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# This file is modified from https://github.com/haotian-liu/LLaVA/
# 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,
PretrainedConfig,
PreTrainedModel,
)
import torch
from llava.model import *
from llava.model.utils import is_mm_model
from llava.model.language_model.llava_llama import LlavaConfig
from llava.constants import (
DEFAULT_IMAGE_PATCH_TOKEN,
DEFAULT_IM_START_TOKEN,
DEFAULT_IM_END_TOKEN,
)
def load_pretrained_model(
model_path,
model_name,
model_base=None,
load_8bit=False,
load_4bit=False,
device_map="auto",
device="cuda",
**kwargs,
):
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
if is_mm_model(model_path):
# Load LLaVA model
## TODO @yunhao: mind fixing lora
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, legacy=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...")
## TODO @yunhao: mind fixing this
elif model_base is not None:
# this may be mm projector only
print("Loading LLaVA from base model...")
cfg_pretrained = AutoConfig.from_pretrained(
model_path, trust_remote_code=True
)
mm_config_wrapper(config, kwargs)
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)
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, legacy=False
)
model = LlavaLlamaForCausalLM.from_pretrained(
model_base, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs
)
else:
config = AutoConfig.from_pretrained(model_path)
config.resume_path = model_path
prepare_config_for_eval(config, kwargs)
if "mpt" in model_name.lower():
model = LlavaMPTForCausalLM.from_pretrained(
model_path, config=config, low_cpu_mem_usage=True, **kwargs
)
elif "mistral" in model_name.lower() or "mixtral" in model_name.lower():
model = LlavaMistralForCausalLM.from_pretrained(
model_path, config=config, low_cpu_mem_usage=True, **kwargs
)
elif "gemma" in model_name.lower():
model = LlavaGemmaForCausalLM.from_pretrained(
model_path, config=config, low_cpu_mem_usage=True, **kwargs
)
else:
# kentang-mit@: llama-2 model
# config._attn_implementation = "flash_attention_2"
model = LlavaLlamaModel(
config=config,
low_cpu_mem_usage=True,
**kwargs
)
tokenizer = model.tokenizer
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.float16)
else:
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, legacy=False
)
model = AutoModelForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
model.eval()
image_processor = None
if is_mm_model(model_path):
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()
vision_tower.to(device=device, dtype=torch.float16)
mm_projector = model.get_mm_projector()
mm_projector.to(device=device, dtype=torch.float16)
image_processor = vision_tower.image_processor
if hasattr(model.llm.config, "max_sequence_length"):
context_len = model.config.max_sequence_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
def parse_model_name_or_path(config: PretrainedConfig, model_name="llm", suffix="_cfg"):
target_model = f"{model_name}{suffix}"
target_cfg = getattr(config, target_model, None)
if isinstance(target_cfg, str):
return target_cfg
elif isinstance(target_cfg, dict):
return target_cfg["architectures"][0]
else:
raise ValueError(f"Invalid {target_model} configuration!")
def prepare_config_for_eval(config: PretrainedConfig, kwargs: dict):
try:
# compatible with deprecated config convention
if getattr(config, "vision_tower_cfg", None) is None:
config.vision_tower_cfg = config.mm_vision_tower
except AttributeError:
raise ValueError(f"Invalid configuration! Cannot find vision_tower in config:\n{config}")
config.model_dtype = kwargs.pop("torch_dtype").__str__()
# siglip does not support device_map = "auto"
vision_tower_name = parse_model_name_or_path(config, "vision_tower")
if "siglip" in vision_tower_name.lower():
kwargs["device_map"] = "cuda" |