import torch torch.backends.cuda.matmul.allow_tf32 = True import copy import warnings from datetime import timedelta from typing import List, Optional, Tuple, Union from accelerate import Accelerator, DistributedType, InitProcessGroupKwargs from accelerate.state import AcceleratorState from packaging import version from tqdm import tqdm 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.utils import stop_sequences_criteria warnings.filterwarnings("ignore") from loguru import logger as eval_logger try: from llava.constants import DEFAULT_IMAGE_TOKEN, IMAGE_TOKEN_INDEX from llava.conversation import conv_templates from llava.mm_utils import ( get_model_name_from_path, process_images, tokenizer_image_token, ) from llava.model.builder import load_pretrained_model except Exception as e: eval_logger.debug("LLaVA is not installed. Please install LLaVA to use this model.\nError: %s" % e) # 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 # if is_flash_attn_2_available: # best_fit_attn_implementation = "flash_attention_2" # flash_attn has a bug that says: ERROR Error query and key must have the same dtype in generating if version.parse(torch.__version__) >= version.parse("2.1.2"): best_fit_attn_implementation = "sdpa" else: best_fit_attn_implementation = "eager" @register_model("llava") class Llava(lmms): """ Llava Model """ def __init__( self, pretrained: str = "liuhaotian/llava-v1.5-7b", truncation: Optional[bool] = True, device: Optional[str] = "cuda:0", batch_size: Optional[Union[int, str]] = 1, model_name=None, attn_implementation=best_fit_attn_implementation, device_map="cuda:0", conv_template="vicuna_v1", use_cache=True, tie_weights: bool = True, truncate_context=False, # whether to truncate the context in generation, set it False for LLaVA-1.6 customized_config=None, # ends in json **kwargs, ) -> None: super().__init__() # Do not use kwargs for now assert kwargs == {}, f"Unexpected kwargs: {kwargs}" accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52)) accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs]) self.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}" llava_model_args = { "multimodal": True, } if customized_config is not None: llava_model_args["customized_config"] = customized_config if attn_implementation is not None: llava_model_args["attn_implementation"] = attn_implementation if "use_flash_attention_2" in kwargs: llava_model_args["use_flash_attention_2"] = kwargs["use_flash_attention_2"] model_name = model_name if model_name is not None else get_model_name_from_path(pretrained) try: # Try to load the model with the multimodal argument self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) except TypeError: # for older versions of LLaVA that don't have multimodal argument llava_model_args.pop("multimodal", None) self._tokenizer, self._model, self._image_processor, self._max_length = load_pretrained_model(pretrained, None, model_name, device_map=self.device_map, **llava_model_args) self._config = self._model.config self.model.eval() if tie_weights: 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 # assert self.batch_size_per_gpu == 1, "Llava currently does not support batched generation. See https://github.com/haotian-liu/LLaVA/issues/754. HF Llava also has this issue." 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.model.to(self._device) 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): # 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 tok_decode(self, tokens): try: return self.tokenizer.decode(tokens) except: return self.tokenizer.decode([tokens]) def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: # TODO 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) image_sizes = [[visual.size[0], visual.size[1]] for visual in visuals] if visuals: image = process_images(visuals, self._image_processor, self._config) if type(image) is list: image = [_image.to(dtype=torch.float16, device=self.device) for _image in image] else: image = image.to(dtype=torch.float16, device=self.device) else: image = None prompts_input = contexts[0] if isinstance(contexts, list) else contexts if image is not None and len(image) != 0 and DEFAULT_IMAGE_TOKEN not in prompts_input: """ Three senarios: 1. No image, and there for, no image token should be added. 2. image token is already specified in the context, so we don't need to add it. 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. """ image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visuals) image_tokens = " ".join(image_tokens) prompts_input = image_tokens + "\n" + (contexts[0] if isinstance(contexts, list) else contexts) # This is much safer for llama3, as we now have some object type in it if "llama_3" in self.conv_template: conv = copy.deepcopy(conv_templates[self.conv_template]) else: conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], prompts_input) conv.append_message(conv.roles[1], None) prompt = conv.get_prompt() pad_token_id = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id contxt_id = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) # Add the answer of the second role conv.messages[1][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=image, use_cache=True, image_sizes=image_sizes) 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): if not input or any(i is None for i in input): return [] new_list = [] for i in input: if i: for j in i: new_list.append(j) return new_list def generate_until(self, requests: List[Instance]) -> List[str]: 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] batched_visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] # [B, N] flattened_visuals = self.flatten(batched_visuals) # [B*N] # 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.tok_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)}") if "image_aspect_ratio" in gen_kwargs.keys() and "image_aspect_ratio" not in self._config.__dict__: # here we should pop it out of gen_kwargs so that it doesn't get passed to the model for next step of generation self._config.image_aspect_ratio = gen_kwargs.pop("image_aspect_ratio") eval_logger.info(f"Setting image aspect ratio: {self._config.image_aspect_ratio}") # encode, pad, and truncate contexts for this batch if flattened_visuals: image_tensor = process_images(flattened_visuals, self._image_processor, self._config) if type(image_tensor) is list: image_tensor = [_image.to(dtype=torch.float16, device=self.device) for _image in image_tensor] else: image_tensor = image_tensor.to(dtype=torch.float16, device=self.device) else: image_tensor = None # prompts_input = contexts[0] question_input = [] for visual, context in zip(batched_visuals, contexts): if image_tensor is not None and len(image_tensor) != 0 and DEFAULT_IMAGE_TOKEN not in context: """ Three senarios: 1. No image, and there for, no image token should be added. 2. image token is already specified in the context, so we don't need to add it. 3. image token is not specified in the context and there is image inputs, so we need to add it. In this case, we add the image token at the beginning of the context and add a new line. """ image_tokens = [DEFAULT_IMAGE_TOKEN] * len(visual) if isinstance(visual, list) else [DEFAULT_IMAGE_TOKEN] image_tokens = " ".join(image_tokens) question = image_tokens + "\n" + context else: question = context # This is much safer for llama3, as we now have some object type in it if "llama_3" in self.conv_template: conv = copy.deepcopy(conv_templates[self.conv_template]) else: conv = conv_templates[self.conv_template].copy() conv.append_message(conv.roles[0], question) conv.append_message(conv.roles[1], None) prompt_question = conv.get_prompt() question_input.append(prompt_question) # input_ids = tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt").unsqueeze(0).to(self.device) # preconfigure gen_kwargs with defaults gen_kwargs["image_sizes"] = [flattened_visuals[idx].size for idx in range(len(flattened_visuals))] if "max_new_tokens" not in gen_kwargs: gen_kwargs["max_new_tokens"] = 1024 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 input_ids_list = [tokenizer_image_token(prompt, self.tokenizer, IMAGE_TOKEN_INDEX, return_tensors="pt") for prompt in question_input] pad_token_ids = self.tokenizer.pad_token_id if self.tokenizer.pad_token_id is not None else self.tokenizer.eos_token_id input_ids = self.pad_sequence(input_ids_list, batch_first=True, padding_value=pad_token_ids).to(self.device) attention_masks = input_ids.ne(pad_token_ids).to(self.device) # These steps are not in LLaVA's original code, but are necessary for generation to work # TODO: attention to this major generation step... try: cont = self.model.generate( input_ids, attention_mask=attention_masks, pad_token_id=pad_token_ids, images=image_tensor, image_sizes=gen_kwargs["image_sizes"], 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, ) text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True) except Exception as e: raise e eval_logger.error(f"Error {e} in generating") cont = "" text_outputs = [""] # cont_toks_list = cont.tolist() # for cont_toks, context in zip(cont_toks_list, contexts): # discard context + left-padding toks if using causal decoder-only LMM # if self.truncate_context: # cont_toks = cont_toks[input_ids.shape[1] :] # use secondary stop seqs to cut off should-have-been-stopped content post-hoc # if self.truncate_context: # for term in until: # if len(term) > 0: # # ignore '' separator, # # for seq2seq case where self.tok_decode(self.eot_token_id) = '' # text_outputs = text_outputs.split(term)[0] res.extend(text_outputs) self.cache_hook.add_partial("generate_until", (context, gen_kwargs), text_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 for LLaVA")