[V1] Cache uses_mrope in GPUModelRunner (#12969)

vllm/v1/worker/gpu_model_runner.py

@@ -92,6 +92,7 @@ def __init__(
         # Multi-modal data support
         self.input_registry = INPUT_REGISTRY
         self.mm_registry = MULTIMODAL_REGISTRY
+        self.uses_mrope = model_config.uses_mrope
 
         # NOTE: Initialized input mapper is only used for processing dummy
         # multimodal data into multimodal kwargs for GPU memory profiling.
@@ -147,7 +148,7 @@ def __init__(
                                      device=self.device)
 
         # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-        if self.model_config.uses_mrope:
+        if self.uses_mrope:
             # NOTE: `mrope_positions` is implemented with one additional dummy
             # position on purpose to make it non-contiguous so that it can work
             # with torch compile.
@@ -284,7 +285,7 @@ def _update_states(self, scheduler_output: "SchedulerOutput") -> bool:
             )
 
             # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-            if self.model_config.uses_mrope:
+            if self.uses_mrope:
                 image_grid_thw = []
                 video_grid_thw = []
                 second_per_grid_ts = []
@@ -411,7 +412,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
 
         # Calculate M-RoPE positions.
         # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
-        if self.model_config.uses_mrope:
+        if self.uses_mrope:
             self._calc_mrope_positions(scheduler_output)
 
         # Get token indices.
@@ -458,7 +459,7 @@ def _prepare_inputs(self, scheduler_output: "SchedulerOutput"):
         # Copy the tensors to the GPU.
         self.input_ids[:total_num_scheduled_tokens].copy_(
             self.input_ids_cpu[:total_num_scheduled_tokens], non_blocking=True)
-        if self.model_config.uses_mrope:
+        if self.uses_mrope:
             # Only relevant for models using M-RoPE (e.g, Qwen2-VL)
             self.mrope_positions[:, :total_num_scheduled_tokens].copy_(
                 self.mrope_positions_cpu[:, :total_num_scheduled_tokens],
@@ -817,13 +818,14 @@ def execute_model(
             # then the embedding layer is not included in the CUDA graph.
             input_ids = self.input_ids[:num_input_tokens]
             inputs_embeds = None
+        if self.uses_mrope:
+            positions = self.mrope_positions[:, :num_input_tokens]
+        else:
+            positions = self.positions[:num_input_tokens]
 
         # Run the decoder.
         # Use persistent buffers for CUDA graphs.
         with set_forward_context(attn_metadata, self.vllm_config):
-            positions = self.mrope_positions[:, :num_input_tokens] \
-                if self.model_config.uses_mrope \
-                else self.positions[:num_input_tokens]
             hidden_states = self.model(
                 input_ids=input_ids,
                 positions=positions,
@@ -1001,10 +1003,11 @@ def _dummy_run(
         else:
             input_ids = self.input_ids[:num_tokens]
             inputs_embeds = None
+        if self.uses_mrope:
+            positions = self.mrope_positions[:, :num_tokens]
+        else:
+            positions = self.positions[:num_tokens]
         with set_forward_context(None, self.vllm_config):
-            positions = self.mrope_positions[:, :num_tokens] \
-                if self.model_config.uses_mrope \
-                else self.positions[:num_tokens]
             hidden_states = model(
                 input_ids=input_ids,
                 positions=positions,