Make Mindspore Training Easier

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MindsporeTrainer

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MindsporeTrainer 是基于昇思MindSpore的训练框架。让Mindspore的算法研究更容易一些。

Mindspore上手不易，希望能帮助各位炼丹师的升级之路更容易些。

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主要特性

主要的几个出发点是：

采用纯python实现，方便多卡训练过程中的调试
易于扩展，对新任务采用插件式接入
方便实现多种模型的训练、评估、预测等

安装

pip

pip install MindsporeTrainer

源码

python setup.py

使用方法

DeBERTa预训练任务示例

1. 定义task

MindsporeTrainer/apps/tasks/deberta.py

from collections import OrderedDict
import numpy as np
import os
import random
from shutil import copyfile
from loguru import logger

from mindspore.communication import get_rank, get_group_size

from MindsporeTrainer.data import ExampleInstance, ExampleSet
from MindsporeTrainer.data.example import *
from MindsporeTrainer.apps.tasks import EvalData, TransformerTask
from MindsporeTrainer.apps.tasks import register_task
from MindsporeTrainer.utils.metrics import *
from MindsporeTrainer.utils.metrics import BertMetric
from MindsporeTrainer.utils.masker import NGramMaskGenerator
from MindsporeTrainer.data.dynamic_dataset import create_dynamic_dataset
from MindsporeTrainer.modeling.tokenizers import BertTokenizer

@register_task(name="DEBERTA", desc="Basic DEBERTA task")
class DEBERTATask(TransformerTask):
    def __init__(self, data_dir, args, **kwargs):
        super().__init__(args, **kwargs)
        self.max_seq_length = 512
        self.model_config = 'data/pretrained_models/deberta-base-v2/model_config.json'
        self.vocab_type = 'BERT'
        self.vocab_path = 'data/pretrained_models/deberta-base-v2/vocab.txt'
        self.data_dir = data_dir
        self.args = args
        self.metric = 'bert'
        self.main_metric = 'perplexity'
        self.optimizer_name = 'Lamb'
        self.tokenizer = BertTokenizer(self.vocab_path)
        if args.distribute:
            self.rank_id = get_rank()
            self.rank_size = get_group_size()
        else:
            self.rank_id = 0
            self.rank_size = 1

    def train_data(self, max_seq_len=512, batch_size=32, **kwargs):
        data_path = os.path.join(self.data_dir, 'daizhige.pkl')
        data = self.load_data(data_path, 'GW', max_seq_len)
        # data = ExampleSet(data)
        output_columns = ["input_ids", "input_mask", "token_type_id", "next_sentence_labels",
                                    "masked_lm_positions", "masked_lm_ids", "masked_lm_weights"]
        return create_dynamic_dataset(data, self.get_feature_fn(), 
                                      batch_size=batch_size,
                                      output_columns=output_columns, 
                                      repeat=self.args.num_train_epochs,
                                      num_workers=self.args.data_workers,
                                      num_shards=self.rank_size, 
                                      shard_id=self.rank_id)

    def eval_data(self, max_seq_len=512, batch_size=32, **kwargs):
        ...

    def get_metrics(self, **kwargs):
        """Calcuate metrics based on prediction results"""
        return OrderedDict(
            bert = BertMetric(self.args.eval_batch_size),
            )

    def get_eval_fn(self, **kwargs):
        data = kwargs.get('data')
        if data is None:
            data = self.eval_data(**kwargs)
        def run_eval(model, name, prefix):
            '''
            args: 
                model: Model instance
                name: evaluate name
                prefix: prefix of file
            return:
                float, main metric of this task, used to save best metric model
            '''
            res = model.eval(data, dataset_sink_mode=False)
            res = res['bert']
            main_metric = res[self.main_metric]
            if self.rank_id == 0:
                output=os.path.join(self.args.output_dir, 'submit-{}-{}.tsv'.format(name, prefix))
                metric_str = '\n'.join([f'{k}: {v:.4f}' for k, v in res.items()])
                metric_str = metric_str + '\n'
                logger.info("====================================")
                logger.info("evaluate result:\n")
                logger.info(metric_str)
                logger.info("====================================")

                with open(output, 'w', encoding='utf-8') as fs:
                    fs.write(f"metrics:\n{metric_str}\n")
            return main_metric
        return run_eval


    def get_feature_fn(self, max_seq_len=512, ext_params=None, rng=None, **kwargs):
        tokenizer = self.tokenizer
        mask_generator = NGramMaskGenerator(tokenizer)
        def example_to_feature(*example):
            '''
            sample: text, label
            return: ["input_ids", "input_mask", "token_type_id", "next_sentence_labels",
                    masked_lm_positions", "masked_lm_ids", "masked_lm_weights"]
            '''
            ......
            return tuple(features)

        return example_to_feature

    def load_data(self, path, type=None, max_seq_len=512):
        examples = []
        ......
        return ExampleSet(examples)


    def get_model(self):
        from MindsporeTrainer.modeling.models import BertPreTraining, DebertaPreTraining
        from MindsporeTrainer import build_transformer_model
        if self.args.fp16:
            compute_type = mstype.float16
        else:
            compute_type = mstype.float32
        model, config = build_transformer_model(self.model_config, 
                                                model=DebertaPreTraining, 
                                                compute_type=compute_type, 
                                                padding_idx=self.tokenizer._convert_token_to_id(self.tokenizer.pad_token))
        with open(os.path.join(self.args.output_dir, 'config.json'), 'w', encoding='utf-8') as f:
            f.write(config.to_json_string())
        copyfile(self.vocab_path, os.path.join(self.args.output_dir, 'vocab.txt'))
        return model
        # return partial_class

    def get_loss(self, *args, **kwargs):
        from MindsporeTrainer.modeling.loss import BertPretrainingLoss
        return BertPretrainingLoss(self.tokenizer.vocab_size)

    def get_eval_head(self, *args, **kwargs):
        from MindsporeTrainer.modeling.models import BertEvalHead
        return BertEvalHead(self.tokenizer.vocab_size)

    def get_opt_fn(self, *args, **kwargs):
        return None

2. 编写启动脚本

run.py

import MindsporeTrainer as mst
mst.launch()

3. 运行任务

python run.py --task_name=RESNETTask --do_train --do_eval --data_dir=data --num_train_epochs=10 --learning_rate=1e-2 --train_batch_size=64 --save_eval_steps=1000 --output_dir=output

多卡训练

官方推荐编写bash脚本，利用mpi进行训练，这里采用了纯python的实现。

定义必须的环境变量

bash

export RANK_SIZE = 8
export RANK_TABLE_FILE = /path/hccl.json

vscode调试环境

"env": {
    "RANK_SIZE": "8",
    "RANK_TABLE_FILE": "/path/hccl.json"
}

设置参数，开始训练

python run.py ...... --device_num=8 --device_id=0,1,2,3,4,5,6,7

模型创建

自定义模型

Mindspore相对TF和PyTorch，有其自身的特点，建模习惯也不同。在本框架中，为了便于模块化与代码复用，将模型分为backbone和predict head两部分。
需要注意的是，模型定义中，construct函数返回都应当是tuple，即使只有一个对象返回，也应当采用(obj,)的形式。

backbone

模型的主体部分定义。

predict head

模型的附属部分，根据模型的用途，通常可以定义为loss层，或者在evaluation过程中定义的eval head，又或者其他用途的头。

使用官方model_zoo中的模型

其代码结构如下

models
├── official                                    # 官方支持模型
│   └── XXX                                     # 模型名
│       ├── README.md                           # 模型说明文档
│       ├── requirements.txt                    # 依赖说明文件
│       ├── eval.py                             # 精度验证脚本
│       ├── export.py                           # 推理模型导出脚本
│       ├── scripts                             # 脚本文件
│       │   ├── run_distributed_train.sh        # 分布式训练脚本
│       │   ├── run_eval.sh                     # 验证脚本
│       │   └── run_standalone_train.sh         # 单机训练脚本
│       ├── src                                 # 模型定义源码目录
│       │   ├── XXXNet.py                       # 模型结构定义
│       │   ├── callback.py                     # 回调函数定义
│       │   ├── config.py                       # 模型配置参数文件
│       │   └── dataset.py                      # 数据集处理定义
│       ├── ascend_infer                        # （可选）用于在Ascend推理设备上进行离线推理的脚本
│       ├── third_party                         # （可选）第三方代码
│       │   └── XXXrepo                         # （可选）完整克隆自第三方仓库的代码
│       └── train.py                            # 训练脚本
├── research                                    # 非官方研究脚本
├── community                                   # 合作方脚本链接
└── utils                                       # 模型通用工具

找到所需的模型目录后，在src/xxxmodel.py中找到相应的定义，如果有定义好的backbone和head，那么可以直接引入使用。
例如使用其中的BERT模型：
git clone https://gitee.com/mindspore/models
复制出bert源码到工作目录models/official/nlp/bert/src
定义task

    from bert.src.bert_for_pre_training import BertPreTraining, BertPretrainingLoss
    ......
    def get_model(self):
        from MindsporeTrainer import build_transformer_model
        if self.args.fp16:
            compute_type = mstype.float16
        else:
            compute_type = mstype.float32
        model, config = build_transformer_model(self.model_config, 
                                                model=BertPreTraining, 
                                                compute_type=compute_type, 
                                                padding_idx=self.tokenizer._convert_token_to_id(self.tokenizer.pad_token))
        with open(os.path.join(self.output_dir, 'config.json'), 'w', encoding='utf-8') as f:
            f.write(config.to_json_string())
        copyfile(self.vocab_path, os.path.join(self.output_dir, 'vocab.txt'))
        return model

    def get_loss(self, *args, **kwargs):
        return BertPretrainingLoss(self.tokenizer.vocab_size)

    def get_eval_head(self, *args, **kwargs):
        from MindsporeTrainer.modeling.models import BertEvalHead
        return BertEvalHead(self.tokenizer.vocab_size)

参数介绍

训练超参数基本上都是通过运行参数来控制的，另外一些则可以在task定义中控制。

commmon args

--accumulation_steps
type=int
default=1
Accumulating gradients N times before weight update, default is 1.
--allreduce_post_accumulation
type=str
default=true
choices=[true, false]
Whether to allreduce after accumulation of N steps or after each step, default is true.
--data_dir
default=None
type=str
required=False
The input data dir. Should contain the .tsv files (or other data files) for the task.
--data_sink_steps
type=int
default=1
Sink steps for each epoch, default is 1.
--do_train
default=False
action='store_true'
Whether to run training.
--do_eval
default=False
action='store_true'
Whether to run eval on the dev set.
--do_predict
default=False
action='store_true'
Whether to run prediction on the test set.
--debug
default=False
action='store_true'
Whether to cache cooked binary features
--device_target
type=str
default='Ascend'
choices=['Ascend 'GPU']
device where the code will be implemented. (Default: Ascend)
--distribute
default=False
action='store_true'
Run distribute, default is false.
--device_id
type=str
default=0
Device id, default is 0.
--device_num
type=int
default=1
Use device nums, default is 1.
--enable_data_sink
default=False
action='store_true'
Enable data sink, default is false.
--load_checkpoint_path
type=str
default=''
Load checkpoint file path
--num_train_epochs
default=1
type=int
Total number of training epochs to perform.
--output_dir
default=None
type=str
required=True
The output directory where the model checkpoints will be written.
--run_mode
type=str
default='GRAPH'
choices=['GRAPH 'PY']
0: GRAPH_MODE, 1: PY_NATIVE_MODE
--save_eval_steps
type=int
default=1000
Save checkpoint and evaluate steps, default is 1000.
--save_checkpoint_num
type=int
default=1
Save checkpoint numbers, default is 1.
--tag
type=str
default='final'
The tag name of current prediction/runs.
--task_dir
default=None
type=str
required=False
The directory to load customized tasks.
--task_name
default=None
type=str
action=LoadTaskAction
required=True
The name of the task to train.

train args

--data_workers
type=int
default=4
The workers to load data.
--enable_graph_kernel
type=str
default=auto
choices=[auto, true, false]
Accelerate by graph kernel, default is auto.
--eval_batch_size
default=32
type=int
Total batch size for eval.
--enable_global_norm
type=bool
default=False
enable global norm
--predict_batch_size
default=32
type=int
Total batch size for prediction.
--report_interval
default=1
type=int
Interval steps for state report.
--save_graphs
default=False
action='store_true'
Whether to save graphs
--seed
type=int
default=1234
random seed for initialization
--thor
default=False
action='store_true' Whether to convert model to thor optimizer
--train_batch_size
default=64
type=int
Total batch size for training.
--train_steps
type=int
default=-1
Training Steps, default is -1, meaning run all steps according to epoch number.

optimizer args

--fp16
default=False
type=boolean_string
Whether to use 16-bit float precision instead of 32-bit
--learning_rate
default=5e-5
type=float
The initial learning rate for Adam.
--loss_scale_value
type=int
default=1024
initial loss scale value
--resume_opt_path
type=str.lower
default=''
The optimizer to be resumed.
--scale_factor
type=int
default=4
loss scale factor
--scale_window
type=int
default=1000
loss window
--warmup
default=0.1
type=float
Proportion of training to perform linear learning rate warmup for. E.g., 0.1 = 10% of training.

task中增加自定义参数

    @classmethod
    def add_arguments(cls, parser):
        """Add task specific arguments
            e.g. parser.add_argument('--data_dir', type=str, help='The path of data directory.')
        """
        parser.add_argument('--task_example_arg', type=str, default=None, help='An example task specific argument')

        return parser

Task

所有的task都应当继承于MindsporeTrainer.apps.tasks.Task,
为transformer定义的MindsporeTrainer.apps.tasks.TransformerTask也继承自Task。
Task类的定义为:

class Task():
    _meta={}

    def __init__(self, args, **kwargs):
        self.args = args
    
    def eval_data(self, **kwargs):
        """
        Get eval dataset object.
        """
        return None

    def train_data(self, **kwargs):
        """
        Get train dataset object.
        """
        return None

    def test_data(self, **kwargs):
        return None

    def get_labels(self):
        """Gets the list of labels for this data set."""
        return None

    def get_eval_fn(self, *args, **kwargs):
        """
        Get the evaluation function
        """
        return None

    def get_eval_head(self, *args, **kwargs):
        """
        Get the evaluate head, the head replace loss function head when evaluation process
        """
        return None

    def get_pred_fn(self, *args, **kwargs):
        """
        Get the predict function
        """
        return None

    def get_loss(self, *args, **kwargs):
        """
        Get the loss function
        """
        return None

    def get_opt_fn(self, *args, **kwargs):
        """
        Get a function wich return the opimizer
        """
        def get_optimizer(*args, **kwargs):
            pass
        return get_optimizer

    def get_metrics(self):
        """Calcuate metrics based on prediction results"""
        return None

    def get_predict_fn(self):
        """Calcuate metrics based on prediction results"""
        def predict_fn(logits, output_dir, name, prefix):
            pass
        return None

    def get_feature_fn(self, **kwargs):
        """
        get the featurize function
        """
        def _example_to_feature(**kwargs):
             return feature
        return _example_to_feature
    
    def get_model(self):
        """
        Get a model instance
        """
        raise NotImplementedError('method not implemented yet.')

    @classmethod
    def add_arguments(cls, parser):
        """Add task specific arguments
        """
        pass

API