sgl-project--sglang/sgl-kernel/python/sgl_kernel/sampling.py

from typing import Optional, Union

import torch
from sgl_kernel.utils import _to_tensor_scalar_tuple

try:
    import flashinfer.sampling as _flashinfer_sampling

    _has_flashinfer = True
except ImportError:
    _has_flashinfer = False


def _top_k_renorm_probs_internal(
    probs: torch.Tensor,
    maybe_top_k_arr: Optional[torch.Tensor],
    top_k_val: int,
) -> torch.Tensor:
    probs = probs.float()
    maybe_top_k_arr = maybe_top_k_arr.int() if maybe_top_k_arr is not None else None
    renorm_probs = torch.empty_like(probs)
    torch.ops.sgl_kernel.top_k_renorm_probs.default(
        probs, renorm_probs, maybe_top_k_arr, top_k_val
    )
    return renorm_probs


def top_k_renorm_probs(
    probs: torch.Tensor,
    top_k: Union[torch.Tensor, int],
) -> torch.Tensor:
    r"""Adapt from https://github.com/flashinfer-ai/flashinfer/flashinfer/sampling.py
    Fused GPU kernel for renormalizing probabilities by top-k thresholding.

    Parameters
    ----------
    probs: torch.Tensor
        Probabilities, shape ``(batch_size, num_classes)``.
    top_k: Union[torch.Tensor, int]
        Either a scalar or a tensor of shape ``(batch_size,)``, representing the top-k threshold for for
        for re-normalizing probabilities, should be in ``(0, num_classes)``.
        If a scalar, the same threshold is used for all requests.
        If a tensor, each request has its own threshold.
        We keep the top-k probabilities, set the rest to zero, and renormalize the probabilities.

    Returns
    -------
    renorm_probs: torch.Tensor
        Renormalized probabilities, shape ``(batch_size, num_classes)``.

    Note
    ----
    This combination of ``top_k_renorm_probs`` and ``sampling_from_probs`` should be equivalent to
    ``top_k_sampling_from_probs``.
    """
    if probs.device.type == "musa" or not _has_flashinfer:
        return _top_k_renorm_probs_internal(probs, *_to_tensor_scalar_tuple(top_k))
    else:
        return _flashinfer_sampling.top_k_renorm_probs(probs, top_k)


top_k_renorm_prob = top_k_renorm_probs


def _top_p_renorm_probs_internal(
    probs: torch.Tensor,
    maybe_top_p_arr: Optional[torch.Tensor],
    top_p_val: float,
) -> torch.Tensor:
    probs = probs.float()
    maybe_top_p_arr = maybe_top_p_arr.float() if maybe_top_p_arr is not None else None
    renorm_probs = torch.empty_like(probs)
    torch.ops.sgl_kernel.top_p_renorm_probs.default(
        probs, renorm_probs, maybe_top_p_arr, top_p_val
    )
    return renorm_probs


def top_p_renorm_probs(
    probs: torch.Tensor,
    top_p: Union[torch.Tensor, float],
) -> torch.Tensor:
    r"""Adapt from https://github.com/flashinfer-ai/flashinfer/flashinfer/sampling.py
    Fused GPU kernel for renormalizing probabilities by top-p thresholding.

    Parameters
    ----------
    probs: torch.Tensor
        Probabilities, shape ``(batch_size, num_classes)``.
    top_p: Union[torch.Tensor, float]
        Either a scalar or a tensor of shape ``(batch_size,)``, representing the top-p threshold for for
        re-normalizing probabilities, should be in ``(0, 1)``.
        If a scalar, the same threshold is used for all requests.
        If a tensor, each request has its own threshold.
        We mask out the probabilities less than `threshold` where the cumulative sum
        of ``probs[probs >= threshold]`` is `top_p`, and renormalize the probabilities.

    Returns
    -------
    renorm_probs: torch.Tensor
        Renormalized probabilities, shape ``(batch_size, num_classes)``.

    Note
    ----
    This combination of ``top_p_renorm_probs`` and ``sampling_from_probs`` should be equivalent to
    ``top_p_sampling_from_probs``.

    """
    if probs.device.type == "musa" or not _has_flashinfer:
        return _top_p_renorm_probs_internal(probs, *_to_tensor_scalar_tuple(top_p))
    else:
        return _flashinfer_sampling.top_p_renorm_probs(probs, top_p)


top_p_renorm_prob = top_p_renorm_probs