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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. | |
# ------------------------------------------------------------------------ | |
# Modified from LLaVA (https://github.com/haotian-liu/LLaVA) | |
# Copyright 2024 Jiachen Li | |
# ------------------------------------------------------------------------ | |
import os | |
import warnings | |
import shutil | |
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig | |
import torch | |
from cumo.model import * | |
from cumo.constants import DEFAULT_IMAGE_PATCH_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN | |
def load_pretrained_model(model_path, model_base, model_name, load_8bit=False, load_4bit=False, device_map="auto", device="cuda", use_flash_attn=False, **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 use_flash_attn: | |
kwargs['attn_implementation'] = 'flash_attention_2' | |
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: | |
if 'vicuna' in model_base: | |
from cumo.model.language_model.llava_llama import LlavaConfig | |
lora_cfg_pretrained = LlavaConfig.from_pretrained(model_path) | |
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) | |
print('Loading 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)) | |
elif 'mistral' in model_base: | |
if '8x' in model_base: | |
from cumo.model.language_model.llava_mixtral import LlavaMixtralConfig | |
lora_cfg_pretrained = LlavaMixtralConfig.from_pretrained(model_path) | |
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) | |
print('Loading from base model...') | |
model = LlavaMixtralForCausalLM.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)) | |
else: | |
from cumo.model.language_model.llava_mistral import LlavaMistralConfig | |
lora_cfg_pretrained = LlavaMistralConfig.from_pretrained(model_path) | |
tokenizer = AutoTokenizer.from_pretrained(model_base, use_fast=False) | |
print('Loading from base model...') | |
model = LlavaMistralForCausalLM.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 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...') | |
else: | |
print('Loading from full model...') | |
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) | |
elif 'mistral' in model_name.lower() or 'mixtral' in model_name.lower(): | |
if '8x' in model_name: | |
print('Loading CuMo 8x7b model...') | |
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
tokenizer.pad_token = tokenizer.unk_token | |
model = LlavaMixtralForCausalLM.from_pretrained( | |
model_path, | |
low_cpu_mem_usage=True, | |
**kwargs | |
) | |
else: | |
print('Loading CuMo 7b model...') | |
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
tokenizer.pad_token = tokenizer.unk_token | |
model = LlavaMistralForCausalLM.from_pretrained( | |
model_path, | |
low_cpu_mem_usage=True, | |
**kwargs | |
) | |
else: | |
print('Loading LLaVA origin from full model...') | |
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False) | |
model = LlavaLlamaForCausalLM.from_pretrained( | |
model_path, | |
low_cpu_mem_usage=True, | |
**kwargs | |
) | |
image_processor = None | |
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 device_map != 'auto': | |
vision_tower.to(device=device_map, dtype=torch.float16) | |
image_processor = vision_tower.image_processor | |
if hasattr(model.config, "max_sequence_length"): | |
context_len = model.config.max_sequence_length | |
else: | |
context_len = 2048 | |
return tokenizer, model, image_processor, context_len | |