52 lines
2.5 KiB
Python
52 lines
2.5 KiB
Python
import os
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import sys
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__package__ = "scripts"
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sys.path.append(os.path.abspath(os.path.join(os.path.dirname(__file__), '..')))
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import json
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import torch
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import transformers
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import warnings
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from transformers import AutoTokenizer, AutoModelForCausalLM
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from model.model_omni import MiniMindOmni, OmniConfig
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warnings.filterwarnings('ignore', category=UserWarning)
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def convert_torch2transformers(torch_path, transformers_path, lm_config, dtype=torch.bfloat16):
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OmniConfig.register_for_auto_class()
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MiniMindOmni.register_for_auto_class("AutoModelForCausalLM")
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model = MiniMindOmni(lm_config, audio_encoder_path="../model/SenseVoiceSmall", vision_model_path="../model/siglip2-base-p32-256-ve")
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state_dict = torch.load(torch_path, map_location='cpu')
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model.load_state_dict(state_dict, strict=False)
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model = model.to(dtype)
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params = sum(p.numel() for p in model.parameters()) / 1e6
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print(f'模型参数: {params:.2f}M')
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del model.audio_encoder
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del model.vision_encoder
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model.save_pretrained(transformers_path, safe_serialization=False)
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tokenizer = AutoTokenizer.from_pretrained('../model/')
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tokenizer.save_pretrained(transformers_path)
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config_path = os.path.join(transformers_path, "config.json")
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config = json.load(open(config_path, 'r', encoding='utf-8'))
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config['tie_word_embeddings'] = True
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if int(transformers.__version__.split('.')[0]) >= 5:
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tokenizer_config_path = os.path.join(transformers_path, "tokenizer_config.json")
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json.dump({**json.load(open(tokenizer_config_path, 'r', encoding='utf-8')), "tokenizer_class": "PreTrainedTokenizerFast", "extra_special_tokens": {}}, open(tokenizer_config_path, 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
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config['rope_theta'] = lm_config.rope_theta; config['rope_scaling'] = None; config.pop('rope_parameters', None)
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json.dump(config, open(config_path, 'w', encoding='utf-8'), indent=2, ensure_ascii=False)
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print(f"已保存为 Transformers 格式: {transformers_path}")
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def convert_transformers2torch(transformers_path, torch_path):
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model = AutoModelForCausalLM.from_pretrained(transformers_path, trust_remote_code=True)
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torch.save(model.state_dict(), torch_path)
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print(f"已保存为 PyTorch 格式: {torch_path}")
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if __name__ == '__main__':
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lm_config = OmniConfig(hidden_size=768, num_hidden_layers=8, use_moe=False)
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torch_path = f"../out/sft_omni_{lm_config.hidden_size}{'_moe' if lm_config.use_moe else ''}.pth"
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transformers_path = '../minimind-3o'
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convert_torch2transformers(torch_path, transformers_path, lm_config)
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