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219 lines
8.1 KiB
219 lines
8.1 KiB
/* Copyright (c) 2019 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 <vector>
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#include "paddle/fluid/framework/eigen.h"
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#include "paddle/fluid/framework/lod_tensor.h"
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/framework/selected_rows.h"
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#include "paddle/fluid/operators/math/blas.h"
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#ifdef PADDLE_WITH_DISTRIBUTE
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#include "paddle/fluid/operators/distributed/parameter_prefetch.h"
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#endif
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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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using SelectedRows = framework::SelectedRows;
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using DDim = framework::DDim;
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constexpr int64_t kNoPadding = -1;
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template <typename T>
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class LookupTableV2Kernel : 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 *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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auto *table_var = context.InputVar("W");
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auto id_name = context.Inputs("Ids").front();
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auto embedding_name = context.Inputs("W").front();
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auto out_name = context.Outputs("Out").front();
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// for remote prefetch
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auto epmap = context.Attr<std::vector<std::string>>("epmap");
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auto remote_prefetch = context.Attr<bool>("remote_prefetch");
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auto height_sections =
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context.Attr<std::vector<int64_t>>("height_sections");
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auto table_names = context.Attr<std::vector<std::string>>("table_names");
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if (remote_prefetch && !epmap.empty()) {
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// if epmap is not empty, then the parameter will be fetched from remote
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// parameter server
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#ifdef PADDLE_WITH_DISTRIBUTE
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operators::distributed::prefetch(id_name, out_name, embedding_name, false,
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table_names, epmap, height_sections,
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context, context.scope());
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#else
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PADDLE_THROW(
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"paddle is not compiled with distribute support, can not do "
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"parameter prefetch!");
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#endif
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} else {
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int64_t padding_idx = context.Attr<int64_t>("padding_idx");
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int64_t *ids = const_cast<int64_t *>(ids_t->data<int64_t>());
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int64_t ids_numel = ids_t->numel();
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if (table_var->IsType<LoDTensor>()) {
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auto *table_t = context.Input<LoDTensor>("W");
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int64_t row_number = table_t->dims()[0];
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int64_t row_width = table_t->dims()[1];
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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_numel; ++i) {
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if (padding_idx != kNoPadding && ids[i] == padding_idx) {
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memset(output + i * row_width, 0, row_width * sizeof(T));
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} else {
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PADDLE_ENFORCE_LT(
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ids[i], row_number,
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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row_number, ids[i]);
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PADDLE_ENFORCE_GE(
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ids[i], 0,
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input "
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"value.",
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row_number, ids[i]);
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memcpy(output + i * row_width, table + ids[i] * row_width,
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row_width * sizeof(T));
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}
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}
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} else if (table_var->IsType<SelectedRows>()) {
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const auto &table_t = table_var->Get<SelectedRows>();
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int64_t row_width = table_t.value().dims()[1];
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const auto *table = table_t.value().data<T>();
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auto *output = output_t->mutable_data<T>(context.GetPlace());
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auto blas = math::GetBlas<platform::CPUDeviceContext, T>(context);
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for (int64_t i = 0; i < ids_numel; ++i) {
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if (padding_idx != kNoPadding && ids[i] == padding_idx) {
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memset(output + i * row_width, 0, row_width * sizeof(T));
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} else {
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PADDLE_ENFORCE_GE(ids[i], 0);
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auto id_index = table_t.Index(ids[i]);
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PADDLE_ENFORCE_GE(id_index, 0, "the input key should be exists.");
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blas.VCOPY(row_width, table + id_index * row_width,
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output + i * row_width);
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}
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}
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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 LookupTableV2GradKernel : 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_var = context.InputVar("W");
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DDim table_dim;
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if (table_var->IsType<LoDTensor>()) {
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table_dim = context.Input<LoDTensor>("W")->dims();
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} else if (table_var->IsType<SelectedRows>()) {
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auto *table_t = context.Input<SelectedRows>("W");
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table_dim = table_t->value().dims();
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} else {
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PADDLE_THROW(
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"The parameter W of a LookupTableV2 "
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"must be either LoDTensor or SelectedRows");
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}
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int64_t padding_idx = context.Attr<int64_t>("padding_idx");
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bool is_sparse = context.Attr<bool>("is_sparse");
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// Since paddings are not trainable and fixed in forward, the gradient of
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// paddings makes no sense and we don't deal with it in backward.
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if (is_sparse) {
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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<SelectedRows>(framework::GradVarName("W"));
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auto *ids_data = ids->data<int64_t>();
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int64_t ids_num = ids->numel();
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std::vector<int64_t> new_rows;
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new_rows.resize(ids_num);
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std::memcpy(&new_rows[0], ids_data, ids_num * sizeof(int64_t));
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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_num, table_dim[1]});
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d_table_value->mutable_data<T>(context.GetPlace());
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d_table->set_height(table_dim[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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auto d_output_dims = d_output->dims();
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PADDLE_ENFORCE_EQ(
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d_table_value->dims(),
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framework::flatten_to_2d(d_output_dims, d_output_dims.size() - 1));
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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 *ids_data = ids->data<int64_t>();
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int64_t N = table_dim[0];
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int64_t D = table_dim[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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if (padding_idx != kNoPadding && ids_data[i] == padding_idx) {
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// the gradient of padding_idx should be 0, already done by memset, so
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// do nothing.
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} else {
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PADDLE_ENFORCE_LT(
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ids_data[i], N,
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input value.",
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N, ids_data[i]);
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PADDLE_ENFORCE_GE(
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ids_data[i], 0,
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"Variable value (input) of OP(fluid.layers.embedding) "
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"expected >= 0 and < %ld, but got %ld. Please check input value.",
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N, ids_data[i]);
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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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};
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} // namespace operators
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} // namespace paddle
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