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76 lines
2.5 KiB
76 lines
2.5 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/op_registry.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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template <typename T>
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class LookupTableKernel : public framework::OpKernel {
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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<Tensor>("W"); // float tensor
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auto ids_t = context.Input<Tensor>("Ids"); // int tensor
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auto output_t = context.Output<Tensor>("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<int32_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 (ssize_t i = 0; i < product(ids_t->dims()); ++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 {
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public:
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void Compute(const framework::ExecutionContext& context) const override {
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auto ids_t = context.Input<Tensor>("Ids");
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auto d_output_t = context.Input<Tensor>(framework::GradVarName("Out"));
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auto d_table_t = context.Output<Tensor>(framework::GradVarName("W"));
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int N = d_table_t->dims()[0];
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int D = d_table_t->dims()[1];
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auto ids = ids_t->data<int32_t>();
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const T* d_output = d_output_t->data<T>();
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T* d_table = d_table_t->mutable_data<T>(context.GetPlace());
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auto t = framework::EigenVector<T>::Flatten(*d_table_t);
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t.device(context.GetEigenDevice<platform::CPUPlace>()) =
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t.constant(static_cast<T>(0));
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for (ssize_t i = 0; i < product(ids_t->dims()); ++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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for (int j = 0; j < D; ++j) {
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d_table[ids[i] * D + j] += d_output[i * D + j];
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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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