aphrodite-engine/aphrodite/modeling/sampling_metadata.py

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

import torch

from aphrodite.common.sampling_params import SamplingParams, SamplingType
from aphrodite.common.sequence import SequenceData
from aphrodite.common.utils import in_wsl

_SAMPLING_EPS = 1e-5


class PersistentMetadata:

    def __init__(self, metadata: Optional[Dict[int, dict]] = None):
        self._metadata: Dict[int, dict] = metadata or {}

    def get(self, seq_id: int) -> dict:
        return self._metadata.get(seq_id, {})


class OutputMetadata(PersistentMetadata):

    def add(self, seq_id: int, key, val) -> None:
        if seq_id not in self._metadata:
            self._metadata[seq_id] = {}
        self._metadata[seq_id][key] = val


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

    Args:
        seq_groups: List of (seq_ids, sampling_params).
        seq_data: Seq_id -> SequenceData.
        prompt_lens: Lengths of prompts.
        selected_token_indices: Token indices selected for sampling.
        categorized_sample_indices: SamplingType -> token indices to sample.
        generators: List of torch.Generators to use for seeded sampling
        perform_sampling: Whether to perform sampling. This option is used to
            make the sampling only happens in the driver worker, and disable
            sampling in other worker processes.
        persistent_metadata: Metadata that persists across iterations.
        output_metadata: the output metadata.
    """

    def __init__(
        self,
        seq_groups: Optional[List[Tuple[List[int], SamplingParams]]],
        seq_data: Optional[Dict[int, SequenceData]],
        prompt_lens: Optional[List[int]],
        selected_token_indices: torch.Tensor,
        categorized_sample_indices: Optional[Dict[SamplingType, torch.Tensor]],
        generators: Optional[List[torch.Generator]] = None,
        perform_sampling: bool = True,
        persistent_metadata: Optional[PersistentMetadata] = None,
        output_metadata: Optional[OutputMetadata] = None,
    ) -> None:
        self.seq_groups = seq_groups
        self.seq_data = seq_data
        self.prompt_lens = prompt_lens
        self.selected_token_indices = selected_token_indices
        self.categorized_sample_indices = categorized_sample_indices
        self.generators = generators
        self.perform_sampling = perform_sampling
        self.persistent_metadata = persistent_metadata or PersistentMetadata()
        self.output_metadata = output_metadata or OutputMetadata()

        self.num_prompts = len(prompt_lens) if prompt_lens is not None else 0

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


@dataclass
class SamplingTensors:
    """Tensors for sampling."""
    temperatures: 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
    tfss: torch.Tensor
    eta_cutoffs: torch.Tensor
    epsilon_cutoffs: torch.Tensor
    typical_ps: torch.Tensor
    typical_p_sigmas: torch.Tensor
    miro_taus: torch.Tensor
    miro_etas: torch.Tensor
    miro_mus: torch.Tensor
    miro_indices: torch.Tensor
    miro_seqids: List[int]  # state writeback done CPU side
    dynatemp_mins: torch.Tensor
    dynatemp_maxs: torch.Tensor
    dynatemp_exps: torch.Tensor
    smoothing_factors: torch.Tensor
    smoothing_curves: 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]:
        prompt_tokens: List[List[int]] = []
        output_tokens: List[List[int]] = []
        top_ks: List[int] = []
        temperatures: List[float] = []
        top_ps: List[float] = []
        top_as: List[float] = []
        min_ps: List[float] = []
        presence_penalties: List[float] = []
        frequency_penalties: List[float] = []
        repetition_penalties: List[float] = []
        tfss: List[float] = []
        eta_cutoffs: List[float] = []
        epsilon_cutoffs: List[float] = []
        typical_ps: List[float] = []
        typical_p_sigmas: List[float] = []
        miro_taus: List[float] = []
        miro_etas: List[float] = []
        miro_mus: List[float] = []
        miro_indices: List[int] = []
        miro_seqids: List[int] = []
        dynatemp_mins: List[float] = []
        dynatemp_maxs: List[float] = []
        dynatemp_exps: List[float] = []
        smoothing_factors: List[float] = []
        smoothing_curves: List[float] = []
        index = 0  # temporary, needed for building miro_indices
        do_temperatures = False
        do_penalties = False
        do_topks = False
        do_topps = False
        do_topas = False
        do_minps = False
        do_tfss = False
        do_eta_cutoffs = False
        do_epsilon_cutoffs = False
        do_typical_ps = False
        do_quadratic = False
        do_mirostat = False
        for i, seq_group in enumerate(sampling_metadata.seq_groups):
            seq_ids, sampling_params = seq_group
            temperature = sampling_params.temperature
            p = sampling_params.presence_penalty
            f = sampling_params.frequency_penalty
            r = sampling_params.repetition_penalty
            top_p = sampling_params.top_p
            # k should not be greater than the vocab size
            top_k = min(sampling_params.top_k, vocab_size)
            top_k = vocab_size if top_k == -1 else top_k
            top_a = sampling_params.top_a
            min_p = sampling_params.min_p
            tfs = sampling_params.tfs
            eta_cutoff = sampling_params.eta_cutoff
            epsilon_cutoff = sampling_params.epsilon_cutoff
            typical_p = sampling_params.typical_p
            typical_p_sigma = sampling_params.typical_p_sigma
            miro_tau = sampling_params.mirostat_tau
            miro_eta = sampling_params.mirostat_eta
            dynatemp_min = sampling_params.dynatemp_min
            dynatemp_max = sampling_params.dynatemp_max
            dynatemp_exp = sampling_params.dynatemp_exponent
            smoothing_factor = sampling_params.smoothing_factor
            smoothing_curve = sampling_params.smoothing_curve

            if do_temperatures is False and temperature > _SAMPLING_EPS:
                do_temperatures = True
            if not do_penalties and (abs(p) >= _SAMPLING_EPS
                                     or abs(f) >= _SAMPLING_EPS
                                     or abs(r - 1.0) >= _SAMPLING_EPS):
                do_penalties = True
            if do_topks is False and top_k != vocab_size:
                do_topks = True
            if do_topps is False and top_p < 1.0 - _SAMPLING_EPS:
                do_topps = True
            if do_topas is False and top_a > 0.0:
                do_topas = True
            if do_minps is False and min_p > _SAMPLING_EPS:
                do_minps = True
            if do_tfss is False and tfs < 1.0 - _SAMPLING_EPS:
                do_tfss = True
            if do_eta_cutoffs is False and eta_cutoff > _SAMPLING_EPS:
                do_eta_cutoffs = True
            if do_epsilon_cutoffs is False and epsilon_cutoff > _SAMPLING_EPS:
                do_epsilon_cutoffs = True
            if do_typical_ps is False and (typical_p < 1.0 - _SAMPLING_EPS
                                           or typical_p_sigma > 0.0):
                do_typical_ps = True
            if do_quadratic is False and (smoothing_factor > _SAMPLING_EPS
                                          or smoothing_curve > 1.0):
                do_quadratic = True
            if do_mirostat is False and sampling_params.mirostat_mode == 2:
                do_mirostat = True

            if (i < sampling_metadata.num_prompts
                    and sampling_params.prompt_logprobs is not None):
                # For tokens in the prompt that we only need to get their
                # logprobs
                prompt_len = sampling_metadata.prompt_lens[i]
                index += sampling_metadata.prompt_lens[i] - 1
                temperatures += [temperature] * (prompt_len - 1)
                top_ps += [top_p] * (prompt_len - 1)
                top_ks += [top_k] * (prompt_len - 1)
                top_as += [top_a] * (prompt_len - 1)
                min_ps += [min_p] * (prompt_len - 1)
                presence_penalties += [0] * (prompt_len - 1)
                frequency_penalties += [0] * (prompt_len - 1)
                repetition_penalties += [1] * (prompt_len - 1)
                tfss += [1] * (prompt_len - 1)
                eta_cutoffs += [0] * (prompt_len - 1)
                epsilon_cutoffs += [0] * (prompt_len - 1)
                typical_ps += [1] * (prompt_len - 1)
                typical_p_sigmas += [0] * (prompt_len - 1)
                dynatemp_mins += [dynatemp_min] * (prompt_len - 1)
                dynatemp_maxs += [dynatemp_max] * (prompt_len - 1)
                dynatemp_exps += [dynatemp_exp] * (prompt_len - 1)
                smoothing_factors += [smoothing_factor] * (prompt_len - 1)
                smoothing_curves += [smoothing_curve] * (prompt_len - 1)
                prompt_tokens.extend([] for _ in range(prompt_len - 1))
                output_tokens.extend([] for _ in range(prompt_len - 1))
            for seq_id in seq_ids:
                seq_data = sampling_metadata.seq_data[seq_id]
                prompt_tokens.append(seq_data.prompt_token_ids)
                output_tokens.append(seq_data.output_token_ids)
            temperatures += [temperature] * len(seq_ids)
            top_ps += [top_p] * len(seq_ids)
            top_ks += [top_k] * len(seq_ids)
            top_as += [top_a] * len(seq_ids)
            min_ps += [min_p] * len(seq_ids)
            presence_penalties += [p] * len(seq_ids)
            frequency_penalties += [f] * len(seq_ids)
            repetition_penalties += [r] * len(seq_ids)
            tfss += [tfs] * len(seq_ids)
            eta_cutoffs += [eta_cutoff] * len(seq_ids)
            epsilon_cutoffs += [epsilon_cutoff] * len(seq_ids)
            typical_ps += [typical_p] * len(seq_ids)
            typical_p_sigmas += [typical_p_sigma] * len(seq_ids)
            dynatemp_mins += [dynatemp_min] * len(seq_ids)
            dynatemp_maxs += [dynatemp_max] * len(seq_ids)
            dynatemp_exps += [dynatemp_exp] * len(seq_ids)
            smoothing_factors += [smoothing_factor] * len(seq_ids)
            smoothing_curves += [smoothing_curve] * len(seq_ids)
            if sampling_params.mirostat_mode == 2:
                miro_indices += [(index + i) for i in range(len(seq_ids))]
                miro_seqids += seq_ids
                miro_taus += [miro_tau] * len(seq_ids)
                miro_etas += [miro_eta] * len(seq_ids)
                miro_mus += [
                    sampling_metadata.persistent_metadata.get(sid).get(
                        "miro_mu", sampling_params.mirostat_tau * 2)
                    for sid in seq_ids
                ]
            index += len(seq_ids)

        sampling_tensors = SamplingTensors.from_lists(
            temperatures, top_ps, top_ks, top_as, min_ps, presence_penalties,
            frequency_penalties, repetition_penalties, tfss, eta_cutoffs,
            epsilon_cutoffs, typical_ps, typical_p_sigmas, dynatemp_mins,
            dynatemp_maxs, dynatemp_exps, smoothing_factors, smoothing_curves,
            miro_taus, miro_etas, miro_mus, miro_indices, miro_seqids,
            prompt_tokens, output_tokens, vocab_size, device, dtype)
        return (sampling_tensors, do_temperatures, do_penalties, do_topks,
                do_topps, do_topas, do_minps, do_tfss, do_eta_cutoffs,
                do_epsilon_cutoffs, do_typical_ps, do_quadratic, do_mirostat)

    @classmethod
    def from_lists(cls, temperatures: List[float], 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], tfss: List[float],
                   eta_cutoffs: List[float], epsilon_cutoffs: List[float],
                   typical_ps: List[float], typical_p_sigmas: List[float],
                   dynatemp_mins: List[float], dynatemp_maxs: List[float],
                   dynatemp_exps: List[float], smoothing_factors: List[float],
                   smoothing_curves: List[float], miro_taus: List[float],
                   miro_etas: List[float], miro_mus: List[float],
                   miro_indices: List[int], miro_seqids: List[int],
                   prompt_tokens: List[List[int]],
                   output_tokens: List[List[int]], vocab_size: int,
                   device: torch.device,
                   dtype: torch.dtype) -> "SamplingTensors":
        # Note that the performance will be very bad without
        # pinned memory.
        pin_memory = not in_wsl()
        prompt_max_len = max(len(tokens) for tokens in prompt_tokens)
        prompt_padded_tokens = [
            tokens + [vocab_size] * (prompt_max_len - len(tokens))
            for tokens in prompt_tokens
        ]
        output_max_len = max(len(tokens) for tokens in output_tokens)
        output_padded_tokens = [
            tokens + [vocab_size] * (output_max_len - len(tokens))
            for tokens in output_tokens
        ]

        temperatures_t = torch.tensor(temperatures,
                                      device="cpu",
                                      dtype=dtype,
                                      pin_memory=pin_memory)
        top_ps_t = torch.tensor(top_ps,
                                device="cpu",
                                dtype=dtype,
                                pin_memory=pin_memory)
        top_ks_t = torch.tensor(top_ks,
                                device="cpu",
                                dtype=torch.int,
                                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)
        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)
        typical_p_sigmas_t = torch.tensor(typical_p_sigmas,
                                          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)
        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)
        miro_taus_t = torch.tensor(miro_taus,
                                   device="cpu",
                                   dtype=dtype,
                                   pin_memory=pin_memory)
        miro_etas_t = torch.tensor(miro_etas,
                                   device="cpu",
                                   dtype=dtype,
                                   pin_memory=pin_memory)
        miro_mus_t = torch.tensor(miro_mus,
                                  device="cpu",
                                  dtype=dtype,
                                  pin_memory=pin_memory)
        miro_indices_t = torch.tensor(miro_indices,
                                      device="cpu",
                                      dtype=torch.int,
                                      pin_memory=pin_memory)
        prompt_tensor = torch.tensor(prompt_padded_tokens,
                                     device=device,
                                     dtype=torch.long,
                                     pin_memory=pin_memory)
        output_tensor = torch.tensor(output_padded_tokens,
                                     device=device,
                                     dtype=torch.long,
                                     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),
            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),
            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),
            typical_ps=typical_ps_t.to(device=device, non_blocking=True),
            typical_p_sigmas=typical_p_sigmas_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),
            smoothing_factors=smoothing_factors_t.to(device=device,
                                                     non_blocking=True),
            smoothing_curves=smoothing_curves_t.to(device=device,
                                                   non_blocking=True),
            miro_taus=miro_taus_t.to(device=device, non_blocking=True),
            miro_etas=miro_etas_t.to(device=device, non_blocking=True),
            miro_mus=miro_mus_t.to(device=device, non_blocking=True),
            miro_indices=miro_indices_t.to(device=device, non_blocking=True),
            miro_seqids=miro_seqids,
            prompt_tokens=prompt_tensor.to(device=device, non_blocking=True),
            output_tokens=output_tensor.to(device=device, non_blocking=True),
        )