huggingface.py 39.8 KB
Newer Older
1
2
import os

3
4
import torch
import transformers
5
6
7
8
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
)
9
from peft import __version__ as PEFT_VERSION, PeftModel
10
11

import copy
12
from collections import defaultdict
13
from tqdm import tqdm
14
from pathlib import Path
15
16
17
18
19
20
21
22
23
24

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

25
from accelerate import Accelerator, find_executable_batch_size, DistributedType
26
from typing import List, Optional, Union
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51


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
52
53


54
@register_model("hf-auto", "hf", "huggingface")
55
class HFLM(LM):
56
57
58
59
60
61
62
    """
    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.
    """

63
    AUTO_MODEL_CLASS = None
64
    _DEFAULT_MAX_LENGTH = 2048
haileyschoelkopf's avatar
haileyschoelkopf committed
65

66
67
    def __init__(
        self,
68
69
70
71
        pretrained: Optional[str] = "gpt2",
        revision: Optional[str] = "main",
        subfolder: Optional[str] = None,
        tokenizer: Optional[str] = None,
lintangsutawika's avatar
lintangsutawika committed
72
        truncation: Optional[bool] = False,
73
74
        max_length: Optional[int] = None,
        device: Optional[str] = "cuda",
75
        dtype: Optional[Union[str, torch.dtype]] = "auto",
Benjamin Fattori's avatar
Benjamin Fattori committed
76
77
        batch_size: Optional[Union[int, str]] = 1,
        max_batch_size: Optional[int] = 64,
78
79
        low_cpu_mem_usage: Optional[bool] = True,
        trust_remote_code: Optional[bool] = False,
haileyschoelkopf's avatar
haileyschoelkopf committed
80
        use_fast_tokenizer: Optional[bool] = True,
lintangsutawika's avatar
lintangsutawika committed
81
        cache_dir: Optional[Union[str, os.PathLike]] = None,
82
        # arguments used for splitting a model across GPUs naively.
83
84
        # only used if `parallelize=True`.
        parallelize: Optional[bool] = False,
85
86
87
88
        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",
89
90
91
92
93
94
95
96
        # 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,
Ethan Smith's avatar
Ethan Smith committed
97
    ) -> None:
98
99
100
101
        super().__init__()

        assert isinstance(device, str)
        assert isinstance(pretrained, str)
Benjamin Fattori's avatar
Benjamin Fattori committed
102
        assert isinstance(batch_size, (int, str))
103
104

        gpus = torch.cuda.device_count()
105
        accelerator = Accelerator()
haileyschoelkopf's avatar
haileyschoelkopf committed
106

107
        if not (parallelize or accelerator.num_processes > 1):
108
            # use user-passed device
109
            device_list = set(
110
                ["cuda", "cpu"]
111
                + [f"cuda:{i}" for i in range(torch.cuda.device_count())]
112
                + ["mps", "mps:0"]
113
            )
114
            if device:
115
                if device not in device_list:
116
117
118
                    device = int(device)
                self._device = torch.device(device)
                eval_logger.info(f"Using device '{device}'")
119
                if device in ("mps", "mps:0") and "dev" not in torch.__version__:
120
                    eval_logger.info(
121
122
123
                        "MPS: Setting dtype to float32. To use float16 with MPS, please install a nightly build of "
                        "PyTorch: pip3 install --pre torch torchvision torchaudio --index-url "
                        "https://download.pytorch.org/whl/nightly/cpu"
124
                    )
125
126
127
128
129
130
131
132
            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")
                )
133
        else:
134
135
136
137
            if device != "cuda":
                eval_logger.info(
                    f"Using `accelerate launch` or `parallelize=True`, device '{device}' will be overridden when placing model."
                )
138
            # TODO: include in warning that `load_in_8bit` etc. affect this too
139
140
141
            self._device = device

        model_kwargs = {}
142
        if parallelize:
143
144
145
146
147
148
            model_kwargs = _get_accelerate_args(
                device_map_option,
                max_memory_per_gpu,
                max_cpu_memory,
                offload_folder,
            )
149
150
151
152
153
154
155

        # 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,
156
            trust_remote_code=trust_remote_code,
157
158
159
160
        )

        if getattr(self._config, "model_type") in MODEL_FOR_CAUSAL_LM_MAPPING_NAMES:
            self.AUTO_MODEL_CLASS = transformers.AutoModelForCausalLM
161
162
163
164
165
166
167
168
169
170
171
172
        elif (
            not getattr(self._config, "model_type")
            in MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES
        ):
            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
173
        else:
haileyschoelkopf's avatar
haileyschoelkopf committed
174
            self.AUTO_MODEL_CLASS = transformers.AutoModelForSeq2SeqLM
175

haileyschoelkopf's avatar
haileyschoelkopf committed
176
177
178
179
        assert self.AUTO_MODEL_CLASS in [
            transformers.AutoModelForCausalLM,
            transformers.AutoModelForSeq2SeqLM,
        ]
180

181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
        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
205
206
207
208
209
210
211
            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]",
                )
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230

            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
            )

231
        # forever after, access self._model through self.model property
232
        self.model.eval()
233
234
235
        self.model.tie_weights()
        if gpus <= 1 and not parallelize:
            # place model onto device, if not using HF Accelerate in any form
236
237
238
239
240
241
            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
242

243
244
245
        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
            pretrained if tokenizer is None else tokenizer,
            revision=revision,
246
            trust_remote_code=trust_remote_code,
haileyschoelkopf's avatar
haileyschoelkopf committed
247
            use_fast=use_fast_tokenizer,
248
249
        )

lintangsutawika's avatar
lintangsutawika committed
250
251
        self.truncation = truncation

252
        self.vocab_size = self.tokenizer.vocab_size
haileyschoelkopf's avatar
haileyschoelkopf committed
253
        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id
254

255
256
        self._max_length = max_length

Benjamin Fattori's avatar
Benjamin Fattori committed
257
258
259
260
261
262
263
264
265
266
        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)
267
268
269
270
271
272
273
274
275
276
277

        # 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:
278
                # TODO: make sure there's still never an edge case where we unintentionally default to CPU
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
                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")
                )
294
295
296
297
298
299
                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."
                    )
300
            else:
Hailey Schoelkopf's avatar
Hailey Schoelkopf committed
301
                assert accelerator.distributed_type in [
lintangsutawika's avatar
lintangsutawika committed
302
303
                    DistributedType.FSDP,
                    DistributedType.MULTI_GPU,
Hailey Schoelkopf's avatar
Hailey Schoelkopf committed
304
                ], "Unsupported distributed type provided. Only DDP and FSDP are supported."
305
                if accelerator.distributed_type == DistributedType.FSDP:
Hailey Schoelkopf's avatar
Hailey Schoelkopf committed
306
                    self._model = accelerator.prepare(self.model)
307
308
                else:
                    self._model = accelerator.prepare_model(
lintangsutawika's avatar
lintangsutawika committed
309
                        self.model, evaluation_mode=True
310
                    )
311
312
313
314
315
316
317
318
                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
319

320
321
322
323
324
    @property
    def config(self):
        # return the associated transformers.AutoConfig for the given pretrained model.
        return self._config

325
326
327
328
329
330
331
332
    @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

333
334
335
336
337
338
339
    @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):
340
341
342
343
344
345
346
347
348
349
350
        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
351

352
    @property
Ethan Smith's avatar
Ethan Smith committed
353
    def max_gen_toks(self) -> int:
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
        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

Ethan Smith's avatar
Ethan Smith committed
372
    def _detect_batch_size(self, requests=None, pos: int = 0):
Benjamin Fattori's avatar
Benjamin Fattori committed
373
374
375
376
377
        if requests:
            _, context_enc, continuation_enc = requests[pos]
            max_length = len(
                (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1]
            )
378
379
            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
380
381
        else:
            max_length = self.max_length
lintangsutawika's avatar
lintangsutawika committed
382

Benjamin Fattori's avatar
Benjamin Fattori committed
383
384
385
        # 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):
386
387
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                length = max(max_context_enc, max_cont_enc)
lintangsutawika's avatar
lintangsutawika committed
388
389
390
                batched_conts = torch.ones(
                    (batch_size, length), device=self.device
                ).long()
391
392
                test_batch = torch.ones((batch_size, length), device=self.device).long()
                call_kwargs = {
lintangsutawika's avatar
lintangsutawika committed
393
394
395
                    "attn_mask": test_batch,
                    "labels": batched_conts,
                }
396
397
            else:
                call_kwargs = {}
lintangsutawika's avatar
lintangsutawika committed
398
399
400
                test_batch = torch.ones(
                    (batch_size, max_length), device=self.device
                ).long()
Benjamin Fattori's avatar
Benjamin Fattori committed
401
            for _ in range(5):
402
                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)
lintangsutawika's avatar
lintangsutawika committed
403
404
                out = out  # Identity process so that it passes pre-commit

Benjamin Fattori's avatar
Benjamin Fattori committed
405
406
407
408
            return batch_size

        batch_size = forward_batch()

409
410
411
412
413
414
415
416
417
418
419
        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)
            utils.clear_torch_cache()
            return batch_size

        utils.clear_torch_cache()
Benjamin Fattori's avatar
Benjamin Fattori committed
420
421
        return batch_size

422
    def tok_encode(self, string: str, left_truncate_len=None, add_special_tokens=None):
haileyschoelkopf's avatar
haileyschoelkopf committed
423
        """ """
424
425
426
427
428
        if add_special_tokens is None:
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
                add_special_tokens = False
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                add_special_tokens = True
429
430

        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)
haileyschoelkopf's avatar
haileyschoelkopf committed
431

432
433
434
        # 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
435

436
437
        return encoding

haileyschoelkopf's avatar
haileyschoelkopf committed
438
    def tok_batch_encode(
lintangsutawika's avatar
lintangsutawika committed
439
440
        self,
        strings: List[str],
lintangsutawika's avatar
lintangsutawika committed
441
        padding_side: str = "left",
442
443
        left_truncate_len: int = None,
        truncation: bool = False,
haileyschoelkopf's avatar
haileyschoelkopf committed
444
445
446
447
448
449
450
451
452
453
454
455
    ):
        # 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,
lintangsutawika's avatar
lintangsutawika committed
456
            truncation=truncation,
haileyschoelkopf's avatar
haileyschoelkopf committed
457
458
459
460
461
462
463
464
465
466
467
468
469
            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"]

470
471
472
473
474
475
476
477
    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
478
        :param inps: torch.Tensor
479
480
481
482
483
484
485
486
487
488
489
490
491
            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():
492
493
            if attn_mask is not None or labels is not None:
                assert attn_mask is not None and labels is not None
494
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
haileyschoelkopf's avatar
haileyschoelkopf committed
495
496
497
                return self.model(
                    input_ids=inps, attention_mask=attn_mask, labels=labels
                ).logits
498
499
500
501
502
503
504
505
506
507
508
509
510
            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]
        )
511
        return self.model.generate(
512
            input_ids=context,
513
514
515
516
517
518
            max_length=max_length,
            stopping_criteria=stopping_criteria,
            pad_token_id=self.eot_token_id,
            use_cache=True,
            **generation_kwargs,
        )
519
520
521

    def _select_cont_toks(self, logits, contlen=None, inplen=None):
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
522
523
524
            assert (
                contlen and inplen
            ), "Must pass input len and cont. len to select scored logits for causal LM"
525
526
527
528
            # 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
529
530
531
532
            assert (
                contlen and not inplen
            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
            # only discard right-padding.
533
            # the logits input to this fn only contain decoder-side tokens.
haileyschoelkopf's avatar
haileyschoelkopf committed
534
535
            logits = logits[:contlen]

536
537
        return logits

538
539
540
541
542
    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]
543
544
545
546
547
548

        whole_enc = self.tok_encode(context + continuation, add_special_tokens=False)
        context_enc = self.tok_encode(context, add_special_tokens=False)

        # whole_enc = self.tok_encode(context + continuation)
        # context_enc = self.tok_encode(context, add_special_tokens=False)
549
550
551
552
        context_enc_len = len(context_enc)
        continuation_enc = whole_enc[context_enc_len:]
        return context_enc, continuation_enc

553
554
555
556
557
    def loglikelihood(self, requests):
        new_reqs = []
        for context, continuation in [req.args for req in requests]:
            if context == "":
                # end of text as context
558
559
560
                context_enc, continuation_enc = [self.eot_token_id], self.tok_encode(
                    continuation
                )
561
            else:
562
                context_enc, continuation_enc = self._encode_pair(context, continuation)
563
564
565
566
567
568
569

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

        return self._loglikelihood_tokens(new_reqs)

    def loglikelihood_rolling(self, requests):
        loglikelihoods = []
Benjamin Fattori's avatar
Benjamin Fattori committed
570
571
572
573
574
575
576
577
578

        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

579
580
581
582
583
584
        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
585
                        prefix_token=self.eot_token_id,
586
587
588
589
590
                        max_seq_len=self.max_length,
                        context_len=1,
                    ),
                )
            )
haileyschoelkopf's avatar
haileyschoelkopf committed
591
592

            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
            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(
lintangsutawika's avatar
lintangsutawika committed
608
609
610
                rolling_token_windows,
                disable_tqdm=True,
                override_bs=adaptive_batch_size,
611
612
613
614
615
616
617
618
619
620
621
622
            )

            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
623

624
625
626
627
628
629
630
631
632
633
634
635
636
    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
637
        self.batch_sizes[sched] = self._detect_batch_size(n_reordered_requests, pos)
638
639
        print(f"Determined largest batch size: {self.batch_sizes[sched]}")
        return self.batch_sizes[sched]
640

Ethan Smith's avatar
Ethan Smith committed
641
642
643
    def _loglikelihood_tokens(
        self, requests, disable_tqdm: bool = False, override_bs=None
    ):
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
        # 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)

        re_ord = utils.Reorderer(requests, _collate)
Benjamin Fattori's avatar
Benjamin Fattori committed
659
660
661
662

        n_reordered_requests = len(re_ord.get_reordered())
        # automatic (variable) batch size detection for vectorization
        # pull longest context sample from request
lintangsutawika's avatar
lintangsutawika committed
663

664
665
        chunks = utils.chunks(
            re_ord.get_reordered(),
666
667
668
669
670
671
672
673
674
675
            n=self.batch_size
            if self.batch_size != "auto"
            else override_bs
            if override_bs is not None
            else 0,
            fn=self._batch_scheduler
            if self.batch_size == "auto"
            and n_reordered_requests > 0
            and not override_bs
            else None,
676
677
        )

haileyschoelkopf's avatar
haileyschoelkopf committed
678
        pbar = tqdm(total=len(requests), disable=(disable_tqdm or (self.rank != 0)))
haileyschoelkopf's avatar
haileyschoelkopf committed
679
        for chunk in chunks:
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
            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
699
                # how this all works (illustrated on a causal decoder-only setup):
700
701
702
703
704
705
706
707
708
709
710
                #          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,
711
712
                        device=self.device,
                    )
713
714
715
716
717
                    (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
718
                        device=self.device,
719
                    )
720
                    (inplen,) = inp.shape
721
722
723
724

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

725
                    cont = torch.tensor(
haileyschoelkopf's avatar
haileyschoelkopf committed
726
                        (continuation_enc)[-self.max_length :],
727
728
                        # TODO: left-shift these?
                        # TODO: our code assumes we never end up truncating conts for either model type
729
                        dtype=torch.long,
730
731
                        device=self.device,
                    )
732
733
                    (contlen,) = cont.shape

734
735
                    conts.append(cont)

haileyschoelkopf's avatar
haileyschoelkopf committed
736
737
738
739
740
                    padding_len_cont = (
                        max(padding_len_cont, contlen)
                        if padding_len_cont is not None
                        else contlen
                    )
741

haileyschoelkopf's avatar
haileyschoelkopf committed
742
743
744
745
746
                padding_len_inp = (
                    max(padding_len_inp, inplen)
                    if padding_len_inp is not None
                    else inplen
                )
747
748
749
750

                inps.append(inp)  # [1, inp_length]
                cont_toks_list.append(continuation_enc)
                inplens.append(inplen)
haileyschoelkopf's avatar
haileyschoelkopf committed
751

752
753
754
            # create encoder attn mask and batched conts, if seq2seq
            call_kwargs = {}
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
haileyschoelkopf's avatar
haileyschoelkopf committed
755
756
757
                batched_inps = utils.pad_and_concat(
                    padding_len_inp, inps, padding_side="right"
                )  # [batch, padding_len_inp]
758
759
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                # TODO: left-pad encoder inps and mask?
haileyschoelkopf's avatar
haileyschoelkopf committed
760
761
762
763
764
765
766
767
768
769
770
771
772
                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,
                }
773
774
775

            multi_logits = F.log_softmax(
                self._model_call(batched_inps, **call_kwargs), dim=-1
776
            )  # [batch, padding_length (inp or cont), vocab]
777
778
779
780
781
782

            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
783
                # take only logits in the continuation
784
                # (discard context toks if decoder-only ; discard right-padding)
785
786
                # 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
787
                ctx_len = (
788
                    inplen + (logits.shape[0] - padding_len_inp)
haileyschoelkopf's avatar
haileyschoelkopf committed
789
790
791
                    if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                    else None
                )
792
                logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
haileyschoelkopf's avatar
haileyschoelkopf committed
793
                logits = logits.unsqueeze(0)  # [1, seq, vocab]
794
795
796

                # Check if per-token argmax is exactly equal to continuation
                greedy_tokens = logits.argmax(dim=-1)
797
798
799
                cont_toks = torch.tensor(
                    cont_toks, dtype=torch.long, device=self.device
                ).unsqueeze(
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
                    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
815
                self.cache_hook.add_partial("loglikelihood", cache_key, answer)
haileyschoelkopf's avatar
haileyschoelkopf committed
816
817
818
                pbar.update(1)

        pbar.close()
haileyschoelkopf's avatar
haileyschoelkopf committed
819

820
821
        return re_ord.get_original(res)

822
    def generate_until(self, requests):
823
824
        res = defaultdict(list)
        re_ords = {}
825
826

        def _collate(x):
827
828
829
830
831
832
            # 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
833
            toks = self.tok_encode(x[0])
haileyschoelkopf's avatar
haileyschoelkopf committed
834
            return -len(toks), x[0]
835

836
837
838
        # 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.
839
840
        grouper = utils.Grouper(requests, lambda x: str(x.args[1]))
        for key, reqs in grouper.get_grouped().items():
841
            # within each set of reqs for given kwargs, we reorder by token length, descending.
842
            re_ords[key] = utils.Reorderer([req.args for req in reqs], _collate)
843

844
        pbar = tqdm(total=len(requests), disable=(self.rank != 0))
845
846
847
848
849
850
        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
851
        # for each different set of kwargs, we execute all requests, by batch.
852
        for key, re_ord in re_ords.items():
853
854
            chunks = utils.chunks(
                re_ord.get_reordered(),
855
856
857
858
859
860
861
862
                n=self.batch_size
                if self.batch_size != "auto"
                else adaptive_batch_size
                if adaptive_batch_size is not None
                else 0,
                fn=self._batch_scheduler
                if self.batch_size == "auto" and not adaptive_batch_size
                else None,
863
            )
haileyschoelkopf's avatar
haileyschoelkopf committed
864
            for chunk in chunks:
865
                contexts, all_gen_kwargs = zip(*chunk)
866
867
868
869
                # 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.
870
871
872
873
874
875
876
877
878
                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(
879
                                f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}"
880
881
882
                            )
                else:
                    raise ValueError(
883
                        f"Expected `kwargs` to be of type `dict` but got {kwargs}"
884
885
886
887
888
889
890
891
                    )
                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
892
                primary_until = [until[0]]
893

894
                # set the max length in tokens of inputs ("context_enc")
haileyschoelkopf's avatar
haileyschoelkopf committed
895
                if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
896
897
898
899
900
                    # 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
901

902
                # encode, pad, and truncate contexts for this batch
903
                context_enc, attn_masks = self.tok_batch_encode(
lintangsutawika's avatar
lintangsutawika committed
904
905
906
                    contexts,
                    left_truncate_len=max_ctx_len,
                    truncation=self.truncation,
907
908
909
910
                )
                context_enc = context_enc.to(self.device)
                attn_masks = attn_masks.to(self.device)

911
                if "max_length" not in kwargs:
Lintang Sutawika's avatar
Lintang Sutawika committed
912
                    kwargs["max_length"] = context_enc.shape[1] + max_gen_toks
913

914
                # perform batched generation
915
916
917
918
919
920
                cont = self._model_generate(
                    context=context_enc,
                    attention_mask=attn_masks,
                    stop=primary_until,
                    **kwargs,
                )
921

922
923
924
925
926
                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] :]
927

928
                    s = self.tok_decode(cont_toks)
929

930
931
                    # use secondary stop seqs to cut off should-have-been-stopped content post-hoc
                    for term in until:
932
933
934
                        if len(term) > 0:
                            # ignore '' separator,
                            # for seq2seq case where self.tok_decode(self.eot_token_id) = ''
935
                            s = s.split(term)[0]
936

937
                    res[key].append(s)
938

939
                    self.cache_hook.add_partial(
940
                        "generate_until", (context, gen_kwargs), s
941
942
                    )
                    pbar.update(1)
943
            # reorder this group of results back to original unsorted form
944
            res[key] = re_ord.get_original(res[key])
945

946
        pbar.close()
947

948
        return grouper.get_original(res)