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123 lines
4.1 KiB
123 lines
4.1 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 "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/gather.h"
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#include "paddle/fluid/operators/scatter.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 T>
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class SequenceScatterOpKernel : 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 = ctx.Input<Tensor>("X");
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auto* ids = ctx.Input<LoDTensor>("Ids");
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auto* updates = ctx.Input<LoDTensor>("Updates");
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auto* out = ctx.Output<Tensor>("Out");
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auto& ids_lod = ids->lod();
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// Initialize out as same as x
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out->mutable_data<T>(ctx.GetPlace());
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framework::TensorCopySync(*x, ctx.GetPlace(), out);
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auto x_dims = x->dims();
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auto out_dims = out->dims();
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for (int i = 0; i < x_dims.size(); ++i)
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PADDLE_ENFORCE(x_dims[i] == out_dims[i],
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"Input and output shape of "
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"sequence scatter op must exactly be the same.");
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size_t slice_size = 1;
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for (int i = 1; i < x_dims.size(); ++i) slice_size *= x_dims[i];
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auto lod_vec = ids_lod[0];
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unsigned int seg = 0;
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for (int i = 0; i < ids->dims()[0]; ++i) {
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PADDLE_ENFORCE_LT(seg, lod_vec.size() - 1,
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"Segment num must not exceed batch size.\n");
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int lower_bound = lod_vec[seg];
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int upper_bound = lod_vec[seg + 1];
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if (i >= lower_bound && i < upper_bound) {
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T* p_out = out->data<T>();
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const T* p_updates = updates->data<T>();
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const int64_t* p_index = ids->data<int64_t>();
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p_out[seg * slice_size + p_index[i]] += p_updates[i];
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} else {
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++seg;
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--i;
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}
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}
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}
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};
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template <typename T>
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class SequenceScatterGradientOpKernel : 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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PADDLE_ENFORCE(platform::is_cpu_place(ctx.GetPlace()),
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"This kernel only runs on CPU.");
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auto* dX = ctx.Output<Tensor>(framework::GradVarName("X"));
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auto* dUpdates = ctx.Output<LoDTensor>(framework::GradVarName("Updates"));
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auto* ids = ctx.Input<LoDTensor>("Ids");
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auto* dOut = ctx.Input<Tensor>(framework::GradVarName("Out"));
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auto& ids_lod = ids->lod();
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dX->mutable_data<T>(ctx.GetPlace());
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framework::TensorCopySync(*dOut, ctx.GetPlace(), dX);
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dUpdates->mutable_data<T>(ctx.GetPlace());
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auto dx_dims = dX->dims();
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auto dout_dims = dOut->dims();
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for (int i = 0; i < dx_dims.size(); ++i)
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PADDLE_ENFORCE(dx_dims[i] == dout_dims[i],
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"Input and output shape of "
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"sequence scatter grad op must exactly be the same.");
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size_t slice_size = 1;
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for (int i = 1; i < dx_dims.size(); ++i) slice_size *= dx_dims[i];
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auto lod_vec = ids_lod[0];
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unsigned int seg = 0;
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for (int i = 0; i < ids->dims()[0]; ++i) {
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PADDLE_ENFORCE_LT(seg, lod_vec.size() - 1,
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"Segment num must not exceed batch size.\n");
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int lower_bound = lod_vec[seg];
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int upper_bound = lod_vec[seg + 1];
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if (i >= lower_bound && i < upper_bound) {
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const T* p_dOut = dOut->data<T>();
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const int64_t* p_index = ids->data<int64_t>();
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T* p_dUpdates = dUpdates->data<T>();
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p_dUpdates[i] = p_dOut[seg * slice_size + p_index[i]];
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} else {
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++seg;
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--i;
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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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