Update spec_decode_worker

vllm/spec_decode/spec_decode_worker.py

@@ -60,8 +60,6 @@ def create_spec_worker(*args, **kwargs) -> "SpecDecodeWorker":
         draft_worker_kwargs=draft_worker_kwargs,
         disable_by_batch_size=speculative_config.
         speculative_disable_by_batch_size,
-        disable_bonus_tokens_in_kv_cache=speculative_config.
-        disable_bonus_tokens_in_kv_cache,
         draft_token_acceptance_method=speculative_config.
         draft_token_acceptance_method,
         typical_acceptance_sampler_posterior_threshold=speculative_config.
@@ -104,7 +102,6 @@ def create_worker(
         scorer_worker: Worker,
         draft_worker_kwargs: Dict[str, Any],
         disable_by_batch_size: Optional[int],
-        disable_bonus_tokens_in_kv_cache: bool,
         draft_token_acceptance_method: str,
         typical_acceptance_sampler_posterior_threshold: float,
         typical_acceptance_sampler_posterior_alpha: float,
@@ -114,18 +111,16 @@ def create_worker(
             draft_worker_kwargs.pop("ngram_prompt_lookup_max"))
         ngram_prompt_lookup_min = (
             draft_worker_kwargs.pop("ngram_prompt_lookup_min"))
-
+
+        disable_bonus_tokens = False
         if ngram_prompt_lookup_max > 0:
-            disable_bonus_tokens = False
             proposer_worker = NGramWorker(**draft_worker_kwargs)
             proposer_worker.set_ngram_window_size(ngram_prompt_lookup_min,
                                                   ngram_prompt_lookup_max)
         elif draft_worker_kwargs[
                 "model_config"].hf_config.model_type == "mlp_speculator":
             proposer_worker = MLPSpeculatorWorker(**draft_worker_kwargs)
-            disable_bonus_tokens = False
         else:
-            disable_bonus_tokens = disable_bonus_tokens_in_kv_cache
             draft_parallel_config: ParallelConfig = draft_worker_kwargs[
                 'parallel_config']
             draft_tp = draft_parallel_config.tensor_parallel_size
@@ -157,18 +152,12 @@ def create_worker(
         return SpecDecodeWorker(proposer_worker,
                                 scorer_worker,
                                 disable_by_batch_size=disable_by_batch_size,
-                                rejection_sampler=RejectionSampler(
-                                    disable_bonus_tokens=disable_bonus_tokens),
-                                disable_bonus_tokens_in_kv_cache=\
-                                    disable_bonus_tokens_in_kv_cache)
                                 spec_decode_sampler=spec_decode_sampler)
 
     def __init__(
         self,
         proposer_worker: ProposerWorkerBase,
         scorer_worker: WorkerBase,
-        rejection_sampler: RejectionSampler,
-        disable_bonus_tokens_in_kv_cache: bool,
         spec_decode_sampler: SpecDecodeBaseSampler,
         metrics_collector: Optional[AsyncMetricsCollector] = None,
         disable_by_batch_size: Optional[int] = None,
@@ -184,10 +173,6 @@ def __init__(
                 Worker.
             rejection_sampler: A Torch module used to perform modified rejection
                 sampling for speculative decoding.
-            disable_bonus_tokens_in_kv_cache: A boolean flag to control the use 
-            of bonus tokens during speculative decoding in models that rely on KV 
-            cache. If set to True, bonus tokens will be disabled and if set to False,
-            bonus tokens will be enabled.
             spec_decode_sampler: A Torch module used to perform acceptance
                 sampling of the draft tokens in the verification step of
                 speculative decoding. Currently we support two different 
@@ -207,13 +192,12 @@ def __init__(
             self.spec_decode_sampler
         ) if metrics_collector is None else metrics_collector
 
-        self.probs_dtype = self.rejection_sampler.probs_dtype
-        self.token_id_dtype = self.rejection_sampler.token_id_dtype
+        self.probs_dtype = self.spec_decode_sampler.probs_dtype
+        self.token_id_dtype = self.spec_decode_sampler.token_id_dtype
         # Tracks the sequence IDs that received a bonus token ID in
         # their last forward pass. Needed only if KV cache is being
         # used for token generation such as in the case of MultiStepWorker.
-        if (isinstance(self.proposer_worker, MultiStepWorker) 
-           and not disable_bonus_tokens_in_kv_cache):
+        if (isinstance(self.proposer_worker, MultiStepWorker)):
            self.seq_with_bonus_token_in_last_step = set()
         else:
             self.seq_with_bonus_token_in_last_step = None