evaluator.py 25.6 KB
Newer Older
lintangsutawika's avatar
lintangsutawika committed
1
import collections
Baber Abbasi's avatar
Baber Abbasi committed
2
import itertools
3
import logging
Baber Abbasi's avatar
Baber Abbasi committed
4
5
6
import random
from typing import Optional, Union

7
import numpy as np
Baber Abbasi's avatar
Baber Abbasi committed
8
import torch
lintangsutawika's avatar
lintangsutawika committed
9

lintangsutawika's avatar
lintangsutawika committed
10
import lm_eval.api.metrics
lintangsutawika's avatar
lintangsutawika committed
11
import lm_eval.api.registry
Baber Abbasi's avatar
Baber Abbasi committed
12
13
import lm_eval.models
from lm_eval.tasks import TaskManager, get_task_dict
lintangsutawika's avatar
lintangsutawika committed
14
from lm_eval.utils import (
Baber Abbasi's avatar
Baber Abbasi committed
15
16
    eval_logger,
    get_git_commit_hash,
lintangsutawika's avatar
lintangsutawika committed
17
18
    positional_deprecated,
    run_task_tests,
lintangsutawika's avatar
lintangsutawika committed
19
    simple_parse_args_string,
lintangsutawika's avatar
lintangsutawika committed
20
)
21

Fabrizio Milo's avatar
Fabrizio Milo committed
22

23
@positional_deprecated
Fabrizio Milo's avatar
Fabrizio Milo committed
24
25
def simple_evaluate(
    model,
Baber Abbasi's avatar
Baber Abbasi committed
26
    model_args: Optional[str] = None,
27
    tasks=None,
Baber Abbasi's avatar
Baber Abbasi committed
28
29
30
31
32
33
    num_fewshot: Optional[int] = None,
    batch_size: Optional[int] = None,
    max_batch_size: Optional[int] = None,
    device: Optional[str] = None,
    use_cache: Optional[str] = None,
    limit: Optional[Union[int, float]] = None,
Ethan Smith's avatar
Ethan Smith committed
34
35
    bootstrap_iters: int = 100000,
    check_integrity: bool = False,
Fabrizio Milo's avatar
Fabrizio Milo committed
36
    decontamination_ngrams_path=None,
Ethan Smith's avatar
Ethan Smith committed
37
38
    write_out: bool = False,
    log_samples: bool = True,
lintangsutawika's avatar
lintangsutawika committed
39
    gen_kwargs: str = None,
40
41
    task_manager: TaskManager = None,
    verbosity: str = "INFO",
Baber Abbasi's avatar
Baber Abbasi committed
42
    predict_only: bool = False,
43
44
45
    random_seed: int = 0,
    numpy_random_seed: int = 1234,
    torch_random_seed: int = 1234,
Fabrizio Milo's avatar
Fabrizio Milo committed
46
):
47
    """Instantiate and evaluate a model on a list of tasks.
48

49
50
51
    :param model: Union[str, LM]
        Name of model or LM object, see lm_eval.models.get_model
    :param model_args: Optional[str]
Fabrizio Milo's avatar
Fabrizio Milo committed
52
        String arguments for each model class, see LM.create_from_arg_string.
53
        Ignored if `model` argument is a LM object.
54
    :param tasks: list[Union[str, dict, Task]]
Leo Gao's avatar
Leo Gao committed
55
        List of task names or Task objects. Task objects will be taken to have name task.EVAL_HARNESS_NAME if defined and type(task).__name__ otherwise.
56
57
    :param num_fewshot: int
        Number of examples in few-shot context
58
    :param batch_size: int or str, optional
59
        Batch size for model
60
61
    :param max_batch_size: int, optional
        Maximal batch size to try with automatic batch size detection
62
    :param device: str, optional
63
        PyTorch device (e.g. "cpu" or "cuda:0") for running models
haileyschoelkopf's avatar
haileyschoelkopf committed
64
65
    :param use_cache: str, optional
        A path to a sqlite db file for caching model responses. `None` if not caching.
66
67
    :param limit: int or float, optional
        Limit the number of examples per task (only use this for testing), If <1, limit is a percentage of the total number of examples.
68
69
    :param bootstrap_iters:
        Number of iterations for bootstrap statistics
Stephen Hogg's avatar
Stephen Hogg committed
70
71
    :param check_integrity: bool
        Whether to run the relevant part of the test suite for the tasks
72
    :param write_out: bool
73
74
75
        If True, write out an example document and model input for checking task integrity
    :param log_samples: bool
        If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis
76
77
78
    :param gen_kwargs: str
        String arguments for model generation
        Ignored for all tasks with loglikelihood output_type
Baber Abbasi's avatar
Baber Abbasi committed
79
80
    :param predict_only: bool
        If true only model outputs will be generated and returned. Metrics will not be evaluated
81
82
83
84
85
86
    :param random_seed: int
        Random seed for python's random module. If set to None, the seed will not be set.
    :param numpy_random_seed: int
        Random seed for numpy. If set to None, the seed will not be set.
    :param torch_random_seed: int
        Random seed for torch. If set to None, the seed will not be set.
Baber Abbasi's avatar
Baber Abbasi committed
87

88
    :return
89
        Dictionary of results
90
    """
91
92
    eval_logger.setLevel(getattr(logging, f"{verbosity}"))

93
94
95
96
97
98
99
100
101
102
103
104
105
    if random_seed is not None:
        # See https://github.com/EleutherAI/lm-evaluation-harness/pull/1412
        eval_logger.info(f"Setting random seed to {random_seed}")
        random.seed(random_seed)

    if numpy_random_seed is not None:
        eval_logger.info(f"Setting numpy seed to {numpy_random_seed}")
        np.random.seed(numpy_random_seed)

    if torch_random_seed is not None:
        eval_logger.info(f"Setting torch manual seed to {torch_random_seed}")
        torch.manual_seed(torch_random_seed)

106
107
    if tasks is None:
        tasks = []
108
109
110
    assert (
        tasks != []
    ), "No tasks specified, or no tasks found. Please verify the task names."
111

lintangsutawika's avatar
lintangsutawika committed
112
113
    if gen_kwargs is not None:
        gen_kwargs = simple_parse_args_string(gen_kwargs)
lintangsutawika's avatar
udate  
lintangsutawika committed
114
        eval_logger.warning(
Baber Abbasi's avatar
Baber Abbasi committed
115
            "generation_kwargs specified through cli, these settings will update set parameters in yaml tasks. Ensure 'do_sample=True' for non-greedy decoding!"
lintangsutawika's avatar
udate  
lintangsutawika committed
116
        )
lintangsutawika's avatar
lintangsutawika committed
117
118
119
        if gen_kwargs == "":
            gen_kwargs = None

120
    if isinstance(model, str):
Fabrizio Milo's avatar
Fabrizio Milo committed
121
122
        if model_args is None:
            model_args = ""
lintangsutawika's avatar
lintangsutawika committed
123
        lm = lm_eval.api.registry.get_model(model).create_from_arg_string(
lintangsutawika's avatar
lintangsutawika committed
124
125
126
127
128
129
            model_args,
            {
                "batch_size": batch_size,
                "max_batch_size": max_batch_size,
                "device": device,
            },
Fabrizio Milo's avatar
Fabrizio Milo committed
130
        )
131
    else:
132
        assert isinstance(model, lm_eval.api.model.LM)
133
        lm = model
134

haileyschoelkopf's avatar
haileyschoelkopf committed
135
136
137
138
139
140
141
    if use_cache is not None:
        print(f"Using cache at {use_cache + '_rank' + str(lm.rank) + '.db'}")
        lm = lm_eval.api.model.CachingLM(
            lm,
            use_cache
            # each rank receives a different cache db.
            # necessary to avoid multiple writes to cache at once
142
143
144
            + "_rank"
            + str(lm.rank)
            + ".db",
haileyschoelkopf's avatar
haileyschoelkopf committed
145
146
        )

147
148
149
150
151
    if task_manager is None:
        task_manager = TaskManager(verbosity)

    eval_logger.info(
        "get_task_dict has been updated to accept an optional argument, `task_manager`"
Baber Abbasi's avatar
Baber Abbasi committed
152
153
        "Read more here:https://github.com/EleutherAI/lm-evaluation-harness/blob/main/docs/interface.md#external-library-usage"
    )
154
    task_dict = get_task_dict(tasks, task_manager)
155
    for task_name in task_dict.keys():
lintangsutawika's avatar
lintangsutawika committed
156
        task_obj = task_dict[task_name]
157
        if isinstance(task_obj, tuple):
158
            _, task_obj = task_obj
159
160
            if task_obj is None:
                continue
lintangsutawika's avatar
lintangsutawika committed
161

Baber Abbasi's avatar
Baber Abbasi committed
162
163
        if task_obj.get_config("output_type") == "generate_until":
            if gen_kwargs is not None:
Baber Abbasi's avatar
Baber Abbasi committed
164
                task_obj.set_config(
Baber Abbasi's avatar
Baber Abbasi committed
165
166
167
168
169
170
171
172
173
174
                    key="generation_kwargs", value=gen_kwargs, update=True
                )

            if predict_only:
                log_samples = True
                eval_logger.info(
                    f"Processing {task_name} in output-only mode. Metrics will not be calculated!"
                )
                # we have to change the class properties post-hoc. This is pretty hacky.
                task_obj.override_metric(metric_name="bypass")
175

176
        if num_fewshot is not None:
Baber Abbasi's avatar
Baber Abbasi committed
177
            if (default_num_fewshot := task_obj.get_config("num_fewshot")) == 0:
178
179
180
                eval_logger.info(
                    f"num_fewshot has been set to 0 for {task_name} in its config. Manual configuration will be ignored."
                )
181
            else:
Baber Abbasi's avatar
Baber Abbasi committed
182
183
184
                eval_logger.warning(
                    f"Overwriting default num_fewshot of {task_name} from {default_num_fewshot} to {num_fewshot}"
                )
Baber Abbasi's avatar
Baber Abbasi committed
185
                task_obj.set_config(key="num_fewshot", value=num_fewshot)
Jonathan Tow's avatar
Merge  
Jonathan Tow committed
186

Stephen Hogg's avatar
Stephen Hogg committed
187
    if check_integrity:
188
        run_task_tests(task_list=tasks)
Stephen Hogg's avatar
Stephen Hogg committed
189

190
191
192
193
    results = evaluate(
        lm=lm,
        task_dict=task_dict,
        limit=limit,
Niklas Muennighoff's avatar
Niklas Muennighoff committed
194
        bootstrap_iters=bootstrap_iters,
Fabrizio Milo's avatar
Fabrizio Milo committed
195
        decontamination_ngrams_path=decontamination_ngrams_path,
196
        write_out=write_out,
197
        log_samples=log_samples,
198
        verbosity=verbosity,
199
    )
200

201
    if lm.rank == 0:
202
203
204
205
206
207
208
        if isinstance(model, str):
            model_name = model
        elif hasattr(model, "config") and hasattr(model.config, "_name_or_path"):
            model_name = model.config._name_or_path
        else:
            model_name = type(model).__name__

209
210
        # add info about the model and few shot config
        results["config"] = {
211
            "model": model_name,
212
213
            "model_args": model_args,
            "batch_size": batch_size,
lintangsutawika's avatar
lintangsutawika committed
214
215
216
            "batch_sizes": list(lm.batch_sizes.values())
            if hasattr(lm, "batch_sizes")
            else [],
217
            "device": device,
haileyschoelkopf's avatar
haileyschoelkopf committed
218
            "use_cache": use_cache,
219
220
            "limit": limit,
            "bootstrap_iters": bootstrap_iters,
lintangsutawika's avatar
lintangsutawika committed
221
            "gen_kwargs": gen_kwargs,
222
        }
223
        results["git_hash"] = get_git_commit_hash()
224
225
226
        return results
    else:
        return None
227

Leo Gao's avatar
Leo Gao committed
228

229
decontaminate_suffix = "_decontaminate"
Leo Gao's avatar
Leo Gao committed
230

Fabrizio Milo's avatar
Fabrizio Milo committed
231

232
@positional_deprecated
Fabrizio Milo's avatar
Fabrizio Milo committed
233
234
235
def evaluate(
    lm,
    task_dict,
Baber Abbasi's avatar
Baber Abbasi committed
236
237
    limit: Optional[int] = None,
    bootstrap_iters: Optional[int] = 100000,
Fabrizio Milo's avatar
Fabrizio Milo committed
238
    decontamination_ngrams_path=None,
Ethan Smith's avatar
Ethan Smith committed
239
240
    write_out: bool = False,
    log_samples: bool = True,
241
    verbosity: str = "INFO",
Fabrizio Milo's avatar
Fabrizio Milo committed
242
):
243
244
245
246
247
    """Instantiate and evaluate a model on a list of tasks.

    :param lm: obj
        Language Model
    :param task_dict: dict[str, Task]
haileyschoelkopf's avatar
haileyschoelkopf committed
248
        Dictionary of tasks. Tasks will be taken to have name type(task).config.task .
249
250
251
252
    :param limit: int, optional
        Limit the number of examples per task (only use this for testing)
    :param bootstrap_iters:
        Number of iterations for bootstrap statistics
253
    :param write_out: bool
254
255
256
        If True, write out an example document and model input for checking task integrity
    :param log_samples: bool
        If True, write out all model outputs and documents for per-sample measurement and post-hoc analysis
257
258
259
    :return
        Dictionary of results
    """
260

261
    eval_logger.setLevel(getattr(logging, f"{verbosity}"))
lintangsutawika's avatar
lintangsutawika committed
262
    # decontaminate = decontamination_ngrams_path is not None
263

Baber Abbasi's avatar
Baber Abbasi committed
264
265
266
267
268
269
270
271
    for task_name, task in task_dict.items():
        if isinstance(task, tuple):
            _, task = task
        if not log_samples:
            assert (
                "bypass" not in getattr(task, "_metric_fn_list", {}).keys()
            ), f"log_samples must be True for 'bypass' only tasks: {task_name}"

272
    # stores the final result for each task, for each metric/filter pair.
Leo Gao's avatar
Leo Gao committed
273
    results = collections.defaultdict(dict)
274
    # Tracks each task's version.
Leo Gao's avatar
Leo Gao committed
275
    versions = collections.defaultdict(dict)
276
    # Tracks the YAML configs of all chosen tasks.
277
    configs = collections.defaultdict(dict)
278
    # logs info about each document evaluated.
lintangsutawika's avatar
lintangsutawika committed
279
    samples = collections.defaultdict(list)
280
    # tracks all Instances/requests a model must generate output on.
Leo Gao's avatar
Leo Gao committed
281
    requests = collections.defaultdict(list)
282
    # Aggregated task scores presented with groups
283
    results_agg = collections.defaultdict(dict)
284
    # Aggregated groups scores only
lintangsutawika's avatar
lintangsutawika committed
285
    groups_agg = collections.defaultdict(dict)
286
287
    # stores the amount to pad out reqs per req. type so that
    # number of fwd passes per distributed rank is equal
288
    padding_requests = collections.defaultdict(int)
lintangsutawika's avatar
lintangsutawika committed
289
    # store the hierarchy to do proper ordering
lintangsutawika's avatar
lintangsutawika committed
290
    task_hierarchy = collections.defaultdict(list)
291
292
    # store num-fewshot value per task
    num_fewshot = collections.defaultdict(int)
293

294
    # get lists of each type of request
295
    for task_name, task in task_dict.items():
296
        if isinstance(task, tuple):
lintangsutawika's avatar
lintangsutawika committed
297
298
            group_name, task = task
            task_hierarchy[group_name].append(task_name)
299
            versions[group_name] = "N/A"
lintangsutawika's avatar
lintangsutawika committed
300

301
        else:
302
            group_name = None
lintangsutawika's avatar
lintangsutawika committed
303
304
305
306
            task_hierarchy[task_name] = []

        if task is None:
            continue
307

Leo Gao's avatar
Leo Gao committed
308
        versions[task_name] = task.VERSION
haileyschoelkopf's avatar
haileyschoelkopf committed
309
310
        configs[task_name] = dict(task.dump_config())

Baber Abbasi's avatar
Baber Abbasi committed
311
312
313
        # Number of few-shots for printing.
        if (n_shot := configs[task_name].get("num_fewshot")) == 0:
            n_shot = configs[task_name].get("metadata", {}).get("num_fewshot", 0)
314
315
        num_fewshot[task_name] = n_shot

lintangsutawika's avatar
lintangsutawika committed
316
        if "task_alias" in configs[task_name]:
Lintang Sutawika's avatar
Lintang Sutawika committed
317
            results[task_name]["alias"] = configs[task_name]["task_alias"]
lintangsutawika's avatar
lintangsutawika committed
318

lintangsutawika's avatar
format  
lintangsutawika committed
319
320
        if (
            ("group_alias" in configs[task_name])
Lintang Sutawika's avatar
Lintang Sutawika committed
321
            and (group_name not in results)
lintangsutawika's avatar
format  
lintangsutawika committed
322
            and (group_name is not None)
lintangsutawika's avatar
lintangsutawika committed
323
        ):
Lintang Sutawika's avatar
Lintang Sutawika committed
324
            results[group_name]["alias"] = configs[task_name]["group_alias"]
lintangsutawika's avatar
lintangsutawika committed
325

Hailey Schoelkopf's avatar
Hailey Schoelkopf committed
326
        if limit is not None:
327
328
329
330
331
332
            if task.has_test_docs():
                task_docs = task.test_docs()
            elif task.has_validation_docs():
                task_docs = task.validation_docs()
            else:
                raise RuntimeError("Task has neither test_docs nor validation_docs")
333
            limit = int(len(task_docs) * limit) if limit < 1.0 else int(limit)
334

335
336
        task.build_all_requests(limit=limit, rank=lm.rank, world_size=lm.world_size)

337
        eval_logger.debug(
haileyschoelkopf's avatar
haileyschoelkopf committed
338
339
340
341
342
343
            f"Task: {task_name}; number of requests on this rank: {len(task.instances)}"
        )

        if write_out:
            for inst in task.instances:
                # print the prompt for the first few documents
Hailey Schoelkopf's avatar
Hailey Schoelkopf committed
344
345
                if inst.doc_id < 1:
                    eval_logger.info(
haileyschoelkopf's avatar
haileyschoelkopf committed
346
347
                        f"Task: {task_name}; document {inst.doc_id}; context prompt (starting on next line):\
\n{inst.args[0]}\n(end of prompt on previous line)\ntarget string or answer choice index (starting on next line):\n{task.doc_to_target(inst.doc)}\n(end of target on previous line)"
haileyschoelkopf's avatar
haileyschoelkopf committed
348
                    )
haileyschoelkopf's avatar
haileyschoelkopf committed
349
                    eval_logger.info(f"Request: {str(inst)}")
haileyschoelkopf's avatar
haileyschoelkopf committed
350

351
        # aggregate Instances by LM method requested to get output.
lintangsutawika's avatar
lintangsutawika committed
352
353
354
        for instance in task.instances:
            reqtype = instance.request_type
            requests[reqtype].append(instance)
355
356

        if lm.world_size > 1:
357
358
359
360
            instances_rnk = torch.tensor(len(task._instances), device=lm.device)
            gathered_item = (
                lm.accelerator.gather(instances_rnk).cpu().detach().numpy().tolist()
            )
361

362
            # compute number of pseudobatches to pad with (FSDP/DDP require even batches among ranks)
363
            numpad = max(gathered_item) - gathered_item[lm.rank]
364
            padding_requests[task.OUTPUT_TYPE] += numpad
365

366
    ### Run LM on inputs, get all outputs ###
Leo Gao's avatar
Leo Gao committed
367
368
    # execute each type of request
    for reqtype, reqs in requests.items():
369
        eval_logger.info(f"Running {reqtype} requests")
370
371
372
373
        # create `K` copies of each request `req` based off `K = req.repeats`
        cloned_reqs = []
        for req in reqs:
            cloned_reqs.extend([req] * req.repeats)
lintangsutawika's avatar
lintangsutawika committed
374

375
376
        if (lm.world_size > 1) and (padding_requests[reqtype] > 0):
            for _ in range(padding_requests[reqtype]):
377
378
                cloned_reqs.extend([req] * req.repeats)

379
380
381
382
383
384
385
        # run requests through model
        resps = getattr(lm, reqtype)(cloned_reqs)

        # put responses from model into a list of length K for each request.
        for x, req in zip(resps, cloned_reqs):
            req.resps.append(x)

386
387
        if lm.world_size > 1:
            lm.accelerator.wait_for_everyone()
388

389
390
391
    ### Postprocess outputs ###
    # TODO: del model here, maybe (idea: allow user to specify device of e.g. reward model separately)
    for task_name, task in task_dict.items():
392
        if isinstance(task, tuple):
393
            group, task = task
394
395
            if task is None:
                continue
396
397
398
        task.apply_filters()

    ### Collect values of metrics on all datapoints ###
Leo Gao's avatar
Leo Gao committed
399
400
401
    vals = collections.defaultdict(list)

    # unpack results and sort back in order and return control to Task
402
    for task_name, task in task_dict.items():
403
        if isinstance(task, tuple):
404
            group, task = task
405
406
            if task is None:
                continue
haileyschoelkopf's avatar
haileyschoelkopf committed
407
408
        # TODO: make it possible to use a different metric per filter
        # iterate over different filters used
409
        for key in task.instances[0].filtered_resps.keys():
410
411
412
413
            doc_iterator = (
                itertools.islice(
                    enumerate(task.test_docs()), lm.rank, limit, lm.world_size
                )
lintangsutawika's avatar
lintangsutawika committed
414
                if task.has_test_docs()
415
416
417
418
                else itertools.islice(
                    enumerate(task.validation_docs()), lm.rank, limit, lm.world_size
                )
            )
419
            for doc_id, doc in doc_iterator:
420
421
                # subset instances to only this document id ; sort by idx
                requests = list(filter(lambda x: x.doc_id == doc_id, task.instances))
422
                requests.sort(key=lambda x: x.idx)
lintangsutawika's avatar
lintangsutawika committed
423
424
425
                metrics = task.process_results(
                    doc, [req.filtered_resps[key] for req in requests]
                )
426
427
428
429
430
431
432
433
434
435
436
437
                if log_samples:
                    target = task.doc_to_target(doc)
                    example = {
                        "doc_id": doc_id,
                        "doc": doc,
                        "target": target,
                        "arguments": [req.args for req in requests],
                        "resps": [req.resps for req in requests],
                        "filtered_resps": [req.filtered_resps[key] for req in requests],
                    }
                    example.update(metrics)
                    samples[task_name].append(example)
438
439
440
                for metric, value in metrics.items():
                    vals[(task_name, key, metric)].append(value)

441
    if lm.world_size > 1:
442
        # if multigpu, then gather data across all ranks
443
444
445
446
447
448
449
450
        # first gather logged samples across all ranks
        for task_name, task_samples in list(samples.items()):
            full_samples = [None] * lm.world_size
            torch.distributed.all_gather_object(full_samples, task_samples)

            samples[task_name] = list(itertools.chain.from_iterable(full_samples))

        # then collect metrics across all ranks
451
452
        vals_torch = collections.defaultdict(list)
        for (task_name, key, metric), items in vals.items():
453
            numitem = 0
454
            if isinstance(items[0], tuple):
455
456
                numitem = len(items[0])

Lintang Sutawika's avatar
Lintang Sutawika committed
457
            if isinstance(items[0], (str, list, tuple)):
458
459
460
                # handle the string case
                gathered_items = [None] * lm.accelerator.num_processes
                torch.distributed.all_gather_object(gathered_items, items)
461

462
                gathered_item = list(itertools.chain.from_iterable(gathered_items))
463
            else:
464
465
466
467
468
469
470
471
472
473
                # distributed gather requires all ranks to have same dimensions
                # so we pad out with float32 min value
                pad_value = torch.finfo(torch.float32).min
                metrics_tensor = torch.tensor(items, device=lm.device)

                original_dtype = metrics_tensor.dtype  # store original dtype
                torch_device_tensor = lm.accelerator.pad_across_processes(
                    metrics_tensor.to(torch.float32), pad_index=pad_value
                )
                gathered_item = lm.accelerator.gather(torch_device_tensor)
474

475
476
477
478
479
480
481
482
483
484
485
                if numitem > 0:
                    gathered_filtered = gathered_item[gathered_item[:, 0] != pad_value]
                else:
                    gathered_filtered = gathered_item[gathered_item != pad_value]

                gathered_item = (
                    gathered_filtered.to(original_dtype).cpu().detach().numpy().tolist()
                )
                # reconvert if we were passed a tuple of values
                if numitem > 0:
                    gathered_item = [tuple(g) for g in gathered_item]
486

487
488
            if lm.rank == 0:
                vals_torch[(task_name, key, metric)] = gathered_item
489

490
        vals = vals_torch
491

492
493
494
495
496
    if lm.rank == 0:
        ### Aggregate results over all datapoints ###
        # aggregate results ; run bootstrap CIs
        for (task_name, key, metric), items in vals.items():
            task = task_dict[task_name]
Baber Abbasi's avatar
Baber Abbasi committed
497
            group_name, task = task if isinstance(task, tuple) else (None, task)
lintangsutawika's avatar
lintangsutawika committed
498

Baber Abbasi's avatar
Baber Abbasi committed
499
            metric_key = f"{metric},{key}"
500
            agg_fn = task.aggregation()[metric]
Baber Abbasi's avatar
Baber Abbasi committed
501

502
503
            results[task_name][metric_key] = agg_fn(items)
            results[task_name]["samples"] = len(items)
lintangsutawika's avatar
lintangsutawika committed
504

505
506
            # hotfix: bleu, chrf, ter seem to be really expensive to bootstrap
            # so we run them less iterations. still looking for a cleaner way to do this
haileyschoelkopf's avatar
haileyschoelkopf committed
507
            if bootstrap_iters > 0:
Baber Abbasi's avatar
Baber Abbasi committed
508
509
                stderr_fn = lm_eval.api.metrics.stderr_for_metric(
                    metric=agg_fn,
haileyschoelkopf's avatar
haileyschoelkopf committed
510
                    bootstrap_iters=min(bootstrap_iters, 100)
haileyschoelkopf's avatar
haileyschoelkopf committed
511
512
513
                    if metric in ["bleu", "chrf", "ter"]
                    else bootstrap_iters,
                )
514

Baber Abbasi's avatar
Baber Abbasi committed
515
516
517
                results[task_name][f"{metric}_stderr,{key}"] = (
                    stderr_fn(items) if (stderr_fn and len(items) > 1) else "N/A"
                )
Fabrizio Milo's avatar
Fabrizio Milo committed
518

lintangsutawika's avatar
lintangsutawika committed
519
        if bool(results):
520
            for group, task_list in reversed(task_hierarchy.items()):
521
522
523
524
525
526
527
                if len(task_list) == 0:
                    # task_hierarchy entries are either
                    # `group_name: [subtask1, subtask2, ...]`
                    # or `task_name: []`.
                    # we only want to operate on groups here.
                    continue
                for metric in [
Baber Abbasi's avatar
Baber Abbasi committed
528
529
530
531
                    key
                    for key in results[task_list[0]].keys()
                    if "_stderr" not in key and key not in ["alias", "samples"]
                ]:  # TODO: what if tasks don't all share the same metrics
532
533
534
                    stderr = "_stderr,".join(metric.split(","))

                    # gather metrics, sizes, and stderrs from subtasks
Baber Abbasi's avatar
Baber Abbasi committed
535
536
537
                    metrics = [
                        results[task][metric] for task in task_list
                    ]  # TODO: copy?
538
539
540
541
                    stderrs = [results[task][stderr] for task in task_list]
                    sizes = [results[task]["samples"] for task in task_list]

                    # compute group's pooled metric and stderr
Baber Abbasi's avatar
Baber Abbasi committed
542
543
544
                    results[group][
                        metric
                    ] = lm_eval.api.metrics.aggregate_subtask_metrics(metrics, sizes)
545
546
547
548
                    # TODO: calculate grouped metric using aggregation fn
                    if "N/A" in stderrs:
                        results[group][stderr] = "N/A"
                    else:
Baber Abbasi's avatar
Baber Abbasi committed
549
550
551
                        results[group][
                            stderr
                        ] = lm_eval.api.metrics.pooled_sample_stderr(stderrs, sizes)
552
553
554
555
556
                        # TODO: allow GroupConfigs to choose which variance formula is used, for back-compatibility
                        # To use the old (likely incorrect) variance formula, comment out the above and uncomment this line:
                        # results[group][stderr] = lm_eval.api.metrics.combined_sample_stderr(stderrs, sizes, metrics=metrics)

                    results[group]["samples"] = sum(sizes)
lintangsutawika's avatar
lintangsutawika committed
557

Lintang Sutawika's avatar
Lintang Sutawika committed
558
        def print_tasks(task_hierarchy, results, tab=0):
559
560
561
            results_agg = collections.defaultdict(dict)
            groups_agg = collections.defaultdict(dict)

Lintang Sutawika's avatar
Lintang Sutawika committed
562
563
            (group_name, task_list), *_ = task_hierarchy.items()
            task_list = sorted(task_list)
564

Lintang Sutawika's avatar
Lintang Sutawika committed
565
566
567
568
            results_agg[group_name] = results[group_name].copy()
            # results_agg[group_name]["tab"] = tab
            if "samples" in results_agg[group_name]:
                results_agg[group_name].pop("samples")
lintangsutawika's avatar
lintangsutawika committed
569

Lintang Sutawika's avatar
Lintang Sutawika committed
570
            tab_string = " " * tab + "- " if tab > 0 else ""
lintangsutawika's avatar
lintangsutawika committed
571

Lintang Sutawika's avatar
Lintang Sutawika committed
572
573
574
575
            if "alias" in results_agg[group_name]:
                results_agg[group_name]["alias"] = (
                    tab_string + results_agg[group_name]["alias"]
                )
lintangsutawika's avatar
lintangsutawika committed
576
            else:
Lintang Sutawika's avatar
Lintang Sutawika committed
577
                results_agg[group_name]["alias"] = tab_string + group_name
lintangsutawika's avatar
lintangsutawika committed
578

Lintang Sutawika's avatar
Lintang Sutawika committed
579
580
581
582
583
            if len(task_list) > 0:
                groups_agg[group_name] = results[group_name].copy()
                # groups_agg[group_name]["tab"] = tab
                if "samples" in groups_agg[group_name]:
                    groups_agg[group_name].pop("samples")
lintangsutawika's avatar
lintangsutawika committed
584

Lintang Sutawika's avatar
Lintang Sutawika committed
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
                if "alias" in groups_agg[group_name]:
                    groups_agg[group_name]["alias"] = (
                        tab_string + groups_agg[group_name]["alias"]
                    )
                else:
                    groups_agg[group_name]["alias"] = tab_string + group_name

                for task_name in task_list:
                    if task_name in task_hierarchy:
                        _task_hierarchy = {
                            **{task_name: task_hierarchy[task_name]},
                            **task_hierarchy,
                        }
                    else:
                        _task_hierarchy = {
                            **{task_name: []},
                            **task_hierarchy,
                        }

                    _results_agg, _groups_agg = print_tasks(
                        _task_hierarchy, results, tab + 1
                    )
                    results_agg = {**results_agg, **_results_agg}
                    groups_agg = {**groups_agg, **_groups_agg}

            return results_agg, groups_agg

        results_agg = collections.defaultdict(dict)
        groups_agg = collections.defaultdict(dict)
        all_tasks_list = list(task_hierarchy.keys())
        left_tasks_list = []
        while True:
            add_tasks_list = list(k for k in results_agg.keys())
            left_tasks_list = sorted(list(set(all_tasks_list) - set(add_tasks_list)))
            if len(left_tasks_list) == 0:
                break

            _task_hierarchy = {
                k: v for k, v in task_hierarchy.items() if k in left_tasks_list
            }
            _results_agg, _groups_agg = print_tasks(_task_hierarchy, results)

            results_agg = {**results_agg, **_results_agg}
            groups_agg = {**groups_agg, **_groups_agg}
lintangsutawika's avatar
lintangsutawika committed
629

630
        for group_name, task_list in task_hierarchy.items():
Baber Abbasi's avatar
Baber Abbasi committed
631
632
633
634
            if task_list:
                num_fewshot[group_name] = num_fewshot[
                    task_list[0]
                ]  # TODO: validate this
635

636
        results_dict = {
637
            "results": dict(results_agg.items()),
lintangsutawika's avatar
lintangsutawika committed
638
            **({"groups": dict(groups_agg.items())} if bool(groups_agg) else {}),
639
            "group_subtasks": {k: v for k, v in reversed(task_hierarchy.items())},
640
641
            "configs": dict(sorted(configs.items())),
            "versions": dict(sorted(versions.items())),
642
            "n-shot": dict(sorted(num_fewshot.items())),
643
        }
644
645
646
647
        if log_samples:
            results_dict["samples"] = dict(samples)

        return results_dict
Fabrizio Milo's avatar
Fabrizio Milo committed
648

649
650
    else:
        return None