sneaxiy
6 years ago
parent
af91d41ab8
commit
4b4af84e67
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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_reverse_op.h"
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namespace ops = paddle::operators;
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REGISTER_OPERATOR(sequence_reverse, ops::SequenceReverseOp,
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ops::SequenceReverseOpMaker,
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ops::SequenceReverseGradOpDescMaker);
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REGISTER_OP_CPU_KERNEL(
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sequence_reverse,
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ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, uint8_t>,
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ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, int64_t>,
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ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SequenceReverseOpKernel<paddle::platform::CPUDeviceContext, double>);
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@ -0,0 +1,25 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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_reverse_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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sequence_reverse,
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ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, uint8_t>,
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ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int>,
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ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, int64_t>,
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ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, float>,
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ops::SequenceReverseOpKernel<paddle::platform::CUDADeviceContext, double>);
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@ -0,0 +1,155 @@
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// Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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//
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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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//
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// http://www.apache.org/licenses/LICENSE-2.0
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//
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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/operators/math/algorithm.h"
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#include "paddle/fluid/platform/for_range.h"
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namespace paddle {
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namespace operators {
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class SequenceReverseOp : 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) must exist");
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PADDLE_ENFORCE(ctx->HasOutput("Y"), "Output(Y) must exist");
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auto x_dim = ctx->GetInputDim("X");
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PADDLE_ENFORCE_GE(x_dim.size(), 2,
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"Rank of Input(X) must be not less than 2.");
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ctx->SetOutputDim("Y", x_dim);
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ctx->ShareLoD("X", "Y");
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}
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};
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class SequenceReverseOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X", "The input LoDTensor of sequence_reverse op.");
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AddOutput("Y", "The output LoDTensor of sequence_reverse op.");
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AddComment(R"DOC(
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SequenceReverse Operator.
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Reverse each sequence in input X along dim 0.
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Assuming X is a LoDTensor with dims [5, 4] and lod [[0, 2, 5]], where:
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X.data() = [
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[1, 2, 3, 4],
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[5, 6, 7, 8], # the 0-th sequence with length 2
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[9, 10, 11, 12],
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[13, 14, 15, 16],
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[17, 18, 19, 20] # the 1-st sequence with length 3
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]
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The output Y would be a LoDTensor sharing the same dims and lod with input X,
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and:
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Y.data() = [
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[5, 6, 7, 8],
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[1, 2, 3, 4], # the reversed 0-th sequence with length 2
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[17, 18, 19, 20],
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[13, 14, 15, 16],
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[9, 10, 11, 12] # the reversed 1-st sequence with length 3
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]
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This Operator is useful to build a reverse dynamic RNN network.
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)DOC");
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}
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};
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template <typename T>
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struct SequenceReverseFunctor {
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SequenceReverseFunctor(const T *x, T *y, const size_t *lod, size_t lod_count,
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size_t row_numel)
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: x_(x), y_(y), lod_(lod), lod_count_(lod_count), row_numel_(row_numel) {}
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HOSTDEVICE void operator()(size_t idx_x) const {
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auto row_idx_x = idx_x / row_numel_;
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auto lod_idx = math::UpperBound(lod_, lod_count_, row_idx_x);
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auto row_idx_y = lod_[lod_idx - 1] + (lod_[lod_idx] - 1 - row_idx_x);
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auto idx_y = row_idx_y * row_numel_ + idx_x % row_numel_;
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y_[idx_y] = x_[idx_x];
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}
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const T *x_;
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T *y_;
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const size_t *lod_;
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size_t lod_count_;
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size_t row_numel_;
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};
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template <typename DeviceContext, typename T>
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class SequenceReverseOpKernel : public framework::OpKernel<T> {
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using LoDTensor = framework::LoDTensor;
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public:
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void Compute(const framework::ExecutionContext &ctx) const override {
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auto &x = *ctx.Input<LoDTensor>("X");
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auto *y = ctx.Output<LoDTensor>("Y");
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PADDLE_ENFORCE_EQ(x.lod().size(), 1,
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"SequenceReverse Op only support one level lod.");
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auto &dev_ctx = ctx.template device_context<DeviceContext>();
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const size_t *lod;
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size_t lod_count = x.lod()[0].size();
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#ifdef PADDLE_WITH_CUDA
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if (platform::is_gpu_place(ctx.GetPlace())) {
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lod = x.lod()[0].CUDAData(ctx.GetPlace());
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} else {
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#endif
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lod = x.lod()[0].data();
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#ifdef PADDLE_WITH_CUDA
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}
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#endif
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size_t limit = static_cast<size_t>(x.numel());
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size_t row_numel = static_cast<size_t>(limit / x.dims()[0]);
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auto *x_data = x.data<T>();
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auto *y_data = y->mutable_data<T>(ctx.GetPlace());
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PADDLE_ENFORCE_NE(x_data, y_data,
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"SequenceReverse Op does not support in-place operation");
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SequenceReverseFunctor<T> functor(x_data, y_data, lod, lod_count,
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row_numel);
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platform::ForRange<DeviceContext> for_range(dev_ctx, limit);
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for_range(functor);
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}
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};
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class SequenceReverseGradOpDescMaker : public framework::SingleGradOpDescMaker {
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public:
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using framework::SingleGradOpDescMaker::SingleGradOpDescMaker;
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protected:
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std::unique_ptr<framework::OpDesc> Apply() const override {
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std::unique_ptr<framework::OpDesc> op(new framework::OpDesc());
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op->SetType("sequence_reverse");
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op->SetInput("X", OutputGrad("Y"));
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op->SetOutput("Y", InputGrad("X"));
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op->SetAttrMap(Attrs());
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return op;
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}
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};
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} // namespace operators
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} // namespace paddle
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@ -0,0 +1,69 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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#
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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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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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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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import unittest
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import paddle.fluid as fluid
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import paddle.fluid.core as core
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from op_test import OpTest
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import numpy as np
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class TestSequenceReverseBase(OpTest):
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def initParameters(self):
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pass
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def setUp(self):
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self.size = (10, 3, 4)
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self.lod = [2, 3, 5]
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self.dtype = 'float32'
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self.initParameters()
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self.op_type = 'sequence_reverse'
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self.x = np.random.random(self.size).astype(self.dtype)
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self.y = self.get_output()
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self.inputs = {'X': (self.x, [self.lod, ]), }
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self.outputs = {'Y': (self.y, [self.lod, ]), }
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def get_output(self):
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tmp_x = np.reshape(self.x, newshape=[self.x.shape[0], -1])
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tmp_y = np.ndarray(tmp_x.shape).astype(self.dtype)
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prev_idx = 0
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for cur_len in self.lod:
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idx_range = range(prev_idx, prev_idx + cur_len)
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tmp_y[idx_range, :] = np.flip(tmp_x[idx_range, :], 0)
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prev_idx += cur_len
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return np.reshape(tmp_y, newshape=self.x.shape).astype(self.dtype)
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def test_output(self):
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self.check_output(0)
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def test_grad(self):
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self.check_grad(['X'], 'Y')
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class TestSequenceReserve1(TestSequenceReverseBase):
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def initParameters(self):
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self.size = (12, 10)
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self.lod = [4, 5, 3]
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class TestSequenceReverse2(TestSequenceReverseBase):
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def initParameters(self):
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self.size = (12, 10)
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self.lod = [12]
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if __name__ == '__main__':
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unittest.main()
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