File size: 8,777 Bytes
3334467 | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 | import os
import shutil
import torch
from llava.constants import (DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN,
DEFAULT_IMAGE_PATCH_TOKEN)
from llava.model import *
from transformers import (AutoConfig, AutoModelForCausalLM, AutoTokenizer,
BitsAndBytesConfig)
def load_pretrained_model(
model_path,
model_base,
model_name,
load_8bit=False,
load_4bit=False,
device_map="auto",
):
kwargs = {"device_map": device_map}
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():
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:
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:
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)
model = LlavaLlamaForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
else:
if model_base is not None:
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
)
elif 'stable' in model_name.lower() or 'obsidian' in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
tokenizer.pad_token = tokenizer.unk_token
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaStableLMEpochForCausalLM.from_pretrained(model_path, config=cfg_pretrained, **kwargs)
print('loading mm')
mm_projector_weights = torch.load(os.path.join('./', '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)
print(model)
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))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(device="cuda", dtype=torch.float16)
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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