BERT Description

The BERT network was proposed by Google in 2018. The network has made a breakthrough in the field of NLP. The network uses pre-training to achieve a large network structure without modifying, and only by adding an output layer to achieve multiple text-based tasks in fine-tuning. The backbone code of BERT adopts the Encoder structure of Transformer. The attention mechanism is introduced to enable the output layer to capture high-latitude global semantic information. The pre-training uses denoising and self-encoding tasks, namely MLM(Masked Language Model) and NSP(Next Sentence Prediction). No need to label data, pre-training can be performed on massive text data, and only a small amount of data to fine-tuning downstream tasks to obtain good results. The pre-training plus fune-tuning mode created by BERT is widely adopted by subsequent NLP networks.

Paper: Jacob Devlin, Ming-Wei Chang, Kenton Lee, Kristina Toutanova. BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding. arXiv preprint arXiv:1810.04805.

Paper: Junqiu Wei, Xiaozhe Ren, Xiaoguang Li, Wenyong Huang, Yi Liao, Yasheng Wang, Jiashu Lin, Xin Jiang, Xiao Chen, Qun Liu. NEZHA: Neural Contextualized Representation for Chinese Language Understanding. arXiv preprint arXiv:1909.00204.

Model Architecture

The backbone structure of BERT is transformer. For BERT_base, the transformer contains 12 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. For BERT_NEZHA, the transformer contains 24 encoder modules, each module contains one self-attention module and each self-attention module contains one attention module. The difference between BERT_base and BERT_NEZHA is that BERT_base uses absolute position encoding to produce position embedding vector and BERT_NEZHA uses relative position encoding.

Dataset

Create pre-training dataset
- Download the zhwiki or enwiki dataset for pre-training.
- Extract and refine texts in the dataset with WikiExtractor. The commands are as follows:
  - pip install wikiextractor
  - python -m wikiextractor.WikiExtractor -o -b
- Extracted text data from WikiExtractor cannot be trained directly, you have to preprocess the data and convert the dataset to TFRecord format. Please refer to create_pretraining_data.py file in BERT repository and download vocab.txt here, if AttributeError: module 'tokenization' has no attribute 'FullTokenizer' occur, please install bert-tensorflow.
Create fine-tune dataset
- Download dataset for fine-tuning and evaluation such as Chinese Named Entity RecognitionCLUENER, Chinese sentences classificationTNEWS, Chinese Named Entity RecognitionChineseNER, English question and answeringSQuAD v1.1 train dataset, SQuAD v1.1 eval dataset, package of English sentences classificationGLUE.
- We haven't provide the scripts to create tfrecord yet, while converting dataset files from JSON format to TFRECORD format, please refer to run_classifier.py or run_squad.py file in BERT repository or the CLUE official repository CLUE and CLUENER

Pretrained models

We have provided several kinds of pretrained checkpoint.

Bert-base-zh, trained on zh-wiki datasets with 128 length.
Bert-large-zh, trained on zh-wiki datasets with 128 length.
Bert-large-en, tarined on en-wiki datasets with 512 length.

Environment Requirements

Hardware（Ascend）
- Prepare hardware environment with Ascend processor.
Framework
- MindSpore
For more information, please check the resources below：
- MindSpore Tutorials
- MindSpore Python API

Quick Start

After installing MindSpore via the official website, you can start pre-training, fine-tuning and evaluation as follows:

Running on Ascend

# run standalone pre-training example
bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128

# run distributed pre-training example
bash scripts/run_distributed_pretrain_ascend.sh /path/cn-wiki-128 /path/hccl.json

# run the evaluation for pre-training example
# Modify the `eval_ckpt` and `eval_data_dir` in pretrain_config.yaml
python pretrain_eval.py

# run fine-tuning and evaluation example
- If you are going to run a fine-tuning task, please prepare a checkpoint generated from pre-training.
- Set bert network config and optimizer hyperparameters in `task_[DOWNSTREAM_TASK]_config.yaml`.

- Classification task: Set task related hyperparameters in scripts/run_classifier.sh.
- Run `bash scripts/run_classifier.sh [DEVICE_ID]` for fine-tuning of BERT-base and BERT-NEZHA model.

  bash scripts/run_classifier.sh DEVICE_ID(optional)

- NER task: Set task related hyperparameters in scripts/run_ner.sh.
- Run `bash scripts/run_ner.sh [DEVICE_ID]` for fine-tuning of BERT-base and BERT-NEZHA model.

  bash scripts/run_ner.sh DEVICE_ID(optional)

- SQuAD task: Set task related hyperparameters in scripts/run_squad.sh.
- Run `bash scripts/run_squad.sh [DEVICE_ID]` for fine-tuning of BERT-base and BERT-NEZHA model.

  bash scripts/run_squad.sh DEVICE_ID(optional)

running on ModelArts

If you want to run in modelarts, please check the official documentation of modelarts, and you can start training as follows

Pretraining with 8 cards on ModelArts

# (1) Upload the code folder to S3 bucket.
# (2) Click to "create training task" on the website UI interface.
# (3) Set the code directory to "/{path}/bert" on the website UI interface.
# (4) Set the startup file to /{path}/bert/train.py" on the website UI interface.
# (5) Perform a or b.
#     a. setting parameters in /{path}/bert/pretrain_config.yaml.
#         1. Set ”enable_modelarts=True“
#         2. Set other parameters, other parameter configuration can refer to `./scripts/run_distributed_pretrain_ascend.sh`
#     b. adding on the website UI interface.
#         1. Add ”enable_modelarts=True“
#         3. Add other parameters, other parameter configuration can refer to `./scripts/run_distributed_pretrain_ascend.sh`
# (6) Upload the dataset to S3 bucket.
# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path (there is only data or zip package under this path).
# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (9) Under the item "resource pool selection", select the specification of 8 cards.
# (10) Create your job.
# After training, the '*.ckpt' file will be saved under the'training output file path'

Running downstream tasks with single card on ModelArts

# (1) Upload the code folder to S3 bucket.
# (2)  Click to "create training task" on the website UI interface.
# (3) Set the code directory to "/{path}/bert" on the website UI interface.
# (4) Set the startup file to /{path}/bert/run_ner.py"(or run_pretrain.py or run_squad.py) on the website UI interface.
# (5) Perform a or b.
#     a. setting parameters in task_ner_config.yaml(or task_squad_config.yaml or task_classifier_config.yaml under the folder `/{path}/bert/`
#         1. Set ”enable_modelarts=True“
#         2. Set other parameters, other parameter configuration can refer to `run_ner.sh`(or run_squad.sh or run_classifier.sh) under the folder '{path}/bert/scripts/'.
#     b. adding on the website UI interface.
#         1. Add ”enable_modelarts=True“
#         2. Set other parameters, other parameter configuration can refer to `run_ner.sh`(or run_squad.sh or run_classifier.sh) under the folder '{path}/bert/scripts/'.
#     Note that vocab_file_path, label_file_path, train_data_file_path, eval_data_file_path, schema_file_path fill in the relative path relative to the path selected in step 7.
#     Finally, "config_path=/path/*.yaml" must be added on the web page (select the *.yaml configuration file according to the downstream task)
# (6) Upload the dataset to S3 bucket.
# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path (there is only data or zip package under this path).
# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (9) Under the item "resource pool selection", select the specification of a single card.
# (10) Create your job.
# After training, the '*.ckpt' file will be saved under the'training output file path'

For distributed training on Ascend, an hccl configuration file with JSON format needs to be created in advance.

Please follow the instructions in the link below to create an hccl.json file in need: https://gitee.com/mindspore/models/tree/master/utils/hccl_tools.

For distributed training among multiple machines, training command should be executed on each machine in a small time interval. Thus, an hccl.json is needed on each machine. merge_hccl is a tool to create hccl.json for multi-machine case.

For pretraining, schema file contains ["input_ids", "input_mask", "segment_ids", "next_sentence_labels", "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"].

For ner or classification task, schema file contains ["input_ids", "input_mask", "segment_ids", "label_ids"].

For squad task, training: schema file contains ["start_positions", "end_positions", "input_ids", "input_mask", "segment_ids"], evaluation: schema file contains ["input_ids", "input_mask", "segment_ids"].

`numRows` is the only option which could be set by user, other values must be set according to the dataset.

For example, the schema file of cn-wiki-128 dataset for pretraining shows as follows:
{
    "datasetType": "TF",
    "numRows": 7680,
    "columns": {
        "input_ids": {
            "type": "int64",
            "rank": 1,
            "shape": [128]
        },
        "input_mask": {
            "type": "int64",
            "rank": 1,
            "shape": [128]
        },
        "segment_ids": {
            "type": "int64",
            "rank": 1,
            "shape": [128]
        },
        "next_sentence_labels": {
            "type": "int64",
            "rank": 1,
            "shape": [1]
        },
        "masked_lm_positions": {
            "type": "int64",
            "rank": 1,
            "shape": [20]
        },
        "masked_lm_ids": {
            "type": "int64",
            "rank": 1,
            "shape": [20]
        },
        "masked_lm_weights": {
            "type": "float32",
            "rank": 1,
            "shape": [20]
        }
    }
}

Script Description

Script and Sample Code

.
└─bert
  ├─ascend310_infer
  ├─README.md
  ├─README_CN.md
  ├─scripts
    ├─ascend_distributed_launcher
        ├─__init__.py
        ├─hyper_parameter_config.ini          # hyper parameter for distributed pretraining
        ├─get_distribute_pretrain_cmd.py          # script for distributed pretraining
        ├─README.md
    ├─run_classifier.sh                       # shell script for standalone classifier task on ascend
    ├─run_ner.sh                              # shell script for standalone NER task on ascend
    ├─run_squad.sh                            # shell script for standalone SQUAD task on ascend
    ├─run_standalone_pretrain_ascend.sh       # shell script for standalone pretrain on ascend
    ├─run_distributed_pretrain_ascend.sh      # shell script for distributed pretrain on ascend
  ├─src
    ├─model_utils
      ├── config.py                           # parse *.yaml parameter configuration file
      ├── devcie_adapter.py                   # distinguish local/ModelArts training
      ├── local_adapter.py                    # get related environment variables in local training
      └── moxing_adapter.py                   # get related environment variables in ModelArts training
    ├─__init__.py
    ├─assessment_method.py                    # assessment method for evaluation
    ├─bert_for_finetune.py                    # backbone code of network
    ├─bert_for_pre_training.py                # backbone code of network
    ├─bert_model.py                           # backbone code of network
    ├─finetune_data_preprocess.py             # data preprocessing
    ├─cluner_evaluation.py                    # evaluation for cluner
    ├─CRF.py                                  # assessment method for clue dataset
    ├─dataset.py                              # data preprocessing
    ├─finetune_eval_model.py                  # backbone code of network
    ├─sample_process.py                       # sample processing
    ├─utils.py                                # util function
  ├─pretrain_config.yaml                      # parameter configuration for pretrain
  ├─task_ner_config.yaml                      # parameter configuration for downstream_task_ner
  ├─task_classifier_config.yaml               # parameter configuration for downstream_task_classifier
  ├─task_squad_config.yaml                    # parameter configuration for downstream_task_squad
  ├─pretrain_eval.py                          # train and eval net  
  ├─run_classifier.py                         # finetune and eval net for classifier task
  ├─run_ner.py                                # finetune and eval net for ner task
  ├─run_pretrain.py                           # train net for pretraining phase
  └─run_squad.py                              # finetune and eval net for squad task

Script Parameters

Pre-Training

usage: run_pretrain.py  [--distribute DISTRIBUTE] [--epoch_size N] [----device_num N] [--device_id N]
                        [--enable_save_ckpt ENABLE_SAVE_CKPT] [--device_target DEVICE_TARGET]
                        [--enable_lossscale ENABLE_LOSSSCALE] [--do_shuffle DO_SHUFFLE]
                        [--enable_data_sink ENABLE_DATA_SINK] [--data_sink_steps N]
                        [--accumulation_steps N]
                        [--allreduce_post_accumulation ALLREDUCE_POST_ACCUMULATION]
                        [--save_checkpoint_path SAVE_CHECKPOINT_PATH]
                        [--load_checkpoint_path LOAD_CHECKPOINT_PATH]
                        [--save_checkpoint_steps N] [--save_checkpoint_num N]
                        [--data_dir DATA_DIR] [--schema_dir SCHEMA_DIR] [train_steps N]

options:
    --device_target                device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
    --distribute                   pre_training by several devices: "true"(training by more than 1 device) | "false", default is "false"
    --epoch_size                   epoch size: N, default is 1
    --device_num                   number of used devices: N, default is 1
    --device_id                    device id: N, default is 0
    --enable_save_ckpt             enable save checkpoint: "true" | "false", default is "true"
    --enable_lossscale             enable lossscale: "true" | "false", default is "true"
    --do_shuffle                   enable shuffle: "true" | "false", default is "true"
    --enable_data_sink             enable data sink: "true" | "false", default is "true"
    --data_sink_steps              set data sink steps: N, default is 1
    --accumulation_steps           accumulate gradients N times before weight update: N, default is 1
    --allreduce_post_accumulation  allreduce after accumulation of N steps or after each step: "true" | "false", default is "true"
    --save_checkpoint_path         path to save checkpoint files: PATH, default is ""
    --load_checkpoint_path         path to load checkpoint files: PATH, default is ""
    --save_checkpoint_steps        steps for saving checkpoint files: N, default is 1000
    --save_checkpoint_num          number for saving checkpoint files: N, default is 1
    --train_steps                  Training Steps: N, default is -1
    --data_dir                     path to dataset directory: PATH, default is ""
    --schema_dir                   path to schema.json file, PATH, default is ""

Fine-Tuning and Evaluation

usage: run_ner.py   [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
                    [--assessment_method ASSESSMENT_METHOD] [--use_crf USE_CRF] [--with_lstm WITH_LSTM]
                    [--device_id N] [--epoch_num N] [--vocab_file_path VOCAB_FILE_PATH]
                    [--label2id_file_path LABEL2ID_FILE_PATH]
                    [--train_data_shuffle TRAIN_DATA_SHUFFLE]
                    [--eval_data_shuffle EVAL_DATA_SHUFFLE]
                    [--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
                    [--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
                    [--train_data_file_path TRAIN_DATA_FILE_PATH]
                    [--eval_data_file_path EVAL_DATA_FILE_PATH]
                    [--schema_file_path SCHEMA_FILE_PATH]
options:
    --device_target                   device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
    --do_train                        whether to run training on training set: true | false
    --do_eval                         whether to run eval on dev set: true | false
    --assessment_method               assessment method to do evaluation: f1 | clue_benchmark
    --use_crf                         whether to use crf to calculate loss: true | false
    --with_lstm                       Whether to use LSTM subnet after the Bert network: true | false
    --device_id                       device id to run task
    --epoch_num                       total number of training epochs to perform
    --train_data_shuffle              Enable train data shuffle, default is true
    --eval_data_shuffle               Enable eval data shuffle, default is true
    --vocab_file_path                 the vocabulary file that the BERT model was trained on
    --label2id_file_path              label to id file, each label name must be consistent with the type name labeled in the original dataset file
    --save_finetune_checkpoint_path   path to save generated finetuning checkpoint
    --load_pretrain_checkpoint_path   initial checkpoint (usually from a pre-trained BERT model)
    --load_finetune_checkpoint_path   give a finetuning checkpoint path if only do eval
    --train_data_file_path            ner tfrecord for training. E.g., train.tfrecord
    --eval_data_file_path             ner tfrecord for predictions if f1 is used to evaluate result, ner json for predictions if clue_benchmark is used to evaluate result
    --dataset_format                  dataset format, support mindrecord or tfrecord
    --schema_file_path                path to datafile schema file

usage: run_squad.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
                    [--device_id N] [--epoch_num N] [--num_class N]
                    [--vocab_file_path VOCAB_FILE_PATH]
                    [--eval_json_path EVAL_JSON_PATH]
                    [--train_data_shuffle TRAIN_DATA_SHUFFLE]
                    [--eval_data_shuffle EVAL_DATA_SHUFFLE]
                    [--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
                    [--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
                    [--load_finetune_checkpoint_path LOAD_FINETUNE_CHECKPOINT_PATH]
                    [--train_data_file_path TRAIN_DATA_FILE_PATH]
                    [--eval_data_file_path EVAL_DATA_FILE_PATH]
                    [--schema_file_path SCHEMA_FILE_PATH]
options:
    --device_target                   device where the code will be implemented: "Ascend" | "GPU", default is "Ascend"
    --do_train                        whether to run training on training set: true | false
    --do_eval                         whether to run eval on dev set: true | false
    --device_id                       device id to run task
    --epoch_num                       total number of training epochs to perform
    --num_class                       number of classes to classify, usually 2 for squad task
    --train_data_shuffle              Enable train data shuffle, default is true
    --eval_data_shuffle               Enable eval data shuffle, default is true
    --vocab_file_path                 the vocabulary file that the BERT model was trained on
    --eval_json_path                  path to squad dev json file
    --save_finetune_checkpoint_path   path to save generated finetuning checkpoint
    --load_pretrain_checkpoint_path   initial checkpoint (usually from a pre-trained BERT model)
    --load_finetune_checkpoint_path   give a finetuning checkpoint path if only do eval
    --train_data_file_path            squad tfrecord for training. E.g., train1.1.tfrecord
    --eval_data_file_path             squad tfrecord for predictions. E.g., dev1.1.tfrecord
    --schema_file_path                path to datafile schema file

usage: run_classifier.py [--device_target DEVICE_TARGET] [--do_train DO_TRAIN] [----do_eval DO_EVAL]
                         [--assessment_method ASSESSMENT_METHOD] [--device_id N] [--epoch_num N] [--num_class N]
                         [--save_finetune_checkpoint_path SAVE_FINETUNE_CHECKPOINT_PATH]
                         [--load_pretrain_checkpoint_path LOAD_PRETRAIN_CHECKPOINT_PATH]
                         [--load_finetune_checkpoint_path LOAD_FINETUNE_CHECKPOINT_PATH]
                         [--train_data_shuffle TRAIN_DATA_SHUFFLE]
                         [--eval_data_shuffle EVAL_DATA_SHUFFLE]
                         [--train_data_file_path TRAIN_DATA_FILE_PATH]
                         [--eval_data_file_path EVAL_DATA_FILE_PATH]
                         [--schema_file_path SCHEMA_FILE_PATH]
options:
    --device_target                   targeted device to run task: Ascend | GPU
    --do_train                        whether to run training on training set: true | false
    --do_eval                         whether to run eval on dev set: true | false
    --assessment_method               assessment method to do evaluation: accuracy | f1 | mcc | spearman_correlation
    --device_id                       device id to run task
    --epoch_num                       total number of training epochs to perform
    --num_class                       number of classes to do labeling
    --train_data_shuffle              Enable train data shuffle, default is true
    --eval_data_shuffle               Enable eval data shuffle, default is true
    --save_finetune_checkpoint_path   path to save generated finetuning checkpoint
    --load_pretrain_checkpoint_path   initial checkpoint (usually from a pre-trained BERT model)
    --load_finetune_checkpoint_path   give a finetuning checkpoint path if only do eval
    --train_data_file_path            tfrecord for training. E.g., train.tfrecord
    --eval_data_file_path             tfrecord for predictions. E.g., dev.tfrecord
    --schema_file_path                path to datafile schema file

Options and Parameters

Parameters for training and downstream task can be set in yaml config file respectively.

Options

config for lossscale and etc.
    bert_network                    version of BERT model: base | nezha, default is base
    batch_size                      batch size of input dataset: N, default is 32
    loss_scale_value                initial value of loss scale: N, default is 2^32
    scale_factor                    factor used to update loss scale: N, default is 2
    scale_window                    steps for once updatation of loss scale: N, default is 1000
    optimizer                       optimizer used in the network: AdamWerigtDecayDynamicLR | Lamb | Momentum, default is "Lamb"

Parameters

Parameters for dataset and network (Pre-Training/Fine-Tuning/Evaluation):
    seq_length                      length of input sequence: N, default is 128
    vocab_size                      size of each embedding vector: N, must be consistent with the dataset you use. Default is 21128.
                                    Usually, we use 21128 for CN vocabs and 30522 for EN vocabs according to the origin paper.
    hidden_size                     size of bert encoder layers: N, default is 768
    num_hidden_layers               number of hidden layers: N, default is 12
    num_attention_heads             number of attention heads: N, default is 12
    intermediate_size               size of intermediate layer: N, default is 3072
    hidden_act                      activation function used: ACTIVATION, default is "gelu"
    hidden_dropout_prob             dropout probability for BertOutput: Q, default is 0.1
    attention_probs_dropout_prob    dropout probability for BertAttention: Q, default is 0.1
    max_position_embeddings         maximum length of sequences: N, default is 512
    type_vocab_size                 size of token type vocab: N, default is 16
    initializer_range               initialization value of TruncatedNormal: Q, default is 0.02
    use_relative_positions          use relative positions or not: True | False, default is False
    dtype                           data type of input: mstype.float16 | mstype.float32, default is mstype.float32
    compute_type                    compute type in BertTransformer: mstype.float16 | mstype.float32, default is mstype.float16

Parameters for optimizer:
    AdamWeightDecay:
    decay_steps                     steps of the learning rate decay: N
    learning_rate                   value of learning rate: Q
    end_learning_rate               value of end learning rate: Q, must be positive
    power                           power: Q
    warmup_steps                    steps of the learning rate warm up: N
    weight_decay                    weight decay: Q
    eps                             term added to the denominator to improve numerical stability: Q

    Lamb:
    decay_steps                     steps of the learning rate decay: N
    learning_rate                   value of learning rate: Q
    end_learning_rate               value of end learning rate: Q
    power                           power: Q
    warmup_steps                    steps of the learning rate warm up: N
    weight_decay                    weight decay: Q

    Momentum:
    learning_rate                   value of learning rate: Q
    momentum                        momentum for the moving average: Q

Training Process

Training

Running on Ascend

bash scripts/run_standalone_pretrain_ascend.sh 0 1 /path/cn-wiki-128

The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the script folder by default. The loss values will be displayed as follows:

# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.000, step: 2, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0821701e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...

The command above will run in the background, you can view the results the file pretraining_log.txt. After training, you will get some checkpoint files under the script folder by default. The loss value will be achieved as follows:

# grep "epoch" pretraining_log.txt
epoch: 0.0, current epoch percent: 0.000, step: 1, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0856101e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.000, step: 2, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.0821701e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...

Attention If you are running with a huge dataset on Ascend, it's better to add an external environ variable to make sure the hccl won't timeout.
export HCCL_CONNECT_TIMEOUT=600
This will extend the timeout limits of hccl from the default 120 seconds to 600 seconds. Attention If you are running with a big bert model, some error of protobuf may occurs while saving checkpoints, try with the following environ set.
export PROTOCOL_BUFFERS_PYTHON_IMPLEMENTATION=python

Distributed Training

Running on Ascend

bash scripts/run_distributed_pretrain_ascend.sh /path/cn-wiki-128 /path/hccl.json

The command above will run in the background, you can view training logs in pretraining_log.txt. After training finished, you will get some checkpoint files under the LOG* folder by default. The loss value will be displayed as follows:

# grep "epoch" LOG*/pretraining_log.txt
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08209e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07566e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...
epoch: 0.0, current epoch percent: 0.001, step: 100, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.08218e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
epoch: 0.0, current epoch percent: 0.002, step: 200, outputs are (Tensor(shape=[1], dtype=Float32, [ 1.07770e+01]), Tensor(shape=[], dtype=Bool, False), Tensor(shape=[], dtype=Float32, 65536))
...

Attention This will bind the processor cores according to the device_num and total processor numbers. If you don't expect to run pretraining with binding processor cores, remove the operations about taskset in scripts/ascend_distributed_launcher/get_distribute_pretrain_cmd.py

Evaluation Process

Evaluation

evaluation on cola dataset when running on Ascend

Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:

--load_pretrain_checkpoint_path="/data/scripts/checkpoint_bert-20000_1.ckpt" \

--train_data_file_path="/data/tnews/train.tf_record" \

--eval_data_file_path="/data/tnews/dev.tf_record" \

--schema_file_path="/data/tnews/dataset.json"

bash scripts/run_classifier.sh

The command above will run in the background, you can view training logs in classfier_log.txt.

If you choose accuracy as assessment method, the result will be as follows:

acc_num XXX, total_num XXX, accuracy 0.588986

evaluation on cluener dataset when running on Ascend

Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:

--label_file_path="/data/cluener/label_file" \

--load_pretrain_checkpoint_path="/data/scripts/checkpoint_bert-20000_1.ckpt" \

--train_data_file_path="/data/cluener/train.tf_record" \

--eval_data_file_path="/data/cluener/dev.tf_record" \

--schema_file_path="/data/cluener/dataset.json"

bash scripts/run_ner.sh

The command above will run in the background, you can view training logs in ner_log.txt.

If you choose F1 as assessment method, the result will be as follows:

Precision 0.868245
Recall 0.865611
F1 0.866926

evaluation on chineseNer dataset when running on Ascend

Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:

--label_file_path="/data/chineseNer/label_file" \

--load_pretrain_checkpoint_path="/data/scripts/checkpoint_bert-20000_1.ckpt" \

--train_data_file_path="/data/chineseNer/train.tf_record" \

--eval_data_file_path="/data/chineseNer/dev.tf_record" \

--schema_file_path="/data/chineseNer/dataset.json"

bash scripts/run_ner.sh

The command above will run in the background, you can view training logs in ner_log.txt.

If you choose F1 as assessment method, the result will be as follows:

F1 0.986526

evaluation on msra dataset when running on Ascend

For preprocess, you can first convert the original txt format of MSRA dataset into mindrecord by run the command as below (please keep in mind that the label names in label2id_file should be consistent with the type names labeled in the original msra_dataset.xml dataset file):

python src/finetune_data_preprocess.py --data_dir=/path/msra_dataset.xml --vocab_file=/path/vacab_file --save_path=/path/msra_dataset.mindrecord --label2id=/path/label2id_file --max_seq_len=seq_len --class_filter="NAMEX" --split_begin=0.0 --split_end=1.0

For finetune and evaluation, just do

bash scripts/run_ner.sh DEVICE_ID(optional)

The command above will run in the background, you can view training logs in ner_log.txt.

If you choose MF1(F1 score with multi-labels) as assessment method, the result will be as follows if evaluation is done after finetuning 10 epoches:

F1 0.931243

evaluation on squad v1.1 dataset when running on Ascend

Before running the command below, please check the load pretrain checkpoint path has been set. Please set the checkpoint path to be the absolute full path, e.g:

--vocab_file_path="/data/squad/vocab_bert_large_en.txt" \

--load_pretrain_checkpoint_path="/data/scripts/bert_converted.ckpt" \

--train_data_file_path="/data/squad/train.tf_record" \

--eval_json_path="/data/squad/dev-v1.1.json" \

bash scripts/squad.sh

The command above will run in the background, you can view training logs in squad_log.txt. The result will be as follows:

{"exact_match": 80.3878923040233284, "f1": 87.6902384023850329}

Export MindIR

Export on local

We only support export with fine-tuned downstream task model and yaml config file, because the pretrained model is useless in inferences task.

python export.py --config_path [/path/*.yaml] --export_ckpt_file [CKPT_PATH] --export_file_name [FILE_NAME] --file_format [FILE_FORMAT]

Export on ModelArts (If you want to run in modelarts, please check the official documentation of modelarts, and you can start as follows)

# (1) Upload the code folder to S3 bucket.
# (2) Click to "create training task" on the website UI interface.
# (3) Set the code directory to "/{path}/bert" on the website UI interface.
# (4) Set the startup file to /{path}/bert/export.py" on the website UI interface.
# (5) Perform a or b.
#     a. setting parameters in task_ner_config.yaml(or task_squad_config.yaml or task_classifier_config.yaml under the folder `/{path}/bert/`
#         1. Set ”enable_modelarts: True“
#         2. Set “export_ckpt_file: ./{path}/*.ckpt”('export_ckpt_file' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
#         3. Set ”export_file_name: bert_ner“
#         4. Set ”file_format：MINDIR“
#         5. Set ”label_file_path：{path}/*.txt“('label_file_path' refers to the relative path relative to the folder selected in step 7.)
#     b. adding on the website UI interface.
#         1. Add ”enable_modelarts=True“
#         2. Add “export_ckpt_file=./{path}/*.ckpt”('export_ckpt_file' indicates the path of the weight file to be exported relative to the file `export.py`, and the weight file must be included in the code directory.)
#         3. Add ”export_file_name=bert_ner“
#         4. Add ”file_format=MINDIR“
#         5. Add ”label_file_path：{path}/*.txt“('label_file_path' refers to the relative path relative to the folder selected in step 7.)
#     Finally, "config_path=/path/*.yaml" must be added on the web page (select the *.yaml configuration file according to the downstream task)
# (7) Check the "data storage location" on the website UI interface and set the "Dataset path" path.
# (8) Set the "Output file path" and "Job log path" to your path on the website UI interface.
# (9) Under the item "resource pool selection", select the specification of a single card.
# (10) Create your job.
# You will see bert_ner.mindir under {Output file path}.

The export_ckpt_file parameter is required, and file_format should be in ["AIR", "MINDIR"]

Inference Process

Usage

Before performing inference, the mindir file must be exported by export.py. Input files must be in bin format.

# Ascend310 inference
bash run_infer_310.sh [MINDIR_PATH] [LABEL_PATH] [DATA_FILE_PATH] [DATASET_FORMAT] [SCHEMA_PATH] [USE_CRF] [NEED_PREPROCESS] [DEVICE_ID]

NEED_PREPROCESS means weather need preprocess or not, it's value is 'y' or 'n'. USE_CRF is mandatory, and must choose from [true|false], it's case-insensitive. DEVICE_ID is optional, default value is 0.

result

Inference result is saved in current path, you can find result in acc.log file.

F1 0.931243

Export ONNX model and inference

Currently, the ONNX model of Bert classification task can be exported, and third-party tools such as ONNXRuntime can be used to load ONNX for inference.

export ONNX

python export.py --config_path [/path/*.yaml] --file_format ["ONNX"] --export_ckpt_file [CKPT_PATH] --num_class [NUM_CLASS] --export_file_name [EXPORT_FILE_NAME]

'CKPT_PATH' is mandatory, it is the path of the CKPT file that has been trained for a certain classification task model. 'NUM_CLASS' is mandatory, it is the number of categories in the classification task model. 'EXPORT_FILE_NAME' is optional, it is the name of the exported ONNX model. If not set, the ONNX model will be saved in the current directory with the default name.

After running, the ONNX model of Bert will be saved in the current file directory.

Load ONNX and inference

python run_eval_onnx.py --config_path [/path/*.yaml] --eval_data_file_path [EVAL_DATA_FILE_PATH] -export_file_name [EXPORT_FILE_NAME]

'EVAL_DATA_FILE_PATH' is mandatory, it is the eval data of the dataset used by the classification task. 'EXPORT_FILE_NAME' is optional, it is the model name of the ONNX in the step of export ONNX, which is used to load the specified ONNX model for inference.

Model Description

Performance

Pretraining Performance

Parameters	Ascend
Model Version	BERT_base
Resource	Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8
uploaded Date	07/05/2021
MindSpore Version	1.3.0
Dataset	cn-wiki-128(4000w)
Training Parameters	pretrain_config.yaml
Optimizer	Lamb
Loss Function	SoftmaxCrossEntropy
outputs	probability
Epoch	40
Batch_size	256*8
Loss	1.7
Speed	284ms/step
Total time	63H
Params (M)	110M
Checkpoint for Fine tuning	1.2G(.ckpt file)
Scripts	BERT_base

Parameters	Ascend
Model Version	BERT_NEZHA
Resource	Ascend 910; cpu 2.60GHz, 192cores; memory 755G; OS Euler2.8
uploaded Date	07/05/2021
MindSpore Version	1.3.0
Dataset	cn-wiki-128(4000w)
Training Parameters	src/config.py
Optimizer	Lamb
Loss Function	SoftmaxCrossEntropy
outputs	probability
Epoch	40
Batch_size	96*8
Loss	1.7
Speed	320ms/step
Total time	180h
Params (M)	340M
Checkpoint for Fine tuning	3.2G(.ckpt file)
Scripts	BERT_NEZHA

Inference Performance

Parameters	Ascend
Model Version
Resource	Ascend 910; OS Euler2.8
uploaded Date	07/05/2021
MindSpore Version	1.3.0
Dataset	cola, 1.2W
batch_size	32(1P)
Accuracy	0.588986
Speed	59.25ms/step
Total time	15min
Model for inference	1.2G(.ckpt file)

Description of Random Situation

In run_standalone_pretrain.sh and run_distributed_pretrain.sh, we set do_shuffle to True to shuffle the dataset by default.

In run_classifier.sh, run_ner.sh and run_squad.sh, we set train_data_shuffle and eval_data_shuffle to True to shuffle the dataset by default.

In config.py, we set the hidden_dropout_prob and attention_pros_dropout_prob to 0.1 to dropout some network node by default.

In run_pretrain.py, we set a random seed to make sure that each node has the same initial weight in distribute training.

ModelZoo Homepage

Please check the official homepage.

FAQ

Refer to the ModelZoo FAQ for some common question.

Q: How to resolve the continually overflow?

A: Continually overflow is usually caused by using too high learning rate. You could try lower learning_rate to use lower base learning rate or higher power to make learning rate decrease faster in config yaml.
Q: Why the training process failed with error for the shape can not match?

A: This is usually caused by the config seq_length of model can't match the dataset. You could check and modified the seq_length in yaml config according to the dataset you used. The parameter of model won't change with seq_length, the shapes of parameter only depends on model config max_position_embeddings.
Q: Why the training process failed with error about operator Gather?

A: Bert use operator Gather for embedding. The size of vocab is configured by vocab_size in yaml config file. If the vocab used to construct the dataset is larger than config, the operator will failed for the violation access.
Q: Why the modification in yaml config file doesn't take effect?

A: Configuration is defined by both yaml file and command line arguments, additionally with the ini file if you are using ascend_distributed_launcher. The priority of these configuration is command line arguments > ini file > yaml file.

Name		Name	Last commit message	Last commit date
Latest commit History 2 Commits
ascend310_infer		ascend310_infer
infer		infer
infer_squad		infer_squad
modelarts		modelarts
scripts		scripts
src		src
.gitignore		.gitignore
LICENSE		LICENSE
README.md		README.md
README_CN.md		README_CN.md
export.py		export.py
mindspore_hub_conf.py		mindspore_hub_conf.py
postprocess.py		postprocess.py
preprocess.py		preprocess.py
pretrain_config.yaml		pretrain_config.yaml
pretrain_config_Ascend_Boost.yaml		pretrain_config_Ascend_Boost.yaml
pretrain_config_Ascend_Thor.yaml		pretrain_config_Ascend_Thor.yaml
pretrain_eval.py		pretrain_eval.py
requirements.txt		requirements.txt
run_classifier.py		run_classifier.py
run_eval_onnx.py		run_eval_onnx.py
run_ner.py		run_ner.py
run_pretrain.py		run_pretrain.py
run_squad.py		run_squad.py
task_classifier_config.yaml		task_classifier_config.yaml
task_ner_config.yaml		task_ner_config.yaml
task_squad_config.yaml		task_squad_config.yaml

License

yangyucheng000/bert_Ascend

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Contents

Pre-Training

Fine-Tuning and Evaluation

Options and Parameters

Options

Parameters

Training

Running on Ascend

Distributed Training

Running on Ascend

Evaluation

evaluation on cola dataset when running on Ascend

evaluation on cluener dataset when running on Ascend

evaluation on chineseNer dataset when running on Ascend

evaluation on msra dataset when running on Ascend

evaluation on squad v1.1 dataset when running on Ascend

Usage

result

Pretraining Performance

Inference Performance

FAQ

About

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