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# GPT-Pretrain
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## Usage
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# Usage
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## Make it simple
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```
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python lit_train.py --model_name gpt2
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python lit_train.py --model_name gpt2 --use_tril_attention_mask
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python lit_export.py --version 0
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python generate.py --model_name_or_path exports/version_0 --tokenizer_name_or_path gpt2
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```
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> :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).
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## Train on multiple GPUs
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy fsdp # default and recommended
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```
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy deepspeed
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```
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --strategy ddp
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```
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## Reduce CUDA memory cost
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- half precision
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16
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```
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --fp16
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```
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- smaller batch size & accumulate grad batches
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
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--train_batch_size 2 --val_batch_size 4 --accumulate_grad_batches 128
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```
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- cpu_offload
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
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--strategy fsdp_cpu_offload
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```
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```
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python lit_train.py --model_name gpt2 --use_tril_attention_mask --bf16 \
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--strategy deepspeed_stage_3_offload
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```
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