forked from p04798526/LLaMA-Factory-Mirror
34 lines
1.2 KiB
Python
34 lines
1.2 KiB
Python
# coding=utf-8
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# Calculates the flops of pre-trained models.
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# Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
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# Inspired by: https://www.deepspeed.ai/tutorials/flops-profiler/
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from typing import Optional
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import fire
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import torch
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from deepspeed.accelerator import get_accelerator # type: ignore
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from deepspeed.profiling.flops_profiler import get_model_profile # type: ignore
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from llmtuner import ChatModel
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def calculate_flops(
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model_name_or_path: str,
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batch_size: Optional[int] = 1,
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seq_length: Optional[int] = 256,
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flash_attn: Optional[bool] = False,
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):
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with get_accelerator().device(0):
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chat_model = ChatModel(dict(model_name_or_path=model_name_or_path, template="vanilla", flash_attn=flash_attn))
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fake_input = torch.ones((batch_size, seq_length), dtype=torch.long, device=chat_model.model.device)
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input_dict = {"input_ids": fake_input, "labels": fake_input.clone()}
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flops, macs, params = get_model_profile(chat_model.model, kwargs=input_dict, print_profile=True, detailed=True)
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print("FLOPs:", flops)
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print("MACs:", macs)
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print("Params:", params)
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if __name__ == "__main__":
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fire.Fire(calculate_flops)
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