gpt-pretrain/README.md

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# GPT-Pretrain
# Usage
## Make it simple
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask
python lit_export.py --version 0
python generate.py --model_name_or_path exports/version_0 --tokenizer_name_or_path gpt2
```
> :memo: **Note:** Training with a "--use_tril_attention_mask" is recommended. However, huggingface model implementions might not support 2D attention mask. You may write a custom model to support 2D attention mask, just like what I did in [custom_models/gpt2](https://github.com/Yiqing-Zhou/gpt-pretrain/tree/main/custom_models/gpt2).
## Train on multiple GPUs
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy fsdp # default and recommended
```
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy deepspeed
```
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy ddp
```
## Reduce CUDA memory cost
- half precision
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16
```
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --fp16
```
- smaller batch size & accumulate grad batches
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
--train_batch_size 2 --val_batch_size 4 --accumulate_grad_batches 128
```
- cpu_offload
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
--strategy fsdp_cpu_offload
```
```
python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
--strategy deepspeed_stage_3_offload
```