Spaces:
Running
on
Zero
Running
on
Zero
File size: 11,588 Bytes
bc752b1 |
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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 |
import os
import warnings
import torch
from transformers import AutoConfig, AutoTokenizer, BitsAndBytesConfig, logging
from vita.constants import GLOBAL_WEIGHTS_PATH
from vita.model import *
logging.set_verbosity_error()
warnings.filterwarnings("ignore")
def load_pretrained_model(
model_path,
model_base,
model_name,
model_type,
load_8bit=False,
load_4bit=False,
device_map="auto",
device="cuda",
**kwargs,
):
if model_type not in {"mixtral-8x7b", "nemo", "qwen2p5_instruct", "qwen2p5_fo_instruct"}:
raise ValueError(f"Unknown Model Type {model_type}")
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
# Load VITA 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."
)
if "lora" in model_name.lower() and model_base is not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
print("Loading VITA from base model...")
if model_type == "mixtral-8x7b":
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = VITAMixtralForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **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 VITA 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 VITA from base model...")
cfg_pretrained = AutoConfig.from_pretrained(model_path)
if model_type == "mixtral-8x7b":
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=True)
model = VITAMixtralForCausalLM.from_pretrained(
model_base, low_cpu_mem_usage=True, **kwargs
)
# load vision encoder
from types import SimpleNamespace
model_args = {
"vision_tower": f"{GLOBAL_WEIGHTS_PATH}/InternViT-300M-448px",
"pretrain_mm_mlp_adapter": None,
"mm_projector_type": "mlp2x_gelu",
}
model_args = SimpleNamespace(**model_args)
model.get_model().initialize_vision_modules(model_args=model_args)
# load audio encoder
from types import SimpleNamespace
model_args = {
'audio_encoder': f"{GLOBAL_WEIGHTS_PATH}/audio-encoder-2wh_zh_en_audioset_Mixtral-8x7B_New-base-tunning",
'freeze_audio_encoder': True,
'freeze_audio_encoder_adapter': True
}
model_args = SimpleNamespace(**model_args)
model.get_model().initialize_audio_modules(model_args=model_args)
audio_encoder = model.get_audio_encoder()
device = torch.device('cuda:0')
audio_encoder = audio_encoder.to(device)
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)
model.model.mm_projector.to(device="cuda", dtype=torch.float16)
model.model.vision_tower.to(device="cuda", dtype=torch.float16)
else:
if model_type == "mixtral-8x7b":
# import pdb; pdb.set_trace()
device_map = {
"model.embed_tokens": 0,
"model.layers.0": 0,
"model.layers.1": 0,
"model.layers.2": 0,
"model.layers.3": 0,
"model.layers.4": 0,
"model.layers.5": 0,
"model.layers.6": 0,
"model.layers.7": 0,
"model.layers.8": 0,
"model.layers.9": 0,
"model.layers.10": 0,
"model.layers.11": 0,
"model.layers.12": 0,
"model.layers.13": 0,
"model.layers.14": 0,
"model.layers.15": 0,
"model.layers.16": 1,
"model.layers.17": 1,
"model.layers.18": 1,
"model.layers.19": 1,
"model.layers.20": 1,
"model.layers.21": 1,
"model.layers.22": 1,
"model.layers.23": 1,
"model.layers.24": 1,
"model.layers.25": 1,
"model.layers.26": 1,
"model.layers.27": 1,
"model.layers.28": 1,
"model.layers.29": 1,
"model.layers.30": 1,
"model.layers.31": 1,
"model.norm": 1,
"model.vision_tower": 1,
"model.mm_projector": 1,
"model.audio_encoder": 1,
"lm_head": 1,
}
device_map["model.audio_encoder"] = 0
kwargs.update(device_map=device_map)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = VITAMixtralForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
# model.hf_device_map
elif model_type == "nemo":
# import pdb; pdb.set_trace()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = VITAMistralForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
elif model_type == "qwen2p5_instruct":
# import pdb; pdb.set_trace()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = VITAQwen2ForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
elif model_type == "qwen2p5_fo_instruct":
# import pdb; pdb.set_trace()
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = VITAFOQwen2ForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
model.resize_token_embeddings(len(tokenizer))
vision_tower = model.get_vision_tower()
if not vision_tower.is_loaded:
vision_tower.load_model()
num_params = sum(p.numel() for p in vision_tower.parameters())
print("the number of vision encoder params: {}M".format(num_params / 1024 / 1024))
if getattr(model.config, "unfreeze_vision_tower", False):
if "lora" in model_name.lower():
assert model_base is not None
vision_non_lora_trainables = {
k[19:]: v
for k, v in non_lora_trainables.items()
if k.startswith("model.vision_tower.")
}
vision_tower.load_state_dict(vision_non_lora_trainables, strict=False)
else:
assert model_base is None
from safetensors.torch import load_file
vision_weights = {}
for file_name in os.listdir(model_path):
if file_name.endswith("safetensors"):
vision_weights.update(
{
k[19:]: v
for k, v in load_file(os.path.join(model_path, file_name)).items()
if k.startswith("model.vision_tower.")
}
)
vision_tower.load_state_dict(vision_weights, strict=True)
# import pdb; pdb.set_trace()
# if (not getattr(model.config, "freeze_audio_encoder", True)) and (not getattr(model.config, "freeze_audio_encoder_adapter", True)):
# from safetensors.torch import load_file
# audio_weights = {}
# for file_name in os.listdir(model_path):
# if file_name.endswith('safetensors'):
# audio_weights.update(
# {k[20:]: v for k, v in load_file(os.path.join(model_path, file_name)).items() if
# k.startswith('model.audio_encoder.')})
# audio_encoder.load_state_dict(audio_weights, strict=True)
# audio_encoder.eval()
# import pdb; pdb.set_trace()
# import pdb; pdb.set_trace()
# from safetensors.torch import load_file
# audio_weights = {}
# for file_name in os.listdir(model_path):
# if file_name.endswith('safetensors'):
# audio_weights.update(
# {k[20:]: v for k, v in load_file(os.path.join(model_path, file_name)).items() if
# k.startswith('model.audio_encoder.')})
# import pdb; pdb.set_trace()
vision_tower.to(dtype=torch.float16)
image_processor = vision_tower.image_processor
#import pdb; pdb.set_trace()
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
if model.generation_config.pad_token_id is None:
model.generation_config.pad_token_id = model.generation_config.eos_token_id
if model_type == "phi-3":
model.generation_config.eos_token_id = tokenizer.eos_token_id
return tokenizer, model, image_processor, context_len
|