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126 lines
4.6 KiB
126 lines
4.6 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 <iterator>
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#include <set>
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#include <unordered_map>
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#include <vector>
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#include "paddle/fluid/framework/op_registry.h"
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#include "paddle/fluid/operators/math/selected_rows_functor.h"
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namespace paddle {
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namespace operators {
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template <typename DeviceContext, typename T>
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class SplitIdsOpKernel : 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 place = ctx.GetPlace();
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if (!platform::is_cpu_place(place)) {
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PADDLE_THROW("SplitIds do not support GPU kernel");
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}
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const auto ids_vars = ctx.MultiInputVar("Ids");
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PADDLE_ENFORCE_GT(ids_vars.size(), 0, "The number of Ids should > 0");
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auto *ids_var = ids_vars[0];
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if (ids_var->IsType<framework::LoDTensor>()) {
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int batch_size = 0;
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const auto ids_tensors = ctx.MultiInput<framework::LoDTensor>("Ids");
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for (size_t i = 0; i < ids_tensors.size(); ++i) {
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batch_size += ids_tensors[i]->dims()[0];
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}
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VLOG(4) << "Get Total BatchSize is: " << batch_size;
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std::vector<T> all_ids(batch_size);
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int offset = 0;
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for (size_t i = 0; i < ids_tensors.size(); ++i) {
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const auto *ids = ids_tensors[i];
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std::memcpy(all_ids.data() + offset, ids->data<T>(),
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ids->numel() * sizeof(T));
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offset += ids->numel();
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}
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std::set<T> st(all_ids.begin(), all_ids.end());
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all_ids.assign(st.begin(), st.end());
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auto outs = ctx.MultiOutput<framework::LoDTensor>("Out");
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const size_t shard_num = outs.size();
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std::vector<std::vector<T>> out_ids;
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out_ids.resize(outs.size());
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// split id by their shard_num.
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for (size_t i = 0; i < all_ids.size(); ++i) {
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T id = all_ids[i];
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size_t shard_id = static_cast<size_t>(id) % shard_num;
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out_ids[shard_id].push_back(id);
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}
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// create tensor for each shard and send to parameter server
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for (size_t i = 0; i < out_ids.size(); ++i) {
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auto *shard_t = outs[i];
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std::vector<T> ids = out_ids[i];
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auto *shard_data = shard_t->mutable_data<T>(
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framework::make_ddim({static_cast<int64_t>(ids.size()), 1}), place);
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for (size_t i = 0; i < ids.size(); ++i) {
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shard_data[i] = ids[i];
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}
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}
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} else if (ids_var->IsType<framework::SelectedRows>()) {
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const auto *ids_selected_rows = ctx.Input<framework::SelectedRows>("Ids");
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auto &ids_dims = ids_selected_rows->value().dims();
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PADDLE_ENFORCE_EQ(ids_dims[0],
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static_cast<int64_t>(ids_selected_rows->rows().size()),
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"");
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const T *ids_data = ids_selected_rows->value().data<T>();
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const auto &ids_rows = ids_selected_rows->rows();
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auto outs = ctx.MultiOutput<framework::SelectedRows>("Out");
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const size_t shard_num = outs.size();
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for (auto &out : outs) {
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out->mutable_rows()->clear();
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}
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// get rows for outputs
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std::unordered_map<int64_t, size_t> id_to_index;
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for (size_t i = 0; i < ids_rows.size(); ++i) {
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id_to_index[ids_rows[i]] = i;
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size_t shard_id = static_cast<size_t>(ids_rows[i]) % shard_num;
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outs[shard_id]->mutable_rows()->push_back(ids_rows[i]);
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}
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int64_t row_width = ids_dims[1];
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for (auto &out : outs) {
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out->set_height(ids_selected_rows->height());
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framework::DDim ddim = framework::make_ddim(
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{static_cast<int64_t>(out->rows().size()), row_width});
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T *output = out->mutable_value()->mutable_data<T>(ddim, place);
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for (int64_t i = 0; i < ddim[0]; ++i) {
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memcpy(output + i * row_width,
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ids_data + id_to_index[out->rows()[i]] * row_width,
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row_width * sizeof(T));
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}
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}
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} else {
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PADDLE_THROW(
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"% should be LoDTensor or SelectedRows, but the received type is %s",
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ctx.Inputs("Ids")[0], framework::ToTypeName(ids_var->Type()));
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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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