#
#
cur_dir = os.path.dirname(os.path.abspath(__file__))
title_markdown = ("""
# arXiv 버튼 옆에 추가?
block_css = """
#buttons button {
min-width: min(120px,100%);
}
"""
tos_markdown = ("""
### Terms of use
By using this service, users are required to agree to the following terms:
The service is a research preview intended for non-commercial use only. It only provides limited safety measures and may generate offensive content. It must not be used for any illegal, harmful, violent, racist, or sexual purposes. The service may collect user dialogue data for future research.
Please click the "Flag" button if you get any inappropriate answer! We will collect those to keep improving our moderator.
For an optimal experience, please use desktop computers for this demo, as mobile devices may compromise its quality.
""")
learn_more_markdown = ("""
### License
The service is a research preview intended for non-commercial use only, subject to the model [License](https://github.com/facebookresearch/llama/blob/main/MODEL_CARD.md) of LLaMA, [Terms of Use](https://openai.com/policies/terms-of-use) of the data generated by OpenAI, and [Privacy Practices](https://chrome.google.com/webstore/detail/sharegpt-share-your-chatg/daiacboceoaocpibfodeljbdfacokfjb) of ShareGPT. Please contact us if you find any potential violation.
""")
class Chat:
def __init__(self, model_path, conv_mode, model_base=None, load_8bit=False, load_4bit=False, device='cuda', cache_dir=None):
# model_base = '/dataset/yura/vlm-rlaif/pretrained/final_models/Video_LLaVA_SFT'
# model_base='/dataset/yura/vlm-rlaif/pretrained/llava-v1.5-7b-lora_w_lora_16_sftv2_short1632_and_then_long_rank32_alpha32_lr1e4_allmodels/SFT_merged'
# model_path = '/dataset/yura/vlm-rlaif/pretrained/LLaVA_Video-RL-Fact-RLHF-7b_SFTv2_RM_13b_v1_40k-v1.5-336-lora-padding/checkpoint-180/adapter_model/lora_policy'
disable_torch_init()
model_name = get_model_name_from_path(model_path)
# self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name,
# load_8bit, load_4bit,
# device=device, cache_dir=cache_dir)
is_rlhf_checkpoint = 'rlhf' in model_path.lower()
print("MODEL_PATH", model_path)
print("RLHF Checkpoint: ", is_rlhf_checkpoint)
if not model_base or model_base == "none": model_base = None
if is_rlhf_checkpoint:
model_name = model_path
print("Config?", os.path.exists(os.path.join(model_path, "config.json")))
if not os.path.exists(os.path.join(model_path, "config.json")):
print("Copying")
shutil.copy(os.path.join(model_base, "config.json"), os.path.join(model_path, "config.json")) # Copy SFT model's config -> to RLHF folder
print("Listed", os.listdir(model_path))
print("Copying done")
# return(model_name)
# return
# self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name, load_8bit, load_4bit, device=device)
self.tokenizer, self.model, image_processor, context_len = load_pretrained_model(model_path, model_base, model_name, False, False, device=device)
self.image_processor = image_processor
# self.image_processor = processor['image']
# self.video_processor = processor['video']
self.conv_mode = conv_mode
self.conv = conv_templates[conv_mode].copy()
self.device = self.model.device
print(self.model)
def get_prompt(self, qs, state):
state.append_message(state.roles[0], qs)
state.append_message(state.roles[1], None)
return state
def _get_latest_prompt(self, state):
new_state = state.copy()
new_state.messages = state.messages[-2:]
return new_state
@torch.inference_mode()
# def generate(self, images_tensor: list, prompt: str, first_run: bool, state):
def generate(self, images_tensor: torch.Tensor, prompt: str, first_run: bool, state):
tokenizer, model, image_processor = self.tokenizer, self.model, self.image_processor
state = self.get_prompt(prompt, state)
# prompt = state.get_prompt()
latest_state = self._get_latest_prompt(state)
prompt = latest_state.get_prompt()
# print('\n\n\n')
# print(prompt)
input_ids = tokenizer_image_token(prompt, tokenizer, IMAGE_TOKEN_INDEX, return_tensors='pt').unsqueeze(0).to(self.device)
temperature = 0.2
max_new_tokens = 1024
stop_str = self.conv.sep if self.conv.sep_style != SeparatorStyle.TWO else self.conv.sep2
keywords = [stop_str]
stopping_criteria = KeywordsStoppingCriteria(keywords, tokenizer, input_ids)
streamer = TextStreamer(tokenizer, skip_prompt=True, skip_special_tokens=True)
print(prompt, input_ids.shape, images_tensor.shape)
# print(images_tensor)
with torch.inference_mode():
output_ids = model.generate(
input_ids,
images=images_tensor,
do_sample=True,
temperature=temperature,
max_new_tokens=max_new_tokens,
streamer=streamer,
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] {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]
outputs = outputs.strip()
outputs = outputs.replace("QA_GT_caption_based_noisy", "")
if outputs.endswith(stop_str):
outputs = outputs[:-len(stop_str)]
outputs = outputs.strip()
print('response', outputs)
return outputs, state