[WIP] Add Imdb train demo (#18895)

* add train demo for imdb text classification task

* make inference library release data_feed dataset dataset_factory data_feed_factory

* add String Data Generator

* new feature of train demo: save model params

* New feature of train demo: set training config using gflags

* change code style for CI

* add readme and dataset for imdb demo trainer

paddle/fluid/inference/CMakeLists.txt

@@ -47,7 +47,7 @@ if (ANAKIN_FOUND)
     set(ANAKIN_SHARED_INFERENCE_SRCS ${CMAKE_CURRENT_SOURCE_DIR}/api/api_anakin_engine.cc)
 endif()
 set(SHARED_INFERENCE_SRCS
-    io.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
+    io.cc ${CMAKE_CURRENT_SOURCE_DIR}/../framework/data_feed.cc ${CMAKE_CURRENT_SOURCE_DIR}/../framework/data_set.cc ${CMAKE_CURRENT_SOURCE_DIR}/../framework/data_feed_factory.cc ${CMAKE_CURRENT_SOURCE_DIR}/../framework/dataset_factory.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api.cc ${CMAKE_CURRENT_SOURCE_DIR}/api/api_impl.cc
     ${CMAKE_CURRENT_SOURCE_DIR}/api/analysis_predictor.cc
     ${mkldnn_quantizer_src}
     ${CMAKE_CURRENT_SOURCE_DIR}/api/details/zero_copy_tensor.cc

paddle/fluid/train/imdb_demo/CMakeLists.txt

+cmake_minimum_required(VERSION 3.0)
+
+project(cpp_imdb_train_demo CXX C)
+
+set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -std=c++11")
+
+if(NOT DEFINED PADDLE_LIB)
+  message(FATAL_ERROR "please set PADDLE_LIB with -DPADDLE_LIB=/paddle/lib/dir")
+endif()
+
+option(WITH_MKLDNN     "Compile PaddlePaddle with MKLDNN"                                   OFF)
+option(WITH_MKL        "Compile PaddlePaddle with MKL support, default use openblas."       OFF)
+
+include_directories("${PADDLE_LIB}")
+include_directories("${PADDLE_LIB}/third_party/install/protobuf/include")
+include_directories("${PADDLE_LIB}/third_party/install/glog/include")
+include_directories("${PADDLE_LIB}/third_party/install/gflags/include")
+include_directories("${PADDLE_LIB}/third_party/install/xxhash/include")
+include_directories("${PADDLE_LIB}/third_party/install/snappy/include")
+include_directories("${PADDLE_LIB}/third_party/install/snappystream/include")
+include_directories("${PADDLE_LIB}/third_party/install/zlib/include")
+
+include_directories("${PADDLE_LIB}/third_party/boost")
+include_directories("${PADDLE_LIB}/third_party/eigen3")
+
+link_directories("${PADDLE_LIB}/third_party/install/snappy/lib")
+link_directories("${PADDLE_LIB}/third_party/install/snappystream/lib")
+link_directories("${PADDLE_LIB}/third_party/install/protobuf/lib")
+link_directories("${PADDLE_LIB}/third_party/install/glog/lib")
+link_directories("${PADDLE_LIB}/third_party/install/gflags/lib")
+link_directories("${PADDLE_LIB}/third_party/install/xxhash/lib")
+link_directories("${PADDLE_LIB}/third_party/install/zlib/lib")
+
+add_executable(demo_trainer save_model.cc demo_trainer.cc)
+
+if(WITH_MKLDNN)
+  include_directories("${PADDLE_LIB}/third_party/install/mkldnn/include")
+  if(WIN32)
+    set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/mkldnn.lib)
+  else(WIN32)
+    set(MKLDNN_LIB ${PADDLE_LIB}/third_party/install/mkldnn/lib/libmkldnn.so.0)
+  endif(WIN32)
+endif(WITH_MKLDNN)
+
+if(WITH_MKL)
+  include_directories("${PADDLE_LIB}/third_party/install/mklml/include")
+  if(WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/mklml.lib)
+  else(WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/mklml/lib/libmklml_intel.so)
+  endif(WIN32)
+else()
+  if(APPLE)
+    set(MATH_LIB cblas)
+  elseif(WIN32)
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.lib)
+  else()
+    set(MATH_LIB ${PADDLE_LIB}/third_party/install/openblas/lib/libopenblas.a)
+  endif(APPLE)
+endif()
+
+if(APPLE)
+  set(MACOS_LD_FLAGS "-undefined dynamic_lookup -Wl,-all_load -framework CoreFoundation -framework Security")
+else(APPLE)
+  set(ARCHIVE_START "-Wl,--whole-archive")
+  set(ARCHIVE_END "-Wl,--no-whole-archive")
+  set(EXTERNAL_LIB "-lrt -ldl -lpthread")
+endif(APPLE)
+
+target_link_libraries(demo_trainer
+	${MACOS_LD_FLAGS}
+	${ARCHIVE_START}
+	${PADDLE_LIB}/paddle/fluid/inference/libpaddle_fluid.so
+	${ARCHIVE_END}
+	${MATH_LIB}
+	${MKLDNN_LIB}
+	glog gflags protobuf snappystream snappy z xxhash
+	${EXTERNAL_LIB})

paddle/fluid/train/imdb_demo/README.md

+# Train with C++ inference API
+
+What is C++ inference API and how to install it:
+
+see: [PaddlePaddle Fluid 提供了 C++ API 来支持模型的部署上线](https://paddlepaddle.org.cn/documentation/docs/zh/1.5/advanced_usage/deploy/inference/index_cn.html)
+
+## IMDB task
+
+see: [IMDB Dataset of 50K Movie Reviews | Kaggle](https://www.kaggle.com/lakshmi25npathi/imdb-dataset-of-50k-movie-reviews)
+
+## Quick Start
+
+### prepare data
+
+```shell
+    wget https://fleet.bj.bcebos.com/text_classification_data.tar.gz
+    tar -zxvf text_classification_data.tar.gz
+```
+### build
+
+```shell
+    mkdir build
+    cd build
+    rm -rf *
+    PADDLE_LIB=path/to/your/fluid_inference_install_dir/
+    cmake .. -DPADDLE_LIB=$PADDLE_LIB  -DWITH_MKLDNN=OFF -DWITH_MKL=OFF
+    make
+```
+
+### generate program description
+
+```
+    python generate_program.py bow
+```
+
+### run
+
+```shell
+   # After editing train.cfg
+   sh run.sh
+```
+
+## results
+
+Below are training logs on BOW model, the losses go down as expected.
+
+```
+WARNING: Logging before InitGoogleLogging() is written to STDERR
+I0731 22:39:06.974232 10965 demo_trainer.cc:130] Start training...
+I0731 22:39:57.395229 10965 demo_trainer.cc:164] epoch: 0; average loss: 0.405706
+I0731 22:40:50.262344 10965 demo_trainer.cc:164] epoch: 1; average loss: 0.110746
+I0731 22:41:49.731079 10965 demo_trainer.cc:164] epoch: 2; average loss: 0.0475805
+I0731 22:43:31.398355 10965 demo_trainer.cc:164] epoch: 3; average loss: 0.0233249
+I0731 22:44:58.744391 10965 demo_trainer.cc:164] epoch: 4; average loss: 0.00701507
+I0731 22:46:30.451735 10965 demo_trainer.cc:164] epoch: 5; average loss: 0.00258187
+I0731 22:48:14.396687 10965 demo_trainer.cc:164] epoch: 6; average loss: 0.00113157
+I0731 22:49:56.242744 10965 demo_trainer.cc:164] epoch: 7; average loss: 0.000698234
+I0731 22:51:11.585919 10965 demo_trainer.cc:164] epoch: 8; average loss: 0.000510136
+I0731 22:52:50.573947 10965 demo_trainer.cc:164] epoch: 9; average loss: 0.000400932
+I0731 22:54:02.686152 10965 demo_trainer.cc:164] epoch: 10; average loss: 0.000329259
+I0731 22:54:55.233342 10965 demo_trainer.cc:164] epoch: 11; average loss: 0.000278644
+I0731 22:56:15.496256 10965 demo_trainer.cc:164] epoch: 12; average loss: 0.000241055
+I0731 22:57:45.015926 10965 demo_trainer.cc:164] epoch: 13; average loss: 0.000212085
+I0731 22:59:18.419997 10965 demo_trainer.cc:164] epoch: 14; average loss: 0.000189109
+I0731 23:00:15.409077 10965 demo_trainer.cc:164] epoch: 15; average loss: 0.000170465
+I0731 23:01:38.795770 10965 demo_trainer.cc:164] epoch: 16; average loss: 0.000155051
+I0731 23:02:57.289487 10965 demo_trainer.cc:164] epoch: 17; average loss: 0.000142106
+I0731 23:03:48.032507 10965 demo_trainer.cc:164] epoch: 18; average loss: 0.000131089
+I0731 23:04:51.195230 10965 demo_trainer.cc:164] epoch: 19; average loss: 0.000121605
+I0731 23:06:27.008040 10965 demo_trainer.cc:164] epoch: 20; average loss: 0.00011336
+I0731 23:07:56.568284 10965 demo_trainer.cc:164] epoch: 21; average loss: 0.000106129
+I0731 23:09:23.948290 10965 demo_trainer.cc:164] epoch: 22; average loss: 9.97393e-05
+I0731 23:10:56.062590 10965 demo_trainer.cc:164] epoch: 23; average loss: 9.40532e-05
+I0731 23:12:23.014047 10965 demo_trainer.cc:164] epoch: 24; average loss: 8.89622e-05
+I0731 23:13:21.439818 10965 demo_trainer.cc:164] epoch: 25; average loss: 8.43784e-05
+I0731 23:14:56.171597 10965 demo_trainer.cc:164] epoch: 26; average loss: 8.02322e-05
+I0731 23:16:01.513542 10965 demo_trainer.cc:164] epoch: 27; average loss: 7.64629e-05
+I0731 23:17:18.709139 10965 demo_trainer.cc:164] epoch: 28; average loss: 7.30239e-05
+I0731 23:18:41.421555 10965 demo_trainer.cc:164] epoch: 29; average loss: 6.98716e-05
+```
+
+I trained a Bow model and a CNN model on IMDB dataset using the trainer. At the same time, I also trained the same models using traditional Python training methods. 
+Results show that the two methods achieve almost the same dev accuracy:
+
+CNN:
+ 
+<img src="https://user-images.githubusercontent.com/23031310/62356234-32217300-b543-11e9-89fd-a07614904a08.png" width="300">
+
+BOW:
+
+<img src="https://user-images.githubusercontent.com/23031310/62356253-39488100-b543-11e9-9fa2-a399fc1119d6.png" width="300">
+
+I also recorded the training speed of the C++ Trainer and the python training methods, C++ trainer is quicker on CNN model: 
+
+<img src="https://user-images.githubusercontent.com/23031310/62356444-af4ce800-b543-11e9-88c8-f3bde1321ea1.png" width="300">
+
+#TODO (mapingshuo): find the reason why C++ trainer is quicker on CNN model than python method.

paddle/fluid/train/imdb_demo/demo_trainer.cc

+//   Copyright (c) 2019 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 <time.h>
+#include <fstream>
+
+#include "include/save_model.h"
+#include "paddle/fluid/framework/data_feed_factory.h"
+#include "paddle/fluid/framework/dataset_factory.h"
+#include "paddle/fluid/framework/executor.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/tensor_util.h"
+#include "paddle/fluid/framework/variable_helper.h"
+#include "paddle/fluid/platform/device_context.h"
+#include "paddle/fluid/platform/init.h"
+#include "paddle/fluid/platform/place.h"
+#include "paddle/fluid/platform/profiler.h"
+
+#include "gflags/gflags.h"
+
+DEFINE_string(filelist, "train_filelist.txt", "filelist for fluid dataset");
+DEFINE_string(data_proto_desc, "data.proto", "data feed protobuf description");
+DEFINE_string(startup_program_file, "startup_program",
+              "startup program description");
+DEFINE_string(main_program_file, "", "main program description");
+DEFINE_string(loss_name, "mean_0.tmp_0",
+              "loss tensor name in the main program");
+DEFINE_string(save_dir, "cnn_model", "directory to save trained models");
+DEFINE_int32(epoch_num, 30, "number of epochs to run when training");
+
+namespace paddle {
+namespace train {
+
+void ReadBinaryFile(const std::string& filename, std::string* contents) {
+  std::ifstream fin(filename, std::ios::in | std::ios::binary);
+  PADDLE_ENFORCE(static_cast<bool>(fin), "Cannot open file %s", filename);
+  fin.seekg(0, std::ios::end);
+  contents->clear();
+  contents->resize(fin.tellg());
+  fin.seekg(0, std::ios::beg);
+  fin.read(&(contents->at(0)), contents->size());
+  fin.close();
+}
+
+std::unique_ptr<paddle::framework::ProgramDesc> LoadProgramDesc(
+    const std::string& model_filename) {
+  VLOG(3) << "loading model from " << model_filename;
+  std::string program_desc_str;
+  ReadBinaryFile(model_filename, &program_desc_str);
+  std::unique_ptr<paddle::framework::ProgramDesc> main_program(
+      new paddle::framework::ProgramDesc(program_desc_str));
+  return main_program;
+}
+
+bool IsPersistable(const paddle::framework::VarDesc* var) {
+  if (var->Persistable() &&
+      var->GetType() != paddle::framework::proto::VarType::FEED_MINIBATCH &&
+      var->GetType() != paddle::framework::proto::VarType::FETCH_LIST &&
+      var->GetType() != paddle::framework::proto::VarType::RAW) {
+    return true;
+  }
+  return false;
+}
+
+}  // namespace train
+}  // namespace paddle
+
+int main(int argc, char* argv[]) {
+  gflags::ParseCommandLineFlags(&argc, &argv, true);
+
+  std::cerr << "filelist: " << FLAGS_filelist << std::endl;
+  std::cerr << "data_proto_desc: " << FLAGS_data_proto_desc << std::endl;
+  std::cerr << "startup_program_file: " << FLAGS_startup_program_file
+            << std::endl;
+  std::cerr << "main_program_file: " << FLAGS_main_program_file << std::endl;
+  std::cerr << "loss_name: " << FLAGS_loss_name << std::endl;
+  std::cerr << "save_dir: " << FLAGS_save_dir << std::endl;
+  std::cerr << "epoch_num: " << FLAGS_epoch_num << std::endl;
+
+  std::string filelist = std::string(FLAGS_filelist);
+  std::vector<std::string> file_vec;
+  std::ifstream fin(filelist);
+  if (fin) {
+    std::string filename;
+    while (fin >> filename) {
+      file_vec.push_back(filename);
+    }
+  }
+  PADDLE_ENFORCE_GE(file_vec.size(), 1, "At least one file to train");
+  paddle::framework::InitDevices(false);
+  const auto cpu_place = paddle::platform::CPUPlace();
+  paddle::framework::Executor executor(cpu_place);
+  paddle::framework::Scope scope;
+  auto startup_program =
+      paddle::train::LoadProgramDesc(std::string(FLAGS_startup_program_file));
+  auto main_program =
+      paddle::train::LoadProgramDesc(std::string(FLAGS_main_program_file));
+
+  executor.Run(*startup_program, &scope, 0);
+
+  std::string data_feed_desc_str;
+  paddle::train::ReadBinaryFile(std::string(FLAGS_data_proto_desc),
+                                &data_feed_desc_str);
+  VLOG(3) << "load data feed desc done.";
+  std::unique_ptr<paddle::framework::Dataset> dataset_ptr;
+  dataset_ptr =
+      paddle::framework::DatasetFactory::CreateDataset("MultiSlotDataset");
+  VLOG(3) << "initialize dataset ptr done";
+
+  // find all params
+  std::vector<std::string> param_names;
+  const paddle::framework::BlockDesc& global_block = main_program->Block(0);
+  for (auto* var : global_block.AllVars()) {
+    if (paddle::train::IsPersistable(var)) {
+      VLOG(3) << "persistable variable's name: " << var->Name();
+      param_names.push_back(var->Name());
+    }
+  }
+
+  int epoch_num = FLAGS_epoch_num;
+  std::string loss_name = FLAGS_loss_name;
+  auto loss_var = scope.Var(loss_name);
+
+  LOG(INFO) << "Start training...";
+
+  for (int epoch = 0; epoch < epoch_num; ++epoch) {
+    VLOG(3) << "Epoch:" << epoch;
+    // get reader
+    dataset_ptr->SetFileList(file_vec);
+    VLOG(3) << "set file list done";
+    dataset_ptr->SetThreadNum(1);
+    VLOG(3) << "set thread num done";
+    dataset_ptr->SetDataFeedDesc(data_feed_desc_str);
+    VLOG(3) << "set data feed desc done";
+    dataset_ptr->CreateReaders();
+    const std::vector<paddle::framework::DataFeed*> readers =
+        dataset_ptr->GetReaders();
+    PADDLE_ENFORCE_EQ(readers.size(), 1,
+                      "readers num should be equal to thread num");
+    const std::vector<std::string>& input_feed_names =
+        readers[0]->GetUseSlotAlias();
+    for (auto name : input_feed_names) {
+      readers[0]->AddFeedVar(scope.Var(name), name);
+    }
+    VLOG(3) << "get reader done";
+    readers[0]->Start();
+    VLOG(3) << "start a reader";
+    VLOG(3) << "readers size: " << readers.size();
+
+    int step = 0;
+    std::vector<float> loss_vec;
+
+    while (readers[0]->Next() > 0) {
+      executor.Run(*main_program, &scope, 0, false, true);
+      loss_vec.push_back(
+          loss_var->Get<paddle::framework::LoDTensor>().data<float>()[0]);
+    }
+    float average_loss =
+        accumulate(loss_vec.begin(), loss_vec.end(), 0.0) / loss_vec.size();
+
+    LOG(INFO) << "epoch: " << epoch << "; average loss: " << average_loss;
+    dataset_ptr->DestroyReaders();
+
+    // save model
+    std::string save_dir_root = FLAGS_save_dir;
+    std::string save_dir =
+        save_dir_root + "/epoch" + std::to_string(epoch) + ".model";
+    paddle::framework::save_model(main_program, &scope, param_names, save_dir,
+                                  false);
+  }
+}

paddle/fluid/train/imdb_demo/generate_program.py

+#   Copyright (c) 2019 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.
+import os
+import sys
+import paddle
+import logging
+import paddle.fluid as fluid
+
+logging.basicConfig(format='%(asctime)s - %(levelname)s - %(message)s')
+logger = logging.getLogger("fluid")
+logger.setLevel(logging.INFO)
+
+
+def load_vocab(filename):
+    vocab = {}
+    with open(filename) as f:
+        wid = 0
+        for line in f:
+            vocab[line.strip()] = wid
+            wid += 1
+    vocab["<unk>"] = len(vocab)
+    return vocab
+
+
+if __name__ == "__main__":
+    vocab = load_vocab('imdb.vocab')
+    dict_dim = len(vocab)
+    model_name = sys.argv[1]
+    data = fluid.layers.data(
+        name="words", shape=[1], dtype="int64", lod_level=1)
+    label = fluid.layers.data(name="label", shape=[1], dtype="int64")
+
+    dataset = fluid.DatasetFactory().create_dataset()
+    dataset.set_batch_size(128)
+    dataset.set_pipe_command("python imdb_reader.py")
+
+    dataset.set_use_var([data, label])
+    desc = dataset.proto_desc
+
+    with open("data.proto", "w") as f:
+        f.write(dataset.desc())
+
+    from nets import *
+    if model_name == 'cnn':
+        logger.info("Generate program description of CNN net")
+        avg_cost, acc, prediction = cnn_net(data, label, dict_dim)
+    elif model_name == 'bow':
+        logger.info("Generate program description of BOW net")
+        avg_cost, acc, prediction = bow_net(data, label, dict_dim)
+    else:
+        logger.error("no such model: " + model_name)
+        exit(0)
+    # optimizer = fluid.optimizer.SGD(learning_rate=0.01)
+    optimizer = fluid.optimizer.Adagrad(learning_rate=0.01)
+    optimizer.minimize(avg_cost)
+
+    with open(model_name + "_main_program", "wb") as f:
+        f.write(fluid.default_main_program().desc.serialize_to_string())
+
+    with open(model_name + "_startup_program", "wb") as f:
+        f.write(fluid.default_startup_program().desc.serialize_to_string())

paddle/fluid/train/imdb_demo/imdb_reader.py

+#   Copyright (c) 2019 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.
+
+import sys
+import os
+import paddle
+import re
+import paddle.fluid.incubate.data_generator as dg
+
+
+class IMDBDataset(dg.MultiSlotDataGenerator):
+    def load_resource(self, dictfile):
+        self._vocab = {}
+        wid = 0
+        with open(dictfile) as f:
+            for line in f:
+                self._vocab[line.strip()] = wid
+                wid += 1
+        self._unk_id = len(self._vocab)
+        self._pattern = re.compile(r'(;|,|\.|\?|!|\s|\(|\))')
+        self.return_value = ("words", [1, 2, 3, 4, 5, 6]), ("label", [0])
+
+    def get_words_and_label(self, line):
+        send = '|'.join(line.split('|')[:-1]).lower().replace("<br />",
+                                                              " ").strip()
+        label = [int(line.split('|')[-1])]
+
+        words = [x for x in self._pattern.split(send) if x and x != " "]
+        feas = [
+            self._vocab[x] if x in self._vocab else self._unk_id for x in words
+        ]
+        return feas, label
+
+    def infer_reader(self, infer_filelist, batch, buf_size):
+        def local_iter():
+            for fname in infer_filelist:
+                with open(fname, "r") as fin:
+                    for line in fin:
+                        feas, label = self.get_words_and_label(line)
+                        yield feas, label
+
+        import paddle
+        batch_iter = paddle.batch(
+            paddle.reader.shuffle(
+                local_iter, buf_size=buf_size),
+            batch_size=batch)
+        return batch_iter
+
+    def generate_sample(self, line):
+        def memory_iter():
+            for i in range(1000):
+                yield self.return_value
+
+        def data_iter():
+            feas, label = self.get_words_and_label(line)
+            yield ("words", feas), ("label", label)
+
+        return data_iter
+
+
+if __name__ == "__main__":
+    imdb = IMDBDataset()
+    imdb.load_resource("imdb.vocab")
+    imdb.run_from_stdin()

paddle/fluid/train/imdb_demo/include/save_model.h

+/* Copyright (c) 2016 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 <fcntl.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/message.h>
+#include <google/protobuf/text_format.h>
+#include <stdio.h>
+#include <string.h>
+#include <unistd.h>
+#include <fstream>
+#include <iostream>
+#include <memory>
+#include <string>
+#include <vector>
+#include "gflags/gflags.h"
+#include "paddle/fluid/framework/feed_fetch_method.h"
+#include "paddle/fluid/framework/feed_fetch_type.h"
+#include "paddle/fluid/framework/lod_rank_table.h"
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/prune.h"
+#include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/platform/place.h"
+
+namespace paddle {
+namespace framework {
+void save_model(const std::unique_ptr<ProgramDesc>& main_program, Scope* scope,
+                const std::vector<std::string>& param_names,
+                const std::string& model_name, bool save_combine);
+}
+}

paddle/fluid/train/imdb_demo/nets.py

+# Copyright (c) 2019 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.
+
+import sys
+import time
+import numpy as np
+
+import paddle
+import paddle.fluid as fluid
+
+
+def bow_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2):
+    """
+    bow net
+    """
+    emb = fluid.layers.embedding(
+        input=data, size=[dict_dim, emb_dim], is_sparse=True)
+    bow = fluid.layers.sequence_pool(input=emb, pool_type='sum')
+    bow_tanh = fluid.layers.tanh(bow)
+    fc_1 = fluid.layers.fc(input=bow_tanh, size=hid_dim, act="tanh")
+    fc_2 = fluid.layers.fc(input=fc_1, size=hid_dim2, act="tanh")
+    prediction = fluid.layers.fc(input=[fc_2], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def cnn_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2,
+            win_size=3):
+    """
+    conv net
+    """
+    emb = fluid.layers.embedding(
+        input=data, size=[dict_dim, emb_dim], is_sparse=True)
+    conv_3 = fluid.nets.sequence_conv_pool(
+        input=emb,
+        num_filters=hid_dim,
+        filter_size=win_size,
+        act="tanh",
+        pool_type="max")
+
+    fc_1 = fluid.layers.fc(input=[conv_3], size=hid_dim2)
+
+    prediction = fluid.layers.fc(input=[fc_1], size=class_dim, act="softmax")
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def lstm_net(data,
+             label,
+             dict_dim,
+             emb_dim=128,
+             hid_dim=128,
+             hid_dim2=96,
+             class_dim=2,
+             emb_lr=30.0):
+    """
+    lstm net
+    """
+    emb = fluid.layers.embedding(
+        input=data,
+        size=[dict_dim, emb_dim],
+        param_attr=fluid.ParamAttr(learning_rate=emb_lr),
+        is_sparse=True)
+
+    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 4)
+
+    lstm_h, c = fluid.layers.dynamic_lstm(
+        input=fc0, size=hid_dim * 4, is_reverse=False)
+
+    lstm_max = fluid.layers.sequence_pool(input=lstm_h, pool_type='max')
+    lstm_max_tanh = fluid.layers.tanh(lstm_max)
+
+    fc1 = fluid.layers.fc(input=lstm_max_tanh, size=hid_dim2, act='tanh')
+
+    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
+
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction
+
+
+def gru_net(data,
+            label,
+            dict_dim,
+            emb_dim=128,
+            hid_dim=128,
+            hid_dim2=96,
+            class_dim=2,
+            emb_lr=400.0):
+    """
+    gru net
+    """
+    emb = fluid.layers.embedding(
+        input=data,
+        size=[dict_dim, emb_dim],
+        param_attr=fluid.ParamAttr(learning_rate=emb_lr))
+
+    fc0 = fluid.layers.fc(input=emb, size=hid_dim * 3)
+    gru_h = fluid.layers.dynamic_gru(input=fc0, size=hid_dim, is_reverse=False)
+    gru_max = fluid.layers.sequence_pool(input=gru_h, pool_type='max')
+    gru_max_tanh = fluid.layers.tanh(gru_max)
+    fc1 = fluid.layers.fc(input=gru_max_tanh, size=hid_dim2, act='tanh')
+    prediction = fluid.layers.fc(input=fc1, size=class_dim, act='softmax')
+
+    cost = fluid.layers.cross_entropy(input=prediction, label=label)
+    avg_cost = fluid.layers.mean(x=cost)
+    acc = fluid.layers.accuracy(input=prediction, label=label)
+
+    return avg_cost, acc, prediction

paddle/fluid/train/imdb_demo/run.sh

+
+set -exu
+build/demo_trainer --flagfile="train.cfg"

paddle/fluid/train/imdb_demo/save_model.cc

+/* Copyright (c) 2016 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 "include/save_model.h"
+#include <fcntl.h>
+#include <google/protobuf/io/zero_copy_stream_impl.h>
+#include <google/protobuf/message.h>
+#include <google/protobuf/text_format.h>
+#include <stdio.h>
+#include <string.h>
+#include <unistd.h>
+#include <fstream>
+#include <iostream>
+#include "gflags/gflags.h"
+#include "paddle/fluid/framework/feed_fetch_method.h"
+#include "paddle/fluid/framework/feed_fetch_type.h"
+#include "paddle/fluid/framework/lod_rank_table.h"
+#include "paddle/fluid/framework/lod_tensor_array.h"
+#include "paddle/fluid/framework/op_registry.h"
+#include "paddle/fluid/framework/program_desc.h"
+#include "paddle/fluid/framework/prune.h"
+#include "paddle/fluid/framework/reader.h"
+#include "paddle/fluid/platform/place.h"
+
+using std::unique_ptr;
+
+namespace paddle {
+namespace framework {
+void save_model(const unique_ptr<ProgramDesc>& main_program, Scope* scope,
+                const std::vector<std::string>& param_names,
+                const std::string& model_name, bool save_combine) {
+  auto place = platform::CPUPlace();
+  const BlockDesc& global_block = main_program->Block(0);
+  std::vector<std::string> paralist;
+  for (auto* var : global_block.AllVars()) {
+    bool is_model_param = false;
+    for (auto param_name : param_names) {
+      if (var->Name() == param_name) {
+        is_model_param = true;
+        break;
+      }
+    }
+
+    if (!is_model_param) continue;
+
+    if (!save_combine) {
+      VLOG(3) << "model var name: %s" << var->Name().c_str();
+
+      paddle::framework::AttributeMap attrs;
+      attrs.insert({"file_path", model_name + "/" + var->Name()});
+      auto save_op = paddle::framework::OpRegistry::CreateOp(
+          "save", {{"X", {var->Name()}}}, {}, attrs);
+
+      save_op->Run(*scope, place);
+    } else {
+      paralist.push_back(var->Name());
+    }
+  }
+  if (save_combine) {
+    std::sort(paralist.begin(), paralist.end());
+    paddle::framework::AttributeMap attrs;
+    attrs.insert({"file_path", model_name});
+    auto save_op = paddle::framework::OpRegistry::CreateOp(
+        "save_combine", {{"X", paralist}}, {}, attrs);
+    save_op->Run(*scope, place);
+  }
+}
+}  // namespace framework
+}  // namespace paddle

paddle/fluid/train/imdb_demo/train.cfg

+--filelist=train_filelist.txt
+--data_proto_desc=data.proto
+--loss_name=mean_0.tmp_0
+--startup_program_file=bow_startup_program
+--main_program_file=bow_main_program
+--save_dir=bow_model
+--epoch_num=30

paddle/fluid/train/imdb_demo/train_filelist.txt

+train_data/part-0
+train_data/part-1
+train_data/part-10
+train_data/part-11
+train_data/part-2
+train_data/part-3
+train_data/part-4
+train_data/part-5
+train_data/part-6
+train_data/part-7
+train_data/part-8
+train_data/part-9

python/paddle/fluid/incubate/data_generator/__init__.py

@@ -15,7 +15,7 @@
 import os
 import sys
 
-__all__ = ['MultiSlotDataGenerator']
+__all__ = ['MultiSlotDataGenerator', 'MultiSlotStringDataGenerator']
 
 
 class DataGenerator(object):