diff --git a/PaddleNLP/dialogue_system/dialogue_general_understanding/README.md b/PaddleNLP/dialogue_system/dialogue_general_understanding/README.md index 330d55118412e74be68be93413d6bd0f18367e1f..7bc45c087ae7b6c961976f1bfee57b762ef75b24 100644 --- a/PaddleNLP/dialogue_system/dialogue_general_understanding/README.md +++ b/PaddleNLP/dialogue_system/dialogue_general_understanding/README.md @@ -145,7 +145,7 @@ batch_size: 一个batch内输入的样本个数 do_lower_case: 是否进行大小写转换 random_seed: 随机种子设置 use_cuda: 是否使用cuda, 如果是gpu训练时，设置成true -in_tokens: 是否采用in_tokens模式来计算batch_siz数量, 如果in_tokens为false, 则batch_size等于真实设置的batch_size大小, 如果in_tokens为true, 则batch_size=batch_size*max_seq_len，即按照token计数 +in_tokens: false do_save_inference_model: 是否保存inference model encable_ce: 是否开启ce ``` @@ -213,9 +213,8 @@ python -u main.py \ --task_name=${TASK_NAME} \ --use_cuda=${use_cuda} \ --do_train=true \ - --in_tokens=true \ --epoch=20 \ - --batch_size=4096 \ + --batch_size=32 \ --do_lower_case=true \ --data_dir="./data/input/data/atis/${TASK_NAME}" \ --bert_config_path="${BERT_BASE_PATH}/bert_config.json" \ @@ -236,7 +235,7 @@ python -u main.py \ #### windows环境下 ``` -python -u main.py --task_name=atis_intent --use_cuda=false --do_train=true --in_tokens=true --epoch=20 --batch_size=4096 --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_param=params --save_steps=100 --learning_rate=2e-5 --weight_decay=0.01 --max_seq_len=128 --print_steps=10 ``` ### 模型预测 @@ -292,8 +291,7 @@ python -u main.py \ --task_name=${TASK_NAME} \ --use_cuda=${use_cuda} \ --do_predict=true \ - --in_tokens=true \ - --batch_size=4096 \ + --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" \ @@ -307,7 +305,7 @@ python -u main.py \ #### windows环境下 ``` -python -u main.py --task_name=atis_intent --use_cuda=false --do_predict=true --in_tokens=true --batch_size=4096 --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 --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 diff --git a/PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py b/PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py index 8cc64f1b2457213471f5760eebdb9d22dd0dd228..0d781b0ff9fb2d26b717840c49e6b8c16e8c75c3 100644 --- a/PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py +++ b/PaddleNLP/dialogue_system/dialogue_general_understanding/predict.py @@ -71,7 +71,7 @@ def do_predict(args): name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64') input_mask = fluid.data( name='input_mask', - shape=[-1, args.max_seq_len], + shape=[-1, args.max_seq_len, 1], dtype='float32') if args.task_name == 'atis_slot': labels = fluid.data( diff --git a/PaddleNLP/dialogue_system/dialogue_general_understanding/run.sh b/PaddleNLP/dialogue_system/dialogue_general_understanding/run.sh index 1cf3aa3fcbbdf83e707a337854dbe09fa391c9f1..3759d859660fedd0465c3d8ea75f1101e13e51ad 100644 --- a/PaddleNLP/dialogue_system/dialogue_general_understanding/run.sh +++ b/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=0 +export CUDA_VISIBLE_DEVICES=1 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="python" +PYTHON_PATH="python3" if [ -f ${SAVE_MODEL_PATH} ]; then rm ${SAVE_MODEL_PATH} @@ -37,8 +37,7 @@ then save_steps=1000 max_seq_len=210 print_steps=1000 - batch_size=6720 - in_tokens=true + batch_size=32 epoch=2 learning_rate=2e-5 elif [ "${TASK_NAME}" = "swda" ] @@ -46,8 +45,7 @@ then save_steps=500 max_seq_len=128 print_steps=200 - batch_size=6720 - in_tokens=true + batch_size=32 epoch=3 learning_rate=2e-5 elif [ "${TASK_NAME}" = "mrda" ] @@ -55,8 +53,7 @@ then save_steps=500 max_seq_len=128 print_steps=200 - batch_size=4096 - in_tokens=true + batch_size=32 epoch=7 learning_rate=2e-5 elif [ "${TASK_NAME}" = "atis_intent" ] @@ -64,8 +61,7 @@ then save_steps=100 max_seq_len=128 print_steps=10 - batch_size=4096 - in_tokens=true + batch_size=32 epoch=20 learning_rate=2e-5 INPUT_PATH="./data/input/data/atis/${TASK_NAME}" @@ -75,7 +71,6 @@ then max_seq_len=128 print_steps=10 batch_size=32 - in_tokens=False epoch=50 learning_rate=2e-5 INPUT_PATH="./data/input/data/atis/${TASK_NAME}" @@ -83,22 +78,23 @@ elif [ "${TASK_NAME}" = "dstc2" ] then save_steps=400 print_steps=20 - batch_size=8192 - in_tokens=true epoch=40 learning_rate=5e-5 INPUT_PATH="./data/input/data/dstc2/${TASK_NAME}" if [ "${TASK_TYPE}" = "train" ] then max_seq_len=256 + batch_size=32 else max_seq_len=512 + batch_size=16 fi else echo "not support ${TASK_NAME} dataset.." exit 255 fi + #training function train() { @@ -106,7 +102,6 @@ function train() --task_name=${TASK_NAME} \ --use_cuda=$1 \ --do_train=true \ - --in_tokens=${in_tokens} \ --epoch=${epoch} \ --batch_size=${batch_size} \ --do_lower_case=true \ @@ -130,7 +125,6 @@ function predict() --task_name=${TASK_NAME} \ --use_cuda=$1 \ --do_predict=true \ - --in_tokens=${in_tokens} \ --batch_size=${batch_size} \ --data_dir=${INPUT_PATH} \ --do_lower_case=true \ diff --git a/PaddleNLP/dialogue_system/dialogue_general_understanding/train.py b/PaddleNLP/dialogue_system/dialogue_general_understanding/train.py index 2ea2a0395a26400ac29d1adadf47f7cea2d9ec32..5d9d14ece472284cf10f23ab1de8914bf7e47974 100644 --- a/PaddleNLP/dialogue_system/dialogue_general_understanding/train.py +++ b/PaddleNLP/dialogue_system/dialogue_general_understanding/train.py @@ -67,7 +67,7 @@ def do_train(args): name='sent_ids', shape=[-1, args.max_seq_len], dtype='int64') input_mask = fluid.data( name='input_mask', - shape=[-1, args.max_seq_len], + shape=[-1, args.max_seq_len, 1], dtype='float32') if args.task_name == 'atis_slot': labels = fluid.data( @@ -80,8 +80,9 @@ def do_train(args): input_inst = [src_ids, pos_ids, sent_ids, input_mask, labels] input_field = InputField(input_inst) - data_reader = fluid.io.PyReader( - 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, max_seq_len=args.max_seq_len, @@ -108,10 +109,8 @@ def do_train(args): accuracy.persistable = True num_seqs.persistable = True - if args.use_cuda: - dev_count = fluid.core.get_cuda_device_count() - else: - dev_count = int(os.environ.get('CPU_NUM', 1)) + places = fluid.cuda_places() if args.use_cuda else fluid.cpu_places() + dev_count = len(places) batch_generator = processor.data_generator( batch_size=args.batch_size, phase='train', shuffle=True) @@ -140,7 +139,7 @@ def do_train(args): use_fp16=False, loss_scaling=args.loss_scaling) - data_reader.decorate_batch_generator(batch_generator) + data_reader.set_batch_generator(batch_generator, places=places) if args.use_cuda: place = fluid.CUDAPlace(int(os.getenv('FLAGS_selected_gpus', '0')))