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Initial implementation of multiplication operator for MKLDNN #9949
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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"); | ||
you may not use this file except in compliance with the License. | ||
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 | ||
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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@@ -13,9 +13,8 @@ See the License for the specific language governing permissions and | |
limitations under the License. */ | ||
#pragma once | ||
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#include <mkldnn.h> | ||
#include <vector> | ||
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#include "mkldnn/include/mkldnn.hpp" | ||
#include "paddle/fluid/framework/operator.h" | ||
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namespace paddle { | ||
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@@ -34,6 +33,32 @@ typedef std::unique_ptr<MKLDNNMemory> MKLDNNMemoryPtr; | |
typedef std::unique_ptr<MKLDNNPrimitive> MKLDNNPrimitivePtr; | ||
typedef std::unique_ptr<MKLDNNPrimitiveDesc> MKLDNNPrimitiveDescPtr; | ||
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template <typename Type> | ||
void* to_void_cast(const Type* t) { | ||
return static_cast<void*>(const_cast<Type*>(t)); | ||
} | ||
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template <class Type> | ||
using tf_desc = typename Type::desc; | ||
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template <class Type> | ||
using tf_pd = typename Type::primitive_desc; | ||
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template <typename Type, typename Engine, typename... Args> | ||
std::shared_ptr<tf_pd<Type>> MKLDNNFwdPrimitiveDesc(const Engine& e, | ||
Args&&... args) { | ||
auto desc = tf_desc<Type>(mkldnn::prop_kind::forward, (args)...); | ||
auto pd = new tf_pd<Type>(desc, e); | ||
return std::shared_ptr<tf_pd<Type>>(pd); | ||
} | ||
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template <typename Type, typename Engine, typename Primitive, typename... Args> | ||
tf_pd<Type> MKLDNNBwdPrimitiveDesc(const Engine& e, const Primitive& p, | ||
Args&&... args) { | ||
auto desc = tf_desc<Type>(args...); | ||
return tf_pd<Type>(desc, e, p); | ||
} | ||
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There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. Are line 36-61 only used in There was a problem hiding this comment. Choose a reason for hiding this commentThe reason will be displayed to describe this comment to others. Learn more. At this moment, yes, but this code may be uses by the all mkldnn operators. |
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inline mkldnn::memory::desc MKLDNNMemDesc(const std::vector<int>& dims, | ||
mkldnn::memory::data_type data_type, | ||
mkldnn::memory::format format) { | ||
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A small question, why named
tf_pd
here?There was a problem hiding this comment.
Choose a reason for hiding this comment
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tf = type_function.