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278 lines
10 KiB
278 lines
10 KiB
/* 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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#include "paddle/fluid/operators/sequence_ops/sequence_expand_op.h"
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#include <memory>
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namespace paddle {
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namespace operators {
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using framework::LoDTensor;
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class SequenceExpandOp : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceExpand");
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OP_INOUT_CHECK(ctx->HasInput("Y"), "Input", "Y", "SequenceExpand");
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OP_INOUT_CHECK(ctx->HasOutput("Out"), "Output", "Out", "SequenceExpand");
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auto x_dims = ctx->GetInputDim("X");
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auto out_dims = x_dims;
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int ref_level = ctx->Attrs().Get<int>("ref_level");
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PADDLE_ENFORCE_GE(
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x_dims.size(), 2,
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platform::errors::InvalidArgument(
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"Dimension number of Input(X) should be at least 2. But "
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"received: input rank %u, input shape [%s].",
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x_dims.size(), x_dims));
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if (ctx->IsRuntime()) {
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framework::Variable* x_var =
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BOOST_GET(framework::Variable*, ctx->GetInputVarPtrs("X")[0]);
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framework::Variable* y_var =
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BOOST_GET(framework::Variable*, ctx->GetInputVarPtrs("Y")[0]);
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auto& x_lod = x_var->Get<LoDTensor>().lod();
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auto& y_lod = y_var->Get<LoDTensor>().lod();
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PADDLE_ENFORCE_LE(x_lod.size(), 1UL,
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platform::errors::InvalidArgument(
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"Level of Input(X)'s lod should not be "
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"greater than 1. But received: lod level %u.",
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x_lod.size()));
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PADDLE_ENFORCE_GT(
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y_lod.size(), 0UL,
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platform::errors::InvalidArgument(
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"Level of Input(Y)'s lod should be greater than 0. But "
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"received: lod level %u.",
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y_lod.size()));
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PADDLE_ENFORCE_EQ(
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ref_level == -1 ||
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(ref_level >= 0 && ref_level < static_cast<int>(y_lod.size())),
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true, platform::errors::InvalidArgument(
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"Invlid `ref_level`, which should be either equal to -1 "
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"or in [0, %d), but received `ref_level` = %u.",
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y_lod.size(), ref_level));
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if (ref_level == -1) ref_level = y_lod.size() - 1;
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if (x_lod.size() > 0) {
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PADDLE_ENFORCE_EQ(
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x_lod[0].size(), y_lod[ref_level].size(),
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platform::errors::InvalidArgument(
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"Level number of Input(X)'s lod could be 0. Otherwise "
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"size of Input(X)'s first level lod should be equal to "
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"size of Input(Y)'s referred level lod. But received: "
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"Input(X).lod[0].size() = %u, Input(Y).lod[%d].size() = "
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"%u",
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x_lod[0].size(), ref_level, y_lod[ref_level].size()));
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} else {
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PADDLE_ENFORCE_EQ(
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x_dims[0], static_cast<int64_t>(y_lod[ref_level].size()) - 1,
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platform::errors::InvalidArgument(
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"When Input(X)'s lod is null, the dims[0] of "
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"Input(X) should match the "
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"size of Input(Y)'s referred level lod. But received "
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"Input(X): input rank %u, input shape [%s]; received "
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"Input(Y).lod[%d].size() - 1 = %d.",
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x_dims.size(), x_dims, ref_level,
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static_cast<int64_t>(y_lod[ref_level].size()) - 1));
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}
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int64_t out_first_dim = 0;
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if (y_lod[ref_level].size() <= 1) {
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out_first_dim = x_dims[0];
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} else {
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for (size_t i = 1; i < y_lod[ref_level].size(); ++i) {
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int x_seq_len = 1;
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if (x_lod.size() == 1) {
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x_seq_len = x_lod[0][i] - x_lod[0][i - 1];
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}
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out_first_dim +=
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(y_lod[ref_level][i] - y_lod[ref_level][i - 1]) * x_seq_len;
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}
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}
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out_dims[0] = out_first_dim;
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} else {
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out_dims[0] = -1;
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}
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ctx->SetOutputDim("Out", out_dims);
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ctx->ShareLoD("X", /*->*/ "Out");
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(
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OperatorWithKernel::IndicateVarDataType(ctx, "X"), ctx.GetPlace());
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}
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};
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class SequenceExpandOpMaker : public framework::OpProtoAndCheckerMaker {
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public:
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void Make() override {
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AddInput("X",
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"(LoDTensor, default LoDTensor<float>) A 2-D LoDTensor whose lod "
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"level is at most 1.");
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AddInput("Y",
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"(LoDTensor, default LoDTensor<float>) Referred LoDTensor whose "
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"lod (specified level) is referred by Input(X).");
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AddOutput("Out",
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"(LodTensor, default LoDTensor<float>) Output LoDTensor which is "
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"generated from Input(X) by referring lod of Input(Y).");
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AddAttr<int>("ref_level", "Specify lod level of Input(Y).").SetDefault(-1);
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AddComment(R"DOC(
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Sequence Expand Operator.
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This operator expands `X` according to specified level lod of `Y`. Current
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implementation constaints that lod level of `X` should be at most 1. Attribute
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`ref_level` is used to specify which level lod of `Y` is referred to expand `X`.
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If set `ref_level` to -1, then last level lod of `Y` would be referred.
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Please note, rank of `X` should be at least 2, when the rank exceeds 2, `X`
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would be viewed as a 2-D tensor.
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Following are cases to better explain how this works:
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Case 1:
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Given a 1-level LoDTensor input(X)
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X.lod = [[0, 2, 4]]
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X.data = [[a], [b], [c], [d]]
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X.dims = [4, 1]
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and input(Y)
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Y.lod = [[0, 2, 4],
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[0, 3, 6, 7, 8]]
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ref_level: 0
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then we get 1-level LoDTensor
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Out.lod = [[0, 2, 4, 6, 8]]
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Out.data = [[a], [b], [a], [b], [c], [d], [c], [d]]
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Out.dims = [8, 1]
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Case 2:
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Given 1-level LoDTensor input(X)
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X.lod = [[0, 1, 4]]
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X.data = [[a], [b], [c], [d]]
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X.dims = [4, 1]
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and input(Y)
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Y.lod = [[0, 2, 4],
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[0, 3, 6, 6, 8]]
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ref_level: 0
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then we get 1-level LoDTensor
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Out.lod = [[0, 1, 2, 5, 8]]
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Out.data = [[a], [a], [b], [c], [d], [b], [c], [d]]
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Out.dims = [8, 1]
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Case 3:
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Given a common Tensor input(X)
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X.data = [[a], [b], [c]]
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X.dims = [3, 1]
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and input(Y)
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Y.lod = [[0, 2, 3, 6]]
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ref_level: -1
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then we get a common Tensor
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Out.data = [[a], [a], [b], [c], [c], [c]]
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Out.dims = [6, 1]
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Case 4:
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Given a common Tensor input(X)
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X.data = [[a, b], [c, d], [e, f]]
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X.dims = [3, 2]
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and input(Y)
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Y.lod = [[0, 2, 3, 6]]
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ref_level: 0
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then we get a common LoDTensor
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Out.data = [[a, b], [a, b] [c, d], [e, f], [e, f], [e, f]]
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Out.dims = [6, 2]
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)DOC");
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}
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};
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class SequenceExpandOpGrad : public framework::OperatorWithKernel {
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public:
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using framework::OperatorWithKernel::OperatorWithKernel;
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protected:
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void InferShape(framework::InferShapeContext* ctx) const override {
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OP_INOUT_CHECK(ctx->HasInput("X"), "Input", "X", "SequenceExpandOpGrad");
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OP_INOUT_CHECK(ctx->HasInput(framework::GradVarName("Out")), "Input",
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framework::GradVarName("Out"), "SequenceExpandOpGrad");
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auto x_dims = ctx->GetInputDim("X");
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auto x_grad_name = framework::GradVarName("X");
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if (ctx->HasOutput(x_grad_name)) {
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ctx->SetOutputDim(x_grad_name, x_dims);
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}
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}
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framework::OpKernelType GetExpectedKernelType(
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const framework::ExecutionContext& ctx) const override {
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return framework::OpKernelType(OperatorWithKernel::IndicateVarDataType(
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ctx, framework::GradVarName("Out")),
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ctx.GetPlace());
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}
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};
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template <typename T>
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class SequenceExpandOpGradMaker : public framework::SingleGradOpMaker<T> {
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public:
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using framework::SingleGradOpMaker<T>::SingleGradOpMaker;
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protected:
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void Apply(GradOpPtr<T> op) const override {
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op->SetType("sequence_expand_grad");
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op->SetInput("X", this->Input("X"));
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op->SetInput("Y", this->Input("Y"));
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op->SetInput(framework::GradVarName("Out"), this->OutputGrad("Out"));
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op->SetOutput(framework::GradVarName("X"), this->InputGrad("X"));
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op->SetAttrMap(this->Attrs());
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}
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};
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(SequenceExpandOpNoNeedBufferVarsInferer,
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"Y");
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DECLARE_NO_NEED_BUFFER_VARS_INFERER(SequenceExpandGradOpNoNeedBufferVarsInferer,
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"X", "Y");
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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_expand, ops::SequenceExpandOp,
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ops::SequenceExpandOpMaker,
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ops::SequenceExpandOpGradMaker<paddle::framework::OpDesc>,
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ops::SequenceExpandOpGradMaker<paddle::imperative::OpBase>,
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ops::SequenceExpandOpNoNeedBufferVarsInferer);
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REGISTER_OPERATOR(sequence_expand_grad, ops::SequenceExpandOpGrad,
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ops::SequenceExpandGradOpNoNeedBufferVarsInferer);
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REGISTER_OP_CPU_KERNEL(
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sequence_expand,
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ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SequenceExpandKernel<paddle::platform::CPUDeviceContext, int64_t>);
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REGISTER_OP_CPU_KERNEL(
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sequence_expand_grad,
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ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, float>,
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ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, double>,
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ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int>,
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ops::SequenceExpandGradKernel<paddle::platform::CPUDeviceContext, int64_t>);
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