Skip to content
New issue

Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

Sign up for GitHub

By clicking “Sign up for GitHub”, you agree to our terms of service and privacy statement. We’ll occasionally send you account related emails.

Already on GitHub? Sign in to your account

Jump to bottom

Move BilinearTensorProduct OP to phi #39903

Merged
merged 2 commits into from
Feb 26, 2022
Merged

Move BilinearTensorProduct OP to phi #39903

Show file tree
Hide file tree
Changes from all commits
Commits
Show all changes
2 commits
Select commit Hold shift + click to select a range
a11094a
Move BilinearTensorProduct OP to phi
From00 Feb 24, 2022
b7acb5d
Set dtype for Infermeta
From00 Feb 25, 2022
File filter

Filter by extension

Filter by extension
Conversations
Failed to load comments.
Loading
Jump to
Jump to file
Failed to load files.
Loading
Diff view
Diff view
154 changes: 17 additions & 137 deletions paddle/fluid/operators/bilinear_tensor_product_op.cc
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -12,84 +12,18 @@ 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/bilinear_tensor_product_op.h"
#include <memory>
#include <string>
#include <vector>
#include "paddle/fluid/framework/infershape_utils.h"
#include "paddle/fluid/framework/op_registry.h"
#include "paddle/phi/core/infermeta_utils.h"
#include "paddle/phi/infermeta/backward.h"
#include "paddle/phi/infermeta/multiary.h"

namespace paddle {
namespace operators {

using framework::Tensor;

class BilinearTensorProductOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;

protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("X"), true,
platform::errors::InvalidArgument("Input(X) should not be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Y"), true,
platform::errors::InvalidArgument("Input(Y) should not be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Weight"), true,
platform::errors::InvalidArgument("Input(Weight) should not be null."));
PADDLE_ENFORCE_EQ(
ctx->HasOutput("Out"), true,
platform::errors::InvalidArgument("Output(Out) should not be null."));
auto x_dims = ctx->GetInputDim("X");
auto y_dims = ctx->GetInputDim("Y");
auto weight_dims = ctx->GetInputDim("Weight");

PADDLE_ENFORCE_EQ(
x_dims.size(), 2UL,
platform::errors::InvalidArgument("The input(X) must be a 2D Tensor."));
PADDLE_ENFORCE_EQ(
y_dims.size(), 2UL,
platform::errors::InvalidArgument("The input(Y) must be a 2D Tensor."));
PADDLE_ENFORCE_EQ(
weight_dims.size(), 3UL,
platform::errors::InvalidArgument("Expected the input(Weight) is a 3D "
"tensor. But received %dD tensor.",
weight_dims.size()));
if (ctx->IsRuntime() || (x_dims[0] > 0 && y_dims[0] > 0)) {
PADDLE_ENFORCE_EQ(
x_dims[0], y_dims[0],
platform::errors::InvalidArgument(
"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],
platform::errors::InvalidArgument(
"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],
platform::errors::InvalidArgument(
"The second dimension of input(Y) must be equal to "
"the third dimension of the input(Weight)."));

if (ctx->HasInput("Bias")) {
auto bias_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(bias_dims.size(), 2UL,
platform::errors::InvalidArgument(
"The Input(Bias) must be a 2-D tensor with "
"the 2nd dimension fixed to 1 (a row vector)."));
PADDLE_ENFORCE_EQ(bias_dims[0], 1UL,
platform::errors::InvalidArgument(
"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],
platform::errors::InvalidArgument(
"The second dimension of input(Bias) must be equal "
"to the first dimension of the input(Weight)."));
}

ctx->SetOutputDim("Out", {x_dims[0], weight_dims[0]});
ctx->ShareLoD("X", /*->*/ "Out");
}
};

class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker {
Expand Down Expand Up @@ -125,59 +59,6 @@ Where $W_i$ is the $i$-th slice of Input(Weight);
class BilinearTensorProductOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;

protected:
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE_EQ(
ctx->HasInput("X"), true,
platform::errors::InvalidArgument("Input(X) should not be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Y"), true,
platform::errors::InvalidArgument("Input(Y) should not be null."));
PADDLE_ENFORCE_EQ(
ctx->HasInput("Weight"), true,
platform::errors::InvalidArgument("Input(Weight) should not be null."));
PADDLE_ENFORCE_EQ(ctx->HasInput(framework::GradVarName("Out")), true,
platform::errors::InvalidArgument(
"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"));

PADDLE_ENFORCE_EQ(out_dims.size(), 2UL,
platform::errors::InvalidArgument(
"The input(Out@GRAD) must be a 2D Tensor."));
PADDLE_ENFORCE_EQ(
x_dims[0], out_dims[0],
platform::errors::InvalidArgument(
"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],
platform::errors::InvalidArgument(
"The second dimension of input(Out@GRAD) must be equal to "
"the third dimension of the Input(Weight)."));

auto bias_grad_name = framework::GradVarName("Bias");
if (ctx->HasOutput(bias_grad_name)) {
ctx->SetOutputDim(bias_grad_name, {1, out_dims[1]});
}

auto x_grad_name = framework::GradVarName("X");
auto y_grad_name = framework::GradVarName("Y");
auto weight_grad_name = framework::GradVarName("Weight");

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);
}
}
};

template <typename T>
Expand Down Expand Up @@ -208,21 +89,20 @@ class BilinearTensorProductGradOpMaker
} // namespace paddle

namespace ops = paddle::operators;

DELCARE_INFER_SHAPE_FUNCTOR(bilinear_tensor_product,
BilinearTensorProductInferShapeFunctor,
PT_INFER_META(phi::BilinearTensorProductInferMeta));
DELCARE_INFER_SHAPE_FUNCTOR(
bilinear_tensor_product_grad, BilinearTensorProductGradInferShapeFunctor,
PT_INFER_META(phi::BilinearTensorProductGradInferMeta));

REGISTER_OPERATOR(
bilinear_tensor_product, ops::BilinearTensorProductOp,
ops::BilinearTensorProductOpMaker,
ops::BilinearTensorProductGradOpMaker<paddle::framework::OpDesc>,
ops::BilinearTensorProductGradOpMaker<paddle::imperative::OpBase>);
ops::BilinearTensorProductGradOpMaker<paddle::imperative::OpBase>,
BilinearTensorProductInferShapeFunctor);
REGISTER_OPERATOR(bilinear_tensor_product_grad,
ops::BilinearTensorProductOpGrad);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::CPUDeviceContext, float>,
ops::BilinearTensorProductKernel<paddle::platform::CPUDeviceContext,
double>);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUDeviceContext,
float>,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUDeviceContext,
double>);
ops::BilinearTensorProductOpGrad,
BilinearTensorProductGradInferShapeFunctor);
29 changes: 0 additions & 29 deletions paddle/fluid/operators/bilinear_tensor_product_op.cu
View file
Open in desktop

This file was deleted.

181 changes: 0 additions & 181 deletions paddle/fluid/operators/bilinear_tensor_product_op.h
View file
Open in desktop

This file was deleted.

Loading