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import argparse
from transformers import AutoTokenizer, AutoModelForCausalLM, AutoConfig
import torch
import os
import json
from tqdm import tqdm
import shortuuid
from llava import LlavaLlamaForCausalLM
from llava.conversation import conv_templates
from llava.utils import disable_torch_init
from transformers import CLIPVisionModel, CLIPImageProcessor, StoppingCriteria
from PIL import Image
import random
import math
def split_list(lst, n):
"""Split a list into n (roughly) equal-sized chunks"""
chunk_size = math.ceil(len(lst) / n) # integer division
return [lst[i:i+chunk_size] for i in range(0, len(lst), chunk_size)]
def get_chunk(lst, n, k):
chunks = split_list(lst, n)
return chunks[k]
DEFAULT_IMAGE_TOKEN = "<image>"
DEFAULT_IMAGE_PATCH_TOKEN = "<im_patch>"
DEFAULT_IM_START_TOKEN = "<im_start>"
DEFAULT_IM_END_TOKEN = "<im_end>"
def patch_config(config):
patch_dict = {
"use_mm_proj": True,
"mm_vision_tower": "openai/clip-vit-large-patch14",
"mm_hidden_size": 1024
}
cfg = AutoConfig.from_pretrained(config)
if not hasattr(cfg, "mm_vision_tower"):
print(f'`mm_vision_tower` not found in `{config}`, applying patch and save to disk.')
for k, v in patch_dict.items():
setattr(cfg, k, v)
cfg.save_pretrained(config)
def eval_model(args):
# Model
disable_torch_init()
model_name = os.path.expanduser(args.model_name)
if 'lora' in model_name.lower():
lora_cfg_pretrained = AutoConfig.from_pretrained(model_name)
tokenizer = AutoTokenizer.from_pretrained(args.base_model_path)
else:
tokenizer = AutoTokenizer.from_pretrained(model_name)
if args.mm_projector is None:
patch_config(model_name)
if 'lora' in model_name.lower():
print('Loading LLaVA from base model...')
llama_state_dict = AutoModelForCausalLM.from_pretrained(args.base_model_path, torch_dtype=torch.float16).state_dict()
model = LlavaLlamaForCausalLM.from_pretrained(args.base_model_path, config=lora_cfg_pretrained, state_dict=llama_state_dict, torch_dtype=torch.float16, ignore_mismatched_sizes=True)
print('Loading additional LLaVA weights...')
if os.path.exists(os.path.join(model_name, 'non_lora_trainables.bin')):
non_lora_trainables = torch.load(os.path.join(model_name, '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_name, '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.embed_tokens') 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()}
non_lora_trainables = {k: v.to(torch.float16) 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_name)
print('Merging LoRA weights...')
model = model.merge_and_unload()
print('Moving to CUDA...')
model = model.cuda()
else:
model = LlavaLlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).cuda()
image_processor = CLIPImageProcessor.from_pretrained(model.config.mm_vision_tower, torch_dtype=torch.float16)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
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)
vision_tower = model.model.vision_tower[0]
vision_tower.to(device='cuda', dtype=torch.float16)
vision_config = vision_tower.config
vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
vision_config.use_im_start_end = mm_use_im_start_end
if mm_use_im_start_end:
vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2
else:
# in case of using a pretrained model with only a MLP projector weights
model = LlavaLlamaForCausalLM.from_pretrained(model_name, torch_dtype=torch.float16, use_cache=True).cuda()
vision_tower = CLIPVisionModel.from_pretrained(args.vision_tower, torch_dtype=torch.float16).cuda()
image_processor = CLIPImageProcessor.from_pretrained(args.vision_tower, torch_dtype=torch.float16)
mm_use_im_start_end = getattr(model.config, "mm_use_im_start_end", False)
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)
vision_config = vision_tower.config
vision_config.im_patch_token = tokenizer.convert_tokens_to_ids([DEFAULT_IMAGE_PATCH_TOKEN])[0]
vision_config.use_im_start_end = mm_use_im_start_end
if mm_use_im_start_end:
vision_config.im_start_token, vision_config.im_end_token = tokenizer.convert_tokens_to_ids([DEFAULT_IM_START_TOKEN, DEFAULT_IM_END_TOKEN])
image_token_len = (vision_config.image_size // vision_config.patch_size) ** 2
mm_projector = torch.nn.Linear(vision_config.hidden_size, model.config.hidden_size)
mm_projector_weights = torch.load(args.mm_projector, map_location='cpu')
mm_projector.load_state_dict({k.split('.')[-1]: v for k, v in mm_projector_weights.items()})
model.model.mm_projector = mm_projector.cuda().half()
model.model.vision_tower = [vision_tower]
questions = [json.loads(q) for q in open(os.path.expanduser(args.question_file), "r")]
questions = get_chunk(questions, args.num_chunks, args.chunk_idx)
answers_file = os.path.expanduser(args.answers_file)
os.makedirs(os.path.dirname(answers_file), exist_ok=True)
ans_file = open(answers_file, "w")
for i, line in enumerate(tqdm(questions)):
idx = line["question_id"]
image_file = line["image"]
qs = line["text"]
cur_prompt = qs
if mm_use_im_start_end:
qs = qs + '\n' + DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len + DEFAULT_IM_END_TOKEN
else:
qs = qs + '\n' + DEFAULT_IMAGE_PATCH_TOKEN * image_token_len
conv = conv_templates[args.conv_mode].copy()
conv.append_message(conv.roles[0], qs)
if args.conv_mode != 'simple':
conv.append_message(conv.roles[1], "")
prompt = conv.get_prompt()
inputs = tokenizer([prompt])
image = Image.open(os.path.join(args.image_folder, image_file))
image_tensor = image_processor.preprocess(image, return_tensors='pt')['pixel_values'][0]
input_ids = torch.as_tensor(inputs.input_ids).cuda()
# new stopping implementation
class KeywordsStoppingCriteria(StoppingCriteria):
def __init__(self, keywords, tokenizer, input_ids):
self.keywords = keywords
self.tokenizer = tokenizer
self.start_len = None
self.input_ids = input_ids
def __call__(self, output_ids: torch.LongTensor, scores: torch.FloatTensor, **kwargs) -> bool:
if self.start_len is None:
self.start_len = self.input_ids.shape[1]
else:
outputs = self.tokenizer.batch_decode(output_ids[:, self.start_len:], skip_special_tokens=True)[0]
for keyword in self.keywords:
if keyword in outputs:
return True
return False
if args.conv_mode == 'simple':
keywords = ['###']
else:
keywords = [conv.sep2]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=image_tensor.unsqueeze(0).half().cuda(),
do_sample=True,
temperature=0.7,
max_new_tokens=1024,
use_cache=True,
stopping_criteria=[stopping_criteria])
input_token_len = input_ids.shape[1]
n_diff_input_output = (input_ids != output_ids[:, :input_token_len]).sum().item()
if n_diff_input_output > 0:
print(f'[Warning] Sample {i}: {n_diff_input_output} output_ids are not the same as the input_ids')
outputs = tokenizer.batch_decode(output_ids[:, input_token_len:], skip_special_tokens=True)[0].strip()
if args.conv_mode == 'simple':
while True:
cur_len = len(outputs)
outputs = outputs.strip()
for pattern in ['###', 'Assistant:', 'Response:']:
if outputs.startswith(pattern):
outputs = outputs[len(pattern):].strip()
if len(outputs) == cur_len:
break
try:
index = outputs.index(conv.sep)
except ValueError:
outputs += conv.sep
index = outputs.index(conv.sep)
outputs = outputs[:index].strip()
else:
outputs = outputs.strip()
ans_id = shortuuid.uuid()
ans_file.write(json.dumps({"question_id": idx,
"prompt": cur_prompt,
"text": outputs,
"answer_id": ans_id,
"model_id": model_name,
"metadata": {}}) + "\n")
ans_file.flush()
ans_file.close()
if __name__ == "__main__":
parser = argparse.ArgumentParser()
parser.add_argument("--model-name", type=str, default="facebook/opt-350m")
parser.add_argument("--base-model-path", type=str, default=None)
parser.add_argument("--image-folder", type=str, default="")
parser.add_argument("--question-file", type=str, default="tables/question.jsonl")
parser.add_argument("--answers-file", type=str, default="answer.jsonl")
parser.add_argument("--mm-projector", type=str, default=None)
parser.add_argument("--vision-tower", type=str, default=None)
parser.add_argument("--conv-mode", type=str, default="simple")
parser.add_argument("--num-chunks", type=int, default=1)
parser.add_argument("--chunk-idx", type=int, default=0)
args = parser.parse_args()
eval_model(args)
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