import os os.environ["LOWRES_RESIZE"] = "384x32" os.environ["HIGHRES_BASE"] = "0x32" os.environ["VIDEO_RESIZE"] = "0x64" os.environ["VIDEO_MAXRES"] = "480" os.environ["VIDEO_MINRES"] = "288" os.environ["MAXRES"] = "1536" os.environ["MINRES"] = "0" os.environ["FORCE_NO_DOWNSAMPLE"] = "1" os.environ["LOAD_VISION_EARLY"] = "1" os.environ["PAD2STRIDE"] = "1" os.environ["USE_SPEECH"] = "1" import copy import logging from datetime import timedelta from pathlib import Path from typing import List, Optional, Tuple, Union import librosa import numpy as np import PIL import soundfile as sf import torch from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState from decord import VideoReader, cpu from tqdm import tqdm from transformers import AutoConfig 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 downsample_audio from lmms_eval.models.model_utils.load_video import read_video_pyav eval_logger = logging.getLogger("lmms-eval") import sys wd = Path(__file__).parent.parent.parent.parent.resolve() sys.path.append(os.path.join(str(wd), "Ola")) import whisper from ola.constants import ( DEFAULT_IM_END_TOKEN, DEFAULT_IM_START_TOKEN, DEFAULT_IMAGE_TOKEN, DEFAULT_SPEECH_TOKEN, IMAGE_TOKEN_INDEX, SPEECH_TOKEN_INDEX, ) from ola.conversation import SeparatorStyle, conv_templates from ola.datasets.preprocess import ( tokenizer_image_token, tokenizer_speech_image_token, tokenizer_speech_token, ) from ola.mm_utils import ( KeywordsStoppingCriteria, get_model_name_from_path, process_anyres_highres_image, process_anyres_video, ) from ola.model.builder import load_pretrained_model try: from ola.model.language_model.ola_qwen import OlaConfigQwen AutoConfig.register("ola_qwen", OlaConfigQwen) except: eval_logger.debug("") from moviepy.video.io.VideoFileClip import VideoFileClip if "USE_SPEECH" in os.environ: USE_SPEECH = os.environ["USE_SPEECH"] print("USE_SPEECH is set") else: USE_SPEECH = None @register_model("ola") class Ola(lmms): """ How to run lmms-eval with Ola model: 1. Install Ola: https://github.com/Ola-Omni/Ola?tab=readme-ov-file#installation 2. Download the pretrained weight from https://huggingface.co/THUdyh/Ola-7b or skip this step to use the online weights directly 3.Download audio encoder from https://huggingface.co/THUdyh/Ola_speech_encoders/tree/main and put the weights large-v3.pt and BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt under llms-eval repository (ensure your current directory can see large-v3.pt and BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt) The path for the project should be like this: Folder/contains/lmms-eval/and/Ola ├── lmms-eval (current directory) │ ├── lmms_eval/ │ ├── ... │ ├── large-v3.pt │ ├── BEATs_iter3_plus_AS2M_finetuned_on_AS2M_cpt2.pt ├── Ola │ ├── ... 4. Run the the command to start evaluate the modeL. For example: ```bash python3 -m accelerate.commands.launch \ --num_processes=8 \ -m lmms_eval \ --model ola\ --tasks mme \ --batch_size 1 \ --log_samples \ --log_samples_suffix mme_ola \ --output_path ./logs/ ``` """ def __init__( self, pretrained: str = "THUdyh/Ola-7b", truncation: Optional[bool] = True, device: Optional[str] = "cuda:0", batch_size: Optional[Union[int, str]] = 1, attn_implementation=( "sdpa" if torch.__version__ >= "2.1.2" else "eager" ), # inference implementation for attention, can be "sdpa", "eager", "flash_attention_2". Seems FA2 is not effective during inference: https://discuss.huggingface.co/t/flash-attention-has-no-effect-on-inference/73453/5 device_map="", conv_template="qwen_1_5", use_cache=True, truncate_context=False, max_frames_num: int = 64, mm_resampler_type: str = "spatial_pool", overwrite: bool = True, video_decode_backend: str = "decord", **kwargs, ) -> None: super().__init__() assert kwargs == {}, f"Unexpected kwargs: {kwargs}" accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) 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}" self.pretrained = pretrained self.model_name = get_model_name_from_path(pretrained) self.video_decode_backend = video_decode_backend # self._config = AutoConfig.from_pretrained(self.pretrained) self.overwrite = overwrite self.mm_resampler_type = mm_resampler_type self.max_frames_num = int(max_frames_num) if self.overwrite == True: overwrite_config = {} overwrite_config["patchify_video_feature"] = False overwrite_config["attn_implementation"] = attn_implementation cfg_pretrained = AutoConfig.from_pretrained(self.pretrained) self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, device=self.device_map) else: self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model( pretrained, None, device_map=self.device_map, ) self._config = self._model.config self.model.to(self.device).eval().bfloat16() self.model.tie_weights() self.truncation = truncation self.batch_size_per_gpu = int(batch_size) self.conv_template = conv_template self.use_cache = use_cache self.truncate_context = truncate_context if accelerator.num_processes > 1: assert accelerator.distributed_type in [DistributedType.FSDP, DistributedType.MULTI_GPU, DistributedType.DEEPSPEED], "Unsupported distributed type provided. Only DDP and FSDP are supported." # If you want to use DistributedType.DEEPSPEED, you have to run accelerate config before using the model # Also, you have to select zero stage 0 (equivalent to DDP) in order to make the prepare model works # I tried to set different parameters in the kwargs to let default zero 2 stage works, but it didn't work. if accelerator.distributed_type == DistributedType.DEEPSPEED: kwargs = { "train_micro_batch_size_per_gpu": self.batch_size_per_gpu, "train_batch_size": self.batch_size_per_gpu * accelerator.num_processes, } AcceleratorState().deepspeed_plugin.deepspeed_config_process(must_match=True, **kwargs) eval_logger.info("Detected that you are using DistributedType.DEEPSPEED. Make sure you run `accelerate config` and set zero stage to 0") if accelerator.distributed_type == DistributedType.FSDP or accelerator.distributed_type == DistributedType.DEEPSPEED: 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 elif accelerator.num_processes == 1 and device_map == "auto": eval_logger.info(f"Using {accelerator.num_processes} devices with tensor parallelism") self._rank = 0 self._world_size = 1 else: eval_logger.info(f"Using single device: {self._device}") self._rank = 0 self._world_size = 1 self.accelerator = accelerator @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): # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence* return self.tokenizer.eos_token_id @property def max_length(self): return self._max_length def pad_sequence(self, input_ids, batch_first, padding_value): if self.tokenizer.padding_side == "left": input_ids = [torch.flip(_input_ids, [0]) for _input_ids in input_ids] input_ids = torch.nn.utils.rnn.pad_sequence(input_ids, batch_first=batch_first, padding_value=padding_value) if self.tokenizer.padding_side == "left": input_ids = torch.flip(input_ids, [1]) return input_ids @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 tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None) -> List[int]: """ """ add_special_tokens = False if add_special_tokens is None else add_special_tokens encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens) # left-truncate the encoded context to be at most `left_truncate_len` tokens long if left_truncate_len: encoding = encoding[-left_truncate_len:] return encoding def load_video(self, video_path, max_frames_num): vr = VideoReader(video_path, ctx=cpu(0)) total_frame_num = len(vr) fps = round(vr.get_avg_fps()) uniform_sampled_frames = np.linspace(0, total_frame_num - 1, max_frames_num, dtype=int) frame_idx = uniform_sampled_frames.tolist() spare_frames = vr.get_batch(frame_idx).asnumpy() video = [PIL.Image.fromarray(frame) for frame in spare_frames] return video, frame_idx def tok_decode(self, tokens): return self.tokenizer.decode(tokens) def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: res = [] pbar = tqdm(total=len(requests), disable=(self.rank != 0), desc="Model Responding") for contexts, doc_to_target, doc_to_visual, doc_id, task, split in [reg.args for reg in requests]: # encode, pad, and truncate contexts for this batch if type(doc_to_target) == str: continuation = doc_to_target else: continuation = doc_to_target(self.task_dict[task][split][doc_id]) visuals = [doc_to_visual(self.task_dict[task][split][doc_id])] visuals = self.flatten(visuals) videos = [] for visual in visuals: video = self.load_video(visual, self.max_frames_num) video = self._image_processor.preprocess(video, return_tensors="pt")["pixel_values"].bfloat16().to(self.device) videos.append(video) qs = contexts if self.model.config.mm_use_im_start_end: qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs else: qs = DEFAULT_IMAGE_TOKEN + "\n" + qs conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], continuation) prompt = conv.get_prompt() input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) labels = input_ids.clone() # Context part no need to calculate for loss labels[0, : contxt_id.shape[1]] = -100 with torch.inference_mode(): outputs = self.model(input_ids=input_ids, labels=labels, images=videos, modalities="video") loss = outputs["loss"] # loss = torch.exp(loss) logits = outputs["logits"] greedy_tokens = logits.argmax(dim=-1) cont_toks = input_ids[:, contxt_id.shape[1] :] # [1, seq] greedy_tokens = greedy_tokens[:, contxt_id.shape[1] : input_ids.shape[1]] # [1, seq] max_equal = (greedy_tokens == cont_toks).all() res.append((float(loss.item()), bool(max_equal))) pbar.update(1) pbar.close() return res def flatten(self, input): new_list = [] for i in input: for j in i: new_list.append(j) return new_list def extract_audio(self, videos_file_path): my_clip = VideoFileClip(videos_file_path) return my_clip.audio def load_audio(self, audio_file_name): CHUNK_LIM = 480000 import librosa audio, samplerate = librosa.load(audio_file_name, sr=16000) audio = audio.astype(np.float32) if len(audio.shape) > 1: audio = audio[:, 0] speechs = [] speech_wavs = [] if len(audio) <= CHUNK_LIM: audio = whisper.pad_or_trim(audio) speechs.append(audio) speech_wavs.append(torch.from_numpy(audio).unsqueeze(0)) else: for i in range(0, len(audio), CHUNK_LIM): chunk = audio[i : i + CHUNK_LIM] if len(chunk) < CHUNK_LIM: chunk = whisper.pad_or_trim(chunk) speechs.append(chunk) speech_wavs.append(torch.from_numpy(chunk).unsqueeze(0)) mels = [] for chunk in speechs: chunk = whisper.log_mel_spectrogram(chunk, n_mels=128).permute(1, 0).unsqueeze(0) mels.append(chunk) mels = torch.cat(mels, dim=0) speech_wavs = torch.cat(speech_wavs, dim=0) if mels.shape[0] > 20: mels = mels[:20] speech_wavs = speech_wavs[:20] speech_length = torch.LongTensor([mels.shape[1]] * mels.shape[0]) speech_chunks = torch.LongTensor([mels.shape[0]]) return mels, speech_length, speech_chunks, speech_wavs def process_audio(self, audio_array, sampling_rate): """ Process audio array to format of Ola model """ audio = audio_array.astype(np.float32) if len(audio.shape) > 1: audio = audio[:, 0] target_sr = 16000 CHUNK_LIM = 480000 if sampling_rate != target_sr: speech_wav = librosa.resample(audio_array, orig_sr=sampling_rate, target_sr=target_sr).astype(np.float32) else: speech_wav = audio_array.astype(np.float32) speechs = [] speech_wavs = [] if len(speech_wav) <= CHUNK_LIM: speech = whisper.pad_or_trim(speech_wav) speech_wav = whisper.pad_or_trim(speech_wav) speechs.append(speech) speech_wavs.append(torch.from_numpy(speech_wav).unsqueeze(0)) else: for i in range(0, len(speech_wav), CHUNK_LIM): chunk = speech_wav[i : i + CHUNK_LIM] if len(chunk) < CHUNK_LIM: chunk = whisper.pad_or_trim(chunk) speechs.append(chunk) speech_wavs.append(torch.from_numpy(chunk).unsqueeze(0)) mels = [] for chunk in speechs: chunk = whisper.log_mel_spectrogram(chunk, n_mels=128).permute(1, 0).unsqueeze(0) mels.append(chunk) mels = torch.cat(mels, dim=0) speech_wavs = torch.cat(speech_wavs, dim=0) if mels.shape[0] > 25: mels = mels[:25] speech_wavs = speech_wavs[:25] speech_length = torch.LongTensor([mels.shape[1]] * mels.shape[0]) speech_chunks = torch.LongTensor([mels.shape[0]]) return mels, speech_length, speech_chunks, speech_wavs def generate_until(self, requests) -> List[str]: MODALITY = None res = [] 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.tok_encode(x[0]) return -len(toks), x[0] # 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) num_iters = len(requests) // self.batch_size if len(requests) % self.batch_size == 0 else len(requests) // self.batch_size + 1 pbar = tqdm(total=num_iters, disable=(self.rank != 0), desc="Model Responding") for chunk in chunks: contexts, all_gen_kwargs, doc_to_visual, doc_id, task, split = zip(*chunk) task = task[0] split = split[0] context = contexts[0] visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] visuals = self.flatten(visuals) # Len = 1. just an audio tho speechs, speech_lengths, speech_wavs, speech_chunks = [], [], [], [] images, images_highres = [], [] # For dummy image passed in audio modality image_sizes = [] image_tensor, image_highres_tensor = [], [] # For image video_processed = [] # For video only for visual in visuals: if isinstance(visual, str): # For Video if MODALITY is None: MODALITY = "VIDEO" # Process audio of video try: video, frame_idx = self.load_video(visual, self.max_frames_num) except Exception as e: eval_logger.info(f"{e}") eval_logger.info(f"Video {visuals} can not load, check the source") continue audio = self.extract_audio(visual) audio.write_audiofile(f"./video_audio_{self.rank}.wav") video_audio_path = f"./video_audio_{self.rank}.wav" speech, speech_length, speech_chunk, speech_wav = self.load_audio(video_audio_path) speechs.append(speech.bfloat16().to(self.device)) speech_lengths.append(speech_length.to(self.device)) speech_chunks.append(speech_chunk.to(self.device)) speech_wavs.append(speech_wav.to(self.device)) os.remove(video_audio_path) # Process images of video for idx, frame in enumerate(video): self._image_processor.do_resize = False self._image_processor.do_center_crop = False frame = process_anyres_video(frame, self._image_processor) if frame_idx is not None and idx in frame_idx: video_processed.append(frame.unsqueeze(0)) elif frame_idx is None: video_processed.append(frame.unsqueeze(0)) if frame_idx is None: frame_idx = np.arange(0, len(video_processed), dtype=int).tolist() video_processed = torch.cat(video_processed, dim=0).bfloat16().to(self.device) video_processed = (video_processed, video_processed) video_data = (video_processed, (384, 384), "video") elif isinstance(visual, PIL.Image.Image): # For Image if MODALITY is None: MODALITY = "IMAGE" self._image_processor.do_resize = False self._image_processor.do_center_crop = False image_sizes.append(visual.size) image_tensor_, image_highres_tensor_ = process_anyres_highres_image(visual, self._image_processor) image_tensor.append(image_tensor_) image_highres_tensor.append(image_highres_tensor_) elif isinstance(visual, dict) and "array" in visual: # For Audio if MODALITY is None: MODALITY = "AUDIO" mels, speech_length, speech_chunk, speech_wav = self.process_audio(visual["array"], visual["sampling_rate"]) speechs.append(mels.bfloat16().to(self.device)) speech_lengths.append(speech_length.to(self.device)) speech_chunks.append(speech_chunk.to(self.device)) speech_wavs.append(speech_wav.to(self.device)) # Processing dummy images, as required by model images.append(torch.zeros(1, 3, 224, 224).to(dtype=torch.bfloat16, device=self.device, non_blocking=True)) images_highres.append(torch.zeros(1, 3, 224, 224).to(dtype=torch.bfloat16, device=self.device, non_blocking=True)) image_sizes.append((224, 224)) if not video_processed and MODALITY == "VIDEO": # If video is not processed, skip the iteration pbar.update(1) continue if MODALITY == "IMAGE": if all(x.shape == image_tensor[0].shape for x in image_tensor): image_tensor = torch.stack(image_tensor, dim=0) if all(x.shape == image_highres_tensor[0].shape for x in image_highres_tensor): image_highres_tensor = torch.stack(image_highres_tensor, dim=0) if type(image_tensor) is list: image_tensor = [_image.bfloat16().to("cuda") for _image in image_tensor] else: image_tensor = image_tensor.bfloat16().to("cuda") if type(image_highres_tensor) is list: image_highres_tensor = [_image.bfloat16().to("cuda") for _image in image_highres_tensor] else: image_highres_tensor = image_highres_tensor.bfloat16().to("cuda") # Processing dummy audio, as required by model speechs.append(torch.zeros(1, 3000, 128).bfloat16().to("cuda")) speech_lengths.append(torch.LongTensor([3000]).to("cuda")) speech_wavs.append(torch.zeros([1, 480000]).to("cuda")) speech_chunks.append(torch.LongTensor([1]).to("cuda")) # we assume all gen kwargs in the batch are the same # this is safe to assume because the `grouper` object ensures it. 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)}") assert self.batch_size_per_gpu == 1, "Do not support batch_size_per_gpu > 1 for now" # Okay be I am assuming bs always == 1 qs = list(contexts)[0] if self.model.config.mm_use_im_start_end: if MODALITY == "AUDIO": qs = DEFAULT_IM_START_TOKEN + DEFAULT_SPEECH_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs elif MODALITY == "IMAGE": qs = DEFAULT_IM_START_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs elif MODALITY == "VIDEO": qs = DEFAULT_IM_START_TOKEN + DEFAULT_SPEECH_TOKEN + DEFAULT_IMAGE_TOKEN + DEFAULT_IM_END_TOKEN + "\n" + qs else: if MODALITY == "AUDIO": qs = DEFAULT_SPEECH_TOKEN + "\n" + qs elif MODALITY == "IMAGE": qs = DEFAULT_IMAGE_TOKEN + "\n" + qs elif MODALITY == "VIDEO": qs = DEFAULT_SPEECH_TOKEN + DEFAULT_IMAGE_TOKEN + "\n" + qs conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], qs) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() if self.accelerator.is_main_process and doc_id[0] % 100 == 0: eval_logger.debug(f"Prompt for doc ID {doc_id[0]}:\n\n{prompt}\n") if MODALITY == "AUDIO": input_ids = tokenizer_speech_token(prompt, self.tokenizer, SPEECH_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) elif MODALITY == "IMAGE": input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) elif MODALITY == "VIDEO": input_ids = tokenizer_speech_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) pad_token_ids = 151643 attention_masks = input_ids.ne(pad_token_ids).long().to(self.device) stop_str = conv.sep if conv.sep_style != SeparatorStyle.TWO else conv.sep2 keywords = [stop_str] stopping_criteria = KeywordsStoppingCriteria(keywords, self.tokenizer, input_ids) if "max_new_tokens" not in gen_kwargs: gen_kwargs["max_new_tokens"] = 256 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 try: with torch.inference_mode(): if MODALITY == "AUDIO": output_ids = self.model.generate( input_ids, images=images, images_highres=images_highres, image_sizes=image_sizes, modalities=["text"], speech=speechs, speech_lengths=speech_lengths, speech_chunks=speech_chunks, speech_wav=speech_wavs, attention_mask=attention_masks, use_cache=True, stopping_criteria=[stopping_criteria], 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"], ) elif MODALITY == "IMAGE": output_ids = self.model.generate( inputs=input_ids, images=image_tensor, images_highres=image_highres_tensor, image_sizes=image_sizes, modalities=["image"], speech=speechs, speech_lengths=speech_lengths, speech_chunks=speech_chunks, speech_wav=speech_wavs, attention_mask=attention_masks, use_cache=True, stopping_criteria=[stopping_criteria], 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"], ) elif MODALITY == "VIDEO": output_ids = self.model.generate( inputs=input_ids, images=video_data[0][0], images_highres=video_data[0][1], modalities=video_data[2], speech=speechs, speech_lengths=speech_lengths, speech_chunks=speech_chunks, speech_wav=speech_wavs, attention_mask=attention_masks, use_cache=True, stopping_criteria=[stopping_criteria], 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"], ) except Exception as e: eval_logger.error(f"Error {e} in generating") outputs = "" res.append(outputs) pbar.update(1) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), outputs) continue outputs = self.tokenizer.batch_decode(output_ids, skip_special_tokens=True)[0].strip() if self.accelerator.is_main_process and doc_id[0] % 100 == 0: eval_logger.debug(f"Generated text for doc ID {doc_id[0]}:\n\n{outputs}\n") res.append(outputs) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), outputs) pbar.update(1) # reorder this group of results back to original unsorted form 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")