[Model Runner V2] Fuse probabilistic rejection sample kernels (#38496)

Signed-off-by: Giancarlo Delfin <gdelfin@inferact.ai>

.buildkite/test_areas/model_runner_v2.yaml

@@ -100,11 +100,13 @@ steps:
   - vllm/v1/worker/gpu/
   - vllm/v1/worker/gpu_worker.py
   - tests/v1/spec_decode/test_max_len.py
+  - tests/v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
   - tests/v1/spec_decode/test_synthetic_rejection_sampler_utils.py
   - tests/v1/e2e/spec_decode/test_spec_decode.py
   commands:
   - set -x
   - export VLLM_USE_V2_MODEL_RUNNER=1
   - pytest -v -s v1/spec_decode/test_max_len.py -k "eagle or mtp"
+  - pytest -v -s v1/spec_decode/test_probabilistic_rejection_sampler_utils.py
   - pytest -v -s v1/spec_decode/test_synthetic_rejection_sampler_utils.py
   - pytest -v -s v1/e2e/spec_decode/test_spec_decode.py -k "eagle or mtp"

tests/v1/spec_decode/test_probabilistic_rejection_sampler_utils.py

+# SPDX-License-Identifier: Apache-2.0
+# SPDX-FileCopyrightText: Copyright contributors to the vLLM project
+
+import math
+
+import pytest
+import torch
+
+from vllm.v1.worker.gpu.spec_decode.probabilistic_rejection_sampler_utils import (
+    probabilistic_rejection_sample,
+)
+
+VOCAB_SIZE = 4096
+
+# Skip if no CUDA - Triton kernel requires GPU
+pytest.importorskip("triton")
+if not torch.cuda.is_available():
+    pytest.skip("CUDA required for rejection sampler tests", allow_module_level=True)
+
+
+def _build_rejection_sample_inputs(
+    target_logits_1d: torch.Tensor,
+    draft_logits_1d: torch.Tensor,
+    num_speculative_steps: int,
+    temperature: float,
+    num_trials: int,
+) -> dict:
+    device = target_logits_1d.device
+    vocab_size = target_logits_1d.shape[0]
+    K = num_speculative_steps
+    num_logits = num_trials * (K + 1)
+
+    target_logits = target_logits_1d.unsqueeze(0).expand(num_logits, -1).contiguous()
+    draft_logits = (
+        draft_logits_1d.view(1, 1, vocab_size).expand(num_trials, K, -1).contiguous()
+    )
+
+    draft_probs = torch.softmax(draft_logits_1d, dim=0)
+    draft_tokens = torch.multinomial(
+        draft_probs.expand(num_trials, -1), K, replacement=True
+    )
+    draft_sampled_2d = torch.zeros(num_trials, K + 1, dtype=torch.int64, device=device)
+    draft_sampled_2d[:, 1:] = draft_tokens
+    draft_sampled = draft_sampled_2d.reshape(-1)
+
+    cu_num_logits = torch.arange(num_trials + 1, dtype=torch.int32, device=device) * (
+        K + 1
+    )
+    pos = torch.arange(num_logits, dtype=torch.int32, device=device)
+    idx_mapping = torch.arange(num_trials, dtype=torch.int32, device=device)
+    expanded_idx_mapping = torch.arange(
+        num_trials, dtype=torch.int32, device=device
+    ).repeat_interleave(K + 1)
+    expanded_local_pos = torch.arange(K + 1, dtype=torch.int32, device=device).repeat(
+        num_trials
+    )
+    temp_tensor = torch.full(
+        (num_trials,), temperature, dtype=torch.float32, device=device
+    )
+    seed = torch.arange(num_trials, dtype=torch.int64, device=device)
+
+    return dict(
+        target_logits=target_logits,
+        draft_logits=draft_logits,
+        draft_sampled=draft_sampled,
+        cu_num_logits=cu_num_logits,
+        pos=pos,
+        idx_mapping=idx_mapping,
+        expanded_idx_mapping=expanded_idx_mapping,
+        expanded_local_pos=expanded_local_pos,
+        temperature=temp_tensor,
+        seed=seed,
+    )
+
+
+def _assert_distribution_match(
+    sampled_tokens: torch.Tensor,
+    target_probs: torch.Tensor,
+    device: str,
+    label: str = "",
+    min_expected: float = 5.0,
+):
+    """
+    Assert sampled tokens match the target distribution via a
+    chi-squared goodness-of-fit test. This is done by computing
+    observed vs expected token counts (target_probs * num_samples),
+    then checking that the chi-squared statistic is below a conservative
+    threshold. The threshold is set at df + 10*sqrt(2*df), which
+    corresponds to ~10 sigma under the chi-squared distribution's
+    normal approximation, effectively disallowing false positives.
+
+    NOTE: Tokens with expected count < min_expected are merged into
+    a single "other" bin to minimize chi-squared noise.
+    """
+    num_samples = sampled_tokens.shape[0]
+    vocab_size = target_probs.shape[0]
+
+    observed = torch.zeros(vocab_size, device=device, dtype=torch.float32)
+    observed.scatter_add_(0, sampled_tokens, torch.ones(num_samples, device=device))
+    expected = target_probs * num_samples
+
+    sufficient = expected >= min_expected
+    obs_main = observed[sufficient]
+    exp_main = expected[sufficient]
+
+    obs_other = observed[~sufficient].sum().unsqueeze(0)
+    exp_other = expected[~sufficient].sum().unsqueeze(0)
+
+    if exp_other.item() >= min_expected:
+        obs_all = torch.cat([obs_main, obs_other])
+        exp_all = torch.cat([exp_main, exp_other])
+    else:
+        obs_all = obs_main
+        exp_all = exp_main
+
+    chi2 = ((obs_all - exp_all) ** 2 / exp_all).sum().item()
+    df = obs_all.shape[0] - 1
+    if df < 1:
+        # All samples were merged into < 2 bins, which is too
+        # few to evaluate.
+        return
+
+    threshold = df + 10 * math.sqrt(2 * df)
+    prefix = f"[{label}] " if label else ""
+    assert chi2 < threshold, (
+        f"{prefix}Chi-squared test failed: chi2={chi2:.1f}, "
+        f"df={df}, threshold={threshold:.1f}. "
+        f"Output distribution does not match target distribution."
+    )
+
+
+@pytest.mark.parametrize(
+    "num_speculative_steps,temperature",
+    [
+        (1, 0.6),
+        (3, 0.6),
+        (1, 1.0),
+        (3, 1.0),
+    ],
+)
+def test_stochastic_rejection_sample(num_speculative_steps: int, temperature: float):
+    """
+    Verify that rejection sampling produces the target distribution.
+    This is done by simulating many independent trials of speculative
+    decoding (from a fixed target and draft distribution). We then
+    run rejection sample on all of the trials (requests), and verify
+    that the sampled tokens at every position follow the target
+    distribution p(x).
+    """
+
+    torch.manual_seed(42)
+    device = "cuda"
+    num_trials = 10 * VOCAB_SIZE
+
+    target_logits_1d = torch.randn(VOCAB_SIZE, device=device, dtype=torch.float32)
+    draft_logits_1d = torch.randn(VOCAB_SIZE, device=device, dtype=torch.float32)
+
+    if temperature > 0:
+        target_logits_1d /= temperature
+        draft_logits_1d /= temperature
+
+    inputs = _build_rejection_sample_inputs(
+        target_logits_1d,
+        draft_logits_1d,
+        num_speculative_steps,
+        temperature=temperature,
+        num_trials=num_trials,
+    )
+
+    sampled, num_sampled = probabilistic_rejection_sample(
+        **inputs, num_speculative_steps=num_speculative_steps
+    )
+
+    target_probs = torch.softmax(target_logits_1d, dim=0)
+    for pos in range(num_speculative_steps + 1):
+        accepted_mask = num_sampled >= pos + 1
+        _assert_distribution_match(
+            sampled[accepted_mask, pos], target_probs, device, label=f"position {pos}"
+        )
+
+
+@pytest.mark.parametrize("num_speculative_steps", [1, 3])
+def test_greedy_rejection_sample(num_speculative_steps: int):
+    """
+    Verify that greedy (temperature=0) always outputs the target argmax
+    at every accepted position.
+    """
+
+    torch.manual_seed(42)
+    device = "cuda"
+    num_trials = 10 * VOCAB_SIZE
+
+    target_logits_1d = torch.randn(VOCAB_SIZE, device=device, dtype=torch.float32)
+    draft_logits_1d = torch.randn(VOCAB_SIZE, device=device, dtype=torch.float32)
+
+    inputs = _build_rejection_sample_inputs(
+        target_logits_1d,
+        draft_logits_1d,
+        num_speculative_steps,
+        temperature=0.0,
+        num_trials=num_trials,
+    )
+
+    sampled, num_sampled = probabilistic_rejection_sample(
+        **inputs, num_speculative_steps=num_speculative_steps
+    )
+
+    target_argmax = target_logits_1d.argmax().item()
+
+    steps = torch.arange(num_speculative_steps + 1, device=device).unsqueeze(0)
+    accepted_mask = steps < num_sampled.unsqueeze(1)
+
+    assert (sampled[accepted_mask] == target_argmax).all(), (
+        "Greedy sampling produced tokens that are not the target argmax"
+    )

vllm/v1/worker/gpu/sample/gumbel.py

@@ -65,36 +65,20 @@ def tl_rand64(seed, offset, includes_zero: tl.constexpr):
 
 
 @triton.jit
-def _gumbel_sample_kernel(
-    local_argmax_ptr,
-    local_argmax_stride,
-    local_max_ptr,
-    local_max_stride,
-    processed_logits_ptr,
-    processed_logits_stride,
-    logits_ptr,
-    logits_stride,
+def gumbel_block_argmax(
+    logits,
+    block,
+    mask,
+    token_idx,
     expanded_idx_mapping_ptr,
+    temp_ptr,
     seeds_ptr,
     pos_ptr,
-    temp_ptr,
-    vocab_size,
-    BLOCK_SIZE: tl.constexpr,
+    processed_logits_ptr,
+    processed_logits_stride,
     APPLY_TEMPERATURE: tl.constexpr,
 ):
-    token_idx = tl.program_id(0)
     req_state_idx = tl.load(expanded_idx_mapping_ptr + token_idx)
-
-    block_idx = tl.program_id(1)
-    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    mask = block < vocab_size
-    logits = tl.load(
-        logits_ptr + token_idx * logits_stride + block,
-        mask=mask,
-        other=float("-inf"),
-    )
-    logits = logits.to(tl.float32)
-
     temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
     if temp != 0.0 and APPLY_TEMPERATURE:
         # Apply temperature.
@@ -102,8 +86,8 @@ def _gumbel_sample_kernel(
         # E.g., if the kernel uses tl.div_rn, we should use tl.div_rn here too.
         logits = logits / temp
 
-    # Store the temperature-applied logits.
     if processed_logits_ptr is not None:
+        # Store the temperature-applied logits.
         tl.store(
             processed_logits_ptr + req_state_idx * processed_logits_stride + block,
             logits,
@@ -126,6 +110,51 @@ def _gumbel_sample_kernel(
         logits = tl.where(mask, logits + gumbel_noise, float("-inf"))
 
     value, idx = tl.max(logits, axis=0, return_indices=True)
+    return value, idx
+
+
+@triton.jit
+def _gumbel_sample_kernel(
+    local_argmax_ptr,
+    local_argmax_stride,
+    local_max_ptr,
+    local_max_stride,
+    processed_logits_ptr,
+    processed_logits_stride,
+    logits_ptr,
+    logits_stride,
+    expanded_idx_mapping_ptr,
+    seeds_ptr,
+    pos_ptr,
+    temp_ptr,
+    vocab_size,
+    BLOCK_SIZE: tl.constexpr,
+    APPLY_TEMPERATURE: tl.constexpr,
+):
+    token_idx = tl.program_id(0)
+    block_idx = tl.program_id(1)
+    block = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
+    mask = block < vocab_size
+    logits = tl.load(
+        logits_ptr + token_idx * logits_stride + block,
+        mask=mask,
+        other=float("-inf"),
+    )
+    logits = logits.to(tl.float32)
+
+    value, idx = gumbel_block_argmax(
+        logits,
+        block,
+        mask,
+        token_idx,
+        expanded_idx_mapping_ptr,
+        temp_ptr,
+        seeds_ptr,
+        pos_ptr,
+        processed_logits_ptr,
+        processed_logits_stride,
+        APPLY_TEMPERATURE=APPLY_TEMPERATURE,
+    )
     token_id = block_idx * BLOCK_SIZE + idx
     tl.store(local_argmax_ptr + token_idx * local_argmax_stride + block_idx, token_id)
     tl.store(local_max_ptr + token_idx * local_max_stride + block_idx, value)

vllm/v1/worker/gpu/spec_decode/rejection_sampler.py

@@ -7,11 +7,13 @@ from vllm.triton_utils import tl, triton
 from vllm.v1.outputs import LogprobsTensors
 from vllm.v1.worker.gpu.input_batch import InputBatch
 from vllm.v1.worker.gpu.metrics.logits import get_num_nans
-from vllm.v1.worker.gpu.sample.gumbel import gumbel_sample, tl_rand64
 from vllm.v1.worker.gpu.sample.logprob import compute_topk_logprobs
 from vllm.v1.worker.gpu.sample.output import SamplerOutput
 from vllm.v1.worker.gpu.sample.sampler import Sampler
 from vllm.v1.worker.gpu.sample.states import NO_LOGPROBS
+from vllm.v1.worker.gpu.spec_decode.probabilistic_rejection_sampler_utils import (
+    probabilistic_rejection_sample,
+)
 from vllm.v1.worker.gpu.spec_decode.synthetic_rejection_sampler_utils import (
     compute_synthetic_rejection_sampler_params,
     synthetic_rejection_sample,
@@ -75,357 +77,6 @@ def strict_rejection_sample(
     return sampled, num_sampled
 
 
-@triton.jit
-def _gather_draft_logits_and_target_argmax_kernel(
-    local_target_argmax_ptr,
-    local_target_argmax_stride,
-    local_target_max_ptr,
-    local_target_max_stride,
-    # [num_logits, V]
-    out_draft_logits_ptr,
-    out_draft_logits_stride,
-    # [num_logits, V]
-    target_logits_ptr,
-    target_logits_stride,
-    # [max_num_reqs, num_speculative_steps, V]
-    draft_logits_ptr,
-    draft_logits_stride_0,
-    draft_logits_stride_1,
-    # [num_logits]
-    expanded_idx_mapping_ptr,
-    # [num_logits]
-    expanded_local_pos_ptr,
-    # [max_num_reqs]
-    temp_ptr,
-    vocab_size,
-    num_speculative_steps,
-    BLOCK_SIZE: tl.constexpr,
-):
-    logit_idx = tl.program_id(0)
-    req_state_idx = tl.load(expanded_idx_mapping_ptr + logit_idx)
-    draft_step_idx = tl.load(expanded_local_pos_ptr + logit_idx)
-
-    block_idx = tl.program_id(1)
-    block_offsets = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    mask = block_offsets < vocab_size
-    temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
-
-    if temp == 0.0:
-        # Greedy sampling. Get the target logits argmax.
-        target_logits = tl.load(
-            target_logits_ptr + logit_idx * target_logits_stride + block_offsets,
-            mask=mask,
-            other=float("-inf"),
-        ).to(tl.float32)
-        value, idx = tl.max(target_logits, axis=0, return_indices=True)
-        token_id = block_idx * BLOCK_SIZE + idx
-        tl.store(
-            local_target_argmax_ptr
-            + logit_idx * local_target_argmax_stride
-            + block_idx,
-            token_id,
-        )
-        tl.store(
-            local_target_max_ptr + logit_idx * local_target_max_stride + block_idx,
-            value,
-        )
-    elif draft_step_idx < num_speculative_steps:
-        draft_logits = tl.load(
-            draft_logits_ptr
-            + req_state_idx * draft_logits_stride_0
-            + draft_step_idx * draft_logits_stride_1
-            + block_offsets,
-            mask=mask,
-            other=float("-inf"),
-        ).to(tl.float32)
-        tl.store(
-            out_draft_logits_ptr + logit_idx * out_draft_logits_stride + block_offsets,
-            draft_logits,
-            mask=mask,
-        )
-
-
-@triton.jit
-def _probabilistic_rejection_kernel(
-    # [num_reqs, num_speculative_steps + 1]
-    sampled_ptr,
-    sampled_stride,
-    # [num_reqs]
-    rejected_steps_ptr,
-    # [num_reqs]
-    rejected_pos_ptr,
-    # [num_logits]
-    draft_sampled_ptr,
-    # [num_logits, V]
-    target_probs_ptr,
-    target_probs_stride,
-    # [num_logits, V]
-    draft_probs_ptr,
-    draft_probs_stride,
-    # [num_logits, num_blocks]
-    local_target_argmax_ptr,
-    local_target_argmax_stride,
-    # [num_logits, num_blocks]
-    local_target_max_ptr,
-    local_target_max_stride,
-    # [num_reqs + 1]
-    cu_num_logits_ptr,
-    # [num_logits]
-    pos_ptr,
-    # [num_reqs]
-    idx_mapping_ptr,
-    # [max_num_reqs]
-    temp_ptr,
-    # [max_num_reqs]
-    seeds_ptr,
-    NUM_BLOCKS: tl.constexpr,
-    PADDED_NUM_BLOCKS: tl.constexpr,
-):
-    req_idx = tl.program_id(0)
-    start_idx = tl.load(cu_num_logits_ptr + req_idx)
-    num_tokens = tl.load(cu_num_logits_ptr + req_idx + 1) - start_idx
-    req_state_idx = tl.load(idx_mapping_ptr + req_idx)
-    seed = tl.load(seeds_ptr + req_state_idx)
-    temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
-
-    rejected_step = 0
-    accepted = True
-    for i in range(num_tokens - 1):
-        if accepted:
-            logit_idx = start_idx + i
-            draft_sampled = tl.load(draft_sampled_ptr + logit_idx + 1)
-            if temp == 0.0:
-                # Greedy sampling. Only accept the sampled draft token if
-                # it exactly matches the target argmax.
-                block_offsets = tl.arange(0, PADDED_NUM_BLOCKS)
-                block_mask = block_offsets < NUM_BLOCKS
-                local_max = tl.load(
-                    local_target_max_ptr
-                    + logit_idx * local_target_max_stride
-                    + block_offsets,
-                    mask=block_mask,
-                    other=float("-inf"),
-                )
-                max_block = tl.argmax(local_max, axis=0)
-                target_argmax = tl.load(
-                    local_target_argmax_ptr
-                    + logit_idx * local_target_argmax_stride
-                    + max_block
-                )
-                accepted &= target_argmax == draft_sampled
-            else:
-                target_prob = tl.load(
-                    target_probs_ptr + logit_idx * target_probs_stride + draft_sampled
-                ).to(tl.float64)
-                draft_prob = tl.load(
-                    draft_probs_ptr + logit_idx * draft_probs_stride + draft_sampled
-                ).to(tl.float64)
-                pos = tl.load(pos_ptr + logit_idx)
-                u = tl_rand64(seed, pos, includes_zero=False)
-                accepted &= target_prob > u * draft_prob
-            tl.store(sampled_ptr + req_idx * sampled_stride + i, draft_sampled)
-            rejected_step += accepted
-    tl.store(rejected_steps_ptr + req_idx, rejected_step)
-    pos_val = tl.load(pos_ptr + start_idx + rejected_step)
-    tl.store(rejected_pos_ptr + req_idx, pos_val)
-
-
-@triton.jit
-def _compute_residual_logits_kernel(
-    # [num_reqs, V]
-    residual_logits_ptr,
-    residual_logits_stride,
-    # [num_logits, V]
-    target_probs_ptr,
-    target_probs_stride,
-    # [num_logits, V]
-    draft_probs_ptr,
-    draft_probs_stride,
-    # [num_logits, V]
-    target_logits_ptr,
-    target_logits_stride,
-    # [num_reqs]
-    rejected_step_ptr,
-    # [num_reqs + 1]
-    cu_num_logits_ptr,
-    # [num_reqs]
-    idx_mapping_ptr,
-    # [max_num_reqs]
-    temp_ptr,
-    vocab_size,
-    BLOCK_SIZE: tl.constexpr,
-):
-    req_idx = tl.program_id(0)
-    block_idx = tl.program_id(1)
-
-    req_state_idx = tl.load(idx_mapping_ptr + req_idx)
-    start_idx = tl.load(cu_num_logits_ptr + req_idx)
-    end_idx = tl.load(cu_num_logits_ptr + req_idx + 1)
-    rejected_logit_idx = start_idx + tl.load(rejected_step_ptr + req_idx)
-    temp = tl.load(temp_ptr + req_state_idx).to(tl.float32)
-    block_offsets = block_idx * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
-    mask = block_offsets < vocab_size
-
-    if temp == 0.0 or (rejected_logit_idx == end_idx - 1):
-        # Greedy sampling / bonus token. In either case, use the
-        # target logits directly to reduce numerical error.
-        residual_logits = tl.load(
-            target_logits_ptr
-            + rejected_logit_idx * target_logits_stride
-            + block_offsets,
-            mask=mask,
-            other=float("-inf"),
-        )
-    else:
-        target_probs = tl.load(
-            target_probs_ptr + rejected_logit_idx * target_probs_stride + block_offsets,
-            mask=mask,
-            other=0.0,
-        )
-        draft_probs = tl.load(
-            draft_probs_ptr + rejected_logit_idx * draft_probs_stride + block_offsets,
-            mask=mask,
-            other=0.0,
-        )
-        residual_probs = tl.maximum(target_probs - draft_probs, 0.0)
-        residual_logits = tl.log(residual_probs)
-
-    tl.store(
-        residual_logits_ptr + req_idx * residual_logits_stride + block_offsets,
-        residual_logits,
-        mask=mask,
-    )
-
-
-def probabilistic_rejection_sample(
-    # [num_logits, V]
-    target_logits: torch.Tensor,
-    # [max_num_reqs, num_speculative_steps, V]
-    draft_logits: torch.Tensor,
-    # [num_logits]
-    draft_sampled: torch.Tensor,
-    # [num_reqs + 1]
-    cu_num_logits: torch.Tensor,
-    # [num_logits]
-    pos: torch.Tensor,
-    # [num_reqs]
-    idx_mapping: torch.Tensor,
-    # [num_logits]
-    expanded_idx_mapping: torch.Tensor,
-    # [num_logits]
-    expanded_local_pos: torch.Tensor,
-    # [max_num_reqs]
-    temperature: torch.Tensor,
-    # [max_num_reqs]
-    seed: torch.Tensor,
-    num_speculative_steps: int,
-) -> tuple[torch.Tensor, torch.Tensor]:
-    num_reqs = cu_num_logits.shape[0] - 1
-    num_logits, vocab_size = target_logits.shape
-
-    BLOCK_SIZE = 1024
-    num_blocks = triton.cdiv(vocab_size, BLOCK_SIZE)
-
-    # Gather draft logits and target argmax for greedy sampling.
-    gathered_draft_logits = target_logits.new_empty(target_logits.shape)
-    local_target_argmax = target_logits.new_empty(
-        num_logits, num_blocks, dtype=torch.int64
-    )
-    local_target_max = target_logits.new_empty(
-        num_logits, num_blocks, dtype=torch.float32
-    )
-    _gather_draft_logits_and_target_argmax_kernel[(num_logits, num_blocks)](
-        local_target_argmax,
-        local_target_argmax.stride(0),
-        local_target_max,
-        local_target_max.stride(0),
-        gathered_draft_logits,
-        gathered_draft_logits.stride(0),
-        target_logits,
-        target_logits.stride(0),
-        draft_logits,
-        draft_logits.stride(0),
-        draft_logits.stride(1),
-        expanded_idx_mapping,
-        expanded_local_pos,
-        temperature,
-        vocab_size,
-        num_speculative_steps,
-        BLOCK_SIZE=BLOCK_SIZE,
-    )
-
-    # Compute target and draft probs.
-    target_probs = torch.softmax(target_logits, dim=-1)
-    draft_probs = torch.softmax(gathered_draft_logits, dim=-1)
-
-    # Rejection sample.
-    # [num_reqs, num_speculative_steps + 1]
-    sampled = draft_sampled.new_empty(
-        num_reqs, num_speculative_steps + 1, dtype=torch.int64
-    )
-    # [num_reqs]
-    rejected_steps = sampled.new_empty(num_reqs)
-    # [num_reqs]
-    rejected_pos = pos.new_empty(num_reqs)
-    _probabilistic_rejection_kernel[(num_reqs,)](
-        sampled,
-        sampled.stride(0),
-        rejected_steps,
-        rejected_pos,
-        draft_sampled,
-        target_probs,
-        target_probs.stride(0),
-        draft_probs,
-        draft_probs.stride(0),
-        local_target_argmax,
-        local_target_argmax.stride(0),
-        local_target_max,
-        local_target_max.stride(0),
-        cu_num_logits,
-        pos,
-        idx_mapping,
-        temperature,
-        seed,
-        num_warps=1,
-        NUM_BLOCKS=num_blocks,
-        PADDED_NUM_BLOCKS=triton.next_power_of_2(num_blocks),
-    )
-
-    # Compute the logits and positions to resample the rejected/bonus
-    # tokens from.
-    # [num_reqs, vocab_size]
-    residual_logits = target_logits.new_empty(num_reqs, vocab_size)
-    _compute_residual_logits_kernel[(num_reqs, num_blocks)](
-        residual_logits,
-        residual_logits.stride(0),
-        target_probs,
-        target_probs.stride(0),
-        draft_probs,
-        draft_probs.stride(0),
-        target_logits,
-        target_logits.stride(0),
-        rejected_steps,
-        cu_num_logits,
-        idx_mapping,
-        temperature,
-        vocab_size,
-        BLOCK_SIZE=BLOCK_SIZE,
-    )
-
-    # Gumbel sample tokens from the residual distribution.
-    resampled = gumbel_sample(
-        residual_logits,
-        idx_mapping,
-        temperature,
-        seed,
-        rejected_pos,
-        apply_temperature=False,
-    )
-    sampled.scatter_(1, rejected_steps.unsqueeze(1), resampled.unsqueeze(1))
-
-    return sampled, rejected_steps + 1
-
-
 @triton.jit
 def _flatten_sampled_kernel(
     # [num_logits]