import copy import warnings from typing import List, Optional, Tuple, Union import torch import transformers from accelerate import Accelerator, DistributedType from accelerate.state import AcceleratorState from tqdm import tqdm from transformers import InstructBlipForConditionalGeneration, InstructBlipProcessor 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.tasks.mmmu.utils_group_img import process_images from lmms_eval.utils import stop_sequences_criteria warnings.filterwarnings("ignore") from loguru import logger as eval_logger @register_model("instructblip") class InstructBLIP(lmms): """ InstructBLIP Model """ def __init__( self, pretrained: str = "Salesforce/instructblip-vicuna-7b", device: Optional[str] = "cuda", dtype: Optional[Union[str, torch.dtype]] = "auto", batch_size: Optional[Union[int, str]] = 1, **kwargs, ) -> None: super().__init__() # Do not use kwargs for now assert kwargs == {}, f"Unexpected kwargs: {kwargs}" accelerator = Accelerator() if accelerator.num_processes > 1: self._device = torch.device(f"cuda:{accelerator.local_process_index}") else: self._device = device self._model = InstructBlipForConditionalGeneration.from_pretrained(pretrained, device_map=self._device) self._image_processor = InstructBlipProcessor.from_pretrained(pretrained) self._tokenizer = self._image_processor.tokenizer self._config = self._model.config self.model.eval() self.model.tie_weights() self.batch_size_per_gpu = int(batch_size) 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 else: 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 @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): return self.tokenizer.decode(tokens) def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]: # TODO assert False, "We have not implemented this function for InstructBLIP yet" def flatten(self, input): new_list = [] for i in input: 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] visuals = [doc_to_visual[0](self.task_dict[task][split][ids]) for ids in doc_id] visuals = self.flatten(visuals) # 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)}") assert self.batch_size_per_gpu == 1, "Do not support batch_size_per_gpu > 1 for now" context = contexts[0] if "" in context: # instruct blip does not expect the tag context = context.replace("", "") # Set trunction equals true here, the max length for qformer tokenizer is 512 # if not truncate, some questions will cause size mismatch # The transformer implementation can't handle multi images for blip # Concat it into one image if len(visuals) > 1: visuals = [process_images(visuals)] inputs = self._image_processor(images=visuals, text=context, return_tensors="pt", truncation=True).to(self.device) gen_kwargs["image_sizes"] = [visuals[idx].size for idx in range(len(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 try: cont = self.model.generate( **inputs, 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") cont = "" text_outputs = self.tokenizer.batch_decode(cont, skip_special_tokens=True)[0].strip() res.append(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 InstructBlip")