[Model Runner V2] Minor optimization for eagle input processing (#32535)

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

vllm/v1/worker/gpu/model_runner.py

@@ -827,20 +827,14 @@ class GPUModelRunner(LoRAModelRunnerMixin, KVConnectorModelRunnerMixin):
         num_rejected: torch.Tensor,
     ) -> torch.Tensor:
         assert self.speculator is not None
-        last_sampled_tokens = self.req_states.last_sampled_tokens[
-            input_batch.idx_mapping
-        ]
-        next_prefill_tokens = self.req_states.next_prefill_tokens[
-            input_batch.idx_mapping
-        ]
         draft_tokens = self.speculator.propose(
             input_batch,
             last_hidden_states,
             aux_hidden_states,
             num_sampled,
             num_rejected,
-            last_sampled_tokens,
-            next_prefill_tokens,
+            self.req_states.last_sampled_tokens,
+            self.req_states.next_prefill_tokens,
             self.sampler.sampling_states.temperature.gpu,
             self.sampler.sampling_states.seeds.gpu,
         )

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

@@ -195,9 +195,9 @@ class EagleSpeculator:
         num_sampled: torch.Tensor,
         # [num_reqs]
         num_rejected: torch.Tensor,
-        # [num_reqs]
+        # [max_num_reqs]
         last_sampled: torch.Tensor,
-        # [num_reqs]
+        # [max_num_reqs]
         next_prefill_tokens: torch.Tensor,
         # [max_num_reqs]
         temperature: torch.Tensor,
@@ -320,6 +320,7 @@ def _prepare_eagle_inputs_kernel(
     eagle_positions_ptr,
     target_input_ids_ptr,
     target_positions_ptr,
+    idx_mapping_ptr,
     last_sampled_ptr,
     next_prefill_tokens_ptr,
     num_sampled_ptr,
@@ -328,6 +329,8 @@ def _prepare_eagle_inputs_kernel(
     BLOCK_SIZE: tl.constexpr,
 ):
     batch_idx = tl.program_id(0)
+    req_state_idx = tl.load(idx_mapping_ptr + batch_idx)
+
     query_start = tl.load(query_start_loc_ptr + batch_idx)
     query_end = tl.load(query_start_loc_ptr + batch_idx + 1)
     query_len = query_end - query_start
@@ -338,11 +341,11 @@ def _prepare_eagle_inputs_kernel(
 
     num_sampled = tl.load(num_sampled_ptr + batch_idx)
     if num_sampled > 0:
-        next_token = tl.load(last_sampled_ptr + batch_idx).to(tl.int32)
+        next_token = tl.load(last_sampled_ptr + req_state_idx).to(tl.int32)
     else:
         # Chunked prefilling.
         # Get the next prefill token.
-        next_token = tl.load(next_prefill_tokens_ptr + batch_idx)
+        next_token = tl.load(next_prefill_tokens_ptr + req_state_idx)
 
     # Shift target_input_ids by one.
     for i in range(1, query_len, BLOCK_SIZE):
@@ -370,9 +373,9 @@ def prepare_eagle_inputs(
     num_sampled: torch.Tensor,
     # [num_reqs]
     num_rejected: torch.Tensor,
-    # [num_reqs]
+    # [max_num_reqs]
     last_sampled: torch.Tensor,
-    # [num_reqs]
+    # [max_num_reqs]
     next_prefill_tokens: torch.Tensor,
 ) -> torch.Tensor:
     num_reqs = input_batch.num_reqs
@@ -387,6 +390,7 @@ def prepare_eagle_inputs(
         input_buffers.positions,
         input_batch.input_ids,
         input_batch.positions,
+        input_batch.idx_mapping,
         last_sampled,
         next_prefill_tokens,
         num_sampled,