test=developrevert-16839-cmakelist_change
tensor-tang
6 years ago
commit
be18636e59
@ -0,0 +1,154 @@
|
||||
/* Copyright (c) 2019 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/fluid/operators/cvm_op.h"
|
||||
#include <memory>
|
||||
#include "paddle/fluid/operators/math/math_function.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
using Tensor = framework::Tensor;
|
||||
|
||||
class CVMOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
void InferShape(framework::InferShapeContext* ctx) const override {
|
||||
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
|
||||
PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) should be not null.");
|
||||
|
||||
auto x_dims = ctx->GetInputDim("X");
|
||||
auto cvm_dims = ctx->GetInputDim("CVM");
|
||||
PADDLE_ENFORCE_EQ(x_dims.size(), 2UL, "Input(X)'s rank should be 2.");
|
||||
PADDLE_ENFORCE_EQ(cvm_dims.size(), 2UL, "Input(CVM)'s rank should be 2.");
|
||||
PADDLE_ENFORCE_EQ(cvm_dims[1], 2UL,
|
||||
"The 2nd dimension of "
|
||||
"Input(CVM) should be 2.");
|
||||
|
||||
if (ctx->Attrs().Get<bool>("use_cvm")) {
|
||||
ctx->SetOutputDim("Y", {x_dims[0], x_dims[1]});
|
||||
} else {
|
||||
ctx->SetOutputDim("Y", {x_dims[0], x_dims[1] - 2});
|
||||
}
|
||||
ctx->ShareLoD("X", /*->*/ "Y");
|
||||
}
|
||||
|
||||
protected:
|
||||
// Explicitly set that the data type of computation kernel of
|
||||
// cvm
|
||||
// is determined by its input "X".
|
||||
framework::OpKernelType GetExpectedKernelType(
|
||||
const framework::ExecutionContext& ctx) const override {
|
||||
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
|
||||
platform::CPUPlace());
|
||||
}
|
||||
};
|
||||
|
||||
class CVMGradientOp : public framework::OperatorWithKernel {
|
||||
public:
|
||||
using framework::OperatorWithKernel::OperatorWithKernel;
|
||||
|
||||
void InferShape(framework::InferShapeContext* ctx) const override {
|
||||
PADDLE_ENFORCE(ctx->HasInput("X"), "Input(X) should be not null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput("CVM"), "Input(CVM) should be not null.");
|
||||
PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Y")),
|
||||
"Input(Y@GRAD) should be not null.");
|
||||
PADDLE_ENFORCE(ctx->HasOutput(framework::GradVarName("X")),
|
||||
"Output(X@GRAD) should be not null.");
|
||||
|
||||
auto x_dims = ctx->GetInputDim("X");
|
||||
auto cvm_dims = ctx->GetInputDim("CVM");
|
||||
auto dy_dims = ctx->GetInputDim(framework::GradVarName("Y"));
|
||||
PADDLE_ENFORCE_EQ(x_dims.size(), 2, "Input(X)'s rank should be 2.");
|
||||
PADDLE_ENFORCE_EQ(dy_dims.size(), 2, "Input(Y@Grad)'s rank should be 2.");
|
||||
PADDLE_ENFORCE_EQ(cvm_dims.size(), 2, "Input(CVM)'s rank should be 2.");
|
||||
|
||||
PADDLE_ENFORCE_EQ(x_dims[0], dy_dims[0],
|
||||
"The 1st dimension of Input(X) and Input(Y@Grad) should "
|
||||
"be equal.");
|
||||
|
||||
PADDLE_ENFORCE_EQ(cvm_dims[1], 2,
|
||||
"When Attr(soft_label) == false, the 2nd dimension of "
|
||||
"Input(CVM) should be 2.");
|
||||
ctx->SetOutputDim(framework::GradVarName("X"), x_dims);
|
||||
ctx->ShareLoD("X", framework::GradVarName("X"));
|
||||
}
|
||||
|
||||
protected:
|
||||
// Explicitly set that the data type of computation kernel of
|
||||
// cvm
|
||||
// is determined by its input "X".
|
||||
framework::OpKernelType GetExpectedKernelType(
|
||||
const framework::ExecutionContext& ctx) const override {
|
||||
return framework::OpKernelType(ctx.Input<Tensor>("X")->type(),
|
||||
platform::CPUPlace());
|
||||
}
|
||||
};
|
||||
|
||||
class CVMOpMaker : public framework::OpProtoAndCheckerMaker {
|
||||
public:
|
||||
void Make() override {
|
||||
AddInput("X",
|
||||
"(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
|
||||
"[N x D],"
|
||||
" where N is the batch size and D is the emebdding dim. ");
|
||||
AddInput("CVM",
|
||||
"(Tensor), a 2-D Tensor with shape [N x 2], where N is the batch "
|
||||
"size, 2 is show and click.");
|
||||
AddOutput("Y",
|
||||
"(LodTensor, default LodTensor<float>), a 2-D tensor with shape "
|
||||
"[N x K].");
|
||||
AddAttr<bool>("use_cvm", "bool, use cvm or not").SetDefault(true);
|
||||
AddComment(R"DOC(
|
||||
CVM Operator.
|
||||
|
||||
We assume that input X is a embedding vector with cvm_feature(show and click), which shape is [N * D] (D is 2(cvm_feature) + embedding dim, N is batch_size)
|
||||
if use_cvm is True, we will log(cvm_feature), and output shape is [N * D].
|
||||
if use_cvm is False, we will remove cvm_feature from input, and output shape is [N * (D - 2)].
|
||||
|
||||
)DOC");
|
||||
}
|
||||
};
|
||||
|
||||
class CVMGradOpDescMaker : public framework::SingleGradOpDescMaker {
|
||||
public:
|
||||
using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
|
||||
|
||||
protected:
|
||||
std::unique_ptr<framework::OpDesc> Apply() const override {
|
||||
std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
|
||||
op->SetType("cvm_grad");
|
||||
op->SetInput("X", Input("X"));
|
||||
op->SetInput("CVM", Input("CVM"));
|
||||
op->SetInput(framework::GradVarName("Y"), OutputGrad("Y"));
|
||||
op->SetOutput(framework::GradVarName("X"), InputGrad("X"));
|
||||
op->SetAttrMap(Attrs());
|
||||
return op;
|
||||
}
|
||||
};
|
||||
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
|
||||
namespace ops = paddle::operators;
|
||||
REGISTER_OPERATOR(cvm, ops::CVMOp, ops::CVMOpMaker, ops::CVMGradOpDescMaker);
|
||||
|
||||
REGISTER_OPERATOR(cvm_grad, ops::CVMGradientOp);
|
||||
|
||||
REGISTER_OP_CPU_KERNEL(cvm, ops::CVMOpKernel<float>, ops::CVMOpKernel<double>);
|
||||
|
||||
REGISTER_OP_CPU_KERNEL(cvm_grad, ops::CVMGradOpKernel<float>,
|
||||
ops::CVMGradOpKernel<double>);
|
||||
@ -0,0 +1,105 @@
|
||||
/* Copyright (c) 2019 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 "paddle/fluid/framework/eigen.h"
|
||||
#include "paddle/fluid/framework/op_registry.h"
|
||||
|
||||
namespace paddle {
|
||||
namespace operators {
|
||||
|
||||
using Tensor = framework::Tensor;
|
||||
using LoDTensor = framework::LoDTensor;
|
||||
|
||||
template <typename T>
|
||||
class CVMOpKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& context) const override {
|
||||
const LoDTensor* x = context.Input<LoDTensor>("X");
|
||||
const T* x_data = x->data<T>();
|
||||
auto lod = x->lod()[0];
|
||||
int64_t item_size = x->numel() / x->dims()[0];
|
||||
int offset = 2;
|
||||
if (!context.Attr<bool>("use_cvm")) {
|
||||
item_size -= offset;
|
||||
}
|
||||
LoDTensor* y = context.Output<LoDTensor>("Y");
|
||||
T* y_data = y->mutable_data<T>(context.GetPlace());
|
||||
|
||||
int seq_num = static_cast<int>(lod.size()) - 1;
|
||||
for (int i = 0; i < seq_num; ++i) {
|
||||
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
|
||||
|
||||
for (int j = 0; j < seq_len; ++j) {
|
||||
if (context.Attr<bool>("use_cvm")) {
|
||||
std::memcpy(y_data, x_data, item_size * sizeof(T));
|
||||
y_data[0] = log(y_data[0] + 1);
|
||||
y_data[1] = log(y_data[1] + 1) - y_data[0];
|
||||
x_data += item_size;
|
||||
y_data += item_size;
|
||||
} else {
|
||||
std::memcpy(y_data, x_data + offset, item_size * sizeof(T));
|
||||
x_data += item_size + offset;
|
||||
y_data += item_size;
|
||||
}
|
||||
}
|
||||
}
|
||||
}
|
||||
};
|
||||
|
||||
template <typename T>
|
||||
class CVMGradOpKernel : public framework::OpKernel<T> {
|
||||
public:
|
||||
void Compute(const framework::ExecutionContext& context) const override {
|
||||
LoDTensor* dx = context.Output<LoDTensor>(framework::GradVarName("X"));
|
||||
T* dx_data = dx->mutable_data<T>(context.GetPlace());
|
||||
|
||||
const Tensor* cvm = context.Input<Tensor>("CVM");
|
||||
const T* cvm_data = cvm->data<T>();
|
||||
int offset = 2;
|
||||
const framework::LoDTensor* dOut =
|
||||
context.Input<framework::LoDTensor>(framework::GradVarName("Y"));
|
||||
const T* dout_data = dOut->data<T>();
|
||||
|
||||
auto lod = dx->lod()[0];
|
||||
int64_t item_size = dx->numel() / dx->dims()[0];
|
||||
if (!context.Attr<bool>("use_cvm")) {
|
||||
item_size -= offset;
|
||||
}
|
||||
|
||||
int seq_num = static_cast<int>(lod.size()) - 1;
|
||||
for (int i = 0; i < seq_num; ++i) {
|
||||
int64_t seq_len = static_cast<int64_t>(lod[i + 1] - lod[i]);
|
||||
|
||||
for (int j = 0; j < seq_len; ++j) {
|
||||
if (context.Attr<bool>("use_cvm")) {
|
||||
std::memcpy(dx_data, dout_data, item_size * sizeof(T));
|
||||
dx_data[0] = cvm_data[0];
|
||||
dx_data[1] = cvm_data[1];
|
||||
dx_data += item_size;
|
||||
dout_data += item_size;
|
||||
} else {
|
||||
std::memcpy(dx_data + offset, dout_data, item_size * sizeof(T));
|
||||
dx_data[0] = cvm_data[0];
|
||||
dx_data[1] = cvm_data[1];
|
||||
dx_data += item_size + offset;
|
||||
dout_data += item_size;
|
||||
}
|
||||
}
|
||||
cvm_data += offset;
|
||||
}
|
||||
}
|
||||
};
|
||||
} // namespace operators
|
||||
} // namespace paddle
|
||||
Some files were not shown because too many files have changed in this diff Show More
Loading…
Reference in new issue