm::eltwise_abs>; | ||
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| template <typename T> | ||
| using ReluMkldnnGradFunctor = | ||
| MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_relu>; | ||
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| template <typename T> | ||
| using TanhMkldnnGradFunctor = | ||
| MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_tanh>; | ||
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| template <typename T> | ||
| using SqrtMkldnnGradFunctor = | ||
| MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_sqrt>; | ||
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| template <typename T> | ||
| using AbsMkldnnGradFunctor = | ||
| MKLDNNActivationGradFunc<T, mkldnn::algorithm::eltwise_abs>; | ||
| } // namespace operators | ||
| } // namespace paddle | ||
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| namespace ops = paddle::operators; | ||
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| #define REGISTER_ACTIVATION_MKLDNN_KERNEL(act_type, functor, grad_functor) \ | ||
| REGISTER_OP_KERNEL(act_type, MKLDNN, ::paddle::platform::CPUPlace, \ | ||
| ops::MKLDNNActivationKernel<ops::functor<float>>); \ | ||
| REGISTER_OP_KERNEL( \ | ||
| act_type##_grad, MKLDNN, ::paddle::platform::CPUPlace, \ | ||
| ops::MKLDNNActivationGradKernel<ops::grad_functor<float>>); | ||
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| #define FOR_EACH_MKLDNN_KERNEL_FUNCTOR(__macro) \ | ||
| __macro(relu, ReluMkldnnFunctor, ReluMkldnnGradFunctor); \ | ||
| __macro(tanh, TanhMkldnnFunctor, TanhMkldnnGradFunctor); \ | ||
| __macro(sqrt, SqrtMkldnnFunctor, SqrtMkldnnGradFunctor); \ | ||
| __macro(abs, AbsMkldnnFunctor, AbsMkldnnGradFunctor); | ||
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| FOR_EACH_MKLDNN_KERNEL_FUNCTOR(REGISTER_ACTIVATION_MKLDNN_KERNEL); |
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