m53297601
/
Paddle
1
0
Fork 0
Code Issues Pull Requests Releases Wiki Activity

add bilinear tensor product op

Browse Source
mobile_baidu
peterzhang2029 8 years ago
parent 154e1d0491
commit 611ee68b78
4 changed files with 383 additions and 0 deletions
Show Stats Download Patch File Download Diff File

153
paddle/operators/bilinear_tensor_product_op.cc
Unescape View File

@ -0,0 +1,153 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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/operators/bilinear_tensor_product_op.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(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");
PADDLE_ENFORCE_EQ(x_dims.size(), 1, "The input X must be a vector.");
PADDLE_ENFORCE_EQ(y_dims.size(), 1, "The input Y must be a vector.");
PADDLE_ENFORCE_EQ(weight_dims.size(), 3,
"The input Weight must be a 3D tensor.");
PADDLE_ENFORCE_GT(weight_dims[0], 0,
"The first dimension of Weight must be larger than 0.");
PADDLE_ENFORCE_GT(weight_dims[1], 0,
"The second dimension of Weight must be larger than 0.");
PADDLE_ENFORCE_GT(weight_dims[2], 0,
"The third dimension of Weight must be larger than 0.");
PADDLE_ENFORCE_EQ(x_dims[0], weight_dims[1],
"The dimension of X must be equal with the second "
"dimension of the Weight.");
PADDLE_ENFORCE_EQ(y_dims[0], weight_dims[2],
"The dimension of Y must be equal with the third "
"dimension of the Weight.");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
auto bias_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(bias_dims.size(), 1,
"The input Bias must be a vector.");
PADDLE_ENFORCE_EQ(bias_dims[0], weight_dims[0],
"The dimension of Bias must be equal with the first "
"dimension of the Weight.");
}
ctx->SetOutputDim("Out", {weight_dims[0]});
}
};
class BilinearTensorProductOpMaker : public framework::OpProtoAndCheckerMaker {
public:
BilinearTensorProductOpMaker(framework::OpProto* proto,
framework::OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "The first input of tensor op");
AddInput("Y", "The second input of tensor op");
AddInput("Weight", "The input weight of tensor op");
AddInput("Bias", "The input bias of tensor op");
AddOutput("Out", "The output of tensor op");
AddComment(R"DOC(
Bilinear Tensor Product operator.
Given input X and Y, a 3D tensor weight, and bias. Each entry of the output is
computed by one slice i = 1, . . . , k of the tensor: Out_i = X*W_i*Y + Bias_i .
The equation of this operator is:
Out = \sum_{i} X*W_i*Y + Bias
)DOC");
}
};
class BilinearTensorProductOpGrad : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
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"));
PADDLE_ENFORCE_EQ(out_dims.size(), 1, "The Out@GRAD must be a vector.");
PADDLE_ENFORCE_EQ(
weight_dims[0], out_dims[0],
"The dimension of Out@GRAD must be equal with the third dimension of "
"the Weight.");
auto bias = Input("Bias");
if (bias != framework::kEmptyVarName) {
auto bias_dims = ctx->GetInputDim("Bias");
PADDLE_ENFORCE_EQ(bias_dims.size(), 1, "Input Bias must be a vector.");
PADDLE_ENFORCE_EQ(
bias_dims[0], out_dims[0],
"The dimension of Bias must be equal with the Out@GRAD ");
auto bias_grad_name = framework::GradVarName("Bias");
if (ctx->HasOutput(bias_grad_name))
ctx->SetOutputDim(bias_grad_name, bias_dims);
}
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);
}
}
};
} // namespace operators
} // namespace paddle
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>);
REGISTER_OP_CPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::CPUPlace, float>);

24
paddle/operators/bilinear_tensor_product_op.cu
Unescape View File

@ -0,0 +1,24 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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. */
#define EIGEN_USE_GPU
#include "paddle/operators/bilinear_tensor_product_op.h"
namespace ops = paddle::operators;
REGISTER_OP_GPU_KERNEL(
bilinear_tensor_product,
ops::BilinearTensorProductKernel<paddle::platform::GPUPlace, float>);
REGISTER_OP_GPU_KERNEL(
bilinear_tensor_product_grad,
ops::BilinearTensorProductGradKernel<paddle::platform::GPUPlace, float>);

176
paddle/operators/bilinear_tensor_product_op.h
Unescape View File

@ -0,0 +1,176 @@
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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 "paddle/framework/op_registry.h"
#include "paddle/operators/math/math_function.h"
#include "paddle/platform/transform.h"
namespace paddle {
namespace operators {
using Tensor = framework::Tensor;
using platform::Transform;
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());
auto weight_dims = weight->dims();
Tensor left_mul_vec;
left_mul_vec.mutable_data<T>(framework::make_ddim({weight_dims[2]}),
ctx.GetPlace());
if (bias) {
out->CopyFrom(*bias, ctx.GetPlace(), ctx.device_context());
}
for (int i = 0; i < weight_dims[0]; ++i) {
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, 1,
weight_dims[2], weight_dims[1], 1, x->data<T>(),
weight_mat.data<T>(), 0, left_mul_vec.data<T>());
if (bias) {
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
1, 1, weight_dims[2], 1, left_mul_vec.data<T>(),
y->data<T>(), 1, &(out->data<T>()[i]));
} else {
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasNoTrans,
1, 1, weight_dims[2], 1, left_mul_vec.data<T>(),
y->data<T>(), 0, &(out->data<T>()[i]));
}
}
}
};
template <typename T>
class ScaleFunctor {
public:
explicit ScaleFunctor(const T* scale) : scale_(scale) {}
HOSTDEVICE T operator()(const T& x) const { return x * (*scale_); }
private:
const T* scale_;
};
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"));
auto* d_out_ptr = d_out->data<T>();
auto weight_dims = weight->dims();
// Get the first matrix of Weight.
Tensor weight_mat_0 = weight->Slice(0, 1).Resize(
framework::make_ddim({weight_dims[1], weight_dims[2]}));
// Create the intermediate variable for gradient.
int numel_x = x->numel();
int numel_y = y->numel();
const T* x_ptr = x->data<T>();
const T* y_ptr = y->data<T>();
Tensor x_scale;
T* x_scale_ptr = x_scale.mutable_data<T>(
framework::make_ddim({weight_dims[1]}), ctx.GetPlace());
Tensor y_scale;
T* y_scale_ptr = y_scale.mutable_data<T>(
framework::make_ddim({weight_dims[2]}), ctx.GetPlace());
Transform<Place> trans;
// Caculate the gradient of X according to the first matrix of Weight.
if (d_x) {
d_x->mutable_data<T>(ctx.GetPlace());
trans(ctx.device_context(), y_ptr, y_ptr + numel_y, y_scale_ptr,
ScaleFunctor<T>(&d_out_ptr[0]));
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans, 1,
weight_dims[1], weight_dims[2], 1, y_scale.data<T>(),
weight_mat_0.data<T>(), 0, d_x->data<T>());
}
// Caculate the gradient of Y according to the first matrix of Weight.
if (d_y) {
d_y->mutable_data<T>(ctx.GetPlace());
trans(ctx.device_context(), x_ptr, x_ptr + numel_x, x_scale_ptr,
ScaleFunctor<T>(&d_out_ptr[0]));
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
weight_dims[2], 1, weight_dims[1], 1,
weight_mat_0.data<T>(), x_scale.data<T>(), 0,
d_y->data<T>());
}
// Caculate the gradient of X and Y completly.
if (d_x || d_y) {
for (int i = 1; i < weight_dims[0]; ++i) {
Tensor weight_mat = weight->Slice(i, i + 1).Resize(
framework::make_ddim({weight_dims[1], weight_dims[2]}));
if (d_x) {
trans(ctx.device_context(), y_ptr, y_ptr + numel_y, y_scale_ptr,
ScaleFunctor<T>(&d_out_ptr[i]));
math::gemm<Place, T>(ctx.device_context(), CblasNoTrans, CblasTrans,
1, weight_dims[1], weight_dims[2], 1,
y_scale.data<T>(), weight_mat.data<T>(), 1,
d_x->data<T>());
}
if (d_y) {
trans(ctx.device_context(), x_ptr, x_ptr + numel_x, x_scale_ptr,
ScaleFunctor<T>(&d_out_ptr[i]));
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
weight_dims[2], 1, weight_dims[1], 1,
weight_mat.data<T>(), x_scale.data<T>(), 1,
d_y->data<T>());
}
}
}
// Caculate the gradient of Weight.
if (d_weight) {
d_weight->mutable_data<T>(ctx.GetPlace());
for (int i = 0; i < weight_dims[0]; ++i) {
Tensor d_weight_mat = d_weight->Slice(i, i + 1).Resize(
framework::make_ddim({weight_dims[1], weight_dims[2]}));
trans(ctx.device_context(), x_ptr, x_ptr + numel_x, x_scale_ptr,
ScaleFunctor<T>(&d_out_ptr[i]));
math::gemm<Place, T>(ctx.device_context(), CblasTrans, CblasNoTrans,
weight_dims[1], weight_dims[2], 1, 1,
x_scale.data<T>(), y->data<T>(), 0,
d_weight_mat.data<T>());
}
}
// Caculate the gradient of Bias.
if (d_bias) {
d_bias->mutable_data<T>(ctx.GetPlace());
d_bias->CopyFrom(*d_out, ctx.GetPlace(), ctx.device_context());
}
}
};
} // namespace operators
} // namespace paddle

30
python/paddle/v2/framework/tests/test_bilinear_tensor_product_op.py
Unescape View File

@ -0,0 +1,30 @@
import unittest
import numpy as np
from op_test import OpTest
class TestBilinearTensorProductOp(OpTest):
def setUp(self):
self.op_type = "bilinear_tensor_product"
self.inputs = {
'X': np.random.random(3).astype("float32"),
'Y': np.random.random(4).astype("float32"),
'Weight': np.random.random((5, 3, 4)).astype("float32"),
'Bias': np.random.random(5).astype("float32")
}
self.outputs = {
'Out': np.matmul(
np.matmul(self.inputs['Weight'], self.inputs['Y']),
self.inputs['X']) + self.inputs['Bias']
}
def test_check_output(self):
self.check_output()
def test_check_grad_normal(self):
self.check_grad(
['X', 'Y', 'Weight', 'Bias'], 'Out', max_relative_error=0.5)
if __name__ == "__main__":
unittest.main()
Write Preview
Loading…
Cancel
Save