148 lines
7.9 KiB
Python
148 lines
7.9 KiB
Python
# Copyright 2023 Haotian Liu
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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 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 llava.model import *
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from llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
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def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
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kwargs = {"device_map": device_map}
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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.float16
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if 'llava' in model_name.lower():
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# Load LLaVA model
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if 'lora' in model_name.lower() and model_base is None:
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warnings.warn('There is `lora` in model name but no `model_base` is provided. If you are loading a LoRA model, please provide the `model_base` argument. Detailed instruction: https://github.com/haotian-liu/LLaVA#launch-a-model-worker-lora-weights-unmerged.')
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if 'lora' in model_name.lower() and model_base is not None:
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lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
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print('Loading LLaVA from base model...')
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model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **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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print('Loading additional LLaVA 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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else:
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# this is probably from HF Hub
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from huggingface_hub import hf_hub_download
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def load_from_hf(repo_id, filename, subfolder=None):
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cache_file = hf_hub_download(
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repo_id=repo_id,
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filename=filename,
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subfolder=subfolder)
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return torch.load(cache_file, map_location='cpu')
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non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
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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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# this may be mm projector only
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print('Loading LLaVA from base model...')
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if 'mpt' in model_name.lower():
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if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
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shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
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cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
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model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
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else:
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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 = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
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mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
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mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
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model.load_state_dict(mm_projector_weights, strict=False)
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else:
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if 'mpt' in model_name.lower():
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
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model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
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else:
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
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else:
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# Load language model
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if model_base is not None:
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# PEFT model
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from peft import PeftModel
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tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(model_base, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
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print(f"Loading LoRA weights from {model_path}")
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model = PeftModel.from_pretrained(model, model_path)
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print(f"Merging weights")
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model = model.merge_and_unload()
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print('Convert to FP16...')
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model.to(torch.float16)
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else:
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use_fast = False
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if 'mpt' in model_name.lower():
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
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model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
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else:
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tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
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model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
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image_processor = None
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if 'llava' in model_name.lower():
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mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
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mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
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if mm_use_im_patch_token:
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tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
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if mm_use_im_start_end:
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tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
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model.resize_token_embeddings(len(tokenizer))
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vision_tower = model.get_vision_tower()
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if not vision_tower.is_loaded:
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vision_tower.load_model()
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vision_tower.to(device=device, dtype=torch.float16)
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image_processor = vision_tower.image_processor
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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, image_processor, context_len
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