task.py 49.8 KB
Newer Older
1
import abc
2
import ast
lintangsutawika's avatar
lintangsutawika committed
3
import logging
4
import random
5
6
7
8
import re
from collections.abc import Callable
from dataclasses import asdict, dataclass
from typing import Any, List, Literal, Tuple, Union
9
10
11
12
13

import datasets
import numpy as np

from lm_eval import utils
14
from lm_eval.api import samplers
haileyschoelkopf's avatar
haileyschoelkopf committed
15
from lm_eval.api.instance import Instance
lintangsutawika's avatar
lintangsutawika committed
16
from lm_eval.api.metrics import (
17
    bits_per_byte,
lintangsutawika's avatar
lintangsutawika committed
18
19
20
21
    mean,
    weighted_perplexity,
)
from lm_eval.api.registry import (
22
23
    AGGREGATION_REGISTRY,
    DEFAULT_METRIC_REGISTRY,
haileyschoelkopf's avatar
haileyschoelkopf committed
24
    get_aggregation,
25
    get_metric,
26
    get_metric_aggregation,
haileyschoelkopf's avatar
haileyschoelkopf committed
27
    is_higher_better,
lintangsutawika's avatar
lintangsutawika committed
28
)
29
30
31
from lm_eval.filters import build_filter_ensemble
from lm_eval.prompts import get_prompt

32

33
34
35
36
ALL_OUTPUT_TYPES = [
    "loglikelihood",
    "multiple_choice",
    "loglikelihood_rolling",
37
    "generate_until",
38
39
]

lintangsutawika's avatar
lintangsutawika committed
40

41
eval_logger = logging.getLogger("lm-eval")
42

lintangsutawika's avatar
lintangsutawika committed
43

44
45
@dataclass
class TaskConfig(dict):
46
    # task naming/registry
47
    task: str = None
lintangsutawika's avatar
lintangsutawika committed
48
    task_alias: str = None
49
    group: Union[str, list] = None
lintangsutawika's avatar
lintangsutawika committed
50
    group_alias: Union[str, list] = None
51
52
53
    # HF dataset options.
    # which dataset to use,
    # and what splits for what purpose
54
55
    dataset_path: str = None
    dataset_name: str = None
56
    dataset_kwargs: dict = None
57
58
59
    training_split: str = None
    validation_split: str = None
    test_split: str = None
lintangsutawika's avatar
lintangsutawika committed
60
    fewshot_split: str = None  # TODO: assert that this not None if num_fewshot > 0. (?) assert if this is same split as one evaling (?)
61
62
    # formatting / prompting options.
    # see docs/advanced_task_guide.md for more info
63
    process_docs: Callable = None
64
65
    doc_to_text: Union[Callable, str] = None
    doc_to_target: Union[Callable, str] = None
lintangsutawika's avatar
lintangsutawika committed
66
    doc_to_choice: Union[Callable, str, dict, list] = None
lintangsutawika's avatar
lintangsutawika committed
67
    process_results: Union[Callable, str] = None
68
    use_prompt: str = None
69
    description: str = ""
70
71
    target_delimiter: str = " "
    fewshot_delimiter: str = "\n\n"
haileyschoelkopf's avatar
haileyschoelkopf committed
72
    fewshot_config: dict = None
73
    # runtime configuration options
74
    num_fewshot: int = None
75
    # scoring options
76
    metric_list: list = None
77
    output_type: str = "generate_until"
78
    generation_kwargs: dict = None
79
    repeats: int = 1
lintangsutawika's avatar
lintangsutawika committed
80
    filter_list: Union[str, list] = None
81
82
    should_decontaminate: bool = False
    doc_to_decontamination_query: str = None
lintangsutawika's avatar
lintangsutawika committed
83
    weight_by_size: bool = False
84

lintangsutawika's avatar
lintangsutawika committed
85
86
87
    metadata: Union[
        str, list
    ] = None  # by default, not used in the code. allows for users to pass arbitrary info to tasks
88

Ethan Smith's avatar
Ethan Smith committed
89
    def __post_init__(self) -> None:
Lintang Sutawika's avatar
Lintang Sutawika committed
90
        if self.generation_kwargs is not None:
91
            if self.output_type != "generate_until":
Lintang Sutawika's avatar
Lintang Sutawika committed
92
                eval_logger.warning(
93
                    f"[{self.task}] passed `generation_kwargs`, but not using `output_type: generate_until`!"
Lintang Sutawika's avatar
Lintang Sutawika committed
94
                )
95
                assert self.output_type != "generate_until"
Lintang Sutawika's avatar
Lintang Sutawika committed
96
97
98
99
100
101
102

            if "temperature" in self.generation_kwargs:
                self.generation_kwargs["temperature"] = float(
                    self.generation_kwargs["temperature"]
                )

            if "until" not in self.generation_kwargs:
103
                self.generation_kwargs["until"] = [self.fewshot_delimiter]
Lintang Sutawika's avatar
Lintang Sutawika committed
104
        else:
105
            if self.output_type == "generate_until":
Lintang Sutawika's avatar
Lintang Sutawika committed
106
107
                # ensure that we greedily generate in absence of explicit arguments otherwise
                self.generation_kwargs = {
Lintang Sutawika's avatar
Lintang Sutawika committed
108
                    "until": None
109
110
                    if self.fewshot_delimiter is None
                    else [self.fewshot_delimiter],
Lintang Sutawika's avatar
Lintang Sutawika committed
111
112
                    "do_sample": False,
                }
113

haileyschoelkopf's avatar
haileyschoelkopf committed
114
115
        # TODO: how to make TaskConfigs be de- and re-serializable, even when using the !function constructor?

116
117
118
    def __getitem__(self, item):
        return getattr(self, item)

119
120
121
    def __setitem__(self, item, value):
        return setattr(self, item, value)

Lintang Sutawika's avatar
Lintang Sutawika committed
122
    def to_dict(self, keep_callable=False):
123
124
        """dumps the current config as a dictionary object, as a printable format.
        null fields will not be printed.
haileyschoelkopf's avatar
haileyschoelkopf committed
125
        Used for dumping results alongside full task configuration
126

haileyschoelkopf's avatar
haileyschoelkopf committed
127
128
129
130
131
132
133
134
135
136
        :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)
haileyschoelkopf's avatar
haileyschoelkopf committed
137
            elif isinstance(v, Callable):
Lintang Sutawika's avatar
Lintang Sutawika committed
138
139
140
141
142
                if keep_callable:
                    cfg_dict[k] = v
                else:
                    # TODO: this should handle Promptsource template objects as a separate case?
                    cfg_dict[k] = str(v)
haileyschoelkopf's avatar
haileyschoelkopf committed
143
        return cfg_dict
144

145
146
147
148
149
150
151
152
153
154
155
156

class Task(abc.ABC):
    """A task represents an entire benchmark including its dataset, problems,
    answers, and evaluation methods. See BoolQ 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
157

158
159
160
161
162
163
164
165
    # 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
lintangsutawika's avatar
lintangsutawika committed
166

167
168
169
170
171
172
    def __init__(
        self,
        data_dir=None,
        cache_dir=None,
        download_mode=None,
        config=None,
Ethan Smith's avatar
Ethan Smith committed
173
    ) -> None:
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
        """
        :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

lintangsutawika's avatar
lintangsutawika committed
200
        self._config = TaskConfig({**config}) if config else TaskConfig()
201

lintangsutawika's avatar
lintangsutawika committed
202
        self._filters = [build_filter_ensemble("none", [["take_first", None]])]
203

Ethan Smith's avatar
Ethan Smith committed
204
    def download(self, data_dir=None, cache_dir=None, download_mode=None) -> None:
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
        """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.
        """
229
230
231
232
233
234
235
        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,
        )
236

237
238
239
240
241
    @property
    def config(self):
        """Returns the TaskConfig associated with this class."""
        return self._config

242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
    @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 []

278
279
280
281
282
283
284
285
286
287
    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:
lintangsutawika's avatar
lintangsutawika committed
288
            eval_logger.warning(
289
                "has_training_docs and has_validation_docs are False"
290
                ", using test_docs as fewshot_docs but this is not recommended."
lintangsutawika's avatar
lintangsutawika committed
291
            )
292
293
            return self.test_docs()

294
295
296
297
298
299
300
301
302
303
    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
lintangsutawika's avatar
lintangsutawika committed
304

305
306
307
308
309
310
311
312
313
314
315
316
317
    @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)

Ethan Smith's avatar
Ethan Smith committed
318
    def doc_to_decontamination_query(self, doc) -> None:
319
320
321
322
323
324
325
326
327
328
329
330
331
        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

Ethan Smith's avatar
Ethan Smith committed
332
    def build_all_requests(self, limit=None, rank=None, world_size=None) -> None:
333
334
335
336
337
338
        """Build a set of Instances for a task, and store them in task.instances"""
        if self.has_test_docs():
            docs = self.test_docs()
        elif self.has_validation_docs():
            docs = self.validation_docs()
        else:
339
            assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"
340

341
        eval_logger.info(f"Building contexts for task on rank {rank}...")
342

343
        instances = []
344
345
        for doc_id, doc in utils.create_iterator(
            enumerate(docs), rank, world_size, limit
lintangsutawika's avatar
lintangsutawika committed
346
        ):
347
            # sample fewshot context #TODO: need to offset doc_id by rank now!
348
            fewshot_ctx = self.fewshot_context(
349
                doc,
350
                0 if self.config.num_fewshot is None else self.config.num_fewshot,
351
            )
352

353
            # TODO: we should override self.config.repeats if doing greedy gen so users don't waste time+compute
lintangsutawika's avatar
lintangsutawika committed
354
355
356
            inst = self.construct_requests(
                doc=doc,
                ctx=fewshot_ctx,
357
                metadata=(self.config["task"], doc_id, self.config.repeats),
lintangsutawika's avatar
lintangsutawika committed
358
            )
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383

            if not isinstance(inst, list):
                inst = [inst]

            instances.extend(inst)

        self._instances = instances
        assert len(self._instances) != 0, "task.build_requests() did not find any docs!"

    @abc.abstractmethod
    def construct_requests(self, doc, ctx, **kwargs):
        """Uses RequestFactory to construct Requests and returns an iterable of
        Requests which will be sent to the LM.

        :param doc:
            The document as returned from training_docs, validation_docs, or test_docs.
        :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 doc_idx: int
            The index of a document within `self.test_docs()` or `self.validation_docs()`,
            whichever is the main split used.
        :param repeats: int
        TODO: update this docstring
lintangsutawika's avatar
lintangsutawika committed
384
            The number of times each instance in a dataset is inferred on. Defaults to 1,
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
            can be increased for techniques like majority voting.
        """
        pass

    @abc.abstractmethod
    def process_results(self, doc, results):
        """Take a single document and the LM 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

haileyschoelkopf's avatar
haileyschoelkopf committed
420
421
422
423
424
425
426
427
428
429
    @classmethod
    def count_bytes(cls, doc):
        """Used for byte-level perplexity metrics in rolling loglikelihood"""
        return len(doc.encode("utf-8"))

    @classmethod
    def count_words(cls, doc):
        """Downstream loglikelihood_rolling perplexity tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))

430
    @utils.positional_deprecated
lintangsutawika's avatar
lintangsutawika committed
431
    def fewshot_context(
432
433
434
435
436
        self,
        doc,
        num_fewshot,
        rnd=random.Random(1234),
        description=None,
lintangsutawika's avatar
lintangsutawika committed
437
    ):
438
439
440
441
442
443
444
        """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.
lintangsutawika's avatar
lintangsutawika committed
445
446
447
448
449
        :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.
450
451
452
        :returns: str
            The fewshot context.
        """
lintangsutawika's avatar
lintangsutawika committed
453
454
455
456
        assert (
            rnd is not None
        ), "A `random.Random` generator argument must be provided to `rnd`"

457
        description = description if description else ""
458
459

        if num_fewshot == 0:
lintangsutawika's avatar
lintangsutawika committed
460
            labeled_examples = ""
461
        else:
lintangsutawika's avatar
lintangsutawika committed
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
            # 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"
lintangsutawika's avatar
lintangsutawika committed
486
            )
487
488

        example = self.doc_to_text(doc)
lintangsutawika's avatar
lintangsutawika committed
489
        return description + labeled_examples + example
490
491

    def apply_filters(self):
lintangsutawika's avatar
lintangsutawika committed
492
493
        if hasattr(self, "_filters"):
            for f in self._filters:
lintangsutawika's avatar
lintangsutawika committed
494
                f.apply(self._instances, None)
lintangsutawika's avatar
lintangsutawika committed
495
496
497
        else:
            eval_logger.warning("No filter defined, passing through instances")
            return self._instances
498

baberabb's avatar
baberabb committed
499
    def dump_config(self) -> dict:
500
        """Returns a dictionary representing the task's config.
501
502
503
504
505

        :returns: str
            The fewshot context.
        """
        # TODO: this should only return the overrides applied to a non-YAML task's configuration.
506
        # (num_fewshot)
507
        return self.config.to_dict()
508

509
510

class ConfigurableTask(Task):
511
    VERSION = "Yaml"
512
    OUTPUT_TYPE = None
513
    CONFIG = None
514
515
516

    def __init__(
        self, data_dir=None, cache_dir=None, download_mode=None, config: dict = None
Ethan Smith's avatar
Ethan Smith committed
517
    ) -> None:  # TODO no super() call here
518
        # Get pre-configured attributes
519
        self._config = self.CONFIG
520

521
        # Use new configurations if there was no preconfiguration
522
        if self.config is None:
523
            self._config = TaskConfig(**config)
524
525
        # Overwrite configs
        else:
lintangsutawika's avatar
lintangsutawika committed
526
            if config is not None:
527
                self._config.__dict__.update(config)
528

529
        if self.config is None:
lintangsutawika's avatar
lintangsutawika committed
530
531
532
            raise ValueError(
                "Must pass a config to ConfigurableTask, either in cls.CONFIG or `config` kwarg"
            )
533

534
535
536
537
        if isinstance(self.config.metadata, dict):
            if "version" in self.config.metadata:
                self.VERSION = self.config.metadata["version"]

538
539
540
        if self.config.output_type is not None:
            assert self.config.output_type in ALL_OUTPUT_TYPES
            self.OUTPUT_TYPE = self.config.output_type
541

542
543
        if self.config.dataset_path is not None:
            self.DATASET_PATH = self.config.dataset_path
544

545
546
        if self.config.dataset_name is not None:
            self.DATASET_NAME = self.config.dataset_name
547

548
549
550
551
        self._metric_fn_list = {}
        self._metric_fn_kwargs = {}
        self._aggregation_list = {}
        self._higher_is_better = {}
552

553
        if self.config.metric_list is None:
554
            # TODO: handle this in TaskConfig.__post_init__ ?
555
556
            _metric_list = DEFAULT_METRIC_REGISTRY[self.config.output_type]

557
            for metric_name in _metric_list:
haileyschoelkopf's avatar
haileyschoelkopf committed
558
                self._metric_fn_list[metric_name] = get_metric(metric_name)
lintangsutawika's avatar
lintangsutawika committed
559
                self._metric_fn_kwargs[metric_name] = {}
560
561
562
                self._aggregation_list[metric_name] = get_metric_aggregation(
                    metric_name
                )
haileyschoelkopf's avatar
haileyschoelkopf committed
563
                self._higher_is_better[metric_name] = is_higher_better(metric_name)
564
        else:
565
            for metric_config in self.config.metric_list:
566
567
568
569
570
                assert "metric" in metric_config
                metric_name = metric_config["metric"]
                kwargs = {
                    key: metric_config[key]
                    for key in metric_config
Chris's avatar
Chris committed
571
572
                    if key
                    not in ["metric", "aggregation", "higher_is_better", "hf_evaluate"]
573
                }
Chris's avatar
Chris committed
574
575
576
577
                hf_evaluate_metric = (
                    "hf_evaluate" in metric_config
                    and metric_config["hf_evaluate"] is True
                )
578

579
                if self.config.process_results is not None:
580
581
                    self._metric_fn_list[metric_name] = None
                    self._metric_fn_kwargs[metric_name] = {}
582
583
584
585
586
587
                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:
Chris's avatar
Chris committed
588
589
590
                    self._metric_fn_list[metric_name] = get_metric(
                        metric_name, hf_evaluate_metric
                    )
591
                    self._metric_fn_kwargs[metric_name] = kwargs
lintangsutawika's avatar
lintangsutawika committed
592

593
                if "aggregation" in metric_config:
594
                    agg_name = metric_config["aggregation"]
595
                    if isinstance(agg_name, str):
haileyschoelkopf's avatar
haileyschoelkopf committed
596
                        self._aggregation_list[metric_name] = get_aggregation(agg_name)
597
                    elif callable(agg_name):  # noqa: E721
598
599
600
                        self._aggregation_list[metric_name] = metric_config[
                            "aggregation"
                        ]
601
                else:
602
                    INV_AGG_REGISTRY = {v: k for k, v in AGGREGATION_REGISTRY.items()}
lintangsutawika's avatar
lintangsutawika committed
603
                    metric_agg = get_metric_aggregation(metric_name)
604
                    eval_logger.warning(
baberabb's avatar
baberabb committed
605
                        f"[Task: {self._config.task}] metric {metric_name} is defined, but aggregation is not. "
606
607
                        f"using default "
                        f"aggregation={INV_AGG_REGISTRY[metric_agg]}"
608
                    )
609
                    self._aggregation_list[metric_name] = metric_agg
lintangsutawika's avatar
lintangsutawika committed
610

611
612
613
614
615
616
                if "higher_is_better" in metric_config:
                    self._higher_is_better[metric_name] = metric_config[
                        "higher_is_better"
                    ]
                else:
                    eval_logger.warning(
baberabb's avatar
baberabb committed
617
                        f"[Task: {self._config.task}] metric {metric_name} is defined, but higher_is_better is not. "
618
                        f"using default "
haileyschoelkopf's avatar
haileyschoelkopf committed
619
                        f"higher_is_better={is_higher_better(metric_name)}"
620
                    )
haileyschoelkopf's avatar
haileyschoelkopf committed
621
                    self._higher_is_better[metric_name] = is_higher_better(metric_name)
622

623
        self.download(self.config.dataset_kwargs)
624
625
626
        self._training_docs = None
        self._fewshot_docs = None

627
        if self.config.filter_list is not None:
lintangsutawika's avatar
lintangsutawika committed
628
            self._filters = []
629
            for filter_config in self.config.filter_list:
lintangsutawika's avatar
lintangsutawika committed
630
631
632
633
634
635
636
                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"
lintangsutawika's avatar
lintangsutawika committed
637
638
639
                        }
                        components.append([function["function"], kwargs])
                    filter_pipeline = build_filter_ensemble(filter_name, components)
lintangsutawika's avatar
lintangsutawika committed
640
                self._filters.append(filter_pipeline)
lintangsutawika's avatar
lintangsutawika committed
641
        else:
642
            self._filters = [build_filter_ensemble("none", [["take_first", None]])]
643

644
645
        if self.config.use_prompt is not None:
            eval_logger.info(f"loading prompt {self.config.use_prompt}")
646
            self.prompt = get_prompt(
647
                self.config.use_prompt, self.DATASET_PATH, self.DATASET_NAME
lintangsutawika's avatar
lintangsutawika committed
648
            )
649
650
651
        else:
            self.prompt = None

lintangsutawika's avatar
lintangsutawika committed
652
        if self.fewshot_docs() is not None:
haileyschoelkopf's avatar
haileyschoelkopf committed
653
            self.sampler = samplers.get_sampler(
haileyschoelkopf's avatar
haileyschoelkopf committed
654
655
656
                self.config.fewshot_config.get("sampler", "default")
                if self.config.fewshot_config
                else "default"
haileyschoelkopf's avatar
haileyschoelkopf committed
657
            )(list(self.fewshot_docs()), self, rnd=random.Random(1234))
658

659
        if self.has_test_docs():
660
            self.task_docs = self.test_docs()
661
        elif self.has_validation_docs():
662
            self.task_docs = self.validation_docs()
663
        else:
664
            assert False, f"Task dataset (path={self.DATASET_PATH}, name={self.DATASET_NAME}) must have valid or test docs!"
665

666
        # Test One Doc
667
        self.features = list(self.task_docs.features.keys())
668
669
        self.multiple_input = 0
        self.multiple_target = 0
670
        test_doc = self.task_docs[0]
671
        test_text = self.doc_to_text(test_doc)
672
        test_target = self.doc_to_target(test_doc)
lintangsutawika's avatar
lintangsutawika committed
673

674
        if self.config.doc_to_choice is not None:
lintangsutawika's avatar
lintangsutawika committed
675
            test_choice = self.doc_to_choice(test_doc)
676
            if not isinstance(test_choice, list):
lintangsutawika's avatar
lintangsutawika committed
677
                eval_logger.error("doc_to_choice must return list")
678
679
            else:
                num_choice = len(test_choice)
680

681
            if isinstance(test_text, int):
682
                self.multiple_input = num_choice
683
684
        else:
            test_choice = None
685

686
        if isinstance(test_target, list):
687
            self.multiple_target = len(test_target)
688
        else:
689
            if (isinstance(test_target, int)) and (test_choice is not None):
lintangsutawika's avatar
lintangsutawika committed
690
                test_target = test_choice[test_target]
691
            else:
lintangsutawika's avatar
lintangsutawika committed
692
                test_target = str(test_target)
693

694
695
696
        if test_choice is not None:
            check_choices = test_choice
        else:
lintangsutawika's avatar
lintangsutawika committed
697
            check_choices = [test_target]
698
699
700
701
        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 = (
702
703
                    True
                    if self.config.target_delimiter.rstrip()
704
                    != self.config.target_delimiter
705
                    else False
706
                )
707

708
                if delimiter_has_whitespace and choice_has_whitespace:
709
710
                    eval_logger.debug(
                        f'Both target_delimiter "{self.config.target_delimiter}" and target choice: "{choice}" have whitespace'
711
712
                    )
                elif (not delimiter_has_whitespace) and (not choice_has_whitespace):
713
                    eval_logger.debug(
714
                        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'
715
716
                    )

Ethan Smith's avatar
Ethan Smith committed
717
    def download(self, dataset_kwargs=None) -> None:
718
719
720
721
722
723
        self.dataset = datasets.load_dataset(
            path=self.DATASET_PATH,
            name=self.DATASET_NAME,
            **dataset_kwargs if dataset_kwargs is not None else {},
        )

baberabb's avatar
baberabb committed
724
    def has_training_docs(self) -> bool:
725
        if self.config.training_split is not None:
726
727
728
729
            return True
        else:
            return False

baberabb's avatar
baberabb committed
730
    def has_validation_docs(self) -> bool:
731
        if self.config.validation_split is not None:
732
733
734
735
            return True
        else:
            return False

baberabb's avatar
baberabb committed
736
    def has_test_docs(self) -> bool:
737
        if self.config.test_split is not None:
738
739
740
741
            return True
        else:
            return False

baberabb's avatar
baberabb committed
742
    def training_docs(self) -> datasets.Dataset:
743
        if self.has_training_docs():
744
745
746
            if self.config.process_docs is not None:
                return self.config.process_docs(
                    self.dataset[self.config.training_split]
747
                )
748
            return self.dataset[self.config.training_split]
749

baberabb's avatar
baberabb committed
750
    def validation_docs(self) -> datasets.Dataset:
751
        if self.has_validation_docs():
752
753
754
            if self.config.process_docs is not None:
                return self.config.process_docs(
                    self.dataset[self.config.validation_split]
755
                )
756
            return self.dataset[self.config.validation_split]
757

baberabb's avatar
baberabb committed
758
    def test_docs(self) -> datasets.Dataset:
759
        if self.has_test_docs():
760
761
762
            if self.config.process_docs is not None:
                return self.config.process_docs(self.dataset[self.config.test_split])
            return self.dataset[self.config.test_split]
763

764
    def fewshot_docs(self):
765
        if self.config.fewshot_split is not None:
766
767
            if self.config.process_docs is not None:
                return self.config.process_docs(self.dataset[self.config.fewshot_split])
768
            return self.dataset[self.config.fewshot_split]
769
        else:
770
            if (self.config.num_fewshot is not None) and (self.config.num_fewshot > 0):
771
                eval_logger.warning(
772
                    f"Task '{self.config.task}': "
773
774
775
776
                    "num_fewshot > 0 but fewshot_split is None. "
                    "using preconfigured rule."
                )
            return super().fewshot_docs()
777

lintangsutawika's avatar
lintangsutawika committed
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
    @utils.positional_deprecated
    def fewshot_context(self, doc, num_fewshot):
        """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.
        :returns: str
            The fewshot context.
        """

        if num_fewshot == 0:
            # always prepend the (possibly empty) task description
            labeled_examples = self.config.description
        else:
            labeled_examples = self.config.description + self.sampler.get_context(
                doc, num_fewshot
            )

        example = self.doc_to_text(doc)
800
801
802
803
804
805
806
807
808
809
810
811
812
        if self.multiple_input:
            return labeled_examples
        else:
            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)
lintangsutawika's avatar
lintangsutawika committed
813

814
815
816
817
818
819
820
821
    def apply_filters(self):
        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

822
    def should_decontaminate(self):
823
        return self.config.should_decontaminate
824
825

    def doc_to_decontamination_query(self, doc):
826
        if self.config.should_decontaminate:
827
828
            if self.config.doc_to_decontamination_query is None:
                return self.doc_to_text(doc)
829
            else:
830
831
832
833
834
835
836
837
838
839
840
                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
                        )
                    )
841

842
843
844
845
846
847
848
849
850
851
852
853
    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):
854
855
        if self.prompt is not None:
            doc_to_text = self.prompt
856
        else:
857
            doc_to_text = self.config.doc_to_text
lintangsutawika's avatar
lintangsutawika committed
858

859
        if isinstance(doc_to_text, int):
860
            return doc_to_text
861
        elif isinstance(doc_to_text, str):
862
            if doc_to_text in self.features:
863
                # if self.config.doc_to_choice is not None:
864
865
                #     return self.doc_to_choice(doc)[doc[doc_to_text]]
                # else:
866
867
                return doc[doc_to_text]
            else:
lintangsutawika's avatar
lintangsutawika committed
868
                text_string = utils.apply_template(doc_to_text, doc)
lintangsutawika's avatar
lintangsutawika committed
869
                if text_string.isdigit() and self._config.doc_to_choice is not None:
lintangsutawika's avatar
lintangsutawika committed
870
871
872
                    return ast.literal_eval(text_string)
                else:
                    return text_string
873
        elif callable(doc_to_text):
874
            return doc_to_text(doc)
lintangsutawika's avatar
lintangsutawika committed
875
        # Used when applying a Promptsource template
876
        elif hasattr(doc_to_text, "apply"):
877
878
879
880
881
            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")
882
                return self.config.fewshot_delimiter
883
        else:
884
            print(type(doc_to_text))
885
            raise TypeError
886

887
    def doc_to_target(self, doc: dict) -> Union[int, str, list]:
888
889
        if self.prompt is not None:
            doc_to_target = self.prompt
890
        else:
891
            doc_to_target = self.config.doc_to_target
892

893
        if isinstance(doc_to_target, int):
894
            return doc_to_target
895
        elif isinstance(doc_to_target, str):
896
            if doc_to_target in self.features:
897
                # if self.config.doc_to_choice is not None:
898
899
900
                #     return self.doc_to_choice(doc)[doc[doc_to_target]]
                # else:
                return doc[doc_to_target]
901
            else:
lintangsutawika's avatar
lintangsutawika committed
902
                target_string = utils.apply_template(doc_to_target, doc)
lintangsutawika's avatar
lintangsutawika committed
903
                if target_string.isdigit() and self._config.doc_to_choice is not None:
lintangsutawika's avatar
lintangsutawika committed
904
                    return ast.literal_eval(target_string)
lintangsutawika's avatar
lintangsutawika committed
905
906
907
908
909
                elif (
                    len(target_string) >= 2
                    and (target_string[0] == "[")
                    and (target_string[-1] == "]")
                ):
baberabb's avatar
baberabb committed
910
911
912
913
                    try:
                        return ast.literal_eval(target_string)
                    except (SyntaxError, ValueError):
                        return target_string
lintangsutawika's avatar
lintangsutawika committed
914
915
                else:
                    return target_string
916
        elif isinstance(doc_to_target, list):
917
            return doc_to_target
918
        elif callable(doc_to_target):
919
            return doc_to_target(doc)
lintangsutawika's avatar
lintangsutawika committed
920
        # Used when applying a Promptsource template
921
        elif hasattr(doc_to_target, "apply"):
922
            applied_prompt = doc_to_target.apply(doc)
923
924
925
926
            if len(applied_prompt) == 2:
                return applied_prompt[1]
            else:
                eval_logger.warning("Applied prompt returns empty string")
927
                return self.config.fewshot_delimiter
928
929
        else:
            raise TypeError
930

baberabb's avatar
baberabb committed
931
    def doc_to_choice(self, doc: Any) -> List[str]:
932
933
        if self.prompt is not None:
            doc_to_choice = self.prompt
934
        elif self.config.doc_to_choice is None:
935
936
            eval_logger.error("doc_to_choice was called but not set in config")
        else:
937
            doc_to_choice = self.config.doc_to_choice
938

939
        if isinstance(doc_to_choice, str):
940
941
942
943
            if doc_to_choice in self.features:
                return doc[doc_to_choice]
            else:
                return ast.literal_eval(utils.apply_template(doc_to_choice, doc))
944
        elif isinstance(doc_to_choice, list):
945
            return doc_to_choice
946
        elif isinstance(doc_to_choice, dict):
947
948
949
950
951
952
953
            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
954

baberabb's avatar
baberabb committed
955
956
957
    def construct_requests(
        self, doc: dict, ctx: str, **kwargs
    ) -> Union[List[Instance], Instance]:
958
        if self.OUTPUT_TYPE == "loglikelihood":
lintangsutawika's avatar
lintangsutawika committed
959
            arguments = (ctx, self.doc_to_target(doc))
960
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
lintangsutawika's avatar
lintangsutawika committed
961
            arguments = (self.doc_to_target(doc),)
962
        elif self.OUTPUT_TYPE == "multiple_choice":
963
            choices = self.doc_to_choice(doc)
964
            target_delimiter = self.config.target_delimiter
965
966
            if self.multiple_input:
                # If there are multiple inputs, choices are placed in the ctx
967
                cont = self.doc_to_target(doc)
968
969
970
                arguments = [
                    (ctx + choice, f"{target_delimiter}{cont}") for choice in choices
                ]
971
            else:
972
                # Otherwise they are placed in the continuation
973
                arguments = [(ctx, f"{target_delimiter}{cont}") for cont in choices]
974

975
            request_list = [
976
977
                Instance(
                    request_type="loglikelihood",
lintangsutawika's avatar
lintangsutawika committed
978
                    doc=doc,
979
                    arguments=arg,
980
                    idx=i,
981
982
                    **kwargs,
                )
983
                for i, arg in enumerate(arguments)
984
            ]
985
            # TODO: we should raise a warning telling users this will at most ~2x runtime.
986
            if "acc_mutual_info" in self._metric_fn_list.keys():
987
988
989
                # if we are calculating multiple choice accuracy
                # using mutual information instead of raw loglikelihood as metric, need unconditional lls.

lintangsutawika's avatar
lintangsutawika committed
990
                # here mutual info refers to calculating
991
992
993
994
995
996
                # 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",
lintangsutawika's avatar
lintangsutawika committed
997
                            doc=doc,
998
                            arguments=("", "{}".format(choice)),
999
1000
1001
                            idx=i,
                            **kwargs,
                        )
lintangsutawika's avatar
lintangsutawika committed
1002
                        for i, choice in enumerate(choices)
1003
1004
1005
                    ]
                )
            return request_list
lintangsutawika's avatar
lintangsutawika committed
1006

1007
        elif self.OUTPUT_TYPE == "generate_until":
1008
            arguments = (ctx, self.config.generation_kwargs)
lintangsutawika's avatar
lintangsutawika committed
1009
1010

        return Instance(
lintangsutawika's avatar
lintangsutawika committed
1011
1012
            request_type=self.OUTPUT_TYPE, doc=doc, arguments=arguments, idx=0, **kwargs
        )
1013
1014

    def process_results(self, doc, results):
1015
1016
        if callable(self.config.process_results):
            return self.config.process_results(doc, results)
lintangsutawika's avatar
lintangsutawika committed
1017

1018
        result_dict = {}
1019
        use_metric = list(self._metric_fn_list.keys())
1020
1021
1022
        if self.OUTPUT_TYPE == "loglikelihood":
            results = results[0]
            ll, is_greedy = results
1023
1024
1025
1026
            return {
                **({"perplexity": ll} if "perplexity" in use_metric else {}),
                **({"acc": int(is_greedy)} if "acc" in use_metric else {}),
            }
1027
        elif self.OUTPUT_TYPE == "loglikelihood_rolling":
haileyschoelkopf's avatar
haileyschoelkopf committed
1028
            (loglikelihood,) = results
1029
1030
            _words = self.count_words(self.doc_to_target(doc))
            _bytes = self.count_bytes(self.doc_to_target(doc))
haileyschoelkopf's avatar
haileyschoelkopf committed
1031
            return {
1032
1033
1034
1035
1036
1037
1038
1039
1040
1041
1042
1043
1044
1045
1046
                **(
                    {"word_perplexity": (loglikelihood, _words)}
                    if "word_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"byte_perplexity": (loglikelihood, _bytes)}
                    if "byte_perplexity" in use_metric
                    else {}
                ),
                **(
                    {"bits_per_byte": (loglikelihood, _bytes)}
                    if "bits_per_byte" in use_metric
                    else {}
                ),
haileyschoelkopf's avatar
haileyschoelkopf committed
1047
            }
1048
        elif self.OUTPUT_TYPE == "multiple_choice":
1049
            lls, is_greedy = zip(*results)
lintangsutawika's avatar
lintangsutawika committed
1050

1051
            # retrieve choices in List[str] form, to compute choice lengths, etc.
1052
            choices = self.doc_to_choice(doc)
1053
1054
            completion_len = np.array([float(len(i)) for i in choices])

1055
1056
            if (
                2 * len(choices) == len(lls)
1057
                and "acc_mutual_info" in self._metric_fn_list.keys()
1058
1059
1060
1061
1062
1063
1064
            ):
                # 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]
1065

1066
1067
            pred = np.argmax(lls)
            pred_norm = np.argmax(lls / completion_len)
lintangsutawika's avatar
lintangsutawika committed
1068

1069
1070
            if self.multiple_input:
                gold = self.doc_to_text(doc)
1071
            else:
1072
                gold = self.doc_to_target(doc)
1073
1074

            gold_index_error = False
1075
            if isinstance(gold, list):
Lintang Sutawika's avatar
Lintang Sutawika committed
1076
1077
                gold = [i if i < len(choices) else -100 for i in gold]
                if -100 in gold:
1078
1079
                    gold_index_error = True
            else:
1080
                if isinstance(gold, int):
Lintang Sutawika's avatar
Lintang Sutawika committed
1081
                    gold = gold if gold < len(choices) else -100
1082
                elif isinstance(gold, str):
Lintang Sutawika's avatar
Lintang Sutawika committed
1083
                    gold = choices.index(gold) if gold in choices else -100
lintangsutawika's avatar
lintangsutawika committed
1084

Lintang Sutawika's avatar
Lintang Sutawika committed
1085
                if gold == -100:
1086
1087
1088
1089
                    gold_index_error = True

            if gold_index_error:
                eval_logger.warning(
lintangsutawika's avatar
lintangsutawika committed
1090
                    f"Label index was not in within range of available choices,"
1091
1092
                    f"Sample:\n\n{doc}\n\n"
                )
lintangsutawika's avatar
lintangsutawika committed
1093

1094
            if self.multiple_target:
lintangsutawika's avatar
lintangsutawika committed
1095
1096
                acc = 1.0 if pred in gold else 0.0
                acc_norm = 1.0 if pred_norm in gold else 0.0
Lintang Sutawika's avatar
Lintang Sutawika committed
1097
                exact_match = int(any([is_greedy[i] if i != -100 else 0 for i in gold]))
lintangsutawika's avatar
lintangsutawika committed
1098
1099
1100
            else:
                acc = 1.0 if pred == gold else 0.0
                acc_norm = 1.0 if pred_norm == gold else 0.0
1101
                # TODO: this gets score of 0 on arc_challenge for pythia-70m. need to test that this works properly
Lintang Sutawika's avatar
Lintang Sutawika committed
1102
                exact_match = int(is_greedy[gold]) if gold != -100 else 0
1103
1104

            result_dict = {
1105
                **({"acc": acc} if "acc" in use_metric else {}),
1106
1107
                **({"f1": (gold, pred)} if "f1" in use_metric else {}),
                **({"mcc": (gold, pred)} if "mcc" in use_metric else {}),
1108
                **({"acc_norm": acc_norm} if "acc_norm" in use_metric else {}),
1109
                **({"exact_match": exact_match} if "exact_match" in use_metric else {}),
1110
1111
            }

1112
            if "acc_mutual_info" in use_metric:
lintangsutawika's avatar
lintangsutawika committed
1113
1114
1115
                lls_mutual_info = [
                    ll_c - ll_u for ll_c, ll_u in zip(lls, lls_unconditional)
                ]
1116
1117
1118
                acc_mutual_info = 1.0 if np.argmax(lls_mutual_info) == gold else 0.0
                result_dict["acc_mutual_info"] = acc_mutual_info

1119
        elif self.OUTPUT_TYPE == "generate_until":
1120
            gold = self.doc_to_target(doc)
Chris's avatar
Chris committed
1121
            result = results[0]
1122
            if self.config.doc_to_choice is not None:
lintangsutawika's avatar
lintangsutawika committed
1123
                # If you set doc_to_choice,
lintangsutawika's avatar
lintangsutawika committed
1124
                # it assumes that doc_to_target returns a number.
1125
1126
                choices = self.doc_to_choice(doc)
                gold = choices[gold]
1127
1128
            # we expect multiple_targets to be a list.
            elif self.multiple_target:
baberabb's avatar
baberabb committed
1129
                gold = list(gold)
Chris's avatar
Chris committed
1130
1131
1132
            elif type(gold) != type(result):
                # cast gold to the same type as result
                gold = type(result)(gold)
1133

lintangsutawika's avatar
lintangsutawika committed
1134
            for metric in self._metric_fn_list.keys():
haileyschoelkopf's avatar
haileyschoelkopf committed
1135
1136
1137
1138
1139
                if self.multiple_target:
                    # 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 = []
haileyschoelkopf's avatar
haileyschoelkopf committed
1140
1141
1142
1143
                    if not isinstance(gold, list):
                        # sometimes, a multiple_target dataset has exceptions where one doc has only one string answer
                        # print(gold)
                        gold = [gold]
1144
1145
1146
1147
1148
1149
1150
1151
                    if metric == "exact_match":
                        result = [result for _ in range(len(gold))]
                        scores = self._metric_fn_list[metric](
                            references=gold,
                            predictions=result,
                            **self._metric_fn_kwargs[metric],
                        )[metric]
                        result_score = 1.0 if scores > 0.0 else 0.0
haileyschoelkopf's avatar
haileyschoelkopf committed
1152
                    else:
1153
1154
1155
1156
1157
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
1171
1172
1173
                        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
haileyschoelkopf's avatar
haileyschoelkopf committed
1174
                else:
1175
                    try:
1176
                        result_score = self._metric_fn_list[metric](
1177
1178
                            references=[gold],
                            predictions=[result],
1179
                            **self._metric_fn_kwargs[metric],
1180
                        )
1181
                    except TypeError:  # needed for now in order to use a different interface between our own metrics and HF Evaluate metrics
1182
                        result_score = self._metric_fn_list[metric]([gold, result])
1183
1184
1185
1186
                    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
1187
        else:
lintangsutawika's avatar
lintangsutawika committed
1188
1189
            raise ValueError(
                f"Passed invalid output_type '{self.OUTPUT_TYPE}' ! Please use one of ",
1190
                "'loglikelihood', 'loglikelihood_rolling', 'generate_until' or 'multiple_choice'",
1191
            )
1192
1193
1194
1195
1196
1197
1198

        return result_dict

    def aggregation(self):
        return self._aggregation_list

    def higher_is_better(self):
haileyschoelkopf's avatar
haileyschoelkopf committed
1199
        return self._higher_is_better
1200
1201
1202
1203
1204


class MultipleChoiceTask(Task):
    OUTPUT_TYPE: str = "loglikelihood"

baberabb's avatar
baberabb committed
1205
    def doc_to_target(self, doc: dict) -> str:
1206
1207
        return " " + doc["choices"][doc["gold"]]

baberabb's avatar
baberabb committed
1208
    def construct_requests(self, doc: dict, ctx: str, **kwargs) -> List[Instance]:
1209
        # TODO: add mutual info here?
lintangsutawika's avatar
lintangsutawika committed
1210
1211
        return [
            Instance(
haileyschoelkopf's avatar
haileyschoelkopf committed
1212
                request_type="loglikelihood",
lintangsutawika's avatar
lintangsutawika committed
1213
                doc=doc,
1214
                arguments=(ctx, " {}".format(choice)),
1215
                idx=i,
1216
1217
                **kwargs,
            )
lintangsutawika's avatar
lintangsutawika committed
1218
1219
            for i, choice in enumerate(doc["choices"])
        ]
1220

baberabb's avatar
baberabb committed
1221
    def process_results(self, doc: dict, results: List[Tuple[float, bool]]) -> dict:
lintangsutawika's avatar
lintangsutawika committed
1222
1223
1224
        results = [
            res[0] for res in results
        ]  # only retain loglikelihoods, discard is_greedy TODO: do we need is_greedy anywhere?
1225
1226
1227
1228
1229
1230
1231
1232
1233
1234
1235
        gold = doc["gold"]

        acc = 1.0 if np.argmax(results) == gold else 0.0
        completion_len = np.array([float(len(i)) for i in doc["choices"]])
        acc_norm = 1.0 if np.argmax(results / completion_len) == gold else 0.0

        return {
            "acc": acc,
            "acc_norm": acc_norm,
        }

baberabb's avatar
baberabb committed
1236
    def higher_is_better(self) -> dict:
1237
1238
1239
1240
1241
        return {
            "acc": True,
            "acc_norm": True,
        }

baberabb's avatar
baberabb committed
1242
    def aggregation(self) -> dict:
1243
1244
1245
1246
1247
1248
        return {
            "acc": mean,
            "acc_norm": mean,
        }


lintangsutawika's avatar
lintangsutawika committed
1249
class PerplexityTask(Task):
1250
1251
    OUTPUT_TYPE = "loglikelihood_rolling"

baberabb's avatar
baberabb committed
1252
    def has_training_docs(self) -> bool:
1253
1254
        return False

baberabb's avatar
baberabb committed
1255
    def fewshot_examples(self, k: int, rnd) -> List:
1256
1257
1258
        assert k == 0
        return []

baberabb's avatar
baberabb committed
1259
    def fewshot_context(self, doc: dict, num_fewshot: int) -> Literal[""]:
1260
1261
1262
1263
1264
1265
        assert (
            num_fewshot == 0
        ), "The number of fewshot examples must be 0 for perplexity tasks."

        return ""

baberabb's avatar
baberabb committed
1266
    def higher_is_better(self) -> dict:
1267
1268
1269
1270
1271
1272
1273
1274
1275
        return {
            "word_perplexity": False,
            "byte_perplexity": False,
            "bits_per_byte": False,
        }

    def doc_to_decontamination_query(self, doc):
        return doc

Ethan Smith's avatar
Ethan Smith committed
1276
    def doc_to_text(self, doc) -> str:
1277
1278
1279
1280
1281
        return ""

    def doc_to_target(self, doc):
        return doc

baberabb's avatar
baberabb committed
1282
    def construct_requests(self, doc: dict, ctx: Union[str, None], **kwargs):
1283
1284
        assert not ctx

lintangsutawika's avatar
lintangsutawika committed
1285
1286
1287
1288
1289
1290
1291
        return Instance(
            request_type=self.OUTPUT_TYPE,
            doc=doc,
            arguments=(self.doc_to_target(doc),),
            idx=0,
            **kwargs,
        )
1292

baberabb's avatar
baberabb committed
1293
    def process_results(self, doc: dict, results: float) -> dict:
1294
        (loglikelihood,) = results
haileyschoelkopf's avatar
haileyschoelkopf committed
1295
1296
        words = self.count_words(self.doc_to_target(doc))
        bytes_ = self.count_bytes(self.doc_to_target(doc))
1297
1298
1299
1300
1301
1302
        return {
            "word_perplexity": (loglikelihood, words),
            "byte_perplexity": (loglikelihood, bytes_),
            "bits_per_byte": (loglikelihood, bytes_),
        }

baberabb's avatar
baberabb committed
1303
    def aggregation(self) -> dict:
1304
1305
1306
1307
1308
1309
1310
        return {
            "word_perplexity": weighted_perplexity,
            "byte_perplexity": weighted_perplexity,
            "bits_per_byte": bits_per_byte,
        }

    @classmethod
baberabb's avatar
baberabb committed
1311
    def count_bytes(cls, doc) -> int:
1312
1313
1314
        return len(doc.encode("utf-8"))

    @classmethod
baberabb's avatar
baberabb committed
1315
    def count_words(cls, doc) -> int:
1316
1317
        """Downstream tasks with custom word boundaries should override this!"""
        return len(re.split(r"\s+", doc))