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import re |
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import logging |
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import base64 |
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from io import BytesIO |
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import os |
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from transformers import Qwen2VLForConditionalGeneration, AutoTokenizer, AutoProcessor |
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from qwen_vl_utils import process_vision_info |
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import torch |
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from transformers import Qwen2_5_VLForConditionalGeneration, AutoProcessor |
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from vllm import LLM, SamplingParams |
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def encode_image_to_base64(image): |
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buffered = BytesIO() |
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image.save(buffered, format="PNG") |
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img_str = base64.b64encode(buffered.getvalue()).decode("utf-8") |
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return img_str |
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def create_message(sample): |
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query = sample['query'] |
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all_contents = [] |
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matches = re.findall(r"<(image_\d+)>", query) |
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split_text = re.split(r"<image_\d+>", query) |
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for i, fragment in enumerate(split_text): |
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if fragment.strip(): |
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all_contents.extend([ |
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{"type": "text", "text": fragment} |
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]) |
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if i < len(matches): |
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if sample[matches[i]]: |
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img_base64 = encode_image_to_base64(sample[matches[i]]) |
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all_contents.extend([ |
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{ |
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"type": "image", |
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"image": f"data:image/png;base64,{img_base64}" |
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} |
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]) |
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else: |
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logging.error( |
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f"The image token {matches[i]} is in the query, but there is no corresponding image provided by the data") |
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messages = [ |
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{ |
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"role": "user", |
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"content": all_contents |
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} |
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] |
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return messages |
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class Qwen_Model: |
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def __init__( |
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self, |
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model_path, |
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temperature=0, |
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max_tokens=1024 |
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): |
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self.model_path = model_path |
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self.temperature = temperature |
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self.max_tokens = max_tokens |
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self.model = Qwen2VLForConditionalGeneration.from_pretrained(self.model_path, torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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device_map="auto", ) |
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self.processor = AutoProcessor.from_pretrained(self.model_path) |
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def get_response(self, sample): |
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model = self.model |
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processor = self.processor |
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try: |
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messages = create_message(sample) |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True, add_vision_id=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=self.max_tokens, temperature=self.temperature) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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response = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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return response[0] |
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except Exception as e: |
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print(e) |
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return None |
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class Qwen2_5_Model: |
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def __init__( |
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self, |
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model_path="Qwen/Qwen2.5-VL-72B-Instruct", |
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temperature=0, |
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max_tokens=1024 |
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): |
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self.model_path = model_path |
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self.temperature = temperature |
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self.max_tokens = max_tokens |
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self.model = Qwen2_5_VLForConditionalGeneration.from_pretrained( |
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self.model_path, |
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torch_dtype=torch.bfloat16, |
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attn_implementation="flash_attention_2", |
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device_map="auto" |
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) |
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self.processor = AutoProcessor.from_pretrained(self.model_path) |
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def get_response(self, sample): |
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model = self.model |
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processor = self.processor |
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try: |
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messages = create_message(sample) |
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text = processor.apply_chat_template( |
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messages, tokenize=False, add_generation_prompt=True, add_vision_id=True |
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) |
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image_inputs, video_inputs = process_vision_info(messages) |
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inputs = processor( |
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text=[text], |
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images=image_inputs, |
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videos=video_inputs, |
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padding=True, |
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return_tensors="pt", |
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) |
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inputs = inputs.to("cuda") |
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generated_ids = model.generate(**inputs, max_new_tokens=self.max_tokens, temperature=self.temperature) |
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generated_ids_trimmed = [ |
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out_ids[len(in_ids):] for in_ids, out_ids in zip(inputs.input_ids, generated_ids) |
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] |
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response = processor.batch_decode( |
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generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False |
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) |
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return response[0] |
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except Exception as e: |
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print(e) |
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return None |
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class Qwen_vllm_Model: |
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def __init__( |
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self, |
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model_path, |
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greedy=1, |
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max_tokens=1024, |
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parallel=1, |
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seed=42, |
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device=0 |
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): |
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self.model_path = model_path |
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self.max_tokens = max_tokens |
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self.model = LLM( |
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model=model_path, |
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enable_prefix_caching=True, |
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trust_remote_code=True, |
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limit_mm_per_prompt={"image": 8, "video": 1}, |
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tensor_parallel_size=parallel, |
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device=device |
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) |
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self.sampling_params = SamplingParams( |
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temperature=0 if greedy else 1, |
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top_p=0.001 if greedy else 1, |
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top_k=1 if greedy else -1, |
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repetition_penalty=1, |
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max_tokens=max_tokens, |
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stop_token_ids=[], |
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seed=seed |
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) |
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self.processor = AutoProcessor.from_pretrained(self.model_path) |
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def get_response(self, sample): |
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try: |
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messages = create_message(sample) |
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text = self.processor.apply_chat_template(messages, tokenize=False, add_generation_prompt=True) |
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image_inputs, _ = process_vision_info([messages]) |
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inputs = { |
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"prompt": text, |
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"multi_modal_data": {'image': image_inputs}, |
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} |
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out = self.model.generate( |
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inputs, |
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sampling_params=self.sampling_params, |
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use_tqdm=False |
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) |
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response = out[0].outputs[0].text |
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return response |
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except Exception as e: |
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print(e) |
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return None |
