diff --git a/vllm_spyre/v1/worker/spyre_worker.py b/vllm_spyre/v1/worker/spyre_worker.py
index 2f5bd536b..95c60f249 100644
--- a/vllm_spyre/v1/worker/spyre_worker.py
+++ b/vllm_spyre/v1/worker/spyre_worker.py
@@ -357,44 +357,46 @@ def _warmup_spyre_dynamic_size(self, special_token_ids):
                 logger.info("[WARMUP] Prefill %d/%d...", i + 1, batch_size)
                 self.execute_model(scheduler_output)
 
-        # one decode iteration across all sequences
-        req_ids = []
-        new_token_ids = []
-        new_block_ids = []
-        num_computed_tokens = []
-        for req in dummy_requests:
-            req_ids.append(req.req_id)
-            new_token_ids.append([
-                valid_token_ids_tensor[torch.randint(
-                    0, len(valid_token_ids_tensor), (1, )).item()]
-            ])  # placeholder token
-            new_block_ids.append([req.block_ids])
-            num_computed_tokens.append(prompt_len)
-        cached_request_data = CachedRequestData(
-            req_ids=req_ids,
-            resumed_from_preemption=False,
-            new_token_ids=new_token_ids,
-            new_block_ids=new_block_ids,
-            num_computed_tokens=num_computed_tokens,
-        )
+            # one decode iteration across all sequences
+            req_ids = []
+            new_token_ids = []
+            new_block_ids = []
+            num_computed_tokens = []
+            for req in dummy_requests:
+                req_ids.append(req.req_id)
+                new_token_ids.append([
+                    valid_token_ids_tensor[torch.randint(
+                        0, len(valid_token_ids_tensor), (1, )).item()]
+                ])  # placeholder token
+                new_block_ids.append([req.block_ids])
+                num_computed_tokens.append(prompt_len)
+            cached_request_data = CachedRequestData(
+                req_ids=req_ids,
+                resumed_from_preemption=False,
+                new_token_ids=new_token_ids,
+                new_block_ids=new_block_ids,
+                num_computed_tokens=num_computed_tokens,
+            )
 
-        scheduler_output = SchedulerOutput(
-            scheduled_new_reqs=[],
-            scheduled_cached_reqs=cached_request_data,
-            num_scheduled_tokens={f"warmup-{i}": 1
-                                  for i in range(batch_size)},
-            total_num_scheduled_tokens=batch_size,
-            scheduled_spec_decode_tokens={},
-            scheduled_encoder_inputs={},
-            num_common_prefix_blocks=0,
-            finished_req_ids=set(),
-            free_encoder_input_ids=[],
-            structured_output_request_ids={},
-            grammar_bitmask=None,
-        )
-        logger.info("[WARMUP] Decode...")
-        self.execute_model(scheduler_output)
-        self._cleanup_model_runner(request=dummy_requests)
+            scheduler_output = SchedulerOutput(
+                scheduled_new_reqs=[],
+                scheduled_cached_reqs=cached_request_data,
+                num_scheduled_tokens={
+                    f"warmup-{i}": 1
+                    for i in range(batch_size)
+                },
+                total_num_scheduled_tokens=batch_size,
+                scheduled_spec_decode_tokens={},
+                scheduled_encoder_inputs={},
+                num_common_prefix_blocks=0,
+                finished_req_ids=set(),
+                free_encoder_input_ids=[],
+                structured_output_request_ids={},
+                grammar_bitmask=None,
+            )
+            logger.info("[WARMUP] Decode...")
+            self.execute_model(scheduler_output)
+            self._cleanup_model_runner(request=dummy_requests)
 
         # warmup_mode completes the graph compilation, but we need to do
         # one additional prefill to deploy the compiled program to the device,