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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_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN
from llava.utils import rank0_print
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", attn_implementation="flash_attention_2", customized_config=None, overwrite_config=None, **kwargs):
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 customized_config is not None:
kwargs["config"] = customized_config
if "multimodal" in kwargs:
if kwargs["multimodal"] is True:
is_multimodal = True
kwargs.pop("multimodal")
else:
is_multimodal = False
if "llava" in model_name.lower() or is_multimodal:
# 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)
rank0_print("Loading LLaVA from base model...")
if "mixtral" in model_name.lower():
from llava.model.language_model.llava_mixtral import LlavaMixtralConfig
lora_cfg_pretrained = LlavaMixtralConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaMixtralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "mistral" in model_name.lower():
from llava.model.language_model.llava_mistral import LlavaMistralConfig
lora_cfg_pretrained = LlavaMistralConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaMistralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
from llava.model.language_model.llava_gemma import LlavaGemmaConfig
lora_cfg_pretrained = LlavaGemmaConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaGemmaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
else:
from llava.model.language_model.llava_llama import LlavaConfig
lora_cfg_pretrained = LlavaConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=lora_cfg_pretrained, attn_implementation=attn_implementation, **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))
rank0_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
rank0_print("Loading LoRA weights...")
model = PeftModel.from_pretrained(model, model_path)
rank0_print("Merging LoRA weights...")
model = model.merge_and_unload()
rank0_print("Model is loaded...")
elif model_base is not None: # this may be mm projector only, loading projector with preset language mdoel
rank0_print(f"Loading LLaVA from base model {model_base}...")
if "mixtral" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaMixtralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "mistral" in model_name.lower() or "zephyr" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaGemmaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
elif (
"wizardlm-2" in model_name.lower()
and "vicuna" in model_name.lower()
or "llama" in model_name.lower()
or "yi" in model_name.lower()
or "nous-hermes" in model_name.lower()
or "llava-v1.6-34b" in model_name.lower()
or "llava-v1.5" in model_name.lower()
):
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_name.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False)
llava_cfg = LlavaConfig.from_pretrained(model_path)
model = LlavaLlamaForCausalLM.from_pretrained(model_base, low_cpu_mem_usage=True, config=llava_cfg, **kwargs)
else:
raise ValueError(f"Model {model_name} not supported")
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:
rank0_print(f"Loaded LLaVA model: {model_path}")
if "mixtral" in model_name.lower():
from llava.model.language_model.llava_mixtral import LlavaMixtralConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaMixtralConfig.from_pretrained(model_path)
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMixtralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
elif "mistral" in model_name.lower() or "zephyr" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
model = LlavaMistralForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
elif (
"wizardlm-2" in model_name.lower()
and "vicuna" in model_name.lower()
or "llama" in model_name.lower()
or "yi" in model_name.lower()
or "nous-hermes" in model_name.lower()
or "llava-v1.6-34b" in model_name.lower()
or "llava-v1.5" in model_name.lower()
):
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_name.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
elif "qwen" in model_name.lower() or "quyen" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path)
if "moe" in model_name.lower() or "A14B" in model_name.lower():
from llava.model.language_model.llava_qwen_moe import LlavaQwenMoeConfig
if overwrite_config is not None:
llava_cfg = LlavaQwenMoeConfig.from_pretrained(model_path)
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaQwenMoeForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
else:
model = LlavaQwenMoeForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
else:
from llava.model.language_model.llava_qwen import LlavaQwenConfig
if overwrite_config is not None:
llava_cfg = LlavaQwenConfig.from_pretrained(model_path)
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
else:
model = LlavaQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, **kwargs)
elif "gemma" in model_name.lower():
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
model = LlavaGemmaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, attn_implementation=attn_implementation, **kwargs)
else:
try:
from llava.model.language_model.llava_llama import LlavaConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if customized_config is None:
llava_cfg = LlavaConfig.from_pretrained(model_path)
if "v1.5" in model_path.lower():
llava_cfg.delay_load = True # a workaround for correctly loading v1.5 models
else:
llava_cfg = customized_config
if overwrite_config is not None:
rank0_print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(llava_cfg, k, v)
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, attn_implementation=attn_implementation, config=llava_cfg, **kwargs)
except:
raise ValueError(f"Model {model_name} not supported")
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().replace("prompt", ""):
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)
rank0_print(f"Model Class: {model.__class__.__name__}")
image_processor = None
if "llava" in model_name.lower() or is_multimodal:
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(device_map=device_map)
if device_map != "auto":
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
elif hasattr(model.config, "max_position_embeddings"):
context_len = model.config.max_position_embeddings
elif hasattr(model.config, "tokenizer_model_max_length"):
context_len = model.config.tokenizer_model_max_length
else:
context_len = 2048
return tokenizer, model, image_processor, context_len
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