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# Copyright 2024 Zhenwei Shao and MILVLG team.
# Licensed under the Apache License, Version 2.0.
# Adopted from https://github.com/haotian-liu/LLaVA. Below is the original copyright:
# Copyright 2023 Haotian Liu
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/licenses/LICENSE-2.0
#
# Unless required by applicable law or agreed to in writing, software
# distributed under the License is distributed on an "AS IS" BASIS,
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
# See the License for the specific language governing permissions and
# limitations under the License.
import os
import warnings
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from flashsloth.model import *
from flashsloth.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from flashsloth.model.multimodal_encoder.builder import build_vision_tower
from .multimodal_projector.builder import build_vision_projector
from flashsloth import logger
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"):
kwargs = {"device_map": device_map}
if device != "cuda":
kwargs['device_map'] = {"": device}
if load_8bit:
kwargs['load_in_8bit'] = True
elif load_4bit:
kwargs['load_in_4bit'] = True
kwargs['quantization_config'] = BitsAndBytesConfig(
load_in_4bit=True,
bnb_4bit_compute_dtype=torch.float16,
bnb_4bit_use_double_quant=True,
bnb_4bit_quant_type='nf4'
)
else:
kwargs['torch_dtype'] = torch.float16
logger.info(f'load cfg kwargs: {kwargs}')
if 'llava' in model_name.lower() or 'flashsloth' in model_name.lower():
if 'lora' in model_name.lower() and model_base is None:
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.')
exit()
if 'lora' in model_name.lower() and model_base is not None:
# Load model trained with LoRA
logger.info(f'Load model name trained with LoRA, model base: {model_base}')
assert 'flashsloth' in model_name.lower(), 'The model name must contain `flashsloth`'
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False, trust_remote_code=True)
if 'phi-2' in model_name.lower() or 'phi2' in model_name.lower():
lora_cfg_pretrained = FlashSlothConfig.from_pretrained(model_path)
model = FlashSlothForCausalLM.from_pretrained(model_base, config=lora_cfg_pretrained, **kwargs)
else:
lora_cfg_pretrained = FlashSlothConfig.from_pretrained(model_path)
model = FlashSlothForCausalLM.from_pretrained(model_base, config=lora_cfg_pretrained, **kwargs)
logger.info('Loading additional weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables.bin'), map_location='cpu')
else:
# this is probably from HF Hub
from huggingface_hub import hf_hub_download
def load_from_hf(repo_id, filename, subfolder=None):
cache_file = hf_hub_download(
repo_id=repo_id,
filename=filename,
subfolder=subfolder)
return torch.load(cache_file, map_location='cpu')
non_lora_trainables = load_from_hf(model_path, 'non_lora_trainables.bin')
non_lora_trainables = {(k[11:] if k.startswith('base_model.') else k): v for k, v in non_lora_trainables.items()}
if any(k.startswith(f'model.{model.base_model_prefix}.') for k in non_lora_trainables):
non_lora_trainables = {(k[6:] if k.startswith('model.') else k): v for k, v in non_lora_trainables.items()}
logger.info(f'Loading additional weights: f{[*non_lora_trainables.keys()]}')
model.load_state_dict(non_lora_trainables, strict=False)
from peft import PeftModel
logger.info('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
logger.info('Merging LoRA weights...')
model = model.merge_and_unload()
logger.info('Model is loaded...')
elif model_base is not None:
logger.info('Load mm projector only model...')
if 'phi2' in model_name.lower() or 'phi-2' in model_name.lower():
logger.info(f'model_base:, {model_base}')
config = FlashSlothConfig.from_pretrained(model_path, trust_remote_code=True)
model = FlashSlothForCausalLM.from_pretrained(model_base, **kwargs)
model.model.vision_tower = build_vision_tower(config)
model.model.mm_projector = build_vision_projector(config)
tokenizer = AutoTokenizer.from_pretrained(model_base)
else:
logger.info(f'model_base:, {model_base}')
config = FlashSlothConfig.from_pretrained(model_path, trust_remote_code=True)
model = FlashSlothForCausalLM.from_pretrained(model_base, **kwargs)
model.model.vision_tower = build_vision_tower(config)
model.model.mm_projector = build_vision_projector(config)
tokenizer = AutoTokenizer.from_pretrained(model_base)
mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.float16) for k, v in mm_projector_weights.items()}
logger.info(f'loading mm projector weights: {[*mm_projector_weights.keys()]}')
model.load_state_dict(mm_projector_weights, strict=False)
# model.to(device)
logger.info('Model is loaded...')
else:
logger.info(f'load fully fine-tuned model or HF Hub model: {model_path}')
#hg version
if 'phi2' in model_name.lower() or 'phi-2' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = FlashSlothForCausalLM.from_pretrained(model_path, **kwargs)
logger.info('Model is loaded...')
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, trust_remote_code=True)
model = FlashSlothForCausalLM.from_pretrained(model_path, **kwargs)
logger.info('Model is loaded...')
else:
raise NotImplementedError
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, **kwargs)
logger.info(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
logger.info(f"Merging weights")
model = model.merge_and_unload()
logger.info('Convert to FP16...')
model.to(torch.float16)
else:
if 'phi2' in model_name.lower() or 'flashsloth' in model_name.lower():
raise NotImplementedError
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
image_processor = None
if 'llava' in model_name.lower() or 'flashsloth' in model_name.lower():
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
mm_use_im_patch_token = getattr(model.config, "mm_use_im_patch_token", True)
if mm_use_im_patch_token:
tokenizer.add_tokens([DEFAULT_IMAGE_PATCH_TOKEN], special_tokens=True)
if mm_use_im_start_end:
tokenizer.add_tokens([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN], special_tokens=True)
# FIXME: phi-2 has unused embeddings.
# [Edited by zhenwei - 2024-01-31 13:50]
# model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
logger.info('Delayed vision tower loaded.')
vision_tower.to(device=model.device, dtype=model.dtype)
image_processor = vision_tower.image_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
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