[Speculative decoding 1/9] Optimized rejection sampler (#2336)

tests/samplers/test_rejection_sampler.py

+"""Tests for rejection sampling."""
+import pytest
+from typing import List, Tuple
+
+import torch
+import torch.nn.functional as F
+
+from vllm.model_executor.utils import set_random_seed
+
+from vllm.model_executor.layers.rejection_sampler import RejectionSampler
+
+
+def mock_causal_accepted_tensor(
+        k: int, last_accepted_indices: torch.Tensor) -> torch.Tensor:
+    """Generate an "accepted" tensor which should yield causally-accepted tokens
+    up to last accepted indices.
+
+    Tokens after last_accepted_indices+1 may also be accepted, although they
+    will not be causally accepted.
+    """
+    batch_size = last_accepted_indices.shape[0]
+
+    accepted = (torch.arange(k).expand(batch_size, k) <=
+                last_accepted_indices.unsqueeze(-1).broadcast_to(
+                    batch_size, k)).to(device="cuda")
+
+    # Sprinkle accepted values after the contiguous initial accepted values.
+    # This replicates the behavior of rejection sampling, which may "accept"
+    # a token that cannot be accepted because of causality.
+    sprinkle_candidates = (
+        torch.arange(k).expand(batch_size, k) >
+        last_accepted_indices.unsqueeze(-1).broadcast_to(batch_size, k) + 1)
+    sprinkle = torch.rand(batch_size, k, device="cuda") > 0.5
+    accepted[sprinkle_candidates] = sprinkle[sprinkle_candidates]
+    return accepted
+
+
+@pytest.mark.parametrize("seed", list(range(10)))
+@pytest.mark.parametrize(
+    "which_tokens_accepted",
+    ["all_tokens_accepted", "no_tokens_accepted", "some_tokens_accepted"])
+@torch.inference_mode()
+def test_correct_output_format(which_tokens_accepted: str, seed: int):
+    """Verify the output has correct format given predetermined accepted matrix.
+    """
+    set_random_seed(seed)
+
+    batch_size = 10
+    k = 5
+    vocab_size = 3000
+
+    if which_tokens_accepted == "all_tokens_accepted":
+        accepted = mock_causal_accepted_tensor(
+            k, -1 + k * torch.ones((batch_size, ), dtype=torch.long))
+    elif which_tokens_accepted == "no_tokens_accepted":
+        accepted = mock_causal_accepted_tensor(
+            k, -torch.ones((batch_size, ), dtype=torch.long))
+    elif which_tokens_accepted == "some_tokens_accepted":
+        last_accepted_indices = torch.randint(low=-1,
+                                              high=k,
+                                              size=(batch_size, ))
+        accepted = mock_causal_accepted_tensor(k, last_accepted_indices)
+    else:
+        raise AssertionError()
+
+    recovered_token_ids = torch.randint(low=0,
+                                        high=vocab_size,
+                                        size=(batch_size, k),
+                                        dtype=torch.int64,
+                                        device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    rejection_sampler = RejectionSampler()
+    rejection_sampler.init_gpu_tensors(rank=0)
+    output_token_ids = rejection_sampler._create_output(  # pylint: disable=protected-access
+        accepted,
+        recovered_token_ids,
+        draft_token_ids,
+        bonus_token_ids,
+    )
+
+    if which_tokens_accepted == "all_tokens_accepted":
+        # Expect all tokens to be equal to draft tokens.
+        assert torch.equal(output_token_ids[:, :-1], draft_token_ids)
+
+        # Expect all bonus tokens to be included.
+        assert torch.equal(output_token_ids[:, -1:], bonus_token_ids)
+    elif which_tokens_accepted == "no_tokens_accepted":
+        # Expect first token to be equal to recovered tokens.
+        assert torch.equal(output_token_ids[:, 0], recovered_token_ids[:, 0])
+
+        # Expect everything else to be -1.
+        assert torch.equal(output_token_ids[:, 1:],
+                           torch.ones_like(output_token_ids[:, 1:]) * -1)
+    elif which_tokens_accepted == "some_tokens_accepted":
+        recovered_plus_bonus = torch.cat(
+            (recovered_token_ids, bonus_token_ids), dim=-1)
+        # Assert first rejected token is a recovered token or bonus token.
+        assert torch.equal(
+            recovered_plus_bonus[torch.arange(0, batch_size),
+                                 last_accepted_indices + 1],
+            output_token_ids[torch.arange(0, batch_size),
+                             last_accepted_indices + 1])
+
+        # Assert every subsequent token is -1.
+        subsequent_mask = torch.arange(0, k + 1).expand(
+            batch_size, k + 1) >= (last_accepted_indices + 2).unsqueeze(-1)
+        assert torch.all(output_token_ids[subsequent_mask] == -1)
+
+
+@pytest.mark.parametrize("k", list(range(1, 6)))
+@pytest.mark.parametrize("vocab_size", [30_000, 50_000])
+@pytest.mark.parametrize("batch_size", list(range(1, 32)))
+@torch.inference_mode()
+def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int):
+    rejection_sampler = RejectionSampler()
+    rejection_sampler.init_gpu_tensors(rank=0)
+
+    draft_probs = torch.rand(batch_size,
+                             k,
+                             vocab_size,
+                             dtype=torch.float32,
+                             device="cuda")
+    target_probs = torch.rand(batch_size,
+                              k,
+                              vocab_size,
+                              dtype=torch.float32,
+                              device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    rejection_sampler(target_probs, bonus_token_ids, draft_probs,
+                      draft_token_ids)
+
+
+@pytest.mark.parametrize("above_or_below_vocab_range", ["above", "below"])
+@pytest.mark.parametrize("which_token_ids",
+                         ["bonus_token_ids", "draft_token_ids"])
+@torch.inference_mode()
+def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
+                               which_token_ids: str):
+    k = 3
+    batch_size = 5
+    vocab_size = 30_000
+
+    rejection_sampler = RejectionSampler(strict_mode=True)
+    rejection_sampler.init_gpu_tensors(rank=0)
+
+    draft_probs = torch.rand(batch_size,
+                             k,
+                             vocab_size,
+                             dtype=torch.float32,
+                             device="cuda")
+    target_probs = torch.rand(batch_size,
+                              k,
+                              vocab_size,
+                              dtype=torch.float32,
+                              device="cuda")
+    bonus_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, 1),
+                                    dtype=torch.int64,
+                                    device="cuda")
+    draft_token_ids = torch.randint(low=0,
+                                    high=vocab_size,
+                                    size=(batch_size, k),
+                                    dtype=torch.int64,
+                                    device="cuda")
+
+    oob_token_ids = None
+    if which_token_ids == "bonus_token_ids":
+        oob_token_ids = bonus_token_ids
+    elif which_token_ids == "draft_token_ids":
+        oob_token_ids = draft_token_ids
+    else:
+        raise AssertionError()
+
+    if above_or_below_vocab_range == "above":
+        rogue_token_id = vocab_size + 1
+    elif above_or_below_vocab_range == "below":
+        rogue_token_id = -1
+    else:
+        raise AssertionError()
+
+    oob_token_ids[0][0] = rogue_token_id
+
+    with pytest.raises(AssertionError):
+        rejection_sampler(target_probs, bonus_token_ids, draft_probs,
+                          draft_token_ids)
+
+
+@pytest.mark.parametrize("draft_and_target_probs_equal", [True, False])
+@pytest.mark.parametrize("seed", list(range(5)))
+@torch.inference_mode()
+def test_rejection_sampling_approximates_target_distribution(
+        seed: int, draft_and_target_probs_equal: bool):
+    """Verify rejection sampling approximates target distribution,
+    despite sampling from a potentially distinct draft distribution.
+
+    This is done by first creating a random target probability
+    distribution and a random draft probability distribution. We then
+    sample token ids from the rejection sampler using these draft
+    and target distributions. The samples are used to estimate
+    the output probability distribution, which we expect to approximate
+    the target distribution.
+
+    A basic distance metric is used to determine similarity between
+    distributions.
+
+    We expect that as we increase the number of samples,
+    the distance between the observed distribution and the target
+    distribution decreases. To measure this, we compare the distance
+    of the observed distribution against both the target distribution
+    and a uniform random distribution. We expect the distance between
+    the observed distribution and the target distribution to improve
+    much more than the distance improvement between the observed
+    distribution and the random distribution.
+
+    When draft_and_target_probs_equal=True, the draft and target
+    probabilities are exactly equal. Rejection sampling should
+    still work without any NaNs or exceptions.
+    """
+    set_random_seed(seed)
+
+    helper = _CorrectnessTestHelper(
+        vocab_size=10,
+        rejection_sampler=RejectionSampler(),
+    )
+
+    draft_probs, target_probs, reference_probs = helper.generate_probs_for_test(
+        draft_and_target_probs_equal)
+
+    sample_sizes = [10, 100, 1_000, 10_000, 100_000]
+    distance_wrt_reference = []
+    distance_wrt_target = []
+
+    for num_samples in sample_sizes:
+        (reference_vs_rejsample_dist,
+         target_vs_rejsample_dist) = helper.run_and_compare_distributions(
+             draft_probs,
+             target_probs,
+             reference_probs,
+             num_samples,
+         )
+
+        distance_wrt_reference.append(reference_vs_rejsample_dist)
+        distance_wrt_target.append(target_vs_rejsample_dist)
+
+        relative_change_in_distance_wrt_target = get_ratio_first_to_last(
+            distance_wrt_target)
+        relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
+            distance_wrt_reference)
+
+        print(f"{num_samples=} {target_vs_rejsample_dist=:.05f} "
+              f"{reference_vs_rejsample_dist=:.05f}")
+        print(f"{num_samples=} {relative_change_in_distance_wrt_target=:.02f} "
+              f"{relative_change_in_distance_wrt_reference=:.02f}")
+
+    relative_change_in_distance_wrt_target = get_ratio_first_to_last(
+        distance_wrt_target)
+    relative_change_in_distance_wrt_reference = get_ratio_first_to_last(
+        distance_wrt_reference)
+
+    expected_improvement_multiplier = 20
+    assert (relative_change_in_distance_wrt_target >
+            relative_change_in_distance_wrt_reference *
+            expected_improvement_multiplier)
+
+
+def get_ratio_first_to_last(elements: List[float]) -> float:
+    return elements[0] / elements[-1]
+
+
+class _CorrectnessTestHelper:
+    """Class that packages together logic required for the unit-level
+    rejection sampling correctness test.
+    """
+
+    def __init__(self, vocab_size: int, rejection_sampler: RejectionSampler):
+        self.rejection_sampler = rejection_sampler
+        self.vocab_size = vocab_size
+        self.vocab_range = (0, vocab_size)
+
+        self.rejection_sampler.init_gpu_tensors(rank=0)
+
+        # Keep test simple, use k=1
+        self.k = 1
+
+        # Bonus tokens not used, but rejection sampler requires
+        # correct shape.
+        self.num_bonus_tokens = 1
+
+    def generate_probs_for_test(
+        self, draft_and_target_probs_equal: bool
+    ) -> Tuple[torch.Tensor, torch.Tensor, torch.Tensor]:
+        draft_probs, target_probs = [
+            F.softmax(
+                torch.rand(self.vocab_size, dtype=torch.float32),
+                dim=-1,
+            ) for _ in range(2)
+        ]
+
+        num_reference_probs = 100
+        reference_probs = F.softmax(
+            torch.rand(num_reference_probs,
+                       self.vocab_size,
+                       dtype=torch.float32),
+            dim=-1,
+        )
+
+        if draft_and_target_probs_equal:
+            target_probs = draft_probs.clone()
+
+        return draft_probs, target_probs, reference_probs
+
+    def run_and_compare_distributions(self, draft_probs: torch.Tensor,
+                                      target_probs: torch.Tensor,
+                                      reference_probs: torch.Tensor,
+                                      num_samples: int) -> Tuple[float, float]:
+        # Sample using rejection sampling.
+        rej_sample_probs = self._estimate_rejection_sampling_pdf(
+            draft_probs, target_probs, num_samples)
+
+        # Average distance from reference probs.
+        reference_vs_rejsample_dist = torch.dist(
+            reference_probs,
+            rej_sample_probs).item() / reference_probs.shape[0]
+        target_vs_rejsample_dist = torch.dist(target_probs,
+                                              rej_sample_probs).item()
+
+        return reference_vs_rejsample_dist, target_vs_rejsample_dist
+
+    def _estimate_rejection_sampling_pdf(
+        self,
+        draft_probs: torch.Tensor,
+        target_probs: torch.Tensor,
+        num_samples: int,
+    ) -> torch.Tensor:
+        # Repeat draft probs num_samples times.
+        draft_probs = draft_probs.reshape(1, self.k, self.vocab_size).repeat(
+            num_samples, 1, 1)
+
+        # Repeat target probs num_samples * k times.
+        # Rejection sampler requires bonus token probs, but they aren't used.
+        target_probs = target_probs.reshape(1, 1, self.vocab_size).repeat(
+            num_samples, self.k, 1)
+
+        # Randomly sample draft token ids from draft probs.
+        draft_token_ids = torch.multinomial(draft_probs[:, 0, :],
+                                            num_samples=1,
+                                            replacement=True).reshape(
+                                                num_samples, self.k)
+
+        # Bonus tokens not used but required.
+        bonus_token_ids = torch.zeros((1, self.num_bonus_tokens),
+                                      dtype=torch.int64,
+                                      device="cuda").repeat(num_samples, 1)
+
+        # Get output tokens via rejection sampling.
+        output_token_ids = self.rejection_sampler(target_probs.to("cuda"),
+                                                  bonus_token_ids.to("cuda"),
+                                                  draft_probs.to("cuda"),
+                                                  draft_token_ids.to("cuda"))
+
+        # Remove bonus tokens
+        output_token_ids = output_token_ids[:, :-1].flatten()
+
+        # Estimate probability density function
+        hist = torch.histogram(output_token_ids.to(dtype=torch.float,
+                                                   device="cpu"),
+                               bins=self.vocab_size,
+                               range=self.vocab_range,
+                               density=True)
+
+        return hist.hist

vllm/model_executor/layers/rejection_sampler.py

+from typing import Tuple, Optional
+from functools import cached_property
+
+import torch
+import torch.nn as nn
+import torch.jit
+
+
+class RejectionSampler(nn.Module):
+    """Apply modified rejection sampling as described in "Accelerating Large
+        Language Model Decoding with Speculative Sampling"
+        https://arxiv.org/pdf/2302.01318.pdf.
+    """
+
+    def __init__(self, strict_mode: bool = False):
+        """Create a rejection sampler.
+
+        Args:
+            strict_mode: Whether or not to perform shape/device/dtype checks
+                during sampling. This catches correctness issues but adds
+                nontrivial latency.
+        """
+        super().__init__()
+        self.probs_dtype = torch.float32
+        self.token_id_dtype = torch.int64
+        self._strict_mode = strict_mode
+
+        # NOTE: A "bonus token" is accepted iff all proposal tokens are
+        # accepted. There is always only one possible bonus token. We store this
+        # value in a variable for readability.
+        self._num_bonus_tokens = 1
+
+        self.num_accepted_tokens: Optional[torch.Tensor] = None
+        self.num_emitted_tokens: Optional[torch.Tensor] = None
+        self.num_draft_tokens: int = 0
+
+    def init_gpu_tensors(self, rank: int) -> None:
+        assert self.num_accepted_tokens is None
+        device = f"cuda:{rank}"
+        self.num_accepted_tokens = torch.tensor(0,
+                                                dtype=torch.long,
+                                                device=device)
+        self.num_emitted_tokens = torch.tensor(0,
+                                               dtype=torch.long,
+                                               device=device)
+
+    def forward(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> torch.Tensor:
+        """Sample token ids using rejection sampling. This accepts or rejects
+        tokens proposed by the draft model using the probability of each token
+        according to the draft and target models.
+
+        In the worst case where all draft tokens are rejected, it is guaranteed
+        one correct token will be emitted.
+
+        In the case where all draft tokens are accepted, a bonus token will be
+        accepted as its cheap to have the target model score this speculative
+        sequence.
+
+        Args:
+            target_probs: The probability distribution over token ids given
+                context according to the target model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            bonus_token_ids: The "bonus" token ids that are accepted iff all
+                speculative tokens in a sequence are accepted.
+            shape = [batch_size, num_bonus_tokens]
+
+            draft_probs: The probability distribution over token ids given
+                context according to the draft model.
+            shape = [batch_size, num_speculative_tokens, vocab_size]
+
+            draft_token_ids: The token ids that were sampled from the draft
+                probabilities.
+            shape = [batch_size, num_speculative_tokens]
+
+        Returns:
+            output_token_ids: The token ids sampled via rejection sampling,
+                or -1 if unable to sample a token because the previous token
+                was rejected.
+            shape = [batch_size, num_speculative_tokens + num_bonus_tokens]
+        """
+        # Only perform shape/dtype/device checking in strict mode, as it adds
+        # overhead.
+        if self._strict_mode:
+            self._raise_if_incorrect_shape(target_probs, bonus_token_ids,
+                                           draft_probs, draft_token_ids)
+            self._raise_if_incorrect_dtype(target_probs, bonus_token_ids,
+                                           draft_probs, draft_token_ids)
+            self._raise_if_inconsistent_device(target_probs, bonus_token_ids,
+                                               draft_probs, draft_token_ids)
+            self._raise_if_out_of_bounds_vocab(target_probs.shape[-1],
+                                               bonus_token_ids,
+                                               draft_token_ids)
+
+        accepted, recovered_token_ids = self._batch_modified_rejection_sampling(
+            target_probs,
+            draft_probs,
+            draft_token_ids,
+        )
+
+        output_token_ids = self._create_output(
+            accepted,
+            recovered_token_ids,
+            draft_token_ids,
+            bonus_token_ids,
+        )
+        return output_token_ids
+
+    def _batch_modified_rejection_sampling(
+            self,
+            target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+    ) -> Tuple[torch.Tensor, torch.Tensor]:
+        """Perform modified rejection sampling on each sequence.
+
+        Returns:
+            A tuple of two tensors:
+            0: A bool tensor of which tokens in each sequence is accepted.
+                shape = [batch_size, k]
+            1: Token ids sampled from a recovered distribution, to be used
+                when a token is rejected.
+                shape = [batch_size, k]
+        """
+
+        batch_size, k, vocab_size = draft_probs.shape
+
+        # shape [batch_size, k]
+        accepted = self._get_accepted(target_probs, draft_probs,
+                                      draft_token_ids)
+
+        recovered_probs = self._get_recovered_probs(
+            target_probs, draft_probs).reshape(batch_size * k, vocab_size)
+
+        recovered_token_ids = _multinomial(recovered_probs,
+                                           num_samples=1).reshape(
+                                               batch_size, k)
+        return accepted, recovered_token_ids
+
+    def _get_accepted(
+            self,
+            target_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_probs: torch.Tensor,  # [batch_size, k, vocab_size]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+    ) -> torch.Tensor:
+        r"""Create bool matrix over the proposed draft tokens. If
+        True, then a token can be accepted, else it should be
+        rejected.
+
+        Given :math:`q(\hat{x}_{n+1}|x_1, \dots, x_n)`, the probability of
+        :math:`\hat{x}_{n+1}` given context :math:`x_1, \dots, x_n` according
+        to the target model, and :math:`p(\hat{x}_{n+1}|x_1, \dots, x_n)`, the
+        same conditional probability according to the draft model, the token
+        is accepted with probability:
+
+        .. math::
+            \min\left(1, \frac{q(\hat{x}_{n+1}|x_1, \dots, x_n)}
+                           {p(\hat{x}_{n+1}|x_1, \dots, x_n)}\right)
+
+        This implementation does not apply causality. When using the output,
+        if a token is rejected, subsequent tokens should not be used.
+
+        Returns a bool tensor of shape [batch_size, k] specifying which tokens
+        are accepted.
+        """
+        batch_size, k, _ = draft_probs.shape
+        batch_indices = torch.arange(batch_size,
+                                     device=target_probs.device)[:, None]
+        probs_indicies = torch.arange(k, device=target_probs.device)
+
+        # shape [batch_size, k]
+        selected_draft_probs = draft_probs[batch_indices, probs_indicies,
+                                           draft_token_ids]
+
+        # shape [batch_size, k]
+        selected_target_probs = target_probs[batch_indices, probs_indicies,
+                                             draft_token_ids]
+
+        uniform_rand = torch.rand(batch_size,
+                                  k,
+                                  dtype=self.probs_dtype,
+                                  device=target_probs.device)
+        capped_ratio = torch.minimum(
+            selected_target_probs / selected_draft_probs,
+            torch.full((1, ), 1, device=target_probs.device))
+        accepted = uniform_rand < capped_ratio
+
+        return accepted
+
+    def _get_recovered_probs(
+            self,
+            target_probs: torch.Tensor,  # [k, vocab_size]
+            draft_probs: torch.Tensor,  # [k, vocab_size]
+    ) -> torch.Tensor:
+        r"""Create a probability distribution for each proposed token which can
+        be sampled if the proposed token is rejected.
+
+        When this routine is applied sequentially, the true distribution of the
+        target model is recovered (within hardware numerics).
+
+        The probability distribution used in this rejection case is constructed
+        as follows. Given :math:`q(x|x_1, \dots, x_n)`, the probability of
+        :math:`x` given context :math:`x_1, \dots, x_n` according to the target
+        model and :math:`p(x|x_1, \dots, x_n)`, the same conditional probability
+        according to the draft model:
+
+        .. math::
+            x_{n+1} \sim (q(x|x_1, \dots, x_n) - p(x|x_1, \dots, x_n))_+
+
+        where :math:`(f(x))_+` is defined as:
+
+        .. math::
+            (f(x))_+ = \frac{\max(0, f(x))}{\sum_x \max(0, f(x))}
+
+        See https://github.com/vllm-project/vllm/pull/2336 for a visualization
+        of the draft, target, and recovered probability distributions.
+
+        Returns a tensor of shape [batch_size, k, vocab_size].
+
+        Note: This batches operations on GPU and thus constructs the recovered
+        distribution for all tokens, even if they are accepted. This causes
+        division-by-zero errors, so we use self._smallest_positive_value to
+        avoid that. This introduces some drift to the distribution.
+        """
+        _, k, _ = draft_probs.shape
+
+        # shape [batch_size, k, vocab_size]
+        difference = target_probs - draft_probs
+
+        # TODO(cade): Can we use logprobs instead of probs, and avoid the
+        # division-by-zero errors without introducing distribution drift?
+
+        # shape [batch_size, k, vocab_size]
+        f = torch.clamp(difference, min=self._smallest_positive_value)
+
+        # shape [batch_size, k, vocab_size]
+        recovered_probs = f / torch.sum(f, dim=-1).reshape(-1, k, 1)
+
+        return recovered_probs
+
+    @cached_property
+    def _smallest_positive_value(self) -> float:
+        """Return the smallest positive value representable by the probs dtype.
+        This value is used when constructing a distribution from which to sample
+        recovered tokens in the first rejection case.
+
+        See _get_recovered_probs for more details
+
+        Note that this isn't actually the smallest positive value representable
+        by float32, but the smallest positive normal value.
+        See https://en.wikipedia.org/wiki/Subnormal_number for more information.
+        """
+        return torch.finfo(self.probs_dtype).tiny
+
+    def _create_output(
+            self,
+            accepted: torch.Tensor,  # [batch_size, k]
+            recovered_token_ids: torch.Tensor,  # [batch_size, k]
+            draft_token_ids: torch.Tensor,  # [batch_size, k]
+            bonus_token_ids: torch.Tensor,  # [batch_size]
+    ) -> torch.Tensor:
+        """Format output. Returns a matrix of token ids. When
+        a token is rejected via rejection sampling, all subsequent
+        token ids are set to -1 for the sequence.
+
+        shape = [batch_size, k + num_bonus_tokens]
+        """
+        bonus_token_ids = bonus_token_ids.squeeze()
+        batch_size, k = recovered_token_ids.shape
+
+        # Determine the index of the first False value for each row.
+        limits = (accepted == 0).max(1).indices
+        limits[~(accepted == 0).any(1)] = k
+
+        # Create masks using the indices.
+        indices = torch.arange(k, device=accepted.device).unsqueeze(0)
+        accepted_mask = indices < limits.unsqueeze(1)
+        after_false_mask = indices == limits.unsqueeze(1)
+
+        # Create an extended output tensor
+        output_with_bonus_tokens = -torch.ones(
+            (batch_size, k + self._num_bonus_tokens),
+            dtype=self.token_id_dtype,
+            device=accepted.device)
+        output = output_with_bonus_tokens[:, :k]
+
+        # Fill in the first k columns of the output tensor using masks and data
+        # tensors.
+        output[:, :k] = torch.where(accepted_mask, draft_token_ids,
+                                    -torch.ones_like(draft_token_ids))
+
+        # Fill the last column.
+        # We check output directly as accepted may have True values inconsistent
+        # with causal acceptance.
+        output_with_bonus_tokens[:, -1] = torch.where(output[:, -1] != -1,
+                                                      bonus_token_ids, -1)
+
+        # Fill the recovered token ids.
+        output.mul_(~after_false_mask).add_(
+            recovered_token_ids.mul(after_false_mask))
+
+        self.num_accepted_tokens += accepted.sum()
+        self.num_emitted_tokens += (output_with_bonus_tokens != -1).sum()
+        self.num_draft_tokens += batch_size * k
+
+        return output_with_bonus_tokens
+
+    def _raise_if_incorrect_shape(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        (target_batch_size, num_target_probs,
+         target_vocab_size) = target_probs.shape
+        bonus_batch_size, num_bonus_tokens = bonus_token_ids.shape
+        draft_batch_size, num_draft_probs, draft_vocab_size = draft_probs.shape
+        draft_token_ids_batch_size, num_draft_token_ids = draft_token_ids.shape
+
+        assert draft_batch_size == target_batch_size
+        assert num_draft_probs == num_target_probs
+        assert (draft_vocab_size == target_vocab_size
+                ), f"{draft_vocab_size=} {target_vocab_size=}"
+
+        assert draft_token_ids_batch_size == draft_batch_size
+        assert num_draft_token_ids == num_draft_probs
+
+        assert bonus_batch_size == target_batch_size
+        assert num_bonus_tokens == self._num_bonus_tokens
+
+    def _raise_if_incorrect_dtype(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        assert all(probs.dtype == self.probs_dtype
+                   for probs in [target_probs, draft_probs])
+        assert all(token_ids.dtype == self.token_id_dtype
+                   for token_ids in [bonus_token_ids, draft_token_ids])
+
+    def _raise_if_inconsistent_device(
+        self,
+        target_probs: torch.Tensor,
+        bonus_token_ids: torch.Tensor,
+        draft_probs: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        devices = [
+            t.device for t in
+            [target_probs, bonus_token_ids, draft_probs, draft_token_ids]
+        ]
+        assert all([devices[0] == device for device in devices])
+
+    def _raise_if_out_of_bounds_vocab(
+        self,
+        vocab_size: int,
+        bonus_token_ids: torch.Tensor,
+        draft_token_ids: torch.Tensor,
+    ) -> None:
+        assert torch.all(bonus_token_ids < vocab_size)
+        assert torch.all(bonus_token_ids >= 0)
+        assert torch.all(draft_token_ids < vocab_size)
+        assert torch.all(draft_token_ids >= 0)
+
+
+# torch.multinomial forces a GPU<->CPU sync.
+# Therefore, we use an optimized implementation instead that skips the sync.
+# Note that we always sample with replacement.
+# probs will be modified in place, but this is fine, as we pass
+# in a copy already.
+@torch.jit.script
+def _multinomial(
+    probs: torch.Tensor,
+    num_samples: int,
+) -> torch.Tensor:
+    if num_samples > 1:
+        # This is equivalent to torch.repeat_interleaved (which also
+        # forces a GPU<->CPU sync).
+        probs = probs[:, None, :].expand(probs.shape[0], num_samples,
+                                         probs.shape[1]).contiguous().view(
+                                             -1, probs.shape[1])
+    q = torch.empty_like(probs).exponential_(1.0)
+    return probs.div_(q).argmax(dim=1).view(-1, num_samples)