aphrodite-engine/aphrodite/modeling/sampling_metadata.py

from array import array
from dataclasses import dataclass
from typing import Dict, List, Optional, Tuple

import torch

from aphrodite.common.sampling_params import SamplingParams, SamplingType
from aphrodite.common.sequence import SequenceData, SequenceGroupMetadata
from aphrodite.common.utils import (PyObjectCache, async_tensor_h2d,
                                    is_pin_memory_available,
                                    make_tensor_with_pad)
from aphrodite.constants import APHRODITE_TOKEN_ID_ARRAY_TYPE

_SAMPLING_EPS = 1e-5


@dataclass
class SequenceGroupToSample:
    # |---------- N-1 iteration --------|
    # |---------------- N iteration ---------------------|
    # |- tokenA -|......................|-- newTokens ---|
    # |---------- context_len ----------|
    # |-------------------- seq_len ----------------------|
    #                                   |-- query_len ---|
    # Sequence ids for the sequence group in a previous step.
    seq_ids: List[int]
    sampling_params: SamplingParams
    # seq_id -> sequence data.
    seq_data: Dict[int, SequenceData]
    # The length of the sequence (all tokens seen in the past + new token to
    # compute attention) of the sequence group. None if it is in a decode
    # stage.
    seq_len: Optional[int]
    # The length of new query tokens to compute in the current step. None if it
    # is in a decode stage. The length of query_len <= seq_len if chunked
    # prefill is enabled.
    query_len: Optional[int]
    # A random number generator for sampling.
    generator: Optional[torch.Generator]
    # True if the sequence group is in prefill stage. False if it is in a
    # decode stage.
    is_prompt: bool
    # Query token indices from logits. to compute prompt logprob. Empty if
    # prompt logprob is not required.
    prompt_logprob_indices: List[int]
    # Sample token indices from logits. Empty if sampling is not required.
    sample_indices: List[int]

    @property
    def do_sample(self):
        return len(self.sample_indices) > 0

    def __post_init__(self):
        if len(self.prompt_logprob_indices) > 0:
            assert self.sampling_params.prompt_logprobs is not None
        if self.is_prompt:
            assert self.seq_len is not None
            assert self.query_len is not None


def gen_seq_group_to_sample_builder(num_seqs: int):
    return lambda: SequenceGroupToSample(
        seq_ids=[0] * num_seqs,
        sampling_params=None,
        seq_data=None,  # type: ignore
        seq_len=0,
        query_len=0,
        generator=None,
        is_prompt=True,
        prompt_logprob_indices=[],
        sample_indices=[])


class SamplingMetadataCache:
    """Used to cache SamplingMetadata objects between scheduler iterations
    """

    def __init__(self):
        self._seq_group_to_sample_cache: Dict[int, PyObjectCache] = {}

    def get_cached_seq_group_to_sample(self, num_seqs):
        if num_seqs not in self._seq_group_to_sample_cache:
            self._seq_group_to_sample_cache[num_seqs] = PyObjectCache(
                gen_seq_group_to_sample_builder(num_seqs))

        obj = self._seq_group_to_sample_cache[num_seqs].get_object()
        return obj

    def reset(self):
        for cache in self._seq_group_to_sample_cache.values():
            cache.reset()


class SamplingMetadata:
    """Metadata for input sequences. Used in sampler.

    The usage is as follow;
    ```
    hidden_states = execute_model(...)
    logits = hidden_states[sampling_metadata.selected_token_indices]
    sample(logits)

    def sample(logits):
        # Use categorized_sample_indices for sampling....
    ```

    Args:
        seq_groups: List of batched sequence groups.
        selected_token_indices: (num_query_tokens_to_logprob). Indices to find
            logits from the initial model output hidden states.
        categorized_sample_indices: SamplingType -> token indices to sample.
            Each token indices is 2D tensor of (num_indices, num_indices) where
            the first item means the sample index within the returned logit
            (before pruning padding), and the second item means the sample
            index after pruning using selected_token_indices.
            For example, if the returned logit is [1, 2, 3], and we select
            [1, 2] for sampling, the pruned logit will be [2, 3]. In this case,
            The first tuple is [1, 2] (sampled index within original logit),
            and the second tuple is [0, 1] (sampled index within pruned logit).
        num_prompts: Number of prompt sequence groups in seq_groups.
        skip_sampler_cpu_output: Indicates if we want to skip the GPU=>CPU 
            serialization of token outputs.
        reuse_sampling_tensors: Indicates if we want to reuse sampling 
            tensors that are part of the sampler forward pass. Currently,
            it is mainly used for multi-step decode.
    """

    def __init__(
        self,
        seq_groups: List[SequenceGroupToSample],
        selected_token_indices: torch.Tensor,
        categorized_sample_indices: Dict[SamplingType, torch.Tensor],
        num_prompts: int,
        skip_sampler_cpu_output: bool = False,
        reuse_sampling_tensors: bool = False,
    ) -> None:
        self.seq_groups = seq_groups
        self.selected_token_indices = selected_token_indices
        self.categorized_sample_indices = categorized_sample_indices
        self.num_prompts = num_prompts
        self.skip_sampler_cpu_output = skip_sampler_cpu_output
        self.reuse_sampling_tensors = reuse_sampling_tensors

    @staticmethod
    def prepare(
        seq_group_metadata_list: List[SequenceGroupMetadata],
        seq_lens: List[int],
        query_lens: Optional[List[int]],
        device: str,
        pin_memory: bool,
        generators: Optional[Dict[str, torch.Generator]] = None,
        cache: Optional[SamplingMetadataCache] = None
    ) -> "SamplingMetadata":
        (
            seq_groups,
            selected_token_indices,
            categorized_sample_indices,
            num_prompts,
        ) = _prepare_seq_groups(seq_group_metadata_list, seq_lens, query_lens,
                                device, generators, cache)
        selected_token_indices = async_tensor_h2d(selected_token_indices,
                                                  dtype=torch.long,
                                                  target_device=device,
                                                  pin_memory=pin_memory)
        categorized_sample_indices = {
            t: async_tensor_h2d(
                seq_ids,
                dtype=torch.int,
                target_device=device,
                pin_memory=pin_memory,
            )
            for t, seq_ids in categorized_sample_indices.items()
        }

        sampling_metadata = SamplingMetadata(
            seq_groups=seq_groups,
            selected_token_indices=selected_token_indices,
            categorized_sample_indices=categorized_sample_indices,
            num_prompts=num_prompts,
        )
        return sampling_metadata

    def __repr__(self) -> str:
        return (
            "SamplingMetadata("
            f"seq_groups={self.seq_groups}, "
            f"selected_token_indices={self.selected_token_indices}, "
            f"categorized_sample_indices={self.categorized_sample_indices}), ")


def _prepare_seq_groups(
    seq_group_metadata_list: List[SequenceGroupMetadata],
    seq_lens: List[int],
    query_lens: Optional[List[int]],
    device: str,
    generators: Optional[Dict[str, torch.Generator]] = None,
    cache: Optional[SamplingMetadataCache] = None,
) -> Tuple[List[SequenceGroupToSample], List[int], Dict[SamplingType,
                                                        List[int]], int, ]:
    """Prepare sequence groups and indices for sampling.

    Args:
        seq_group_metadata_list: A list of sequence group to batch.
        seq_lens: A list of sequence lens per sequence group.
            Index of prompt len should match with seq_group_metadata_list.
        query_lens: A list of query lengths. Prompt lens include the length
            of entire prompt tokens, and it could be shorter.
        device: A device to use for random number generators,
            `SequenceGroupToSample.generator`.
        generators: A store of per-request random number generators used
            for seeded requests.

    Returns:
        seq_groups: A list of sequence group to sample.
        selected_token_indices: See the definition from `SamplingMetadata`.
        categorized_sample_indices: See the definition from `SamplingMetadata`.
        num_prompts: Total number of prompts from `seq_group_metadata_list`.
    """
    # Batched sequence groups for the current model forward stsep.
    seq_groups: List[SequenceGroupToSample] = []
    # A list of token indices to sample/compute logprob. It is used to
    # prune the outcome logits from the model for the performance.
    selected_token_indices: List[int] = []
    # Used for selected_token_indices.
    model_output_idx = 0

    # Sampling type -> (
    # indices to sample/prompt logprob within pruned output logits,
    # indices to sample within pruned logits)
    categorized_sample_indices: Dict[SamplingType, List[int]] = {
        t: []
        for t in SamplingType
    }
    # Index of logits to compute logprob. Logits include both prompt logprob
    # and sample logprob indices.
    logit_idx = 0
    # Total number of prompts from given sequence groups.
    num_prompts = 0

    for i, seq_group_metadata in enumerate(seq_group_metadata_list):
        seq_ids = seq_group_metadata.seq_data.keys()

        if cache is not None:
            sample_obj = cache.get_cached_seq_group_to_sample(len(seq_ids))

            for j, seq_id in enumerate(seq_ids):
                sample_obj.seq_ids[j] = seq_id

            sample_obj.prompt_logprob_indices.clear()
            sample_obj.sample_indices.clear()
        sampling_params = seq_group_metadata.sampling_params
        is_prompt = seq_group_metadata.is_prompt
        generator: Optional[torch.Generator] = None
        # If the current seq group is in decode stage, it is None.
        seq_len: Optional[int] = None
        query_len: Optional[int] = None
        prompt_logprob_indices: List[int] = (sample_obj.prompt_logprob_indices
                                             if cache is not None else [])
        sample_indices: List[int] = (sample_obj.sample_indices
                                     if cache is not None else [])
        do_sample = seq_group_metadata.do_sample

        if seq_group_metadata.is_prompt:
            if sampling_params.seed is not None:
                generator = torch.Generator(device=device).manual_seed(
                    sampling_params.seed)
                if generators is not None:
                    generators[seq_group_metadata.request_id] = generator

            num_prompts += 1
            num_prefill_sample = len(seq_ids)
            assert num_prefill_sample == 1
            assert query_lens is not None and seq_lens is not None
            query_len, seq_len = query_lens[i], seq_lens[i]
            # If we need sampling, exclude num_prefill_sample tokens from
            # prompt logprob.
            prompt_logprob_len = (query_len - num_prefill_sample
                                  if do_sample else query_len)
            sample_len = num_prefill_sample if do_sample else 0
        else:
            # Decode
            prompt_logprob_len = 0
            sample_len = len(seq_ids) if do_sample else 0

            if sampling_params.seed is not None and generators is not None:
                generator = generators.get(seq_group_metadata.request_id)

        # Update indices to select from the model output.
        """
        This blocks computes selected_token_indices which is used in the
        following way.

        hidden_states = model(...)
        logits = hidden_states[selected_token_indices]
        """

        if sampling_params.prompt_logprobs is not None:
            selected_token_indices.extend(
                range(model_output_idx, model_output_idx + prompt_logprob_len))
        model_output_idx += prompt_logprob_len
        if do_sample:
            selected_token_indices.extend(
                range(model_output_idx, model_output_idx + sample_len))
        model_output_idx += sample_len

        # We now find indices for logprob computation and sampling.
        """
        This block computes categorized_sample_indices which is used in the
        following way.

        hidden_states = model(...)
        logits = hidden_states[selected_token_indices]
        def sample(logits):
           # Use categorized_sample_indices for sampling.
           # prompt_logprob_indices to find prompt logprob indices.
           # sample_indices to find sample indices.
        """

        if sampling_params.prompt_logprobs is not None:
            prompt_logprob_indices.extend(
                range(logit_idx, logit_idx + prompt_logprob_len))
            logit_idx += prompt_logprob_len
        if do_sample:
            sample_indices.extend(range(logit_idx, logit_idx + sample_len))
            categorized_sample_indices[sampling_params.sampling_type].extend(
                list(range(logit_idx, logit_idx + sample_len)))

        if cache is not None:
            sample_obj.sampling_params = sampling_params
            sample_obj.seq_data = seq_group_metadata.seq_data
            sample_obj.seq_len = seq_len
            sample_obj.query_len = query_len
            sample_obj.generator = generator
            sample_obj.is_prompt = is_prompt
        else:
            sample_obj = SequenceGroupToSample(
                seq_ids=list(seq_ids),
                sampling_params=sampling_params,
                seq_data=seq_group_metadata.seq_data,
                seq_len=seq_len,
                query_len=query_len,
                generator=generator,
                is_prompt=is_prompt,
                prompt_logprob_indices=list(prompt_logprob_indices),
                sample_indices=list(sample_indices),
            )

        seq_groups.append(sample_obj)

    if cache is not None:
        cache.reset()
    return (seq_groups, selected_token_indices, categorized_sample_indices,
            num_prompts)


@dataclass
class SamplingTensors:
    """Tensors for sampling."""

    temperatures: torch.Tensor
    dynatemp_mins: torch.Tensor
    dynatemp_maxs: torch.Tensor
    dynatemp_exps: torch.Tensor
    temperature_lasts: torch.Tensor
    top_ps: torch.Tensor
    top_ks: torch.Tensor
    top_as: torch.Tensor
    min_ps: torch.Tensor
    presence_penalties: torch.Tensor
    frequency_penalties: torch.Tensor
    repetition_penalties: torch.Tensor
    no_repeat_ngram_sizes: torch.Tensor
    tfss: torch.Tensor
    eta_cutoffs: torch.Tensor
    epsilon_cutoffs: torch.Tensor
    typical_ps: torch.Tensor
    smoothing_factors: torch.Tensor
    smoothing_curves: torch.Tensor
    xtc_thresholds: torch.Tensor
    xtc_probabilities: torch.Tensor
    nsigmas: torch.Tensor
    dry_multipliers: torch.Tensor
    dry_bases: torch.Tensor
    dry_allowed_lengths: torch.Tensor
    dry_sequence_breaker_ids: torch.Tensor
    dry_ranges: torch.Tensor
    skews: torch.Tensor
    prompt_tokens: torch.Tensor
    output_tokens: torch.Tensor

    @classmethod
    def from_sampling_metadata(
        cls,
        sampling_metadata: "SamplingMetadata",
        vocab_size: int,
        device: torch.device,
        dtype: torch.dtype,
    ) -> Tuple["SamplingTensors", bool, bool, bool, bool, bool, bool, bool,
               bool, bool, bool, bool, bool, bool, bool, bool, bool]:
        prompt_tokens: List[array] = []
        output_tokens: List[array] = []
        top_ks: List[int] = []
        temperatures: List[float] = []
        dynatemp_mins: List[float] = []
        dynatemp_maxs: List[float] = []
        dynatemp_exps: List[float] = []
        temperature_lasts: List[bool] = []
        top_ps: List[float] = []
        top_as: List[float] = []
        min_ps: List[float] = []
        presence_penalties: List[float] = []
        frequency_penalties: List[float] = []
        repetition_penalties: List[float] = []
        no_repeat_ngram_sizes: List[int] = []
        tfss: List[float] = []
        eta_cutoffs: List[float] = []
        epsilon_cutoffs: List[float] = []
        typical_ps: List[float] = []
        smoothing_factors: List[float] = []
        smoothing_curves: List[float] = []
        xtc_thresholds: List[float] = []
        xtc_probabilities: List[float] = []
        nsigmas: List[float] = []
        dry_multipliers: List[float] = []
        dry_bases: List[float] = []
        dry_allowed_lengths: List[int] = []
        dry_sequence_breaker_ids: List[List[int]] = []
        dry_ranges: List[int] = []
        skews: List[float] = []

        do_penalties = False
        do_no_repeat_ngrams = False
        do_temperatures = False
        do_top_p_top_k = False
        do_top_as = False
        do_min_p = False
        do_tfss = False
        do_eta_cutoffs = False
        do_epsilon_cutoffs = False
        do_typical_ps = False
        do_quadratic = False
        do_xtc = False
        do_nsigmas = False
        do_dry = False
        do_skews = False
        do_temp_last = False

        assert sampling_metadata.seq_groups is not None
        for seq_group in sampling_metadata.seq_groups:
            seq_ids = seq_group.seq_ids
            params = seq_group.sampling_params

            # k should not be greater than the vocab size.
            top_k = min(params.top_k, vocab_size)
            top_k = vocab_size if top_k == -1 else top_k

            temperature = params.temperature
            if temperature < _SAMPLING_EPS:
                # NOTE: Zero temperature means deterministic sampling
                # (i.e., greedy sampling or beam search).
                # Set the temperature to 1 to avoid division by zero.
                temperature = 1.0

            do_temperatures |= (temperature != 1.0 or
                                params.dynatemp_min > _SAMPLING_EPS or
                                params.dynatemp_max > _SAMPLING_EPS)
            do_top_p_top_k |= (params.top_p < 1.0 - _SAMPLING_EPS or
                               top_k != vocab_size)
            do_top_as |= params.top_a > 0.0
            do_min_p |= params.min_p > _SAMPLING_EPS
            do_penalties |= (abs(params.presence_penalty) >= _SAMPLING_EPS or
                             abs(params.frequency_penalty) >= _SAMPLING_EPS or
                             params.repetition_penalty > 1.0)
            do_no_repeat_ngrams |= params.no_repeat_ngram_size > 0
            do_tfss |= params.tfs < 1.0 - _SAMPLING_EPS
            do_eta_cutoffs |= params.eta_cutoff > _SAMPLING_EPS
            do_epsilon_cutoffs |= params.epsilon_cutoff > _SAMPLING_EPS
            do_typical_ps |= params.typical_p < 1.0 - _SAMPLING_EPS
            do_quadratic |= (params.smoothing_factor > _SAMPLING_EPS or
                             params.smoothing_curve > 1.0)
            do_xtc |= params.xtc_probability > _SAMPLING_EPS
            do_nsigmas |= params.nsigma > _SAMPLING_EPS
            do_dry |= params.dry_multiplier > _SAMPLING_EPS
            do_skews |= abs(params.skew) > _SAMPLING_EPS

            do_temp_last |= params.temperature_last

            wants_prompt_logprobs = params.prompt_logprobs is not None

            n_seqs = 0
            if seq_group.is_prompt and wants_prompt_logprobs:
                assert seq_group.query_len is not None
                n_seqs += len(seq_group.prompt_logprob_indices)

            if seq_group.do_sample:
                assert len(seq_group.sample_indices) == len(seq_ids)
                n_seqs += len(seq_ids)

            temperatures += [temperature] * n_seqs
            dynatemp_mins += [params.dynatemp_min] * n_seqs
            dynatemp_maxs += [params.dynatemp_max] * n_seqs
            dynatemp_exps += [params.dynatemp_exponent] * n_seqs
            temperature_lasts += [params.temperature_last] * n_seqs
            top_ps += [params.top_p] * n_seqs
            top_ks += [top_k] * n_seqs
            top_as += [params.top_a] * n_seqs
            min_ps += [params.min_p] * n_seqs
            presence_penalties += [params.presence_penalty] * n_seqs
            frequency_penalties += [params.frequency_penalty] * n_seqs
            repetition_penalties += [params.repetition_penalty] * n_seqs
            no_repeat_ngram_sizes += [params.no_repeat_ngram_size] * n_seqs
            tfss += [params.tfs] * n_seqs
            eta_cutoffs += [params.eta_cutoff] * n_seqs
            epsilon_cutoffs += [params.epsilon_cutoff] * n_seqs
            typical_ps += [params.typical_p] * n_seqs
            smoothing_factors += [params.smoothing_factor] * n_seqs
            smoothing_curves += [params.smoothing_curve] * n_seqs
            xtc_thresholds += [params.xtc_threshold] * n_seqs
            xtc_probabilities += [params.xtc_probability] * n_seqs
            nsigmas += [params.nsigma] * n_seqs
            dry_multipliers += [params.dry_multiplier] * n_seqs
            dry_bases += [params.dry_base] * n_seqs
            dry_allowed_lengths += [params.dry_allowed_length] * n_seqs
            dry_sequence_breaker_ids += (
                [params.dry_sequence_breaker_ids] * n_seqs)
            dry_ranges += [params.dry_range] * n_seqs
            skews += [params.skew] * n_seqs

        if do_penalties or do_dry or do_no_repeat_ngrams:
            for seq_group in sampling_metadata.seq_groups:
                seq_ids = seq_group.seq_ids
                if (seq_group.is_prompt
                        and params.prompt_logprobs is not None):
                    prefill_len = len(seq_group.prompt_logprob_indices)
                    prompt_tokens.extend(
                        array(APHRODITE_TOKEN_ID_ARRAY_TYPE)
                        for _ in range(prefill_len))
                    output_tokens.extend(
                        array(APHRODITE_TOKEN_ID_ARRAY_TYPE)
                        for _ in range(prefill_len))
                if seq_group.do_sample:
                    for seq_id in seq_ids:
                        seq_data = seq_group.seq_data[seq_id]
                        prompt_tokens.append(seq_data.prompt_token_ids_array)
                        output_tokens.append(seq_data.output_token_ids_array)

        sampling_tensors = SamplingTensors.from_lists(
            temperatures,
            dynatemp_mins,
            dynatemp_maxs,
            dynatemp_exps,
            temperature_lasts,
            top_ps,
            top_ks,
            top_as,
            min_ps,
            presence_penalties,
            frequency_penalties,
            repetition_penalties,
            no_repeat_ngram_sizes,
            tfss,
            eta_cutoffs,
            epsilon_cutoffs,
            typical_ps,
            smoothing_factors,
            smoothing_curves,
            xtc_thresholds,
            xtc_probabilities,
            nsigmas,
            dry_multipliers,
            dry_bases,
            dry_allowed_lengths,
            dry_sequence_breaker_ids,
            dry_ranges,
            skews,
            prompt_tokens,
            output_tokens,
            vocab_size,
            device,
            dtype)
        return (
            sampling_tensors,
            do_penalties,
            do_no_repeat_ngrams,
            do_temperatures,
            do_top_p_top_k,
            do_top_as,
            do_min_p,
            do_tfss,
            do_eta_cutoffs,
            do_epsilon_cutoffs,
            do_typical_ps,
            do_quadratic,
            do_xtc,
            do_nsigmas,
            do_dry,
            do_skews,
            do_temp_last)

    @classmethod
    def from_lists(
        cls,
        temperatures: List[float],
        dynatemp_mins: List[float],
        dynatemp_maxs: List[float],
        dynatemp_exps: List[float],
        temperature_lasts: List[bool],
        top_ps: List[float],
        top_ks: List[int],
        top_as: List[float],
        min_ps: List[float],
        presence_penalties: List[float],
        frequency_penalties: List[float],
        repetition_penalties: List[float],
        no_repeat_ngram_sizes: List[int],
        tfss: List[float],
        eta_cutoffs: List[float],
        epsilon_cutoffs: List[float],
        typical_ps: List[float],
        smoothing_factors: List[float],
        smoothing_curves: List[float],
        xtc_thresholds: List[float],
        xtc_probabilities: List[float],
        nsigmas: List[float],
        dry_multipliers: List[float],
        dry_bases: List[float],
        dry_allowed_lengths: List[int],
        dry_sequence_breaker_ids: List[List[int]],
        dry_ranges: List[int],
        skews: List[float],
        prompt_tokens: List[array],
        output_tokens: List[array],
        vocab_size: int,
        device: torch.device,
        dtype: torch.dtype) -> "SamplingTensors":
        # Note that the performance will be very bad without
        # pinned memory.
        pin_memory = is_pin_memory_available()
        do_penalties = prompt_tokens or output_tokens

        if do_penalties:
            prompt_t = make_tensor_with_pad(
                prompt_tokens,
                vocab_size,
                device="cpu",
                dtype=torch.int64,
                pin_memory=pin_memory,
            )
            output_t = make_tensor_with_pad(
                output_tokens,
                vocab_size,
                device="cpu",
                dtype=torch.int64,
                pin_memory=pin_memory,
            )
        else:
            empty_tensor = torch.empty(0, device=device, dtype=torch.long)
            prompt_t = empty_tensor
            output_t = empty_tensor

        temperatures_t = torch.tensor(
            temperatures,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        dynatemp_mins_t = torch.tensor(
            dynatemp_mins,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        dynatemp_maxs_t = torch.tensor(
            dynatemp_maxs,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        dynatemp_exps_t = torch.tensor(
            dynatemp_exps,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        temp_lasts_t = torch.tensor(
            temperature_lasts,
            device="cpu",
            dtype=torch.bool,
            pin_memory=pin_memory,
        )
        top_ps_t = torch.tensor(
            top_ps,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        top_as_t = torch.tensor(top_as,
                                device="cpu",
                                dtype=dtype,
                                pin_memory=pin_memory)
        min_ps_t = torch.tensor(
            min_ps,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        presence_penalties_t = torch.tensor(
            presence_penalties,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        frequency_penalties_t = torch.tensor(
            frequency_penalties,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        repetition_penalties_t = torch.tensor(
            repetition_penalties,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )
        no_repeat_ngram_sizes_t = torch.tensor(
            no_repeat_ngram_sizes,
            device="cpu",
            dtype=torch.int,
            pin_memory=pin_memory,
        )
        top_ks_t = torch.tensor(
            top_ks,
            device="cpu",
            dtype=torch.int,
            pin_memory=pin_memory,
        )
        tfss_t = torch.tensor(tfss,
                              device="cpu",
                              dtype=dtype,
                              pin_memory=pin_memory)
        eta_cutoffs_t = torch.tensor(eta_cutoffs,
                                     device="cpu",
                                     dtype=dtype,
                                     pin_memory=pin_memory)
        epsilon_cutoffs_t = torch.tensor(epsilon_cutoffs,
                                         device="cpu",
                                         dtype=dtype,
                                         pin_memory=pin_memory)
        typical_ps_t = torch.tensor(typical_ps,
                                    device="cpu",
                                    dtype=dtype,
                                    pin_memory=pin_memory)
        smoothing_factors_t = torch.tensor(smoothing_factors,
                                           device="cpu",
                                           dtype=dtype,
                                           pin_memory=pin_memory)
        smoothing_curves_t = torch.tensor(smoothing_curves,
                                          device="cpu",
                                          dtype=dtype,
                                          pin_memory=pin_memory)
        xtc_thresholds_t = torch.tensor(xtc_thresholds,
                                        device="cpu",
                                        dtype=dtype,
                                        pin_memory=pin_memory)
        xtc_probabilities_t = torch.tensor(xtc_probabilities,
                                           device="cpu",
                                           dtype=dtype,
                                           pin_memory=pin_memory)
        nsigmas_t = torch.tensor(nsigmas,
                                 device="cpu",
                                 dtype=dtype,
                                 pin_memory=pin_memory)
        dry_multipliers_t = torch.tensor(
            dry_multipliers,
            device="cpu", 
            dtype=dtype,
            pin_memory=pin_memory,
        )
        dry_bases_t = torch.tensor(
            dry_bases,
            device="cpu",
            dtype=dtype, 
            pin_memory=pin_memory,
        )
        dry_allowed_lengths_t = torch.tensor(
            dry_allowed_lengths,
            device="cpu",
            dtype=torch.int,
            pin_memory=pin_memory,
        )
        dry_sequence_breakers_t = torch.tensor(
            [seq + [0] * (max(len(s) for s in
                              dry_sequence_breaker_ids) - len(seq)) 
             for seq in dry_sequence_breaker_ids],
            device="cpu",
            dtype=torch.long,
            pin_memory=pin_memory,
        )
        dry_ranges_t = torch.tensor(
            dry_ranges,
            device="cpu",
            dtype=torch.int,
            pin_memory=pin_memory,
        )
        skews_t = torch.tensor(
            skews,
            device="cpu",
            dtype=dtype,
            pin_memory=pin_memory,
        )

        # Because the memory is pinned, we can do non-blocking
        # transfer to device.

        return cls(
            temperatures=temperatures_t.to(device=device, non_blocking=True),
            dynatemp_mins=dynatemp_mins_t.to(device=device, non_blocking=True),
            dynatemp_maxs=dynatemp_maxs_t.to(device=device, non_blocking=True),
            dynatemp_exps=dynatemp_exps_t.to(device=device, non_blocking=True),
            temperature_lasts=temp_lasts_t.to(device=device, non_blocking=True),
            top_ps=top_ps_t.to(device=device, non_blocking=True),
            top_ks=top_ks_t.to(device=device, non_blocking=True),
            top_as=top_as_t.to(device=device, non_blocking=True),
            min_ps=min_ps_t.to(device=device, non_blocking=True),
            presence_penalties=presence_penalties_t.to(device=device,
                                                       non_blocking=True),
            frequency_penalties=frequency_penalties_t.to(device=device,
                                                         non_blocking=True),
            repetition_penalties=repetition_penalties_t.to(device=device,
                                                           non_blocking=True),
            no_repeat_ngram_sizes=no_repeat_ngram_sizes_t.to(device=device,
                                                             non_blocking=True),
            tfss=tfss_t.to(device=device, non_blocking=True),
            eta_cutoffs=eta_cutoffs_t.to(device=device, non_blocking=True),
            epsilon_cutoffs=epsilon_cutoffs_t.to(device=device,
                                                 non_blocking=True),
            smoothing_factors=smoothing_factors_t.to(device=device,
                                                     non_blocking=True),
            smoothing_curves=smoothing_curves_t.to(device=device,
                                                   non_blocking=True),
            xtc_thresholds=xtc_thresholds_t.to(device=device,
                                               non_blocking=True),
            xtc_probabilities=xtc_probabilities_t.to(device=device,
                                                     non_blocking=True),
            nsigmas=nsigmas_t.to(device=device, non_blocking=True),
            dry_multipliers=dry_multipliers_t.to(device=device,
                                                 non_blocking=True),
            dry_bases=dry_bases_t.to(device=device, non_blocking=True),
            dry_allowed_lengths=dry_allowed_lengths_t.to(device=device,
                                                         non_blocking=True),
            dry_sequence_breaker_ids=dry_sequence_breakers_t.to(device=device,
                                                                non_blocking=True),
            dry_ranges=dry_ranges_t.to(device=device, non_blocking=True),
            skews=skews_t.to(device=device, non_blocking=True),
            typical_ps=typical_ps_t.to(device=device, non_blocking=True),
            prompt_tokens=prompt_t.to(device=device, non_blocking=True),
            output_tokens=output_t.to(device=device, non_blocking=True),
        )