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Merge pull request #9949 from mozga-intel/mozga-intel/Mul_mkldnn
Initial implementation of multiplication operator for MKLDNN
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/* Copyright (c) 2018 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. */ | ||
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#include "mkldnn.hpp" | ||
#include "paddle/fluid/framework/tensor.h" | ||
#include "paddle/fluid/operators/mul_op.h" | ||
#include "paddle/fluid/platform/device_context.h" | ||
#include "paddle/fluid/platform/mkldnn_helper.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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template <typename Format = mkldnn::memory::format> | ||
mkldnn::memory::desc type(const std::vector<int>& dims, Format&& f) { | ||
return platform::MKLDNNMemDesc(dims, mkldnn::memory::data_type::f32, f); | ||
} | ||
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template <typename T> | ||
class MulMKLDNNOpKernel : public paddle::framework::OpKernel<T> { | ||
void Compute(const paddle::framework::ExecutionContext& ctx) const override { | ||
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>(); | ||
auto mkldnn_engine = dev_ctx.GetEngine(); | ||
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auto input = ctx.Input<Tensor>("X"); | ||
auto weight = ctx.Input<Tensor>("Y"); | ||
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PADDLE_ENFORCE(input->dims().size() & (2 | 4), | ||
"Input must be with 2 or 4 dimensions, i.e. NC or NCHW"); | ||
PADDLE_ENFORCE(weight->dims().size() & (2 | 4), | ||
"Weights must be with 2 or 4 dimensions, i.e. OI or OIHW"); | ||
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std::vector<int> w_tz = paddle::framework::vectorize2int(weight->dims()); | ||
std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims()); | ||
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auto src_md = | ||
src_tz.size() != 2 | ||
? type(src_tz, mkldnn::memory::format::nchw) | ||
: type({src_tz[0], src_tz[1]}, mkldnn::memory::format::nc); | ||
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auto dst_md = type({src_tz[0], w_tz[1]}, mkldnn::memory::format::nc); | ||
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auto weights_md = | ||
src_tz.size() != 2 | ||
? type({w_tz[1], src_tz[1], src_tz[2], src_tz[3]}, | ||
mkldnn::memory::format::oihw) | ||
: type({w_tz[1], src_tz[1]}, mkldnn::memory::format::oi); | ||
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auto output = ctx.Output<Tensor>("Out"); | ||
T* output_data = output->mutable_data<T>(ctx.GetPlace()); | ||
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const std::string key = ctx.op().Output("Out"); | ||
const std::string key_fc_pd = key + "@mul_pd"; | ||
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const T* input_data = input->data<T>(); | ||
const T* w_data = weight->data<T>(); | ||
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auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine}, output_data); | ||
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auto src_memory = mkldnn::memory({src_md, mkldnn_engine}, | ||
platform::to_void_cast(input_data)); | ||
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auto weights_memory = mkldnn::memory({weights_md, mkldnn_engine}, | ||
platform::to_void_cast(w_data)); | ||
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auto pd = platform::MKLDNNFwdPrimitiveDesc<mkldnn::inner_product_forward>( | ||
mkldnn_engine, src_md, weights_md, dst_md); | ||
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dev_ctx.SetBlob(key_fc_pd, pd); | ||
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auto forward = mkldnn::inner_product_forward(*pd, src_memory, | ||
weights_memory, dst_memory); | ||
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std::vector<mkldnn::primitive> pipeline = {forward}; | ||
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
} | ||
}; | ||
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template <typename T> | ||
class MulMKLDNNGradOpKernel : public paddle::framework::OpKernel<T> { | ||
public: | ||
void Compute(const paddle::framework::ExecutionContext& ctx) const override { | ||
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>(); | ||
auto mkldnn_engine = dev_ctx.GetEngine(); | ||
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const Tensor* input = ctx.Input<Tensor>("X"); | ||
const Tensor* w = ctx.Input<Tensor>("Y"); | ||
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const Tensor* out_grad = ctx.Input<Tensor>(framework::GradVarName("Out")); | ||
Tensor* input_grad = ctx.Output<Tensor>(framework::GradVarName("X")); | ||
Tensor* w_grad = ctx.Output<Tensor>(framework::GradVarName("Y")); | ||
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const std::string key = ctx.op().Input("Out"); | ||
const std::string key_fc_pd = key + "@mul_pd"; | ||
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const T* input_data = input->data<T>(); | ||
const T* w_data = w->data<T>(); | ||
const T* out_grad_data = out_grad->data<T>(); | ||
T* input_grad_data = nullptr; | ||
T* w_grad_data = nullptr; | ||
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if (input_grad) { | ||
input_grad_data = input_grad->mutable_data<T>(ctx.GetPlace()); | ||
} | ||
if (w_grad) { | ||
w_grad_data = w_grad->mutable_data<T>(ctx.GetPlace()); | ||
} | ||
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std::vector<int> src_tz = paddle::framework::vectorize2int(input->dims()); | ||
std::vector<int> w_tz = paddle::framework::vectorize2int(w->dims()); | ||
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auto src_md = | ||
src_tz.size() != 2 | ||
? type(src_tz, mkldnn::memory::format::nchw) | ||
: type({src_tz[0], src_tz[1]}, mkldnn::memory::format::nc); | ||
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auto dst_md = type({src_tz[0], w_tz[1]}, mkldnn::memory::format::nc); | ||
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auto weights_md = | ||
src_tz.size() != 2 | ||
? type({w_tz[1], src_tz[1], src_tz[2], src_tz[3]}, | ||
mkldnn::memory::format::oihw) | ||
: type({w_tz[1], src_tz[1]}, mkldnn::memory::format::oi); | ||
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auto src_memory = mkldnn::memory({src_md, mkldnn_engine}, | ||
platform::to_void_cast(input_data)); | ||
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auto dst_memory = mkldnn::memory({dst_md, mkldnn_engine}, | ||
platform::to_void_cast(out_grad_data)); | ||
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auto weight_memory = mkldnn::memory({weights_md, mkldnn_engine}, | ||
platform::to_void_cast(w_data)); | ||
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auto pd = | ||
std::static_pointer_cast<mkldnn::inner_product_forward::primitive_desc>( | ||
dev_ctx.GetBlob(key_fc_pd)); | ||
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PADDLE_ENFORCE(pd != nullptr, "Fail to find pd in device context"); | ||
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if (w_grad) { | ||
auto weights_grad_memory = mkldnn::memory( | ||
{weights_md, mkldnn_engine}, platform::to_void_cast(w_grad_data)); | ||
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auto bwd_weight_pd = platform::MKLDNNBwdPrimitiveDesc< | ||
mkldnn::inner_product_backward_weights>(mkldnn_engine, *pd, src_md, | ||
weights_md, dst_md); | ||
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auto bwd_weights_prim = mkldnn::inner_product_backward_weights( | ||
bwd_weight_pd, src_memory, dst_memory, weights_grad_memory); | ||
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std::vector<mkldnn::primitive> pipeline{bwd_weights_prim}; | ||
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
} | ||
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if (input_grad) { | ||
auto src_grad_memory = mkldnn::memory( | ||
{src_md, mkldnn_engine}, platform::to_void_cast(input_grad_data)); | ||
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auto bwd_data_pd = | ||
platform::MKLDNNBwdPrimitiveDesc<mkldnn::inner_product_backward_data>( | ||
mkldnn_engine, *pd, src_md, weights_md, dst_md); | ||
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auto bwd_data_prim = mkldnn::inner_product_backward_data( | ||
bwd_data_pd, dst_memory, weight_memory, src_grad_memory); | ||
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std::vector<mkldnn::primitive> pipeline{bwd_data_prim}; | ||
mkldnn::stream(mkldnn::stream::kind::eager).submit(pipeline).wait(); | ||
} | ||
} | ||
}; | ||
} // namespace operators | ||
} // namespace paddle | ||
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REGISTER_OP_KERNEL(mul, MKLDNN, ::paddle::platform::CPUPlace, | ||
paddle::operators::MulMKLDNNOpKernel<float>); | ||
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REGISTER_OP_KERNEL(mul_grad, MKLDNN, ::paddle::platform::CPUPlace, | ||
paddle::operators::MulMKLDNNGradOpKernel<float>); |
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