import os
import warnings
from transformers import (
AutoTokenizer,
AutoModelForCausalLM,
AutoConfig,
BitsAndBytesConfig,
CLIPImageProcessor,
)
import torch
from .language_model.llava_phi import LlavaPhiForCausalLM
from .language_model.configuration_llava_phi import LlavaPhiConfig
IGNORE_INDEX = -100
IMAGE_TOKEN_INDEX = -200
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
def load_pretrained_model(
model_path,
model_base,
model_name,
load_8bit=False,
load_4bit=False,
device_map="cuda",
device="cuda",
):
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: # TODO: after fine-tuning LLava-Phi, load the model weights with fp16 will pose nan
# kwargs['torch_dtype'] = torch.float16
if "phi" in model_name.lower():
# Load LLaVA-Phi 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)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
print("Loading LLaVA-Phi from base model...")
model = LlavaPhiForCausalLM.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-Phi 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-Phi from base model...")
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaPhiForCausalLM.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:
print("load llaVA-Phi MLLM!!!")
config = LlavaPhiConfig.from_pretrained(model_path, trust_remote_code=True)
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=True)
model = LlavaPhiForCausalLM.from_pretrained(
model_path, config=config, use_safetensors=True, **kwargs
).to("cuda")
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:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
image_processor = CLIPImageProcessor.from_pretrained(model_path)
if "phi" 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)
# TODO: the tokenizer length of phi-2 is 50295, but the output class of lm_head is 51200
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))
else:
raise ValueError(f"Unsupported model name: {model_name}")
if hasattr(model.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
model.to(device="cuda")
print(kwargs)
return tokenizer, model, image_processor, context_len