mirror of
https://github.com/jingyaogong/minimind.git
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75 lines
3.4 KiB
Python
75 lines
3.4 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 torch
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import warnings
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from transformers import AutoTokenizer, AutoModelForCausalLM, LlamaConfig, LlamaForCausalLM
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from model.model_minimind import MiniMindConfig, MiniMindForCausalLM
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warnings.filterwarnings('ignore', category=UserWarning)
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# MoE模型需使用此函数转换
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def convert_torch2transformers_minimind(torch_path, transformers_path, dtype=torch.bfloat16):
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MiniMindConfig.register_for_auto_class()
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MiniMindForCausalLM.register_for_auto_class("AutoModelForCausalLM")
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lm_model = MiniMindForCausalLM(lm_config)
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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state_dict = torch.load(torch_path, map_location=device)
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lm_model.load_state_dict(state_dict, strict=False)
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lm_model = lm_model.to(dtype) # 转换模型权重精度
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model_params = sum(p.numel() for p in lm_model.parameters() if p.requires_grad)
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print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)')
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lm_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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print(f"模型已保存为 Transformers-MiniMind 格式: {transformers_path}")
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# LlamaForCausalLM结构兼容第三方生态
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def convert_torch2transformers_llama(torch_path, transformers_path, dtype=torch.bfloat16):
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device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
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state_dict = torch.load(torch_path, map_location=device)
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llama_config = LlamaConfig(
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vocab_size=lm_config.vocab_size,
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hidden_size=lm_config.hidden_size,
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intermediate_size=64 * ((int(lm_config.hidden_size * 8 / 3) + 64 - 1) // 64),
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num_hidden_layers=lm_config.num_hidden_layers,
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num_attention_heads=lm_config.num_attention_heads,
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num_key_value_heads=lm_config.num_key_value_heads,
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max_position_embeddings=lm_config.max_seq_len,
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rms_norm_eps=lm_config.rms_norm_eps,
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rope_theta=lm_config.rope_theta,
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)
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llama_model = LlamaForCausalLM(llama_config)
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llama_model.load_state_dict(state_dict, strict=False)
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llama_model = llama_model.to(dtype) # 转换模型权重精度
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llama_model.save_pretrained(transformers_path)
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model_params = sum(p.numel() for p in llama_model.parameters() if p.requires_grad)
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print(f'模型参数: {model_params / 1e6} 百万 = {model_params / 1e9} B (Billion)')
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tokenizer = AutoTokenizer.from_pretrained('../model/')
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tokenizer.save_pretrained(transformers_path)
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print(f"模型已保存为 Transformers-Llama 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"模型已保存为 PyTorch 格式: {torch_path}")
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if __name__ == '__main__':
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lm_config = MiniMindConfig(hidden_size=768, num_hidden_layers=16, max_seq_len=8192, use_moe=True)
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torch_path = f"../out/full_sft_{lm_config.hidden_size}{'_moe' if lm_config.use_moe else ''}.pth"
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transformers_path = '../MiniMind2-MoE'
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convert_torch2transformers_minimind(torch_path, transformers_path)
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# # # convert transformers to torch model
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# # convert_transformers2torch(transformers_path, torch_path)
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