import abc import ast import copy import inspect import itertools import json import os import random import re import shutil import subprocess from collections.abc import Callable from dataclasses import asdict, dataclass, field from functools import partial from glob import glob from typing import ( Any, Dict, Iterable, Iterator, List, Literal, Mapping, Optional, Tuple, Union, ) import datasets import numpy as np from accelerate import Accelerator from datasets import Audio, DownloadConfig, Image, Sequence from huggingface_hub import snapshot_download from loguru import logger as eval_logger from PIL import Image as PIL_Image from PIL import ImageFile from tenacity import retry, stop_after_attempt, stop_after_delay, wait_fixed from tqdm import tqdm from lmms_eval import utils from lmms_eval.api import samplers from lmms_eval.api.instance import Instance from lmms_eval.api.registry import ( AGGREGATION_REGISTRY, DEFAULT_METRIC_REGISTRY, METRIC_REGISTRY, OUTPUT_TYPE_REGISTRY, get_aggregation, get_metric, get_metric_aggregation, is_higher_better, ) from lmms_eval.caching.cache import load_from_cache, save_to_cache from lmms_eval.filters import build_filter_ensemble # HuggingfaceM4/NoCaps contains truncated image in test split # Include this inside code block to avoid error ImageFile.LOAD_TRUNCATED_IMAGES = True ALL_OUTPUT_TYPES = [ "loglikelihood", "multiple_choice", "generate_until", "generate_until_multi_round", ] @dataclass class TaskConfig(dict): # task naming/registry task: str = None task_alias: str = None tag: str = None group: Union[str, list] = None group_alias: Union[str, list] = None # HF dataset options. # which dataset to use, # and what splits for what purpose dataset_path: str = None dataset_name: str = None dataset_kwargs: dict = None training_split: str = None validation_split: str = None test_split: str = None fewshot_split: str = None # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?) full_docs: bool = False # formatting / prompting options. # see docs/advanced_task_guide.md for more info process_results_use_image: bool = False process_docs: Callable = None doc_to_visual: Union[Callable, str] = None doc_to_text: Union[Callable, str] = None doc_to_target: Union[Callable, str] = None doc_to_choice: Union[Callable, str, dict, list] = None doc_to_messages: Callable = None process_results: Union[Callable, str] = None use_prompt: str = None description: str = "" target_delimiter: str = " " fewshot_delimiter: str = "\n\n" fewshot_config: dict = None # runtime configuration options num_fewshot: int = None # scoring options metric_list: list = None output_type: str = "generate_until" generation_kwargs: dict = None repeats: int = 1 filter_list: Union[str, list] = None should_decontaminate: bool = False doc_to_decontamination_query: str = None metadata: Union[str, list] = None # by default, not used in the code. allows for users to pass arbitrary info to tasks lmms_eval_specific_kwargs: dict = None model_specific_generation_kwargs: dict = None model_specific_target_kwargs: dict = None def __post_init__(self) -> None: if self.dataset_path and os.path.exists(os.path.dirname(self.dataset_path)): import inspect from importlib import import_module # self.dataset_path = inspect.getfile(import_module(self.dataset_path)) if self.group is not None: eval_logger.warning( "A task YAML file was found to contain a `group` key. Groups which provide aggregate scores over several subtasks now require a separate config file--if not aggregating, you may want to use the `tag` config option instead within your config. Setting `group` within a TaskConfig will be deprecated in v0.4.4. Please see https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/task_guide.md for more information." ) if self.tag is None: self.tag = self.group else: raise ValueError("Got both a `group` and `tag` entry within a TaskConfig. Please use one or the other--`group` values will be deprecated in v0.4.4.") if self.generation_kwargs is not None: if "generate_until" not in self.output_type: eval_logger.warning(f"[{self.task}] passed `generation_kwargs`, but not using `output_type: generate_until`!") assert "generate_until" not in self.output_type if "temperature" in self.generation_kwargs: self.generation_kwargs["temperature"] = float(self.generation_kwargs["temperature"]) if "until" not in self.generation_kwargs: self.generation_kwargs["until"] = [self.fewshot_delimiter] else: if "generate_until" in self.output_type: # ensure that we greedily generate in absence of explicit arguments otherwise self.generation_kwargs = { "until": None if self.fewshot_delimiter is None else [self.fewshot_delimiter], "do_sample": False, } # TODO: how to make TaskConfigs be de- and re-serializable, even when using the !function constructor? def __getitem__(self, item): return getattr(self, item) def __setitem__(self, item, value): return setattr(self, item, value) def to_dict(self): """dumps the current config as a dictionary object, as a printable format. null fields will not be printed. Used for dumping results alongside full task configuration :return: dict A printable dictionary version of the TaskConfig object. # TODO: should any default value in the TaskConfig not be printed? """ cfg_dict = asdict(self) # remove values that are `None` for k, v in list(cfg_dict.items()): if v is None: cfg_dict.pop(k) elif isinstance(v, Callable): # TODO: this should handle Promptsource template objects as a separate case? cfg_dict[k] = str(v) return cfg_dict class Task(abc.ABC): """A task represents an entire benchmark including its dataset, problems, answers, and evaluation methods. See MME for a simple example implementation A `doc` can be any python object which represents one instance of evaluation. This is usually a dictionary e.g. {"question": ..., "answer": ...} or {"question": ..., question, answer) """ VERSION = None # The name of the `Task` benchmark as denoted in the HuggingFace datasets Hub # or a path to a custom `datasets` loading script. DATASET_PATH: str = None # The name of a subset within `DATASET_PATH`. DATASET_NAME: str = None OUTPUT_TYPE: str = None def __init__( self, data_dir=None, cache_dir=None, download_mode=None, config=None, ) -> None: """ :param data_dir: str Stores the path to a local folder containing the `Task`'s data files. Use this to specify the path to manually downloaded data (usually when the dataset is not publicly accessible). :param cache_dir: str The directory to read/write the `Task` dataset. This follows the HuggingFace `datasets` API with the default cache directory located at: `~/.cache/huggingface/datasets` NOTE: You can change the cache location globally for a given process to another directory: `export HF_DATASETS_CACHE="/path/to/another/directory"` :param download_mode: datasets.DownloadMode How to treat pre-existing `Task` downloads and data. - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS` Reuse download and reuse dataset. - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS` Reuse download with fresh dataset. - `datasets.DownloadMode.FORCE_REDOWNLOAD` Fresh download and fresh dataset. """ self.download(data_dir, cache_dir, download_mode) self._training_docs = None self._fewshot_docs = None self._instances = None self._config = TaskConfig({**config}) if config else TaskConfig() self._filters = [build_filter_ensemble("none", [["take_first", None]])] def download(self, data_dir=None, cache_dir=None, download_mode=None) -> None: """Downloads and returns the task dataset. Override this method to download the dataset from a custom API. :param data_dir: str Stores the path to a local folder containing the `Task`'s data files. Use this to specify the path to manually downloaded data (usually when the dataset is not publicly accessible). :param cache_dir: str The directory to read/write the `Task` dataset. This follows the HuggingFace `datasets` API with the default cache directory located at: `~/.cache/huggingface/datasets` NOTE: You can change the cache location globally for a given process by setting the shell environment variable, `HF_DATASETS_CACHE`, to another directory: `export HF_DATASETS_CACHE="/path/to/another/directory"` :param download_mode: datasets.DownloadMode How to treat pre-existing `Task` downloads and data. - `datasets.DownloadMode.REUSE_DATASET_IF_EXISTS` Reuse download and reuse dataset. - `datasets.DownloadMode.REUSE_CACHE_IF_EXISTS` Reuse download with fresh dataset. - `datasets.DownloadMode.FORCE_REDOWNLOAD` Fresh download and fresh dataset. """ self.dataset = datasets.load_dataset( path=self.DATASET_PATH, name=self.DATASET_NAME, data_dir=data_dir, cache_dir=cache_dir, download_mode=download_mode, ) self.dataset_no_image = datasets.load_dataset( path=self.DATASET_PATH, name=self.DATASET_NAME, data_dir=data_dir, cache_dir=cache_dir, download_mode=download_mode, ) for doc_name in self.dataset_no_image: remove_cols = [] features = self.dataset_no_image[doc_name].features # If it is an Image instance or a Sequence of Image instance. Remove it for feature in features: if isinstance(features[feature], Image): remove_cols.append(feature) elif isinstance(features[feature], Sequence) and isinstance(features[feature].feature, Image): remove_cols.append(feature) for remove_col in remove_cols: self.dataset_no_image[doc_name] = self.dataset_no_image[doc_name].remove_columns(remove_col) @property def config(self): """Returns the TaskConfig associated with this class.""" return self._config @abc.abstractmethod def has_training_docs(self): """Whether the task has a training set""" pass @abc.abstractmethod def has_validation_docs(self): """Whether the task has a validation set""" pass @abc.abstractmethod def has_test_docs(self): """Whether the task has a test set""" pass def training_docs(self): """ :return: Iterable[obj] A iterable of any object, that doc_to_text can handle """ return [] def validation_docs(self): """ :return: Iterable[obj] A iterable of any object, that doc_to_text can handle """ return [] def test_docs(self): """ :return: Iterable[obj] A iterable of any object, that doc_to_text can handle """ return [] def fewshot_docs(self): """ :return: Iterable[obj] A iterable of any object, that doc_to_text can handle """ if self.has_training_docs(): return self.training_docs() elif self.has_validation_docs(): return self.validation_docs() else: if self.config.num_fewshot is not None: eval_logger.warning("has_training_docs and has_validation_docs are False" ", using test_docs as fewshot_docs but this is not recommended.") return self.test_docs() def _process_doc(self, doc): """ Override this to process (detokenize, strip, replace, etc.) individual documents. This can be used in a map over documents of a data split. E.g. `map(self._process_doc, self.dataset["validation"])` :return: dict The processed version of the specified `doc`. """ return doc @property def instances(self): """After calling `task.build_all_requests()`, tasks maintain a list of the dataset instances which will be evaluated. """ return self._instances def fewshot_examples(self, k, rnd): if self._training_docs is None: self._training_docs = list(self.training_docs()) return rnd.sample(self._training_docs, k) def doc_to_decontamination_query(self, doc) -> None: print("Override doc_to_decontamination_query with document specific decontamination query.") assert False @abc.abstractmethod def doc_to_text(self, doc): pass @abc.abstractmethod def doc_to_target(self, doc): pass # @profile def build_all_requests( self, *, limit: Union[int, None] = None, rank: int = 0, world_size: int = 1, cache_requests: bool = False, rewrite_requests_cache: bool = False, system_instruction: Optional[str] = None, apply_chat_template: bool = False, fewshot_as_multiturn: bool = False, chat_template: Optional[Callable] = None, tokenizer_name: str = "", ) -> None: """Build a set of Instances for a task, and store them in task.instances""" if self.has_test_docs(): docs = self.test_docs() split = self.config.test_split elif self.has_validation_docs(): docs = self.validation_docs() split = self.config.validation_split else: assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!" # used with caching og_limit = limit cache_key = f"requests-{self._config.task}-{self.config.num_fewshot}shot-rank{rank}-world_size{world_size}" cache_key += "-chat_template" if apply_chat_template else "" cache_key += "-fewshot_as_multiturn" if fewshot_as_multiturn else "" cache_key += f"-system_prompt_hash{utils.hash_string(system_instruction)}" if system_instruction is not None else "" cache_key += f"-tokenizer{tokenizer_name}" cached_instances = load_from_cache(file_name=cache_key) if cache_requests and cached_instances and not rewrite_requests_cache: cached_instances = cached_instances[:limit] flattened_instances = [instance for instance_group in cached_instances for instance in instance_group] self._instances = flattened_instances return eval_logger.info(f"Building contexts for {self.config.task} on rank {rank}...") instances = [] # process all documents when caching is specified for simplicity if cache_requests and (not cached_instances or rewrite_requests_cache) and limit is not None: limit = None doc_id_docs = utils.create_iterator(enumerate(self.eval_docs_no_media), rank=rank, limit=int(limit) if limit else None, world_size=world_size) doc_iterator_for_counting = itertools.islice(range(len(self.test_docs())), rank, limit, world_size) if self.has_test_docs() else itertools.islice(range(len(self.validation_docs())), rank, limit, world_size) num_docs = sum(1 for _ in doc_iterator_for_counting) for doc_id, doc in tqdm( doc_id_docs, total=num_docs, ): # sample fewshot context #TODO: need to offset doc_id by rank now! fewshot_ctx = self.fewshot_context( doc, 0 if self.config.num_fewshot is None else self.config.num_fewshot, system_instruction, apply_chat_template, fewshot_as_multiturn, chat_template, ) # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute per_task_metadata = {"task": self.config["task"], "doc_id": doc_id, "repeats": self.config.repeats, "split": split} if self.config.metadata and type(self.config.metadata) == dict: # TODO: temporary fix for metadata loading, ignore the list of dict type. per_task_metadata.update(self.config.metadata) inst = self.construct_requests(doc_id=doc_id, ctx=fewshot_ctx, metadata=per_task_metadata) if not isinstance(inst, list): inst = [inst] instances.append(inst) # now flatten, this is to allow slicing to work with pickles sliced_instances = instances[:og_limit] flattened_instances = [instance for instance_group in sliced_instances for instance in instance_group] self._instances = flattened_instances if len(self._instances) == 0: raise ValueError("task.build_requests() did not find any docs!") if cache_requests and (not cached_instances or rewrite_requests_cache): save_to_cache(file_name=cache_key, obj=instances) # FIXME: Bo - We need to check if the doc_to_visual if it's exists and restore it. If we use cache, the doc_to_visual will be None since it's not serializable for instance in self._instances: if instance.arguments[2] is None: arguments = (instance.arguments[0], instance.arguments[1], self.doc_to_visual, *instance.arguments[3:]) else: arguments = instance.arguments instance.arguments = arguments @abc.abstractmethod def construct_requests(self, doc_id, ctx, **kwargs): """Uses RequestFactory to construct Requests and returns an iterable of Requests which will be sent to the LMM. :param doc_id: int The index of a document within `self.test_docs()` or `self.validation_docs()`, whichever is the main split used. :param ctx: str The context string, generated by fewshot_context. This includes the natural language description, as well as the few shot examples, and the question part of the document for `doc`. :param repeats: int TODO: update this docstring The number of times each instance in a dataset is inferred on. Defaults to 1, can be increased for techniques like majority voting. """ pass @abc.abstractmethod def process_results(self, doc, results): """Take a single document and the LMM results and evaluates, returning a dict where keys are the names of submetrics and values are the values of the metric for that one document :param doc: The document as returned from training_docs, validation_docs, or test_docs. :param results: The results of the requests created in construct_requests. """ pass @abc.abstractmethod def aggregation(self): """ :returns: {str: [metric_score] -> float} A dictionary where keys are the names of submetrics and values are functions that aggregate a list of metric scores """ pass @abc.abstractmethod def higher_is_better(self): """ :returns: {str: bool} A dictionary where keys are the names of submetrics and values are whether a higher value of the submetric is better """ pass @classmethod def count_bytes(cls, doc): """Used for byte-level perplexity metrics in rolling loglikelihood""" return len(doc.encode("utf-8")) @utils.positional_deprecated def fewshot_context( self, doc_id, num_fewshot, split, rnd=random.Random(1234), description=None, ): """Returns a fewshot context string that is made up of a prepended description (if provided), the `num_fewshot` number of examples, and an appended prompt example. :param doc_id: int The document id as returned from training_docs, validation_docs, or test_docs. :param num_fewshot: int The number of fewshot examples to provide in the returned context string. :param split: str The split of the document to retrieve from the dataset :param rnd: random.Random The pseudo-random number generator used to randomly sample examples. WARNING: This is currently a required arg although it's optionalized with a default `None`. :param description: str The task's description that will be prepended to the fewshot examples. :returns: str The fewshot context. """ assert rnd is not None, "A `random.Random` generator argument must be provided to `rnd`" description = description if description else "" doc = self.dataset_no_image[split][doc_id] if num_fewshot == 0: labeled_examples = "" else: # for sets with no training docs, draw from other set *but ensure no overlap with current doc* if self.has_training_docs(): fewshotex = self.fewshot_examples(k=num_fewshot, rnd=rnd) else: if self._fewshot_docs is None: self._fewshot_docs = list(self.validation_docs() if self.has_validation_docs() else self.test_docs()) fewshotex = rnd.sample(self._fewshot_docs, num_fewshot + 1) # get rid of the doc that's the one we're evaluating, if it's in the fewshot fewshotex = [x for x in fewshotex if x != doc][:num_fewshot] labeled_examples = "\n\n".join([self.doc_to_text(doc) + self.doc_to_target(doc) for doc in fewshotex]) + "\n\n" example = self.doc_to_text(doc) return description + labeled_examples + example def apply_filters(self) -> Optional[List[Instance]]: """Iterates over FilterEnsembles and applies them to instances""" if hasattr(self, "_filters"): for f in self._filters: f.apply(self._instances, None) else: eval_logger.warning("No filter defined, passing through instances") return self._instances def dump_config(self) -> dict: """Returns a dictionary representing the task's config. :returns: str The fewshot context. """ # TODO: this should only return the overrides applied to a non-YAML task's configuration. # (num_fewshot) return self.config.to_dict() def set_config(self, key: str, value: Any, update: bool = False) -> None: """Set or update the configuration for a given key.""" if key is None: raise ValueError("Key must be provided.") if update: current_value = getattr(self._config, key, {}) if not isinstance(current_value, dict): raise TypeError(f"Expected a dict for key '{key}', got {type(current_value).__name__} instead.") current_value.update(value) else: setattr(self._config, key, value) def override_metric(self, metric_name: str) -> None: """ Override the default metrics used for evaluation with custom metrics. Parameters: - metric_name (str): The name of the custom metric to override. Should be registered in api.metrics. """ ( self._metric_fn_list, self._aggregation_list, self._metric_fn_kwargs, self._higher_is_better, ) = ({}, {}, {}, {}) self._metric_fn_list[metric_name] = get_metric(metric_name) self._aggregation_list[metric_name] = get_metric_aggregation(metric_name) self._higher_is_better[metric_name] = is_higher_better(metric_name) self._metric_fn_kwargs[metric_name] = {} if not isinstance(self, ConfigurableTask): self.process_results = lambda x, y: {metric_name: get_metric(metric_name)} self.aggregation = lambda: {metric_name: get_metric_aggregation(metric_name)} setattr(self._config, "metric_list", [{"metric": metric_name}]) setattr(self._config, "process_results", None) def set_fewshot_seed(self, seed: Optional[int] = None) -> None: self.fewshot_rnd = random.Random(seed) if hasattr(self, "sampler"): self.sampler.rnd = self.fewshot_rnd @property def eval_docs(self) -> Union[datasets.Dataset, List[dict]]: if self.has_test_docs(): return self.test_docs() elif self.has_validation_docs(): return self.validation_docs() else: raise ValueError(f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!") def doc_iterator(self, *, rank: int = 0, limit: Union[int, None] = None, world_size: int = 1) -> Iterator[Tuple[int, Any]]: limit = int(limit) if limit else None doc_iterator = utils.create_iterator( enumerate(self.eval_docs), rank=int(rank), limit=limit, world_size=int(world_size), ) return doc_iterator class ConfigurableTask(Task): VERSION = "Yaml" OUTPUT_TYPE = None CONFIG = None def __init__( self, data_dir=None, cache_dir=None, download_mode=None, config: Optional[dict] = None, model_name: Optional[str] = None, ) -> None: # TODO no super() call here # Get pre-configured attributes self._config = self.CONFIG # Use new configurations if there was no preconfiguration if self.config is None: self._config = TaskConfig(**config) # Overwrite configs else: if config is not None: self._config.__dict__.update(config) if self.config is None: raise ValueError("Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg") if isinstance(self.config.metadata, dict): if "version" in self.config.metadata: self.VERSION = self.config.metadata["version"] self.model_name = model_name self._prepare_model_specific_config() if self.config.output_type is not None: if self.config.output_type not in ALL_OUTPUT_TYPES: raise ValueError(f"Got invalid output_type '{self.config.output_type}', must be in '{','.join(ALL_OUTPUT_TYPES)}'") self.OUTPUT_TYPE = self.config.output_type if self.config.dataset_path is not None: self.DATASET_PATH = self.config.dataset_path if self.config.dataset_name is not None: self.DATASET_NAME = self.config.dataset_name self._prepare_metric_and_aggregation() self.download(self.config.dataset_kwargs) self._training_docs = None self._fewshot_docs = None if self.config.filter_list is not None: self._filters = [] for filter_config in self.config.filter_list: for filter_pipeline in filter_config: filter_name = filter_config["name"] filter_functions = filter_config["filter"] components = [] for function in filter_functions: kwargs = {key: function[key] for key in function if key != "function"} components.append([function["function"], kwargs]) filter_pipeline = build_filter_ensemble(filter_name, components) self._filters.append(filter_pipeline) else: self._filters = [build_filter_ensemble("none", [["take_first", None]])] if self.config.fewshot_config is not None: self.sampler = samplers.get_sampler(self.config.fewshot_config.get("sampler", "default") if self.config.fewshot_config else "default")(list(self.fewshot_docs()), self, rnd=random.Random(1234)) if self.has_test_docs(): self.task_docs = self.test_docs() elif self.has_validation_docs(): self.task_docs = self.validation_docs() else: assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!" # Test One Doc self.features = list(self.task_docs.features.keys()) self.multiple_input = 0 self.multiple_target = 0 test_doc = self.task_docs[0] test_text = self.doc_to_text(test_doc) test_target = self.doc_to_target(test_doc) if self.config.doc_to_choice is not None: test_choice = self.doc_to_choice(test_doc) if type(test_choice) is not list: eval_logger.error("doc_to_choice must return list") else: num_choice = len(test_choice) if type(test_text) is int: self.multiple_input = num_choice else: test_choice = None if type(test_target) is list: self.multiple_target = len(test_target) else: if (type(test_target) is int) and (test_choice is not None): test_target = test_choice[test_target] else: test_target = str(test_target) if test_choice is not None: check_choices = test_choice else: check_choices = [test_target] if self.config.doc_to_choice is not None: for choice in check_choices: choice_has_whitespace = True if choice[0].isspace() else False delimiter_has_whitespace = True if self.config.target_delimiter.rstrip() != self.config.target_delimiter else False if delimiter_has_whitespace and choice_has_whitespace: eval_logger.warning(f'Both target_delimiter and target choice: "{choice}" have whitespace') elif (not delimiter_has_whitespace) and (not choice_has_whitespace): eval_logger.warning(f'Both target_delimiter "{self.config.target_delimiter}" and target choice: "{choice}" do not have whitespace, ignore if the language you are evaluating on does not require/use whitespace') def _prepare_model_specific_config(self): self.lmms_eval_specific_kwargs = self.config.lmms_eval_specific_kwargs if self.lmms_eval_specific_kwargs is not None: if self.model_name in self.lmms_eval_specific_kwargs: self.lmms_eval_specific_kwargs = self.lmms_eval_specific_kwargs[self.model_name] elif "default" in self.lmms_eval_specific_kwargs: self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("default", {})) elif "dataset" in self.lmms_eval_specific_kwargs: self.lmms_eval_specific_kwargs.update(self.lmms_eval_specific_kwargs.get("dataset", {})) self.model_specific_target_kwargs = self.config.model_specific_target_kwargs if self.model_specific_target_kwargs is not None: if self.model_name in self.model_specific_target_kwargs: self.model_specific_target_kwargs = self.model_specific_target_kwargs[self.model_name] else: self.model_specific_target_kwargs = self.model_specific_target_kwargs.get("default", None) self.model_specific_generation_kwargs = self.config.model_specific_generation_kwargs if self.model_specific_generation_kwargs is not None: if self.model_name in self.model_specific_generation_kwargs: self.model_specific_generation_kwargs = self.model_specific_generation_kwargs[self.model_name] else: self.model_specific_generation_kwargs = self.model_specific_generation_kwargs.get("default", {}) self.config.generation_kwargs.update(self.model_specific_generation_kwargs) def _prepare_metric_and_aggregation(self): self._metric_fn_list = {} self._metric_fn_kwargs = {} self._aggregation_list = {} self._higher_is_better = {} if self.config.metric_list is None: # TODO: handle this in TaskConfig.__post_init__ ? _metric_list = DEFAULT_METRIC_REGISTRY[self.config.output_type] for metric_name in _metric_list: self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name] self._metric_fn_kwargs[metric_name] = {} self._aggregation_list[metric_name] = get_metric_aggregation(metric_name) self._higher_is_better[metric_name] = is_higher_better(metric_name) else: for metric_config in self.config.metric_list: assert "metric" in metric_config metric_name = metric_config["metric"] kwargs = {key: metric_config[key] for key in metric_config if key not in ["metric", "aggregation", "higher_is_better"]} if self.config.process_results is not None: self._metric_fn_list[metric_name] = None self._metric_fn_kwargs[metric_name] = {} elif callable(metric_name): metric_fn = metric_name.__call__ metric_name = metric_name.__name__ self._metric_fn_list[metric_name] = metric_fn self._metric_fn_kwargs[metric_name] = kwargs else: self._metric_fn_list[metric_name] = METRIC_REGISTRY[metric_name] self._metric_fn_kwargs[metric_name] = kwargs if "aggregation" in metric_config: agg_name = metric_config["aggregation"] if type(agg_name) == str: self._aggregation_list[metric_name] = get_aggregation(agg_name) elif callable(agg_name): self._aggregation_list[metric_name] = metric_config["aggregation"] else: INV_AGG_REGISTRY = {v: k for k, v in AGGREGATION_REGISTRY.items()} metric_agg = get_metric_aggregation(metric_name) eval_logger.warning(f"[Task: {self._config.task}] metric {metric_name} is defined, but aggregation is not. " f"using default " f"aggregation={INV_AGG_REGISTRY[metric_agg]}") self._aggregation_list[metric_name] = metric_agg if "higher_is_better" in metric_config: self._higher_is_better[metric_name] = metric_config["higher_is_better"] else: eval_logger.warning(f"[Task: {self._config.task}] metric {metric_name} is defined, but higher_is_better is not. " f"using default " f"higher_is_better={is_higher_better(metric_name)}") self._higher_is_better[metric_name] = is_higher_better(metric_name) @retry(stop=(stop_after_attempt(5) | stop_after_delay(60)), wait=wait_fixed(2)) def download(self, dataset_kwargs=None) -> None: # If the dataset is a video dataset, # Recursively search whether their is a zip and unzip it to the huggingface home download_config = DownloadConfig() download_config.max_retries = dataset_kwargs.get("max_retries", 10) if dataset_kwargs is not None else 10 download_config.num_proc = dataset_kwargs.get("num_proc", 8) if dataset_kwargs is not None else 8 download_config.local_files_only = dataset_kwargs.get("local_files_only", False) if dataset_kwargs is not None else False if dataset_kwargs is not None: if "From_YouTube" in dataset_kwargs: def _download_from_youtube(path): try: for video in tqdm(self.all_dataset[split]): video_id = video["videoID"] target_path = os.path.join(path, f"{video_id}.mp4") assert shutil.which("yt-dlp") is not None, "yt-dlp must be installed and available in the system's PATH" command = f"yt-dlp -o {target_path} -f mp4 https://www.youtube.com/watch?v={video_id}" subprocess.run(command, shell=True) with open(os.path.join(cache_path, f"{task}_download_status.json"), "w") as f: f.write(json.dumps({task: "downloaded"})) except Exception as e: eval_logger.error(f"Error while downloading {task} data: {e}") with open(os.path.join(cache_path, f"{task}_download_status.json"), "w") as f: f.write(json.dumps({task: "not downloaded"})) hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/") accelerator = Accelerator() if accelerator.is_main_process: dataset_kwargs.pop("From_YouTube") assert "load_from_disk" not in dataset_kwargs, "load_from_disk must not be True when From_YouTube is True" self.all_dataset = datasets.load_dataset( path=self.DATASET_PATH, name=self.DATASET_NAME, download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, **dataset_kwargs if dataset_kwargs is not None else {}, ) dataset_kwargs["From_YouTube"] = True cache_path = snapshot_download(repo_id=self.DATASET_PATH, repo_type="dataset") # download_parquet split = vars(self.config)["test_split"] task = vars(self.config)["task"] video_path = os.path.join(hf_home, task) if os.path.exists(os.path.join(cache_path, f"{task}_download_status.json")): download_status = json.load(open(os.path.join(cache_path, f"{task}_download_status.json"), "r")) if download_status[task] == "downloaded": eval_logger.info(f"Data for {task} already download!") else: eval_logger.info(f"Start downloading YouTube data to {video_path}...") _download_from_youtube(video_path) else: eval_logger.info(f"Start downloading YouTube data to {video_path}...") _download_from_youtube(video_path) accelerator.wait_for_everyone() if "builder_script" in dataset_kwargs: builder_script = dataset_kwargs["builder_script"] self.DATASET_PATH = os.path.join(cache_path, builder_script) dataset_kwargs.pop("builder_script") downloaded_video_ids = [i.split(".mp4")[0] for i in os.listdir(os.path.expanduser(video_path)) if i.endswith(".mp4")] # Filtered the existing dataset with the downloaded video ids self.dataset = datasets.DatasetDict({split: self.all_dataset[split].filter(lambda x: x["videoID"] in downloaded_video_ids)}) self.dataset_no_image = self.dataset dataset_kwargs.pop("From_YouTube") return if "video" in dataset_kwargs and dataset_kwargs["video"]: hf_home = os.getenv("HF_HOME", "~/.cache/huggingface/") hf_home = os.path.expanduser(hf_home) cache_dir = dataset_kwargs["cache_dir"] cache_dir = os.path.join(hf_home, cache_dir) accelerator = Accelerator() if accelerator.is_main_process: force_download = dataset_kwargs.get("force_download", False) force_unzip = dataset_kwargs.get("force_unzip", False) revision = dataset_kwargs.get("revision", "main") create_link = dataset_kwargs.get("create_link", False) # If the user already has a cache dir, we skip download the zip files if not os.path.exists(cache_dir): cache_path = snapshot_download(repo_id=self.DATASET_PATH, revision=revision, repo_type="dataset", force_download=force_download, etag_timeout=60) zip_files = glob(os.path.join(cache_path, "**/*.zip"), recursive=True) tar_files = glob(os.path.join(cache_path, "**/*.tar*"), recursive=True) else: zip_files = [] tar_files = [] def unzip_video_data(zip_file): import os import zipfile with zipfile.ZipFile(zip_file, "r") as zip_ref: for file_info in zip_ref.infolist(): target_path = os.path.join(cache_dir, file_info.filename) if not os.path.exists(target_path): zip_ref.extract(file_info, cache_dir) else: eval_logger.info(f"Skipping existing file: {target_path}") eval_logger.info(f"Extracted all files from {zip_file} to {cache_dir}") def untar_video_data(tar_file): import tarfile with tarfile.open(tar_file, "r") as tar_ref: tar_ref.extractall(cache_dir) eval_logger.info(f"Extracted all files from {tar_file} to {cache_dir}") def concat_tar_parts(tar_parts, output_tar): with open(output_tar, "wb") as out_tar: from tqdm import tqdm for part in tqdm(sorted(tar_parts)): with open(part, "rb") as part_file: out_tar.write(part_file.read()) eval_logger.info(f"Concatenated parts {tar_parts} into {output_tar}") # Unzip zip files if needed if force_unzip or (not os.path.exists(cache_dir) and len(zip_files) > 0): for zip_file in zip_files: unzip_video_data(zip_file) # Concatenate and extract tar files if needed if force_unzip or (not os.path.exists(cache_dir) and len(tar_files) > 0): tar_parts_dict = {} # Group tar parts together for tar_file in tar_files: base_name = tar_file.split(".tar")[0] base_name = re.sub(r"_\d+$", "", base_name) if base_name not in tar_parts_dict: tar_parts_dict[base_name] = [] tar_parts_dict[base_name].append(tar_file) # Concatenate and untar split parts for base_name, parts in tar_parts_dict.items(): eval_logger.info(f"Extracting following tar files: {parts}") output_tar = base_name + ".tar" if not os.path.exists(output_tar): eval_logger.info(f"Start concatenating tar files") concat_tar_parts(parts, output_tar) eval_logger.info(f"Finish concatenating tar files") if not os.path.exists(os.path.join(cache_dir, os.path.basename(base_name))): untar_video_data(output_tar) # Link cache_path to cache_dir if needed. if create_link: if not os.path.exists(cache_dir) or os.path.islink(cache_dir): if os.path.islink(cache_dir): os.remove(cache_dir) eval_logger.info(f"Removed existing symbolic link: {cache_dir}") # Create a new symbolic link os.symlink(cache_path, cache_dir) eval_logger.info(f"Symbolic link created successfully: {cache_path} -> {cache_dir}") accelerator.wait_for_everyone() dataset_kwargs.pop("cache_dir") dataset_kwargs.pop("video") if "builder_script" in dataset_kwargs: builder_script = dataset_kwargs["builder_script"] self.DATASET_PATH = os.path.join(cache_path, builder_script) dataset_kwargs.pop("builder_script") if "force_download" in dataset_kwargs: dataset_kwargs.pop("force_download") if "force_unzip" in dataset_kwargs: dataset_kwargs.pop("force_unzip") if "local_files_only" in dataset_kwargs: dataset_kwargs.pop("local_files_only") if "create_link" in dataset_kwargs: dataset_kwargs.pop("create_link") if dataset_kwargs is not None and "load_from_disk" in dataset_kwargs and dataset_kwargs["load_from_disk"]: # using local task in offline environment, need to process the online dataset into local format via # `ds = load_datasets("lmms-lab/MMMU")` self.dataset = datasets.load_from_disk(dataset_path=self.DATASET_PATH) else: self.dataset = datasets.load_dataset( path=self.DATASET_PATH, name=self.DATASET_NAME, download_mode=datasets.DownloadMode.REUSE_DATASET_IF_EXISTS, download_config=download_config, **dataset_kwargs if dataset_kwargs is not None else {}, ) if self.config.process_docs is not None: for split in self.dataset: if split in [self.config.training_split, self.config.validation_split, self.config.test_split, self.config.fewshot_split]: self.dataset[split] = self.config.process_docs(self.dataset[split]) # copy dataset, remove image features self.dataset_no_image = self.dataset.copy() for doc_name in self.dataset_no_image: remove_cols = [] features = self.dataset_no_image[doc_name].features # If it is an Image instance or a Sequence of Image instance. Remove it for feature in features: if isinstance(features[feature], Image): remove_cols.append(feature) elif isinstance(features[feature], Sequence) and isinstance(features[feature].feature, Image): remove_cols.append(feature) elif isinstance(features[feature], Audio): remove_cols.append(feature) for remove_col in remove_cols: self.dataset_no_image[doc_name] = self.dataset_no_image[doc_name].remove_columns(remove_col) def has_training_docs(self) -> bool: if self.config.training_split is not None: return True else: return False def has_validation_docs(self) -> bool: if self.config.validation_split is not None: return True else: return False def has_test_docs(self) -> bool: if self.config.test_split is not None: return True else: return False def training_docs(self) -> datasets.Dataset: if self.has_training_docs(): return self.dataset[self.config.training_split] def validation_docs(self) -> datasets.Dataset: if self.has_validation_docs(): return self.dataset[self.config.validation_split] def validation_docs_no_media(self) -> datasets.Dataset: if self.has_validation_docs(): return self.dataset_no_image[self.config.validation_split] def test_docs(self) -> datasets.Dataset: if self.has_test_docs(): return self.dataset[self.config.test_split] def test_docs_no_media(self) -> datasets.Dataset: if self.has_test_docs(): return self.dataset_no_image[self.config.test_split] @property def eval_docs_no_media(self) -> Union[datasets.Dataset, List[dict]]: if self.has_test_docs(): return self.test_docs_no_media() elif self.has_validation_docs(): return self.validation_docs_no_media() else: raise ValueError(f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!") def fewshot_docs(self): if self.config.fewshot_split is not None: return self.dataset[self.config.fewshot_split] else: if (self.config.num_fewshot is not None) and (self.config.num_fewshot > 0): eval_logger.warning(f"Task '{self.config.task}': " "num_fewshot > 0 but fewshot_split is None. " "using preconfigured rule.") return super().fewshot_docs() @utils.positional_deprecated def fewshot_context( self, doc: str, num_fewshot: int, system_instruction: Optional[str] = None, apply_chat_template: bool = False, fewshot_as_multiturn: bool = False, chat_template: Optional[Callable] = None, is_multimodal: bool = False, ) -> str: """Returns a fewshot context string that is made up of a prepended description (if provided), the `num_fewshot` number of examples, and an appended prompt example. :param doc: str The document as returned from training_docs, validation_docs, or test_docs. :param num_fewshot: int The number of fewshot examples to provide in the returned context string. :param system_instruction: str System instruction to be applied to the prompt. :param apply_chat_template: bool Whether to apply the chat template to the fewshot context. :param fewshot_as_multiturn: bool Whether to provide the fewshot examples as a multiturn conversation or a single user turn. :param chat_template: callable (from lm.apply_chat_template) that takes in a list[Dict] chat transcript and renders it into a string. :returns: str The fewshot context. """ if apply_chat_template: labeled_examples = [] else: labeled_examples = "" # get task description if description := self.config.description: description = utils.apply_template(self.config.description, doc) # create system prompt based on the provided system instruction and description if system_instruction is not None and description: system_prompt = f"{system_instruction}{self.sampler.fewshot_delimiter}{description}" elif system_instruction is not None: system_prompt = system_instruction elif description: system_prompt = description else: system_prompt = "" # add system prompt if specified if system_prompt: if apply_chat_template: labeled_examples.append({"role": "system", "content": system_prompt}) else: labeled_examples = system_prompt # if few-shot - append examples after the system prompt if num_fewshot > 0: if is_multimodal is False: if apply_chat_template: labeled_examples.extend(self.sampler.get_chat_context(doc, num_fewshot, fewshot_as_multiturn)) else: labeled_examples += self.sampler.get_context(doc, num_fewshot) else: if apply_chat_template: labeled_examples_text, labeled_examples_multimodal = self.sampler.get_multimodal_chat_context(doc, num_fewshot, fewshot_as_multiturn) labeled_examples.extend(labeled_examples_text) else: labeled_examples_text, labeled_examples_multimodal = self.sampler.get_multimodal_context(doc, num_fewshot) labeled_examples += labeled_examples_text example = self.doc_to_text(doc) if is_multimodal is False: if apply_chat_template: if self.multiple_input: return chat_template(labeled_examples) if isinstance(example, str): self.append_target_question(labeled_examples, example, fewshot_as_multiturn) # for loglikelihood create a list of questions with appended choices elif isinstance(example, list): labeled_examples_list = [] # copy chat history for each example and append the answer for ex in example: chat = copy.deepcopy(labeled_examples) self.append_target_question(chat, ex, fewshot_as_multiturn) labeled_examples_list.append(chat_template(chat)) return labeled_examples_list # if example is an integer, append the choice or convert to string elif isinstance(example, int): if self.config.doc_to_choice is not None: choices = self.doc_to_choice(doc) self.append_target_question(labeled_examples, choices[example], fewshot_as_multiturn) else: self.append_target_question(labeled_examples, str(example), fewshot_as_multiturn) # return lm.apply_chat_template(labeled_examples) return chat_template(labeled_examples) else: if self.multiple_input: return labeled_examples if isinstance(example, str): return labeled_examples + example elif isinstance(example, list): return [labeled_examples + ex for ex in example] elif isinstance(example, int): if self.config.doc_to_choice is not None: choices = self.doc_to_choice(doc) return labeled_examples + choices[example] else: return labeled_examples + str(example) else: if apply_chat_template: raise NotImplementedError("Multimodal chat template not implemented yet") else: if self.multiple_input: return labeled_examples + " " + example, labeled_examples_multimodal if isinstance(example, str): return labeled_examples + " " + example, labeled_examples_multimodal else: raise NotImplementedError("Multimodal not implemented yet") # elif isinstance(example, list): # return [labeled_examples + ex for ex in example] # elif isinstance(example, int): # if self.config.doc_to_choice is not None: # choices = self.doc_to_choice(doc) # return labeled_examples + choices[example], labeled_examples_multimodal # else: # return labeled_examples + str(example), labeled_examples_multimodal def apply_filters(self) -> Optional[List[Instance]]: """Iterates over FilterEnsembles and applies them to instances""" if hasattr(self, "_filters"): for f in self._filters: f.apply(self._instances, self.task_docs) else: eval_logger.warning("No filter defined, passing through instances") return self._instances def should_decontaminate(self): return self.config.should_decontaminate def doc_to_decontamination_query(self, doc): if self.config.should_decontaminate: if self.config.doc_to_decontamination_query is None: return self.doc_to_text(doc) else: doc_to_decontamination_query = self.config.doc_to_decontamination_query if doc_to_decontamination_query in self.features: return doc[doc_to_decontamination_query] elif callable(doc_to_decontamination_query): return doc_to_decontamination_query(doc) else: return ast.literal_eval(utils.apply_template(self.config.doc_to_decontamination_query, doc)) def _process_doc(self, doc): """ Override this to process (detokenize, strip, replace, etc.) individual documents. This can be used in a map over documents of a data split. E.g. `map(self._process_doc, self.dataset["validation"])` :return: dict The processed version of the specified `doc`. """ return doc def doc_to_text(self, doc): doc_to_text = self.config.doc_to_text if type(doc_to_text) == int: return doc_to_text elif type(doc_to_text) == str: if doc_to_text in self.features: # if self.config.doc_to_choice is not None: # return self.doc_to_choice(doc)[doc[doc_to_text]] # else: return doc[doc_to_text] else: text_string = utils.apply_template(doc_to_text, doc) if text_string.isdigit() and self._config.doc_to_choice is not None: return ast.literal_eval(text_string) else: return text_string elif callable(doc_to_text): return ( doc_to_text(doc, self.lmms_eval_specific_kwargs) if self.lmms_eval_specific_kwargs is not None else doc_to_text( doc, ) ) # Used when applying a Promptsource template elif hasattr(doc_to_text, "apply"): applied_prompt = doc_to_text.apply(doc) if len(applied_prompt) == 2: return applied_prompt[0] else: eval_logger.warning("Applied prompt returns empty string") return self.config.fewshot_delimiter else: print(type(doc_to_text)) raise TypeError def doc_to_target(self, doc: dict) -> Union[int, str, list]: doc_to_target = self.config.doc_to_target if type(doc_to_target) == int: return doc_to_target elif type(doc_to_target) == str: if doc_to_target in self.features: # if self.config.doc_to_choice is not None: # return self.doc_to_choice(doc)[doc[doc_to_target]] # else: return doc[doc_to_target] else: target_string = utils.apply_template(doc_to_target, doc) if target_string.isdigit() and self._config.doc_to_choice is not None: return ast.literal_eval(target_string) elif len(target_string) >= 2 and (target_string[0] == "[") and (target_string[-1] == "]"): try: return ast.literal_eval(target_string) except (SyntaxError, ValueError): return target_string else: return target_string elif type(doc_to_target) == list: return doc_to_target elif callable(doc_to_target): return doc_to_target(doc, self.model_specific_target_kwargs) if self.model_specific_target_kwargs is not None else doc_to_target(doc) # Used when applying a Promptsource template elif hasattr(doc_to_target, "apply"): applied_prompt = doc_to_target.apply(doc) if len(applied_prompt) == 2: return applied_prompt[1] else: eval_logger.warning("Applied prompt returns empty string") return self.config.fewshot_delimiter else: raise TypeError def doc_to_visual(self, doc: dict) -> Union[int, str, list]: self.config.doc_to_visual if type(self.config.doc_to_visual) == str: assert self.config.doc_to_visual in self.features # Single image. Still return a list for consistency. return [doc[self.config.doc_to_visual]] elif callable(self.config.doc_to_visual): return ( self.config.doc_to_visual(doc, self.lmms_eval_specific_kwargs) if self.lmms_eval_specific_kwargs is not None and len(inspect.signature(self.config.doc_to_visual).parameters) == 2 else self.config.doc_to_visual( doc, ) ) else: # eval_logger.warning("Note that doc_to_visual was called but not set in config. Please check if this is a text-only task.") return self.config.doc_to_visual def doc_to_choice(self, doc: Any) -> List[str]: if self.config.doc_to_choice is None: eval_logger.error("Note that doc_to_choice was called but not set in config.") else: doc_to_choice = self.config.doc_to_choice if type(doc_to_choice) == str: if doc_to_choice in self.features: return doc[doc_to_choice] else: return ast.literal_eval(utils.apply_template(doc_to_choice, doc)) elif type(doc_to_choice) == list: return doc_to_choice elif type(doc_to_choice) == dict: return list(doc_to_choice.values()) elif callable(doc_to_choice): return doc_to_choice(doc) elif hasattr(doc_to_choice, "get_answer_choices_list"): return doc_to_choice.get_answer_choices_list(doc) else: raise TypeError def construct_requests(self, doc_id: int, ctx: str, **kwargs) -> Union[List[Instance], Instance]: split = kwargs.get("metadata").get("split") # kwargs.pop("split") if self.OUTPUT_TYPE == "loglikelihood": arguments = (ctx, self.doc_to_target, self.doc_to_visual, doc_id, self.config.task, split) elif self.OUTPUT_TYPE == "multiple_choice": doc = self.dataset[split][doc_id] choices = self.doc_to_choice(doc) target_delimiter = self.config.target_delimiter if self.multiple_input: # If there are multiple inputs, choices are placed in the ctx cont = self.doc_to_target(doc) arguments = [(ctx, f"{target_delimiter}{cont}", self.doc_to_visual, doc_id, self.config.task, split) for ctx in choices] else: # Otherwise they are placed in the continuation arguments = [(ctx, f"{target_delimiter}{cont}", self.doc_to_visual, doc_id, self.config.task, split) for cont in choices] request_list = [ Instance( request_type="loglikelihood", # doc=doc, arguments=arg, idx=i, task_name=self.config.task, doc_id=doc_id, **kwargs, ) for i, arg in enumerate(arguments) ] # TODO: we should raise a warning telling users this will at most ~2x runtime. if "acc_mutual_info" in self._metric_fn_list.keys(): # if we are calculating multiple choice accuracy # using mutual information instead of raw loglikelihood as metric, need unconditional lls. # here mutual info refers to calculating # log(P(choice|ctx) / P(choice)) = log(P(choice|ctx)) - log(P(choice)) # in other words normalizing by subtracting the unconditional logprob of each choice. request_list.extend( [ Instance( request_type="loglikelihood", # doc=doc, arguments=("", "{}".format(choice)), idx=i, task_name=self.config.task, doc_id=doc_id, **kwargs, ) for i, choice in enumerate(choices) ] ) return request_list elif self.OUTPUT_TYPE == "generate_until": arguments = (ctx, copy.deepcopy(self.config.generation_kwargs), self.doc_to_visual, doc_id, self.config.task, split) elif self.OUTPUT_TYPE == "generate_until_multi_round": arguments = (ctx, copy.deepcopy(self.config.generation_kwargs), self.doc_to_visual, partial(self.config.doc_to_text, lmms_eval_specific_kwargs=self.lmms_eval_specific_kwargs), doc_id, self.config.task, split) return Instance(request_type=self.OUTPUT_TYPE, arguments=arguments, idx=0, **kwargs) # TODO: we add a full_docs interface here for some evaluations that needs to access the full datasets during process_results function. we may have better ways to handle this. @retry(stop=(stop_after_attempt(5) | stop_after_delay(1200)), wait=wait_fixed(2)) def process_results(self, doc, results, full_docs=None): if self.OUTPUT_TYPE == "generate_until": if isinstance(results, list) and isinstance(results[0], list): results = [res.strip() for res in results[0]] else: results = [res.strip() for res in results] kwargs = {} if full_docs is not None: kwargs["full_docs"] = full_docs if callable(self.config.process_results): return self.config.process_results(doc, results, **kwargs) result_dict = {} use_metric = list(self._metric_fn_list.keys()) if self.OUTPUT_TYPE == "loglikelihood": ll, is_greedy = results return { **({"perplexity": ll} if "perplexity" in use_metric else {}), **({"acc": int(is_greedy)} if "acc" in use_metric else {}), } elif self.OUTPUT_TYPE == "multiple_choice": lls, is_greedy = zip(*results) # retrieve choices in List[str] form, to compute choice lengths, etc. choices = self.doc_to_choice(doc) completion_len = np.array([float(len(i)) for i in choices]) if 2 * len(choices) == len(lls) and "acc_mutual_info" in self._metric_fn_list.keys(): # then we are doing mutual info. # this stores the "dryrun" / unconditional answer loglikelihoods lls_unconditional = lls[1::2] assert len(lls_unconditional) == len(choices) # and this stores our "regular" conditional loglikelihoods lls = lls[::2] # Warning : # Here may be different from original lm-eval # since we return the actual loss in many model loglikelihood # we just use the argmin here pred = np.argmin(lls) pred_norm = np.argmin(lls / completion_len) if self.multiple_input: gold = self.doc_to_text(doc) else: gold = self.doc_to_target(doc) gold_index_error = False if type(gold) is list: gold = [i if i < len(choices) else -100 for i in gold] if -100 in gold: gold_index_error = True else: if type(gold) is int: gold = gold if gold < len(choices) else -100 elif type(gold) is str: gold = choices.index(gold) if gold in choices else -100 if gold == -100: gold_index_error = True if gold_index_error: eval_logger.warning(f"Label index was not in within range of available choices," f"Sample:\n\n{doc}\n\n") if self.multiple_target: acc = 1.0 if pred in gold else 0.0 acc_norm = 1.0 if pred_norm in gold else 0.0 exact_match = int(any([is_greedy[i] if i != -100 else 0 for i in gold])) else: acc = 1.0 if pred == gold else 0.0 acc_norm = 1.0 if pred_norm == gold else 0.0 # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly exact_match = int(is_greedy[gold]) if gold != -100 else 0 result_dict = { **({"acc": acc} if "acc" in use_metric else {}), **({"f1": (gold, pred)} if "f1" in use_metric else {}), **({"mcc": (gold, pred)} if "mcc" in use_metric else {}), **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}), **({"exact_match": exact_match} if "exact_match" in use_metric else {}), } if "acc_mutual_info" in use_metric: lls_mutual_info = [ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)] acc_mutual_info = 1.0 if np.argmax(lls_mutual_info) == gold else 0.0 result_dict["acc_mutual_info"] = acc_mutual_info elif "generate_until" in self.OUTPUT_TYPE: gold = self.doc_to_target(doc) result = [res.strip() for res in results] if self.config.doc_to_choice is not None: # If you set doc_to_choice, # it assumes that doc_to_target returns a number. choices = self.doc_to_choice(doc) gold = choices[gold] # we expect multiple_targets to be a list. elif self.multiple_target: gold = list(gold) # elif type(gold) != type(result): # # cast gold to the same type as result # gold = type(result)(gold) for metric in self._metric_fn_list.keys(): if self.multiple_target and metric != "anls": # in the case where we have multiple targets, # return true if any are true # TODO: this may break for multipLe_target, non zero-or-1 metrics scores = [] if not isinstance(gold, list): # sometimes, a multiple_target dataset has exceptions where one doc has only one string answer # print(gold) gold = [gold] for gold_option in gold: try: result_score = self._metric_fn_list[metric]( references=[gold_option], predictions=result, **self._metric_fn_kwargs[metric], ) except TypeError: # TODO: this is hacky and I don't want to do it result_score = self._metric_fn_list[metric]([gold_option, result]) if isinstance(result_score, dict): # TODO: this handles the case where HF evaluate returns a dict. result_score = result_score[metric] scores.append(result_score) if any(scores): result_score = 1.0 else: result_score = 0.0 else: if not isinstance(gold, list): gold = [gold] try: result_score = self._metric_fn_list[metric]( references=gold, predictions=result, **self._metric_fn_kwargs[metric], ) except TypeError: # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics result_score = self._metric_fn_list[metric]([gold, result]) if isinstance(result_score, dict): # TODO: this handles the case where HF evaluate returns a dict. result_score = result_score[metric] result_dict[metric] = result_score else: raise ValueError( f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ", "'loglikelihood','generate_until', 'generate_until_multi_round', or 'multiple_choice'", ) return result_dict def aggregation(self): return self._aggregation_list def higher_is_better(self): return self._higher_is_better def get_config(self, key: str) -> Any: return getattr(self._config, key, None) @property def task_name(self) -> Any: return getattr(self.config, "task", None) def __repr__(self): return f"ConfigurableTask(task_name={getattr(self.config, 'task', None)}," f"output_type={self.OUTPUT_TYPE}," f"num_fewshot={getattr(self.config, 'num_fewshot', None)}," f"num_samples={len(self.eval_docs)})" class ConfigurableMessagesTask(ConfigurableTask): def __init__(self, data_dir=None, cache_dir=None, download_mode=None, config=None, model_name=None): super().__init__(data_dir, cache_dir, download_mode, config, model_name) def doc_to_messages(self, doc: dict) -> Union[int, str, list]: if callable(self.config.doc_to_messages): return ( self.config.doc_to_messages(doc, self.lmms_eval_specific_kwargs) if self.lmms_eval_specific_kwargs is not None and len(inspect.signature(self.config.doc_to_messages).parameters) == 2 else self.config.doc_to_messages( doc, ) ) elif self.config.doc_to_messages is None and (self.config.doc_to_visual is not None or self.config.doc_to_text is not None): # An auto doc to messages function def auto_doc_to_messages(doc): visuals = self.doc_to_visual(doc) if visuals is None: visuals = [] text = self.doc_to_text(doc) messages = [{"role": "user", "content": []}] content = [] for visual in visuals: if isinstance(visual, PIL_Image.Image): content.append({"type": "image", "url": visual}) elif isinstance(visual, dict): content.append({"type": "audio", "url": visual}) elif isinstance(visual, str): content.append({"type": "video", "url": visual}) content.append({"type": "text", "text": text}) messages[0]["content"] = content return messages return auto_doc_to_messages(doc) else: # eval_logger.warning("Note that doc_to_visual was called but not set in config. Please check if this is a text-only task.") return self.config.doc_to_messages def construct_requests(self, doc_id: int, ctx: str, **kwargs) -> Union[List[Instance], Instance]: split = kwargs.get("metadata").get("split") # kwargs.pop("split") assert self.OUTPUT_TYPE == "generate_until", "Currently messages is used for generation only" arguments = (ctx, self.doc_to_messages, copy.deepcopy(self.config.generation_kwargs), doc_id, self.config.task, split) return Instance(request_type=self.OUTPUT_TYPE, arguments=arguments, idx=0, task_name=self.config.task, doc_id=doc_id, **kwargs) def __repr__(self): return f"ConfigurableMessagesTask(task_name={getattr(self.config, 'task', None)}," f"output_type={self.OUTPUT_TYPE}," f"num_fewshot={getattr(self.config, 'num_fewshot', None)}," f"num_samples={len(self.eval_docs)})"