forked from p04798526/LLaMA-Factory-Mirror
49 lines
1.8 KiB
Python
49 lines
1.8 KiB
Python
# coding=utf-8
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# Copyright 2024 Microsoft Corporation and the LlamaFactory team.
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#
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# This code is inspired by the Microsoft's DeepSpeed library.
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# https://www.deepspeed.ai/tutorials/flops-profiler/
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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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 llamafactory.chat import ChatModel
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def calculate_flops(
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model_name_or_path: str,
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batch_size: int = 1,
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seq_length: int = 256,
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flash_attn: str = "auto",
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):
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r"""
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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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"""
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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="empty", 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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