[Model Runner V2] Fix draft logits not populated during cudagraph replay

vllm/v1/worker/gpu/model_runner.py

@@ -195,7 +195,6 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
             num_speculative_steps=self.num_speculative_steps,
             vocab_size=self.vocab_size,
             device=self.device,
-            cache_draft_logits=not use_strict_rejection_sampling,
         )
         self.input_buffers = InputBuffers(
             max_num_reqs=self.max_num_reqs,
@@ -447,7 +446,6 @@ def _dummy_run(
                 next_prefill_tokens=self.req_states.next_prefill_tokens,
                 temperature=self.sampler.sampling_states.temperature.gpu,
                 seeds=self.sampler.sampling_states.seeds.gpu,
-                draft_logits_out=self.req_states.draft_logits,
                 num_tokens_across_dp=num_tokens_across_dp,
                 dummy_run=True,
                 skip_attn_for_dummy_run=skip_attn,
@@ -816,11 +814,12 @@ def sample(
         else:
             # Rejection sampling for spec decoding.
             assert self.rejection_sampler is not None
+            assert self.speculator is not None
             sampler_output = self.rejection_sampler(
                 logits,
                 input_batch,
                 # Draft logits are needed for probabilistic rejection sampling.
-                self.req_states.draft_logits,
+                self.speculator.draft_logits,
             )
 
         # Get the number of sampled and rejected tokens.
@@ -1146,7 +1145,6 @@ def sample_tokens(
                 self.req_states.next_prefill_tokens,
                 self.sampler.sampling_states.temperature.gpu,
                 self.sampler.sampling_states.seeds.gpu,
-                self.req_states.draft_logits,
                 num_tokens_across_dp=num_tokens_across_dp,
             )
             self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens

vllm/v1/worker/gpu/spec_decode/eagle/speculator.py

@@ -75,6 +75,17 @@ def __init__(self, vllm_config: VllmConfig, device: torch.device):
             device=device,
         )
 
+        cache_draft_logits = self.speculative_config.rejection_sample_method != "strict"
+        self.draft_logits: torch.Tensor | None = None
+        if cache_draft_logits:
+            self.draft_logits = torch.zeros(
+                self.max_num_reqs,
+                self.num_speculative_steps,
+                self.vocab_size,
+                dtype=torch.float32,
+                device=device,
+            )
+
         # currently we don't  support PIECEWISE for Eagle.
         cudagraph_mode = vllm_config.compilation_config.cudagraph_mode
         if cudagraph_mode.decode_mode() == CUDAGraphMode.FULL:
@@ -140,7 +151,6 @@ def generate_draft(
         slot_mappings: dict[str, torch.Tensor] | None,
         num_tokens_across_dp: torch.Tensor | None,
         cudagraph_runtime_mode: CUDAGraphMode = CUDAGraphMode.NONE,
-        draft_logits_out: torch.Tensor | None = None,
     ) -> None:
         pos = self.input_buffers.positions[:num_reqs]
         query_start_loc = self.input_buffers.query_start_loc[: num_reqs + 1]
@@ -167,8 +177,8 @@ def generate_draft(
                 self.seeds,
                 pos + 1,
                 apply_temperature=True,
-                processed_logits_out=draft_logits_out[:, step]
-                if draft_logits_out is not None
+                processed_logits_out=self.draft_logits[:, step]
+                if self.draft_logits is not None
                 else None,
             )
             self.draft_tokens[:num_reqs, step] = draft_tokens
@@ -223,8 +233,6 @@ def propose(
         temperature: torch.Tensor,
         # [max_num_reqs]
         seeds: torch.Tensor,
-        # [max_num_reqs, num_speculative_steps, vocab_size]
-        draft_logits_out: torch.Tensor | None,
         num_tokens_across_dp: torch.Tensor | None = None,
         dummy_run: bool = False,
         skip_attn_for_dummy_run: bool = False,
@@ -290,8 +298,8 @@ def propose(
             self.seeds,
             pos + 1,
             apply_temperature=True,
-            processed_logits_out=draft_logits_out[:, 0]
-            if draft_logits_out is not None
+            processed_logits_out=self.draft_logits[:, 0]
+            if self.draft_logits is not None
             else None,
         )
 
@@ -376,7 +384,6 @@ def propose(
             slot_mappings_updated,
             num_tokens_across_dp=num_tokens_across_dp,
             cudagraph_runtime_mode=batch_desc.cg_mode,
-            draft_logits_out=draft_logits_out,
         )
         return self.draft_tokens[:num_reqs]
 

vllm/v1/worker/gpu/states.py

@@ -15,7 +15,6 @@ def __init__(
         num_speculative_steps: int,
         vocab_size: int,
         device: torch.device,
-        cache_draft_logits: bool,
     ):
         self.max_num_reqs = max_num_reqs
         self.max_model_len = max_model_len
@@ -71,18 +70,6 @@ def __init__(
             dtype=torch.int64,
             device=device,
         )
-        # Draft token logits.
-        # NOTE: This tensor maintains the "processed" logits after applying temperature,
-        # top-p, etc.
-        self.draft_logits: torch.Tensor | None = None
-        if cache_draft_logits:
-            self.draft_logits = torch.zeros(
-                self.max_num_reqs,
-                self.num_speculative_steps,
-                self.vocab_size,
-                dtype=torch.float32,
-                device=device,
-            )
 
         self.next_prefill_tokens = torch.zeros(
             self.max_num_reqs, dtype=torch.int32, device=device