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Add sequence_reshape_op.

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add_depthwiseConv_op_gpu
yangyaming 8 years ago
parent 38c61053ff
commit 9bd9d8b5ca
2 changed files with 205 additions and 0 deletions
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78
paddle/operators/sequence_reshape_op.cc
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/* 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/sequence_reshape_op.h"
namespace paddle {
namespace operators {
class SequenceReshapeOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceReshapeOp should not be null.");
PADDLE_ENFORCE(ctx->HasOutput("Out"),
"Output(Out) of SequenceReshapeOp should not be null.");
auto x_dims = ctx->GetInputDim("X");
PADDLE_ENFORCE_EQ(x_dims.size(), 2U, "Rank of Input(X) should be 2.");
int dimension = ctx->Attrs().Get<int>("dimension");
ctx->SetOutputDim("Out", {{x_dims[0], static_cast<int64_t>(dimension)}});
ctx->ShareLoD("X", /*->*/ "Out");
}
};
class SequenceReshapeOpMaker : public framework::OpProtoAndCheckerMaker {
public:
SequenceReshapeOpMaker(OpProto* proto, OpAttrChecker* op_checker)
: OpProtoAndCheckerMaker(proto, op_checker) {
AddInput("X", "");
AddOutput("Out", "");
AddAttr<int>("dimension", "");
AddAttr<bool>("is_padding", "Default padding zero.");
AddComment(R"DOC()DOC");
}
};
class SequenceReshapeGradOp : public framework::OperatorWithKernel {
public:
using framework::OperatorWithKernel::OperatorWithKernel;
void InferShape(framework::InferShapeContext* ctx) const override {
PADDLE_ENFORCE(
ctx->HasInput(framework::GradVarName("Out")),
"Input(Out@GRAD) of SequenceReshapeGradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("Out"),
"Input(Out) of SequenceReshapeGradOp should not be null.");
PADDLE_ENFORCE(ctx->HasInput("X"),
"Input(X) of SequenceReshapeGradOp should not be null.");
ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
}
};
} // namespace operators
} // namespace paddle
namespace ops = paddle::operators;
REGISTER_OPERATOR(sequence_reshape, ops::SequenceReshapeOp,
ops::SequenceReshapeOpMaker);
REGISTER_OPERATOR(sequence_reshape_grad, ops::SequenceReshapeGradOp);
REGISTER_OP_CPU_KERNEL(
sequence_reshape,
ops::SequenceReshapeKernel<paddle::platform::CPUDeviceContext, float>);
REGISTER_OP_CPU_KERNEL(
sequence_reshape_grad,
ops::SequenceReshapeGradKernel<paddle::platform::CPUDeviceContext, float>);

127
paddle/operators/sequence_reshape_op.h
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/* 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"
namespace paddle {
namespace operators {
using LoDTensor = framework::LoDTensor;
template <typename DeviceContext, typename T>
class SequenceReshapeKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* in = context.Input<LoDTensor>("X");
auto* out = context.Output<LoDTensor>("Out");
int out_width = context.Attr<int>("dimension");
bool whether_padding = context.Attr<bool>("whether_padding");
const T* p_in_data = in->data<T>();
T* p_out_data = out->mutable_data<T>(context.GetPlace());
// compute shape for output
auto in_dims = in->dims();
int64_t in_width = in_dims[1];
auto& in_lod = in->lod();
PADDLE_ENFORCE_EQ(in_lod.size(), 1UL,
"Only support one level sequence now.");
PADDLE_ENFORCE_GE(
in_dims[0],
/* batch size = */ static_cast<int64_t>(in_lod[0].size() - 1),
"The 1st dimension of Input(X) must be equal or larger than batch "
"size.");
auto in_lod_l0 = in_lod[0];
int seq_num = in_lod_l0.size() - 1;
auto& out_lod = *out->mutable_lod();
out_lod.push_back(std::vector<size_t>({0}));
size_t offset = 0;
for (int i = 0; i < seq_num; ++i) {
size_t seq_len = in_lod_l0[i + 1] - in_lod_l0[i];
if (whether_padding) {
offset += std::ceil((float)(seq_len * in_width) / out_width);
} else {
offset += (seq_len * in_width) / out_width;
}
out_lod[0].push_back(offset);
}
out->Resize({{static_cast<int64_t>(out_lod[0].back()), out_width}});
math::set_constant(context.device_context(), out, 0.0f);
for (int i = 0; i < seq_num; ++i) {
size_t in_offset = in_lod_l0[i] * in_width;
size_t out_offset = out_lod[0][i] * out_width;
size_t bytes = sizeof(T) * (in_lod_l0[i + 1] - in_lod_l0[i]) * in_width;
if (platform::is_cpu_place(context.GetPlace())) {
std::memcpy(p_out_data + out_offset, p_in_data + in_offset, bytes);
} else {
#ifdef PADDLE_WITH_CUDA
auto& dev_ctx = context.template device_context<DeviceContext>();
memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
p_out_data + out_offset,
boost::get<platform::CUDAPlace>(context.GetPlace()),
p_in_data + in_offset, bytes, dev_ctx.stream());
#endif
}
}
}
};
template <typename DeviceContext, typename T>
class SequenceReshapeGradKernel : public framework::OpKernel<T> {
public:
void Compute(const framework::ExecutionContext& context) const override {
auto* x_tensor_ptr = context.Input<LoDTensor>("X");
auto* out_tensor_ptr = context.Input<LoDTensor>("Out");
auto* out_grad_tensor_ptr =
context.Input<LoDTensor>(framework::GradVarName("Out"));
auto* x_grad_tensor_ptr =
context.Output<LoDTensor>(framework::GradVarName("X"));
T* p_x_grad_data = x_grad_tensor_ptr->mutable_data<T>(context.GetPlace());
const T* p_out_grad_data = out_grad_tensor_ptr->data<T>();
auto& x_lod = x_tensor_ptr->lod();
int seq_num = x_lod[0].size() - 1;
int x_width = x_tensor_ptr->dims()[1];
auto& out_lod = out_tensor_ptr->lod();
int out_width = out_tensor_ptr->dims()[1];
for (int i = 0; i < seq_num; ++i) {
size_t src_offset = out_lod[0][i] * out_width;
size_t dst_offset = x_lod[0][i] * x_width;
size_t bytes = sizeof(T) * (x_lod[0][i + 1] - x_lod[0][i]) * x_width;
if (platform::is_cpu_place(context.GetPlace())) {
std::memcpy(p_x_grad_data + dst_offset, p_out_grad_data + src_offset,
bytes);
} else {
#ifdef PADDLE_WITH_CUDA
auto& dev_ctx = context.template device_context<DeviceContext>();
memory::Copy(boost::get<platform::CUDAPlace>(context.GetPlace()),
p_x_grad_data + dst_offset,
boost::get<platform::CUDAPlace>(context.GetPlace()),
p_out_grad_data + src_offset, bytes, dev_ctx.stream());
#endif
}
}
}
};
} // namespace operators
} // namespace paddle
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