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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.model import *
from llava.constants import DEFAULT_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN
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,
# "offload_folder": model_path,
"cache_dir": r'./'
}
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
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)
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))
print('Loading additional LLaVA 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('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.float16) 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)
# config = AutoConfig.from_pretrained(model_path)
# model1 = LlavaLlamaForCausalLM(config)
# a = torch.load(rf'{model_path}/pytorch_model-00001-of-00003.bin')
# b = torch.load(rf'{model_path}/pytorch_model-00002-of-00003.bin')
# c = torch.load(rf'{model_path}/pytorch_model-00003-of-00003.bin')
# model1.load_state_dict(a, strict=False)
# model1.load_state_dict(b, strict=False)
# model1.load_state_dict(c, strict=False)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
print()
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, torch_dtype=torch.float16, low_cpu_mem_usage=True, device_map="auto")
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.float16)
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)
processor = {}
if 'llava' in model_name.lower():
mm_use_x_start_end = getattr(model.config, "mm_use_x_start_end", False)
mm_use_x_patch_token = getattr(model.config, "mm_use_x_patch_token", True)
X = model.config.X
if mm_use_x_patch_token:
for x in X:
tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True)
if mm_use_x_start_end:
for x in X:
tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], special_tokens=True)
model.resize_token_embeddings(len(tokenizer))
print(X)
if 'Image' in X:
image_tower = model.get_image_tower()
if not image_tower.is_loaded:
image_tower.load_model()
image_tower.to(device=device, dtype=torch.float16)
image_processor = image_tower.image_processor
processor['image'] = image_processor
if 'Video' in X:
video_tower = model.get_video_tower()
if not video_tower.is_loaded:
video_tower.load_model()
video_tower.to(device=device, dtype=torch.float16)
video_processor = video_tower.video_processor
processor['video'] = video_processor
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, processor, context_len
# return tokenizer, model1, processor, context_len
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