yangyaming
7 years ago
parent
170ac721b6
commit
7c67146632
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/sequence_pad_op.h"
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namespace paddle {
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namespace operators {
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class SequencePadOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequencePadOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequencePadOp should not be null.");
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auto x_dims = ctx->GetInputDim("X");
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PADDLE_ENFORCE_EQ(x_dims.size(), 2,
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"Only support 2-D tensor, rank of Input(X) should be 2.");
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auto out_dims = x_dims;
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if (ctx->IsRuntime()) {
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framework::Variable* x_var =
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boost::get<framework::Variable*>(ctx->GetInputVarPtrs("X")[0]);
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auto& x_lod = x_var->Get<LoDTensor>().lod();
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PADDLE_ENFORCE_GE(x_lod.size(), 1,
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"Input(X) should be sequences containing lod.");
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auto last_level_lod = x_lod[x_lod.size() - 1];
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size_t max_len = 0;
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for (size_t i = 1; i < last_level_lod.size(); ++i) {
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auto seq_len = last_level_lod[i] - last_level_lod[i - 1];
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max_len = max_len < seq_len ? seq_len : max_len;
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}
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out_dims[0] = max_len * (last_level_lod.size() - 1);
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} else {
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framework::VarDesc* x_desc =
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boost::get<framework::VarDesc*>(ctx->GetInputVarPtrs("X")[0]);
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PADDLE_ENFORCE_GE(x_desc->GetLoDLevel(), 1,
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"Input(X) should be sequences containing lod.");
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out_dims[0] = -1;
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}
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ctx->SetOutputDim("Out", out_dims);
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}
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protected:
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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framework::ToDataType(ctx.Input<framework::LoDTensor>("X")->type()),
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ctx.device_context());
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}
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};
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class SequencePadOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequencePadOpMaker(OpProto* proto, OpAttrChecker* op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X",
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"(LoDTensor, default LoDTensor<float>) Input variable which "
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"should contain lod information. Length of each sequence would "
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"be computed from the most bottom level lod.");
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AddOutput("Out",
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"(Tensor) Output variable which would be a common tensor "
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"without lod. Each sequence would be padded to the maximum "
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"length.");
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AddAttr<float>("pad_value",
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"(float, default 0.0) Value to be padded "
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"to the end of each sequence.");
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AddComment(R"DOC(
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)DOC");
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}
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};
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class SequencePadGradOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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void InferShape(framework::InferShapeContext* ctx) const override {
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of SequencePadGradOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) of SequencePadGradOp should not be null.");
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if (ctx->HasOutput(framework::GradVarName("X"))) {
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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ctx->ShareLoD("X", /*->*/ framework::GradVarName("X"));
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(sequence_pad, ops::SequencePadOp, ops::SequencePadOpMaker,
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paddle::framework::DefaultGradOpDescMaker<true>);
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REGISTER_OPERATOR(sequence_pad_grad, ops::SequencePadGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_pad,
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ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SequencePadOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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sequence_pad_grad,
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ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SequencePadGradOpKernel<paddle::platform::CPUDeviceContext, int64_t>);
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@ -0,0 +1,23 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#include "paddle/fluid/operators/sequence_pad_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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sequence_pad,
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ops::SequencePadOpKernel<paddle::platform::CUDADeviceContext, float>);
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REGISTER_OP_CUDA_KERNEL(
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sequence_pad_grad,
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ops::SequencePadGradOpKernel<paddle::platform::CUDADeviceContext, float>);
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@ -0,0 +1,97 @@
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/* Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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Licensed under the Apache License, Version 2.0 (the "License");
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you may not use this file except in compliance with the License.
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You may obtain a copy of the License at
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http://www.apache.org/licenses/LICENSE-2.0
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Unless required by applicable law or agreed to in writing, software
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distributed under the License is distributed on an "AS IS" BASIS,
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WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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See the License for the specific language governing permissions and
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limitations under the License. */
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#pragma once
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/memory/memcpy.h"
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#include "paddle/fluid/operators/math/math_function.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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using LoD = framework::LoD;
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// @TODO clean code
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template <typename DeviceContext, typename T>
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class SequencePadOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x_ptr = ctx.Input<LoDTensor>("X");
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auto* out_ptr = ctx.Output<LoDTensor>("Out");
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out_ptr->mutable_data<T>(ctx.GetPlace());
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T pad_value = static_cast<T>(ctx.Attr<float>("pad_value"));
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math::SetConstant<DeviceContext, T> set_func;
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set_func(ctx.template device_context<DeviceContext>(), out_ptr, pad_value);
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auto& x_lod = x_ptr->lod();
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auto& x_last_level_lod = x_lod[x_lod.size() - 1];
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auto seq_num = x_last_level_lod.size() - 1;
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auto max_len = out_ptr->dims()[0] / seq_num;
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PADDLE_ENFORCE_EQ(max_len * seq_num, out_ptr->dims()[0],
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"First dimension of `Out` should be equal to "
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"maximum length mulplied by sequence number.");
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for (size_t i = 1; i < x_last_level_lod.size(); ++i) {
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auto x_start = x_last_level_lod[i - 1];
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auto x_end = x_last_level_lod[i];
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auto out_start = (i - 1) * max_len;
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auto out_end = out_start + (x_end - x_start);
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auto x_sub_tensor = x_ptr->Slice(x_start, x_end);
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auto out_sub_tensor = out_ptr->Slice(out_start, out_end);
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framework::TensorCopy(x_sub_tensor, ctx.GetPlace(), &out_sub_tensor);
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}
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}
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};
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template <typename DeviceContext, typename T>
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class SequencePadGradOpKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const override {
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auto* x_ptr = ctx.Input<LoDTensor>("X");
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auto* g_out_ptr = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* g_x_ptr = ctx.Output<LoDTensor>(framework::GradVarName("X"));
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math::SetConstant<DeviceContext, T> set_func;
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set_func(ctx.template device_context<DeviceContext>(), g_x_ptr,
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static_cast<T>(0));
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auto& x_lod = x_ptr->lod();
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auto& x_last_level_lod = x_lod[x_lod.size() - 1];
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auto seq_num = x_last_level_lod.size() - 1;
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int64_t max_len = g_out_ptr->dims()[0] / seq_num;
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PADDLE_ENFORCE_EQ(max_len * seq_num, g_out_ptr->dims()[0],
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"First dimension of `Out` should be equal to "
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"maximum length mulplied by sequence number.");
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for (size_t i = 1; i < x_last_level_lod.size(); ++i) {
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auto x_start = x_last_level_lod[i - 1];
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auto x_end = x_last_level_lod[i];
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auto out_start = (i - 1) * max_len;
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auto out_end = out_start + (x_end - x_start);
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auto g_out_sub = g_out_ptr->Slice(out_start, out_end);
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auto g_x_sub = g_x_ptr->Slice(x_start, x_end);
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framework::TensorCopy(g_x_sub, ctx.GetPlace(), &g_out_sub);
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}
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}
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};
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} // namespace operators
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} // namespace paddle
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