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PaddleDetection
提交 fcd31d61 编写于 作者: Y Yu Yang
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上级 0a13d3c6
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paddle/fluid/operators/math/blas.cc
浏览文件 @ fcd31d61
......@@ -18,34 +18,26 @@
namespace paddle {
namespace operators {
namespace math {
MatDescriptor GetMatDim(const framework::DDim& dim, int num_flatten_cols,
bool trans) {
MatDescriptor CreateMatrixDescriptor(const framework::DDim &tensor_dim,
int num_flatten_cols, bool trans) {
PADDLE_ENFORCE_GT(tensor_dim.size(), 1);
MatDescriptor retv;
if (num_flatten_cols > 1) {
auto flatten_dim = framework::flatten_to_2d(dim, num_flatten_cols);
auto flatten_dim = framework::flatten_to_2d(tensor_dim, num_flatten_cols);
retv.height_ = flatten_dim[0];
retv.width_ = flatten_dim[1];
} else {
if (dim.size() == 1) {
retv.height_ = 1;
retv.width_ = dim[0];
} else if (dim.size() == 2) {
retv.height_ = dim[0];
retv.width_ = dim[1];
if (tensor_dim.size() == 2) {
retv.height_ = tensor_dim[0];
retv.width_ = tensor_dim[1];
} else {
if (dim.size() == 3) {
retv.batch_size_ = dim[0];
retv.height_ = dim[1];
retv.width_ = dim[2];
} else {
auto dim_vec = framework::vectorize(dim);
retv.batch_size_ = 1;
for (size_t i = 0; i < dim_vec.size() - 2; ++i) {
retv.batch_size_ *= dim_vec[i];
retv.height_ = dim_vec[dim_vec.size() - 2];
retv.width_ = dim_vec[dim_vec.size() - 1];
}
auto dim_vec = framework::vectorize(tensor_dim);
retv.batch_size_ = 1;
for (size_t i = 0; i < dim_vec.size() - 2; ++i) {
retv.batch_size_ *= dim_vec[i];
}
retv.height_ = dim_vec[dim_vec.size() - 2];
retv.width_ = dim_vec[dim_vec.size() - 1];
retv.stride_ = retv.height_ * retv.width_;
}
}
......
paddle/fluid/operators/math/blas.h
浏览文件 @ fcd31d61
......@@ -46,6 +46,25 @@ namespace paddle {
namespace operators {
namespace math {
/**
* Matrix Descriptor of a memory buffer.
*
* It is used for Blas::MatMul. MatMul operator can be batched.
* if Mat A is [BatchSize, H, W], Mat B is [BatchSize, H, W]. It will be a
* `batch_size` times of GEMM. The batched GEMM could be faster base on the
* implementation of the blas library. The batch size could be zero. If any
* matrix of `matmul` has a batch size, the will be a batched GEMM, too. e.g.,
* Mat A is [BatchSize, H1, W2], and Mat B [H2, W2], The result matrix wil be
* [BatchSize, H1, W2]
*
* The boolean flag, `trans`, describe the memory is the transpose of matrix or
* not. If the trans is true, the last two dims of matrix are transposed. The
* memory layout of the matrix is [Width, Height] or [BatchSize, Width, Height].
*
* The MatDescriptor is not only the dimension or shape of a matrix, it also
* contains the layout, stride of matrix. It is clearer to have a structure than
* reuse `DDim`.
*/
struct MatDescriptor {
int64_t height_;
int64_t width_;
......@@ -54,8 +73,22 @@ struct MatDescriptor {
bool trans_;
};
extern MatDescriptor GetMatDim(const framework::DDim& tensor,
int num_flatten_cols, bool trans);
/**
* Create Matrix Descriptor from a tensor dim, num_flatten_cols, and transpose
* flag
*
* @param tensor_dim: The dimension of the tensor. The rank of this dimension
* must larger than 1.
*
* @param num_flatten_cols: Reshape a tensor to a matrix. The matrix's first
* dimension(column length) will be the product of tensor's first `num_col_dims`
* dimensions. If num_flatten_cols is zero, the first N-2 dimension will be the
* batch_size of descriptor.
*
* @param trans: True if the matrix is transposed.
*/
extern MatDescriptor CreateMatrixDescriptor(const framework::DDim& tensor_dim,
int num_flatten_cols, bool trans);
template <typename DeviceContext>
class Blas {
......
paddle/fluid/operators/matmul_op.cc
浏览文件 @ fcd31d61
......@@ -12,14 +12,257 @@ 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. */
#include "paddle/fluid/operators/matmul_op.h"
#include <algorithm>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
namespace paddle {
namespace operators {
/**
* Get row matrix shape from a vector shape. If the rank of x_dim > 1, the
* original x_dim is returned.
*/
static framework::DDim RowMatrixFromVector(const framework::DDim& x_dim) {
if (x_dim.size() > 1) {
return x_dim;
}
return framework::make_ddim({1, x_dim[0]});
}
/**
* Get column matrix shape from a vector shape. If the ran of y_dim > 1, the
* original y_dim is returned.
*/
static framework::DDim ColumnMatrixFromVector(const framework::DDim& y_dim) {
if (y_dim.size() > 1) {
return y_dim;
}
return framework::make_ddim({y_dim[0], 1});
}
template <typename DeviceContext, typename T>
class MatMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& x =
detail::Ref(context.Input<framework::Tensor>("X"), "Cannot find X");
auto& y =
detail::Ref(context.Input<framework::Tensor>("Y"), "Cannot find Y");
auto* out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(context);
auto mat_dim_a = math::CreateMatrixDescriptor(
RowMatrixFromVector(x.dims()), 0, context.Attr<bool>("transpose_X"));
auto mat_dim_b = math::CreateMatrixDescriptor(
ColumnMatrixFromVector(y.dims()), 0, context.Attr<bool>("transpose_Y"));
blas.MatMul(x, mat_dim_a, y, mat_dim_b, T(1), out, T(0));
}
};
// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
// Identity op if the tensor is not of rank 3.
static framework::Tensor FoldInitDims(const framework::Tensor& input) {
auto output = input;
auto in_dims = input.dims();
if (in_dims.size() == 3) {
output.Resize({in_dims[0] * in_dims[1], in_dims[2]});
}
return output;
}
// Reshape a rank-3 tensor from P x M x N to M x (P * N).
// (Warning: This requires transposing data and writes into new memory.)
// Identity op if the tensor is not of rank 3.
template <typename DeviceContext, typename T>
static framework::Tensor FoldHeadAndLastDims(const DeviceContext& context,
const framework::Tensor& input) {
auto in_dims = input.dims();
if (in_dims.size() != 3) {
return input;
}
framework::Tensor output;
output.Resize({in_dims[1], in_dims[0], in_dims[2]});
output.mutable_data<T>(context.GetPlace());
std::vector<int> axis = {1, 0, 2};
math::Transpose<DeviceContext, T, 3> trans;
trans(context, input, &output, axis);
output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
using framework::Tensor;
return output;
}
/**
* Reshape a tensor to 3-D or 2-D tensor by matrix descriptor.
*
* The shape would be [BatchSize, H, W] or [H, W].
* If transposed, `H,W` will be swapped.
*/
static void ReshapeTensorIntoMatrixSequence(
framework::Tensor* x, const math::MatDescriptor& descriptor) {
int64_t h, w;
h = descriptor.height_;
w = descriptor.width_;
if (descriptor.trans_) {
std::swap(w, h);
}
if (descriptor.batch_size_) {
x->Resize({descriptor.batch_size_, h, w});
} else {
x->Resize({h, w});
}
}
/**
* Reshape the x,y,out tensor to 3-D or 2-D tensor by matrix descriptor
* Out = matmul(x, y)
*
* This method will first calculate X,Y matrix sequence, and then calculate
* the out shape.
*
* Assume X = [BatchSize, H1, W1], Y = [BatchSize, H2, W2]
* The out = [BatchSize, H1, W2]
*
* If there is no batch size in `X` and `Y`, the out will be [H1, W2]
* If any of `X` and `Y` has batch size BatchSize, the out will have the
* BatchSize.
*/
static void ReshapeXYOutIntoMatrixSequence(framework::Tensor* x,
framework::Tensor* y,
framework::Tensor* out, bool trans_x,
bool trans_y) {
auto x_dim = RowMatrixFromVector(x->dims());
auto y_dim = ColumnMatrixFromVector(y->dims());
auto mat_dim_x = math::CreateMatrixDescriptor(x_dim, 0, trans_x);
auto mat_dim_y = math::CreateMatrixDescriptor(y_dim, 0, trans_y);
if (mat_dim_x.batch_size_ == 0 && mat_dim_y.batch_size_ == 0) {
out->Resize({mat_dim_x.height_, mat_dim_y.width_});
} else {
out->Resize({std::max(mat_dim_x.batch_size_, mat_dim_y.batch_size_),
mat_dim_x.height_, mat_dim_y.width_});
}
ReshapeTensorIntoMatrixSequence(x, mat_dim_x);
ReshapeTensorIntoMatrixSequence(y, mat_dim_y);
}
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
//
// transpose_X | False | True | False | True
// transpose_Y | False | False | True | True
// -----------+----------+----------+----------+-----------
// dX = | dOut Y^T | Y dOut^T | dOut Y | Y^T dOut^T
// dY = | X^T dOut | X dOut | dOut^T X | dOut^T X^T
//
// When X is a vector of size K, we treat it instead as a matrix of shape
// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
// a matrix of shape (K, 1).
//
// When X and Y are both 3-dimensional tensors, then the first dimension
// the batch dimension can be ignored and the exact same formulas apply
// as for two matrices.
//
// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
// up with formulas like
//
// dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
//
// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
// to X: (P * M) x K, dOut: (P * M) x N.
template <typename DeviceContext, typename T>
class MatMulGradKernel : public framework::OpKernel<T> {
public:
void MatMul(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
const framework::Tensor& b, bool trans_b,
framework::Tensor* out) const {
out->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(context);
auto mat_dim_a = math::CreateMatrixDescriptor(a.dims(), 0, trans_a);
auto mat_dim_b = math::CreateMatrixDescriptor(b.dims(), 0, trans_b);
blas.MatMul(a, mat_dim_a, b, mat_dim_b, T(1), out, T(0));
}
void CalcInputGrad(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
bool is_fold_init_dims_a, const framework::Tensor& b,
bool trans_b, bool is_fold_init_dims_b,
framework::Tensor* out) const {
if (out == nullptr) return;
bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
out->dims().size() == 2;
if (!need_combine) {
MatMul(context, a, trans_a, b, trans_b, out);
} else {
auto& ctx = context.template device_context<DeviceContext>();
MatMul(context, is_fold_init_dims_a
? FoldInitDims(a)
: FoldHeadAndLastDims<DeviceContext, T>(ctx, a),
trans_a, is_fold_init_dims_b
? FoldInitDims(b)
: FoldHeadAndLastDims<DeviceContext, T>(ctx, b),
trans_b, out);
}
}
void Compute(const framework::ExecutionContext& context) const override {
auto x = *context.Input<framework::Tensor>("X");
auto y = *context.Input<framework::Tensor>("Y");
auto dout =
*context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
bool transpose_x = context.Attr<bool>("transpose_X");
bool transpose_y = context.Attr<bool>("transpose_Y");
ReshapeXYOutIntoMatrixSequence(&x, &y, &dout, transpose_x, transpose_y);
framework::DDim dx_dims;
if (dx) {
dx_dims = dx->dims();
if (dx_dims != x.dims()) {
dx->Resize(x.dims());
}
}
framework::DDim dy_dims;
if (dy) {
dy_dims = dy->dims();
if (dy_dims != y.dims()) {
dy->Resize(y.dims());
}
}
if (transpose_x && transpose_y) {
CalcInputGrad(context, y, true, true, dout, true, false, dx);
CalcInputGrad(context, dout, true, true, x, true, false, dy);
} else if (transpose_x) {
CalcInputGrad(context, y, false, false, dout, true, false, dx);
CalcInputGrad(context, x, false, false, dout, false, true, dy);
} else if (transpose_y) {
CalcInputGrad(context, dout, false, false, y, false, true, dx);
CalcInputGrad(context, dout, true, true, x, false, true, dy);
} else {
CalcInputGrad(context, dout, false, false, y, true, false, dx);
CalcInputGrad(context, x, true, true, dout, false, true, dy);
}
if (dx) {
if (dx_dims != x.dims()) {
dx->Resize(dx_dims);
}
}
if (dy) {
if (dy_dims != y.dims()) {
dy->Resize(dy_dims);
}
}
}
};
class MatMulOp : public framework::OperatorWithKernel {
public:
......@@ -37,9 +280,11 @@ class MatMulOp : public framework::OperatorWithKernel {
auto dim_x = context->GetInputDim("X");
auto dim_y = context->GetInputDim("Y");
auto mat_dim_x = math::GetMatDim(GetXDim(dim_x), 0,
auto mat_dim_x =
math::CreateMatrixDescriptor(RowMatrixFromVector(dim_x), 0,
context->Attrs().Get<bool>("transpose_X"));
auto mat_dim_y = math::GetMatDim(GetYDim(dim_y), 0,
auto mat_dim_y =
math::CreateMatrixDescriptor(ColumnMatrixFromVector(dim_y), 0,
context->Attrs().Get<bool>("transpose_Y"));
PADDLE_ENFORCE_EQ(mat_dim_x.width_, mat_dim_y.height_);
......@@ -151,15 +396,40 @@ class MatMulOpGrad : public framework::OperatorWithKernel {
}
};
class MatMulOpGradMaker : public framework::SingleGradOpDescMaker {
public:
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
protected:
std::unique_ptr<framework::OpDesc> Apply() const override {
auto* retv = new framework::OpDesc();
retv->SetType("matmul_grad");
retv->SetInput("X", Input("X"));
retv->SetInput("Y", Input("Y"));
retv->SetInput(framework::GradVarName("Out"), OutputGrad("Out"));
retv->SetOutput(framework::GradVarName("X"), InputGrad("X"));
retv->SetOutput(framework::GradVarName("Y"), InputGrad("Y"));
retv->SetAttrMap(Attrs());
return std::unique_ptr<framework::OpDesc>(retv);
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(matmul, ops::MatMulOp, ops::MatMulOpMaker,
paddle::framework::DefaultGradOpDescMaker<true>);
ops::MatMulOpGradMaker);
REGISTER_OPERATOR(matmul_grad, ops::MatMulOpGrad);
REGISTER_OP_CPU_KERNEL(
matmul, ops::MatMulKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
matmul_grad,
ops::MatMulGradKernel<paddle::platform::CPUDeviceContext, float>);
#ifdef PADDLE_WITH_CUDA
REGISTER_OP_CUDA_KERNEL(
matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
matmul_grad,
ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>);
#endif
paddle/fluid/operators/matmul_op.cu.cc 已删除 100644 → 0
浏览文件 @ 0a13d3c6
/* Copyright (c) 2016 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. */
#include "paddle/fluid/operators/matmul_op.h"
namespace ops = paddle::operators;
REGISTER_OP_CUDA_KERNEL(
matmul, ops::MatMulKernel<paddle::platform::CUDADeviceContext, float>);
REGISTER_OP_CUDA_KERNEL(
matmul_grad,
ops::MatMulGradKernel<paddle::platform::CUDADeviceContext, float>);
paddle/fluid/operators/matmul_op.h 已删除 100644 → 0
浏览文件 @ 0a13d3c6
/* Copyright (c) 2016 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. */
#pragma once
#include <algorithm>
#include <functional>
#include <utility>
#include <vector>
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/fluid/operators/detail/safe_ref.h"
#include "paddle/fluid/operators/math/blas.h"
#include "paddle/fluid/operators/math/math_function.h"
namespace paddle {
namespace operators {
inline framework::DDim GetXDim(const framework::DDim& x_dim) {
if (x_dim.size() > 1) {
return x_dim;
}
return framework::make_ddim({1, x_dim[0]});
}
inline framework::DDim GetYDim(const framework::DDim& y_dim) {
if (y_dim.size() > 1) {
return y_dim;
}
return framework::make_ddim({y_dim[0], 1});
}
template <typename DeviceContext, typename T>
class MatMulKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto& x =
detail::Ref(context.Input<framework::Tensor>("X"), "Cannot find X");
auto& y =
detail::Ref(context.Input<framework::Tensor>("Y"), "Cannot find Y");
auto* out = context.Output<framework::Tensor>("Out");
out->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(context);
auto mat_dim_a = math::GetMatDim(GetXDim(x.dims()), 0,
context.Attr<bool>("transpose_X"));
auto mat_dim_b = math::GetMatDim(GetYDim(y.dims()), 0,
context.Attr<bool>("transpose_Y"));
blas.MatMul(x, mat_dim_a, y, mat_dim_b, T(1), out, T(0));
}
};
// Reshape a rank-3 tensor from P x M x N to (P * M) x N.
// Identity op if the tensor is not of rank 3.
inline framework::Tensor CombineBatchAndM(const framework::Tensor& input) {
auto output = input;
auto in_dims = input.dims();
if (in_dims.size() == 3) {
output.Resize({in_dims[0] * in_dims[1], in_dims[2]});
}
return output;
}
// Reshape a rank-3 tensor from P x M x N to M x (P * N).
// (Warning: This requires transposing data and writes into new memory.)
// Identity op if the tensor is not of rank 3.
template <typename DeviceContext, typename T>
inline framework::Tensor CombineBatchAndN(const DeviceContext& context,
const framework::Tensor& input) {
auto in_dims = input.dims();
if (in_dims.size() != 3) {
return input;
}
framework::Tensor output;
output.Resize({in_dims[1], in_dims[0], in_dims[2]});
output.mutable_data<T>(context.GetPlace());
std::vector<int> axis = {1, 0, 2};
math::Transpose<DeviceContext, T, 3> trans;
trans(context, input, &output, axis);
output.Resize({in_dims[1], in_dims[0] * in_dims[2]});
return output;
}
inline void NormalizeTensorShape(framework::Tensor* x,
const math::MatDescriptor& mat_dim_x) {
int64_t h, w;
h = mat_dim_x.height_;
w = mat_dim_x.width_;
if (mat_dim_x.trans_) {
std::swap(w, h);
}
if (mat_dim_x.batch_size_) {
x->Resize({mat_dim_x.batch_size_, h, w});
} else {
x->Resize({h, w});
}
}
inline void NormalizeXYOutTensorShape(framework::Tensor* x,
framework::Tensor* y,
framework::Tensor* out, bool trans_a,
bool trans_b) {
auto x_dim = GetXDim(x->dims());
auto y_dim = GetYDim(y->dims());
auto mat_dim_x = math::GetMatDim(x_dim, 0, trans_a);
auto mat_dim_y = math::GetMatDim(y_dim, 0, trans_b);
if (mat_dim_x.batch_size_ == 0 && mat_dim_y.batch_size_ == 0) {
out->Resize({mat_dim_x.height_, mat_dim_y.width_});
} else {
out->Resize({std::max(mat_dim_x.batch_size_, mat_dim_y.batch_size_),
mat_dim_x.height_, mat_dim_y.width_});
}
NormalizeTensorShape(x, mat_dim_x);
NormalizeTensorShape(y, mat_dim_y);
}
// Using dimensional constraints on matrix multiplication, it is
// straight-forward to check the following table for when X and Y
// are both matrices.
//
// transpose_X | False | True | False | True
// transpose_Y | False | False | True | True
// -----------+----------+----------+----------+-----------
// dX = | dOut Y^T | Y dOut^T | dOut Y | Y^T dOut^T
// dY = | X^T dOut | X dOut | dOut^T X | dOut^T X^T
//
// When X is a vector of size K, we treat it instead as a matrix of shape
// (1, K). Similarly, when Y is a vector of size K, we treat it instead as
// a matrix of shape (K, 1).
//
// When X and Y are both 3-dimensional tensors, then the first dimension
// the batch dimension can be ignored and the exact same formulas apply
// as for two matrices.
//
// Finally, when, e.g., X is a 3-dimensional tensor but Y is a matrix, we end
// up with formulas like
//
// dY_{ij} = \sum_{p, m} X_{pmi} dOut_{pmj}
//
// To handle this sort of scenario, we reshape X : P x M x K, dOut: P x M x N
// to X: (P * M) x K, dOut: (P * M) x N.
template <typename DeviceContext, typename T>
class MatMulGradKernel : public framework::OpKernel<T> {
public:
void MatMul(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
const framework::Tensor& b, bool trans_b,
framework::Tensor* out) const {
out->mutable_data<T>(context.GetPlace());
auto blas = math::GetBlas<DeviceContext, T>(context);
auto mat_dim_a = math::GetMatDim(a.dims(), 0, trans_a);
auto mat_dim_b = math::GetMatDim(b.dims(), 0, trans_b);
blas.MatMul(a, mat_dim_a, b, mat_dim_b, T(1), out, T(0));
}
void CalcInputGrad(const framework::ExecutionContext& context,
const framework::Tensor& a, bool trans_a,
bool is_combine_m_a, const framework::Tensor& b,
bool trans_b, bool is_combine_m_b,
framework::Tensor* out) const {
if (out == nullptr) return;
bool need_combine = (a.dims().size() == 3 || b.dims().size() == 3) &&
out->dims().size() == 2;
if (!need_combine) {
MatMul(context, a, trans_a, b, trans_b, out);
} else {
auto& ctx = context.template device_context<DeviceContext>();
MatMul(
context, is_combine_m_a ? CombineBatchAndM(a)
: CombineBatchAndN<DeviceContext, T>(ctx, a),
trans_a, is_combine_m_b ? CombineBatchAndM(b)
: CombineBatchAndN<DeviceContext, T>(ctx, b),
trans_b, out);
}
}
void Compute(const framework::ExecutionContext& context) const override {
auto x = *context.Input<framework::Tensor>("X");
auto y = *context.Input<framework::Tensor>("Y");
auto dout =
*context.Input<framework::Tensor>(framework::GradVarName("Out"));
auto* dx = context.Output<framework::Tensor>(framework::GradVarName("X"));
auto* dy = context.Output<framework::Tensor>(framework::GradVarName("Y"));
bool transpose_x = context.Attr<bool>("transpose_X");
bool transpose_y = context.Attr<bool>("transpose_Y");
NormalizeXYOutTensorShape(&x, &y, &dout, transpose_x, transpose_y);
framework::DDim dx_dims;
if (dx) {
dx_dims = dx->dims();
if (dx_dims != x.dims()) {
dx->Resize(x.dims());
}
}
framework::DDim dy_dims;
if (dy) {
dy_dims = dy->dims();
if (dy_dims != y.dims()) {
dy->Resize(y.dims());
}
}
if (transpose_x && transpose_y) {
CalcInputGrad(context, y, true, true, dout, true, false, dx);
CalcInputGrad(context, dout, true, true, x, true, false, dy);
} else if (transpose_x && !transpose_y) {
CalcInputGrad(context, y, false, false, dout, true, false, dx);
CalcInputGrad(context, x, false, false, dout, false, true, dy);
} else if (!transpose_x && transpose_y) {
CalcInputGrad(context, dout, false, false, y, false, true, dx);
CalcInputGrad(context, dout, true, true, x, false, true, dy);
} else {
CalcInputGrad(context, dout, false, false, y, true, false, dx);
CalcInputGrad(context, x, true, true, dout, false, true, dy);
}
if (dx) {
if (dx_dims != x.dims()) {
dx->Resize(dx_dims);
}
}
if (dy) {
if (dy_dims != y.dims()) {
dy->Resize(dy_dims);
}
}
}
};
} // namespace operators
} // namespace paddle
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