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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_X_PATCH_TOKEN, DEFAULT_X_START_TOKEN, DEFAULT_X_END_TOKEN | |
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda"): | |
kwargs = {"device_map": device_map, | |
# "offload_folder": model_path, | |
"cache_dir": r'./' | |
} | |
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(): | |
# 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) | |
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...') | |
elif model_base is not None: | |
# this may be mm projector only | |
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) | |
# config = AutoConfig.from_pretrained(model_path) | |
# model1 = LlavaLlamaForCausalLM(config) | |
# a = torch.load(rf'{model_path}/pytorch_model-00001-of-00003.bin') | |
# b = torch.load(rf'{model_path}/pytorch_model-00002-of-00003.bin') | |
# c = torch.load(rf'{model_path}/pytorch_model-00003-of-00003.bin') | |
# model1.load_state_dict(a, strict=False) | |
# model1.load_state_dict(b, strict=False) | |
# model1.load_state_dict(c, strict=False) | |
model = LlavaLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs) | |
print() | |
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(): | |
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) | |
processor = {} | |
if 'llava' in model_name.lower(): | |
mm_use_x_start_end = getattr(model.config, "mm_use_x_start_end", False) | |
mm_use_x_patch_token = getattr(model.config, "mm_use_x_patch_token", True) | |
''' | |
X = model.config.X | |
if mm_use_x_patch_token: | |
for x in X: | |
tokenizer.add_tokens([DEFAULT_X_PATCH_TOKEN[x.upper()]], special_tokens=True) | |
if mm_use_x_start_end: | |
for x in X: | |
tokenizer.add_tokens([DEFAULT_X_START_TOKEN[x.upper()], DEFAULT_X_END_TOKEN[x.upper()]], special_tokens=True) | |
''' | |
model.resize_token_embeddings(len(tokenizer)) | |
#print(X) | |
#if 'Image' in X: | |
image_tower = model.get_image_tower() | |
if not image_tower.is_loaded: | |
image_tower.load_model() | |
image_tower.to(device=device, dtype=torch.float16) | |
image_processor = image_tower.image_processor | |
processor['image'] = image_processor | |
#if 'Video' in X: | |
video_tower = model.get_video_tower() | |
if not video_tower.is_loaded: | |
video_tower.load_model() | |
video_tower.to(device=device, dtype=torch.float16) | |
video_processor = video_tower.video_processor | |
processor['video'] = video_processor | |
if hasattr(model.config, "max_sequence_length"): | |
context_len = model.config.max_sequence_length | |
else: | |
context_len = 2048 | |
return tokenizer, model, processor, context_len | |
# return tokenizer, model1, processor, context_len | |