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import os |
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import shutil |
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from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig |
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import torch |
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from vtimellm.model import * |
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from peft import PeftModel |
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def load_lora(model, lora_path): |
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non_lora_trainables_path = os.path.join(lora_path, 'non_lora_trainables.bin') |
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if os.path.exists(non_lora_trainables_path): |
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non_lora_trainables = torch.load(non_lora_trainables_path, map_location='cpu') |
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non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()} |
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if any(k.startswith('model.model.') for k in non_lora_trainables): |
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non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()} |
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model.load_state_dict(non_lora_trainables, strict=False) |
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print('Loading LoRA weights...') |
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model = PeftModel.from_pretrained(model, lora_path) |
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return model |
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def load_pretrained_model(args, stage2=None, stage3=None, stage4=None, stage5=None): |
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kwargs = {'torch_dtype': torch.float16} |
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model_base = args.model_base |
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print('Loading VTimeLLM from base model...') |
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if 'chatglm' in model_base: |
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tokenizer = AutoTokenizer.from_pretrained(model_base, trust_remote_code=True) |
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model = VTimeLLMChatGLMForCausalLM.from_pretrained(model_base) |
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else: |
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) |
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model = VTimeLLMLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs) |
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token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features |
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if model.lm_head.weight.shape[0] != token_num: |
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model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) |
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model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype)) |
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model = model.cuda() |
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model.get_model().initialize_vision_modules(args) |
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if stage2 is not None and stage2 != "": |
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print('Loading stage2 weights...') |
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model = load_lora(model, stage2) |
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print('Merging stage2 weights...') |
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model = model.merge_and_unload() |
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if stage3 is not None and stage3 != "" : |
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print('Loading stage3 weights...') |
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model = load_lora(model, stage3) |
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print('Merging stage3 weights...') |
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model = model.merge_and_unload() |
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if stage4 is not None and stage4 != "": |
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print('Loading stage4 weights...') |
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model = load_lora(model, stage4) |
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print('Merging stage4 weights...') |
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model = model.merge_and_unload() |
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if stage5 is not None and stage5 != "": |
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print('Loading stage5 weights...') |
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model = load_lora(model, stage5) |
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print('Merging stage5 weights...') |
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model = model.merge_and_unload() |
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if hasattr(model.config, "max_sequence_length"): |
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context_len = model.config.max_sequence_length |
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else: |
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context_len = 2048 |
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return tokenizer, model, context_len |
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