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92 lines
4.0 KiB
Python
92 lines
4.0 KiB
Python
import os
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import warnings
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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 opencompass.models.ola.model import *
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from opencompass.models.ola.model.speech_encoder.builder import build_speech_encoder
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def load_pretrained_model(model_path, model_base, is_lora=False, s2s=False, load_8bit=False, load_4bit=False, device="cuda", use_flash_attn=False, **kwargs):
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if load_8bit:
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kwargs['load_in_8bit'] = True
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elif load_4bit:
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kwargs['load_in_4bit'] = True
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kwargs['quantization_config'] = BitsAndBytesConfig(
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load_in_4bit=True,
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bnb_4bit_compute_dtype=torch.float16,
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bnb_4bit_use_double_quant=True,
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bnb_4bit_quant_type='nf4'
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)
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else:
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kwargs['torch_dtype'] = torch.bfloat16
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if use_flash_attn:
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kwargs['attn_implementation'] = 'flash_attention_2'
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model_cls = OlaQwenForCausalLM
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# Load Ola model
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if is_lora:
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assert model_base is not None, "model_base is required for LoRA models."
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from ola.model.language_model.ola_qwen import OlaConfigQwen
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lora_cfg_pretrained = OlaConfigQwen.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
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print('Loading Ola from base model...')
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model = model_cls.from_pretrained(model_base, low_cpu_mem_usage=False, config=lora_cfg_pretrained, **kwargs)
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print('Loading additional Ola weights...')
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if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
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non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), 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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from peft import PeftModel
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print('Loading LoRA weights...')
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model = PeftModel.from_pretrained(model, model_path)
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print('Merging LoRA weights...')
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model = model.merge_and_unload()
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print('Model is loaded...')
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elif model_base is not None:
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print('Loading Ola from base model...')
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
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cfg_pretrained = AutoConfig.from_pretrained(model_path)
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model = model_cls.from_pretrained(model_base, low_cpu_mem_usage=False, config=cfg_pretrained, **kwargs)
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speech_projector_weights = torch.load(os.path.join(model_path, 'speech_projector.bin'), map_location='cpu')
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speech_projector_weights = {k: v.to(torch.float16) for k, v in speech_projector_weights.items()}
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model.load_state_dict(speech_projector_weights, strict=False)
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model = model.to(device=device)
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else:
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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model = model_cls.from_pretrained(
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model_path,
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low_cpu_mem_usage=False,
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**kwargs
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)
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model = model.to(device=device)
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model.get_model().speech_encoder = build_speech_encoder(model.config)
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model.get_model().speech_encoder.to(device=device, dtype=torch.float16)
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image_processor = None
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model.resize_token_embeddings(len(tokenizer))
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vision_tower = model.get_vision_tower()
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print("Loading vision tower...")
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if not vision_tower.is_loaded:
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vision_tower.load_model(device_map=device)
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if device != "auto":
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vision_tower.to(device="cuda", dtype=torch.bfloat16)
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else:
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vision_tower.to(device="cuda:0", dtype=torch.bfloat16)
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image_processor = vision_tower.image_processor
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print("Loading vision tower succeeded.")
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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 = 16384
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return tokenizer, model, image_processor, context_len
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