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Running
on
Zero
File size: 4,193 Bytes
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import os
import warnings
import shutil
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig, BitsAndBytesConfig
import torch
from oryx.model import *
from oryx.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", overwrite_config=None):
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.bfloat16, bnb_4bit_use_double_quant=True, bnb_4bit_quant_type="nf4")
else:
kwargs["torch_dtype"] = torch.bfloat16
if "oryx" in model_name.lower():
# Load Oryx model
if "7b" in model_name.lower():
from oryx.model.language_model.oryx_qwen import OryxQwenConfig
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
if overwrite_config is not None:
cfg_pretrained = OryxQwenConfig.from_pretrained(model_path)
print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(cfg_pretrained, k, v)
model = OryxQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
else:
model = OryxQwenForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
else:
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
cfg_pretrained = AutoConfig.from_pretrained(model_path)
if overwrite_config is not None:
print(f"Overwriting config with {overwrite_config}")
for k, v in overwrite_config.items():
setattr(cfg_pretrained, k, v)
model = OryxLlamaForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, config=cfg_pretrained, **kwargs)
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.bfloat16, 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.bfloat16)
else:
use_fast = False
tokenizer = AutoTokenizer.from_pretrained(model_path, use_fast=False)
model = AutoModelForCausalLM.from_pretrained(model_path, low_cpu_mem_usage=True, **kwargs)
image_processor = None
assert "oryx" in model_name.lower(), "Only Oryx models are supported for video chatbot."
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()
print("Loading 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.bfloat16)
else:
vision_tower.to(device="cuda:0", dtype=torch.bfloat16)
image_processor = vision_tower.image_processor
print("Loading vision tower succeeded.")
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
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