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vllm.v1.sample.ops.topk_topp_sampler

is_flashinfer_available module-attribute 

is_flashinfer_available = True

logger module-attribute 

logger = init_logger(__name__)

TopKTopPSampler

Bases: Module

Module that performs optional top-k and top-p filtering followed by weighted random sampling of logits.

Implementations may update the logits tensor in-place.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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class TopKTopPSampler(nn.Module):
    """
    Module that performs optional top-k and top-p filtering followed by
    weighted random sampling of logits.

    Implementations may update the logits tensor in-place.
    """

    def __init__(
            self,
            logprobs_mode: LogprobsMode = LogprobsMode.RAW_LOGPROBS) -> None:
        super().__init__()
        self.logprobs_mode = logprobs_mode
        # flashinfer optimization does not apply if intermediate
        # logprobs/logits after top_k/top_p need to be returned
        if logprobs_mode not in (LogprobsMode.PROCESSED_LOGITS,
                                 LogprobsMode.PROCESSED_LOGPROBS
                                 ) and current_platform.is_cuda():
            if is_flashinfer_available:
                flashinfer_version = flashinfer.__version__
                if version.parse(flashinfer_version) < version.parse("0.2.3"):
                    logger.warning_once(
                        "FlashInfer version >= 0.2.3 required. "
                        "Falling back to default sampling implementation.")
                    self.forward = self.forward_native
                elif envs.VLLM_USE_FLASHINFER_SAMPLER is not False:
                    # NOTE(woosuk): The V0 sampler doesn't use FlashInfer for
                    # sampling unless VLLM_USE_FLASHINFER_SAMPLER=1 (i.e., by
                    # default it is unused). For backward compatibility, we set
                    # `VLLM_USE_FLASHINFER_SAMPLER` as None by default and
                    # interpret it differently in V0 and V1 samplers: In V0,
                    # None means False, while in V1, None means True. This is
                    # why we use the condition
                    # `envs.VLLM_USE_FLASHINFER_SAMPLER is not False` here.
                    logger.info_once(
                        "Using FlashInfer for top-p & top-k sampling.")
                    self.forward = self.forward_cuda
                else:
                    logger.warning_once(
                        "FlashInfer is available, but it is not enabled. "
                        "Falling back to the PyTorch-native implementation of "
                        "top-p & top-k sampling. For the best performance, "
                        "please set VLLM_USE_FLASHINFER_SAMPLER=1.")
                    self.forward = self.forward_native
            else:
                logger.warning_once(
                    "FlashInfer is not available. Falling back to the PyTorch-"
                    "native implementation of top-p & top-k sampling. For the "
                    "best performance, please install FlashInfer.")
                self.forward = self.forward_native
        else:
            self.forward = self.forward_native

        self.apply_top_k_top_p = apply_top_k_top_p

    def forward_native(
        self,
        logits: torch.Tensor,
        generators: dict[int, torch.Generator],
        k: Optional[torch.Tensor],
        p: Optional[torch.Tensor],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """
        PyTorch-native implementation of top-k and top-p sampling.

        The logits tensor may be updated in-place.
        """
        logits = self.apply_top_k_top_p(logits, k, p)
        logits_to_return = None
        if self.logprobs_mode == LogprobsMode.PROCESSED_LOGITS:
            logits_to_return = logits
        elif self.logprobs_mode == LogprobsMode.PROCESSED_LOGPROBS:
            logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
        probs = logits.softmax(dim=-1, dtype=torch.float32)
        return random_sample(probs, generators), logits_to_return

    def forward_cuda(
        self,
        logits: torch.Tensor,
        generators: dict[int, torch.Generator],
        k: Optional[torch.Tensor],
        p: Optional[torch.Tensor],
    ) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
        """More optimized implementation for top-k and top-p sampling."""
        # We prefer `random_sample` over `flashinfer_sample` when sorting is
        # not needed. This is because `random_sample` does not require
        # CPU-GPU synchronization while `flashinfer_sample` does.
        if (k is None and p is None) or generators:
            if generators:
                logger.warning_once("FlashInfer 0.2.3+ does not support "
                                    "per-request generators. Falling back to "
                                    "PyTorch-native implementation.")
            return self.forward_native(logits, generators, k, p)
        assert self.logprobs_mode not in (
            LogprobsMode.PROCESSED_LOGITS, LogprobsMode.PROCESSED_LOGPROBS
        ), "FlashInfer does not support returning logits/logprobs"
        # flashinfer sampling functions expect contiguous logits.
        # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
        # because of slicing operation in logits_processor.
        return flashinfer_sample(logits.contiguous(), k, p, generators), None

apply_top_k_top_p instance-attribute 

apply_top_k_top_p = apply_top_k_top_p

forward instance-attribute 

forward = forward_native

logprobs_mode instance-attribute 

logprobs_mode = logprobs_mode

__init__ 

__init__(
    logprobs_mode: LogprobsMode = RAW_LOGPROBS,
) -> None
Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def __init__(
        self,
        logprobs_mode: LogprobsMode = LogprobsMode.RAW_LOGPROBS) -> None:
    super().__init__()
    self.logprobs_mode = logprobs_mode
    # flashinfer optimization does not apply if intermediate
    # logprobs/logits after top_k/top_p need to be returned
    if logprobs_mode not in (LogprobsMode.PROCESSED_LOGITS,
                             LogprobsMode.PROCESSED_LOGPROBS
                             ) and current_platform.is_cuda():
        if is_flashinfer_available:
            flashinfer_version = flashinfer.__version__
            if version.parse(flashinfer_version) < version.parse("0.2.3"):
                logger.warning_once(
                    "FlashInfer version >= 0.2.3 required. "
                    "Falling back to default sampling implementation.")
                self.forward = self.forward_native
            elif envs.VLLM_USE_FLASHINFER_SAMPLER is not False:
                # NOTE(woosuk): The V0 sampler doesn't use FlashInfer for
                # sampling unless VLLM_USE_FLASHINFER_SAMPLER=1 (i.e., by
                # default it is unused). For backward compatibility, we set
                # `VLLM_USE_FLASHINFER_SAMPLER` as None by default and
                # interpret it differently in V0 and V1 samplers: In V0,
                # None means False, while in V1, None means True. This is
                # why we use the condition
                # `envs.VLLM_USE_FLASHINFER_SAMPLER is not False` here.
                logger.info_once(
                    "Using FlashInfer for top-p & top-k sampling.")
                self.forward = self.forward_cuda
            else:
                logger.warning_once(
                    "FlashInfer is available, but it is not enabled. "
                    "Falling back to the PyTorch-native implementation of "
                    "top-p & top-k sampling. For the best performance, "
                    "please set VLLM_USE_FLASHINFER_SAMPLER=1.")
                self.forward = self.forward_native
        else:
            logger.warning_once(
                "FlashInfer is not available. Falling back to the PyTorch-"
                "native implementation of top-p & top-k sampling. For the "
                "best performance, please install FlashInfer.")
            self.forward = self.forward_native
    else:
        self.forward = self.forward_native

    self.apply_top_k_top_p = apply_top_k_top_p

forward_cuda 

forward_cuda(
    logits: Tensor,
    generators: dict[int, Generator],
    k: Optional[Tensor],
    p: Optional[Tensor],
) -> tuple[Tensor, Optional[Tensor]]

More optimized implementation for top-k and top-p sampling.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def forward_cuda(
    self,
    logits: torch.Tensor,
    generators: dict[int, torch.Generator],
    k: Optional[torch.Tensor],
    p: Optional[torch.Tensor],
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
    """More optimized implementation for top-k and top-p sampling."""
    # We prefer `random_sample` over `flashinfer_sample` when sorting is
    # not needed. This is because `random_sample` does not require
    # CPU-GPU synchronization while `flashinfer_sample` does.
    if (k is None and p is None) or generators:
        if generators:
            logger.warning_once("FlashInfer 0.2.3+ does not support "
                                "per-request generators. Falling back to "
                                "PyTorch-native implementation.")
        return self.forward_native(logits, generators, k, p)
    assert self.logprobs_mode not in (
        LogprobsMode.PROCESSED_LOGITS, LogprobsMode.PROCESSED_LOGPROBS
    ), "FlashInfer does not support returning logits/logprobs"
    # flashinfer sampling functions expect contiguous logits.
    # In flex_attn/triton_attn fp32 inference, logits can be non-contiguous
    # because of slicing operation in logits_processor.
    return flashinfer_sample(logits.contiguous(), k, p, generators), None

forward_native 

forward_native(
    logits: Tensor,
    generators: dict[int, Generator],
    k: Optional[Tensor],
    p: Optional[Tensor],
) -> tuple[Tensor, Optional[Tensor]]

PyTorch-native implementation of top-k and top-p sampling.

The logits tensor may be updated in-place.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def forward_native(
    self,
    logits: torch.Tensor,
    generators: dict[int, torch.Generator],
    k: Optional[torch.Tensor],
    p: Optional[torch.Tensor],
) -> tuple[torch.Tensor, Optional[torch.Tensor]]:
    """
    PyTorch-native implementation of top-k and top-p sampling.

    The logits tensor may be updated in-place.
    """
    logits = self.apply_top_k_top_p(logits, k, p)
    logits_to_return = None
    if self.logprobs_mode == LogprobsMode.PROCESSED_LOGITS:
        logits_to_return = logits
    elif self.logprobs_mode == LogprobsMode.PROCESSED_LOGPROBS:
        logits_to_return = logits.log_softmax(dim=-1, dtype=torch.float32)
    probs = logits.softmax(dim=-1, dtype=torch.float32)
    return random_sample(probs, generators), logits_to_return

apply_top_k_only 

apply_top_k_only(logits: Tensor, k: Tensor) -> Tensor

Apply top-k mask to the logits.

This implementation doesn't involve sorting the entire vocab.

The logits tensor may be updated in-place.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def apply_top_k_only(
    logits: torch.Tensor,
    k: torch.Tensor,
) -> torch.Tensor:
    """
    Apply top-k mask to the logits.

    This implementation doesn't involve sorting the entire vocab.

    The logits tensor may be updated in-place.
    """
    no_top_k_mask = k == logits.shape[1]
    # Set non-top-k rows to 1 so that we can gather.
    k = k.masked_fill(no_top_k_mask, 1)
    max_top_k = k.max()
    # topk.values tensor has shape [batch_size, max_top_k].
    # Convert top k to 0-based index in range [0, max_top_k).
    k_index = k.sub_(1).unsqueeze(1)
    top_k_mask = logits.topk(max_top_k, dim=1).values.gather(1, k_index.long())
    # Handle non-topk rows.
    top_k_mask.masked_fill_(no_top_k_mask.unsqueeze(1), -float("inf"))
    logits.masked_fill_(logits < top_k_mask, -float("inf"))
    return logits

apply_top_k_top_p 

apply_top_k_top_p(
    logits: Tensor, k: Optional[Tensor], p: Optional[Tensor]
) -> Tensor

Apply top-k and top-p masks to the logits.

If a top-p is used, this function will sort the logits tensor, which can be slow for large batches.

The logits tensor may be updated in-place.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def apply_top_k_top_p(
    logits: torch.Tensor,
    k: Optional[torch.Tensor],
    p: Optional[torch.Tensor],
) -> torch.Tensor:
    """Apply top-k and top-p masks to the logits.

    If a top-p is used, this function will sort the logits tensor,
    which can be slow for large batches.

    The logits tensor may be updated in-place.
    """
    if p is None:
        if k is None:
            return logits

        # Avoid sorting vocab for top-k only case.
        return apply_top_k_only(logits, k)

    logits_sort, logits_idx = logits.sort(dim=-1, descending=False)

    if k is not None:
        # Apply top-k.
        top_k_mask = logits_sort.size(1) - k.to(torch.long)  # shape: B
        # Get all the top_k values.
        top_k_mask = logits_sort.gather(1, top_k_mask.unsqueeze(dim=1))
        top_k_mask = logits_sort < top_k_mask
        logits_sort.masked_fill_(top_k_mask, -float("inf"))

    if p is not None:
        # Apply top-p.
        probs_sort = logits_sort.softmax(dim=-1)
        probs_sum = torch.cumsum(probs_sort, dim=-1, out=probs_sort)
        top_p_mask = probs_sum <= 1 - p.unsqueeze(dim=1)
        # at least one
        top_p_mask[:, -1] = False
        logits_sort.masked_fill_(top_p_mask, -float("inf"))

    # Re-sort the probabilities.
    logits = logits_sort.scatter(dim=-1, index=logits_idx, src=logits_sort)
    return logits

flashinfer_sample 

flashinfer_sample(
    logits: Tensor,
    k: Optional[Tensor],
    p: Optional[Tensor],
    generators: dict[int, Generator],
) -> Tensor

Sample from the logits using FlashInfer.

Statistically, this function is equivalent to the random_sample function. However, this function is faster because it avoids sorting the logits tensor via rejection sampling.

NOTE: The outputs of this function do not necessarily match the outputs of the random_sample function. It only guarantees that the outputs are statistically equivalent.

NOTE: This function includes CPU-GPU synchronization, while random_sample does not. Call this function at the end of the forward pass to minimize the synchronization overhead.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def flashinfer_sample(
    logits: torch.Tensor,
    k: Optional[torch.Tensor],
    p: Optional[torch.Tensor],
    generators: dict[int, torch.Generator],
) -> torch.Tensor:
    """Sample from the logits using FlashInfer.

    Statistically, this function is equivalent to the `random_sample` function.
    However, this function is faster because it avoids sorting the logits tensor
    via rejection sampling.

    NOTE: The outputs of this function do not necessarily match the outputs of
    the `random_sample` function. It only guarantees that the outputs are
    statistically equivalent.

    NOTE: This function includes CPU-GPU synchronization, while `random_sample`
    does not. Call this function at the end of the forward pass to minimize
    the synchronization overhead.
    """
    assert not (k is None and p is None)
    if k is None:
        # Top-p only.
        probs = logits.softmax(dim=-1, dtype=torch.float32)
        next_token_ids = flashinfer.sampling.top_p_sampling_from_probs(
            probs, p, deterministic=True)
    elif p is None:
        # Top-k only.
        probs = logits.softmax(dim=-1, dtype=torch.float32)
        next_token_ids = flashinfer.sampling.top_k_sampling_from_probs(
            probs, k, deterministic=True)
    else:
        # Both top-k and top-p.
        next_token_ids = flashinfer.sampling.top_k_top_p_sampling_from_logits(
            logits, k, p, deterministic=True)

    return next_token_ids.view(-1)

random_sample 

random_sample(
    probs: Tensor, generators: dict[int, Generator]
) -> Tensor

Randomly sample from the probabilities.

We use this function instead of torch.multinomial because torch.multinomial causes CPU-GPU synchronization.

Source code in vllm/v1/sample/ops/topk_topp_sampler.py 
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def random_sample(
    probs: torch.Tensor,
    generators: dict[int, torch.Generator],
) -> torch.Tensor:
    """Randomly sample from the probabilities.

    We use this function instead of torch.multinomial because torch.multinomial
    causes CPU-GPU synchronization.
    """
    q = torch.empty_like(probs)
    # NOTE(woosuk): To batch-process the requests without their own seeds,
    # which is the common case, we first assume that every request does
    # not have its own seed. Then, we overwrite the values for the requests
    # that have their own seeds.
    if len(generators) != probs.shape[0]:
        q.exponential_()
    if generators:
        # TODO(woosuk): This can be slow because we handle each request
        # one by one. Optimize this.
        for i, generator in generators.items():
            q[i].exponential_(generator=generator)
    return probs.div_(q).argmax(dim=-1).view(-1)