TypeError: _extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format' #204
Description
System Info / 系統信息
transformers 4.47.0
Who can help? / 谁可以帮助到您？
No response
Information / 问题信息
- The official example scripts / 官方的示例脚本
- My own modified scripts / 我自己修改的脚本和任务
Reproduction / 复现过程
video_demo/inference.py
import io
import os
import numpy as np
import torch
from decord import cpu, VideoReader, bridge
from transformers import AutoModelForCausalLM, AutoTokenizer
MODEL_PATH = "/data/benchmark_weight/cogvlm2-video-llama3-chat"
VIDEO_PATH = "/data/video-llama2/VideoLLaMA2-main/datasets/custom_sft/videos/MER2023/sample_00000003.mp4"
PROMPT = "Could you describe the features of the individual in the video? "
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
TORCH_TYPE = torch.bfloat16 if torch.cuda.is_available() and torch.cuda.get_device_capability()[0] >= 8 else torch.float16
Disable tokenizer parallelism warning
os.environ["TOKENIZERS_PARALLELISM"] = "false"
def load_video(video_path, strategy='chat'):
bridge.set_bridge('torch')
with open(video_path, 'rb') as f:
mp4_stream = f.read()
num_frames = 24
decord_vr = VideoReader(io.BytesIO(mp4_stream), ctx=cpu(0))
frame_id_list = None
total_frames = len(decord_vr)
if strategy == 'base':
clip_end_sec = 60
clip_start_sec = 0
start_frame = int(clip_start_sec * decord_vr.get_avg_fps())
end_frame = min(total_frames,
int(clip_end_sec * decord_vr.get_avg_fps())) if clip_end_sec is not None else total_frames
frame_id_list = np.linspace(start_frame, end_frame - 1, num_frames, dtype=int)
elif strategy == 'chat':
timestamps = decord_vr.get_frame_timestamp(np.arange(total_frames))
timestamps = [i[0] for i in timestamps]
max_second = round(max(timestamps)) + 1
frame_id_list = []
for second in range(max_second):
closest_num = min(timestamps, key=lambda x: abs(x - second))
index = timestamps.index(closest_num)
frame_id_list.append(index)
if len(frame_id_list) >= num_frames:
break
video_data = decord_vr.get_batch(frame_id_list)
video_data = video_data.permute(3, 0, 1, 2)
return video_data
Initialize tokenizer and model
tokenizer = AutoTokenizer.from_pretrained(
MODEL_PATH,
trust_remote_code=True
)
model = AutoModelForCausalLM.from_pretrained(
MODEL_PATH,
torch_dtype=TORCH_TYPE,
trust_remote_code=True
).eval().to(DEVICE)
def predict(prompt, video_path, temperature=0.7):
strategy = 'chat'
video = load_video(video_path, strategy=strategy)
history = []
query = prompt
inputs = model.build_conversation_input_ids(
tokenizer=tokenizer,
query=query,
images=[video],
history=history,
template_version=strategy
)
inputs = {
'input_ids': inputs['input_ids'].unsqueeze(0).to(DEVICE),
'token_type_ids': inputs['token_type_ids'].unsqueeze(0).to(DEVICE),
'attention_mask': inputs['attention_mask'].unsqueeze(0).to(DEVICE),
'images': [[inputs['images'][0].to(DEVICE).to(TORCH_TYPE)]],
}
gen_kwargs = {
"max_new_tokens": 2048,
"pad_token_id": 128002,
"top_k": 1,
"do_sample": True, # Enable sampling to match `temperature` usage
"top_p": 0.1,
"temperature": temperature,
}
with torch.no_grad():
outputs = model.generate(**inputs, **gen_kwargs)
outputs = outputs[:, inputs['input_ids'].shape[1]:]
response = tokenizer.decode(outputs[0], skip_special_tokens=True)
return response
if name == "main":
response = predict(PROMPT, VIDEO_PATH)
print(response)
TypeError: _extract_past_from_model_output() got an unexpected keyword argument 'standardize_cache_format'
Expected behavior / 期待表现
cache_name, cache = self._extract_past_from_model_output(
outputs, standardize_cache_format=standardize_cache_format
)
我明白需要将上面的改为 cache_name, cache = self._extract_past_from_model_output(
outputs)
但是推理代码中有trust_remote_code=True，自动更新，当trust_remote_code=False后报错安全问题，我尝试了transformers的多个版本包括4.44.0，4.42.4，4.40.2，4.41.2以及最新版本均4.47.0均无法解决，期待得到您的帮助。