Upgrade dialogue models to paddle 1.8 (#4594)

* upgrade_dialogue_models

* fix_upgrade_models_comments

PaddleNLP/dialogue_domain_classification/README.MD

@@ -10,11 +10,11 @@
 
 
 ## 快速开始
-**目前模型要求使用PaddlePaddle 1.6及以上版本或适当的develop版本运行。**
+**目前模型要求使用PaddlePaddle 1.8及以上版本或适当的develop版本运行。**
 
 ### 1. Paddle版本安装
 
-本项目训练模块兼容Python2.7.x以及Python3.7.x， 依赖PaddlePaddle 1.6版本以及CentOS系统环境， 安装请参考官网 [快速安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/install/index_cn.html)。
+本项目训练模块兼容Python2.7.x以及Python3.7.x， 依赖PaddlePaddle 1.8版本以及CentOS系统环境， 安装请参考官网 [快速安装](https://www.paddlepaddle.org.cn/documentation/docs/zh/beginners_guide/install/index_cn.html)。
 
 注意：该模型同时支持cpu和gpu训练和预测，用户可以根据自身需求，选择安装对应的paddlepaddle-gpu或paddlepaddle版本。
 

PaddleNLP/dialogue_domain_classification/run_classifier.py

@@ -452,7 +452,7 @@ def main(args):
     if args.use_cuda:
         test_place = fluid.cuda_places(0)
         place = fluid.cuda_places()
-        DEV_COUNT = fluid.core.get_cuda_device_count()
+        DEV_COUNT = len(place)
     else:
         test_place = fluid.cpu_places(1)
         os.environ['CPU_NUM'] = str(args.cpu_num)

PaddleNLP/dialogue_domain_classification/utils.py

@@ -130,12 +130,11 @@ class DataReader(object):
         assert os.path.exists(data_path), "The given data file does not exist."
         if mode == "train":
             train_reader = fluid.io.batch(
-                paddle.reader.shuffle(
+                fluid.io.shuffle(
                     self.data_reader(
                         data_path, self.max_len, shuffle=True),
                     buf_size=batch_size * 100),
                 batch_size)
-            # train_reader = fluid.io.batch(self.data_reader(data_path), batch_size)                   
             return train_reader
         else:
             test_reader = fluid.io.batch(

PaddleNLP/dialogue_system/auto_dialogue_evaluation/README.md

@@ -30,7 +30,7 @@
 - cuda >= 9.0
 - cudnn >= 7.0
 - pandas >= 0.20.1
-- PaddlePaddle >= 1.7.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
+- PaddlePaddle >= 1.8.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 本模块使用bert作为pretrain model进行模型的finetuning训练，训练速度较慢，建议安装GPU版本的PaddlePaddle
 
 ####   b、下载代码
 
@@ -119,13 +119,10 @@ emb_size: embedding层大小
 vocab_size: 词表大小
 sample_pro: 采样比率
 output_prediction_file: 输出的预测文件
-init_from_checkpoint: 加载断点模型
 init_from_params: 训练好的模型参数文件，一般用于预测
 init_from_pretrain_model: 预训练模型路径，如bert的模型参数
 inference_model_dir: inference model的保存路径
 save_model_path: 训练产出模型的输出路径
-save_checkpoint: 调用paddle的io接口save_persistables(把传入的层中所有参数以及优化器进行保存)来保存模型参数
-save_param: 调用paddle的io接口save_params(从main_program中取出所有参数然后保存到文件中)来保存模型参数
 evaluation_file: 参与评估的inference 文件
 vocab_path: 词表路径
 max_seq_len: 输入最大序列长度
@@ -199,7 +196,6 @@ python -u main.py \
       --loss_type="CLS" \
       --max_seq_len=50 \
       --save_model_path="data/saved_models/matching_pretrained" \
-      --save_param="params" \
       --training_file="data/input/data/unlabel_data/train.ids" \
       --epoch=20 \
       --print_step=1 \
@@ -217,7 +213,7 @@ python -u main.py \
 #### windows环境下：
 训练：
 ```
-python -u main.py --do_train=true --use_cuda=false --loss_type=CLS --max_seq_len=50 --save_model_path=data\saved_models\matching_pretrained  --save_param=params  --training_file=data\input\data\unlabel_data\train.ids --epoch=20 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
+python -u main.py --do_train=true --use_cuda=false --loss_type=CLS --max_seq_len=50 --save_model_path=data\saved_models\matching_pretrained --training_file=data\input\data\unlabel_data\train.ids --epoch=20 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
 ```
 
 #### 2、第二阶段finetuning模型的训练：
@@ -271,9 +267,8 @@ python -u main.py \
       --use_cuda=${use_cuda} \
       --loss_type="L2" \
       --max_seq_len=50 \
-      --init_from_pretrain_model="data/saved_models/trained_models/matching_pretrained/params" \
+      --init_from_pretrain_model="data/saved_models/trained_models/matching_pretrained/params/params" \
       --save_model_path="data/saved_models/human_finetuned" \
-      --save_param="params" \
       --training_file="data/input/data/label_data/human/train.ids" \
       --epoch=50 \
       --print_step=1 \
@@ -288,7 +283,7 @@ python -u main.py \
 
 #### windows环境下：
 ```
-python -u main.py --do_train=true --use_cuda=false --loss_type=L2 --max_seq_len=50 --save_model_path=data\saved_models\human_finetuned  --save_param=params  --training_file=data\input\data\label_data\human\train.ids --epoch=50 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
+python -u main.py --do_train=true --use_cuda=false --loss_type=L2 --max_seq_len=50 --save_model_path=data\saved_models\human_finetuned --training_file=data\input\data\label_data\human\train.ids --epoch=50 --print_step=1 --save_step=400 --batch_size=256 --hidden_size=256 --emb_size=256 --vocab_size=484016 --learning_rate=0.001 --sample_pro=0.1
 ```
 
 ### 模型预测

PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade/prepare_data_and_model.py

@@ -29,7 +29,7 @@ DATA_MODEL_PATH = {
     "DATA_PATH":
     "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_dataset-1.0.0.tar.gz",
     "TRAINED_MODEL":
-    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.2.0.0.tar.gz"
+    "https://baidu-nlp.bj.bcebos.com/auto_dialogue_evaluation_models.3.0.0.tar.gz"
 }
 
 PATH_MAP = {'DATA_PATH': "./data/input", 'TRAINED_MODEL': './data/saved_models'}

PaddleNLP/dialogue_system/auto_dialogue_evaluation/ade_net.py

@@ -34,7 +34,7 @@ def create_net(is_training,
     label = model_input.labels
 
     #emb
-    context_emb = fluid.input.embedding(
+    context_emb = fluid.embedding(
         input=context_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,
@@ -42,7 +42,7 @@ def create_net(is_training,
             name=word_emb_name,
             initializer=fluid.initializer.Normal(scale=0.1)))
 
-    response_emb = fluid.input.embedding(
+    response_emb = fluid.embedding(
         input=response_wordseq,
         size=[args.vocab_size, args.emb_size],
         is_sparse=True,

PaddleNLP/dialogue_system/auto_dialogue_evaluation/data/config/ade.yaml

@@ -14,13 +14,10 @@ emb_size: 256
 vocab_size: 484016
 sample_pro: 1.0
 output_prediction_file: ""
-init_from_checkpoint: ""
 init_from_params: ""
 init_from_pretrain_model: ""
 inference_model_dir: ""
 save_model_path: ""
-save_checkpoint: ""
-save_param: ""
 evaluation_file: ""
 vocab_path: ""
 max_seq_len: 128

PaddleNLP/dialogue_system/auto_dialogue_evaluation/inference_model.py

@@ -27,7 +27,6 @@ from ade_net import create_net
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 
 def do_save_inference_model(args):
@@ -55,7 +54,7 @@ def do_save_inference_model(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
@@ -72,9 +71,9 @@ def do_save_inference_model(args):
     assert (args.init_from_params) or (args.init_from_pretrain_model)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
     elif args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_pretrain_model)
 
     # saving inference model
     fluid.io.save_inference_model(

PaddleNLP/dialogue_system/auto_dialogue_evaluation/predict.py

@@ -29,7 +29,6 @@ from ade_net import create_net
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 
 def do_predict(args):
@@ -59,12 +58,11 @@ def do_predict(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             logits = create_net(
                 is_training=False, model_input=input_field, args=args)
-            logits.persistable = True
 
             fetch_list = [logits.name]
     #for_test is True if change the is_test attribute of operators to True
@@ -79,9 +77,9 @@ def do_predict(args):
 
     assert (args.init_from_params) or (args.init_from_pretrain_model)
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params, executor=exe)
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_pretrain_model, executor=exe)
 
     compiled_test_prog = fluid.CompiledProgram(test_prog)
 
@@ -94,7 +92,7 @@ def do_predict(args):
         place=place, phase="test", shuffle=False, sample_pro=1)
     num_test_examples = processor.get_num_examples(phase='test')
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
     data_reader.start()
 
     scores = []
@@ -110,7 +108,7 @@ def do_predict(args):
     print("Write the predicted results into the output_prediction_file")
     fw = io.open(args.output_prediction_file, 'w', encoding="utf8")
     for index, score in enumerate(scores):
-        fw.write("%s\t%s\n" % (index, score))
+        fw.write(u"%s\t%s\n" % (index, score[0]))
     print("finish........................................")
 
 

PaddleNLP/dialogue_system/auto_dialogue_evaluation/run.sh

@@ -67,7 +67,6 @@ function pretrain_train()
       --loss_type="CLS" \
       --max_seq_len=50 \
       --save_model_path=${pretrain_model_path} \
-      --save_param="params" \
       --training_file="${INPUT_PATH}/unlabel_data/train.ids" \
       --epoch=20 \
       --print_step=1 \
@@ -99,9 +98,8 @@ function finetuning_train()
       --use_cuda=${1} \
       --loss_type="L2" \
       --max_seq_len=50 \
-      --init_from_pretrain_model="${SAVED_MODELS}/matching_pretrained/params/step_final" \
+      --init_from_pretrain_model="${SAVED_MODELS}/matching_pretrained/step_final" \
       --save_model_path=${save_model_path} \
-      --save_param="params" \
       --training_file="${INPUT_PATH}/label_data/${2}/train.ids" \
       --epoch=50 \
       --print_step=1 \
@@ -121,7 +119,7 @@ function pretrain_predict()
       --do_predict=true \
       --use_cuda=${1} \
       --predict_file="${INPUT_PATH}/unlabel_data/test.ids" \
-      --init_from_params="${SAVED_MODELS}/trained_models/matching_pretrained/params" \
+      --init_from_params="${SAVED_MODELS}/trained_models/matching_pretrained/params/params" \
       --loss_type="CLS" \
       --output_prediction_file="${OUTPUT_PATH}/pretrain_matching_predict" \
       --max_seq_len=50 \
@@ -137,7 +135,7 @@ function finetuning_predict()
       --do_predict=true \
       --use_cuda=${1} \
       --predict_file="${INPUT_PATH}/label_data/${2}/test.ids" \
-      --init_from_params=${SAVED_MODELS}/trained_models/${2}_finetuned/params \
+      --init_from_params="${SAVED_MODELS}/trained_models/${2}_finetuned/params/params" \
       --loss_type="L2" \
       --output_prediction_file="${OUTPUT_PATH}/finetuning_${2}_predict" \
       --max_seq_len=50 \

PaddleNLP/dialogue_system/auto_dialogue_evaluation/train.py

@@ -29,7 +29,6 @@ from ade_net import create_net, set_word_embedding
 from ade.utils.configure import PDConfig
 from ade.utils.input_field import InputField
 from ade.utils.model_check import check_cuda
-import ade.utils.save_load_io as save_load_io
 
 try:
     import cPickle as pickle  #python 2
@@ -62,24 +61,27 @@ def do_train(args):
 
             input_inst = [context_wordseq, response_wordseq, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             loss = create_net(
                 is_training=True, model_input=input_field, args=args)
-            loss.persistable = True
+
             # gradient clipping
-            fluid.clip.set_gradient_clip(clip=fluid.clip.GradientClipByValue(
-                max=1.0, min=-1.0))
-            optimizer = fluid.optimizer.Adam(learning_rate=args.learning_rate)
+            optimizer = fluid.optimizer.AdamOptimizer(
+                learning_rate=args.learning_rate,
+                grad_clip=fluid.clip.GradientClipByValue(
+                    max=1.0, min=-1.0))
             optimizer.minimize(loss)
 
             if args.use_cuda:
-                dev_count = fluid.core.get_cuda_device_count()
+                places = fluid.cuda_places()
+                dev_count = len(places)
                 place = fluid.CUDAPlace(
                     int(os.getenv('FLAGS_selected_gpus', '0')))
             else:
-                dev_count = int(os.environ.get('CPU_NUM', 1))
+                places = fluid.cpu_places()
+                dev_count = len(places)
                 place = fluid.CPUPlace()
 
             processor = reader.DataProcessor(
@@ -99,20 +101,20 @@ def do_train(args):
             print("Num train examples: %d" % num_train_examples)
             print("Max train steps: %d" % max_train_steps)
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
 
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_checkpoint == "") or (
+    assert (args.init_from_params == "") or (
         args.init_from_pretrain_model == "")
 
     #init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint:
-        save_load_io.init_from_checkpoint(args, exe, train_prog)
+    if args.init_from_params:
+        fluid.load(train_prog, args.init_from_params, exe)
     #init from some pretrain models, to better solve the current task
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, train_prog)
+        fluid.load(train_prog, args.init_from_pretrain_model, exe)
 
     if args.word_emb_init:
         print("start loading word embedding init ...")
@@ -163,21 +165,17 @@ def do_train(args):
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
-                    if args.save_checkpoint:
-                        save_load_io.save_checkpoint(args, exe, train_prog,
-                                                     "step_" + str(steps))
-                    if args.save_param:
-                        save_load_io.save_param(args, exe, train_prog,
-                                                "step_" + str(steps))
+                    model_path = os.path.join(args.save_model_path,
+                                              "step_" + str(steps))
+                    fluid.save(train_prog, model_path)
+
                 steps += 1
             except fluid.core.EOFException:
                 data_reader.reset()
                 break
 
-    if args.save_checkpoint:
-        save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
-    if args.save_param:
-        save_load_io.save_param(args, exe, train_prog, "step_final")
+    model_path = os.path.join(args.save_model_path, "step_final")
+    fluid.save(train_prog, model_path)
 
     def get_cards():
         num = 0

PaddleNLP/dialogue_system/dialogue_general_understanding/README.md

@@ -23,7 +23,7 @@
 - Python >= 2.7
 - cuda >= 9.0
 - cudnn >= 7.0
-- PaddlePaddle >= 1.7.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
+- PaddlePaddle >= 1.8.0，请参考[安装指南](http://www.paddlepaddle.org/#quick-start)进行安装, 由于模块内模型基于bert做finetuning, 训练速度较慢, 建议用户安装GPU版本PaddlePaddle进行训练。
 
 ####   b、下载代码
 
@@ -123,13 +123,10 @@ format：conversation_content \t question \1 answer \t state1 state2 state3.....
 task_name: 任务名称，可选udc、swda、mrda、atis_intent、atis_slot、dstc2
 data_dir: 数据路径，如./data/input/data/udc
 bert_config_path: 预训练模型bert的网络配置./data/pretrain_model/uncased_L-12_H-768_A-12/bert_config.json
-init_from_checkpoint: 加载断点模型
 init_from_params: 训练好的模型参数文件，一般用于预测
 init_from_pretrain_model: 预训练模型路径，如bert的模型参数
 inference_model_dir: inference model的保存路径
 save_model_path: 训练产出模型的输出路径
-save_checkpoint: 调用paddle的io接口save_persistables(把传入的层中所有参数以及优化器进行保存)来保存模型参数
-save_param: 调用paddle的io接口save_params(从main_program中取出所有参数然后保存到文件中)来保存模型参数
 lr_scheduler: learning rate scheduler
 weight_decay: learning rate 权重衰减因子
 warmup_proportion: warmup比率
@@ -221,7 +218,6 @@ python -u main.py \
        --vocab_path="${BERT_BASE_PATH}/vocab.txt" \
        --init_from_pretrain_model="${BERT_BASE_PATH}/params" \
        --save_model_path="./data/saved_models/${TASK_NAME}" \
-       --save_param="params" \
        --save_steps=100 \
        --learning_rate=2e-5 \
        --weight_decay=0.01 \
@@ -235,7 +231,7 @@ python -u main.py \
 
 #### windows环境下
 ```
-python -u main.py --task_name=atis_intent --use_cuda=false --do_train=true --epoch=20 --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --init_from_pretrain_model=data\pretrain_model\uncased_L-12_H-768_A-12\params --save_model_path=data\saved_models\atis_intent --save_param=params --save_steps=100 --learning_rate=2e-5 --weight_decay=0.01 --max_seq_len=128 --print_steps=10
+python -u main.py --task_name=atis_intent --use_cuda=false --do_train=true --epoch=20 --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --init_from_pretrain_model=data\pretrain_model\uncased_L-12_H-768_A-12\params --save_model_path=data\saved_models\atis_intent --save_steps=100 --learning_rate=2e-5 --weight_decay=0.01 --max_seq_len=128 --print_steps=10
 ```
 
 ### 模型预测
@@ -294,7 +290,7 @@ python -u main.py \
        --batch_size=32 \
        --do_lower_case=true \
        --data_dir="./data/input/data/atis/${TASK_NAME}" \
-       --init_from_params="./data/saved_models/trained_models/${TASK_NAME}/params" \
+       --init_from_params="./data/saved_models/trained_models/${TASK_NAME}/params/params" \
        --bert_config_path="${BERT_BASE_PATH}/bert_config.json" \
        --vocab_path="${BERT_BASE_PATH}/vocab.txt" \
        --output_prediction_file="./data/output/pred_${TASK_NAME}" \
@@ -305,7 +301,7 @@ python -u main.py \
 
 #### windows环境下
 ```
-python -u main.py --task_name=atis_intent --use_cuda=false --do_predict=true --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --init_from_params=data\saved_models\trained_models\atis_intent\params --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --output_prediction_file=data\output\pred_atis_intent --max_seq_len=128
+python -u main.py --task_name=atis_intent --use_cuda=false --do_predict=true --batch_size=32 --do_lower_case=true --data_dir=data\input\data\atis\atis_intent --init_from_params=data\saved_models\trained_models\atis_intent\params\params --bert_config_path=data\pretrain_model\uncased_L-12_H-768_A-12\bert_config.json --vocab_path=data\pretrain_model\uncased_L-12_H-768_A-12\vocab.txt --output_prediction_file=data\output\pred_atis_intent --max_seq_len=128
 ```
 
 ### 模型评估

PaddleNLP/dialogue_system/dialogue_general_understanding/data/config/dgu.yaml

 task_name: ""
 data_dir: ""
 bert_config_path: ""
-init_from_checkpoint: ""
 init_from_params: ""
 init_from_pretrain_model: ""
 inference_model_dir: ""
 save_model_path: ""
-save_checkpoint: ""
-save_param: ""
 lr_scheduler: "linear_warmup_decay"
 weight_decay: 0.01
 warmup_proportion: 0.1

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/bert.py

@@ -87,21 +87,21 @@ class BertModel(object):
 
     def _build_model(self, src_ids, position_ids, sentence_ids, input_mask):
         # padding id in vocabulary must be set to 0
-        emb_out = fluid.input.embedding(
+        emb_out = fluid.embedding(
             input=src_ids,
             size=[self._voc_size, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._word_emb_name, initializer=self._param_initializer),
             is_sparse=False)
-        position_emb_out = fluid.input.embedding(
+        position_emb_out = fluid.embedding(
             input=position_ids,
             size=[self._max_position_seq_len, self._emb_size],
             dtype=self._dtype,
             param_attr=fluid.ParamAttr(
                 name=self._pos_emb_name, initializer=self._param_initializer))
 
-        sent_emb_out = fluid.input.embedding(
+        sent_emb_out = fluid.embedding(
             sentence_ids,
             size=[self._sent_types, self._emb_size],
             dtype=self._dtype,

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_paradigm.py

@@ -48,8 +48,8 @@ class Paradigm(object):
                 initializer=fluid.initializer.TruncatedNormal(scale=0.02)),
             bias_attr=fluid.ParamAttr(
                 name="cls_out_b", initializer=fluid.initializer.Constant(0.)))
-
-        if not params['is_training']:
+        
+        if not params['is_training']: 
             probs = fluid.layers.softmax(logits)
             results = {"probs": probs}
             return results

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/define_predict_pack.py

@@ -17,7 +17,6 @@ import re
 import sys
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/optimization.py

@@ -59,7 +59,13 @@ def optimization(loss,
                  weight_decay,
                  scheduler='linear_warmup_decay',
                  use_fp16=False,
-                 loss_scaling=1.0):
+                 loss_scaling=1.0,
+                 clip_norm_thres=1.0):
+    # When using mixed precision training, scale the gradient clip threshold
+    # by loss_scaling
+    if use_fp16 and loss_scaling > 1.0:
+        clip_norm_thres *= loss_scaling
+
     if warmup_steps > 0:
         if scheduler == 'noam_decay':
             scheduled_lr = fluid.layers.learning_rate_scheduler\
@@ -71,19 +77,17 @@ def optimization(loss,
         else:
             raise ValueError("Unkown learning rate scheduler, should be "
                              "'noam_decay' or 'linear_warmup_decay'")
-        optimizer = fluid.optimizer.Adam(learning_rate=scheduled_lr)
+        optimizer = fluid.optimizer.AdamOptimizer(
+            learning_rate=scheduled_lr,
+            grad_clip=fluid.clip.GradientClipByGlobalNorm(
+                clip_norm=clip_norm_thres))
     else:
-        optimizer = fluid.optimizer.Adam(learning_rate=learning_rate)
+        optimizer = fluid.optimizer.AdamOptimizer(
+            learning_rate=learning_rate,
+            grad_clip=fluid.clip.GradientClipByGlobalNorm(
+                clip_norm=clip_norm_thres))
         scheduled_lr = learning_rate
 
-    clip_norm_thres = 1.0
-    # When using mixed precision training, scale the gradient clip threshold
-    # by loss_scaling
-    if use_fp16 and loss_scaling > 1.0:
-        clip_norm_thres *= loss_scaling
-    fluid.clip.set_gradient_clip(
-        clip=fluid.clip.GradientClipByGlobalNorm(clip_norm=clip_norm_thres))
-
     def exclude_from_weight_decay(name):
         if name.find("layer_norm") > -1:
             return True

PaddleNLP/dialogue_system/dialogue_general_understanding/dgu/prepare_data_and_model.py

@@ -29,7 +29,7 @@ DATA_MODEL_PATH = {
     "DATA_PATH": "https://baidu-nlp.bj.bcebos.com/dmtk_data_1.0.0.tar.gz",
     "PRETRAIN_MODEL":
     "https://bert-models.bj.bcebos.com/uncased_L-12_H-768_A-12.tar.gz",
-    "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_2.0.0.tar.gz"
+    "TRAINED_MODEL": "https://baidu-nlp.bj.bcebos.com/dgu_models_3.0.0.tar.gz"
 }
 
 PATH_MAP = {

PaddleNLP/dialogue_system/dialogue_general_understanding/inference_model.py

@@ -25,7 +25,6 @@ import paddle.fluid as fluid
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 
 import dgu.reader as reader
 from dgu_net import create_net
@@ -97,12 +96,10 @@ def do_save_inference_model(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_params) or (args.init_from_pretrain_model)
+    assert (args.init_from_params)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
-    elif args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
 
     # saving inference model
     fluid.io.save_inference_model(

PaddleNLP/dialogue_system/dialogue_general_understanding/main.py

@@ -16,7 +16,6 @@ import os
 import sys
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 from eval import do_eval

PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py

@@ -19,7 +19,6 @@ import sys
 import numpy as np
 import argparse
 import collections
-import paddle
 import paddle.fluid as fluid
 
 import dgu.reader as reader
@@ -30,7 +29,6 @@ import dgu.define_predict_pack as define_predict_pack
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 from dgu.utils.py23 import tab_tok, rt_tok
 
 
@@ -84,7 +82,7 @@ def do_predict(args):
 
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            data_reader = fluid.io.PyReader(
+            data_reader = fluid.io.DataLoader.from_generator(
                 feed_list=input_inst, capacity=4, iterable=False)
 
             results = create_net(
@@ -95,9 +93,6 @@ def do_predict(args):
                 args=args)
 
             probs = results.get("probs", None)
-
-            probs.persistable = True
-
             fetch_list = [probs.name]
 
     #for_test is True if change the is_test attribute of operators to True
@@ -111,12 +106,10 @@ def do_predict(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_params) or (args.init_from_pretrain_model)
+    assert (args.init_from_params)
 
     if args.init_from_params:
-        save_load_io.init_from_params(args, exe, test_prog)
-    if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, test_prog)
+        fluid.load(test_prog, args.init_from_params)
 
     compiled_test_prog = fluid.CompiledProgram(test_prog)
 
@@ -130,7 +123,7 @@ def do_predict(args):
     batch_generator = processor.data_generator(
         batch_size=args.batch_size, phase='test', shuffle=False)
 
-    data_reader.decorate_batch_generator(batch_generator)
+    data_reader.set_batch_generator(batch_generator, places=place)
     data_reader.start()
 
     all_results = []

PaddleNLP/dialogue_system/dialogue_general_understanding/run.sh

@@ -3,7 +3,7 @@
 export FLAGS_sync_nccl_allreduce=0
 export FLAGS_eager_delete_tensor_gb=1
 
-export CUDA_VISIBLE_DEVICES=1
+export CUDA_VISIBLE_DEVICES=
 if  [ ! "$CUDA_VISIBLE_DEVICES" ]
 then
     export CPU_NUM=1
@@ -21,7 +21,7 @@ SAVE_MODEL_PATH="./data/saved_models/${TASK_NAME}"
 TRAIN_MODEL_PATH="./data/saved_models/trained_models"
 OUTPUT_PATH="./data/output"
 INFERENCE_MODEL="data/inference_models"
-PYTHON_PATH="python3"
+PYTHON_PATH="python"
 
 if [ -f ${SAVE_MODEL_PATH} ]; then
     rm ${SAVE_MODEL_PATH}
@@ -94,7 +94,6 @@ else
   exit 255
 fi
 
-
 #training
 function train()
 {
@@ -110,7 +109,6 @@ function train()
        --vocab_path=${BERT_BASE_PATH}/vocab.txt \
        --init_from_pretrain_model=${BERT_BASE_PATH}/params \
        --save_model_path=${SAVE_MODEL_PATH} \
-       --save_param="params" \
        --save_steps=${save_steps} \
        --learning_rate=${learning_rate} \
        --weight_decay=0.01 \
@@ -128,7 +126,7 @@ function predict()
        --batch_size=${batch_size} \
        --data_dir=${INPUT_PATH} \
        --do_lower_case=true \
-       --init_from_params=${TRAIN_MODEL_PATH}/${TASK_NAME}/params \
+       --init_from_params=${TRAIN_MODEL_PATH}/${TASK_NAME}/params/params \
        --bert_config_path=${BERT_BASE_PATH}/bert_config.json \
        --vocab_path=${BERT_BASE_PATH}/vocab.txt \
        --output_prediction_file=${OUTPUT_PATH}/pred_${TASK_NAME} \

PaddleNLP/dialogue_system/dialogue_general_understanding/train.py

@@ -22,7 +22,6 @@ import sys
 import time
 import numpy as np
 
-import paddle
 import paddle.fluid as fluid
 
 from dgu_net import create_net
@@ -32,7 +31,6 @@ import dgu.define_paradigm as define_paradigm
 from dgu.utils.configure import PDConfig
 from dgu.utils.input_field import InputField
 from dgu.utils.model_check import check_cuda
-import dgu.utils.save_load_io as save_load_io
 
 
 def do_train(args):
@@ -80,8 +78,9 @@ def do_train(args):
 
             input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels]
             input_field = InputField(input_inst)
-            
-            data_reader = fluid.io.DataLoader.from_generator(feed_list=input_inst, capacity=4, iterable=False)
+
+            data_reader = fluid.io.DataLoader.from_generator(
+                feed_list=input_inst, capacity=4, iterable=False)
 
             processor = processors[task_name](data_dir=args.data_dir,
                                               vocab_path=args.vocab_path,
@@ -103,13 +102,8 @@ def do_train(args):
             accuracy = results.get("accuracy", None)
             num_seqs = results.get("num_seqs", None)
 
-            loss.persistable = True
-            probs.persistable = True
-            if accuracy:
-                accuracy.persistable = True
-            num_seqs.persistable = True
-
-            places = fluid.cuda_places() if args.use_cuda else fluid.cpu_places()
+            places = fluid.cuda_places() if args.use_cuda else fluid.cpu_places(
+            )
             dev_count = len(places)
 
             batch_generator = processor.data_generator(
@@ -149,16 +143,13 @@ def do_train(args):
     exe = fluid.Executor(place)
     exe.run(startup_prog)
 
-    assert (args.init_from_checkpoint == "") or (
-        args.init_from_pretrain_model == "")
+    assert args.init_from_params or args.init_from_pretrain_model
 
     # init from some checkpoint, to resume the previous training
-    if args.init_from_checkpoint:
-        save_load_io.init_from_checkpoint(args, exe, train_prog)
-
-    # init from some pretrain models, to better solve the current task
+    if args.init_from_params:
+        fluid.load(train_prog, args.init_from_params, exe)
     if args.init_from_pretrain_model:
-        save_load_io.init_from_pretrain_model(args, exe, train_prog)
+        fluid.load(train_prog, args.init_from_pretrain_model, exe)
 
     build_strategy = fluid.compiler.BuildStrategy()
     build_strategy.enable_inplace = True
@@ -234,21 +225,16 @@ def do_train(args):
                     time_begin = time.time()
 
                 if steps % args.save_steps == 0:
-                    save_path = "step_" + str(steps)
-                    if args.save_checkpoint:
-                        save_load_io.save_checkpoint(args, exe, train_prog,
-                                                     save_path)
-                    if args.save_param:
-                        save_load_io.save_param(args, exe, train_prog,
-                                                save_path)
+                    model_path = os.path.join(args.save_model_path,
+                                              "step_" + str(steps))
+                    fluid.save(train_prog, model_path)
 
             except fluid.core.EOFException:
                 data_reader.reset()
                 break
-    if args.save_checkpoint:
-        save_load_io.save_checkpoint(args, exe, train_prog, "step_final")
-    if args.save_param:
-        save_load_io.save_param(args, exe, train_prog, "step_final")
+
+    model_path = os.path.join(args.save_model_path, "step_final")
+    fluid.save(train_prog, model_path)
 
     def get_cards():
         num = 0