"test/vscode:/vscode.git/clone" did not exist on "23471f9aa3d4fb7bea89d2f9c0b471025292406e"
huggingface.py 51.6 KB
Newer Older
1
import copy
2
import os
Jeevan's avatar
Jeevan committed
3
from datetime import timedelta
4
5
6
from pathlib import Path
from typing import List, Literal, Optional, Tuple, Union

7
import torch
8
import torch.nn.functional as F
9
import transformers
Jeevan's avatar
Jeevan committed
10
11
12
13
14
15
from accelerate import (
    Accelerator,
    DistributedType,
    InitProcessGroupKwargs,
    find_executable_batch_size,
)
16
17
18
19
from packaging import version
from peft import PeftModel
from peft import __version__ as PEFT_VERSION
from tqdm import tqdm
20
21
22
23
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
)
24
25

from lm_eval import utils
baberabb's avatar
baberabb committed
26
from lm_eval.api.instance import Instance
27
from lm_eval.api.model import TemplateLM
28
from lm_eval.api.registry import register_model
29
30
31
32
33
34
35
from lm_eval.models.utils import (
    Collator,
    clear_torch_cache,
    get_dtype,
    pad_and_concat,
    stop_sequences_criteria,
)
36

37

38
eval_logger = utils.eval_logger
39

lintangsutawika's avatar
lintangsutawika committed
40

41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
def _get_accelerate_args(
    device_map_option: Optional[str] = "auto",
    max_memory_per_gpu: Optional[Union[int, str]] = None,
    max_cpu_memory: Optional[Union[int, str]] = None,
    offload_folder: Optional[str] = "./offload",
) -> dict:
    """Returns the kwargs needed to apply `accelerate` in `AutoModel.from_pretrained`."""
    max_memory = {}
    if max_memory_per_gpu is not None:
        max_memory_per_gpu_map = {
            device_idx: max_memory_per_gpu
            for device_idx in range(torch.cuda.device_count())
        }
        max_memory.update(max_memory_per_gpu_map)
    if max_cpu_memory is not None:
        max_memory["cpu"] = max_cpu_memory

    args = {}
    if max_memory:
        args["max_memory"] = max_memory
    args["device_map"] = device_map_option
    args["offload_folder"] = offload_folder
    return args
64
65


66
@register_model("hf-auto", "hf", "huggingface")
67
class HFLM(TemplateLM):
68
69
70
71
72
73
74
    """
    An abstracted Huggingface model class. Enables usage with both models of
    `transformers.AutoModelForCausalLM` and `transformers.AutoModelForSeq2SeqLM` classes.

    Supports data-parallel multi-GPU with HF Accelerate.
    """

75
    AUTO_MODEL_CLASS = None
76
    _DEFAULT_MAX_LENGTH = 2048
haileyschoelkopf's avatar
haileyschoelkopf committed
77

78
79
    def __init__(
        self,
80
        pretrained: Optional[Union[str, transformers.PreTrainedModel]] = "gpt2",
Baber Abbasi's avatar
Baber Abbasi committed
81
82
        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
        # override whether the model should be treated as decoder-only (causal) or encoder-decoder (seq2seq)
83
84
        revision: Optional[str] = "main",
        subfolder: Optional[str] = None,
85
86
87
88
89
90
91
        tokenizer: Optional[
            Union[
                str,
                transformers.PreTrainedTokenizer,
                transformers.PreTrainedTokenizerFast,
            ]
        ] = None,
lintangsutawika's avatar
lintangsutawika committed
92
        truncation: Optional[bool] = False,
Baber Abbasi's avatar
Baber Abbasi committed
93
        logits_cache: bool = True,
94
95
        max_length: Optional[int] = None,
        device: Optional[str] = "cuda",
96
        dtype: Optional[Union[str, torch.dtype]] = "auto",
Benjamin Fattori's avatar
Benjamin Fattori committed
97
98
        batch_size: Optional[Union[int, str]] = 1,
        max_batch_size: Optional[int] = 64,
99
        trust_remote_code: Optional[bool] = False,
haileyschoelkopf's avatar
haileyschoelkopf committed
100
        use_fast_tokenizer: Optional[bool] = True,
101
        add_bos_token: Optional[bool] = False,
102
        prefix_token_id: Optional[int] = None,
103
        # arguments used for splitting a model across GPUs naively.
104
105
        # only used if `parallelize=True`.
        parallelize: Optional[bool] = False,
106
107
108
        device_map_option: Optional[str] = "auto",
        max_memory_per_gpu: Optional[Union[int, str]] = None,
        max_cpu_memory: Optional[Union[int, str]] = None,
109
        offload_folder: Optional[Union[str, os.PathLike]] = "./offload",
110
111
        # PEFT and quantization options
        peft: Optional[str] = None,
112
113
        autogptq: Optional[Union[bool, str]] = False,
        **kwargs,
Ethan Smith's avatar
Ethan Smith committed
114
    ) -> None:
115
116
        super().__init__()

117
118
119
120
        # optionally: take in an already-initialized transformers.PreTrainedModel
        if not isinstance(pretrained, str):
            eval_logger.warning(
                "`pretrained` model kwarg is not of type `str`. Many other model arguments may be ignored. Please do not launch via accelerate or use `parallelize=True` if passing an existing model this way."
121
            )
122
            assert not parallelize, "`parallelize=True` is not compatible with passing pre-initialized model to `pretrained`"
123
124
125
            self._model = pretrained
            self._device = self._model.device
            self._config = self._model.config
Baber Abbasi's avatar
Baber Abbasi committed
126
            gpus = 0
127
128
129
130
131
132

            if tokenizer:
                assert isinstance(
                    tokenizer, transformers.PreTrainedTokenizer
                ) or isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
                self.tokenizer = tokenizer
133
            else:
134
135
136
137
138
139
140
                # Get tokenizer
                model_name = self._model.name_or_path
                self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                    model_name,
                    revision=revision,
                    trust_remote_code=trust_remote_code,
                    use_fast=use_fast_tokenizer,
141
                )
142

143
        else:
144
145
146
147
148
            assert isinstance(device, str)
            assert isinstance(pretrained, str)
            assert isinstance(batch_size, (int, str))

            gpus = torch.cuda.device_count()
Jeevan's avatar
Jeevan committed
149
150
            accelerator_kwargs = InitProcessGroupKwargs(timeout=timedelta(weeks=52))
            accelerator = Accelerator(kwargs_handlers=[accelerator_kwargs])
151
152
            if accelerator.num_processes > 1:
                self.accelerator = accelerator
153
154
155
156
157
158
159

            if not (parallelize or accelerator.num_processes > 1):
                # use user-passed device
                device_list = set(
                    ["cuda", "cpu"]
                    + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
                    + ["mps", "mps:0"]
160
                )
161
                if device and device in device_list:
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
                    self._device = torch.device(device)
                    eval_logger.info(f"Using device '{device}'")
                    if device in ("mps", "mps:0") and version.parse(
                        torch.__version__
                    ) < version.parse("2.1"):
                        raise RuntimeError(
                            f"mps requires torch >= 2.1. You have {torch.__version__}"
                        )
                else:
                    eval_logger.info("Device not specified")
                    eval_logger.info(f"Cuda Available? {torch.cuda.is_available()}")
                    self._device = (
                        torch.device("cuda")
                        if torch.cuda.is_available()
                        else torch.device("cpu")
                    )
            else:
                if device != "cuda":
                    eval_logger.info(
                        f"Using `accelerate launch` or `parallelize=True`, device '{device}' will be overridden when placing model."
                    )
                # TODO: include in warning that `load_in_8bit` etc. affect this too
184
                self._device = torch.device(device)
185

186
187
            # TODO: update this to be less of a hack once subfolder is fixed in HF
            revision = revision + ("/" + subfolder if subfolder is not None else "")
188

189
            self._get_config(
190
191
192
193
194
                pretrained,
                revision=revision,
                trust_remote_code=trust_remote_code,
            )

195
196
197
198
        # determine which of 'causal' and 'seq2seq' backends to use
        self._get_backend(
            config=self.config, backend=backend, trust_remote_code=trust_remote_code
        )
199

200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
        # if we passed `pretrained` as a string, initialize our model now
        if isinstance(pretrained, str):
            self._create_model(
                pretrained=pretrained,
                revision=revision,
                dtype=dtype,
                trust_remote_code=trust_remote_code,
                parallelize=parallelize,
                device_map_option=device_map_option,
                max_memory_per_gpu=max_memory_per_gpu,
                max_cpu_memory=max_cpu_memory,
                offload_folder=offload_folder,
                peft=peft,
                autogptq=autogptq,
                **kwargs,
215
216
            )

217
        # access self._model through self.model property outside this method
218
219
220
        if isinstance(self.model, torch.nn.Module):
            self.model.eval()
            self.model.tie_weights()
haileyschoelkopf's avatar
haileyschoelkopf committed
221

222
        if isinstance(pretrained, str) and (gpus >= 1 or str(self.device) == "mps"):
223
224
            # TODO: can remove this whole snippet except in the mps case, perhaps?
            if not (parallelize or autogptq or hasattr(self, "accelerator")):
225
226
227
228
229
230
                # place model onto device requested manually,
                # if not using HF Accelerate or device_map
                # or any other option that preloads model onto device
                try:
                    self.model.to(self.device)
                except ValueError:
231
232
                    eval_logger.debug(
                        "Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes` or `device_map` is provided. If the desired GPU is being used, this message is safe to ignore."
233
234
235
236
237
                    )

        self._create_tokenizer(
            pretrained,
            tokenizer,
238
            revision=revision,
239
            trust_remote_code=trust_remote_code,
240
            use_fast_tokenizer=use_fast_tokenizer,
241
242
        )

lintangsutawika's avatar
lintangsutawika committed
243
        self.truncation = truncation
Baber Abbasi's avatar
Baber Abbasi committed
244
        self.logits_cache = logits_cache
245
        self.vocab_size = self.tokenizer.vocab_size
246
247
248
249
250
251
252
253
        # select (or create) a pad token to use
        if self.tokenizer.pad_token:
            pass
        elif self.tokenizer.unk_token:
            self.tokenizer.pad_token_id = self.tokenizer.unk_token_id
        elif self.tokenizer.eos_token:
            self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
        else:
254
            if getattr(self.config, "model_type", None) == "qwen":
255
256
                # Qwen's trust_remote_code tokenizer does not allow for adding special tokens
                self.tokenizer.pad_token = "<|endoftext|>"
257
258
259
260
261
262
263
264
265
266
            elif (
                self.tokenizer.__class__.__name__ == "RWKVWorldTokenizer"
                or self.tokenizer.__class__.__name__ == "Rwkv5Tokenizer"
            ):
                # The RWKV world tokenizer, does not allow for adding special tokens / setting the pad token (which is set as 0)
                # The additional tokenizer name check is needed, as there exists rwkv4 models with neox tokenizer
                # ---
                # Note that the world tokenizer class name, might change in the future for the final huggingface merge
                # https://github.com/huggingface/transformers/pull/26963
                assert self.tokenizer.pad_token_id == 0
267
268
            else:
                self.tokenizer.add_special_tokens({"pad_token": "<|pad|>"})
269

270
271
        # TODO: override this for Gemma
        self.add_bos_token = add_bos_token
272
273
        if getattr(self.config, "model_type", None) == "gemma":
            self.add_bos_token = True
274
            eval_logger.info(
275
                f"Model type is '{self.config.model_type}', a BOS token will be used as Gemma underperforms without it."
276
277
            )

278
279
        self._max_length = max_length

Benjamin Fattori's avatar
Benjamin Fattori committed
280
281
282
283
284
285
286
287
288
289
        self.batch_schedule = 1
        self.batch_sizes = {}
        self.max_batch_size = max_batch_size

        if str(batch_size).startswith("auto"):
            batch_size = batch_size.split(":")
            self.batch_size_per_gpu = batch_size[0]
            self.batch_schedule = float(batch_size[1]) if len(batch_size) > 1 else 1
        else:
            self.batch_size_per_gpu = int(batch_size)
290

291
292
293
294
295
296
297
298
299
300
301
302
303
304
        if isinstance(pretrained, str):
            # multigpu data-parallel support when launched with accelerate
            if gpus > 1:
                if parallelize:
                    if accelerator.num_processes > 1:
                        raise RuntimeError(
                            "Attempted to use both a HF Accelerate `device_map` and to launch via `accelerate launch`. If this is the case, please either remove `parallelize=True` from --model_args or launch outside of the Accelerate launcher."
                        )
                    else:
                        pass
                elif accelerator.num_processes == 1:
                    # if we aren't launching via accelerate, ditch
                    self._rank = 0
                    self._world_size = 1
305
                else:
306
307
308
309
310
311
312
                    if gpus > accelerator.num_processes:
                        eval_logger.warning(
                            "WARNING: The number of total system GPUs does not match the number of spawned processes. "
                            "If you would like to use data parallelism, please launch the script "
                            "with 'accelerate launch *script*'. "
                            f"Current run will proceed with {accelerator.num_processes} devices."
                        )
313
314
315
316
317
318
319
                    assert (
                        accelerator.distributed_type
                        in [
                            DistributedType.FSDP,
                            DistributedType.MULTI_GPU,
                        ]
                    ), "Unsupported distributed type provided. Only DDP and FSDP are supported."
320
321
322
323
324
325
326
327
                    if accelerator.distributed_type == DistributedType.FSDP:
                        self._model = accelerator.prepare(self.model)
                    else:
                        self._model = accelerator.prepare_model(
                            self.model, evaluation_mode=True
                        )
                    self._device = torch.device(
                        f"cuda:{accelerator.local_process_index}"
328
                    )
329
                    self.accelerator = accelerator
330

331
332
                    if self.accelerator.is_local_main_process:
                        eval_logger.info(f"Using {gpus} devices with data parallelism")
333

334
335
336
337
338
339
340
341
342
                    self._rank = self.accelerator.local_process_index
                    self._world_size = self.accelerator.num_processes
        else:
            # if a PreTrainedModel was passed into HFLM, we forgo distributed setup.
            eval_logger.warning(
                "Passed an already-initialized model through `pretrained`, assuming single-process call to evaluate() or custom distributed integration"
            )
            self._rank = 0
            self._world_size = 1
haileyschoelkopf's avatar
haileyschoelkopf committed
343

344
        self.custom_prefix_token_id = prefix_token_id
345
346
347
348
        if prefix_token_id is not None:
            eval_logger.info(
                f"Loglikelihood prefix token id used in evaluation: {self.prefix_token_id}"
            )
349

350
351
352
353
354
    @property
    def config(self):
        # return the associated transformers.AutoConfig for the given pretrained model.
        return self._config

355
356
357
358
359
360
361
362
    @property
    def model(self):
        # returns the model, unwrapping it if using Accelerate
        if hasattr(self, "accelerator"):
            return self.accelerator.unwrap_model(self._model)
        else:
            return self._model

363
364
365
366
367
    @property
    def eot_token_id(self):
        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
        return self.tokenizer.eos_token_id

368
369
370
371
372
373
374
375
376
    @property
    def prefix_token_id(self):
        # it is used as prefix for loglikelihood
        if self.custom_prefix_token_id is not None:
            return self.custom_prefix_token_id
        if self.tokenizer.bos_token_id is not None:
            return self.tokenizer.bos_token_id
        return self.tokenizer.eos_token_id

377
378
    @property
    def max_length(self):
379
380
381
382
383
384
385
386
387
388
389
        if self._max_length:  # if max length manually set, return it
            return self._max_length
        seqlen_config_attrs = ("n_positions", "max_position_embeddings", "n_ctx")
        for attr in seqlen_config_attrs:
            if hasattr(self.model.config, attr):
                return getattr(self.model.config, attr)
        if hasattr(self.tokenizer, "model_max_length"):
            if self.tokenizer.model_max_length == 1000000000000000019884624838656:
                return self._DEFAULT_MAX_LENGTH
            return self.tokenizer.model_max_length
        return self._DEFAULT_MAX_LENGTH
390

391
    @property
Ethan Smith's avatar
Ethan Smith committed
392
    def max_gen_toks(self) -> int:
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
        return 256

    @property
    def batch_size(self):
        return self.batch_size_per_gpu

    @property
    def device(self):
        return self._device

    @property
    def rank(self):
        return self._rank

    @property
    def world_size(self):
        return self._world_size

411
412
    def _get_backend(
        self,
Baber Abbasi's avatar
Baber Abbasi committed
413
        config: Union[transformers.PretrainedConfig, transformers.AutoConfig],
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
        backend: Optional[Literal["default", "causal", "seq2seq"]] = "default",
        trust_remote_code: Optional[bool] = False,
    ) -> None:
        """
        Helper method during initialization.
        Determines the backend ("causal" (decoder-only) or "seq2seq" (encoder-decoder))
        model type to be used.
        """
        assert backend in ["default", "causal", "seq2seq"]

        if backend != "default":
            # if we've settled on non-default backend, use that manually
            if backend == "causal":
                self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM
            elif backend == "seq2seq":
                self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
            eval_logger.info(
                f"Overrode HF model backend type, and using type '{backend}'"
            )
        else:
            # determine and use the default HF backend for this model, based on its config + metadata.
            if (
                getattr(config, "model_type")
                in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
            ):
                # first check if model type is listed under seq2seq models, since some
                # models like MBart are listed in both seq2seq and causal mistakenly in HF transformers.
                # these special cases should be treated as seq2seq models.
                self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
            elif (
                getattr(self.config, "model_type") in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
            ):
                self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM
            else:
                if not trust_remote_code:
                    eval_logger.warning(
                        "HF model type is neither marked as CausalLM or Seq2SeqLM. \
                    This is expected if your model requires `trust_remote_code=True` but may be an error otherwise."
                    )
                # if model type is neither in HF transformers causal or seq2seq model registries
                # then we default to AutoModelForCausalLM
                self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM

        assert self.AUTO_MODEL_CLASS in [
            transformers.AutoModelForCausalLM,
            transformers.AutoModelForSeq2SeqLM,
        ]
        return None

    def _get_config(
        self,
        pretrained: str,
        revision: str = "main",
        trust_remote_code: bool = False,
    ) -> None:
        self._config = transformers.AutoConfig.from_pretrained(
            pretrained,
            revision=revision,
            trust_remote_code=trust_remote_code,
        )

    def _create_model(
        self,
        pretrained: str,
        revision: Optional[str] = "main",
        dtype: Optional[Union[str, torch.dtype]] = "auto",
        trust_remote_code: Optional[bool] = False,
        # arguments used for splitting a model across GPUs naively.
        # only used if `parallelize=True`.
        # (accelerate naive PP (device_map) options)
        parallelize: Optional[bool] = False,
        device_map_option: Optional[str] = "auto",
        max_memory_per_gpu: Optional[Union[int, str]] = None,
        max_cpu_memory: Optional[Union[int, str]] = None,
        offload_folder: Optional[str] = "./offload",
        # PEFT and quantization options
        peft: Optional[str] = None,
        autogptq: Optional[Union[bool, str]] = False,
        **kwargs,
    ) -> None:
        """
        Initializes an HF or HF-compatible PreTrainedModel from scratch
        inside HFLM, using the kwargs passed into self.__init__().

        Also handles functionality such as AutoGPTQ usage and PEFT wrapping.

        For future similar extensions to AutoGPTQ that are not core to HF's ecosystem,
        (such as PyTorch models that are nearly, but not quite, fully mirroring
        HF's public interface relied on in this HFLM class)
        please consider subclassing HFLM and overriding this and other methods as needed.
        """

        model_kwargs = kwargs if kwargs else {}

        if parallelize:
            model_kwargs.update(
                _get_accelerate_args(
511
                    device_map_option,  # TODO: phase out device_map_option?
512
513
514
515
516
                    max_memory_per_gpu,
                    max_cpu_memory,
                    offload_folder,
                )
            )
517
518
519
520
521
522
523
524
525
526
527
528
        elif "device_map" not in model_kwargs:
            # set a device_map to initialize model on the right GPU.
            # this is needed because it seems that the default behavior
            # for quantized models now seems to be device_map="auto"
            # which breaks data-parallel mode.
            if hasattr(self, "accelerator"):
                model_kwargs.update(
                    {"device_map": {"": f"cuda:{self.accelerator.local_process_index}"}}
                )
            else:
                model_kwargs.update({"device_map": {"": str(self.device)}})

529
530
531
532
533
534
535
536
        if not autogptq:
            if model_kwargs.get("load_in_4bit", None):
                assert (
                    transformers.__version__ >= "4.30.0"
                ), "load_in_4bit requires transformers >= 4.30.0"
            if transformers.__version__ >= "4.30.0":
                if model_kwargs.get("load_in_4bit", None):
                    if model_kwargs.get("bnb_4bit_compute_dtype", None):
537
                        model_kwargs["bnb_4bit_compute_dtype"] = get_dtype(
538
539
540
541
542
                            model_kwargs["bnb_4bit_compute_dtype"]
                        )
            self._model = self.AUTO_MODEL_CLASS.from_pretrained(
                pretrained,
                revision=revision,
543
                torch_dtype=get_dtype(dtype),
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
                trust_remote_code=trust_remote_code,
                **model_kwargs,
            )
        else:
            try:
                from auto_gptq import AutoGPTQForCausalLM
            except ModuleNotFoundError:
                raise Exception(
                    "Tried to load auto_gptq, but auto-gptq is not installed ",
                    "please install auto-gptq via pip install lm-eval[gptq] or pip install -e .[gptq]",
                )

            self._model = AutoGPTQForCausalLM.from_quantized(
                pretrained,
                trust_remote_code=trust_remote_code,
                model_basename=None if autogptq is True else Path(autogptq).stem,
                use_safetensors=True
                if autogptq is True
                else autogptq.endswith(".safetensors"),
                **model_kwargs,
            )

        if peft:
            if model_kwargs.get("load_in_4bit", None):
WoosungMyung's avatar
WoosungMyung committed
568
569
                if version.parse(PEFT_VERSION) < version.parse("0.4.0"):
                    raise AssertionError("load_in_4bit requires peft >= 0.4.0")
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
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
            self._model = PeftModel.from_pretrained(
                self._model, peft, revision=revision
            )

        return None

    def _create_tokenizer(
        self,
        pretrained: Union[str, transformers.PreTrainedModel],
        tokenizer: Optional[
            Union[
                str,
                transformers.PreTrainedTokenizer,
                transformers.PreTrainedTokenizerFast,
            ]
        ],
        revision: Optional[str] = "main",
        trust_remote_code: Optional[bool] = False,
        use_fast_tokenizer: Optional[bool] = True,
    ) -> None:
        """
        Helper method during initialization.

        Create a tokenizer object corresponding to the correct
        tokenizer for value of `pretrained`, or use the pre-initialized tokenizer passed.
        """

        if tokenizer:
            if isinstance(tokenizer, str):
                self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                    tokenizer,
                    revision=revision,
                    trust_remote_code=trust_remote_code,
                    use_fast=use_fast_tokenizer,
                )
            else:
                assert isinstance(
                    tokenizer, transformers.PreTrainedTokenizer
                ) or isinstance(tokenizer, transformers.PreTrainedTokenizerFast)
                self.tokenizer = tokenizer
        else:
            # Get tokenizer based on 'pretrained'
            if isinstance(pretrained, str):
                model_name = pretrained
            else:
                # get the HF hub name via accessor on model
                model_name = self.model.name_or_path
            self.tokenizer = transformers.AutoTokenizer.from_pretrained(
                model_name,
                revision=revision,
                trust_remote_code=trust_remote_code,
                use_fast=use_fast_tokenizer,
            )
        return None

Ethan Smith's avatar
Ethan Smith committed
625
    def _detect_batch_size(self, requests=None, pos: int = 0):
Benjamin Fattori's avatar
Benjamin Fattori committed
626
627
628
629
630
        if requests:
            _, context_enc, continuation_enc = requests[pos]
            max_length = len(
                (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1]
            )
631
632
            max_context_enc = len(context_enc[-(self.max_length + 1) :])
            max_cont_enc = len(continuation_enc[-(self.max_length + 1) :])
Benjamin Fattori's avatar
Benjamin Fattori committed
633
634
        else:
            max_length = self.max_length
lintangsutawika's avatar
lintangsutawika committed
635

Benjamin Fattori's avatar
Benjamin Fattori committed
636
637
638
        # if OOM, then halves batch_size and tries again
        @find_executable_batch_size(starting_batch_size=self.max_batch_size)
        def forward_batch(batch_size):
639
640
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                length = max(max_context_enc, max_cont_enc)
lintangsutawika's avatar
lintangsutawika committed
641
642
643
                batched_conts = torch.ones(
                    (batch_size, length), device=self.device
                ).long()
644
645
                test_batch = torch.ones((batch_size, length), device=self.device).long()
                call_kwargs = {
lintangsutawika's avatar
lintangsutawika committed
646
647
648
                    "attn_mask": test_batch,
                    "labels": batched_conts,
                }
649
650
            else:
                call_kwargs = {}
lintangsutawika's avatar
lintangsutawika committed
651
652
653
                test_batch = torch.ones(
                    (batch_size, max_length), device=self.device
                ).long()
Benjamin Fattori's avatar
Benjamin Fattori committed
654
            for _ in range(5):
655
                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)  # noqa: F841
lintangsutawika's avatar
lintangsutawika committed
656

Benjamin Fattori's avatar
Benjamin Fattori committed
657
658
            return batch_size

659
660
661
662
663
664
665
        try:
            batch_size = forward_batch()
        except RuntimeError as e:
            if "No executable batch size found" in str(e):
                batch_size = 1
            else:
                raise
Benjamin Fattori's avatar
Benjamin Fattori committed
666

667
668
669
670
671
672
673
        if self.world_size > 1:
            # if multi-GPU, always take minimum over all selected batch sizes
            max_rnk_bs = torch.tensor([batch_size], device=self.device)
            gathered = (
                self.accelerator.gather(max_rnk_bs).cpu().detach().numpy().tolist()
            )
            batch_size = min(gathered)
674
            clear_torch_cache()
675
676
            return batch_size

677
        clear_torch_cache()
Benjamin Fattori's avatar
Benjamin Fattori committed
678
679
        return batch_size

baberabb's avatar
baberabb committed
680
681
682
    def tok_encode(
        self, string: str, left_truncate_len=None, add_special_tokens=None
    ) -> List[int]:
haileyschoelkopf's avatar
haileyschoelkopf committed
683
        """ """
Lintang Sutawika's avatar
Lintang Sutawika committed
684
685
686
687
688
        # default for None - empty dict, use predefined tokenizer param
        # used for all models except for CausalLM or predefined value
        special_tokens_kwargs = {}

        # by default for CausalLM - false or self.add_bos_token is set
689
690
        if add_special_tokens is None:
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
Lintang Sutawika's avatar
Lintang Sutawika committed
691
692
693
694
695
696
                special_tokens_kwargs = {
                    "add_special_tokens": False or self.add_bos_token
                }
        # otherwise the method explicitly defines the value
        else:
            special_tokens_kwargs = {"add_special_tokens": add_special_tokens}
697

Lintang Sutawika's avatar
Lintang Sutawika committed
698
        encoding = self.tokenizer.encode(string, **special_tokens_kwargs)
haileyschoelkopf's avatar
haileyschoelkopf committed
699

700
701
702
        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
        if left_truncate_len:
            encoding = encoding[-left_truncate_len:]
haileyschoelkopf's avatar
haileyschoelkopf committed
703

704
705
        return encoding

haileyschoelkopf's avatar
haileyschoelkopf committed
706
    def tok_batch_encode(
lintangsutawika's avatar
lintangsutawika committed
707
708
        self,
        strings: List[str],
lintangsutawika's avatar
lintangsutawika committed
709
        padding_side: str = "left",
710
711
        left_truncate_len: int = None,
        truncation: bool = False,
Baber Abbasi's avatar
Baber Abbasi committed
712
    ) -> Tuple[torch.Tensor, torch.Tensor]:
haileyschoelkopf's avatar
haileyschoelkopf committed
713
714
715
716
        # encode a batch of strings. converts to tensors and pads automatically, unlike tok_encode.
        old_padding_side = self.tokenizer.padding_side
        self.tokenizer.padding_side = padding_side

Lintang Sutawika's avatar
Lintang Sutawika committed
717
        add_special_tokens = {}
haileyschoelkopf's avatar
haileyschoelkopf committed
718
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
Lintang Sutawika's avatar
Lintang Sutawika committed
719
            add_special_tokens = {"add_special_tokens": False or self.add_bos_token}
haileyschoelkopf's avatar
haileyschoelkopf committed
720
721
722

        encoding = self.tokenizer(
            strings,
lintangsutawika's avatar
lintangsutawika committed
723
            truncation=truncation,
haileyschoelkopf's avatar
haileyschoelkopf committed
724
725
            padding="longest",
            return_tensors="pt",
Lintang Sutawika's avatar
Lintang Sutawika committed
726
            **add_special_tokens,
haileyschoelkopf's avatar
haileyschoelkopf committed
727
728
729
730
731
732
733
734
735
736
        )
        if left_truncate_len:
            encoding["input_ids"] = encoding["input_ids"][:, -left_truncate_len:]
            encoding["attention_mask"] = encoding["attention_mask"][
                :, -left_truncate_len:
            ]
        self.tokenizer.padding_side = old_padding_side

        return encoding["input_ids"], encoding["attention_mask"]

Lintang Sutawika's avatar
Lintang Sutawika committed
737
738
    def tok_decode(self, tokens, skip_special_tokens=True):
        return self.tokenizer.decode(tokens, skip_special_tokens=skip_special_tokens)
739
740
741

    def _model_call(self, inps, attn_mask=None, labels=None):
        """
haileyschoelkopf's avatar
haileyschoelkopf committed
742
        :param inps: torch.Tensor
743
744
745
746
747
748
749
750
751
752
753
754
755
            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)] or of shape
            [batch, sequence_ctx]. the size of sequence may vary from call to call
        :param attn_mask: torch.Tensor, optional
            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
        :param labels: torch.Tensor, optional
            A torch tensor of shape [batch, (sequence_ctx + sequence_cont)]. Only passed
            (and must be passed) if self.AUTO_MODEL_CLASS is transformers.AutoModelForSeq2SeqLM
        :return
            A torch tensor of shape [batch, sequence, vocab] with the
        logits returned from the model's decoder
        """
        with torch.no_grad():
756
757
            if attn_mask is not None or labels is not None:
                assert attn_mask is not None and labels is not None
758
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
haileyschoelkopf's avatar
haileyschoelkopf committed
759
760
761
                return self.model(
                    input_ids=inps, attention_mask=attn_mask, labels=labels
                ).logits
762
763
764
765
766
            else:
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                return self.model(inps).logits

    def _model_generate(self, context, max_length, stop, **generation_kwargs):
Baber Abbasi's avatar
Baber Abbasi committed
767
        # temperature = 0.0 if not set
768
769
770
        # if do_sample is false and temp==0.0:
        # remove temperature, as do_sample=False takes care of this
        # and we don't want a warning from HF
Baber Abbasi's avatar
Baber Abbasi committed
771
        generation_kwargs["temperature"] = generation_kwargs.get("temperature", 0.0)
772
        do_sample = generation_kwargs.get("do_sample", None)
773
774
775
776
777

        # The temperature has to be a strictly positive float -- if it is 0.0, use greedy decoding strategies
        if generation_kwargs.get("temperature") == 0.0 and do_sample is None:
            generation_kwargs["do_sample"] = do_sample = False

Baber Abbasi's avatar
Baber Abbasi committed
778
779
        if do_sample is False and generation_kwargs.get("temperature") == 0.0:
            generation_kwargs.pop("temperature")
780
781
        # build stopping criteria
        stopping_criteria = stop_sequences_criteria(
782
            self.tokenizer, stop, context.shape[1], context.shape[0]
783
        )
784
        return self.model.generate(
785
            input_ids=context,
786
787
            max_length=max_length,
            stopping_criteria=stopping_criteria,
788
            pad_token_id=self.tokenizer.pad_token_id,
789
790
791
            use_cache=True,
            **generation_kwargs,
        )
792

Baber Abbasi's avatar
Baber Abbasi committed
793
794
795
    def _select_cont_toks(
        self, logits: torch.Tensor, contlen: int = None, inplen: int = None
    ) -> torch.Tensor:
796
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
797
798
799
            assert (
                contlen and inplen
            ), "Must pass input len and cont. len to select scored logits for causal LM"
800
801
802
803
            # discard right-padding.
            # also discard the input/context tokens. we'll only score continuations.
            logits = logits[inplen - contlen : inplen]
        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
804
805
806
807
            assert (
                contlen and not inplen
            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
            # only discard right-padding.
808
            # the logits input to this fn only contain decoder-side tokens.
haileyschoelkopf's avatar
haileyschoelkopf committed
809
810
            logits = logits[:contlen]

811
812
        return logits

813
814
815
    def loglikelihood_rolling(
        self, requests: List[Instance], disable_tqdm: bool = False
    ) -> List[float]:
816
        loglikelihoods = []
Benjamin Fattori's avatar
Benjamin Fattori committed
817
818
819
820
821
822
823
824
825

        adaptive_batch_size = None
        if self.batch_size == "auto":
            # using rolling window with maximum context
            print("Passed argument batch_size = auto. Detecting largest batch size")
            batch_size = self._detect_batch_size()
            print(f"Determined Largest batch size: {batch_size}")
            adaptive_batch_size = batch_size

826
827
828
        for (string,) in tqdm(
            [req.args for req in requests], disable=(disable_tqdm or (self.rank != 0))
        ):
829
830
831
832
833
            rolling_token_windows = list(
                map(
                    utils.make_disjoint_window,
                    utils.get_rolling_token_windows(
                        token_list=self.tok_encode(string),
834
                        prefix_token=self.prefix_token_id,
835
836
837
838
839
                        max_seq_len=self.max_length,
                        context_len=1,
                    ),
                )
            )
haileyschoelkopf's avatar
haileyschoelkopf committed
840
841

            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
            rolling_token_windows = [(None,) + x for x in rolling_token_windows]

            pad_amnt = 0
            if self.world_size > 1:
                # We pad out the external document-level iterator so the inner iterator doesn't hang
                mytensor = torch.tensor(len(rolling_token_windows), device=self.device)
                gathered = (
                    self.accelerator.gather(mytensor).cpu().detach().numpy().tolist()
                )

                pad_amnt = max(gathered) - gathered[self.rank]
                if pad_amnt > 0:
                    rolling_token_windows += pad_amnt * [rolling_token_windows[0]]

            string_nll = self._loglikelihood_tokens(
Baber Abbasi's avatar
Baber Abbasi committed
857
                requests=rolling_token_windows,
lintangsutawika's avatar
lintangsutawika committed
858
859
                disable_tqdm=True,
                override_bs=adaptive_batch_size,
860
861
862
863
864
865
866
867
868
869
870
871
            )

            if (self.world_size > 1) and (pad_amnt > 0):
                string_nll = [x[0] for x in string_nll[:-pad_amnt]]
            else:
                # discard is_greedy
                string_nll = [x[0] for x in string_nll]

            string_nll = sum(string_nll)
            loglikelihoods.append(string_nll)

        return loglikelihoods
Zhiwei Zhuang's avatar
Zhiwei Zhuang committed
872

873
874
875
876
877
878
879
880
881
882
883
884
885
    def _batch_scheduler(self, pos, n_reordered_requests):
        sched = pos // int(len(n_reordered_requests) / self.batch_schedule)
        if sched in self.batch_sizes:
            return self.batch_sizes[sched]
        if (len(self.batch_sizes) > 1) and (
            self.batch_sizes[sched - 1] == self.max_batch_size
        ):
            # if previous batch size is already maximal, skip recomputation
            self.batch_sizes[sched] = self.max_batch_size
            return self.batch_sizes[sched]
        print(
            f"Passed argument batch_size = auto:{self.batch_schedule}. Detecting largest batch size"
        )
Zhiwei Zhuang's avatar
Zhiwei Zhuang committed
886
        self.batch_sizes[sched] = self._detect_batch_size(n_reordered_requests, pos)
887
888
        print(f"Determined largest batch size: {self.batch_sizes[sched]}")
        return self.batch_sizes[sched]
889

Ethan Smith's avatar
Ethan Smith committed
890
    def _loglikelihood_tokens(
baberabb's avatar
baberabb committed
891
892
893
894
895
        self,
        requests: List[Tuple[Tuple[str, str], List[int], List[int]]],
        disable_tqdm: bool = False,
        override_bs: int = None,
    ) -> List[Tuple[float, bool]]:
896
897
898
        # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
        res = []

Baber Abbasi's avatar
Baber Abbasi committed
899
        def _collate(req: Tuple[Tuple[str, str], List[int], List[int]]):
Baber Abbasi's avatar
Baber Abbasi committed
900
            """Defines the key for the sorted method"""
901
902
903
904
905
906
907
            # the negative sign on len(toks) sorts descending - this has a few advantages:
            # - time estimates will always be over not underestimates, which is more useful for planning
            # - to know the size of a batch when going through the list, you know the first one is always the batch
            #   padded context length. this is useful to simplify the batching logic and more importantly to make
            #   automatic adaptive batches much much easier to implement
            # - any OOMs will happen right away rather than near the end

Baber Abbasi's avatar
Baber Abbasi committed
908
            toks = req[1] + req[2]
909
910
            return -len(toks), tuple(toks)

Baber Abbasi's avatar
Baber Abbasi committed
911
912
913
        def _lookup_one_token_cont(req: Tuple[Tuple[str, str], List[int], List[int]]):
            """Defines the key to group and lookup one-token continuations"""
            # Use with group_by="contexts" (optional)"
Baber Abbasi's avatar
Baber Abbasi committed
914
            # allows for the creation of a lookup, so we can reuse logits in case of one-token continuations.
Baber Abbasi's avatar
Baber Abbasi committed
915
916
917
918
919
920
921
922
923
924
925
926
927
            # speeds up some multiple-choice tasks proportionally to the number of choices.
            # groups requests by context+continuation[:-1] and infer on one request/group.
            return req[-2] + req[-1][:-1]

        re_ord = Collator(
            requests,
            sort_fn=_collate,
            group_by="contexts"
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
            and self.logits_cache
            else None,
            group_fn=_lookup_one_token_cont,
        )
Benjamin Fattori's avatar
Benjamin Fattori committed
928
929
930

        # automatic (variable) batch size detection for vectorization
        # pull longest context sample from request
Baber Abbasi's avatar
Baber Abbasi committed
931
932
933
        n_reordered_requests = len(re_ord)
        batch_size = (
            self.batch_size
934
935
936
            if self.batch_size != "auto"
            else override_bs
            if override_bs is not None
Baber Abbasi's avatar
Baber Abbasi committed
937
938
939
940
            else 0
        )
        batch_fn = (
            self._batch_scheduler
941
942
943
            if self.batch_size == "auto"
            and n_reordered_requests > 0
            and not override_bs
Baber Abbasi's avatar
Baber Abbasi committed
944
            else None
945
946
        )

Baber Abbasi's avatar
Baber Abbasi committed
947
        chunks = re_ord.get_batched(n=batch_size, batch_fn=batch_fn)
948
949
950
951
952
        pbar = tqdm(
            total=len(requests),
            disable=(disable_tqdm or (self.rank != 0)),
            desc="Running loglikelihood requests",
        )
haileyschoelkopf's avatar
haileyschoelkopf committed
953
        for chunk in chunks:
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
            inps = []
            cont_toks_list = []
            inplens = []

            conts = []
            encoder_attns = []

            padding_len_inp = None
            padding_len_cont = None
            # because vectorizing is annoying, we first convert each (context, continuation) pair to padded
            # tensors, then we pack them together into a batch, call the model, and then pick it all apart
            # again because vectorizing is annoying

            for _, context_enc, continuation_enc in chunk:
                # sanity check
                assert len(context_enc) > 0
                assert len(continuation_enc) > 0
                assert len(continuation_enc) <= self.max_length

haileyschoelkopf's avatar
haileyschoelkopf committed
973
                # how this all works (illustrated on a causal decoder-only setup):
974
975
976
977
978
979
980
981
982
983
984
                #          CTX      CONT
                # inp    0 1 2 3|4 5 6 7 8 9   <- last token is deleted by inp[:, :-1]
                # model  \               \
                # logits   1 2 3|4 5 6 7 8 9   <- the ctx half gets tossed out by the
                # cont_toks      4 5 6 7 8 9      [:, -len(continuation_enc):, :self.vocab_size] slice

                # when too long to fit in context, truncate from the left
                if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
                    inp = torch.tensor(
                        (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1],
                        dtype=torch.long,
985
986
                        device=self.device,
                    )
987
988
989
990
991
                    (inplen,) = inp.shape
                elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                    inp = torch.tensor(
                        (context_enc)[-self.max_length :],
                        dtype=torch.long,
haileyschoelkopf's avatar
haileyschoelkopf committed
992
                        device=self.device,
993
                    )
994
                    (inplen,) = inp.shape
995
996
997
998

                    # build encoder attn masks
                    encoder_attns.append(torch.ones_like(inp))

999
                    cont = torch.tensor(
haileyschoelkopf's avatar
haileyschoelkopf committed
1000
                        (continuation_enc)[-self.max_length :],
1001
1002
                        # TODO: left-shift these?
                        # TODO: our code assumes we never end up truncating conts for either model type
1003
                        dtype=torch.long,
1004
1005
                        device=self.device,
                    )
1006
1007
                    (contlen,) = cont.shape

1008
1009
                    conts.append(cont)

haileyschoelkopf's avatar
haileyschoelkopf committed
1010
1011
1012
1013
1014
                    padding_len_cont = (
                        max(padding_len_cont, contlen)
                        if padding_len_cont is not None
                        else contlen
                    )
1015

haileyschoelkopf's avatar
haileyschoelkopf committed
1016
1017
1018
1019
1020
                padding_len_inp = (
                    max(padding_len_inp, inplen)
                    if padding_len_inp is not None
                    else inplen
                )
1021
1022
1023
1024

                inps.append(inp)  # [1, inp_length]
                cont_toks_list.append(continuation_enc)
                inplens.append(inplen)
haileyschoelkopf's avatar
haileyschoelkopf committed
1025

1026
1027
1028
            # create encoder attn mask and batched conts, if seq2seq
            call_kwargs = {}
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
1029
                batched_inps = pad_and_concat(
haileyschoelkopf's avatar
haileyschoelkopf committed
1030
1031
                    padding_len_inp, inps, padding_side="right"
                )  # [batch, padding_len_inp]
1032
1033
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                # TODO: left-pad encoder inps and mask?
1034
                batched_inps = pad_and_concat(
haileyschoelkopf's avatar
haileyschoelkopf committed
1035
1036
                    padding_len_inp, inps
                )  # [batch, padding_len_inp]
1037
                batched_conts = pad_and_concat(
haileyschoelkopf's avatar
haileyschoelkopf committed
1038
1039
                    padding_len_cont, conts
                )  # [batch, padding_len_cont]
1040
                batched_encoder_mask = pad_and_concat(
haileyschoelkopf's avatar
haileyschoelkopf committed
1041
1042
1043
1044
1045
1046
                    padding_len_inp, encoder_attns
                )  # [batch, padding_len_inp]
                call_kwargs = {
                    "attn_mask": batched_encoder_mask,
                    "labels": batched_conts,
                }
1047
1048
1049

            multi_logits = F.log_softmax(
                self._model_call(batched_inps, **call_kwargs), dim=-1
1050
            )  # [batch, padding_length (inp or cont), vocab]
1051

Baber Abbasi's avatar
Baber Abbasi committed
1052
            for (request_str, ctx_tokens, _), logits, inplen, cont_toks in zip(
1053
1054
1055
1056
                chunk, multi_logits, inplens, cont_toks_list
            ):
                # Slice to original seq length
                contlen = len(cont_toks)
haileyschoelkopf's avatar
haileyschoelkopf committed
1057
                # take only logits in the continuation
1058
                # (discard context toks if decoder-only ; discard right-padding)
1059
1060
                # also discards + checks for "virtual tokens" in the causal LM's input window
                # from prompt/prefix tuning tokens, if applicable
haileyschoelkopf's avatar
haileyschoelkopf committed
1061
                ctx_len = (
1062
                    inplen + (logits.shape[0] - padding_len_inp)
haileyschoelkopf's avatar
haileyschoelkopf committed
1063
1064
1065
                    if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                    else None
                )
1066
                logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
haileyschoelkopf's avatar
haileyschoelkopf committed
1067
                logits = logits.unsqueeze(0)  # [1, seq, vocab]
1068
1069
1070
1071

                # Check if per-token argmax is exactly equal to continuation
                greedy_tokens = logits.argmax(dim=-1)

Baber Abbasi's avatar
Baber Abbasi committed
1072
1073
1074
1075
1076
1077
1078
1079
1080
1081
1082
1083
1084
1085
1086
1087
1088
1089
1090
1091
1092
1093
1094
1095
1096
1097
1098
1099
1100
                # check for one-token continuation cache hits.
                # noop in case group_by != "contexts" or no cache hit and returns the
                # original args. Otherwise, expands the logits batch dimension and yields each
                # batch along with matching continuation tokens and prompt strings.
                # logits -> [1, seq, vocab]
                for request_str, cont_toks, logits in re_ord.get_cache(
                    req_str=request_str,
                    cxt_toks=ctx_tokens,
                    cont_toks=cont_toks,
                    logits=logits,
                ):
                    cont_toks = torch.tensor(
                        cont_toks, dtype=torch.long, device=self.device
                    ).unsqueeze(0)  # [1, seq]
                    max_equal = (greedy_tokens == cont_toks).all()

                    # Obtain log-probs at the corresponding continuation token indices
                    # last_token_slice = logits[:, -1, :].squeeze(0).tolist()
                    logits = torch.gather(logits, 2, cont_toks.unsqueeze(-1)).squeeze(
                        -1
                    )  # [1, seq]

                    # Answer: (log prob, is-exact-match)
                    answer = (float(logits.sum()), bool(max_equal))

                    res.append(answer)

                    self.cache_hook.add_partial("loglikelihood", request_str, answer)
                    pbar.update(1)
haileyschoelkopf's avatar
haileyschoelkopf committed
1101
1102

        pbar.close()
haileyschoelkopf's avatar
haileyschoelkopf committed
1103

1104
1105
        return re_ord.get_original(res)

1106
1107
1108
    def generate_until(
        self, requests: List[Instance], disable_tqdm: bool = False
    ) -> List[str]:
Baber Abbasi's avatar
Baber Abbasi committed
1109
        res = []
1110

Baber Abbasi's avatar
Baber Abbasi committed
1111
        def _collate(req: Tuple[str, dict]):
Baber Abbasi's avatar
Baber Abbasi committed
1112
            """Defines the key for the sorted method"""
1113
1114
1115
1116
1117
1118
            # the negative sign on len(toks) sorts descending - this has a few advantages:
            # - time estimates will always be over not underestimates, which is more useful for planning
            # - to know the size of a batch when going through the list, you know the first one is always the batch
            #   padded context length. this is useful to simplify the batching logic and more importantly to make
            #   automatic adaptive batches much much easier to implement
            # - any OOMs will happen right away rather than near the end
Baber Abbasi's avatar
Baber Abbasi committed
1119
1120
            toks = self.tok_encode(req[0])
            return -len(toks), req[0]
1121

1122
1123
        pbar = tqdm(
            total=len(requests),
1124
            disable=(disable_tqdm or (self.rank != 0)),
1125
1126
            desc="Running generate_until requests",
        )
Baber Abbasi's avatar
Baber Abbasi committed
1127
        adaptive_batch_size = None
1128
1129
1130
1131
1132
1133
        if self.batch_size == "auto":
            # using rolling window with maximum context
            print("Passed argument batch_size = auto. Detecting largest batch size")
            batch_size = self._detect_batch_size()
            print(f"Determined Largest batch size: {batch_size}")
            adaptive_batch_size = batch_size
1134
        # for each different set of kwargs, we execute all requests, by batch.
Baber Abbasi's avatar
Baber Abbasi committed
1135
1136
1137
1138
1139
1140
1141
1142
1143
1144
1145
1146
        batch_size = (
            self.batch_size
            if self.batch_size != "auto"
            else adaptive_batch_size
            if adaptive_batch_size is not None
            else 0
        )
        batch_fn = (
            self._batch_scheduler
            if self.batch_size == "auto" and not adaptive_batch_size
            else None
        )
1147

Baber Abbasi's avatar
Baber Abbasi committed
1148
1149
1150
        # we group requests by their generation_kwargs,
        # so that we don't try to execute e.g. greedy sampling and temp=0.8 sampling
        # in the same batch.
Baber Abbasi's avatar
Baber Abbasi committed
1151
1152
1153
1154
1155
1156
1157
        # group_fn=lambda x: x[1] -> x=(context, gen_kwargs)
        re_ords = Collator(
            [reg.args for reg in requests],
            sort_fn=_collate,
            group_by="gen_kwargs",
            group_fn=lambda x: x[1],
        )
Baber Abbasi's avatar
Baber Abbasi committed
1158
1159
1160
1161
1162
1163
1164
1165
1166
1167
1168
1169
1170
        chunks = re_ords.get_batched(n=batch_size, batch_fn=batch_fn)
        for chunk in chunks:
            contexts, all_gen_kwargs = zip(*chunk)
            # we assume all gen kwargs in the batch are the same
            # this is safe to assume because the `grouper` object ensures it.
            gen_kwargs = all_gen_kwargs[0]
            # unpack our keyword arguments.
            until = None
            if isinstance(gen_kwargs, dict):
                kwargs = copy.deepcopy(gen_kwargs)  # edge case for repeats > 1
                if "until" in kwargs.keys():
                    until = kwargs.pop("until")
                    if isinstance(until, str):
achervyakov's avatar
achervyakov committed
1171
                        until = [until]
Baber Abbasi's avatar
Baber Abbasi committed
1172
1173
1174
1175
1176
1177
                    elif not isinstance(until, list):
                        raise ValueError(
                            f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}"
                        )
            else:
                raise ValueError(
Baber Abbasi's avatar
Baber Abbasi committed
1178
                    f"Expected `kwargs` to be of type `dict` but got {type(gen_kwargs)}"
1179
                )
1180
            # add EOS token to stop sequences
Lintang Sutawika's avatar
Lintang Sutawika committed
1181
            eos = self.tok_decode(self.eot_token_id, skip_special_tokens=False)
Baber Abbasi's avatar
Baber Abbasi committed
1182
            if not until:
1183
1184
1185
                until = [eos]
            else:
                until.append(eos)
Baber Abbasi's avatar
Baber Abbasi committed
1186
1187
1188
1189
1190
1191
1192
1193
1194
1195
1196
1197
1198
1199
1200
1201
1202
1203
1204
1205
1206
            if "max_gen_toks" in kwargs.keys():
                max_gen_toks = kwargs.pop("max_gen_toks")
            else:
                max_gen_toks = self.max_gen_toks

            # set the max length in tokens of inputs ("context_enc")
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
                # max len for inputs = max length, minus room to generate the max new tokens
                max_ctx_len = self.max_length - max_gen_toks
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                # max len for inputs = encoder's whole max_length
                max_ctx_len = self.max_length

            # encode, pad, and truncate contexts for this batch
            context_enc, attn_masks = self.tok_batch_encode(
                contexts,
                left_truncate_len=max_ctx_len,
                truncation=self.truncation,
            )
            context_enc = context_enc.to(self.device)
            attn_masks = attn_masks.to(self.device)
1207

Baber Abbasi's avatar
Baber Abbasi committed
1208
1209
            if "max_length" not in kwargs:
                kwargs["max_length"] = context_enc.shape[1] + max_gen_toks
1210

Baber Abbasi's avatar
Baber Abbasi committed
1211
1212
1213
1214
1215
1216
1217
            # perform batched generation
            cont = self._model_generate(
                context=context_enc,
                attention_mask=attn_masks,
                stop=until,
                **kwargs,
            )
1218

Baber Abbasi's avatar
Baber Abbasi committed
1219
1220
1221
1222
1223
            cont_toks_list = cont.tolist()
            for cont_toks, context in zip(cont_toks_list, contexts):
                # discard context + left-padding toks if using causal decoder-only LM
                if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
                    cont_toks = cont_toks[context_enc.shape[1] :]
1224

Baber Abbasi's avatar
Baber Abbasi committed
1225
                s = self.tok_decode(cont_toks)
1226

Baber Abbasi's avatar
Baber Abbasi committed
1227
1228
1229
1230
1231
1232
1233
1234
1235
1236
1237
1238
1239
                # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
                for term in until:
                    if len(term) > 0:
                        # ignore '' separator,
                        # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
                        s = s.split(term)[0]

                res.append(s)

                self.cache_hook.add_partial("generate_until", (context, gen_kwargs), s)
                pbar.update(1)
        # reorder this group of results back to original unsorted form
        res = re_ords.get_original(res)
1240

1241
        pbar.close()
1242

Baber Abbasi's avatar
Baber Abbasi committed
1243
        return res