[Model Runner V2] Minor code cleanup (vllm-project#29570)

Signed-off-by: Woosuk Kwon <woosuk.kwon@berkeley.edu>

Author: WoosukKwon

vllm/v1/worker/gpu/cudagraph_utils.py

@@ -16,6 +16,7 @@
 from vllm.v1.kv_cache_interface import KVCacheConfig
 from vllm.v1.worker.gpu.attn_utils import build_attn_metadata
 from vllm.v1.worker.gpu.block_table import BlockTables
+from vllm.v1.worker.gpu.dp_utils import make_num_tokens_across_dp
 from vllm.v1.worker.gpu.input_batch import InputBuffers
 
 
@@ -127,15 +128,7 @@ def capture_graph(
             slot_mappings=slot_mappings,
             kv_cache_config=kv_cache_config,
         )
-        if self.dp_size > 1:
-            num_tokens_across_dp = torch.full(
-                (self.dp_size,),
-                batch_size,
-                dtype=torch.int32,
-                device="cpu",
-            )
-        else:
-            num_tokens_across_dp = None
+        num_tokens_across_dp = make_num_tokens_across_dp(self.dp_size, batch_size)
 
         # Warm up.
         with set_forward_context(

vllm/v1/worker/gpu/dp_utils.py

@@ -20,3 +20,12 @@ def get_batch_metadata_across_dp(
     tensor[1][dp_rank] = cudagraph_size
     dist.all_reduce(tensor, group=group)
     return tensor[0], tensor[1]
+
+
+def make_num_tokens_across_dp(
+    dp_size: int,
+    num_tokens: int,
+) -> torch.Tensor | None:
+    if dp_size == 1:
+        return None
+    return torch.full((dp_size,), num_tokens, dtype=torch.int32, device="cpu")

vllm/v1/worker/gpu/model_runner.py

@@ -35,7 +35,10 @@
 )
 from vllm.v1.worker.gpu.block_table import BlockTables
 from vllm.v1.worker.gpu.cudagraph_utils import CudaGraphManager
-from vllm.v1.worker.gpu.dp_utils import get_batch_metadata_across_dp
+from vllm.v1.worker.gpu.dp_utils import (
+    get_batch_metadata_across_dp,
+    make_num_tokens_across_dp,
+)
 from vllm.v1.worker.gpu.input_batch import (
     InputBatch,
     InputBuffers,
@@ -255,12 +258,7 @@ def _dummy_run(
         if not skip_attn:
             self.prepare_dummy_attn_metadata(input_batch)
 
-        if self.dp_size == 1:
-            num_tokens_across_dp: torch.Tensor | None = None
-        else:
-            num_tokens_across_dp = torch.full(
-                (self.dp_size,), num_tokens, dtype=torch.int32, device="cpu"
-            )
+        num_tokens_across_dp = make_num_tokens_across_dp(self.dp_size, num_tokens)
         num_sampled_tokens = np.ones(input_batch.num_reqs, dtype=np.int32)
         with (
             self.maybe_dummy_run_with_lora(
@@ -816,7 +814,6 @@ def propose_draft(
             self.req_states.last_sampled_tokens,
             next_prefill_tokens,
         )
-        self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
         return draft_tokens
 
     def get_cudagraph_and_dp_padding(
@@ -1006,14 +1003,15 @@ def sample_tokens(
             input_batch, sampler_output.sampled_token_ids, num_sampled, num_rejected
         )
         if self.do_spec_decode:
-            _ = self.propose_draft(
+            draft_tokens = self.propose_draft(
                 input_batch,
                 sampling_metadata,
                 hidden_states,
                 None,  # aux_hidden_states
                 num_sampled,
                 num_rejected,
             )
+            self.req_states.draft_tokens[input_batch.idx_mapping] = draft_tokens
 
         if self.use_async_scheduling:
             return async_output