[V1][spec decode] return logprobs for spec decoding (#26060)

Signed-off-by: Giancarlo Delfin <gdelfin@meta.com>
Co-authored-by: Nick Hill <nhill@redhat.com>

tests/v1/sample/test_logprobs.py

@@ -2,12 +2,14 @@
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
 import itertools
+import math
 from collections.abc import Generator
 from typing import get_args
 
 import pytest
 import torch
 
+from tests.utils import large_gpu_mark
 from tests.v1.sample.utils import (
     BatchLogprobsComposition,
     BatchLogprobsSpecType,
@@ -17,6 +19,7 @@ from tests.v1.sample.utils import (
 )
 from vllm import SamplingParams
 from vllm.config.model import LogprobsMode
+from vllm.distributed import cleanup_dist_env_and_memory
 
 from ...conftest import HfRunner, VllmRunner
 
@@ -508,3 +511,94 @@ def test_logprobs_mode(logprobs_mode: LogprobsMode):
     if logprobs_mode in ("raw_logits", "processed_logits"):
         assert positive_values > 0
     del llm
+
+
+@pytest.mark.parametrize("logprobs_mode", get_args(LogprobsMode))
+@pytest.mark.parametrize(
+    "model_setup",
+    [
+        pytest.param(
+            (
+                "eagle",
+                "meta-llama/Llama-3.1-8B-Instruct",
+                "yuhuili/EAGLE-LLaMA3.1-Instruct-8B",
+            ),
+            marks=large_gpu_mark(min_gb=32),
+        ),
+    ],
+)
+def test_spec_decode_logprobs(
+    logprobs_mode: LogprobsMode,
+    model_setup: tuple[str, str, str],
+    monkeypatch: pytest.MonkeyPatch,
+):
+    """Spec decode logprobs should match those of the base model.
+
+    Args:
+        logprobs_mode: logprobs mode.
+        model_setup: Spec decode method, base model name, and
+        draft model name.
+    """
+    from vllm import LLM
+
+    with monkeypatch.context() as m:
+        m.setenv("VLLM_USE_V1", "1")
+        prompt = "Hello world"
+        sampling_params = SamplingParams(
+            temperature=0, logprobs=3, max_tokens=10, ignore_eos=False
+        )
+        method, model_name, spec_model_name = model_setup
+        max_model_len = 256
+
+        # Run base LLM.
+        ref_llm = LLM(
+            model=model_name,
+            max_logprobs=5,
+            max_model_len=max_model_len,
+            seed=42,
+            logprobs_mode=logprobs_mode,
+            gpu_memory_utilization=0.4,
+        )
+        ref_results = ref_llm.generate([prompt], sampling_params)
+        # Collect logprobs outputs from reference LLM.
+        ref_logprobs = []
+        for output in ref_results[0].outputs:
+            for logprobs in output.logprobs:
+                for token_id in logprobs:
+                    ref_logprobs.append(logprobs[token_id])
+        del ref_llm
+        torch.cuda.empty_cache()
+        cleanup_dist_env_and_memory()
+
+        # Run spec decode LLM.
+        spec_llm = LLM(
+            model_name,
+            speculative_config={
+                "method": method,
+                "model": spec_model_name,
+                "num_speculative_tokens": 3,
+                "max_model_len": max_model_len,
+            },
+            max_logprobs=5,
+            max_model_len=max_model_len,
+            seed=42,
+            logprobs_mode=logprobs_mode,
+            gpu_memory_utilization=0.4,
+        )
+        spec_results = spec_llm.generate([prompt], sampling_params)
+        # Collect logprobs outputs from spec decode LLM.
+        spec_logprobs = []
+        for output in spec_results[0].outputs:
+            for logprobs in output.logprobs:
+                for token_id in logprobs:
+                    spec_logprobs.append(logprobs[token_id])
+        del spec_llm
+        torch.cuda.empty_cache()
+        cleanup_dist_env_and_memory()
+
+        # Per-token logprobs are expected to be the same.
+        assert len(ref_logprobs) == len(spec_logprobs)
+        for ref_logprob, spec_logprob in zip(ref_logprobs, spec_logprobs):
+            assert math.isclose(ref_logprob.logprob, spec_logprob.logprob, abs_tol=1e-3)
+            assert ref_logprob.rank == spec_logprob.rank
+            assert ref_logprob.decoded_token == spec_logprob.decoded_token

tests/v1/sample/test_rejection_sampler.py

@@ -1,6 +1,7 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 from typing import Any
+from unittest.mock import Mock
 
 import pytest
 import torch
@@ -11,6 +12,7 @@ from vllm.platforms import current_platform
 from vllm.v1.sample.logits_processor import LogitsProcessors
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.rejection_sampler import PLACEHOLDER_TOKEN_ID, RejectionSampler
+from vllm.v1.sample.sampler import Sampler, SamplerOutput
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 
 DEVICE = current_platform.device_type
@@ -18,7 +20,28 @@ DEVICE = current_platform.device_type
 
 @pytest.fixture
 def rejection_sampler():
-    return RejectionSampler()
+    mock_sampler = Mock(spec=Sampler)
+    mock_sampler.logprobs_mode = "raw_logprobs"
+    return RejectionSampler(mock_sampler)
+
+
+def mock_sampler_output(
+    rejection_sampler: RejectionSampler, bonus_token_ids: torch.Tensor
+):
+    rejection_sampler.sampler.return_value = SamplerOutput(
+        sampled_token_ids=bonus_token_ids, logprobs_tensors=None
+    )
+
+
+def create_spec_decode_metadata(
+    spec_tokens: list[list[int]], logits: torch.Tensor
+) -> SpecDecodeMetadata:
+    metadata = SpecDecodeMetadata.make_dummy(spec_tokens, device=logits.device)
+    metadata.target_logits_indices = torch.arange(logits.shape[0])
+    # Output bonus token ids are mocked, so the bonus logit indices should
+    # be empty.
+    metadata.bonus_logits_indices = torch.empty(0, dtype=torch.int32)
+    return metadata
 
 
 def create_logits_tensor(
@@ -111,19 +134,17 @@ def test_perfect_match(rejection_sampler):
     metadata = create_sampling_metadata(all_greedy=True)
     logits = create_logits_tensor(output_tokens)
     bonus_token_tensor = torch.tensor([output_tokens[0][-1]], device=logits.device)
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor([[1, 2, 3, 4]], dtype=torch.int, device=logits.device)
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_early_mismatch(rejection_sampler):
@@ -134,15 +155,13 @@ def test_early_mismatch(rejection_sampler):
     metadata = create_sampling_metadata(all_greedy=True)
     logits = create_logits_tensor(output_tokens)
     bonus_token_tensor = torch.tensor([output_tokens[0][-1]], device=logits.device)
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor(
@@ -150,7 +169,7 @@ def test_early_mismatch(rejection_sampler):
         dtype=torch.int,
         device=logits.device,
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_multiple_sequences(rejection_sampler):
@@ -163,21 +182,19 @@ def test_multiple_sequences(rejection_sampler):
     bonus_token_tensor = torch.tensor(
         [output_tokens[0][-1], output_tokens[1][-1]], device=logits.device
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor(
         [[1, 2, 5], [3, 4, PLACEHOLDER_TOKEN_ID]], dtype=torch.int, device=logits.device
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_single_token_sequence(rejection_sampler):
@@ -188,19 +205,17 @@ def test_single_token_sequence(rejection_sampler):
     metadata = create_sampling_metadata(all_greedy=True)
     logits = create_logits_tensor(output_tokens)
     bonus_token_tensor = torch.tensor([output_tokens[0][-1]], device=logits.device)
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor([[1, 2]], dtype=torch.int, device=logits.device)
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_empty_sequence(rejection_sampler):
@@ -211,19 +226,17 @@ def test_empty_sequence(rejection_sampler):
     metadata = create_sampling_metadata(all_greedy=True)
     logits = create_logits_tensor(output_tokens)
     bonus_token_tensor = torch.tensor([output_tokens[0][-1]], device=logits.device)
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor([[5]], dtype=torch.int, device=logits.device)
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_multiple_mismatches(rejection_sampler):
@@ -236,15 +249,13 @@ def test_multiple_mismatches(rejection_sampler):
     bonus_token_tensor = torch.tensor(
         [output_tokens[0][-1], output_tokens[1][-1]], device=logits.device
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor(
@@ -255,7 +266,7 @@ def test_multiple_mismatches(rejection_sampler):
         dtype=torch.int,
         device=logits.device,
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 @pytest.mark.parametrize(
@@ -277,19 +288,17 @@ def test_parametrized_cases(rejection_sampler, spec_tokens, output_tokens, expec
     bonus_token_tensor = torch.tensor(
         [tokens[-1] for tokens in output_tokens], device=logits.device
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
 
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected_tensor = torch.tensor(expected, dtype=torch.int, device=logits.device)
-    assert torch.equal(output, expected_tensor)
+    assert torch.equal(output.sampled_token_ids, expected_tensor)
 
 
 ########################### Tests for Random Sampling ###################
@@ -331,18 +340,19 @@ def test_deterministic_when_seeded(
         sampling_metadata = create_sampling_metadata(
             all_greedy=False, temperature=temperature, generators=seeded_seqs
         )
-        spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-            draft_token_ids.tolist(), device=DEVICE
+        spec_decode_metadata = create_spec_decode_metadata(
+            draft_token_ids.tolist(), target_logits
         )
+
+        mock_sampler_output(rejection_sampler, bonus_token_ids)
         rep_result = rejection_sampler(
             spec_decode_metadata,
-            draft_probs=draft_probs,
-            target_logits=target_logits,
-            bonus_token_ids=bonus_token_ids,
+            draft_probs=None,
+            logits=target_logits,
             sampling_metadata=sampling_metadata,
         )
 
-        results.append(rep_result)
+        results.append(rep_result.sampled_token_ids)
 
     for i in range(batch_size):
         if seeded_mask[i]:
@@ -460,7 +470,9 @@ def estimate_rejection_sampling_pdf(
     Returns:
         Estimated probability distribution of the output tokens.
     """
-    rejection_sampler = RejectionSampler()
+    mock_sampler = Mock(spec=Sampler)
+    mock_sampler.logprobs_mode = "raw_logprobs"
+    rejection_sampler = RejectionSampler(mock_sampler)
     num_tokens = num_samples * k
     # Repeat draft probs num_samples * k times.
     draft_probs = draft_probs.reshape(1, 1, vocab_size).repeat(num_samples, k, 1)
@@ -483,17 +495,18 @@ def estimate_rejection_sampling_pdf(
     sampling_metadata = create_sampling_metadata(
         all_greedy=False, temperature=temperature
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        draft_token_ids.tolist(), device=bonus_token_ids.device
+    spec_decode_metadata = create_spec_decode_metadata(
+        draft_token_ids.tolist(), target_logits
     )
-    output_token_ids = rejection_sampler(
+
+    mock_sampler_output(rejection_sampler, bonus_token_ids)
+    sampler_output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=draft_probs,
-        target_logits=target_logits,
-        bonus_token_ids=bonus_token_ids,
+        logits=target_logits,
         sampling_metadata=sampling_metadata,
     )
-    output_token_ids = output_token_ids[:, :-1].flatten()
+    output_token_ids = sampler_output.sampled_token_ids[:, :-1].flatten()
 
     hist = torch.histogram(
         output_token_ids.to(dtype=torch.float, device="cpu"),
@@ -532,22 +545,19 @@ def _test_masked_logits(
     bonus_token_ids = torch.zeros((batch_size, 1), dtype=torch.int64, device=DEVICE)
 
     # Create spec decode metadata
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        draft_token_ids,
-        device=DEVICE,
-    )
+    spec_decode_metadata = create_spec_decode_metadata(draft_token_ids, target_logits)
 
     # Run rejection sampling
-    output_token_ids = rejection_sampler(
+    mock_sampler_output(rejection_sampler, bonus_token_ids)
+    output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=draft_probs,
-        target_logits=target_logits,
-        bonus_token_ids=bonus_token_ids,
+        logits=target_logits,
         sampling_metadata=sampling_metadata,
     )
 
     # Remove bonus tokens and reshape
-    output_token_ids = output_token_ids[:, :-1].flatten().tolist()
+    output_token_ids = output.sampled_token_ids[:, :-1].flatten().tolist()
 
     # Check that all sampled tokens are within the unmasked indices.
     for i in range(num_tokens):
@@ -665,11 +675,11 @@ def test_frequency_penalties(rejection_sampler):
     spec_decode_metadata = SpecDecodeMetadata.make_dummy(
         spec_tokens, device=logits.device
     )
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
     expected = torch.tensor(
@@ -677,7 +687,7 @@ def test_frequency_penalties(rejection_sampler):
         dtype=torch.int,
         device=logits.device,
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_bad_words(rejection_sampler):
@@ -707,14 +717,12 @@ def test_bad_words(rejection_sampler):
     bonus_token_tensor = torch.tensor(
         [output_tokens[i][-1] for i in range(len(output_tokens))], device=logits.device
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
 
@@ -723,7 +731,7 @@ def test_bad_words(rejection_sampler):
         dtype=torch.int,
         device=logits.device,
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)
 
 
 def test_allowed_token_ids(rejection_sampler):
@@ -756,14 +764,12 @@ def test_allowed_token_ids(rejection_sampler):
     bonus_token_tensor = torch.tensor(
         [output_tokens[i][-1] for i in range(len(output_tokens))], device=logits.device
     )
-    spec_decode_metadata = SpecDecodeMetadata.make_dummy(
-        spec_tokens, device=logits.device
-    )
+    spec_decode_metadata = create_spec_decode_metadata(spec_tokens, logits)
+    mock_sampler_output(rejection_sampler, bonus_token_tensor)
     output = rejection_sampler(
         spec_decode_metadata,
         draft_probs=None,
-        target_logits=logits,
-        bonus_token_ids=bonus_token_tensor,
+        logits=logits,
         sampling_metadata=metadata,
     )
 
@@ -772,4 +778,4 @@ def test_allowed_token_ids(rejection_sampler):
         dtype=torch.int,
         device=logits.device,
     )
-    assert torch.equal(output, expected)
+    assert torch.equal(output.sampled_token_ids, expected)

vllm/v1/engine/logprobs.py

@@ -66,7 +66,7 @@ class LogprobsProcessor:
         assert self.logprobs is not None
         assert self.cumulative_logprob is not None
 
-        token_ids_lst, logprobs_lst, ranks_lst = logprobs_lists
+        token_ids_lst, logprobs_lst, ranks_lst, _ = logprobs_lists
 
         for rank, logprobs, token_ids in zip(ranks_lst, logprobs_lst, token_ids_lst):
             # Detokenize (non-incrementally).

vllm/v1/outputs.py

@@ -14,34 +14,49 @@ else:
 
 
 class LogprobsLists(NamedTuple):
-    # [num_reqs, max_num_logprobs + 1]
+    # [num_reqs x num_generated_tokens, max_num_logprobs + 1]
     logprob_token_ids: list[list[int]]
-    # [num_reqs, max_num_logprobs + 1]
+    # [num_reqs x num_generated_tokens, max_num_logprobs + 1]
     logprobs: list[list[float]]
-    # [num_reqs]
+    # [num_reqs x num_generated_tokens]
     sampled_token_ranks: list[int]
+    # [num_reqs]
+    # Used for slicing the logprobs in cases like speculative
+    # decoding where the number of generated tokens may be
+    # different for each request.
+    cu_num_generated_tokens: list[int] | None = None
 
-    def slice(self, start: int, end: int):
+    def slice(self, start_req_idx: int, end_req_idx: int):
+        if self.cu_num_generated_tokens:
+            start = self.cu_num_generated_tokens[start_req_idx]
+            end = self.cu_num_generated_tokens[end_req_idx]
+        else:
+            start = start_req_idx
+            end = end_req_idx
         return LogprobsLists(
             self.logprob_token_ids[start:end],
             self.logprobs[start:end],
             self.sampled_token_ranks[start:end],
+            self.cu_num_generated_tokens[start_req_idx:end_req_idx]
+            if self.cu_num_generated_tokens
+            else None,
         )
 
 
 class LogprobsTensors(NamedTuple):
-    # [num_reqs, max_num_logprobs + 1]
+    # [num_reqs x num_generated_tokens, max_num_logprobs + 1]
     logprob_token_ids: torch.Tensor
-    # [num_reqs, max_num_logprobs + 1]
+    # [num_reqs x num_generated_tokens, max_num_logprobs + 1]
     logprobs: torch.Tensor
-    # [num_reqs]
+    # [num_reqs x num_generated_tokens]
     selected_token_ranks: torch.Tensor
 
-    def tolists(self):
+    def tolists(self, cu_num_generated_tokens: list[int] | None = None):
         return LogprobsLists(
             self.logprob_token_ids.tolist(),
             self.logprobs.tolist(),
             self.selected_token_ranks.tolist(),
+            cu_num_generated_tokens,
         )
 
     @staticmethod

vllm/v1/sample/rejection_sampler.py

@@ -1,15 +1,19 @@
 # SPDX-License-Identifier: Apache-2.0
 # SPDX-FileCopyrightText: Copyright contributors to the vLLM project
 
+from dataclasses import replace
+
 import torch
 import torch.nn as nn
 
 from vllm.logger import init_logger
 from vllm.triton_utils import tl, triton
+from vllm.v1.outputs import LogprobsTensors, SamplerOutput
 from vllm.v1.sample.metadata import SamplingMetadata
 from vllm.v1.sample.ops.bad_words import apply_bad_words_with_drafts
 from vllm.v1.sample.ops.penalties import apply_all_penalties
 from vllm.v1.sample.ops.topk_topp_sampler import apply_top_k_top_p
+from vllm.v1.sample.sampler import Sampler
 from vllm.v1.spec_decode.metadata import SpecDecodeMetadata
 
 logger = init_logger(__name__)
@@ -44,17 +48,22 @@ class RejectionSampler(nn.Module):
         output tokens = accepted tokens + recovered tokens + bonus tokens
     """
 
+    def __init__(self, sampler: Sampler):
+        super().__init__()
+        self.sampler = sampler
+        logprobs_mode = self.sampler.logprobs_mode
+        self.is_processed_logprobs_mode = logprobs_mode.startswith("processed")
+        self.is_logits_logprobs_mode = logprobs_mode.endswith("logits")
+
     def forward(
         self,
         metadata: SpecDecodeMetadata,
         # [num_tokens, vocab_size]
         draft_probs: torch.Tensor | None,
-        # [num_tokens, vocab_size]
-        target_logits: torch.Tensor,
-        # [batch_size, 1]
-        bonus_token_ids: torch.Tensor,
+        # [num_tokens + batch_size, vocab_size]
+        logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
-    ) -> torch.Tensor:
+    ) -> SamplerOutput:
         """
         Args:
             metadata:
@@ -63,43 +72,65 @@ class RejectionSampler(nn.Module):
                 Probability distribution for the draft tokens. Shape is
                 [num_tokens, vocab_size]. Can be None if probabilities are
                 not provided, which is the case for ngram spec decode.
-            target_logits (torch.Tensor):
+            logits (torch.Tensor):
                 Target model's logits probability distribution.
-                Shape is [num_tokens, vocab_size]. Here, probabilities from
-                different requests are flattened into a single tensor because
-                this is the shape of the output logits.
-                NOTE: `target_logits` can be updated in place to save memory.
-            bonus_token_ids (torch.Tensor):
-                A tensor containing bonus tokens. Shape is [batch_size, 1].
-                Bonus tokens are added to the end of the sequence if all
-                proposed tokens are accepted. We generate the bonus tokens
-                outside of the rejection sampler with the default sampling
-                strategy. It allows for more flexibility in the sampling
-                process such as top_p, top_k sampling.
+                Shape is [num_tokens + batch_size, vocab_size]. Here,
+                probabilities from different requests are flattened into a
+                single tensor because this is the shape of the output logits.
+                NOTE: `logits` can be updated in place to save memory.
             sampling_metadata (vllm.v1.sample.metadata.SamplingMetadata):
                 Additional metadata needed for sampling, such as temperature,
                 top-k/top-p parameters, or other relevant information.
         Returns:
-            output_token_ids (torch.Tensor):
-                A tensor containing the final output token IDs.
+            SamplerOutput:
+                Contains the final output token IDs and their logprobs if
+                requested.
         """
         assert metadata.max_spec_len <= MAX_SPEC_LEN
 
-        # Use float32 for the target_logits.
-        target_logits = target_logits.to(torch.float32)
+        bonus_logits_indices = metadata.bonus_logits_indices
+        target_logits_indices = metadata.target_logits_indices
 
-        target_logits = self.apply_logits_processors(
-            target_logits, sampling_metadata, metadata
+        # When indexing with a tensor (bonus_logits_indices), PyTorch
+        # creates a new tensor with separate storage from the original
+        # logits tensor. This means any in-place operations on bonus_logits
+        # won't affect the original logits tensor.
+        assert logits is not None
+        bonus_logits = logits[bonus_logits_indices]
+        bonus_sampler_output = self.sampler(
+            logits=bonus_logits,
+            sampling_metadata=replace(
+                sampling_metadata,
+                max_num_logprobs=-1,
+            ),
+            predict_bonus_token=True,
+            # Override the logprobs mode to return logits because they are
+            # needed later to compute the accepted token logprobs.
+            logprobs_mode_override="processed_logits"
+            if self.is_processed_logprobs_mode
+            else "raw_logits",
         )
+        bonus_token_ids = bonus_sampler_output.sampled_token_ids
 
+        # Just like `bonus_logits`, `target_logits` is a new tensor with
+        # separate storage from the original `logits` tensor. Therefore,
+        # it is safe to update `target_logits` in place.
+        raw_target_logits = logits[target_logits_indices]
+        # Use float32 for the target_logits.
+        raw_target_logits = raw_target_logits.to(torch.float32)
+        target_logits = self.apply_logits_processors(
+            raw_target_logits, sampling_metadata, metadata
+        )
         # [num_tokens, vocab_size]
         # NOTE(woosuk): `target_logits` can be updated in place inside the
-        # `compute_probs` function.
-        target_probs = compute_probs(
+        # `apply_sampling_constraints` function.
+        target_logits = apply_sampling_constraints(
             target_logits,
             metadata.cu_num_draft_tokens,
             sampling_metadata,
         )
+        # Compute probability distribution from target logits.
+        target_probs = target_logits.softmax(dim=-1, dtype=torch.float32)
 
         output_token_ids = rejection_sample(
             metadata.draft_token_ids,
@@ -111,7 +142,63 @@ class RejectionSampler(nn.Module):
             bonus_token_ids,
             sampling_metadata,
         )
-        return output_token_ids
+
+        logprobs_tensors = None
+        if sampling_metadata.max_num_logprobs:
+            logprobs_tensors = self._get_logprobs_tensors(
+                sampling_metadata.max_num_logprobs,
+                metadata,
+                logits,
+                target_logits if self.is_processed_logprobs_mode else raw_target_logits,
+                bonus_sampler_output.logprobs_tensors.logprobs,
+                output_token_ids,
+            )
+
+        return SamplerOutput(
+            sampled_token_ids=output_token_ids,
+            logprobs_tensors=logprobs_tensors,
+        )
+
+    def _get_logprobs_tensors(
+        self,
+        max_num_logprobs: int,
+        metadata: SpecDecodeMetadata,
+        logits: torch.Tensor,
+        target_logits: torch.Tensor,
+        bonus_logits: torch.Tensor,
+        sampled_token_ids: torch.Tensor,
+    ) -> LogprobsTensors:
+        cu_num_sampled_tokens = torch.zeros_like(metadata.cu_num_sampled_tokens)
+        cu_num_sampled_tokens[1:] = metadata.cu_num_sampled_tokens[:-1]
+
+        # Collect target and bonus logits.
+        bonus_logits_indices = metadata.bonus_logits_indices
+        target_logits_indices = metadata.target_logits_indices
+        final_logits = torch.zeros_like(logits, dtype=torch.float32)
+        final_logits[target_logits_indices] = target_logits.to(torch.float32)
+        final_logits[bonus_logits_indices] = bonus_logits.to(torch.float32)
+
+        # Compute accepted token indices.
+        accepted_mask = sampled_token_ids != PLACEHOLDER_TOKEN_ID
+        num_accepted_tokens = accepted_mask.sum(dim=-1)
+        accepted_logit_indices = accepted_mask.nonzero(as_tuple=True)[1]
+        accepted_logit_indices += cu_num_sampled_tokens.repeat_interleave(
+            num_accepted_tokens
+        )
+
+        # Compute logprobs for accepted tokens.
+        accepted_logits = final_logits[accepted_logit_indices]
+        accepted_logprobs = (
+            accepted_logits
+            if self.is_logits_logprobs_mode
+            else self.sampler.compute_logprobs(accepted_logits)
+        )
+        accepted_tokens = sampled_token_ids[accepted_mask]
+        return self.sampler.gather_logprobs(
+            accepted_logprobs,
+            max_num_logprobs,
+            accepted_tokens.to(torch.int64),
+        )
 
     @staticmethod
     def parse_output(
@@ -119,14 +206,12 @@ class RejectionSampler(nn.Module):
         vocab_size: int,
     ) -> list[list[int]]:
         """Parse the output of the rejection sampler.
-
         Args:
             output_token_ids: The sampled token IDs in shape
                 [batch_size, max_spec_len + 1]. The rejected tokens are
                 replaced with `PLACEHOLDER_TOKEN_ID` by the rejection sampler
                 and will be filtered out in this function.
             vocab_size: The size of the vocabulary.
-
         Returns:
             A list of lists of token IDs.
         """
@@ -328,27 +413,26 @@ def rejection_sample(
     return output_token_ids
 
 
-def compute_probs(
+def apply_sampling_constraints(
     logits: torch.Tensor,  # [num_tokens, vocab_size]
     cu_num_draft_tokens: torch.Tensor,  # [batch_size]
     sampling_metadata: SamplingMetadata,
 ) -> torch.Tensor:
-    """Compute probability distribution from logits based on sampling metadata.
+    """Process logits based on sampling metadata.
 
-    This function applies temperature scaling to the logits and converts
-    them to probabilities using softmax. For greedy decoding, it returns
+    This function applies temperature scaling to the logits,
+    as well as top-k and top-p. For greedy decoding, it returns
     the original logits.
 
     Args:
-        logits: Input logits tensor to be converted to probabilities.
+        logits: Input logits tensor to be processed.
         cu_num_draft_tokens: Cumulative number of draft tokens.
         sampling_metadata: Metadata containing sampling parameters such as
             temperature and whether greedy sampling is used.
 
     Returns:
-        torch.Tensor: Probability distribution (softmax of scaled logits)
-            if non-greedy sampling is used, otherwise returns the
-            original logits.
+        torch.Tensor: Processed logits if non-greedy sampling is used,
+        otherwise returns the original logits.
     """
     assert logits.ndim == 2
     assert cu_num_draft_tokens.ndim == 1
@@ -384,9 +468,7 @@ def compute_probs(
 
     # NOTE(woosuk): `apply_top_k_top_p` uses sorting to calculate the mask,
     # which is slow for large vocab sizes. This may cause performance issues.
-    logits = apply_top_k_top_p(logits, top_k, top_p)
-    output_prob = logits.softmax(dim=-1, dtype=torch.float32)
-    return output_prob
+    return apply_top_k_top_p(logits, top_k, top_p)
 
 
 def expand_batch_to_tokens(

vllm/v1/sample/sampler.py

@@ -69,16 +69,18 @@ class Sampler(nn.Module):
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
         predict_bonus_token: bool = False,
+        logprobs_mode_override: LogprobsMode | None = None,
     ) -> SamplerOutput:
+        logprobs_mode = logprobs_mode_override or self.logprobs_mode
         # NOTE(woosuk): Use the original logits (before any penalties or
         # temperature scaling) for the top-k logprobs.
         # This is different from the V0 sampler, which uses the logits that
         # is used for sampling (after penalties and temperature scaling).
         num_logprobs = sampling_metadata.max_num_logprobs
         if num_logprobs is not None:
-            if self.logprobs_mode == "raw_logprobs":
+            if logprobs_mode == "raw_logprobs":
                 raw_logprobs = self.compute_logprobs(logits)
-            elif self.logprobs_mode == "raw_logits":
+            elif logprobs_mode == "raw_logits":
                 raw_logprobs = logits.clone()
 
         # Use float32 for the logits.
@@ -97,13 +99,18 @@ class Sampler(nn.Module):
         # return int32 (while PyTorch argmax and topk return int64).
         sampled = sampled.long()
 
-        # Gather the logprobs of the topk and sampled token (if requested).
-        # Get logprobs and rank tensors (if requested)
-        logprobs_tensors = (
-            None
-            if num_logprobs is None
-            else self.gather_logprobs(raw_logprobs, num_logprobs, token_ids=sampled)
-        )
+        if num_logprobs is None:
+            logprobs_tensors = None
+        elif num_logprobs == -1:
+            # Return the full unsorted and unranked logprobs.
+            logprobs_tensors = LogprobsTensors(
+                torch.empty(0), raw_logprobs, torch.empty(0)
+            )
+        else:
+            # Gather the logprobs and ranks of the topk and sampled token.
+            logprobs_tensors = self.gather_logprobs(
+                raw_logprobs, num_logprobs, token_ids=sampled
+            )
 
         # Use int32 to reduce the tensor size.
         sampled = sampled.to(torch.int32)
@@ -138,6 +145,7 @@ class Sampler(nn.Module):
         self,
         logits: torch.Tensor,
         sampling_metadata: SamplingMetadata,
+        logprobs_mode_override: LogprobsMode | None = None,
     ) -> tuple[torch.Tensor, torch.Tensor | None]:
         """Sample logits based on sampling metadata.
 
@@ -145,6 +153,7 @@ class Sampler(nn.Module):
         may update the logits tensor in-place.
         """
 
+        logprobs_mode = logprobs_mode_override or self.logprobs_mode
         assert not (sampling_metadata.all_greedy and sampling_metadata.all_random)
         if sampling_metadata.all_random:
             greedy_sampled = None
@@ -153,9 +162,9 @@ class Sampler(nn.Module):
             if sampling_metadata.all_greedy:
                 processed_logprobs = None
                 if sampling_metadata.max_num_logprobs is not None:
-                    if self.logprobs_mode == "processed_logits":
+                    if logprobs_mode == "processed_logits":
                         processed_logprobs = logits
-                    elif self.logprobs_mode == "processed_logprobs":
+                    elif logprobs_mode == "processed_logprobs":
                         processed_logprobs = self.compute_logprobs(logits)
                 return greedy_sampled, processed_logprobs
 

vllm/v1/spec_decode/metadata.py

@@ -14,6 +14,8 @@ class SpecDecodeMetadata:
     num_draft_tokens: list[int]
     # [batch_size]
     cu_num_draft_tokens: torch.Tensor
+    # [batch_size]
+    cu_num_sampled_tokens: torch.Tensor
     # [num_tokens]
     target_logits_indices: torch.Tensor
     # [batch_size]
@@ -32,6 +34,7 @@ class SpecDecodeMetadata:
     ) -> "SpecDecodeMetadata":
         batch_size = len(draft_token_ids)
         num_draft_tokens = [len(ids) for ids in draft_token_ids]
+        num_sampled_tokens = [len(ids) + 1 for ids in draft_token_ids]
         flattened_draft_token_ids = sum(draft_token_ids, [])
         num_tokens = len(flattened_draft_token_ids)
 
@@ -40,6 +43,10 @@ class SpecDecodeMetadata:
         )
         cu_num_draft_tokens = np.cumsum(num_draft_tokens, dtype=np.int32)
         cu_num_draft_tokens_tensor = torch.from_numpy(cu_num_draft_tokens).to(device)
+        cu_num_sampled_tokens = np.cumsum(num_sampled_tokens, dtype=np.int32)
+        cu_num_sampled_tokens_tensor = torch.from_numpy(cu_num_sampled_tokens).to(
+            device
+        )
 
         target_logits_indices = torch.zeros(
             num_tokens, dtype=torch.int32, device=device
@@ -52,6 +59,7 @@ class SpecDecodeMetadata:
             draft_token_ids=draft_token_ids_tensor,
             num_draft_tokens=num_draft_tokens,
             cu_num_draft_tokens=cu_num_draft_tokens_tensor,
+            cu_num_sampled_tokens=cu_num_sampled_tokens_tensor,
             target_logits_indices=target_logits_indices,
             bonus_logits_indices=bonus_logits_indices,
             logits_indices=logits_indices,

vllm/v1/worker/gpu_model_runner.py

@@ -327,7 +327,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                     "Unknown speculative decoding method: "
                     f"{self.speculative_config.method}"
                 )
-            self.rejection_sampler = RejectionSampler()
+            self.rejection_sampler = RejectionSampler(self.sampler)
 
         # Request states.
         self.requests: dict[str, CachedRequestState] = {}
@@ -1624,6 +1624,9 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         cu_num_draft_tokens = torch.from_numpy(cu_num_draft_tokens).to(
             self.device, non_blocking=True
         )
+        cu_num_sampled_tokens = torch.from_numpy(cu_num_sampled_tokens).to(
+            self.device, non_blocking=True
+        )
         logits_indices = torch.from_numpy(logits_indices).to(
             self.device, non_blocking=True
         )
@@ -1639,15 +1642,15 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         draft_token_ids = self.input_ids.gpu[logits_indices]
         draft_token_ids = draft_token_ids[target_logits_indices + 1]
 
-        metadata = SpecDecodeMetadata(
+        return SpecDecodeMetadata(
             draft_token_ids=draft_token_ids,
             num_draft_tokens=num_draft_tokens.tolist(),
             cu_num_draft_tokens=cu_num_draft_tokens,
+            cu_num_sampled_tokens=cu_num_sampled_tokens,
             target_logits_indices=target_logits_indices,
             bonus_logits_indices=bonus_logits_indices,
             logits_indices=logits_indices,
         )
-        return metadata
 
     def _prepare_kv_sharing_fast_prefill(
         self,
@@ -2221,32 +2224,13 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 sampling_metadata=sampling_metadata,
             )
 
-        # When indexing with a tensor (bonus_logits_indices), PyTorch
-        # creates a new tensor with separate storage from the original
-        # logits tensor. This means any in-place operations on bonus_logits
-        # won't affect the original logits tensor.
-        assert logits is not None
-        bonus_logits = logits[spec_decode_metadata.bonus_logits_indices]
-        sampler_output = self.sampler(
-            logits=bonus_logits,
-            sampling_metadata=sampling_metadata,
-            predict_bonus_token=True,
-        )
-        bonus_token_ids = sampler_output.sampled_token_ids
-
-        # Just like `bonus_logits`, `target_logits` is a new tensor with
-        # separate storage from the original `logits` tensor. Therefore,
-        # it is safe to update `target_logits` in place.
-        target_logits = logits[spec_decode_metadata.target_logits_indices]
-        output_token_ids = self.rejection_sampler(
+        sampler_output = self.rejection_sampler(
             spec_decode_metadata,
             None,  # draft_probs
-            target_logits,
-            bonus_token_ids,
+            logits,
             sampling_metadata,
         )
-        sampler_output.sampled_token_ids = output_token_ids
-        self._update_states_after_model_execute(output_token_ids)
+        self._update_states_after_model_execute(sampler_output.sampled_token_ids)
         return sampler_output
 
     def _bookkeeping_sync(
@@ -2256,6 +2240,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         logits: torch.Tensor | None,
         hidden_states: torch.Tensor,
         num_scheduled_tokens: int,
+        spec_decode_metadata: SpecDecodeMetadata | None,
     ) -> tuple[
         dict[str, int],
         LogprobsLists | None,
@@ -2282,19 +2267,6 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         req_ids_output_copy = self.input_batch.req_ids.copy()
         req_id_to_index_output_copy = self.input_batch.req_id_to_index.copy()
 
-        # NOTE: GPU -> CPU Sync happens here.
-        # Move as many CPU operations as possible before this sync point.
-        logprobs_tensors = sampler_output.logprobs_tensors
-        logprobs_lists = (
-            logprobs_tensors.tolists() if logprobs_tensors is not None else None
-        )
-
-        # Compute prompt logprobs if needed.
-        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
-            hidden_states[:num_scheduled_tokens],
-            scheduler_output.num_scheduled_tokens,
-        )
-
         num_sampled_tokens = sampler_output.sampled_token_ids.shape[0]
         sampled_token_ids = sampler_output.sampled_token_ids
         invalid_req_indices = []
@@ -2335,6 +2307,10 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         # the sampled tokens back, because there's no direct communication
         # between the first-stage worker and the last-stage worker.
         req_ids = self.input_batch.req_ids
+        logprobs_tensors = sampler_output.logprobs_tensors
+        cu_num_accepted_tokens = (
+            [0] if spec_decode_metadata and logprobs_tensors else None
+        )
         for req_idx in range(num_sampled_tokens):
             if self.use_async_scheduling:
                 sampled_ids = [-1] if req_idx not in invalid_req_indices_set else None
@@ -2360,6 +2336,25 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             req_state = self.requests[req_id]
             req_state.output_token_ids.extend(sampled_ids)
 
+            if cu_num_accepted_tokens is not None:
+                cu_num_accepted_tokens.append(
+                    cu_num_accepted_tokens[-1] + len(sampled_ids)
+                )
+
+        # NOTE: GPU -> CPU Sync happens here.
+        # Move as many CPU operations as possible before this sync point.
+        logprobs_lists = (
+            logprobs_tensors.tolists(cu_num_accepted_tokens)
+            if logprobs_tensors is not None
+            else None
+        )
+
+        # Compute prompt logprobs if needed.
+        prompt_logprobs_dict = self._get_prompt_logprobs_dict(
+            hidden_states[:num_scheduled_tokens],
+            scheduler_output.num_scheduled_tokens,
+        )
+
         return (
             num_nans_in_logits,
             logprobs_lists,
@@ -2644,6 +2639,7 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
                 logits,
                 hidden_states,
                 num_scheduled_tokens,
+                spec_decode_metadata,
             )
 
         if (
@@ -3560,20 +3556,16 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
             #     num_tokens, logits.shape[-1], device=self.device,
             #     dtype=logits.dtype)
             draft_probs = None
-            target_logits = torch.randn(
-                num_tokens, logits.shape[-1], device=self.device, dtype=logits.dtype
-            )
-            # NOTE(woosuk): Here, we should use int32 because the sampler uses
-            # int32 for bonus_token_ids. If the dtype mismatches, re-compilation
-            # will occur at runtime.
-            bonus_token_ids = torch.zeros(
-                num_reqs, device=self.device, dtype=torch.int32
+            logits = torch.randn(
+                num_tokens + num_reqs,
+                logits.shape[-1],
+                device=self.device,
+                dtype=logits.dtype,
             )
             self.rejection_sampler(
                 dummy_spec_decode_metadata,
                 draft_probs,
-                target_logits,
-                bonus_token_ids,
+                logits,
                 dummy_metadata,
             )
         return sampler_output