forked from mindspore-Ecosystem/mindspore
149 lines
9.1 KiB
Markdown
149 lines
9.1 KiB
Markdown
# BERT Example
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## Description
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This example implements pre-training, fine-tuning and evaluation of [BERT-base](https://github.com/google-research/bert)(the base version of BERT model) and [BERT-NEZHA](https://github.com/huawei-noah/Pretrained-Language-Model)(a Chinese pretrained language model developed by Huawei, which introduced a improvement of Functional Relative Positional Encoding as an effective positional encoding scheme).
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## Requirements
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- Install [MindSpore](https://www.mindspore.cn/install/en).
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- Download the zhwiki dataset for pre-training. Extract and clean text in the dataset with [WikiExtractor](https://github.com/attardi/wikiextractor). Convert the dataset to TFRecord format and move the files to a specified path.
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- Download the CLUE dataset for fine-tuning and evaluation.
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> Notes:
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If you are running a fine-tuning or evaluation task, prepare the corresponding checkpoint file.
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## Running the Example
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### Pre-Training
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- Set options in `config.py`, including lossscale, optimizer and network. Click [here](https://www.mindspore.cn/tutorial/zh-CN/master/use/data_preparation/loading_the_datasets.html#tfrecord) for more information about dataset and the json schema file.
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- Run `run_standalone_pretrain.sh` for non-distributed pre-training of BERT-base and BERT-NEZHA model.
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``` bash
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sh scripts/run_standalone_pretrain.sh DEVICE_ID EPOCH_SIZE DATA_DIR SCHEMA_DIR
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```
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- Run `run_distribute_pretrain.sh` for distributed pre-training of BERT-base and BERT-NEZHA model.
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``` bash
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sh scripts/run_distribute_pretrain.sh DEVICE_NUM EPOCH_SIZE DATA_DIR SCHEMA_DIR MINDSPORE_HCCL_CONFIG_PATH
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```
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### Fine-Tuning
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- Set options in `finetune_config.py`. Make sure the 'data_file', 'schema_file' and 'pre_training_file' are set to your own path. Set the 'pre_training_ckpt' to a saved checkpoint file generated after pre-training.
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- Run `finetune.py` for fine-tuning of BERT-base and BERT-NEZHA model.
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```bash
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python finetune.py
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```
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### Evaluation
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- Set options in `evaluation_config.py`. Make sure the 'data_file', 'schema_file' and 'finetune_ckpt' are set to your own path.
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- Run `evaluation.py` for evaluation of BERT-base and BERT-NEZHA model.
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```bash
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python evaluation.py
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```
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## Usage
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### Pre-Training
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```
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usage: run_pretrain.py [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N]
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[--enable_save_ckpt ENABLE_SAVE_CKPT]
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[--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
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[--enable_data_sink ENABLE_DATA_SINK] [--data_sink_steps N] [--checkpoint_path CHECKPOINT_PATH]
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[--save_checkpoint_steps N] [--save_checkpoint_num N]
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[--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR]
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options:
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--distribute pre_training by serveral devices: "true"(training by more than 1 device) | "false", default is "false"
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--epoch_size epoch size: N, default is 1
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--device_num number of used devices: N, default is 1
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--device_id device id: N, default is 0
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--enable_save_ckpt enable save checkpoint: "true" | "false", default is "true"
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--enable_lossscale enable lossscale: "true" | "false", default is "true"
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--do_shuffle enable shuffle: "true" | "false", default is "true"
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--enable_data_sink enable data sink: "true" | "false", default is "true"
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--data_sink_steps set data sink steps: N, default is 1
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--checkpoint_path path to save checkpoint files: PATH, default is ""
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--save_checkpoint_steps steps for saving checkpoint files: N, default is 1000
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--save_checkpoint_num number for saving checkpoint files: N, default is 1
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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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```
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## Options and Parameters
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It contains of parameters of BERT model and options for training, which is set in file `config.py`, `finetune_config.py` and `evaluation_config.py` respectively.
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### Options:
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```
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config.py:
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bert_network version of BERT model: base | nezha, default is base
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loss_scale_value initial value of loss scale: N, default is 2^32
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scale_factor factor used to update loss scale: N, default is 2
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scale_window steps for once updatation of loss scale: N, default is 1000
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optimizer optimizer used in the network: AdamWerigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
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finetune_config.py:
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task task type: NER | XNLI | LCQMC | SENTIi | OTHERS
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num_labels number of labels to do classification
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data_file dataset file to load: PATH, default is "/your/path/train.tfrecord"
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schema_file dataset schema file to load: PATH, default is "/your/path/schema.json"
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epoch_num repeat counts of training: N, default is 5
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ckpt_prefix prefix used to save checkpoint files: PREFIX, default is "bert"
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ckpt_dir path to save checkpoint files: PATH, default is None
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pre_training_ckpt checkpoint file to load: PATH, default is "/your/path/pre_training.ckpt"
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use_crf whether to use crf for evaluation. use_crf takes effect only when task type is NER, default is False
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optimizer optimizer used in fine-tune network: AdamWeigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"
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evaluation_config.py:
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task task type: NER | XNLI | LCQMC | SENTI | OTHERS
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num_labels number of labels to do classsification
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data_file dataset file to load: PATH, default is "/your/path/evaluation.tfrecord"
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schema_file dataset schema file to load: PATH, default is "/your/path/schema.json"
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finetune_ckpt checkpoint file to load: PATH, default is "/your/path/your.ckpt"
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use_crf whether to use crf for evaluation. use_crf takes effect only when task type is NER, default is False
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clue_benchmark whether to use clue benchmark. clue_benchmark takes effect only when task type is NER, default is False
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```
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### Parameters:
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```
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Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
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batch_size batch size of input dataset: N, default is 16
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seq_length length of input sequence: N, default is 128
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vocab_size size of each embedding vector: N, default is 21136
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hidden_size size of bert encoder layers: N, default is 768
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num_hidden_layers number of hidden layers: N, default is 12
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num_attention_heads number of attention heads: N, default is 12
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intermediate_size size of intermediate layer: N, default is 3072
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hidden_act activation function used: ACTIVATION, default is "gelu"
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hidden_dropout_prob dropout probability for BertOutput: Q, default is 0.1
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attention_probs_dropout_prob dropout probability for BertAttention: Q, default is 0.1
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max_position_embeddings maximum length of sequences: N, default is 512
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type_vocab_size size of token type vocab: N, default is 16
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initializer_range initialization value of TruncatedNormal: Q, default is 0.02
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use_relative_positions use relative positions or not: True | False, default is False
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input_mask_from_dataset use the input mask loaded form dataset or not: True | False, default is True
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token_type_ids_from_dataset use the token type ids loaded from dataset or not: True | False, default is True
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dtype data type of input: mstype.float16 | mstype.float32, default is mstype.float32
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compute_type compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16
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Parameters for optimizer:
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AdamWeightDecayDynamicLR:
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decay_steps steps of the learning rate decay: N
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learning_rate value of learning rate: Q
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end_learning_rate value of end learning rate: Q, must be positive
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power power: Q
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warmup_steps steps of the learning rate warm up: N
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weight_decay weight decay: Q
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eps term added to the denominator to improve numerical stability: Q
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Lamb:
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decay_steps steps of the learning rate decay: N
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learning_rate value of learning rate: Q
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end_learning_rate value of end learning rate: Q
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power power: Q
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warmup_steps steps of the learning rate warm up: N
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weight_decay weight decay: Q
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Momentum:
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learning_rate value of learning rate: Q
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momentum momentum for the moving average: Q
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```
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