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https://github.com/hpcaitech/ColossalAI.git
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d202cc28c0
* update accelerator * fix timer * fix amp * update * fix * update bug * add error raise * fix autocast * fix set device * remove doc accelerator * update doc * update doc * update doc * use nullcontext * update cpu * update null context * change time limit for example * udpate * update * update * update * [npu] polish accelerator code --------- Co-authored-by: Xuanlei Zhao <xuanlei.zhao@gmail.com> Co-authored-by: zxl <43881818+oahzxl@users.noreply.github.com>
Pretraining
- Pretraining roberta through running the script below. Detailed parameter descriptions can be found in the arguments.py.
data_path_prefixis absolute path specifies output of preprocessing. You have to modify the hostfile according to your cluster.
bash run_pretrain.sh
--hostfile: servers' host name from /etc/hosts--include: servers which will be used--nproc_per_node: number of process(GPU) from each server--data_path_prefix: absolute location of train data, e.g., /h5/0.h5--eval_data_path_prefix: absolute location of eval data--tokenizer_path: tokenizer path contains huggingface tokenizer.json, e.g./tokenizer/tokenizer.json--bert_config: config.json which represent model--mlm: model type of backbone, bert or deberta_v2
- if resume training from earlier checkpoint, run the script below.
bash run_pretrain_resume.sh
--resume_train: whether to resume training--load_pretrain_model: absolute path which contains model checkpoint--load_optimizer_lr: absolute path which contains optimizer checkpoint