[SpecDec][Misc] Cleanup, remove bonus token logic. (#8701)

tests/samplers/test_rejection_sampler.py

@@ -42,18 +42,13 @@ def mock_causal_accepted_tensor(
 @pytest.mark.parametrize(
     "which_tokens_accepted",
     ["all_tokens_accepted", "no_tokens_accepted", "some_tokens_accepted"])
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @pytest.mark.parametrize("use_flashinfer", [True, False])
 @torch.inference_mode()
 def test_correct_output_format(which_tokens_accepted: str, seed: int,
-                               disable_bonus_tokens: bool, device: str,
-                               use_flashinfer: bool):
+                               device: str, use_flashinfer: bool):
     """Verify the output has correct format given predetermined accepted matrix.
     """
-    if use_flashinfer and disable_bonus_tokens:
-        pytest.skip("Flashinfer rejection sampler must enable bonus token.")
-
     set_random_seed(seed)
     torch.set_default_device(device)
 
@@ -88,9 +83,7 @@ def test_correct_output_format(which_tokens_accepted: str, seed: int,
                                     size=(batch_size, 1),
                                     dtype=torch.int64)
 
-    rejection_sampler = RejectionSampler(
-        disable_bonus_tokens=disable_bonus_tokens,
-        use_flashinfer=use_flashinfer)
+    rejection_sampler = RejectionSampler(use_flashinfer=use_flashinfer)
     rejection_sampler.init_gpu_tensors(device=device)
     output_token_ids = rejection_sampler._create_output(  # pylint: disable=protected-access
         accepted,
@@ -100,10 +93,6 @@ def test_correct_output_format(which_tokens_accepted: str, seed: int,
     )
 
     expected_bonus_token_ids = bonus_token_ids.clone()
-    # If bonus tokens disabled. Verify they are set to -1.
-    # See https://github.com/vllm-project/vllm/issues/4212
-    if disable_bonus_tokens:
-        expected_bonus_token_ids = expected_bonus_token_ids * 0 - 1
 
     if which_tokens_accepted == "all_tokens_accepted":
         # Expect all tokens to be equal to draft tokens.
@@ -143,8 +132,7 @@ def test_correct_output_format(which_tokens_accepted: str, seed: int,
 def test_no_crash_with_varying_dims(k: int, vocab_size: int, batch_size: int,
                                     device: str, use_flashinfer: bool):
     torch.set_default_device(device)
-    rejection_sampler = RejectionSampler(disable_bonus_tokens=False,
-                                         use_flashinfer=use_flashinfer)
+    rejection_sampler = RejectionSampler(use_flashinfer=use_flashinfer)
     rejection_sampler.init_gpu_tensors(device=device)
 
     draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
@@ -177,8 +165,7 @@ def test_deterministic_when_seeded(k: int, vocab_size: int, batch_size: int,
                                    frac_seeded: float, n_rep: int, device: str,
                                    use_flashinfer: bool):
     torch.set_default_device(device)
-    rejection_sampler = RejectionSampler(disable_bonus_tokens=False,
-                                         use_flashinfer=use_flashinfer)
+    rejection_sampler = RejectionSampler(use_flashinfer=use_flashinfer)
     rejection_sampler.init_gpu_tensors(device=device)
 
     draft_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
@@ -251,8 +238,7 @@ def test_compare_nonflashinfer_backend(k: int, vocab_size: int,
         }
 
     for use_flashinfer in [True, False]:
-        rejection_sampler = RejectionSampler(disable_bonus_tokens=False,
-                                             use_flashinfer=use_flashinfer)
+        rejection_sampler = RejectionSampler(use_flashinfer=use_flashinfer)
         rejection_sampler.init_gpu_tensors(device=device)
         # We use seeded sequences to ensure the same tokens are accepted
         # for both flashinfer and nonflashinfer backends.
@@ -282,8 +268,7 @@ def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
     vocab_size = 30_000
     torch.set_default_device(device)
 
-    rejection_sampler = RejectionSampler(disable_bonus_tokens=False,
-                                         use_flashinfer=use_flashinfer,
+    rejection_sampler = RejectionSampler(use_flashinfer=use_flashinfer,
                                          strict_mode=True)
     rejection_sampler.init_gpu_tensors(device=device)
 
@@ -359,8 +344,7 @@ def test_rejection_sampling_approximates_target_distribution(
     set_random_seed(seed)
     helper = _CorrectnessTestHelper(
         vocab_size=10,
-        rejection_sampler=RejectionSampler(disable_bonus_tokens=False,
-                                           use_flashinfer=use_flashinfer),
+        rejection_sampler=RejectionSampler(use_flashinfer=use_flashinfer),
     )
 
     draft_probs, target_probs, reference_probs = helper.generate_probs_for_test(

tests/samplers/test_typical_acceptance_sampler.py

@@ -55,14 +55,13 @@ def get_draft_token_ids(batch_size: int, k: int, vocab_size: int,
 def get_acceptance_sampler(
     posterior_threshold: float = 0.03,
     posterior_alpha: float = 0.9,
-    disable_bonus_tokens: bool = False,
     strict_mode: bool = False,
 ) -> TypicalAcceptanceSampler:
     """
     Initializes and returns a TypicalAcceptanceSampler.
     """
     return TypicalAcceptanceSampler(posterior_threshold, posterior_alpha,
-                                    disable_bonus_tokens, strict_mode)
+                                    strict_mode)
 
 
 @pytest.mark.parametrize("k", list(range(1, 6)))
@@ -154,11 +153,10 @@ def test_raises_when_vocab_oob(above_or_below_vocab_range: str,
 
 
 @pytest.mark.parametrize("seed", list(range(10)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
 def test_uniform_target_distribution_accepts_all_tokens(
-        seed: int, disable_bonus_tokens: bool, device: str):
+        seed: int, device: str):
     """
      Test the TypicalAcceptanceSampler with a uniform target probability 
      distribution.
@@ -166,17 +164,14 @@ def test_uniform_target_distribution_accepts_all_tokens(
     This test verifies that when provided with a uniform target probability
     distribution, the TypicalAcceptanceSampler accepts all draft tokens. The
     entropy of the uniform target distribution being high should lead to all
-    draft tokens being accepted. The test also ensures that the behavior
-    regarding bonus tokens is consistent with the `disable_bonus_tokens`
-    flag.
+    draft tokens being accepted.
     """
     set_random_seed(seed)
     k = 3
     batch_size = 5
     vocab_size = 30_000
     torch.set_default_device(device)
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_with_bonus_probs = torch.rand(batch_size,
                                          k + 1,
@@ -200,21 +195,15 @@ def test_uniform_target_distribution_accepts_all_tokens(
     # should lead to all draft tokens being accepted. Verify that.
     assert output_token_ids.shape[0] == batch_size
     assert output_token_ids.shape[1] == (k + 1)
-    if disable_bonus_tokens:
-        assert torch.all(output_token_ids[:, -1] == -1)
-    else:
-        assert torch.all(output_token_ids[:, -1] == bonus_token_ids.squeeze())
+    assert torch.all(output_token_ids[:, -1] == bonus_token_ids.squeeze())
 
     assert torch.all(output_token_ids[:, :k] == draft_token_ids)
 
 
 @pytest.mark.parametrize("seed", list(range(10)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
-def test_temperature_zero_target_distribution(seed: int,
-                                              disable_bonus_tokens: bool,
-                                              device: str):
+def test_temperature_zero_target_distribution(seed: int, device: str):
     """
     Test the TypicalAcceptanceSampler with a zero-temperature target
     probability distribution.
@@ -232,8 +221,7 @@ def test_temperature_zero_target_distribution(seed: int,
     vocab_size = 30_000
     torch.set_default_device(device)
 
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Simulate temperature 0 probability distribution for target probabilities
     # and create target probabilities such that only 1 token id has
@@ -267,11 +255,9 @@ def test_temperature_zero_target_distribution(seed: int,
 
 
 @pytest.mark.parametrize("seed", list(range(10)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
-def test_mixed_target_distribution(seed: int, disable_bonus_tokens: bool,
-                                   device: str):
+def test_mixed_target_distribution(seed: int, device: str):
     """
     Test the TypicalAcceptanceSampler with a mixed target probability
     distribution.
@@ -285,16 +271,13 @@ def test_mixed_target_distribution(seed: int, disable_bonus_tokens: bool,
     with a probability of 1.0 is accepted, and all other tokens are rejected.
     - For sequences with a uniform distribution, all draft tokens are
     accepted.
-    - When `disable_bonus_tokens` is False, the bonus tokens are also accepted
-    for sequences with a uniform distribution.
     """
     set_random_seed(seed)
     k = 3
     batch_size = 4
     vocab_size = 30_000
     torch.set_default_device(device)
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     # For sequences 0 and 2 set the distribution to a temperature
     # zero distribution. For sequences 1 and 3 set it to a uniform
@@ -328,21 +311,16 @@ def test_mixed_target_distribution(seed: int, disable_bonus_tokens: bool,
                                                                         0]))
     # For sequences 1 and 3 verify that all tokens are accepted since the
     # target probability distribution is uniform. In addition verify that
-    # if disable_bonus_tokens is false then we also accept the bonus tokens.
+    # we also accept the bonus tokens.
     assert torch.all(
         output_token_ids[[1, 3], :-1] == draft_token_ids[[1, 3], :])
-    if disable_bonus_tokens:
-        assert torch.all(output_token_ids[[1, 3], -1] == -1)
-    else:
-        assert torch.all(output_token_ids[[1, 3], -1] != -1)
+    assert torch.all(output_token_ids[[1, 3], -1] != -1)
 
 
 @pytest.mark.parametrize("seed", list(range(10)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
-def test_accept_tokens_partially(seed: int, disable_bonus_tokens: bool,
-                                 device: str):
+def test_accept_tokens_partially(seed: int, device: str):
     """
     Test the TypicalAcceptanceSampler's behavior when only a subset of draft
     tokens should be accepted.
@@ -362,8 +340,7 @@ def test_accept_tokens_partially(seed: int, disable_bonus_tokens: bool,
     batch_size = 1
     vocab_size = 30_000
     torch.set_default_device(device)
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Create a temperature zero target probability distribution and ensure
     # all draft token ids correspond to the tokens with 1.0 probability.
@@ -384,10 +361,7 @@ def test_accept_tokens_partially(seed: int, disable_bonus_tokens: bool,
     assert output_token_ids.shape[0] == batch_size
     assert output_token_ids.shape[1] == (k + 1)
     assert torch.all(output_token_ids[:, 0:-1] == draft_token_ids)
-    if disable_bonus_tokens:
-        assert torch.all(output_token_ids[:, -1] == -1)
-    else:
-        assert torch.all(output_token_ids[:, -1] == bonus_token_ids)
+    assert torch.all(output_token_ids[:, -1] == bonus_token_ids)
     # Next only keep the first 2 draft tokens same as the zero temperature
     # tokens. For the remaining 3 choose some other tokens. In the
     # response we will expect the first 2 tokens to be the same as the
@@ -408,12 +382,9 @@ def test_accept_tokens_partially(seed: int, disable_bonus_tokens: bool,
 
 
 @pytest.mark.parametrize("seed", list(range(1)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
-def test_accept_tokens_set_non_default_posteriors(seed: int,
-                                                  disable_bonus_tokens: bool,
-                                                  device: str):
+def test_accept_tokens_set_non_default_posteriors(seed: int, device: str):
     """
     Test the TypicalAcceptanceSampler with custom posterior thresholds and 
     alpha values. This test verifies that by modifying the posterior
@@ -425,8 +396,7 @@ def test_accept_tokens_set_non_default_posteriors(seed: int,
     batch_size = 1
     vocab_size = 30_000
     torch.set_default_device(device)
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     # Simulate temperature 0 probability distribution for target
     # probabilities and create target probabilities such that only 1 token
@@ -457,10 +427,7 @@ def test_accept_tokens_set_non_default_posteriors(seed: int,
     # now accept even draft tokens with very low probability in the
     # target distribution. Simulate and verify the same.
     typical_acceptance_sampler = TypicalAcceptanceSampler(
-        strict_mode=True,
-        disable_bonus_tokens=disable_bonus_tokens,
-        posterior_threshold=0.0,
-        posterior_alpha=0.0)
+        strict_mode=True, posterior_threshold=0.0, posterior_alpha=0.0)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     output_token_ids = typical_acceptance_sampler(
         target_probs,
@@ -470,18 +437,13 @@ def test_accept_tokens_set_non_default_posteriors(seed: int,
     assert output_token_ids.shape[0] == batch_size
     assert output_token_ids.shape[1] == (k + 1)
     assert torch.all(output_token_ids[:, 0:-1] == draft_token_ids)
-    if disable_bonus_tokens:
-        assert torch.all(output_token_ids[:, -1] == -1)
-    else:
-        assert torch.all(output_token_ids[:, -1] == bonus_token_ids)
+    assert torch.all(output_token_ids[:, -1] == bonus_token_ids)
 
 
 @pytest.mark.parametrize("seed", list(range(10)))
-@pytest.mark.parametrize("disable_bonus_tokens", [True, False])
 @pytest.mark.parametrize("device", CUDA_DEVICES)
 @torch.inference_mode()
-def test_replacement_token_ids(seed: int, disable_bonus_tokens: bool,
-                               device: str):
+def test_replacement_token_ids(seed: int, device: str):
     """
     Test the TypicalAcceptanceSampler's method for generating
     replacement token IDs.
@@ -497,8 +459,7 @@ def test_replacement_token_ids(seed: int, disable_bonus_tokens: bool,
     batch_size = 5
     vocab_size = 30_000
     torch.set_default_device(device)
-    typical_acceptance_sampler = get_acceptance_sampler(
-        strict_mode=True, disable_bonus_tokens=disable_bonus_tokens)
+    typical_acceptance_sampler = get_acceptance_sampler(strict_mode=True)
     typical_acceptance_sampler.init_gpu_tensors(device=device)
     target_probs = torch.rand(batch_size, k, vocab_size, dtype=torch.float32)
     expected_replacement_tokens = -torch.ones(

tests/spec_decode/e2e/test_medusa_correctness.py

@@ -31,7 +31,7 @@ MAIN_MODEL = "JackFram/llama-68m"
 # speculative model
 SPEC_MODEL = "abhigoyal/vllm-medusa-llama-68m-random"
 
-# max. number of speculative tokens: this corresponds to
+# max number of speculative tokens: this corresponds to
 # num_heads in the config.json of the speculator model.
 MAX_SPEC_TOKENS = 5
 

vllm/model_executor/layers/rejection_sampler.py

@@ -31,15 +31,11 @@ class RejectionSampler(SpecDecodeStochasticBaseSampler):
     """
 
     def __init__(self,
-                 disable_bonus_tokens: bool = True,
                  strict_mode: bool = False,
                  use_flashinfer: Optional[bool] = None):
         """Create a rejection sampler.
 
         Args:
-            disable_bonus_tokens: Whether or not to disable the bonus token.
-            Require when bonus tokens will cause corrupt KV cache for
-            proposal methods that require KV cache.
             strict_mode: Whether or not to perform shape/device/dtype checks
             during sampling. This catches correctness issues but adds
             nontrivial latency.
@@ -48,8 +44,7 @@ class RejectionSampler(SpecDecodeStochasticBaseSampler):
             None, we will use the default value from the environment variable.
             This parameter is only used for testing purposes.
         """
-        super().__init__(disable_bonus_tokens=disable_bonus_tokens,
-                         strict_mode=strict_mode)
+        super().__init__(strict_mode=strict_mode)
         if use_flashinfer is None:
             self.use_flashinfer = envs.VLLM_USE_FLASHINFER_SAMPLER and (
                 chain_speculative_sampling is not None)
@@ -57,8 +52,6 @@ class RejectionSampler(SpecDecodeStochasticBaseSampler):
             self.use_flashinfer = use_flashinfer
 
         if self.use_flashinfer:
-            assert not disable_bonus_tokens, \
-                "flashinfer will enable bonus token by default"
             logger.info("Use flashinfer for rejection sampling.")
         else:
             logger.info("Use pytorch for rejection sampling.")

vllm/model_executor/layers/spec_decode_base_sampler.py

@@ -11,20 +11,14 @@ class SpecDecodeBaseSampler(nn.Module):
         step.
     """
 
-    def __init__(self,
-                 disable_bonus_tokens: bool = True,
-                 strict_mode: bool = False):
+    def __init__(self, strict_mode: bool = False):
         """Base class constructor.
         Args:
-            disable_bonus_tokens: Whether or not to disable the bonus token.
-            Require when bonus tokens will cause corrupt KV cache for
-            proposal methods that require KV cache.
             strict_mode: Whether or not to perform shape/device/dtype checks
                 during sampling. This catches correctness issues but adds
                 nontrivial latency.
         """
         super().__init__()
-        self._disable_bonus_tokens = disable_bonus_tokens
         self._strict_mode = strict_mode
 
         # NOTE: A "bonus token" is accepted iff all proposal tokens are
@@ -111,13 +105,6 @@ class SpecDecodeBaseSampler(nn.Module):
         output_with_bonus_tokens[:, -1] = torch.where(output[:, -1] != -1,
                                                       bonus_token_ids, -1)
 
-        # We disable bonus tokens because it causes corrupt KV cache for
-        # proposal methods that require KV cache. We can fix it by "prefilling"
-        # the bonus token in the proposer. The following issue tracks the fix.
-        # https://github.com/vllm-project/vllm/issues/4212
-        if self._disable_bonus_tokens:
-            output_with_bonus_tokens[:, -1] = -1
-
         # Fill the recovered token ids.
         output.mul_(~after_false_mask).add_(
             substitute_token_ids.mul(after_false_mask))

vllm/model_executor/layers/typical_acceptance_sampler.py

@@ -16,15 +16,11 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
         self,
         posterior_threshold: float,
         posterior_alpha: float,
-        disable_bonus_tokens: bool = False,
         strict_mode: bool = False,
     ):
         """Create a Typical Acceptance Sampler.
 
         Args:
-            disable_bonus_tokens: Whether or not to disable the bonus token.
-            Require when bonus tokens will cause corrupt KV cache for
-            proposal methods that require KV cache.
             strict_mode: Whether or not to perform shape/device/dtype checks
             during sampling. This catches correctness issues but adds
             nontrivial latency.
@@ -36,8 +32,7 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
         """
         self._posterior_threshold = posterior_threshold
         self._posterior_alpha = posterior_alpha
-        super().__init__(disable_bonus_tokens=disable_bonus_tokens,
-                         strict_mode=strict_mode)
+        super().__init__(strict_mode=strict_mode)
 
     def forward(
         self,
@@ -54,7 +49,7 @@ class TypicalAcceptanceSampler(SpecDecodeDeterministicBaseSampler):
         one token will be emitted.
 
         In the case where all draft tokens are accepted, the bonus token will be
-        accepted conditioned on self._disable_bonus_tokens being false.
+        accepted.
 
         Args:
             target_probs: The probability distribution over token ids given

vllm/spec_decode/spec_decode_worker.py

@@ -164,11 +164,9 @@ class SpecDecodeWorker(LoraNotSupportedWorkerBase):
 
         spec_decode_sampler: SpecDecodeBaseSampler = None
         if draft_token_acceptance_method == "rejection_sampler":
-            spec_decode_sampler = RejectionSampler(
-                disable_bonus_tokens=False, )
+            spec_decode_sampler = RejectionSampler()
         elif draft_token_acceptance_method == "typical_acceptance_sampler":
             spec_decode_sampler = TypicalAcceptanceSampler(
-                disable_bonus_tokens=False,
                 posterior_threshold=\
                     typical_acceptance_sampler_posterior_threshold,
                 posterior_alpha=typical_acceptance_sampler_posterior_alpha,