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111 lines
3.8 KiB
111 lines
3.8 KiB
/* Copyright (c) 2016 PaddlePaddle Authors. All Rights Reserve.
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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/framework/eigen.h"
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#include "paddle/framework/lod_tensor.h"
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#include "paddle/framework/op_registry.h"
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#include "paddle/framework/selected_rows.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 SelectedRows = framework::SelectedRows;
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template <typename T>
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class LookupTableKernel : 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* table_t = context.Input<LoDTensor>("W"); // float tensor
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auto* ids_t = context.Input<LoDTensor>("Ids"); // int tensor
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auto* output_t = context.Output<LoDTensor>("Out"); // float tensor
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int N = table_t->dims()[0];
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int D = table_t->dims()[1];
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auto* ids = ids_t->data<int64_t>();
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auto* table = table_t->data<T>();
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auto* output = output_t->mutable_data<T>(context.GetPlace());
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for (int64_t i = 0; i < ids_t->numel(); ++i) {
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PADDLE_ENFORCE_LT(ids[i], N);
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PADDLE_ENFORCE_GE(ids[i], 0);
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memcpy(output + i * D, table + ids[i] * D, D * sizeof(T));
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}
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}
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};
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template <typename T>
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class LookupTableGradKernel : 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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bool is_sparse = context.Attr<bool>("is_sparse");
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if (is_sparse) {
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auto* ids = context.Input<LoDTensor>("Ids");
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auto* table = context.Input<LoDTensor>("W");
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auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* d_table = context.Output<SelectedRows>(framework::GradVarName("W"));
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auto* ids_data = ids->data<int64_t>();
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auto ids_dim = ids->dims();
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framework::Vector<int64_t> new_rows;
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new_rows.reserve(ids_dim[0]);
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for (int64_t i = 0; i < ids_dim[0]; i++) {
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new_rows.push_back(ids_data[i]);
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}
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d_table->set_rows(new_rows);
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auto* d_table_value = d_table->mutable_value();
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d_table_value->Resize({ids_dim[0], table->dims()[1]});
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d_table_value->mutable_data<T>(context.GetPlace());
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d_table->set_height(table->dims()[0]);
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auto* d_output_data = d_output->data<T>();
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auto* d_table_data = d_table_value->data<T>();
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PADDLE_ENFORCE_EQ(d_table_value->dims(), d_output->dims());
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memcpy(d_table_data, d_output_data, sizeof(T) * d_output->numel());
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} else {
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auto* ids = context.Input<LoDTensor>("Ids");
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auto* d_output = context.Input<LoDTensor>(framework::GradVarName("Out"));
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auto* d_table = context.Output<LoDTensor>(framework::GradVarName("W"));
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auto* table = context.Input<LoDTensor>("W");
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auto* ids_data = ids->data<int64_t>();
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auto ids_dim = ids->dims();
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int N = table->dims()[0];
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int D = d_output->dims()[1];
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auto* d_output_data = d_output->data<T>();
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auto* d_table_data = d_table->mutable_data<T>(context.GetPlace());
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memset(d_table_data, 0, d_table->numel() * sizeof(T));
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for (int64_t i = 0; i < ids->numel(); ++i) {
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PADDLE_ENFORCE_LT(ids_data[i], N);
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PADDLE_ENFORCE_GE(ids_data[i], 0);
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for (int j = 0; j < D; ++j) {
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d_table_data[ids_data[i] * D + j] += d_output_data[i * D + j];
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}
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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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