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# 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 LLAVA_Biovil.biovil_t.model import ImageModel
from LLAVA_Biovil.biovil_t.pretrained import _download_biovil_t_image_model_weights
from LLAVA_Biovil.biovil_t.types import ImageEncoderType
from LLAVA_Biovil.llava.model.multimodal_projector.builder import build_vision_projector
try:
from LLAVA_Biovil.llava.model import *
from LLAVA_Biovil.llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
except:
from LLAVA_Biovil.llava.model import *
from LLAVA_Biovil.llava.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", **kwargs):
print("Model base: ", model_base)
kwargs = {"device_map": device_map, **kwargs}
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
kwargs['torch_dtype'] = torch.bfloat16
if 'llava' in model_name.lower():
# Load LLaVA model
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.')
if 'lora' in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
if 'LLaVAMed' in model_base:
lora_cfg_pretrained.mm_projector_type = 'linear' #for LLaVA med
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print('Loading LLaVA from base model...')
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, **kwargs)
token_num, tokem_dim = model.lm_head.out_features, model.lm_head.in_features
if model.lm_head.weight.shape[0] != token_num:
model.lm_head.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
model.model.embed_tokens.weight = torch.nn.Parameter(torch.empty(token_num, tokem_dim, device=model.device, dtype=model.dtype))
if model.config.mm_vision_tower == 'biovil':
# reset mm_projector as wrong shape is loaded from pretrained base model
model.model.mm_projector = build_vision_projector(model.config)
model.model.mm_projector.to(device=model.device, dtype=model.dtype)
print('Loading additional LLaVA weights...')
if os.path.exists(os.path.join(model_path, 'non_lora_trainables_extended.bin')): #TODO only for fixed runs, can be deleted later
non_lora_trainables = torch.load(os.path.join(model_path, 'non_lora_trainables_extended.bin'), map_location='cpu')
non_lora_trainables = {(k[7:] if k.startswith('module.') else k): v for k, v in non_lora_trainables.items()}
elif 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('model.model.') 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()}
model.load_state_dict(non_lora_trainables, strict=False)
from peft import PeftModel
print('Loading LoRA weights...')
model = PeftModel.from_pretrained(model, model_path)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Model is loaded...')
elif model_base is not None:
# this may be mm projector only
print('Loading LLaVA from base model...')
if 'mpt' in model_name.lower():
if not os.path.isfile(os.path.join(model_path, 'configuration_mpt.py')):
shutil.copyfile(os.path.join(model_base, 'configuration_mpt.py'), os.path.join(model_path, 'configuration_mpt.py'))
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
cfg_pretrained = AutoConfig.from_pretrained(model_path, trust_remote_code=True)
model = LlavaMPTForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
mm_projector_weights = torch.load(os.path.join(model_path, 'mm_projector.bin'), map_location='cpu')
mm_projector_weights = {k: v.to(torch.bfloat16) for k, v in mm_projector_weights.items()}
model.load_state_dict(mm_projector_weights, strict=False)
else:
if 'mpt' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = LlavaMPTForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
# 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)
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path)
print(f"Merging weights")
model = model.merge_and_unload()
print('Convert to FP16...')
model.to(torch.bfloat16)
else:
use_fast = False
if 'mpt' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, trust_remote_code=True, **kwargs)
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():
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)
model.resize_token_embeddings(len(tokenizer))
if model.config.mm_vision_tower == 'biovil':
biovilt_checkpoint_path = _download_biovil_t_image_model_weights()
model_type = ImageEncoderType.RESNET50_MULTI_IMAGE
vision_tower = ImageModel(img_encoder_type=model_type,
joint_feature_size=128,
pretrained_model_path=biovilt_checkpoint_path)
model.model.vision_tower = vision_tower
else:
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(device=device, dtype=torch.bfloat16)
image_processor = vision_tower.image_processor
# if non_lora_trainables contains something about vision_tower, load it
if non_lora_trainables is not None and any(k.startswith('model.vision_tower.') for k in non_lora_trainables):
new_vision_tower_state_dict = {}
for k, v in non_lora_trainables.items(): # we need remapping, because state_dict from model is always like model.vision_tower. It should be vision_tower.
if 'model.vision_tower.vision_tower.' in k: #original CLIP
new_k = k.replace('model.vision_tower.', '')
new_vision_tower_state_dict[new_k] = v
elif 'model.vision_tower' in k: #biovil
new_k = k.replace('model.vision_tower.', '')
new_vision_tower_state_dict[new_k] = v
print('Loaded additional vision tower weights...')
vision_tower.load_state_dict(new_vision_tower_state_dict, strict=False)
# weight difference sum([torch.norm(value-vision_tower.state_dict()[key].cpu()) for key,value in new_vision_tower_state_dict.items()])
image_pooler = model.get_image_pooler()
if image_pooler is not None:
image_pooler.to(device=device, dtype=torch.float16)
if non_lora_trainables is not None and any(k.startswith('model.image_pooler.') for k in non_lora_trainables):
new_image_pooler_state_dict = {}
for k, v in non_lora_trainables.items(): # we need remapping, because state_dict from model is always like model.vision_tower. It should be vision_tower.
if 'model.image_pooler.' in k:
new_k = k.replace('model.image_pooler.', '')
new_image_pooler_state_dict[new_k] = v
print('Loading additional image pooler weights...')
image_pooler.load_state_dict(new_image_pooler_state_dict, strict=True)
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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