huggingface.py

import os
from packaging import version
import torch
import transformers
from transformers.models.auto.modeling_auto import (
    MODEL_FOR_CAUSAL_LM_MAPPING_NAMES,
    MODEL_FOR_SEQ_TO_SEQ_CAUSAL_LM_MAPPING_NAMES,
)
from peft import __version__ as PEFT_VERSION, PeftModel

import copy
from collections import defaultdict
from tqdm import tqdm
from pathlib import Path

import torch.nn.functional as F

from lm_eval import utils
from lm_eval.api.instance import Instance
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, find_executable_batch_size, DistributedType
from typing import List, Optional, Union, Tuple

eval_logger = utils.eval_logger


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


@register_model("hf-auto", "hf", "huggingface")
class HFLM(LM):
    """
    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.
    """

    AUTO_MODEL_CLASS = None
    _DEFAULT_MAX_LENGTH = 2048

    def __init__(
        self,
        pretrained: Optional[str] = "gpt2",
        revision: Optional[str] = "main",
        subfolder: Optional[str] = None,
        tokenizer: Optional[str] = None,
        truncation: Optional[bool] = False,
        max_length: Optional[int] = None,
        device: Optional[str] = "cuda",
        dtype: Optional[Union[str, torch.dtype]] = "auto",
        batch_size: Optional[Union[int, str]] = 1,
        max_batch_size: Optional[int] = 64,
        low_cpu_mem_usage: Optional[bool] = True,
        trust_remote_code: Optional[bool] = False,
        use_fast_tokenizer: Optional[bool] = True,
        cache_dir: Optional[Union[str, os.PathLike]] = None,
        # arguments used for splitting a model across GPUs naively.
        # only used if `parallelize=True`.
        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,
        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,
    ) -> None:
        super().__init__()

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

        gpus = torch.cuda.device_count()
        accelerator = Accelerator()

        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"]
            )
            if device:
                if device not in device_list:
                    device = int(device)
                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
            self._device = device

        model_kwargs = {}
        if parallelize:
            model_kwargs = _get_accelerate_args(
                device_map_option,
                max_memory_per_gpu,
                max_cpu_memory,
                offload_folder,
            )

        # 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,
            trust_remote_code=trust_remote_code,
        )

        if (
            getattr(self._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,
        ]

        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:
            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,
                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
            )

        # forever after, access self._model through self.model property
        self.model.eval()
        self.model.tie_weights()
        if gpus <= 1 and not parallelize:
            # place model onto device, if not using HF Accelerate in any form
            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."
                )

        self.tokenizer = transformers.AutoTokenizer.from_pretrained(
            pretrained if tokenizer is None else tokenizer,
            revision=revision,
            trust_remote_code=trust_remote_code,
            use_fast=use_fast_tokenizer,
        )

        self.truncation = truncation

        self.vocab_size = self.tokenizer.vocab_size
        self.tokenizer.pad_token_id = self.tokenizer.eos_token_id

        self._max_length = max_length

        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)

        # 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:
                # TODO: make sure there's still never an edge case where we unintentionally default to CPU
                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")
                )
                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."
                    )
            else:
                assert accelerator.distributed_type in [
                    DistributedType.FSDP,
                    DistributedType.MULTI_GPU,
                ], "Unsupported distributed type provided. Only DDP and FSDP are supported."
                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}")
                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

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

    @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

    @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):
        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

    @property
    def max_gen_toks(self) -> int:
        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 _detect_batch_size(self, requests=None, pos: int = 0):
        if requests:
            _, context_enc, continuation_enc = requests[pos]
            max_length = len(
                (context_enc + continuation_enc)[-(self.max_length + 1) :][:-1]
            )
            max_context_enc = len(context_enc[-(self.max_length + 1) :])
            max_cont_enc = len(continuation_enc[-(self.max_length + 1) :])
        else:
            max_length = self.max_length

        # 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):
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                length = max(max_context_enc, max_cont_enc)
                batched_conts = torch.ones(
                    (batch_size, length), device=self.device
                ).long()
                test_batch = torch.ones((batch_size, length), device=self.device).long()
                call_kwargs = {
                    "attn_mask": test_batch,
                    "labels": batched_conts,
                }
            else:
                call_kwargs = {}
                test_batch = torch.ones(
                    (batch_size, max_length), device=self.device
                ).long()
            for _ in range(5):
                out = F.log_softmax(self._model_call(test_batch, **call_kwargs), dim=-1)
                out = out  # Identity process so that it passes pre-commit

            return batch_size

        batch_size = forward_batch()

        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()
        return batch_size

    def tok_encode(
        self, string: str, left_truncate_len=None, add_special_tokens=None
    ) -> List[int]:
        """ """
        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

        encoding = self.tokenizer.encode(string, add_special_tokens=add_special_tokens)

        # left-truncate the encoded context to be at most `left_truncate_len` tokens long
        if left_truncate_len:
            encoding = encoding[-left_truncate_len:]

        return encoding

    def tok_batch_encode(
        self,
        strings: List[str],
        padding_side: str = "left",
        left_truncate_len: int = None,
        truncation: bool = False,
    ) -> Tuple[List[int], List[int]]:
        # 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,
            truncation=truncation,
            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"]

    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):
        """
        :param inps: torch.Tensor
            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():
            if attn_mask is not None or labels is not None:
                assert attn_mask is not None and labels is not None
                assert self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM
                return self.model(
                    input_ids=inps, attention_mask=attn_mask, labels=labels
                ).logits
            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]
        )
        return self.model.generate(
            input_ids=context,
            max_length=max_length,
            stopping_criteria=stopping_criteria,
            pad_token_id=self.eot_token_id,
            use_cache=True,
            **generation_kwargs,
        )

    def _select_cont_toks(self, logits, contlen=None, inplen=None):
        if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
            assert (
                contlen and inplen
            ), "Must pass input len and cont. len to select scored logits for causal LM"
            # 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:
            assert (
                contlen and not inplen
            ), "Selecting scored logits for Seq2SeqLM requires only cont. len"
            # only discard right-padding.
            # the logits input to this fn only contain decoder-side tokens.
            logits = logits[:contlen]

        return logits

    def _encode_pair(
        self, context: str, continuation: str
    ) -> Tuple[List[int], List[int]]:
        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, 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)
        context_enc_len = len(context_enc)
        continuation_enc = whole_enc[context_enc_len:]
        return context_enc, continuation_enc

    def loglikelihood(self, requests: List[Instance]) -> List[Tuple[float, bool]]:
        new_reqs = []
        for context, continuation in [req.args for req in requests]:
            if context == "":
                # end of text as context
                context_enc, continuation_enc = [self.eot_token_id], self.tok_encode(
                    continuation
                )
            else:
                context_enc, continuation_enc = self._encode_pair(context, continuation)

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

        return self._loglikelihood_tokens(new_reqs)

    def loglikelihood_rolling(self, requests: List[Instance]) -> List[float]:
        loglikelihoods = []

        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

        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),
                        prefix_token=self.eot_token_id,
                        max_seq_len=self.max_length,
                        context_len=1,
                    ),
                )
            )

            # TODO: Right now, we pass single EOT token to the Encoder and the full context to the decoder, in seq2seq case
            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,
                override_bs=adaptive_batch_size,
            )

            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 _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"
        )
        self.batch_sizes[sched] = self._detect_batch_size(n_reordered_requests, pos)
        print(f"Determined largest batch size: {self.batch_sizes[sched]}")
        return self.batch_sizes[sched]

    def _loglikelihood_tokens(
        self,
        requests: List[Tuple[Tuple[str, str], List[int], List[int]]],
        disable_tqdm: bool = False,
        override_bs: int = None,
    ) -> List[Tuple[float, bool]]:
        # 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)

        n_reordered_requests = len(re_ord.get_reordered())
        # automatic (variable) batch size detection for vectorization
        # pull longest context sample from request

        chunks = utils.chunks(
            re_ord.get_reordered(),
            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,
        )

        pbar = tqdm(total=len(requests), disable=(disable_tqdm or (self.rank != 0)))
        for chunk in chunks:
            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

                # how this all works (illustrated on a causal decoder-only setup):
                #          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,
                        device=self.device,
                    )
                    (inplen,) = inp.shape
                elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                    inp = torch.tensor(
                        (context_enc)[-self.max_length :],
                        dtype=torch.long,
                        device=self.device,
                    )
                    (inplen,) = inp.shape

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

                    cont = torch.tensor(
                        (continuation_enc)[-self.max_length :],
                        # TODO: left-shift these?
                        # TODO: our code assumes we never end up truncating conts for either model type
                        dtype=torch.long,
                        device=self.device,
                    )
                    (contlen,) = cont.shape

                    conts.append(cont)

                    padding_len_cont = (
                        max(padding_len_cont, contlen)
                        if padding_len_cont is not None
                        else contlen
                    )

                padding_len_inp = (
                    max(padding_len_inp, inplen)
                    if padding_len_inp is not None
                    else inplen
                )

                inps.append(inp)  # [1, inp_length]
                cont_toks_list.append(continuation_enc)
                inplens.append(inplen)

            # create encoder attn mask and batched conts, if seq2seq
            call_kwargs = {}
            if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM:
                batched_inps = utils.pad_and_concat(
                    padding_len_inp, inps, padding_side="right"
                )  # [batch, padding_len_inp]
            elif self.AUTO_MODEL_CLASS == transformers.AutoModelForSeq2SeqLM:
                # TODO: left-pad encoder inps and mask?
                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,
                }

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

            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)
                # take only logits in the continuation
                # (discard context toks if decoder-only ; discard right-padding)
                # also discards + checks for "virtual tokens" in the causal LM's input window
                # from prompt/prefix tuning tokens, if applicable
                ctx_len = (
                    inplen + (logits.shape[0] - padding_len_inp)
                    if self.AUTO_MODEL_CLASS == transformers.AutoModelForCausalLM
                    else None
                )
                logits = self._select_cont_toks(logits, contlen=contlen, inplen=ctx_len)
                logits = logits.unsqueeze(0)  # [1, seq, vocab]

                # Check if per-token argmax is exactly equal to continuation
                greedy_tokens = logits.argmax(dim=-1)
                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", cache_key, answer)
                pbar.update(1)

        pbar.close()

        return re_ord.get_original(res)

    def generate_until(self, requests: List[Instance]) -> List[str]:
        res = defaultdict(list)
        re_ords = {}

        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 = self.tok_encode(x[0])
            return -len(toks), x[0]

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

        pbar = tqdm(total=len(requests), disable=(self.rank != 0))
        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
        # for each different set of kwargs, we execute all requests, by batch.
        for key, re_ord in re_ords.items():
            chunks = utils.chunks(
                re_ord.get_reordered(),
                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,
            )
            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):
                            until = [kwargs]
                        elif not isinstance(until, list):
                            raise ValueError(
                                f"Expected `kwargs['until']` to be of type Union[str,list] but got {until}"
                            )
                else:
                    raise ValueError(
                        f"Expected `kwargs` to be of type `dict` but got {kwargs}"
                    )
                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

                # 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)

                if "max_length" not in kwargs:
                    kwargs["max_length"] = context_enc.shape[1] + max_gen_toks

                # perform batched generation
                cont = self._model_generate(
                    context=context_enc,
                    attention_mask=attn_masks,
                    stop=until,
                    **kwargs,
                )

                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] :]

                    s = self.tok_decode(cont_toks)

                    # 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[key].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[key] = re_ord.get_original(res[key])

        pbar.close()

        return grouper.get_original(res)