import base64 from io import BytesIO from typing import List, Optional, Tuple, Union import audioread import av import decord import librosa import numpy as np import soundfile as sf import torch from accelerate import Accelerator, DistributedType from loguru import logger as eval_logger from moviepy import VideoFileClip from PIL import Image from tqdm import tqdm from transformers import Qwen2_5OmniForConditionalGeneration, Qwen2_5OmniProcessor from lmms_eval import utils from lmms_eval.api.instance import Instance from lmms_eval.api.model import lmms from lmms_eval.api.registry import register_model from lmms_eval.models.model_utils.audio_processing import split_audio from lmms_eval.models.model_utils.load_video import read_video_pyav_base64 try: from qwen_omni_utils import process_mm_info except ImportError: eval_logger.warning("Failed to import qwen_omni_utils; Please install it via `pip install qwen-omni-utils[decord]`") @register_model("qwen2_5_omni") class Qwen2_5_Omni(lmms): """ Qwen2.5-Omni-7B "https://huggingface.co/Qwen/Qwen2.5-Omni-7B" For better performance, please visit the Qwen-Omni repo to get the latest system prompt based on your running tasks. https://github.com/QwenLM/Qwen2.5-Omni/tree/main/cookbooks """ def __init__( self, pretrained: str = "Qwen/Qwen2.5-Omni-7B", device: Optional[str] = "cuda", device_map: Optional[str] = "auto", batch_size: Optional[Union[int, str]] = 1, use_cache=True, attn_implementation: Optional[bool] = "eager", max_num_frames: int = 768, max_pixels: int = 307200, min_pixels: int = 65536, use_custom_video_loader: Optional[bool] = False, fps: Optional[float] = None, # Only applicable if use_custom_video_loader is True max_image_size: Optional[int] = None, # Only applicable if use_custom_video_loader is True system_prompt: str = "You are Qwen, a virtual human developed by the Qwen Team, Alibaba Group, capable of perceiving auditory and visual inputs, as well as generating text and speech.", **kwargs, ) -> None: super().__init__() # Do not use kwargs for now assert kwargs == {}, f"Unexpected kwargs: {kwargs}" self.use_custom_video_loader = use_custom_video_loader self.fps = fps # if self.fps and not self.use_custom_video_loader: # raise ValueError("FPS is only applicable if use_custom_video_loader is True") self.max_image_size = max_image_size if self.max_image_size and not self.use_custom_video_loader: raise ValueError("max_image_size is only applicable if use_custom_video_loader is True") accelerator = Accelerator() if accelerator.num_processes > 1: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" elif accelerator.num_processes == 1 and device_map == "auto": self._device = torch.device(device) self.device_map = device_map else: self._device = torch.device(f"cuda:{accelerator.local_process_index}") self.device_map = f"cuda:{accelerator.local_process_index}" Qwen2_5OmniForConditionalGeneration._tp_plan = [] if Qwen2_5OmniForConditionalGeneration._tp_plan is None else Qwen2_5OmniForConditionalGeneration._tp_plan self._model = Qwen2_5OmniForConditionalGeneration.from_pretrained(pretrained, torch_dtype=torch.bfloat16, device_map=self.device_map, attn_implementation=attn_implementation).eval() self.processor = Qwen2_5OmniProcessor.from_pretrained(pretrained, max_pixels=max_pixels, min_pixels=min_pixels) self.max_num_frames = max_num_frames self._tokenizer = self.processor.tokenizer self._config = self.model.config self.batch_size_per_gpu = int(batch_size) self.use_cache = use_cache self._model.disable_talker() self.system_prompt = system_prompt if accelerator.num_processes > 1: assert accelerator.distributed_type in [ DistributedType.FSDP, DistributedType.MULTI_GPU, ], "Unsupported distributed type provided. Only DDP and FSDP are supported." if accelerator.distributed_type == DistributedType.FSDP: self._model = accelerator.prepare(self.model) else: self._model = accelerator.prepare_model(self.model, evaluation_mode=True) self.accelerator = accelerator if self.accelerator.is_local_main_process: eval_logger.info(f"Using {accelerator.num_processes} devices with data parallelism") self._rank = self.accelerator.local_process_index self._world_size = self.accelerator.num_processes else: self._rank = 0 self._world_size = 1 @property def config(self): # return the associated transformers.AutoConfig for the given pretrained model. return self._config @property def tokenizer(self): return self._tokenizer @property def model(self): # returns the model, unwrapping it if using Accelerate if hasattr(self, "accelerator"): return self.accelerator.unwrap_model(self._model) else: return self._model @property def eot_token_id(self): return self.tokenizer.eos_token_id @property def max_length(self): return self._max_length @property def batch_size(self): return self.batch_size_per_gpu @property def device(self): return self._device @property def rank(self): return self._rank @property def world_size(self): return self._world_size def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: raise NotImplementedError("Loglikelihood is not implemented for Qwen2.5_Omni") def flatten(self, input): new_list = [] for i in input: for j in i: new_list.append(j) return new_list def resample_audio(self, audio: np.ndarray, current_sample_rate: int): """ Resample the audio to the target sample rate. """ if current_sample_rate != 16000: # The sample rate for Qwen2.5-Omni is 16kHz if isinstance(audio, np.ndarray): audio = librosa.resample(audio, orig_sr=current_sample_rate, target_sr=16000).astype(np.float32) return audio def _check_if_video_has_audio(self, video_path): clip = VideoFileClip(video_path) return clip.audio is not None def generate_until(self, requests: List[Instance]) -> List[str]: res = [] current_use_audio = False # Flag to check whether we are using video or not def _collate(x): # the negative sign on len(toks) sorts descending - this has a few advantages: # - time estimates will always be over not underestimates, which is more useful for planning # - to know the size of a batch when going through the list, you know the first one is always the batch # padded context length. this is useful to simplify the batching logic and more importantly to make # automatic adaptive batches much much easier to implement # - any OOMs will happen right away rather than near the end toks = self.tokenizer.encode(x[0]) return -len(toks), x[0] pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") # we group requests by their generation_kwargs, # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling # in the same batch. re_ords = utils.Collator([reg.args for reg in requests], _collate, grouping=True) chunks = re_ords.get_batched(n=self.batch_size, batch_fn=None) for chunk in chunks: contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) task = task[0] split = split[0] visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] visuals = self.flatten(visuals) gen_kwargs = all_gen_kwargs[0] # Set default values for until and max_new_tokens until = [self.tokenizer.decode(self.eot_token_id)] # Update values from gen_kwargs if present if "until" in gen_kwargs: until = gen_kwargs.pop("until") if isinstance(until, str): until = [until] elif not isinstance(until, list): raise ValueError(f"Expected `gen_kwargs['until']` to be of type Union[str,list] but got {type(until)}") # For better performance, please visit the Qwen-Omni repo to get the latest system prompt based on tasks. # https://github.com/QwenLM/Qwen2.5-Omni/tree/main/cookbooks message = [{"role": "system", "content": [{"type": "text", "text": self.system_prompt}]}] for i, context in enumerate(contexts): if len(visuals) > 0: visual = visuals[i] if i < len(visuals) else None if isinstance(visual, str) and visual.endswith((".mp4", ".avi", ".mov")): # Video file current_use_audio = self._check_if_video_has_audio(visual) if self.use_custom_video_loader: visual = read_video_pyav_base64(visual, num_frm=self.max_num_frames, fps=self.fps, img_format="JPEG", max_image_size=self.max_image_size) image_contents = list(map(lambda x: f"data:image/jpeg;base64,{x}", visual)) message.append({"role": "user", "content": [{"type": "video", "video": image_contents}, {"type": "text", "text": context}]}) else: # Model video loader message.append({"role": "user", "content": [{"type": "video", "video": visual}, {"type": "text", "text": context}]}) elif isinstance(visual, Image.Image): # Single image message.append({"role": "user", "content": [{"type": "image", "image": visual}, {"type": "text", "text": context}]}) elif isinstance(visual, (list, tuple)) and all(isinstance(v, Image.Image) for v in visual): # Multiple images single_message = {"role": "user", "content": []} for v in visual: single_message["content"].append({"type": "image", "image": v}) single_message["content"].append({"type": "text", "text": context}) message.append(single_message) # Fixed code for audio messages elif isinstance(visual, dict): # Single audio current_use_audio = True audio = self.resample_audio(visual["array"], visual["sampling_rate"]) audio_splits = split_audio(audio, 4800000) # Split the audio to 5 min chunks single_message = {"role": "user", "content": []} for i in range(len(audio_splits)): single_message["content"].append({"type": "audio", "audio": audio_splits[i]}) single_message["content"].append({"type": "text", "text": context}) message.append(single_message) elif isinstance(visual, (list, tuple)) and all(isinstance(v, dict) for v in visual): # Multiple audios current_use_audio = True for i, v in enumerate(visual): audio = self.resample_audio(v["array"], v["sampling_rate"]) audio_splits = split_audio(audio, 4800000) # Split the audio to 5 min chunks single_message = {"role": "user", "content": []} for j in range(len(audio_splits)): single_message["content"].append({"type": "audio", "audio": audio_splits[j]}) single_message["content"].append({"type": "text", "text": context}) message.append(single_message) else: raise ValueError(f"Unknown visual type: {type(visual)}") text = self.processor.apply_chat_template(message, add_generation_prompt=True, tokenize=False) audios, images, videos = process_mm_info(message, use_audio_in_video=current_use_audio) inputs = self.processor(text=text, audio=audios, images=images, videos=videos, return_tensors="pt", padding=True, use_audio_in_video=current_use_audio) if self.device_map == "auto": inputs = inputs.to("cuda").to(self.model.dtype) else: inputs = inputs.to(self.model.device).to(self.model.dtype) if "max_new_tokens" not in gen_kwargs: gen_kwargs["max_new_tokens"] = 4096 if "temperature" not in gen_kwargs: gen_kwargs["temperature"] = 0 if "top_p" not in gen_kwargs: gen_kwargs["top_p"] = None if "num_beams" not in gen_kwargs: gen_kwargs["num_beams"] = 1 pad_token_id = self.tokenizer.pad_token_id try: cont = self.model.generate( **inputs, return_audio=False, eos_token_id=self.tokenizer.eos_token_id, pad_token_id=pad_token_id, do_sample=True if gen_kwargs["temperature"] > 0 else False, temperature=gen_kwargs["temperature"], top_p=gen_kwargs["top_p"], num_beams=gen_kwargs["num_beams"], max_new_tokens=gen_kwargs["max_new_tokens"], use_cache=self.use_cache, use_audio_in_video=current_use_audio, thinker_do_sample=False, ) except Exception as e: eval_logger.error(f"Error {e} in generating") answer = "" res.append(answer) pbar.update(1) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), answer) continue generated_ids_trimmed = [out_ids[len(in_ids) :] for in_ids, out_ids in zip(inputs.input_ids, cont)] answers = self.processor.batch_decode(generated_ids_trimmed, skip_special_tokens=True, clean_up_tokenization_spaces=False) for i, ans in enumerate(answers): answers[i] = ans content = [] for ans, context in zip(answers, contexts): res.append(ans) content.append(ans) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), ans) pbar.update(1) res = re_ords.get_original(res) pbar.close() return res def generate_until_multi_round(self, requests) -> List[str]: raise NotImplementedError("TODO: Implement multi-round generation")