// Copyright (c) 2020 PaddlePaddle Authors. All Rights Reserved. // // Licensed under the Apache License, Version 2.0 (the "License"); // you may not use this file except in compliance with the License. // You may obtain a copy of the License at // // http://www.apache.org/licenses/LICENSE-2.0 // // Unless required by applicable law or agreed to in writing, software // distributed under the License is distributed on an "AS IS" BASIS, // WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. // See the License for the specific language governing permissions and // limitations under the License. #pragma once #include #include #include // NOLINT #include #include #include // NOLINT #include #include #include #include #include "gflags/gflags.h" #include "paddle/fluid/distributed/common/utils.h" #include "paddle/fluid/distributed/table/depends/initializers.h" #include "paddle/fluid/framework/generator.h" #include "paddle/fluid/framework/lod_tensor.h" #include "paddle/fluid/framework/rw_lock.h" #include "paddle/fluid/framework/selected_rows.h" #include "paddle/fluid/framework/tensor.h" #include "paddle/fluid/framework/threadpool.h" #include "paddle/fluid/framework/variable.h" #include "paddle/fluid/platform/device_context.h" #include "paddle/fluid/platform/enforce.h" #include "paddle/fluid/platform/place.h" #include "paddle/fluid/platform/port.h" #include "paddle/fluid/string/printf.h" #include "paddle/fluid/string/string_helper.h" namespace paddle { namespace distributed { enum Mode { training, infer }; template inline bool entry(const int count, const T threshold); template <> inline bool entry(const int count, const std::string threshold) { return true; } template <> inline bool entry(const int count, const int threshold) { return count >= threshold; } template <> inline bool entry(const int count, const float threshold) { UniformInitializer uniform = UniformInitializer({"0", "0", "1"}); return uniform.GetValue() >= threshold; } struct VALUE { explicit VALUE(size_t length) : length_(length), count_(1), unseen_days_(0), seen_after_last_save_(true), is_entry_(true) { data_.resize(length); } size_t length_; std::vector data_; int count_; int unseen_days_; bool seen_after_last_save_; bool is_entry_; }; class ValueBlock { public: explicit ValueBlock(const std::vector &value_names, const std::vector &value_dims, const std::vector &value_offsets, const std::unordered_map &value_idx, const std::vector &init_attrs, const std::string &entry_attr) : value_names_(value_names), value_dims_(value_dims), value_offsets_(value_offsets), value_idx_(value_idx) { for (int x = 0; x < value_dims.size(); ++x) { value_length_ += value_dims[x]; } // for Entry { if (entry_attr == "none") { has_entry_ = false; entry_func_ = std::bind(entry, std::placeholders::_1, "none"); } else { has_entry_ = true; auto slices = string::split_string(entry_attr, "&"); if (slices[0] == "count_filter") { int threshold = std::stoi(slices[1]); entry_func_ = std::bind(entry, std::placeholders::_1, threshold); } else if (slices[0] == "probability") { float threshold = std::stof(slices[1]); entry_func_ = std::bind(entry, std::placeholders::_1, threshold); } } } // for Initializer { for (auto &attr : init_attrs) { auto slices = string::split_string(attr, "&"); if (slices[0] == "gaussian_random") { initializers_.emplace_back( std::make_shared(slices)); } else if (slices[0] == "fill_constant") { initializers_.emplace_back( std::make_shared(slices)); } else if (slices[0] == "uniform_random") { initializers_.emplace_back( std::make_shared(slices)); } else { PADDLE_THROW(platform::errors::InvalidArgument( "%s can not be supported", attr)); } } } } ~ValueBlock() {} float *Init(const uint64_t &id) { auto value = std::make_shared(value_length_); for (int x = 0; x < value_names_.size(); ++x) { initializers_[x]->GetValue(value->data_.data() + value_offsets_[x], value_dims_[x]); } values_[id] = value; return value->data_.data(); } std::vector Get(const uint64_t &id, const std::vector &value_names) { auto pts = std::vector(); pts.reserve(value_names.size()); auto &values = values_.at(id); for (int i = 0; i < static_cast(value_names.size()); i++) { pts.push_back(values->data_.data() + value_offsets_.at(value_idx_.at(value_names[i]))); } return pts; } float *Get(const uint64_t &id) { auto pts = std::vector *>(); auto &values = values_.at(id); return values->data_.data(); } float *InitFromInitializer(const uint64_t &id) { if (Has(id)) { if (has_entry_) { Update(id); } return Get(id); } return Init(id); } bool GetEntry(const uint64_t &id) { auto value = values_.at(id); return value->is_entry_; } void Update(const uint64_t id) { auto value = values_.at(id); value->unseen_days_ = 0; auto count = ++value->count_; if (!value->is_entry_) { value->is_entry_ = entry_func_(count); } } private: bool Has(const uint64_t id) { auto got = values_.find(id); if (got == values_.end()) { return false; } else { return true; } } public: std::unordered_map> values_; size_t value_length_ = 0; private: const std::vector &value_names_; const std::vector &value_dims_; const std::vector &value_offsets_; const std::unordered_map &value_idx_; bool has_entry_ = false; std::function entry_func_; std::vector> initializers_; }; } // namespace distributed } // namespace paddle