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Add Bilinear Tensor Product operator. #5014
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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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 "paddle/operators/bilinear_tensor_product_op.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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using framework::Tensor; | ||
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class BilinearTensorProductOp : public framework::OperatorWithKernel { | ||
public: | ||
using framework::OperatorWithKernel::OperatorWithKernel; | ||
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protected: | ||
void InferShape(framework::InferShapeContext* ctx) const override { | ||
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasInput("Weight"), | ||
"Input(Weight) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasOutput("Out"), "Output(Out) should not be null."); | ||
auto x_dims = ctx->GetInputDim("X"); | ||
auto y_dims = ctx->GetInputDim("Y"); | ||
auto weight_dims = ctx->GetInputDim("Weight"); | ||
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PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "The input(X) must be a 2D Tensor."); | ||
PADDLE_ENFORCE_EQ(y_dims.size(), 2UL, "The input(Y) must be a 2D Tensor."); | ||
PADDLE_ENFORCE_EQ(weight_dims.size(), 3UL, | ||
"The input(Weight) must be a 3D tensor."); | ||
PADDLE_ENFORCE_EQ(x_dims[0], y_dims[0], | ||
"The first dimension(batch_size) of input(X) must be " | ||
"equal to the first dimension of the input(Y)."); | ||
PADDLE_ENFORCE_EQ(x_dims[1], weight_dims[1], | ||
"The second dimension of input(X) must be equal to " | ||
"the second dimension of the input(Weight)."); | ||
PADDLE_ENFORCE_EQ(y_dims[1], weight_dims[2], | ||
"The second dimension of input(Y) must be equal to " | ||
"the third dimension of the input(Weight)."); | ||
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if (ctx->HasInput("Bias")) { | ||
auto bias_dims = ctx->GetInputDim("Bias"); | ||
PADDLE_ENFORCE(bias_dims.size() == 2UL && bias_dims[0] == 1UL, | ||
"The Input(Bias) must be a 2-D tensor with " | ||
"the 2nd dimension fixed to 1 (a row vector)."); | ||
PADDLE_ENFORCE_EQ(bias_dims[1], weight_dims[0], | ||
"The second dimension of input(Bias) must be equal " | ||
"to the first dimension of the input(Weight)."); | ||
} | ||
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ctx->SetOutputDim("Out", {x_dims[0], weight_dims[0]}); | ||
ctx->ShareLoD("X", /*->*/ "Out"); | ||
} | ||
}; | ||
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class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker { | ||
public: | ||
BilinearTensorProductOpMaker(framework::OpProto* proto, | ||
framework::OpAttrChecker* op_checker) | ||
: OpProtoAndCheckerMaker(proto, op_checker) { | ||
AddInput("X", "The first input of bilinear_tensor_product operator."); | ||
AddInput("Y", "The second input of bilinear_tensor_product operator."); | ||
AddInput("Weight", | ||
"The learnable parameters of bilinear_tensor_product operator."); | ||
AddInput("Bias", "The learnable bias of bilinear_tensor_product operator.") | ||
.AsDispensable(); | ||
AddOutput("Out", "The output of bilinear_tensor_product operator."); | ||
AddComment(R"DOC( | ||
Bilinear Tensor Product operator. | ||
Given input X and Y, a 3D tensor weight, and bias. Each column of the | ||
output is computed by one slice i = 1, . . . , k of the tensor: | ||
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M = (X W_i) \cdot Y | ||
Out_i = \sum_i {M_i} + Bias_i | ||
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)DOC"); | ||
} | ||
}; | ||
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class BilinearTensorProductOpGrad : public framework::OperatorWithKernel { | ||
public: | ||
using framework::OperatorWithKernel::OperatorWithKernel; | ||
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protected: | ||
void InferShape(framework::InferShapeContext* ctx) const override { | ||
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasInput("Y"), "Input(Y) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasInput("Weight"), | ||
"Input(Weight) should not be null."); | ||
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")), | ||
"Input(Out@GRAD) should not be null."); | ||
auto x_dims = ctx->GetInputDim("X"); | ||
auto y_dims = ctx->GetInputDim("Y"); | ||
auto weight_dims = ctx->GetInputDim("Weight"); | ||
auto out_dims = ctx->GetInputDim(framework::GradVarName("Out")); | ||
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PADDLE_ENFORCE_EQ(out_dims.size(), 2UL, | ||
"The input(Out@GRAD) must be a 2D Tensor."); | ||
PADDLE_ENFORCE_EQ( | ||
x_dims[0], out_dims[0], | ||
"The first dimension(batch_size) of input(Out@GRAD) must be " | ||
"equal to the first dimension of the Input(X)."); | ||
PADDLE_ENFORCE_EQ( | ||
weight_dims[0], out_dims[1], | ||
"The second dimension of input(Out@GRAD) must be equal to " | ||
"the third dimension of the Input(Weight)."); | ||
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if (ctx->HasInput("Bias")) { | ||
auto bias_dims = ctx->GetInputDim("Bias"); | ||
PADDLE_ENFORCE_EQ( | ||
bias_dims[1], out_dims[1], | ||
"The second dimension of input(Out@GRAD) must be equal to " | ||
"the second dimension of the Input(Bias)."); | ||
auto bias_grad_name = framework::GradVarName("Bias"); | ||
if (ctx->HasOutput(bias_grad_name)) | ||
ctx->SetOutputDim(bias_grad_name, bias_dims); | ||
} | ||
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auto x_grad_name = framework::GradVarName("X"); | ||
auto y_grad_name = framework::GradVarName("Y"); | ||
auto weight_grad_name = framework::GradVarName("Weight"); | ||
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if (ctx->HasOutput(x_grad_name)) { | ||
ctx->SetOutputDim(x_grad_name, x_dims); | ||
} | ||
if (ctx->HasOutput(y_grad_name)) { | ||
ctx->SetOutputDim(y_grad_name, y_dims); | ||
} | ||
if (ctx->HasOutput(weight_grad_name)) { | ||
ctx->SetOutputDim(weight_grad_name, weight_dims); | ||
} | ||
} | ||
}; | ||
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} // namespace operators | ||
} // namespace paddle | ||
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namespace ops = paddle::operators; | ||
REGISTER_OP(bilinear_tensor_product, ops::BilinearTensorProductOp, | ||
ops::BilinearTensorProductOpMaker, bilinear_tensor_product_grad, | ||
ops::BilinearTensorProductOpGrad); | ||
REGISTER_OP_CPU_KERNEL( | ||
bilinear_tensor_product, | ||
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, float>, | ||
ops::BilinearTensorProductKernel<paddle::platform::CPUPlace, double>); | ||
REGISTER_OP_CPU_KERNEL( | ||
bilinear_tensor_product_grad, | ||
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, float>, | ||
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, double>); |
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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. License的缩进有问题。按照accuracy_op.h 。 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. Done |
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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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#define EIGEN_USE_GPU | ||
#include "paddle/operators/bilinear_tensor_product_op.h" | ||
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namespace ops = paddle::operators; | ||
REGISTER_OP_GPU_KERNEL( | ||
bilinear_tensor_product, | ||
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, float>, | ||
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, double>); | ||
REGISTER_OP_GPU_KERNEL( | ||
bilinear_tensor_product_grad, | ||
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, float>, | ||
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, double>); |
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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve. | ||
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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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#pragma once | ||
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#include "paddle/framework/eigen.h" | ||
#include "paddle/framework/op_registry.h" | ||
#include "paddle/operators/math/math_function.h" | ||
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namespace paddle { | ||
namespace operators { | ||
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using framework::Tensor; | ||
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template <typename T, int MajorType = Eigen::RowMajor, | ||
typename IndexType = Eigen::DenseIndex> | ||
using EigenMatrix = framework::EigenMatrix<T, MajorType, IndexType>; | ||
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template <typename T, int MajorType = Eigen::RowMajor, | ||
typename IndexType = Eigen::DenseIndex> | ||
using EigenVector = framework::EigenVector<T, MajorType, IndexType>; | ||
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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. 30 ~ 32 行删掉。并没有用到 EigenVector。 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. Done |
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template <typename Place, typename T> | ||
class BilinearTensorProductKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
auto* x = ctx.Input<Tensor>("X"); | ||
auto* y = ctx.Input<Tensor>("Y"); | ||
auto* weight = ctx.Input<Tensor>("Weight"); | ||
auto* bias = ctx.Input<Tensor>("Bias"); | ||
auto* out = ctx.Output<Tensor>("Out"); | ||
out->mutable_data<T>(ctx.GetPlace()); | ||
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auto y_mat = EigenMatrix<T>::From(*y); | ||
auto output_mat = EigenMatrix<T>::From(*out); | ||
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auto batch_size = x->dims()[0]; | ||
auto weight_dims = weight->dims(); | ||
auto place = ctx.GetEigenDevice<Place>(); | ||
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// Create the intermediate variables. | ||
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.
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. Done |
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Tensor left_mul; | ||
left_mul.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[2]}), | ||
ctx.GetPlace()); | ||
auto left_mul_mat = EigenMatrix<T>::From(left_mul); | ||
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for (size_t i = 0; i < weight_dims[0]; ++i) { | ||
auto output_col_vec = output_mat.chip(i, 1); | ||
Tensor weight_mat = weight->Slice(i, i + 1).Resize( | ||
framework::make_ddim({weight_dims[1], weight_dims[2]})); | ||
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans, | ||
batch_size, weight_dims[2], weight_dims[1], 1, | ||
x->data<T>(), weight_mat.data<T>(), 0, | ||
left_mul.data<T>()); | ||
output_col_vec.device(place) = | ||
(left_mul_mat * y_mat).sum(Eigen::DSizes<int, 1>(1)); | ||
} | ||
if (bias) { | ||
auto bias_vec = EigenMatrix<T>::From(*bias); | ||
Eigen::DSizes<int, 2> bcast(batch_size, 1); | ||
output_mat.device(place) = bias_vec.broadcast(bcast) + output_mat; | ||
} | ||
} | ||
}; | ||
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template <typename Place, typename T> | ||
class BilinearTensorProductGradKernel : public framework::OpKernel<T> { | ||
public: | ||
void Compute(const framework::ExecutionContext& ctx) const override { | ||
const Tensor* x = ctx.Input<Tensor>("X"); | ||
const Tensor* y = ctx.Input<Tensor>("Y"); | ||
const Tensor* weight = ctx.Input<Tensor>("Weight"); | ||
Tensor* d_x = ctx.Output<Tensor>(framework::GradVarName("X")); | ||
Tensor* d_y = ctx.Output<Tensor>(framework::GradVarName("Y")); | ||
Tensor* d_weight = ctx.Output<Tensor>(framework::GradVarName("Weight")); | ||
Tensor* d_bias = ctx.Output<Tensor>(framework::GradVarName("Bias")); | ||
const Tensor* d_out = ctx.Input<Tensor>(framework::GradVarName("Out")); | ||
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auto batch_size = x->dims()[0]; | ||
auto weight_dims = weight->dims(); | ||
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auto x_mat = EigenMatrix<T>::From(*x); | ||
auto y_mat = EigenMatrix<T>::From(*y); | ||
auto d_out_mat = EigenMatrix<T>::From(*d_out); | ||
auto place = ctx.GetEigenDevice<Place>(); | ||
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// Create the intermediate variables for gradient. | ||
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. Please complete the comments. There are three gradients need to be computed in backward. 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. Done |
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Tensor x_scale; | ||
x_scale.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[1]}), | ||
ctx.GetPlace()); | ||
auto x_scale_mat = EigenMatrix<T>::From(x_scale); | ||
Tensor y_scale; | ||
y_scale.mutable_data<T>(framework::make_ddim({batch_size, weight_dims[2]}), | ||
ctx.GetPlace()); | ||
auto y_scale_mat = EigenMatrix<T>::From(y_scale); | ||
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math::SetConstant<Place, T> set_zero; | ||
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// Set X@Grad be zero at first. | ||
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. remove "at first". 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. Done |
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if (d_x) { | ||
d_x->mutable_data<T>(ctx.GetPlace()); | ||
set_zero(ctx.device_context(), d_x, static_cast<T>(0)); | ||
} | ||
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. if (d_x) d_x->mutable_data<T>(ctx.GetPlace()); Setting zero is not necessary here. 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. There is an additive operation for d_x:
For this reason, the elements of d_x must be initialized as 0. Otherwise this op will lead to erroneous result. |
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// Set Y@Grad be zero at first. | ||
if (d_y) { | ||
d_y->mutable_data<T>(ctx.GetPlace()); | ||
set_zero(ctx.device_context(), d_y, static_cast<T>(0)); | ||
} | ||
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. if (d_y) d_y->mutable_data<T>(ctx.GetPlace()); 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. The same with d_x 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. the same to ... |
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// Caculate the X@Grad and Y@Grad. | ||
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. Output(X@Grad) and Output(Y@Grad) 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. Done |
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if (d_x || d_y) { | ||
Eigen::DSizes<int, 2> bcast_for_x(1, weight_dims[2]); | ||
Eigen::DSizes<int, 2> bcast_for_y(1, weight_dims[1]); | ||
for (int i = 0; i < weight_dims[0]; ++i) { | ||
Tensor weight_i = weight->Slice(i, i + 1).Resize( | ||
framework::make_ddim({weight_dims[1], weight_dims[2]})); | ||
auto output_vec = d_out_mat.chip(i, 1); | ||
if (d_x) { | ||
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.
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. 这里由于broadcast是在batch的方向展开,且TMP = scaled(X) W,scaled(X)中每一行元素所乘的放缩系数不同,所以无法在矩阵乘法之后做scaling计算。即scaled(X) W != scaled(X W). |
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y_scale_mat.device(place) = | ||
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1)) | ||
.broadcast(bcast_for_x) * | ||
y_mat; | ||
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans, | ||
batch_size, weight_dims[1], weight_dims[2], 1, | ||
y_scale.data<T>(), weight_i.data<T>(), 1, | ||
d_x->data<T>()); | ||
} | ||
if (d_y) { | ||
x_scale_mat.device(place) = | ||
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1)) | ||
.broadcast(bcast_for_y) * | ||
x_mat; | ||
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans, | ||
batch_size, weight_dims[2], weight_dims[1], 1, | ||
x_scale.data<T>(), weight_i.data<T>(), 1, | ||
d_y->data<T>()); | ||
} | ||
} | ||
} | ||
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// Caculate the gradient of Weight. | ||
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. Weight --> Input(Weight) to keep a consistent naming style in comments. 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. Done |
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if (d_weight) { | ||
d_weight->mutable_data<T>(ctx.GetPlace()); | ||
Eigen::DSizes<int, 2> bcast_for_weight(1, weight_dims[1]); | ||
for (int i = 0; i < weight_dims[0]; ++i) { | ||
Tensor d_weight_i = d_weight->Slice(i, i + 1).Resize( | ||
framework::make_ddim({weight_dims[1], weight_dims[2]})); | ||
auto output_vec = d_out_mat.chip(i, 1); | ||
x_scale_mat.device(place) = | ||
output_vec.reshape(Eigen::DSizes<int, 2>(batch_size, 1)) | ||
.broadcast(bcast_for_weight) * | ||
x_mat; | ||
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans, | ||
weight_dims[1], weight_dims[2], batch_size, 1, | ||
x_scale.data<T>(), y->data<T>(), 0, | ||
d_weight_i.data<T>()); | ||
} | ||
} | ||
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// Caculate the gradient of Bias. | ||
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. Bias --> Input(Bias) 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. Done |
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if (d_bias) { | ||
d_bias->mutable_data<T>(ctx.GetPlace()); | ||
auto d_bias_mat = EigenMatrix<T>::From(*d_bias); | ||
d_bias_mat.device(place) = d_out_mat.sum(Eigen::DSizes<int, 1>(0)); | ||
} | ||
} | ||
}; | ||
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} // namespace operators | ||
} // namespace paddle |
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be equal to
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Done