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Yibing Liu
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Merge pull request #4747 from kuke/seq_reshape_op_dev
Add Lod Reset Operator
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‎paddle/operators/lod_reset_op.cc

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/lod_reset_op.h"
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namespace paddle {
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namespace operators {
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class LoDResetOp : 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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// input check
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PADDLE_ENFORCE(ctx->HasInput("X"),
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"Input(X) of LoDResetOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of LoDResetOp should not be null.");
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// If target LoD is not set form Input(), then it must be set from Attr().
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if (!ctx->HasInput("TargetLoD")) {
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auto level0 = ctx->Attrs().Get<std::vector<int>>("target_lod");
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PADDLE_ENFORCE(level0.size() > 1,
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"Target LoD is not found, should be set to be a valid one "
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"through Input() or Attr().");
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}
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ctx->SetOutputDim("Out", ctx->GetInputDim("X"));
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}
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protected:
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framework::OpKernelType GetKernelType(
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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 LoDResetOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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LoDResetOpMaker(framework::OpProto *proto,
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framework::OpAttrChecker *op_checker)
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: OpProtoAndCheckerMaker(proto, op_checker) {
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AddInput("X", "(LoDTensor) The input tensor of lod_reset operator.");
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AddInput("TargetLoD",
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"(Tensor, optional) The target level 0 LoD from Input().")
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.AsDispensable();
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AddOutput("Out", "(LoDTensor) The output tensor of lod_reset operator.");
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AddAttr<std::vector<int>>("target_lod",
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"The target level 0 LoD from Attr().")
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.SetDefault(std::vector<int>{});
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AddComment(R"DOC(LoDReset operator
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Reset LoD of Input(X) into a new one specified by Input(TargetLoD) or
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Attr(target_lod), or set LoD for Input(X) if it doesn't have one.
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Currently the lod_reset operator only supports the reset of level 0 LoD.
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At least one of Input(TargetLoD) and Attr(target_lod) must be set,
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and if both of them are set, Input(TargetLoD) will be chosen as the
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target LoD.
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An example:
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Given a float LoDTensor X with shape (6, 1), its transpose form represents
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[1.0, 2.0, 3.0, 4.0, 5.0, 6.0],
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with LoD = [[0, 2, 5, 6]] and the three (transposed) sequences look like
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[1.0, 2.0], [3.0, 4.0, 5.0], [6.0].
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If target LoD = [0, 4, 6], the lod_reset operator will reset the LoD and
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the sequences that the LoDTensor Output(Out) contains becomes:
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[1.0, 2.0, 3.0, 4.0], [5.0, 6.0].
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)DOC");
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}
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};
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class LoDResetGradOp : 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"), "Input(X) shouldn't be null.");
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PADDLE_ENFORCE(ctx->HasInput(framework::GradVarName("Out")),
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"Input(Out@GRAD) shouldn't be null.");
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ctx->SetOutputDim(framework::GradVarName("X"), ctx->GetInputDim("X"));
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}
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protected:
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framework::OpKernelType GetKernelType(
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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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} // namespace operators
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} // namespace paddle
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namespace ops = paddle::operators;
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REGISTER_OP(lod_reset, ops::LoDResetOp, ops::LoDResetOpMaker, lod_reset_grad,
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ops::LoDResetGradOp);
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REGISTER_OP_CPU_KERNEL(lod_reset,
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ops::LoDResetKernel<paddle::platform::CPUPlace, float>,
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ops::LoDResetKernel<paddle::platform::CPUPlace, double>);
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REGISTER_OP_CPU_KERNEL(
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lod_reset_grad, ops::LoDResetGradKernel<paddle::platform::CPUPlace, float>,
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ops::LoDResetGradKernel<paddle::platform::CPUPlace, double>);

‎paddle/operators/lod_reset_op.cu

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/operators/lod_reset_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(lod_reset,
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ops::LoDResetKernel<paddle::platform::GPUPlace, float>,
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ops::LoDResetKernel<paddle::platform::GPUPlace, double>);
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REGISTER_OP_GPU_KERNEL(
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lod_reset_grad, ops::LoDResetGradKernel<paddle::platform::GPUPlace, float>,
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ops::LoDResetGradKernel<paddle::platform::GPUPlace, double>);

‎paddle/operators/lod_reset_op.h

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/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/framework/eigen.h"
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#include "paddle/framework/op_registry.h"
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namespace paddle {
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namespace operators {
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template <typename Place, typename T>
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class LoDResetKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* out = ctx.Output<framework::LoDTensor>("Out");
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auto* in = ctx.Input<framework::LoDTensor>("X");
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auto* lod_t = ctx.Input<framework::Tensor>("TargetLoD");
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std::vector<int> level0;
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if (lod_t) {
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auto* lod = lod_t->data<int>();
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if (platform::is_gpu_place(ctx.GetPlace())) {
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framework::Tensor lod_cpu;
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lod_cpu.CopyFrom(*lod_t, platform::CPUPlace(), ctx.device_context());
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lod = lod_cpu.data<int>();
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}
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level0 = std::vector<int>(lod, lod + lod_t->numel());
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} else {
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level0 = ctx.Attr<std::vector<int>>("target_lod");
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}
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PADDLE_ENFORCE(level0.size() > 1UL,
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"The size of target LoD should be greater than 1.");
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PADDLE_ENFORCE(level0[0] == 0,
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"Target LoD should be a vector starting from 0.");
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PADDLE_ENFORCE(level0.back() == in->dims()[0],
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"Target LoD should be a vector end with the "
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"first dimension of Input(X).");
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for (size_t i = 0; i < level0.size() - 1; ++i) {
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PADDLE_ENFORCE(level0[i + 1] > level0[i],
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"Target LoD should be an ascending vector.");
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}
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out->ShareDataWith(*in);
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// cast level0 to size_t
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std::vector<size_t> ulevel0(level0.size(), 0);
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std::transform(level0.begin(), level0.end(), ulevel0.begin(),
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[](int a) { return static_cast<size_t>(a); });
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framework::LoD target_lod;
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target_lod.push_back(ulevel0);
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out->set_lod(target_lod);
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}
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};
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template <typename Place, typename T>
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class LoDResetGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& ctx) const {
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auto* d_out = ctx.Input<framework::Tensor>(framework::GradVarName("Out"));
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auto* d_x = ctx.Output<framework::Tensor>(framework::GradVarName("X"));
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d_x->ShareDataWith(*d_out);
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}
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};
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} // namespace operators
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} // namespace paddle

‎python/paddle/v2/framework/tests/test_lod_reset_op.py

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import unittest
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import numpy as np
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from op_test import OpTest
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class TestLodResetOpByAttr(OpTest):
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def setUp(self):
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self.op_type = "lod_reset"
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x = np.random.random((10, 20)).astype("float32")
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lod = [[0, 3, 5, 10]]
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target_lod_0 = [0, 7, 10]
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self.inputs = {'X': (x, lod)}
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self.attrs = {'target_lod': target_lod_0}
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self.outputs = {'Out': (x, [target_lod_0])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out")
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class TestLodResetOpByInput(OpTest):
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def setUp(self):
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self.op_type = "lod_reset"
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x = np.random.random((10, 20)).astype("float32")
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lod = [[0, 3, 5, 10]]
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target_lod_0 = [0, 4, 7, 10]
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self.inputs = {
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'X': (x, lod),
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'TargetLoD': np.array([target_lod_0]).astype('int32')
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}
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self.outputs = {'Out': (x, [target_lod_0])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
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class TestLodResetOpBoth(OpTest):
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def setUp(self):
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self.op_type = "lod_reset"
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x = np.random.random((10, 20)).astype("float32")
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lod = [[0, 3, 5, 10]]
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target_lod_0_attr = [0, 7, 10]
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target_lod_0_in = [0, 4, 7, 10]
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self.inputs = {
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'X': (x, lod),
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'TargetLoD': np.array(target_lod_0_in).astype('int32')
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}
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self.attrs = {'target_lod': target_lod_0_attr}
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self.outputs = {'Out': (x, [target_lod_0_in])}
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def test_check_output(self):
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self.check_output()
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def test_check_grad(self):
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self.check_grad(["X"], "Out", no_grad_set=set("TargetLoD"))
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if __name__ == '__main__':
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unittest.main()

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