huggingface.py 32.6 KB
Newer Older
1
2
3
import torch
import transformers
from transformers.models.auto.modeling_auto import MODEL_FOR_CAUSAL_LM_MAPPING_NAMES
4
from peft import __version__ as PEFT_VERSION, PeftModel
5
6

import copy
7
from collections import defaultdict
8
from tqdm import tqdm
9
from pathlib import Path
10
11
12
13
14
15
16
17
18
19
20

import torch.nn.functional as F

from lm_eval import utils
from lm_eval.logger import eval_logger
from lm_eval.api.model import LM
from lm_eval.api.registry import register_model

from lm_eval.utils import MultiTokenEOSCriteria, stop_sequences_criteria

from accelerate import Accelerator
21
from typing import List, Optional, Union
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46


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
47
48


49
@register_model("hf-auto", "hf", "huggingface")
50
class HFLM(LM):
51
52
53
54
55
56
57
    """
    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.
    """

58
    AUTO_MODEL_CLASS = None
59
    _DEFAULT_MAX_LENGTH = 2048
haileyschoelkopf's avatar
haileyschoelkopf committed
60

61
62
    def __init__(
        self,
63
64
65
66
67
68
        pretrained: Optional[str] = "gpt2",
        revision: Optional[str] = "main",
        subfolder: Optional[str] = None,
        tokenizer: Optional[str] = None,
        max_length: Optional[int] = None,
        device: Optional[str] = "cuda",
69
        dtype: Optional[Union[str, torch.dtype]] = "auto",
70
71
72
        batch_size: Optional[int] = 1,
        low_cpu_mem_usage: Optional[bool] = True,
        trust_remote_code: Optional[bool] = False,
haileyschoelkopf's avatar
haileyschoelkopf committed
73
        use_fast_tokenizer: Optional[bool] = True,
74
        # arguments used for splitting a model across GPUs naively.
75
76
        # only used if `parallelize=True`.
        parallelize: Optional[bool] = False,
77
78
79
80
        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",
81
82
83
84
85
86
87
88
        # PEFT and quantization options
        peft: Optional[str] = None,
        load_in_8bit: Optional[bool] = False,
        load_in_4bit: Optional[bool] = False,
        bnb_4bit_quant_type: Optional[str] = None,
        bnb_4bit_compute_dtype: Optional[Union[str, torch.dtype]] = None,
        gptq: Optional[Union[bool, str]] = False,
        gptq_use_triton: Optional[bool] = False,
89
90
91
92
93
94
95
96
    ):
        super().__init__()

        assert isinstance(device, str)
        assert isinstance(pretrained, str)
        assert isinstance(batch_size, int)

        gpus = torch.cuda.device_count()
97
        accelerator = Accelerator()
haileyschoelkopf's avatar
haileyschoelkopf committed
98

99
        if not (parallelize or accelerator.num_processes > 1):
100
            # use user-passed device
101
            device_list = set(
baberabb's avatar
add mps  
baberabb committed
102
                ["cuda", "cpu", "mps"]
103
104
                + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
            )
105
            if device:
106
                if device not in device_list:
107
108
109
                    device = int(device)
                self._device = torch.device(device)
                eval_logger.info(f"Using device '{device}'")
110
111
                if device == "mps":
                    eval_logger.info(
baberabb's avatar
baberabb committed
112
                        "MPS is still in beta and only supports float32; setting dtype to float32."
113
                    )
114
115
116
117
118
119
120
121
            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")
                )
122
        else:
123
124
125
126
            if device != "cuda":
                eval_logger.info(
                    f"Using `accelerate launch` or `parallelize=True`, device '{device}' will be overridden when placing model."
                )
127
            # TODO: include in warning that `load_in_8bit` etc. affect this too
128
129
130
            self._device = device

        model_kwargs = {}
131
        if parallelize:
132
133
134
135
136
137
            model_kwargs = _get_accelerate_args(
                device_map_option,
                max_memory_per_gpu,
                max_cpu_memory,
                offload_folder,
            )
138
139
140
141
142
143
144

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

        self._config = transformers.AutoConfig.from_pretrained(
            pretrained,
            revision=revision,
145
            trust_remote_code=trust_remote_code,
146
147
148
149
150
        )

        if getattr(self._config, "model_type") in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
            self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM
        else:
haileyschoelkopf's avatar
haileyschoelkopf committed
151
            self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
152

haileyschoelkopf's avatar
haileyschoelkopf committed
153
154
155
156
        assert self.AUTO_MODEL_CLASS in [
            transformers.AutoModelForCausalLM,
            transformers.AutoModelForSeq2SeqLM,
        ]
157

158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
        if not gptq:
            if load_in_4bit:
                assert (
                    transformers.__version__ >= "4.30.0"
                ), "load_in_4bit requires transformers >= 4.30.0"
            if transformers.__version__ >= "4.30.0":
                model_kwargs["load_in_4bit"] = load_in_4bit
                if load_in_4bit:
                    if bnb_4bit_quant_type:
                        model_kwargs["bnb_4bit_quant_type"] = bnb_4bit_quant_type
                    if bnb_4bit_compute_dtype:
                        model_kwargs["bnb_4bit_compute_dtype"] = utils.get_dtype(
                            bnb_4bit_compute_dtype
                        )
            self._model = self.AUTO_MODEL_CLASS.from_pretrained(
                pretrained,
                revision=revision,
                torch_dtype=utils.get_dtype(dtype),
                low_cpu_mem_usage=low_cpu_mem_usage,
                trust_remote_code=trust_remote_code,
                load_in_8bit=load_in_8bit,
                **model_kwargs,
            )
        else:
gk's avatar
gk committed
182
183
184
185
186
187
188
            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]",
                )
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207

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

        if peft:
            if load_in_4bit:
                assert PEFT_VERSION >= "0.4.0", "load_in_4bit requires peft >= 0.4.0"
            self._model = PeftModel.from_pretrained(
                self._model, peft, revision=revision
            )

208
        # forever after, access self._model through self.model property
209
        self.model.eval()
210
211
212
        self.model.tie_weights()
        if gpus <= 1 and not parallelize:
            # place model onto device, if not using HF Accelerate in any form
213
214
215
216
217
218
            try:
                self.model.to(self.device)
            except ValueError:
                eval_logger.info(
                    "Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes`. If the desired GPU is being used, this message is safe to ignore."
                )
haileyschoelkopf's avatar
haileyschoelkopf committed
219

220
221
222
        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
            pretrained if tokenizer is None else tokenizer,
            revision=revision,
223
            trust_remote_code=trust_remote_code,
haileyschoelkopf's avatar
haileyschoelkopf committed
224
            use_fast=use_fast_tokenizer,
225
226
227
        )

        self.vocab_size = self.tokenizer.vocab_size
haileyschoelkopf's avatar
haileyschoelkopf committed
228
        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
229

230
231
        self._max_length = max_length

232
        # multithreading and batching
233
234
235
236
237
238
239
240
241
242
243
244
        self.batch_size_per_gpu = batch_size

        # 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 gpus > accelerator.num_processes:
245
                # TODO: make sure there's still never an edge case where we unintentionally default to CPU
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
                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."
                )
                self._rank = accelerator.local_process_index
                self._world_size = accelerator.num_processes
                # manually set model to use gpu, for case where many GPUs available but
                # only seek to use one
                self._device = (
                    torch.device(f"cuda:{accelerator.local_process_index}")
                    if torch.cuda.is_available()
                    else torch.device("cpu")
                )
261
262
263
264
265
266
                try:
                    self.model.to(self.device)
                except ValueError:
                    eval_logger.info(
                        "Failed to place model onto specified device. This may be because the model is quantized via `bitsandbytes`. If the desired GPU is being used, this message is safe to ignore."
                    )
267
            else:
haileyschoelkopf's avatar
haileyschoelkopf committed
268
                self._model = accelerator.prepare(self.model)
269
270
271
272
273
274
275
276
                self._device = torch.device(f"cuda:{accelerator.local_process_index}")
                self.accelerator = accelerator

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

                self._rank = self.accelerator.local_process_index
                self._world_size = self.accelerator.num_processes
haileyschoelkopf's avatar
haileyschoelkopf committed
277

278
279
280
281
282
    @property
    def config(self):
        # return the associated transformers.AutoConfig for the given pretrained model.
        return self._config

283
284
285
286
287
288
289
290
    @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

291
292
293
294
295
296
297
    @property
    def eot_token_id(self):
        # we use EOT because end of *text* is more accurate for what we're doing than end of *sentence*
        return self.tokenizer.eos_token_id

    @property
    def max_length(self):
298
299
300
301
302
303
304
305
306
307
308
        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
309

310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
    @property
    def max_gen_toks(self):
        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

    def tok_encode(self, string: str, left_truncate_len=None):
haileyschoelkopf's avatar
haileyschoelkopf committed
331
        """ """
332
333
334
335
336
337
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
            add_special_tokens = False
        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
            add_special_tokens = True

        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
haileyschoelkopf's avatar
haileyschoelkopf committed
338

339
340
341
        # 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
342

343
344
        return encoding

haileyschoelkopf's avatar
haileyschoelkopf committed
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
    def tok_batch_encode(
        self, strings: List[str], padding_side="left", left_truncate_len=None
    ):
        # 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

        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
            add_special_tokens = False
        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
            add_special_tokens = True

        encoding = self.tokenizer(
            strings,
            padding="longest",
            return_tensors="pt",
            add_special_tokens=add_special_tokens,
        )
        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"]

372
373
374
375
376
377
378
379
    def tok_decode(self, tokens):
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
            return self.tokenizer.decode(tokens)
        elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
            return self.tokenizer.decode(tokens, skip_special_tokens=True)

    def _model_call(self, inps, attn_mask=None, labels=None):
        """
haileyschoelkopf's avatar
haileyschoelkopf committed
380
        :param inps: torch.Tensor
381
382
383
384
385
386
387
388
389
390
391
392
393
            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():
394
395
            if attn_mask is not None or labels is not None:
                assert attn_mask is not None and labels is not None
396
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
haileyschoelkopf's avatar
haileyschoelkopf committed
397
398
399
                return self.model(
                    input_ids=inps, attention_mask=attn_mask, labels=labels
                ).logits
400
401
402
403
404
405
406
407
408
409
410
411
412
            else:
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                return self.model(inps).logits

    def _model_generate(self, context, max_length, stop, **generation_kwargs):
        # we require users to pass do_sample=True explicitly
        # for non-greedy gen. This should be reevaluated when considering beam search.
        if "do_sample" not in generation_kwargs.keys():
            generation_kwargs["do_sample"] = False
        # build stopping criteria
        stopping_criteria = stop_sequences_criteria(
            self.tokenizer, stop, 1, context.shape[0]
        )
413
414
415
416
417
418
419
420
        return self.model.generate(
            context,
            max_length=max_length,
            stopping_criteria=stopping_criteria,
            pad_token_id=self.eot_token_id,
            use_cache=True,
            **generation_kwargs,
        )
421
422
423

    def _select_cont_toks(self, logits, contlen=None, inplen=None):
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
424
425
426
            assert (
                contlen and inplen
            ), "Must pass input len and cont. len to select scored logits for causal LM"
427
428
429
430
            # 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
431
432
433
434
            assert (
                contlen and not inplen
            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
            # only discard right-padding.
435
            # the logits input to this fn only contain decoder-side tokens.
haileyschoelkopf's avatar
haileyschoelkopf committed
436
437
            logits = logits[:contlen]

438
439
        return logits

440
441
442
443
444
445
446
447
448
449
450
    def _encode_pair(self, context, continuation):
        n_spaces = len(context) - len(context.rstrip())
        if n_spaces > 0:
            continuation = context[-n_spaces:] + continuation
            context = context[:-n_spaces]
        whole_enc = self.tok_encode(context + continuation)
        context_enc = self.tok_encode(context)
        context_enc_len = len(context_enc)
        continuation_enc = whole_enc[context_enc_len:]
        return context_enc, continuation_enc

451
452
453
454
455
    def loglikelihood(self, requests):
        new_reqs = []
        for context, continuation in [req.args for req in requests]:
            if context == "":
                # end of text as context
456
457
458
                context_enc, continuation_enc = [self.eot_token_id], self.tok_encode(
                    continuation
                )
459
            else:
460
                context_enc, continuation_enc = self._encode_pair(context, continuation)
461
462
463
464
465
466
467
468
469
470
471
472
473

            new_reqs.append(((context, continuation), context_enc, continuation_enc))

        return self._loglikelihood_tokens(new_reqs)

    def loglikelihood_rolling(self, requests):
        loglikelihoods = []
        for (string,) in tqdm([req.args for req in requests], disable=(self.rank != 0)):
            rolling_token_windows = list(
                map(
                    utils.make_disjoint_window,
                    utils.get_rolling_token_windows(
                        token_list=self.tok_encode(string),
haileyschoelkopf's avatar
haileyschoelkopf committed
474
                        prefix_token=self.eot_token_id,
475
476
477
478
479
                        max_seq_len=self.max_length,
                        context_len=1,
                    ),
                )
            )
haileyschoelkopf's avatar
haileyschoelkopf committed
480
481

            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
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
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
            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(
                rolling_token_windows, disable_tqdm=True
            )

            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

    def _loglikelihood_tokens(self, requests, disable_tqdm=False):
        # TODO: implement some kind of efficient-request-middleware that lumps together requests with the same context
        res = []

        def _collate(x):
            # the negative sign on len(toks) sorts descending - this has a few advantages:
            # - time estimates will always be over not underestimates, which is more useful for planning
            # - to know the size of a batch when going through the list, you know the first one is always the batch
            #   padded context length. this is useful to simplify the batching logic and more importantly to make
            #   automatic adaptive batches much much easier to implement
            # - any OOMs will happen right away rather than near the end

            toks = x[1] + x[2]
            return -len(toks), tuple(toks)

        # TODO: automatic (variable) batch size detection for vectorization
        re_ord = utils.Reorderer(requests, _collate)
        for chunk in utils.chunks(
            tqdm(re_ord.get_reordered(), disable=(disable_tqdm or (self.rank != 0))),
            self.batch_size,
        ):
            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
551
                # how this all works (illustrated on a causal decoder-only setup):
552
553
554
555
556
557
558
559
560
561
562
                #          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,
563
564
                        device=self.device,
                    )
565
566
567
568
569
                    (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
570
                        device=self.device,
571
                    )
572
                    (inplen,) = inp.shape
573
574
575
576

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

577
                    cont = torch.tensor(
haileyschoelkopf's avatar
haileyschoelkopf committed
578
                        (continuation_enc)[-self.max_length :],
579
580
                        # TODO: left-shift these?
                        # TODO: our code assumes we never end up truncating conts for either model type
581
                        dtype=torch.long,
582
583
                        device=self.device,
                    )
584
585
                    (contlen,) = cont.shape

586
587
                    conts.append(cont)

haileyschoelkopf's avatar
haileyschoelkopf committed
588
589
590
591
592
                    padding_len_cont = (
                        max(padding_len_cont, contlen)
                        if padding_len_cont is not None
                        else contlen
                    )
593

haileyschoelkopf's avatar
haileyschoelkopf committed
594
595
596
597
598
                padding_len_inp = (
                    max(padding_len_inp, inplen)
                    if padding_len_inp is not None
                    else inplen
                )
599
600
601
602

                inps.append(inp)  # [1, inp_length]
                cont_toks_list.append(continuation_enc)
                inplens.append(inplen)
haileyschoelkopf's avatar
haileyschoelkopf committed
603

604
605
606
            # create encoder attn mask and batched conts, if seq2seq
            call_kwargs = {}
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
607
608
609
                batched_inps = utils.pad_and_concat(
                    padding_len_inp, inps, padding_side="right"
                )  # [batch, padding_len_inp]
610
611
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                # TODO: left-pad encoder inps and mask?
haileyschoelkopf's avatar
haileyschoelkopf committed
612
613
614
615
616
617
618
619
620
621
622
623
624
                batched_inps = utils.pad_and_concat(
                    padding_len_inp, inps
                )  # [batch, padding_len_inp]
                batched_conts = utils.pad_and_concat(
                    padding_len_cont, conts
                )  # [batch, padding_len_cont]
                batched_encoder_mask = utils.pad_and_concat(
                    padding_len_inp, encoder_attns
                )  # [batch, padding_len_inp]
                call_kwargs = {
                    "attn_mask": batched_encoder_mask,
                    "labels": batched_conts,
                }
625
626
627

            multi_logits = F.log_softmax(
                self._model_call(batched_inps, **call_kwargs), dim=-1
628
            )  # [batch, padding_length (inp or cont), vocab]
629
630
631
632
633
634

            for (cache_key, _, _), logits, inplen, cont_toks in zip(
                chunk, multi_logits, inplens, cont_toks_list
            ):
                # Slice to original seq length
                contlen = len(cont_toks)
haileyschoelkopf's avatar
haileyschoelkopf committed
635
                # take only logits in the continuation
636
                # (discard context toks if decoder-only ; discard right-padding)
haileyschoelkopf's avatar
haileyschoelkopf committed
637
638
639
640
641
                ctx_len = (
                    inplen
                    if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                    else None
                )
642
                logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
haileyschoelkopf's avatar
haileyschoelkopf committed
643
                logits = logits.unsqueeze(0)  # [1, seq, vocab]
644
645
646

                # Check if per-token argmax is exactly equal to continuation
                greedy_tokens = logits.argmax(dim=-1)
647
648
649
                cont_toks = torch.tensor(
                    cont_toks, dtype=torch.long, device=self.device
                ).unsqueeze(
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
                    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)

haileyschoelkopf's avatar
haileyschoelkopf committed
665
666
                self.cache_hook.add_partial("loglikelihood", cache_key, answer)

667
668
669
        return re_ord.get_original(res)

    def greedy_until(self, requests):
670
671
        res = defaultdict(list)
        re_ords = {}
672
673

        def _collate(x):
674
675
676
677
678
679
            # 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
680
            toks = self.tok_encode(x[0])
haileyschoelkopf's avatar
haileyschoelkopf committed
681
            return -len(toks), x[0]
682

683
684
685
        # 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.
686
687
        grouper = utils.Grouper(requests, lambda x: str(x.args[1]))
        for key, reqs in grouper.get_grouped().items():
688
            # within each set of reqs for given kwargs, we reorder by token length, descending.
689
            re_ords[key] = utils.Reorderer([req.args for req in reqs], _collate)
690

691
692
693
        pbar = tqdm(total=len(requests), disable=(self.rank != 0))

        # for each different set of kwargs, we execute all requests, by batch.
694
695
        for key, re_ord in re_ords.items():
            for chunk in utils.chunks(
haileyschoelkopf's avatar
haileyschoelkopf committed
696
                re_ord.get_reordered(),
697
698
699
                self.batch_size,
            ):
                contexts, all_gen_kwargs = zip(*chunk)
700
701
702
703
                # 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.
704
705
706
707
708
709
710
711
712
                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):
                            until = [kwargs]
                        elif not isinstance(until, list):
                            raise ValueError(
713
                                f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}"
714
715
716
                            )
                else:
                    raise ValueError(
717
                        f"Expected `kwargs` to be of type `dict` but got {kwargs}"
718
719
720
721
722
723
724
725
726
                    )
                if not until:
                    until = [self.tok_decode(self.eot_token_id)]
                if "max_gen_toks" in kwargs.keys():
                    max_gen_toks = kwargs.pop("max_gen_toks")
                else:
                    max_gen_toks = self.max_gen_toks
                # first stop sequence is used to halt generation upon encountering
                (primary_until) = until[0]
727

728
                # set the max length in tokens of inputs ("context_enc")
haileyschoelkopf's avatar
haileyschoelkopf committed
729
                if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
730
731
732
733
734
                    # 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
735

736
                # encode, pad, and truncate contexts for this batch
737
738
739
740
741
742
                context_enc, attn_masks = self.tok_batch_encode(
                    contexts, left_truncate_len=max_ctx_len
                )
                context_enc = context_enc.to(self.device)
                attn_masks = attn_masks.to(self.device)

743
                # perform batched generation
744
745
746
747
748
749
750
                cont = self._model_generate(
                    context=context_enc,
                    attention_mask=attn_masks,
                    max_length=context_enc.shape[1] + max_gen_toks,
                    stop=primary_until,
                    **kwargs,
                )
751

752
753
754
755
756
                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] :]
757

758
                    s = self.tok_decode(cont_toks)
759

760
761
                    # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
                    for term in until:
762
763
764
                        if len(term) > 0:
                            # ignore '' separator,
                            # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
765
                            s = s.split(term)[0]
766

767
                    res[key].append(s)
768

769
770
771
772
                    self.cache_hook.add_partial(
                        "greedy_until", (context, gen_kwargs), s
                    )
                    pbar.update(1)
773
            # reorder this group of results back to original unsorted form
774
            res[key] = re_ord.get_original(res[key])
775

776
        pbar.close()
777

778
        return grouper.get_original(res)