[Chore][Spec Decode] Update check NoneType instead of assigning variables (#18836)

Signed-off-by: Aaron Pham <contact@aarnphm.xyz>

Author: aarnphm

vllm/v1/worker/gpu_model_runner.py

@@ -146,31 +146,27 @@ def __init__(
         # req_id -> (input_id -> encoder_output)
         self.encoder_cache: dict[str, dict[int, torch.Tensor]] = {}
 
-        # Set up speculative decoding.
-        self.use_spec_decode = False
         self.use_aux_hidden_state_outputs = False
-        if self.speculative_config:
-            self.use_spec_decode = True
-
-            # NOTE(Jiayi): currently we put the entire draft model on
-            # the last PP rank. This is not ideal if there are many
-            # layers in the draft model.
-            if get_pp_group().is_last_rank:
-                if self.speculative_config.method == "ngram":
-                    self.drafter = NgramProposer(self.vllm_config)
-                elif self.speculative_config.use_eagle():
-                    self.drafter = EagleProposer(self.vllm_config, self.device,
-                                                 self)  # type: ignore
-                    if self.speculative_config.method == "eagle3":
-                        self.use_aux_hidden_state_outputs = True
-                elif self.speculative_config.method == "medusa":
-                    self.drafter = MedusaProposer(
-                        vllm_config=self.vllm_config,
-                        device=self.device)  # type: ignore
-                else:
-                    raise ValueError("Unknown speculative decoding method: "
-                                     f"{self.speculative_config.method}")
-                self.rejection_sampler = RejectionSampler()
+        # Set up speculative decoding.
+        # NOTE(Jiayi): currently we put the entire draft model on
+        # the last PP rank. This is not ideal if there are many
+        # layers in the draft model.
+        if self.speculative_config and get_pp_group().is_last_rank:
+            if self.speculative_config.method == "ngram":
+                self.drafter = NgramProposer(self.vllm_config)
+            elif self.speculative_config.use_eagle():
+                self.drafter = EagleProposer(self.vllm_config, self.device,
+                                             self)  # type: ignore
+                if self.speculative_config.method == "eagle3":
+                    self.use_aux_hidden_state_outputs = True
+            elif self.speculative_config.method == "medusa":
+                self.drafter = MedusaProposer(
+                    vllm_config=self.vllm_config,
+                    device=self.device)  # type: ignore
+            else:
+                raise ValueError("Unknown speculative decoding method: "
+                                 f"{self.speculative_config.method}")
+            self.rejection_sampler = RejectionSampler()
 
         # Request states.
         self.requests: dict[str, CachedRequestState] = {}
@@ -1318,7 +1314,7 @@ def execute_model(
         for i in discard_sampled_tokens_req_indices:
             valid_sampled_token_ids[i].clear()
 
-        if not self.use_spec_decode:
+        if not self.speculative_config:
             # Speculative decoding is not enabled.
             spec_token_ids = None
         elif self.speculative_config.method == "ngram":
@@ -1740,7 +1736,7 @@ def _dummy_run(
             else:
                 hidden_states = outputs
 
-            if self.use_spec_decode and self.speculative_config.use_eagle():
+            if self.speculative_config and self.speculative_config.use_eagle():
                 assert isinstance(self.drafter, EagleProposer)
                 self.drafter.dummy_run(num_tokens)
 
@@ -1795,7 +1791,7 @@ def _dummy_sampler_run(
                     "initializing the engine.") from e
             else:
                 raise e
-        if self.use_spec_decode:
+        if self.speculative_config:
             draft_token_ids = [[0] for _ in range(num_reqs)]
             dummy_spec_decode_metadata = SpecDecodeMetadata.make_dummy(
                 draft_token_ids, self.device)