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105 lines
3.6 KiB
105 lines
3.6 KiB
/* Copyright (c) 2018 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 <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/memory/memcpy.h"
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#include "paddle/fluid/operators/math/math_function.h"
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#include "paddle/fluid/operators/math/sequence_padding.h"
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namespace paddle {
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namespace operators {
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using LoDTensor = framework::LoDTensor;
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using LoD = framework::LoD;
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template <typename DeviceContext, typename T>
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class SequenceUnpadOpKernel : 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* x_t = ctx.Input<LoDTensor>("X");
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auto* len_t = ctx.Input<LoDTensor>("Length");
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auto* out_t = ctx.Output<LoDTensor>("Out");
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out_t->mutable_data<T>(ctx.GetPlace());
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const int64_t* seq_len_ptr = nullptr;
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if (platform::is_gpu_place(ctx.GetPlace())) {
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LoDTensor seq_len_cpu;
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seq_len_cpu.Resize(len_t->dims());
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seq_len_ptr = seq_len_cpu.mutable_data<int64_t>(platform::CPUPlace());
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framework::TensorCopy(*len_t, platform::CPUPlace(),
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ctx.template device_context<DeviceContext>(),
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&seq_len_cpu);
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} else {
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seq_len_ptr = len_t->data<int64_t>();
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}
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size_t batch_size = x_t->dims()[0];
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std::vector<size_t> out_lod0(batch_size + 1, 0);
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for (size_t i = 0; i < batch_size; ++i) {
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out_lod0[i + 1] = out_lod0[i] + seq_len_ptr[i];
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}
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framework::LoD out_lod;
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out_lod.push_back(out_lod0);
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out_t->set_lod(out_lod);
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std::vector<int64_t> out_dims_vec{static_cast<int64_t>(out_lod0.back())};
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if (x_t->dims().size() == 2) {
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out_dims_vec.push_back(1);
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} else {
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for (int i = 2; i < x_t->dims().size(); ++i) {
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out_dims_vec.push_back(x_t->dims()[i]);
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}
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}
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out_t->Resize(framework::make_ddim(out_dims_vec));
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int64_t padded_length = x_t->dims()[1];
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math::UnpaddingLoDTensorFunctor<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), *x_t, out_t,
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padded_length, 0, false, math::kBatchLengthWidth);
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}
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};
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template <typename DeviceContext, typename T>
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class SequenceUnpadGradOpKernel : 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* d_x = ctx.Output<LoDTensor>(framework::GradVarName("X"));
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if (d_x) {
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const auto* d_out = ctx.Input<LoDTensor>(framework::GradVarName("Out"));
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const auto* x_t = ctx.Input<LoDTensor>("X");
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d_x->mutable_data<T>(ctx.GetPlace());
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int padded_length = x_t->dims()[1];
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LoDTensor zero_pads;
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zero_pads.Resize({1, 1});
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zero_pads.mutable_data<T>(ctx.GetPlace());
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math::SetConstant<DeviceContext, T> set_zero;
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auto& dev_ctx = ctx.template device_context<DeviceContext>();
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set_zero(dev_ctx, &zero_pads, static_cast<T>(0));
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math::PaddingLoDTensorFunctor<DeviceContext, T>()(
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ctx.template device_context<DeviceContext>(), *d_out, d_x, zero_pads,
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padded_length, 0, false, math::kBatchLengthWidth);
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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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