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83ddd7e | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 | # Copyright 2025 Microsoft Corporation and the LlamaFactory team.
#
# This code is inspired by the Microsoft's DeepSpeed library.
# https://www.deepspeed.ai/tutorials/flops-profiler/
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import fire
import torch
from deepspeed.accelerator import get_accelerator # type: ignore
from deepspeed.profiling.flops_profiler import get_model_profile # type: ignore
from llamafactory.chat import ChatModel
def calculate_flops(
model_name_or_path: str,
batch_size: int = 1,
seq_length: int = 512,
flash_attn: str = "auto",
):
r"""Calculate the flops of pre-trained models.
Usage: python cal_flops.py --model_name_or_path path_to_model --batch_size 1 --seq_length 512
"""
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.engine.model.device)
input_dict = {"input_ids": fake_input, "labels": fake_input.clone()}
flops, macs, params = get_model_profile(
chat_model.engine.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)
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