Spaces:
Running
on
Zero
Running
on
Zero
File size: 10,687 Bytes
947767a d419c1d 947767a f2c4ee8 947767a d419c1d 947767a |
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 |
# 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 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,
)
def load_pretrained_model(
model_path,
model_base,
model_name,
load_8bit=False,
load_4bit=False,
device_map="auto",
load_bf16=False,
):
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",
)
elif load_bf16:
kwargs["torch_dtype"] = torch.bfloat16
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 not None:
lora_cfg_pretrained = AutoConfig.from_pretrained(model_path)
tokenizer = AutoTokenizer.from_pretrained(model_path, 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
)
if model.get_vision_tower() is not None and not model.get_vision_tower().is_loaded:
model.get_vision_tower().load_model()
# if the parameters have been ever modified during model training,
# then for some reason, the layer will have the correct shape
# but the weight will have a wrong shape
# the code below fix this weird shape mismatch issue
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)
)
# if the parameters have been ever modified during model training,
# then for some reason, the layer will have the correct shape
# but the weight will have a wrong shape
# the code below fix this weird shape mismatch issue
if model.get_vision_tower() is not None:
mm_projector_in, mm_projector_out = (
model.model.mm_projector.in_features,
model.model.mm_projector.out_features,
)
if (
model.model.mm_projector.weight.shape[1] != mm_projector_in
or model.model.mm_projector.weight.shape[0] != mm_projector_out
):
model.model.mm_projector.weight = torch.nn.Parameter(
torch.empty(
mm_projector_out,
mm_projector_in,
device=model.device,
dtype=model.dtype,
)
)
model.model.mm_projector.bias = torch.nn.Parameter(
torch.empty(mm_projector_out, 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, device_map=device_map)
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_path, 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_path, 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
)
# load mm projector weights (this include the vision tower weights too)
if model.get_vision_tower() is not None:
if not model.get_vision_tower().is_loaded:
model.get_vision_tower().load_model()
mm_projector_weights = torch.load(
os.path.join(model_path, "mm_projector.bin"), map_location="cpu"
)
mm_projector_weights = {k: v for k, v in mm_projector_weights.items()}
model.load_state_dict(
mm_projector_weights, strict=False
) # for 3d point cloud, this will load the vision tower too.
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)
model = LlavaLlamaForCausalLM.from_pretrained(
model_path, low_cpu_mem_usage=True, **kwargs
)
else:
# Load language model
if model_base is not None:
# PEFT model
from peft import PeftModel
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(
model_base,
torch_dtype=torch.bfloat16,
low_cpu_mem_usage=True,
device_map=device_map,
)
print(f"Loading LoRA weights from {model_path}")
model = PeftModel.from_pretrained(model, model_path, device_map=device_map)
print(f"Merging weights")
model = model.merge_and_unload()
print("Convert to BF16...")
model.to(torch.bfloat16)
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
)
image_processor = None
if "llava" 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)
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 vision_tower is not None:
if not vision_tower.is_loaded:
vision_tower.load_model()
vision_tower.to(device=model.device, dtype=model.dtype)
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
|