caoying03
7 years ago
commit
fafd3e0fc0
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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/sequence_slice_op.h"
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namespace paddle {
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namespace operators {
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class SequenceSliceOp : 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 SequenceSliceOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Offset"),
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"Input(Offset) of SequenceSliceOp should not be null.");
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PADDLE_ENFORCE(ctx->HasInput("Length"),
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"Input(Length) of SequenceSliceOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceSliceOp should not be null.");
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auto input_dims = ctx->GetInputDim("X");
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auto offset_dim = ctx->GetInputDim("Offset");
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auto length_dim = ctx->GetInputDim("Length");
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PADDLE_ENFORCE_EQ(
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offset_dim.size(), 2UL,
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"Only support one level sequence now, The rank of offset must be 2.");
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PADDLE_ENFORCE_EQ(
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length_dim.size(), 2UL,
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"Only support one level sequence now, The rank of Length must be 2.");
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// Initialize the output's dims to maximum,
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// and re-set to real dims by the value of Offset and Length at kernel
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ctx->SetOutputDim("Out", input_dims);
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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 SequenceSliceGradOp : 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(framework::GradVarName("Out")),
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"The gradient of Out should not be null.");
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PADDLE_ENFORCE(ctx->HasOutputs(framework::GradVarName("X")),
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"The gradient of X should not be null.");
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ctx->SetOutputsDim(framework::GradVarName("X"), ctx->GetInputsDim("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 SequenceSliceOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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SequenceSliceOpMaker(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",
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"(LoDTensor), "
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"the input of SequenceSliceOp.");
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AddInput("Offset",
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"(Tensor), "
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"a vector<int> to describe the offset of every input sequence for "
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"sub sequence item.");
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AddInput("Length",
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"(Tensor), "
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"a vector<int> to describe the length of every input sequence for "
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"sub sequence item.");
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AddOutput("Out",
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"(LoDTensor), the output of SequenceSliceOp.");
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AddComment(R"DOC(
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Sequence slice operator
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The operator crops a subsequence from given sequence with given start offset and subsequence length.
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It only supports sequence (LoD Tensor with level number is 1).
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- Case:
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X = [[a1, a2;
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b1, b2;
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c1, c2]
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[d1, d2;
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e1, e2]]
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LoD(X) = {{0, 3, 5}}; Dims(X) = (5, 2)
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Offset = [[0], [1]]; Length = [[2], [1]]
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Out = [[a1, a2;
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b1, b2]
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[e1, e2]]
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LoD(Out) = {{0, 2, 3}}; Dims(Out) = (3, 2)
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NOTE: The first dimension size of input, the size of offset and Length, should be equal. The offset start from 0.
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)DOC");
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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(sequence_slice, ops::SequenceSliceOp, ops::SequenceSliceOpMaker,
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sequence_slice_grad, ops::SequenceSliceGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_slice,
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ops::SequenceSliceOpKernel<paddle::platform::CPUPlace, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_slice_grad,
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ops::SequenceSliceGradOpKernel<paddle::platform::CPUPlace, float>);
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@ -0,0 +1,23 @@
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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/sequence_slice_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_GPU_KERNEL(
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sequence_slice,
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ops::SequenceSliceOpKernel<paddle::platform::GPUPlace, float>);
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REGISTER_OP_GPU_KERNEL(
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sequence_slice_grad,
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ops::SequenceSliceGradOpKernel<paddle::platform::GPUPlace, float>);
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@ -0,0 +1,173 @@
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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/op_registry.h"
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#include "paddle/operators/math/math_function.h"
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#include "paddle/operators/strided_memcpy.h"
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namespace paddle {
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namespace operators {
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using Tensor = framework::Tensor;
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using LoDTensor = framework::LoDTensor;
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using LoD = framework::LoD;
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template <typename T>
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inline LoD SequenceSliceLoD(const T& in, const int64_t* offset_data,
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const int64_t* length_data) {
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auto out_lod = in.lod();
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size_t lod_offset = 0;
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auto n = in.lod()[0].size() - 1;
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out_lod[0][0] = 0;
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for (size_t i = 0; i < n; ++i) {
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lod_offset += length_data[i];
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out_lod[0][i+1] = lod_offset;
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}
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return out_lod;
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}
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template <typename Place, typename T>
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class SequenceSliceOpKernel : 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* in = ctx.Input<LoDTensor>("X");
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auto* offset = ctx.Input<Tensor>("Offset");
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auto* length = ctx.Input<Tensor>("Length");
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auto* out = ctx.Output<LoDTensor>("Out");
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auto lod = in->lod();
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auto n = lod[0].size() - 1;
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PADDLE_ENFORCE_EQ(lod.size(), 1UL,
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"Only support one level sequence now.");
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PADDLE_ENFORCE_EQ(
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n, static_cast<size_t>(length->dims()[0]),
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"The size of input-sequence and length-array should be the same")
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PADDLE_ENFORCE_EQ(
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n, static_cast<size_t>(offset->dims()[0]),
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"The size of input-sequence and offset-array should be the same")
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const int64_t* offset_data = offset->data<int64_t>();
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const int64_t* length_data = length->data<int64_t>();
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framework::Tensor offset_cpu;
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framework::Tensor length_cpu;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
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offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
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offset_data = offset_cpu.data<int64_t>();
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length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
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length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
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length_data = length_cpu.data<int64_t>();
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}
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for (size_t i = 0; i < n; ++i) {
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PADDLE_ENFORCE_LT(0, offset_data[i],
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"The offset[%d] must greater than zero.", i)
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PADDLE_ENFORCE_LT(0, length_data[i],
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"The length[%d] must greater than zero.", i)
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PADDLE_ENFORCE_LT(
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lod[0][i] + offset_data[i] + length_data[i],
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lod[0][i + 1],
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"The target tensor's length overflow.")
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}
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out->mutable_data<T>(ctx.GetPlace());
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auto out_lod = SequenceSliceLoD(*in, offset_data, length_data);
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auto out_dims = in->dims();
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out_dims[0] = out_lod[0][out_lod[0].size() - 1];
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out->Resize(out_dims);
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out->set_lod(out_lod);
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auto in_stride = framework::stride(in->dims());
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auto out_stride = framework::stride(out->dims());
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size_t out_offset = 0;
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for (size_t i = 0; i < n; ++i) {
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Tensor in_t =
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in->Slice(static_cast<int>(lod[0][i] + offset_data[i]),
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static_cast<int>(lod[0][i] + offset_data[i] +
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length_data[i]));
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StridedMemcpy<T>(ctx.device_context(), in_t.data<T>(),
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in_stride, in_t.dims(), out_stride,
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out->data<T>() + out_offset);
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out_offset += length_data[i] * in_stride[0];
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}
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}
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};
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template <typename Place, typename T>
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class SequenceSliceGradOpKernel : 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* in = ctx.Input<LoDTensor>("X");
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auto* offset = ctx.Input<Tensor>("Offset");
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auto* length = ctx.Input<Tensor>("Length");
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auto* out_grad =
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ctx.Input<framework::LoDTensor>(framework::GradVarName("Out"));
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auto* x_grad =
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ctx.Output<framework::LoDTensor>(framework::GradVarName("X"));
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const int64_t* offset_data = offset->data<int64_t>();
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const int64_t* length_data = length->data<int64_t>();
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framework::Tensor offset_cpu;
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framework::Tensor length_cpu;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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offset_cpu.mutable_data<T>(offset->dims(), platform::CPUPlace());
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offset_cpu.CopyFrom(*offset, platform::CPUPlace(), ctx.device_context());
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offset_data = offset_cpu.data<int64_t>();
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length_cpu.mutable_data<T>(length->dims(), platform::CPUPlace());
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length_cpu.CopyFrom(*length, platform::CPUPlace(), ctx.device_context());
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length_data = length_cpu.data<int64_t>();
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}
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auto lod = in->lod();
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auto out_lod = out_grad->lod();
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if (x_grad) {
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x_grad->mutable_data<T>(ctx.GetPlace());
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x_grad->set_lod(in->lod());
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math::SetConstant<Place, T> set_zero;
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set_zero(ctx.device_context(), x_grad, static_cast<T>(0));
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auto out_grad_stride = framework::stride(out_grad->dims());
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for (size_t i = 0; i < out_lod[0].size() - 1; ++i) {
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Tensor out_grad_t =
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out_grad->Slice(static_cast<int>(out_lod[0][i]),
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static_cast<int>(out_lod[0][i + 1]));
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auto out_grad_stride = framework::stride(out_grad_t.dims());
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auto x_grad_stride = framework::stride(x_grad->dims());
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Tensor x_grad_t = x_grad->Slice(
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static_cast<int>(lod[0][i] + offset_data[i]),
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static_cast<int>(lod[0][i] + offset_data[i] + length_data[i]));
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StridedMemcpy<T>(ctx.device_context(), out_grad_t.data<T>(),
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out_grad_stride, out_grad_t.dims(), x_grad_stride,
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x_grad_t.data<T>());
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}
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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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@ -0,0 +1,47 @@
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import unittest
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import numpy as np
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import sys
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from op_test import OpTest
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class TestSequenceSliceOp(OpTest):
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def set_data(self):
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self.init_test_case()
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# only supprot one level LoD
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x = np.random.random(self.x_dim).astype('float32')
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lod = self.x_lod
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offset = np.array(self.offset).astype("int64")
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length = np.array(self.length).astype("int64")
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self.inputs = {'X': (x, lod), 'Offset': offset, 'Length': length}
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outs = [] #np.zeros((100, 3, 2)).astype('float32')
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out_lod = [[0]]
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out_lod_offset = 0
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for i in range(len(offset)):
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sub_x = x[lod[0][i] + offset[i, 0]:lod[0][i] + offset[i, 0] +
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length[i, 0], :]
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out_lod_offset = out_lod_offset + len(sub_x)
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outs.append(sub_x)
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out_lod[0].append(out_lod_offset)
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outs = np.concatenate(outs, axis=0)
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self.outputs = {'Out': (outs, out_lod)}
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|
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|
def init_test_case(self):
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|
self.x_dim = (100, 3, 2)
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self.x_lod = [[0, 20, 40, 60, 80, 100]]
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||||||
|
self.offset = [[1], [2], [3], [4], [5]]
|
||||||
|
self.length = [[10], [8], [6], [4], [2]]
|
||||||
|
|
||||||
|
def setUp(self):
|
||||||
|
self.op_type = "sequence_slice"
|
||||||
|
self.set_data()
|
||||||
|
|
||||||
|
def test_check_output(self):
|
||||||
|
self.check_output()
|
||||||
|
|
||||||
|
def test_check_grad(self):
|
||||||
|
self.check_grad(['X'], 'Out')
|
||||||
|
|
||||||
|
|
||||||
|
if __name__ == '__main__':
|
||||||
|
unittest.main()
|
||||||
Loading…
Reference in new issue