forked from mindspore-Ecosystem/mindspore
update tiny bert script and readme
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@ -44,12 +44,12 @@ After installing MindSpore via the official website, you can start general disti
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# run standalone general distill example
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bash scripts/run_standalone_gd.sh
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Before running the shell script, please set the `load_teacher_ckpt_path`, `data_dir` and `schema_dir` in the run_standalone_gd.sh file first. If running on GPU, please set the `device_target=GPU`.
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Before running the shell script, please set the `load_teacher_ckpt_path`, `data_dir`, `schema_dir` and `dataset_type` in the run_standalone_gd.sh file first. If running on GPU, please set the `device_target=GPU`.
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# For Ascend device, run distributed general distill example
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bash scripts/run_distributed_gd_ascend.sh 8 1 /path/hccl.json
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Before running the shell script, please set the `load_teacher_ckpt_path`, `data_dir` and `schema_dir` in the run_distributed_gd_ascend.sh file first.
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Before running the shell script, please set the `load_teacher_ckpt_path`, `data_dir`, `schema_dir` and `dataset_type` in the run_distributed_gd_ascend.sh file first.
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# For GPU device, run distributed general distill example
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bash scripts/run_distributed_gd_gpu.sh 8 1 /path/data/ /path/schema.json /path/teacher.ckpt
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@ -57,7 +57,7 @@ bash scripts/run_distributed_gd_gpu.sh 8 1 /path/data/ /path/schema.json /path/t
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# run task distill and evaluation example
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bash scripts/run_standalone_td.sh
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Before running the shell script, please set the `task_name`, `load_teacher_ckpt_path`, `load_gd_ckpt_path`, `train_data_dir`, `eval_data_dir` and `schema_dir` in the run_standalone_td.sh file first.
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Before running the shell script, please set the `task_name`, `load_teacher_ckpt_path`, `load_gd_ckpt_path`, `train_data_dir`, `eval_data_dir`, `schema_dir` and `dataset_type` in the run_standalone_td.sh file first.
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If running on GPU, please set the `device_target=GPU`.
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```
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@ -101,7 +101,7 @@ usage: run_general_distill.py [--distribute DISTRIBUTE] [--epoch_size N] [----
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[--save_ckpt_path SAVE_CKPT_PATH]
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[--load_teacher_ckpt_path LOAD_TEACHER_CKPT_PATH]
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[--save_checkpoint_step N] [--max_ckpt_num N]
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[--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR] [train_steps N]
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[--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR] [--dataset_type DATASET_TYPE] [train_steps N]
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options:
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--device_target device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
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@ -118,6 +118,7 @@ options:
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--load_teacher_ckpt_path path to load teacher checkpoint files: PATH, default is ""
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--data_dir path to dataset directory: PATH, default is ""
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--schema_dir path to schema.json file, PATH, default is ""
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--dataset_type the dataset type which can be tfrecord/mindrecord, default is tfrecord
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```
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### Task Distill
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@ -132,7 +133,7 @@ usage: run_general_task.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN
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[--load_td1_ckpt_path LOAD_TD1_CKPT_PATH]
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[--train_data_dir TRAIN_DATA_DIR]
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[--eval_data_dir EVAL_DATA_DIR]
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[--task_name TASK_NAME] [--schema_dir SCHEMA_DIR]
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[--task_name TASK_NAME] [--schema_dir SCHEMA_DIR] [--dataset_type DATASET_TYPE]
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options:
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--device_target device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
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@ -153,6 +154,7 @@ options:
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--eval_data_dir path to eval dataset directory: PATH, default is ""
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--task_name classification task: "SST-2" | "QNLI" | "MNLI", default is ""
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--schema_dir path to schema.json file, PATH, default is ""
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--dataset_type the dataset type which can be tfrecord/mindrecord, default is tfrecord
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```
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## Options and Parameters
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@ -344,4 +346,4 @@ In run_general_distill.py, we set the random seed to make sure distribute traini
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# [ModelZoo Homepage](#contents)
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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Please check the official [homepage](https://gitee.com/mindspore/mindspore/tree/master/model_zoo).
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@ -55,7 +55,8 @@ def run_general_distill():
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parser.add_argument("--load_teacher_ckpt_path", type=str, default="", help="Load checkpoint file path")
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parser.add_argument("--data_dir", type=str, default="", help="Data path, it is better to use absolute path")
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parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
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parser.add_argument("--dataset_type", type=str, default="tfrecord", help="dataset type, default is tfrecord")
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parser.add_argument("--dataset_type", type=str, default="tfrecord",
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help="dataset type tfrecord/mindrecord, default is tfrecord")
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args_opt = parser.parse_args()
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context.set_context(mode=context.GRAPH_MODE, device_target=args_opt.device_target, device_id=args_opt.device_id)
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@ -68,7 +68,8 @@ def parse_args():
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parser.add_argument("--schema_dir", type=str, default="", help="Schema path, it is better to use absolute path")
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parser.add_argument("--task_name", type=str, default="", choices=["SST-2", "QNLI", "MNLI"],
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help="The name of the task to train.")
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parser.add_argument("--dataset_type", type=str, default="tfrecord", help="dataset type, default is tfrecord")
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parser.add_argument("--dataset_type", type=str, default="tfrecord",
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help="dataset type tfrecord/mindrecord, default is tfrecord")
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args = parser.parse_args()
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return args
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@ -65,6 +65,7 @@ do
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--max_ckpt_num=1 \
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--load_teacher_ckpt_path="" \
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--data_dir="" \
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--schema_dir="" > log.txt 2>&1 &
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--schema_dir="" \
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--dataset_type="tfrecord" > log.txt 2>&1 &
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cd ../
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done
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@ -37,5 +37,6 @@ mpirun --allow-run-as-root -n $RANK_SIZE \
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--save_ckpt_path="" \
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--data_dir=$DATA_DIR \
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--schema_dir=$SCHEMA_DIR \
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--dataset_type="tfrecord" \
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--enable_data_sink=False \
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--load_teacher_ckpt_path=$TEACHER_CKPT_PATH > log.txt 2>&1 &
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@ -43,5 +43,6 @@ python ${PROJECT_DIR}/../run_task_distill.py \
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--load_td1_ckpt_path="" \
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--train_data_dir="" \
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--eval_data_dir="" \
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--schema_dir="" > log.txt 2>&1 &
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--schema_dir="" \
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--dataset_type="tfrecord" > log.txt 2>&1 &
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