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Merge pull request #13983 from jacquesqiao/add-ctr-reader

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Add ctr reader
local_add_cudnn_lstm
Qiao Longfei 7 years ago committed by GitHub
parent b1dbbb7f88 da387720d7
commit 35b79ab865
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GPG Key ID: 4AEE18F83AFDEB23
8 changed files with 782 additions and 0 deletions
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1
CMakeLists.txt
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@ -214,6 +214,7 @@ if (NOT WIN32)
# there is no official support of warpctc, nccl, cupti in windows
include(external/warpctc) # download, build, install warpctc
include(cupti)
include(external/gzstream)
endif (NOT WIN32)
if(WITH_DISTRIBUTE)

47
cmake/external/gzstream.cmake vendored
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# 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.
#
IF(MOBILE_INFERENCE)
return()
ENDIF()
include (ExternalProject)
# NOTE: gzstream is needed when linking with ctr reader.
SET(GZSTREAM_SOURCES_DIR ${THIRD_PARTY_PATH}/gzstream)
SET(GZSTREAM_INSTALL_DIR ${THIRD_PARTY_PATH}/install/gzstream)
SET(GZSTREAM_INCLUDE_DIR "${GZSTREAM_INSTALL_DIR}/include/" CACHE PATH "gzstream include directory." FORCE)
ExternalProject_Add(
extern_gzstream
GIT_REPOSITORY "https://github.com/jacquesqiao/gzstream.git"
GIT_TAG ""
PREFIX ${GZSTREAM_SOURCES_DIR}
UPDATE_COMMAND ""
CONFIGURE_COMMAND ""
BUILD_IN_SOURCE 1
BUILD_COMMAND make -j8
INSTALL_COMMAND mkdir -p ${GZSTREAM_INSTALL_DIR}/lib/ && mkdir -p ${GZSTREAM_INSTALL_DIR}/include/
&& cp ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/libgzstream.a ${GZSTREAM_INSTALL_DIR}/lib
&& cp -r ${GZSTREAM_SOURCES_DIR}/src/extern_gzstream/gzstream.h ${GZSTREAM_INSTALL_DIR}/include
)
ADD_LIBRARY(gzstream STATIC IMPORTED GLOBAL)
SET_PROPERTY(TARGET gzstream PROPERTY IMPORTED_LOCATION
"${GZSTREAM_INSTALL_DIR}/lib/libgzstream.a")
include_directories(${GZSTREAM_INCLUDE_DIR})
ADD_DEPENDENCIES(gzstream extern_gzstream zlib)

6
paddle/fluid/operators/reader/CMakeLists.txt
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@ -28,6 +28,12 @@ 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)
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)

79
paddle/fluid/operators/reader/create_ctr_reader_op.cc
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// 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 "paddle/fluid/operators/reader/lod_tensor_blocking_queue.h"
#include "paddle/fluid/operators/reader/reader_op_registry.h"
namespace paddle {
namespace operators {
namespace reader {
class CreateCTRReaderOp : public framework::OperatorBase {
public:
using framework::OperatorBase::OperatorBase;
private:
void RunImpl(const framework::Scope& scope,
const platform::Place& dev_place) const override {
auto* out = scope.FindVar(Output("Out"))
->template GetMutable<framework::ReaderHolder>();
if (out->Get() != nullptr) return;
const std::string& queue_name = Input("blocking_queue");
auto* queue_holder_var = scope.FindVar(queue_name);
PADDLE_ENFORCE_NOT_NULL(
queue_holder_var,
"No LoDTensorBlockingQueueHolder variable with name %s found",
queue_name);
auto* queue_holder =
queue_holder_var->template GetMutable<LoDTensorBlockingQueueHolder>();
int thread_num = Attr<int>("thread_num");
std::vector<std::string> slots = Attr<std::vector<std::string>>("slots");
int batch_size = Attr<int>("batch_size");
std::vector<std::string> file_list =
Attr<std::vector<std::string>>("file_list");
out->Reset(std::make_shared<CTRReader>(queue_holder->GetQueue(), batch_size,
thread_num, slots, file_list));
}
};
class CreateCTRReaderOpMaker : public FileReaderMakerBase {
protected:
void Apply() override {
AddInput("blocking_queue",
"Name of the `LoDTensorBlockingQueueHolder` variable");
AddAttr<int>("thread_num", "the thread num to read data");
AddAttr<int>("batch_size", "the batch size of read data");
AddAttr<std::vector<std::string>>("file_list",
"The list of files that need to read");
AddAttr<std::vector<std::string>>(
"slots", "the slots that should be extract from file");
AddComment(R"DOC(
Create CTRReader to support read ctr data with cpp.
)DOC");
}
};
} // namespace reader
} // namespace operators
} // namespace paddle
namespace reader = ::paddle::operators::reader;
REGISTER_FILE_READER_OPERATOR(create_ctr_reader, reader::CreateCTRReaderOp,
reader::CreateCTRReaderOpMaker);

238
paddle/fluid/operators/reader/ctr_reader.cc
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// 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) {
size_t start = 0;
size_t end = s.find_first_of(delimiter);
while (end <= std::string::npos) {
output->emplace_back(s.substr(start, end - start));
if (end == std::string::npos) {
break;
}
start = end + 1;
end = s.find_first_of(delimiter, start);
}
}
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[2]) > 0;
for (size_t i = 3; 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);
}
}
}
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 MultiGzipReader : public Reader {
public:
explicit MultiGzipReader(const std::vector<std::string>& file_list) {
for (auto& file : file_list) {
readers_.emplace_back(std::make_shared<GzipReader>(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<GzipReader>> readers_;
size_t current_reader_index_ = 0;
};
void MonitorThread(std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
VLOG(30) << "monitor thread in";
bool reader_thread_is_running = true;
while (reader_thread_is_running) {
VLOG(30) << "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(30) << "reader is running!";
reader_thread_is_running = true;
}
}
std::this_thread::sleep_for(std::chrono::milliseconds(1000));
}
VLOG(30) << "all reader thread is stopped, push empty data into queue";
queue->Push({});
VLOG(30) << "monitor thread exited";
}
void ReadThread(const std::vector<std::string>& file_list,
const std::vector<std::string>& slots, int batch_size,
int thread_id, std::vector<ReaderThreadStatus>* thread_status,
std::shared_ptr<LoDTensorBlockingQueue> queue) {
VLOG(30) << "[" << thread_id << "]"
<< " reader thread start! thread_id = " << thread_id;
for (auto& file : file_list) {
VLOG(30) << "[" << thread_id << "]"
<< " file " << file;
}
(*thread_status)[thread_id] = Running;
VLOG(30) << "set status to running";
std::unordered_map<std::string, size_t> slot_to_index;
for (size_t i = 0; i < slots.size(); ++i) {
slot_to_index[slots[i]] = i;
}
std::string line;
std::vector<std::unordered_map<std::string, std::vector<int64_t>>> batch_data;
std::vector<int64_t> batch_label;
MultiGzipReader reader(file_list);
VLOG(30) << "reader inited";
while (reader.HasNext()) {
batch_data.clear();
batch_data.reserve(batch_size);
batch_label.clear();
batch_label.reserve(batch_size);
// read batch_size data
for (int i = 0; i < 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 slots
for (auto& slot : slots) {
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({1, static_cast<int64_t>(batch_feasign.size())}),
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({1, static_cast<int64_t>(batch_label.size())}),
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(40) << "push one data, queue_size=" << queue->Size();
}
(*thread_status)[thread_id] = Stopped;
VLOG(30) << "set status to stopped, thread " << thread_id << " exited";
}
} // namespace reader
} // namespace operators
} // namespace paddle

133
paddle/fluid/operators/reader/ctr_reader.h
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// 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 <chrono> // NOLINT
#include <cstdlib>
#include <fstream>
#include <iostream>
#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 };
void ReadThread(const std::vector<std::string>& file_list,
const std::vector<std::string>& slots, int batch_size,
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:
explicit CTRReader(const std::shared_ptr<LoDTensorBlockingQueue>& queue,
int batch_size, int thread_num,
const std::vector<std::string>& slots,
const std::vector<std::string>& file_list)
: batch_size_(batch_size), slots_(slots), file_list_(file_list) {
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(file_list.size(), 0, "file list should not be empty");
thread_num_ =
file_list_.size() > thread_num ? thread_num : file_list_.size();
queue_ = queue;
SplitFiles();
for (size_t i = 0; i < thread_num_; ++i) {
read_thread_status_.push_back(Stopped);
}
}
~CTRReader() {}
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();
}
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 (int thread_id = 0; thread_id < thread_num_; thread_id++) {
read_threads_.emplace_back(new std::thread(
std::bind(&ReadThread, file_groups_[thread_id], slots_, batch_size_,
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 < file_list_.size(); ++i) {
auto& file_name = file_list_[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 int batch_size_;
const std::vector<std::string> slots_;
const std::vector<std::string> file_list_;
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

155
paddle/fluid/operators/reader/ctr_reader_test.cc
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// 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;
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 = {
"aaaa 1 0 0:6002 1:6003 2:6004 3:6005 4:6006 -1\n",
"bbbb 1 0 5:6003 6:6003 7:6003 8:6004 9:6004 -1\n",
"cccc 1 1 10:6002 11:6002 12:6002 13:6002 14:6002 -2\n",
"dddd 1 0 15:6003 16:6003 17:6003 18:6003 19:6004 -3\n",
"1111 1 1 20:6001 21:6001 22:6001 23:6001 24:6001 12\n",
"2222 1 1 25:6004 26:6004 27:6004 28:6005 29:6005 aa\n",
"3333 1 0 30:6002 31:6003 32:6004 33:6004 34:6005 er\n",
"eeee 1 1 35:6003 36:6003 37:6005 38:6005 39:6005 dd\n",
"ffff 1 1 40:6002 41:6003 42:6004 43:6004 44:6005 66\n",
"gggg 1 1 46:6006 45:6006 47:6003 48:6003 49:6003 ba\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 = {{1, 3}, {1, 3}, {1, 3}, {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> slots = {"6002", "6003"};
std::vector<std::string> file_list;
for (int i = 0; i < thread_num; ++i) {
file_list.push_back(gz_file_name);
}
CTRReader reader(queue, batch_size, thread_num, slots, file_list);
reader.Start();
size_t batch_num =
std::ceil(static_cast<float>(ctr_data.size()) / batch_size) * thread_num;
check_all_data(ctr_data, 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, slots, label_dims, label_value, data_slot_6002,
data_slot_6003, batch_num, batch_size, queue, &reader);
reader.Shutdown();
}

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python/paddle/fluid/contrib/reader/ctr_reader.py
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# 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
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()
reader.reset = reset
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_data,
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:
capacity(int): The buffer capacity maintained by :code:`py_reader`.
thread_num(list|tuple): List of tuples which declaring data shapes.
batch_size(list|tuple): List of strs which declaring data type.
file_list(list|tuple): List of ints which declaring data lod_level.
slots(bool): Whether use double buffer or not.
name(basestring): 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:`py_reader` is as follows:
"""
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, shapes)
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={
'thread_num': thread_num,
'batch_size': batch_size,
'file_list': file_list,
'slots': slots,
})
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]}, outputs={'Out': feed_data})
return reader
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