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