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107 lines
4.2 KiB
107 lines
4.2 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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#pragma once
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#include <string>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/operators/math/sequence_pooling.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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template <typename DeviceContext, typename T>
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class SequencePoolKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* in = context.Input<LoDTensor>("X");
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auto* out = context.Output<LoDTensor>("Out");
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std::string pooltype = context.Attr<std::string>("pooltype");
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T pad_value = static_cast<T>(context.Attr<float>("pad_value"));
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auto dims = in->dims();
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auto lod = in->lod();
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auto lod_level = lod.size();
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// InferShape by lod
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PADDLE_ENFORCE_GT(lod_level, 0, platform::errors::InvalidArgument(
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"Input(X) Tensor of SequencePoolOp "
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"does not contain LoD information."));
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PADDLE_ENFORCE_LE(lod_level, 2UL,
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platform::errors::InvalidArgument(
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"The lod level of input shall be no more than 2."
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"Received lod level is %d.",
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lod_level));
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PADDLE_ENFORCE_GE(
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dims[0],
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/*batch size = */ static_cast<int64_t>(lod[lod_level - 1].size() - 1),
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platform::errors::InvalidArgument(
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"The first dimension of Input(X) must be large than batch size."
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"But received first dimension of Input(X) is %d, while batch"
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"size is %d.",
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dims[0], static_cast<int64_t>(lod[lod_level - 1].size() - 1)));
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if (lod_level > 1UL) {
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PADDLE_ENFORCE_EQ(lod[0][lod[0].size() - 1], lod[1].size() - 1,
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platform::errors::InvalidArgument(
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"The input lod information is illegal."));
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framework::LoD out_lod;
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out_lod.push_back(lod[0]);
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out->set_lod(out_lod);
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}
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dims[0] = lod[lod_level - 1].size() - 1;
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out->Resize({dims});
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out->mutable_data<T>(context.GetPlace());
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Tensor* index = nullptr;
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const bool is_test = context.Attr<bool>("is_test");
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// Do not create index buffer for inference (is_test) mode
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// TODO(jczaja): Skip index buffer creation for other devices eg. GPU
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if (pooltype == "MAX" &&
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(is_test == false ||
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platform::is_cpu_place(context.GetPlace()) == false)) {
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index = context.Output<Tensor>("MaxIndex");
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index->Resize({dims});
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index->mutable_data<int>(context.GetPlace());
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}
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math::SequencePoolFunctor<DeviceContext, T> pool;
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pool(context.template device_context<DeviceContext>(), pooltype, pad_value,
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*in, out, is_test, index);
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}
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};
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template <typename DeviceContext, typename T>
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class SequencePoolGradKernel : public framework::OpKernel<T> {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto* out_g = context.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* in_g = context.Output<LoDTensor>(framework::GradVarName("X"));
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std::string pooltype = context.Attr<std::string>("pooltype");
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const Tensor* index = nullptr;
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if (pooltype == "MAX") {
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index = context.Input<Tensor>("MaxIndex");
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}
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in_g->mutable_data<T>(context.GetPlace());
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math::SequencePoolGradFunctor<DeviceContext, T> pool;
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pool(context.template device_context<DeviceContext>(), pooltype, *out_g,
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in_g, index);
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}
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};
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} // namespace operators
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} // namespace paddle
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