Yu Yang
7 years ago
committed by
GitHub
commit
2c31ea9293
@ -1,136 +1,100 @@
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/* Copyright (c) 2016 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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// 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_concat_op.h"
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#include <vector>
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namespace paddle {
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namespace operators {
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class SequenceConcatOp : public framework::OperatorWithKernel {
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class SeqConcatOpMaker : public framework::OpProtoAndCheckerMaker {
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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->HasInputs("X"),
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"Inputs(X) of SequenceConcatOp should not be null.");
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PADDLE_ENFORCE(ctx->HasOutput("Out"),
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"Output(Out) of SequenceConcatOp should not be null.");
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const size_t level = static_cast<size_t>(ctx->Attrs().Get<int>("level"));
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const size_t axis = static_cast<size_t>(ctx->Attrs().Get<int>("axis"));
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PADDLE_ENFORCE(level == 0UL || level == 1UL,
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"The sequence_concat operator only accepts sequence "
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"or a nested sequence as its input.");
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auto ins_dims = ctx->GetInputsDim("X");
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framework::DDim out_dims = ins_dims[0];
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const size_t n = ins_dims.size();
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for (size_t i = 1; i < n; ++i) {
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out_dims[axis] += ins_dims[i][axis];
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}
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ctx->SetOutputDim("Out", out_dims);
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void Make() override {
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AddInput("X", "The inputs of sequence concat op").AsDuplicable();
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AddOutput("Out", "The output of sequence concat op");
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AddComment(
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"Sequence Concat Op\n"
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"It will concat LoD tensors by its sequence information.\n"
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"For example:\n"
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" LoD of X1 = [0, 3, 7]\n"
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" LoD of X2 = [0, 7, 9]\n"
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" Result LoD is [0, (3+7), (7+9)]\n"
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" i.e.[0, 10, 16]\n");
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}
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};
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class SequenceConcatOpMaker : public framework::OpProtoAndCheckerMaker {
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class SeqConcatShapeInferer : public framework::InferShapeBase {
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public:
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void Make() override {
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AddInput("X",
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"(LodTensorArray) Input is a vector of LoDTensor, "
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"each of which is a variable-length sequence or nested sequence.")
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.AsDuplicable();
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AddOutput("Out",
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"(LoDTensor), Variable-length output of "
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"sequence_concat Op.");
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AddAttr<int>("axis",
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"(int, default 0) "
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"The axis along which the inputs will be joined. "
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"If axis is 0, the inputs will be joined with LoD index.")
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.SetDefault(0);
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AddAttr<int>("level",
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"(int, default 0) "
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"The level at which the inputs will be joined. "
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"If the level is 0, the inputs will be joined at the nested "
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"sequence level. "
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"If the level is 1, the inputs will be joined at the "
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"sequence level. "
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"The level should be less than the level number of inputs.")
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.SetDefault(0);
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AddComment(R"DOC(
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The sequence_concat operator concatenates multiple LoDTensors.
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It only supports sequence (LoD Tensor with level number is 1)
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or a nested sequence (LoD tensor with level number is 2) as its input.
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- Case1:
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If the axis is other than 0(here, axis is 1 and level is 1),
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each input should have the same LoD information and the LoD
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information of the output keeps the same as the input.
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LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
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LoD(x1) = {{0,2,4}, {0,1,2,3,4}}; Dims(x1) = (4,4,4)
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LoD(Out) = {{0,2,4}, {0,1,2,3,4}}; Dims(Out) = (4,7,4)
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- Case2:
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If the axis is 0(here, leve is 0), the inputs are concatenated along
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time steps, the LoD information of the output need to re-compute.
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The LoD information of level-1 should be same.
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LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
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LoD(x1) = {{0,2,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
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LoD(Out) = {{0,2,4}, {0,2,5,8,11}}; Dims(Out) = (11,3,4)
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- Case3:
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If the axis is 0(here, level is 1).
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LoD(x0) = {{0,2,4}, {0,1,2,3,4}}; Dims(x0) = (4,3,4)
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LoD(x1) = {{0,3,4}, {0,1,3,5,7}}; Dims(x1) = (7,3,4)
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LoD(Out) = {{0,5,8}, {0,1,2,3,5,7,8,9,11}}; Dims(Out) = (11,3,4)
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- Case4:
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If the LoD number is 1, axis is 0, level is 0
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LoD(x0) = {{0,1,2,3,4}}; Dims(x0) = (4,3,4)
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LoD(x1) = {{0,1,3,5,7}}; Dims(x1) = (7,3,4)
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LoD(Out) = {{0,2,5,8,11}}; Dims(Out) = (11,3,4)
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NOTE: The levels of all the inputs should be the same.
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)DOC");
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void operator()(framework::InferShapeContext *context) const override {
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PADDLE_ENFORCE(context->HasInputs("X"),
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"Input(X) of Sequence Concat Op should not be null.");
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PADDLE_ENFORCE(context->HasOutput("Out"),
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"Output(Out) of Sequence Concat Op should not be null.");
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PADDLE_ENFORCE_GT(context->Inputs("X").size(), 1,
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"The number of input sequences is at least two.");
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auto x_dims = context->GetInputsDim("X");
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int64_t batch_size = 0;
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int64_t feature_size = 0;
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std::vector<int64_t> out_dims;
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for (auto &x_dim : x_dims) {
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if (out_dims.empty()) {
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out_dims = framework::vectorize(x_dim);
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}
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batch_size += x_dim[0];
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if (feature_size == 0) {
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feature_size = framework::product(x_dim) / x_dim[0];
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} else {
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PADDLE_ENFORCE_EQ(
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feature_size, framework::product(x_dim) / x_dim[0],
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"Inputs of sequence concat must have same feature size");
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}
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}
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if (batch_size < 0) {
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batch_size = -1; // Normalize batch size for compile time.
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}
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out_dims[0] = batch_size;
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context->SetOutputDim("Out", framework::make_ddim(out_dims));
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if (!context->IsRuntime()) { // Runtime LoD infershape will be computed
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// in Kernel.
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context->ShareLoD("X", "Out");
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}
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}
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};
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class SequenceConcatGradOp : public framework::OperatorWithKernel {
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class SeqConcatGradShapeInferer : public framework::InferShapeBase {
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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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void operator()(framework::InferShapeContext *context) const override {
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context->SetOutputsDim(framework::GradVarName("X"),
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context->GetInputsDim("X"));
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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_concat, ops::SequenceConcatOp,
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ops::SequenceConcatOpMaker,
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paddle::framework::DefaultGradOpDescMaker<
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false> /* set false to disable empty grad */);
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REGISTER_OPERATOR(sequence_concat_grad, ops::SequenceConcatGradOp);
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REGISTER_OP_CPU_KERNEL(
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sequence_concat,
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ops::SequenceConcatOpKernel<paddle::platform::CPUDeviceContext, float>);
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REGISTER_OP_CPU_KERNEL(
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sequence_concat_grad,
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ops::SequenceConcatGradOpKernel<paddle::platform::CPUDeviceContext, float>);
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namespace op = paddle::operators;
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REGISTER_OPERATOR(sequence_concat, paddle::framework::OperatorWithKernel,
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op::SeqConcatOpMaker, op::SeqConcatShapeInferer,
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paddle::framework::DefaultGradOpDescMaker<false>);
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template <typename T>
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using Kernel = op::SeqConcatKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(sequence_concat, Kernel<float>, Kernel<double>);
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REGISTER_OPERATOR(sequence_concat_grad, paddle::framework::OperatorWithKernel,
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op::SeqConcatGradShapeInferer);
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template <typename T>
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using GradKernel =
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op::SeqConcatGradKernel<paddle::platform::CPUDeviceContext, T>;
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REGISTER_OP_CPU_KERNEL(sequence_concat_grad, GradKernel<float>,
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GradKernel<double>);
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@ -1,23 +1,26 @@
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/* Copyright (c) 2016 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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// 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_concat_op.h"
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namespace ops = paddle::operators;
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REGISTER_OP_CUDA_KERNEL(
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sequence_concat,
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ops::SequenceConcatOpKernel<paddle::platform::CUDADeviceContext, float>);
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REGISTER_OP_CUDA_KERNEL(sequence_concat_grad,
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ops::SequenceConcatGradOpKernel<
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paddle::platform::CUDADeviceContext, float>);
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template <typename T>
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using Kernel =
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paddle::operators::SeqConcatKernel<paddle::platform::CUDADeviceContext, T>;
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REGISTER_OP_CUDA_KERNEL(sequence_concat, Kernel<float>, Kernel<double>);
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template <typename T>
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using GradKernel =
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paddle::operators::SeqConcatGradKernel<paddle::platform::CUDADeviceContext,
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T>;
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REGISTER_OP_CUDA_KERNEL(sequence_concat_grad, GradKernel<float>,
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GradKernel<double>);
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File diff suppressed because it is too large
Load Diff
@ -1,124 +0,0 @@
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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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from __future__ import print_function
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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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def to_abs_offset_lod(lod):
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offset_lod = [[0] for i in lod]
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for i, level in enumerate(lod):
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for seq_len in level:
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offset_lod[i].append(offset_lod[i][-1] + seq_len)
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if len(offset_lod) == 0 or len(offset_lod) == 1:
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return offset_lod
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import copy
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new_offset_lod = copy.deepcopy(offset_lod)
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for idx, val in enumerate(offset_lod[0]):
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new_offset_lod[0][idx] = offset_lod[1][val]
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return new_offset_lod
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def seq_concat(inputs, level):
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lod0 = inputs['X'][0][1][1]
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lod1 = inputs['X'][1][1][1]
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x0 = inputs['X'][0][1][0]
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x1 = inputs['X'][1][1][0]
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level_idx = len(lod0) - level - 1
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outs = []
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for i in range(len(lod0[level_idx])):
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sub_x0 = x0[to_abs_offset_lod(lod0)[level_idx][i]:to_abs_offset_lod(
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lod0)[level_idx][i + 1], :]
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sub_x1 = x1[to_abs_offset_lod(lod1)[level_idx][i]:to_abs_offset_lod(
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lod1)[level_idx][i + 1], :]
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outs.append(np.concatenate((sub_x0, sub_x1), axis=0))
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return np.concatenate(outs, axis=0)
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class TestSeqConcatOp(OpTest):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((4, 8, 3)).astype('float32')
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lod1 = [[2, 2], [1, 1, 1, 1]]
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axis = 1
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level = 1
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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self.outputs = {'Out': (np.concatenate([x0, x1], axis=1), lod0)}
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def setUp(self):
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self.op_type = "sequence_concat"
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self.set_data()
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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(['x0'], 'Out')
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class TestSeqConcatOpLevelZeroNestedSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((7, 6, 3)).astype('float32')
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lod1 = [[2, 2], [1, 2, 2, 2]]
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axis = 0
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level = 0
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[2, 2], [2, 3, 3, 3]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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class TestSeqConcatOplevelOneNestedSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 6, 3)).astype('float32')
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lod0 = [[2, 2], [1, 1, 1, 1]]
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x1 = np.random.random((7, 6, 3)).astype('float32')
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lod1 = [[3, 1], [1, 2, 2, 2]]
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axis = 0
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level = 1
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[5, 3], [1, 1, 1, 2, 2, 1, 1, 2]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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class TestSeqConcatOpLevelZeroSequence(TestSeqConcatOp):
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def set_data(self):
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# two level, batch size is 3
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x0 = np.random.random((4, 3, 4)).astype('float32')
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lod0 = [[1, 1, 1, 1]]
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x1 = np.random.random((7, 3, 4)).astype('float32')
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lod1 = [[1, 2, 2, 2]]
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axis = 0
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level = 0
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self.inputs = {'X': [('x0', (x0, lod0)), ('x1', (x1, lod1))]}
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self.attrs = {'axis': axis, 'level': level}
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out_lod = [[2, 3, 3, 3]]
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self.outputs = {'Out': (seq_concat(self.inputs, level), out_lod)}
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if __name__ == '__main__':
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unittest.main()
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@ -0,0 +1,45 @@
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
|
||||
#
|
||||
# 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.
|
||||
|
||||
from __future__ import print_function
|
||||
|
||||
import unittest
|
||||
import numpy as np
|
||||
from op_test import OpTest
|
||||
|
||||
|
||||
class TestSequenceConcat(OpTest):
|
||||
def setUp(self):
|
||||
x1 = np.random.random(size=(10, 80))
|
||||
lod1 = [7, 3]
|
||||
x2 = np.random.random(size=(20, 80))
|
||||
lod2 = [12, 8]
|
||||
|
||||
out = np.concatenate((x1[0:lod1[0]], x2[0:lod2[0]], x1[lod1[0]:],
|
||||
x2[lod2[0]:]))
|
||||
out_lod = [19, 11]
|
||||
|
||||
self.op_type = "sequence_concat"
|
||||
self.inputs = {'X': [("x1", (x1, [lod1])), ("x2", (x2, [lod2]))]}
|
||||
self.outputs = {"Out": (out, [out_lod])}
|
||||
|
||||
def test_output(self):
|
||||
self.check_output(1e-3)
|
||||
|
||||
def test_dx(self):
|
||||
self.check_grad(inputs_to_check=['x1', 'x2'], output_names="Out")
|
||||
|
||||
|
||||
if __name__ == '__main__':
|
||||
unittest.main()
|
||||
Loading…
Reference in new issue