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未验证 提交 5ed713d5 编写于 作者: G guru4elephant 提交者： GitHub
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remove ctr reader, all functions are satisfied in dataset (#18672) 

* remove ctr reader, all functions are satisfied in dataset
上级 898237c1
隐藏空白更改
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Showing with 0 addition and 989 deletion
paddle/fluid/API.spec
浏览文件 @ 5ed713d5
......@@ -451,7 +451,6 @@ paddle.fluid.contrib.QuantizeTranspiler.convert_to_int8 (ArgSpec(args=['self', '
paddle.fluid.contrib.QuantizeTranspiler.freeze_program (ArgSpec(args=['self', 'program', 'place', 'fuse_bn', 'scope'], varargs=None, keywords=None, defaults=(False, None)), ('document', '909675a1ab055c69b436a7893fcae4fd'))
paddle.fluid.contrib.QuantizeTranspiler.training_transpile (ArgSpec(args=['self', 'program', 'startup_program'], varargs=None, keywords=None, defaults=(None, None)), ('document', '6dd9909f10b283ba2892a99058a72884'))
paddle.fluid.contrib.distributed_batch_reader (ArgSpec(args=['batch_reader'], varargs=None, keywords=None, defaults=None), ('document', 'b60796eb0a481484dd34e345f0eaa4d5'))
paddle.fluid.contrib.reader.ctr_reader.ctr_reader (ArgSpec(args=['feed_dict', 'file_type', 'file_format', 'dense_slot_index', 'sparse_slot_index', 'capacity', 'thread_num', 'batch_size', 'file_list', 'slots', 'name'], varargs=None, keywords=None, defaults=(None,)), ('document', 'b2ebf3de2a6ef1af2c3b88d2db7591ab'))
paddle.fluid.contrib.Compressor ('paddle.fluid.contrib.slim.core.compressor.Compressor', ('document', 'a5417774a94aa9ae5560a42b96527e7d'))
paddle.fluid.contrib.Compressor.__init__ (ArgSpec(args=['self', 'place', 'scope', 'train_program', 'train_reader', 'train_feed_list', 'train_fetch_list', 'eval_program', 'eval_reader', 'eval_feed_list', 'eval_fetch_list', 'teacher_programs', 'checkpoint_path', 'train_optimizer', 'distiller_optimizer', 'search_space'], varargs=None, keywords=None, defaults=(None, None, None, None, None, None, None, [], None, None, None, None)), ('document', 'c195b3bba26169cff9439e8c467557c0'))
paddle.fluid.contrib.Compressor.config (ArgSpec(args=['self', 'config_file'], varargs=None, keywords=None, defaults=None), ('document', '780d9c007276ccbb95b292400d7807b0'))
......
paddle/fluid/operators/reader/CMakeLists.txt
浏览文件 @ 5ed713d5
......@@ -30,12 +30,6 @@ reader_library(create_multi_pass_reader_op SRCS create_multi_pass_reader_op.cc)
reader_library(create_custom_reader_op SRCS create_custom_reader_op.cc)
reader_library(create_py_reader_op SRCS create_py_reader_op.cc DEPS py_reader)
if (NOT WIN32 AND NOT ON_INFER)
cc_library(ctr_reader SRCS ctr_reader.cc DEPS gzstream reader zlib)
cc_test(ctr_reader_test SRCS ctr_reader_test.cc DEPS ctr_reader)
reader_library(create_ctr_reader_op SRCS create_ctr_reader_op.cc DEPS ctr_reader)
endif ()
cc_test(reader_blocking_queue_test SRCS reader_blocking_queue_test.cc)
# Export local libraries to parent
# set(READER_LIBRARY ${LOCAL_READER_LIBS} PARENT_SCOPE)
......
paddle/fluid/operators/reader/ctr_reader.cc 已删除 100644 → 0
浏览文件 @ 898237c1
// Copyright (c) 2018 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.
#include "paddle/fluid/operators/reader/ctr_reader.h"
#include <gzstream.h>
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <sstream>
#include <string>
#include <unordered_map>
#include <algorithm>
#include <random>
namespace paddle {
namespace operators {
namespace reader {
static inline void string_split(const std::string& s, const char delimiter,
std::vector<std::string>* output) {
if (s.empty()) return;
size_t start = 0;
size_t end = s.find(delimiter);
while (end != std::string::npos) {
if (end > start) output->emplace_back(s.substr(start, end - start));
start = end + 1;
end = s.find(delimiter, start);
}
auto term = s.substr(start);
if (!term.empty()) output->emplace_back(term);
}
static inline void parse_line(
const std::string& line,
const std::unordered_map<std::string, size_t>& slot_to_index,
int64_t* label,
std::unordered_map<std::string, std::vector<int64_t>>* slot_to_data) {
std::vector<std::string> ret;
string_split(line, ' ', &ret);
*label = std::stoi(ret[0]) > 0;
for (size_t i = 1; i < ret.size(); ++i) {
const std::string& item = ret[i];
std::vector<std::string> feasign_and_slot;
string_split(item, ':', &feasign_and_slot);
if (feasign_and_slot.size() == 2 &&
slot_to_index.find(feasign_and_slot[1]) != slot_to_index.end()) {
int64_t feasign = std::strtoll(feasign_and_slot[0].c_str(), NULL, 10);
(*slot_to_data)[feasign_and_slot[1]].push_back(feasign);
}
}
// NOTE:: if the slot has no value, then fill [0] as it's data.
for (auto& item : slot_to_index) {
if (slot_to_data->find(item.first) == slot_to_data->end()) {
(*slot_to_data)[item.first].push_back(0);
}
}
}
// label slot1:fea_sign slot2:fea_sign slot1:fea_sign
static inline void parse_svm_line(const std::string& line) {}
class Reader {
public:
virtual ~Reader() {}
virtual bool HasNext() = 0;
virtual void NextLine(std::string* line) = 0;
};
class GzipReader : public Reader {
public:
explicit GzipReader(const std::string& file_name)
: gzstream_(file_name.c_str()) {}
~GzipReader() {}
bool HasNext() override { return gzstream_.peek() != EOF; }
void NextLine(std::string* line) override { std::getline(gzstream_, *line); }
private:
igzstream gzstream_;
};
class PlainFileReader : public Reader {
public:
explicit PlainFileReader(const std::string& file_name)
: stream_(file_name.c_str()) {}
~PlainFileReader() {}
bool HasNext() override { return stream_.peek() != EOF; }
void NextLine(std::string* line) override { std::getline(stream_, *line); }
private:
std::ifstream stream_;
};
template <typename SingleFileReader>
class MultiFileReader : public Reader {
public:
explicit MultiFileReader(const std::vector<std::string>& file_list) {
for (auto& file : file_list) {
readers_.emplace_back(std::make_shared<SingleFileReader>(file));
}
}
bool HasNext() override {
if (current_reader_index_ >= readers_.size()) {
return false;
}
if (!readers_[current_reader_index_]->HasNext()) {
current_reader_index_++;
return HasNext();
}
return true;
}
void NextLine(std::string* line) override {
readers_[current_reader_index_]->NextLine(line);
}
private:
std::vector<std::shared_ptr<SingleFileReader>> readers_;
size_t current_reader_index_ = 0;
};
void MonitorThread(std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
VLOG(3) << "monitor thread in";
bool reader_thread_is_running = true;
while (reader_thread_is_running) {
VLOG(3) << "reader_thread_is_running";
reader_thread_is_running = false;
for (size_t i = 0; i < (*thread_status).size(); ++i) {
if ((*thread_status)[i] == Running) {
VLOG(3) << "reader is running!";
reader_thread_is_running = true;
}
}
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
}
VLOG(3) << "all reader thread is stopped, close the queue";
queue->Close();
VLOG(3) << "monitor thread exited";
}
void ReadSvmData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
std::unordered_map<std::string, size_t> slot_to_index;
for (size_t i = 0; i < data_desc.sparse_slot_ids_.size(); ++i) {
slot_to_index[data_desc.sparse_slot_ids_[i]] = i;
}
std::string line;
std::vector<std::unordered_map<std::string, std::vector<int64_t>>> batch_data;
std::vector<int64_t> batch_label;
while (reader->HasNext()) {
batch_data.clear();
batch_data.reserve(data_desc.batch_size_);
batch_label.clear();
batch_label.reserve(data_desc.batch_size_);
// read batch_size data
for (int i = 0; i < data_desc.batch_size_; ++i) {
if (reader->HasNext()) {
reader->NextLine(&line);
std::unordered_map<std::string, std::vector<int64_t>> slot_to_data;
int64_t label;
parse_line(line, slot_to_index, &label, &slot_to_data);
batch_data.push_back(slot_to_data);
batch_label.push_back(label);
} else {
break;
}
}
std::vector<framework::LoDTensor> lod_datas;
// first insert tensor for each sparse_slots
for (auto& slot : data_desc.sparse_slot_ids_) {
std::vector<size_t> lod_data{0};
std::vector<int64_t> batch_feasign;
for (size_t i = 0; i < batch_data.size(); ++i) {
auto& feasign = batch_data[i][slot];
lod_data.push_back(lod_data.back() + feasign.size());
batch_feasign.insert(batch_feasign.end(), feasign.begin(),
feasign.end());
}
framework::LoDTensor lod_tensor;
framework::LoD lod{lod_data};
lod_tensor.set_lod(lod);
int64_t* tensor_data = lod_tensor.mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(batch_feasign.size()), 1}),
platform::CPUPlace());
memcpy(tensor_data, batch_feasign.data(),
batch_feasign.size() * sizeof(int64_t));
lod_datas.push_back(lod_tensor);
}
// insert label tensor
framework::LoDTensor label_tensor;
auto* label_tensor_data = label_tensor.mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(batch_label.size()), 1}),
platform::CPUPlace());
memcpy(label_tensor_data, batch_label.data(),
batch_label.size() * sizeof(int64_t));
lod_datas.push_back(label_tensor);
queue->Push(lod_datas);
VLOG(4) << "push one data, queue_size=" << queue->Size();
}
}
// label dense_fea,dense_fea sparse_fea,sparse_fea
static inline void parse_csv_line(
const std::string& line, const DataDesc& data_desc, int64_t* label,
std::vector<std::vector<float>>* dense_datas,
std::vector<std::vector<int64_t>>* sparse_datas) {
std::vector<std::string> ret;
string_split(line, ' ', &ret);
*label = std::stol(ret[0]);
dense_datas->resize(data_desc.dense_slot_index_.size());
for (size_t i = 0; i < data_desc.dense_slot_index_.size(); ++i) {
int slot_idx = data_desc.dense_slot_index_[i];
auto& slot_data = ret[slot_idx];
std::vector<std::string> data_in_slot_str;
string_split(slot_data, ',', &data_in_slot_str);
std::vector<float> data_in_slot;
for (auto& data_str : data_in_slot_str) {
(*dense_datas)[i].push_back(std::stof(data_str));
}
}
sparse_datas->resize(data_desc.sparse_slot_index_.size());
for (size_t i = 0; i < data_desc.sparse_slot_index_.size(); ++i) {
int slot_idx = data_desc.sparse_slot_index_[i];
auto& slot_data = ret[slot_idx];
std::vector<std::string> data_in_slot_str;
string_split(slot_data, ',', &data_in_slot_str);
std::vector<int64_t> data_in_slot;
for (auto& data_str : data_in_slot_str) {
auto id = std::stol(data_str);
(*sparse_datas)[i].push_back(id);
}
}
}
void ReadCsvData(const DataDesc& data_desc, std::shared_ptr<Reader> reader,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
std::string line;
while (reader->HasNext()) {
std::vector<int64_t> batch_label;
batch_label.reserve(data_desc.batch_size_);
std::vector<std::vector<std::vector<float>>> batch_dense_data;
batch_dense_data.reserve(data_desc.batch_size_);
std::vector<std::vector<std::vector<int64_t>>> batch_sparse_data;
batch_sparse_data.reserve(data_desc.batch_size_);
// read batch_size data
for (int i = 0; i < data_desc.batch_size_; ++i) {
if (reader->HasNext()) {
reader->NextLine(&line);
int64_t label;
std::vector<std::vector<float>> dense_datas;
std::vector<std::vector<int64_t>> sparse_datas;
parse_csv_line(line, data_desc, &label, &dense_datas, &sparse_datas);
batch_label.push_back(label);
if (!batch_dense_data.empty()) {
PADDLE_ENFORCE_EQ(batch_dense_data[0].size(), dense_datas.size(),
"dense data should have the same shape");
}
batch_dense_data.push_back(dense_datas);
batch_sparse_data.push_back(sparse_datas);
} else {
break;
}
}
// the order of output data is label, dense_datas, sparse_datas
std::vector<framework::LoDTensor> lod_datas;
// insert label tensor
framework::LoDTensor label_tensor;
auto* label_tensor_data = label_tensor.mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(batch_label.size()), 1}),
platform::CPUPlace());
memcpy(label_tensor_data, batch_label.data(),
batch_label.size() * sizeof(int64_t));
lod_datas.push_back(label_tensor);
// insert tensor for each dense_slots
for (size_t i = 0; i < data_desc.dense_slot_index_.size(); ++i) {
framework::LoDTensor lod_tensor;
size_t width = batch_dense_data[0][i].size();
auto* tensor_data = lod_tensor.mutable_data<float>(
framework::make_ddim(
{static_cast<int64_t>(batch_dense_data.size()), // batch_size
static_cast<int64_t>(width)}),
platform::CPUPlace());
for (size_t j = 0; j < batch_dense_data.size(); ++j) {
auto& dense_data_row = batch_dense_data[j][i];
memcpy(tensor_data + j * width, dense_data_row.data(),
width * sizeof(float));
}
lod_datas.push_back(lod_tensor);
}
// insert tensor for each sparse_slots
for (size_t i = 0; i < data_desc.sparse_slot_index_.size(); ++i) {
std::vector<size_t> lod_data{0};
std::vector<int64_t> batch_feasign;
for (size_t row_idx = 0; row_idx < batch_sparse_data.size(); ++row_idx) {
auto& sparse_ids = batch_sparse_data[row_idx][i];
lod_data.push_back(lod_data.back() + sparse_ids.size());
batch_feasign.insert(batch_feasign.end(), sparse_ids.begin(),
sparse_ids.end());
}
framework::LoDTensor lod_tensor;
framework::LoD lod{lod_data};
lod_tensor.set_lod(lod);
int64_t* tensor_data = lod_tensor.mutable_data<int64_t>(
framework::make_ddim({static_cast<int64_t>(batch_feasign.size()), 1}),
platform::CPUPlace());
memcpy(tensor_data, batch_feasign.data(),
batch_feasign.size() * sizeof(int64_t));
lod_datas.push_back(lod_tensor);
}
queue->Push(lod_datas);
VLOG(4) << "push one data, queue_size=" << queue->Size();
}
}
void ReadThread(const std::vector<std::string>& file_list,
const DataDesc& data_desc, int thread_id,
std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
VLOG(3) << "[" << thread_id << "]"
<< " reader thread start! thread_id = " << thread_id;
for (auto& file : file_list) {
VLOG(3) << "[" << thread_id << "]"
<< " file " << file;
}
(*thread_status)[thread_id] = Running;
VLOG(3) << "set status to running";
std::shared_ptr<Reader> reader;
if (data_desc.file_type_ == "gzip") {
reader.reset(new MultiFileReader<GzipReader>(file_list));
} else if (data_desc.file_type_ == "plain") {
reader.reset(new MultiFileReader<PlainFileReader>(file_list));
} else {
PADDLE_THROW("do not support file format %s", data_desc.file_type_);
}
VLOG(3) << "reader inited";
if (data_desc.file_format_ == "svm") {
ReadSvmData(data_desc, reader, queue);
} else if (data_desc.file_format_ == "csv") {
ReadCsvData(data_desc, reader, queue);
}
(*thread_status)[thread_id] = Stopped;
VLOG(3) << "set status to stopped, thread " << thread_id << " exited";
}
} // namespace reader
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reader/ctr_reader.h 已删除 100644 → 0
浏览文件 @ 898237c1
// Copyright (c) 2018 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 <sys/time.h>
#include <algorithm>
#include <chrono> // NOLINT
#include <cstdlib>
#include <fstream>
#include <iostream>
#include <memory>
#include <sstream>
#include <string>
#include <unordered_map>
#include <vector>
#include "paddle/fluid/framework/reader.h"
#include "paddle/fluid/framework/threadpool.h"
#include "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
namespace paddle {
namespace operators {
namespace reader {
enum ReaderThreadStatus { Running, Stopped };
struct DataDesc {
DataDesc(int batch_size, const std::vector<std::string>& file_names,
const std::string& file_type, const std::string& file_format,
const std::vector<int>& dense_slot_index,
const std::vector<int>& sparse_slot_index,
const std::vector<std::string>& sparse_slot_ids)
: batch_size_(batch_size),
file_names_(file_names),
file_type_(file_type),
file_format_(file_format),
dense_slot_index_(dense_slot_index),
sparse_slot_index_(sparse_slot_index),
sparse_slot_ids_(sparse_slot_ids) {}
const int batch_size_;
const std::vector<std::string> file_names_;
const std::string file_type_; // gzip or plain
const std::string file_format_; // csv or svm
// used for csv data format
const std::vector<int> dense_slot_index_;
const std::vector<int> sparse_slot_index_;
// used for svm data format
const std::vector<std::string> sparse_slot_ids_;
};
inline std::ostream& operator<<(std::ostream& os, const DataDesc& data_desc) {
os << "data_desc:\n";
os << "\tbatch_size -> " << data_desc.batch_size_ << "\n";
os << "\tfile_type -> " << data_desc.file_type_ << "\n";
os << "\tfile_format -> " << data_desc.file_format_ << "\n";
os << "\tfile_names -> {";
for (auto& file_name : data_desc.file_names_) {
os << file_name << ",";
}
os << "}\n";
os << "\tdense_slot_index -> {";
for (auto& slot : data_desc.dense_slot_index_) {
os << slot << ",";
}
os << "}\n";
os << "\tsparse_slot_index_ -> {";
for (auto& slot : data_desc.sparse_slot_index_) {
os << slot << ",";
}
os << "}\n";
os << "\tsparse_slot_ids_ -> {";
for (auto& slot : data_desc.sparse_slot_ids_) {
os << slot << ",";
}
os << "}\n";
return os;
}
void ReadThread(const std::vector<std::string>& file_list,
const DataDesc& data_desc, int thread_id,
std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue);
// monitor all running thread, if they are all stopped,
// then push an empty data into LoDTensorBlockingQueue
void MonitorThread(std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue);
class CTRReader : public framework::FileReader {
public:
CTRReader(const std::shared_ptr<LoDTensorBlockingQueue>& queue,
int thread_num, const DataDesc& data_desc)
: data_desc_(data_desc) {
PADDLE_ENFORCE_GT(thread_num, 0, "thread num should be larger then 0!");
PADDLE_ENFORCE(queue != nullptr, "LoDTensorBlockingQueue must not be null");
PADDLE_ENFORCE_GT(data_desc_.file_names_.size(), 0,
"file list should not be empty");
thread_num_ = std::min<size_t>(data_desc_.file_names_.size(), thread_num);
queue_ = queue;
SplitFiles();
for (size_t i = 0; i < thread_num_; ++i) {
read_thread_status_.push_back(Stopped);
}
}
~CTRReader() { Shutdown(); }
void ReadNext(std::vector<framework::LoDTensor>* out) override {
bool success;
*out = queue_->Pop(&success);
if (!success) out->clear();
}
void Shutdown() override {
VLOG(3) << "Shutdown reader";
if (status_ == ReaderStatus::kStopped) {
return;
}
// shutdown should stop all the reader thread
for (auto& read_thread : read_threads_) {
read_thread->join();
}
if (monitor_thread_) {
monitor_thread_->join();
}
read_threads_.clear();
monitor_thread_.reset(nullptr);
queue_->Close();
status_ = ReaderStatus::kStopped;
}
void Start() override {
VLOG(3) << "Start reader";
PADDLE_ENFORCE_EQ(read_threads_.size(), 0, "read thread should be empty!");
queue_->ReOpen();
VLOG(3) << "reopen success";
VLOG(3) << "thread_num " << thread_num_;
for (size_t thread_id = 0; thread_id < thread_num_; thread_id++) {
read_threads_.emplace_back(new std::thread(std::bind(
&ReadThread, file_groups_[thread_id], data_desc_,
static_cast<int>(thread_id), &read_thread_status_, queue_)));
}
monitor_thread_.reset(new std::thread(
std::bind(&MonitorThread, &read_thread_status_, queue_)));
status_ = ReaderStatus::kRunning;
}
private:
void SplitFiles() {
file_groups_.resize(thread_num_);
for (size_t i = 0; i < data_desc_.file_names_.size(); ++i) {
auto& file_name = data_desc_.file_names_[i];
std::ifstream f(file_name.c_str());
PADDLE_ENFORCE(f.good(), "file %s not exist!", file_name);
file_groups_[i % thread_num_].push_back(file_name);
}
}
private:
size_t thread_num_;
const DataDesc data_desc_;
std::shared_ptr<LoDTensorBlockingQueue> queue_;
std::vector<std::unique_ptr<std::thread>> read_threads_;
std::unique_ptr<std::thread> monitor_thread_;
std::vector<ReaderThreadStatus> read_thread_status_;
std::vector<std::vector<std::string>> file_groups_;
};
} // namespace reader
} // namespace operators
} // namespace paddle
paddle/fluid/operators/reader/ctr_reader_test.cc 已删除 100644 → 0
浏览文件 @ 898237c1
// Copyright (c) 2018 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.
#include "paddle/fluid/operators/reader/ctr_reader.h"
#include <gzstream.h>
#include <time.h>
#include <math.h>
#include <stdio.h>
#include <cstring>
#include <fstream>
#include <tuple>
#include "gtest/gtest.h"
#include "paddle/fluid/framework/lod_tensor.h"
#include "paddle/fluid/operators/reader/blocking_queue.h"
using paddle::operators::reader::LoDTensorBlockingQueue;
using paddle::operators::reader::LoDTensorBlockingQueueHolder;
using paddle::operators::reader::CTRReader;
using paddle::framework::LoDTensor;
using paddle::framework::LoD;
using paddle::framework::DDim;
using paddle::platform::CPUPlace;
using paddle::framework::make_ddim;
using paddle::operators::reader::DataDesc;
static void generatedata(const std::vector<std::string>& data,
const std::string& file_name) {
std::ifstream in(file_name.c_str());
if (in.good()) {
VLOG(3) << "file " << file_name << " exist, delete it first!";
remove(file_name.c_str());
} else {
in.close();
}
ogzstream out(file_name.c_str());
PADDLE_ENFORCE(out.good(), "open file %s failed!", file_name);
for (auto& c : data) {
out << c;
}
out.close();
PADDLE_ENFORCE(out.good(), "save file %s failed!", file_name);
}
static inline void check_all_data(
const std::vector<std::string>& ctr_data,
const std::vector<std::string>& slots, const std::vector<DDim>& label_dims,
const std::vector<int64_t>& label_value,
const std::vector<std::tuple<LoD, std::vector<int64_t>>>& data_slot_6002,
const std::vector<std::tuple<LoD, std::vector<int64_t>>>& data_slot_6003,
size_t batch_num, size_t batch_size,
std::shared_ptr<LoDTensorBlockingQueue> queue, CTRReader* reader) {
std::vector<LoDTensor> out;
for (size_t i = 0; i < batch_num; ++i) {
reader->ReadNext(&out);
ASSERT_EQ(out.size(), slots.size() + 1);
auto& label_tensor = out.back();
ASSERT_EQ(label_tensor.dims(), label_dims[i]);
for (size_t j = 0; j < batch_size && i * batch_num + j < ctr_data.size();
++j) {
auto& label = label_tensor.data<int64_t>()[j];
ASSERT_TRUE(label == 0 || label == 1);
ASSERT_EQ(label, label_value[i * batch_size + j]);
}
auto& tensor_6002 = out[0];
ASSERT_EQ(std::get<0>(data_slot_6002[i]), tensor_6002.lod());
ASSERT_EQ(std::memcmp(std::get<1>(data_slot_6002[i]).data(),
tensor_6002.data<int64_t>(),
tensor_6002.dims()[1] * sizeof(int64_t)),
0);
}
reader->ReadNext(&out);
ASSERT_EQ(out.size(), 0);
ASSERT_EQ(queue->Size(), 0);
}
TEST(CTR_READER, read_data) {
const std::vector<std::string> ctr_data = {
"0 0:6002 1:6003 2:6004 3:6005 4:6006 \n",
"0 5:6003 6:6003 7:6003 8:6004 9:6004 \n",
"1 10:6002 11:6002 12:6002 13:6002 14:6002 \n",
"0 15:6003 16:6003 17:6003 18:6003 19:6004 \n",
"1 20:6001 21:6001 22:6001 23:6001 24:6001 \n",
"1 25:6004 26:6004 27:6004 28:6005 29:6005 \n",
"0 30:6002 31:6003 32:6004 33:6004 34:6005 \n",
"1 35:6003 36:6003 37:6005 38:6005 39:6005 \n",
"1 40:6002 41:6003 42:6004 43:6004 44:6005 \n",
"1 46:6006 45:6006 47:6003 48:6003 49:6003 \n",
};
std::string gz_file_name = "test_ctr_reader_data.gz";
generatedata(ctr_data, gz_file_name);
std::vector<int64_t> label_value = {0, 0, 1, 0, 1, 1, 0, 1, 1, 1};
std::tuple<LoD, std::vector<int64_t>> a1({{0, 1, 2, 7}},
{0, 0, 10, 11, 12, 13, 14});
std::tuple<LoD, std::vector<int64_t>> a2({{0, 1, 2, 3}}, {0, 0, 0});
std::tuple<LoD, std::vector<int64_t>> a3({{0, 1, 2, 3}}, {30, 0, 40});
std::tuple<LoD, std::vector<int64_t>> a4({{0, 1}}, {0});
std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6002{a1, a2, a3,
a4};
std::tuple<LoD, std::vector<int64_t>> b1({{0, 1, 4, 5}}, {1, 5, 6, 7, 0});
std::tuple<LoD, std::vector<int64_t>> b2({{0, 4, 5, 6}},
{15, 16, 17, 18, 0, 0});
std::tuple<LoD, std::vector<int64_t>> b3({{0, 1, 3, 4}}, {31, 35, 36, 41});
std::tuple<LoD, std::vector<int64_t>> b4({{0, 3}}, {47, 48, 49});
std::vector<std::tuple<LoD, std::vector<int64_t>>> data_slot_6003{b1, b2, b3,
b4};
std::vector<DDim> label_dims = {{3, 1}, {3, 1}, {3, 1}, {1, 1}};
LoDTensorBlockingQueueHolder queue_holder;
int capacity = 64;
queue_holder.InitOnce(capacity, false);
std::shared_ptr<LoDTensorBlockingQueue> queue = queue_holder.GetQueue();
int batch_size = 3;
int thread_num = 1;
std::vector<std::string> sparse_slots = {"6002", "6003"};
std::vector<std::string> file_list;
for (int i = 0; i < thread_num; ++i) {
file_list.push_back(gz_file_name);
}
DataDesc data_desc(batch_size, file_list, "gzip", "svm", {}, {},
sparse_slots);
CTRReader reader(queue, thread_num, data_desc);
reader.Start();
size_t batch_num =
std::ceil(static_cast<float>(ctr_data.size()) / batch_size) * thread_num;
check_all_data(ctr_data, sparse_slots, label_dims, label_value,
data_slot_6002, data_slot_6003, batch_num, batch_size, queue,
&reader);
reader.Shutdown();
reader.Start();
check_all_data(ctr_data, sparse_slots, label_dims, label_value,
data_slot_6002, data_slot_6003, batch_num, batch_size, queue,
&reader);
reader.Shutdown();
}
static void GenereteCsvData(const std::string& file_name,
const std::vector<std::string>& data) {
std::ofstream out(file_name.c_str());
PADDLE_ENFORCE(out.good(), "open file %s failed!", file_name);
for (auto& c : data) {
out << c;
}
out.close();
PADDLE_ENFORCE(out.good(), "save file %s failed!", file_name);
}
static void CheckReadCsvOut(const std::vector<LoDTensor>& out) {
ASSERT_EQ(out.size(), 3);
ASSERT_EQ(out[0].dims()[1], 1);
ASSERT_EQ(out[1].dims()[1], 2);
ASSERT_EQ(out[2].dims()[1], 1);
for (size_t i = 0; i < out[0].numel(); ++i) {
int64_t label = out[0].data<int64_t>()[i];
auto& dense_dim = out[1].dims();
for (size_t j = 0; j < dense_dim[1]; ++j) {
ASSERT_EQ(out[1].data<float>()[i * dense_dim[1] + j],
static_cast<float>(label + 0.1));
}
auto& sparse_lod = out[2].lod();
for (size_t j = sparse_lod[0][i]; j < sparse_lod[0][i + 1]; ++j) {
ASSERT_EQ(out[2].data<int64_t>()[j], label);
}
}
}
TEST(CTR_READER, read_csv_data) {
std::string file_name = "test_ctr_reader_data.csv";
const std::vector<std::string> csv_data = {
"0 0.1,0.1 0,0,0,0\n", "1 1.1,1.1 1,1,1,1\n", "2 2.1,2.1 2,2,2,2\n",
"3 3.1,3.1 3,3,3,3\n",
};
GenereteCsvData(file_name, csv_data);
LoDTensorBlockingQueueHolder queue_holder;
int capacity = 64;
queue_holder.InitOnce(capacity, false);
std::shared_ptr<LoDTensorBlockingQueue> queue = queue_holder.GetQueue();
int batch_size = 3;
int thread_num = 1;
std::vector<std::string> file_list;
for (int i = 0; i < thread_num; ++i) {
file_list.push_back(file_name);
}
DataDesc data_desc(batch_size, file_list, "plain", "csv", {1}, {2}, {});
CTRReader reader(queue, thread_num, data_desc);
for (size_t i = 0; i < 2; ++i) {
reader.Start();
std::vector<LoDTensor> out;
while (true) {
reader.ReadNext(&out);
if (out.empty()) {
break;
}
CheckReadCsvOut(out);
}
reader.Shutdown();
}
}
python/paddle/fluid/contrib/reader/__init__.py
浏览文件 @ 5ed713d5
......@@ -14,9 +14,7 @@
from __future__ import print_function
from . import ctr_reader
from .distributed_reader import *
__all__ = []
__all__ += distributed_reader.__all__
__all__ += ctr_reader.__all__
python/paddle/fluid/contrib/reader/ctr_reader.py 已删除 100644 → 0
浏览文件 @ 898237c1
# Copyright (c) 2018 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.
from __future__ import print_function
from paddle.fluid import core
from paddle.fluid.executor import global_scope
from paddle.fluid.framework import default_main_program, \
default_startup_program, Variable
from paddle.fluid.unique_name import generate as unique_name
__all__ = ['ctr_reader']
def monkey_patch_reader_methods(reader):
def __get_reader__():
scope = global_scope()
var = scope.find_var(reader.name)
return var.get_reader()
def reset():
return __get_reader__().reset()
def start():
return __get_reader__().start()
reader.reset = reset
reader.start = start
reader.stop_gradient = True
reader.persistable = True
return reader
def _copy_reader_var_(block, var):
new_var = block.create_var(name=var.name, type=core.VarDesc.VarType.READER)
new_var.desc.set_shapes(var.desc.shapes())
new_var.desc.set_dtypes(var.desc.dtypes())
new_var.persistable = True
return new_var
def ctr_reader(
feed_dict,
file_type, # gzip or plain
file_format, # csv or svm
dense_slot_index,
sparse_slot_index,
capacity,
thread_num,
batch_size,
file_list,
slots,
name=None):
"""
Create a CTR reader for data feeding in Python
This layer returns a Reader Variable.
The Reader provides :code:`decorate_paddle_reader()` and
:code:`decorate_tensor_provider()` to set a Python generator as the data
source in Python side. When :code:`Executor::Run()` is invoked in C++
side, the data from the generator would be read automatically. Unlike
:code:`DataFeeder.feed()`, the data reading process and
:code:`Executor::Run()` process can run in parallel using
:code:`py_reader`. The :code:`start()` method of the Reader should be
called when each pass begins, while the :code:`reset()` method should be
called when the pass ends and :code:`fluid.core.EOFException` raises.
Note that :code:`Program.clone()` method cannot clone :code:`py_reader`.
Args:
feed_dict(list(variable)): a list of data variable.
file_type('gzip'|'plain'): the type of the data file
file_format('csv'|'svm'): csv data or svm data format.
cvs data format is :
label dense_fea,dense_fea sparse_fea,sparse_fea
the svm data format is :
label slot1:fea_sign slot2:fea_sign slot1:fea_sign
dense_slot_index(list(int)): the index of dense slots
sparse_slot_index(list(int)): the index of sparse slots
capacity(int): The buffer capacity maintained by :code:`py_reader`.
thread_num(int): the thread num to read files by cpp reader.
batch_size(int): batch size of data.
file_list(list(str)): List of file names that need to read.
slots(list(int64)): list of slot id.
name(string): The prefix Python queue name and Reader name. None will
be generated automatically.
Returns:
Variable: A Reader from which we can get feeding data.
Examples:
1. The basic usage of :code:`ctr_reader` is as follows:
.. code-block:: python
py_reader = fluid.contrib.ctr_reader.ctr_reader(
feed_dict=datas, file_type='plain', file_format='csv',
file_list=file_list, dense_slot_indexs=[1, 2, 3, 4], sparse_slot_indexs=[],
capacity=64, thread_num=20, batch_size=1000, slots=[], name='ctr_reader')
"""
if name is None:
queue_name = unique_name('lod_tensor_blocking_queue')
reader_name = unique_name('create_ctr_reader')
else:
queue_name = "_".join([name, "queue"])
reader_name = "_".join([name, "reader"])
var = global_scope().var(queue_name)
feed_queue = core.init_lod_tensor_blocking_queue(var, capacity)
startup_blk = default_startup_program().current_block()
reader_var = startup_blk.create_var(name=reader_name)
startup_blk.append_op(
type='create_ctr_reader',
inputs={'blocking_queue': [queue_name]},
outputs={'Out': [reader_var]},
attrs={
'use_data_config': False,
'thread_num': thread_num,
'batch_size': batch_size,
'file_list': file_list,
'file_type': file_type,
'file_format': file_format,
'dense_slot_index': dense_slot_index,
'sparse_slot_index': sparse_slot_index,
'sparse_slots': slots,
'ranks': [],
'lod_levels': [],
'shape_concat': []
})
dtypes = [data.dtype for data in feed_dict]
reader_var.desc.set_dtypes(dtypes)
reader_var.persistable = True
main_prog_reader_var = _copy_reader_var_(
default_main_program().current_block(), reader_var)
reader = monkey_patch_reader_methods(main_prog_reader_var)
# monkey patch py_reader special methods
reader.queue = feed_queue
reader.exited = False
main_blk = default_main_program().current_block()
main_blk.append_op(
type='read',
inputs={'Reader': [reader]},
attrs={'infer_out': False},
outputs={'Out': feed_dict})
return reader
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