[V1] Check all pooling tasks during profiling

vllm/sequence.py

@@ -1173,6 +1173,10 @@ class PoolingSequenceGroupOutput(
     # The actual type is in SequenceGroup.pooled_data
     data: Any
 
+    def get_data_nbytes(self) -> int:
+        data: torch.Tensor = self.data
+        return data.nbytes
+
     def __repr__(self) -> str:
         return f"PoolingSequenceGroupOutput(data={self.data}"
 
@@ -1234,6 +1238,9 @@ class PoolerOutput(
     """The output from a pooling operation in the pooling model."""
     outputs: list[PoolingSequenceGroupOutput]
 
+    def get_data_nbytes(self) -> int:
+        return sum(o.get_data_nbytes() for o in self.outputs)
+
     def __getitem__(self, idx: int) -> PoolingSequenceGroupOutput:
         return self.outputs[idx]
 

vllm/v1/worker/gpu_model_runner.py

@@ -41,7 +41,7 @@
 from vllm.multimodal.utils import group_mm_inputs_by_modality
 from vllm.pooling_params import PoolingParams, PoolingTask
 from vllm.sampling_params import SamplingType
-from vllm.sequence import IntermediateTensors
+from vllm.sequence import IntermediateTensors, PoolerOutput
 from vllm.utils import (STR_DTYPE_TO_TORCH_DTYPE, DeviceMemoryProfiler,
                         GiB_bytes, LazyLoader, check_use_alibi, get_dtype_size,
                         is_pin_memory_available, round_up)
@@ -1819,7 +1819,7 @@ def load_model(self, eep_scale_up: bool = False) -> None:
             old_global_expert_indices = None
             rank_mapping = None
 
-        with DeviceMemoryProfiler() as m:  # noqa: SIM117
+        with DeviceMemoryProfiler() as m:
             time_before_load = time.perf_counter()
             model_loader = get_model_loader(self.load_config)
             if not hasattr(self, "model"):
@@ -2215,12 +2215,11 @@ def _dummy_sampler_run(
             )
         return sampler_output
 
-    @torch.inference_mode()
-    def _dummy_pooler_run(
+    def _dummy_pooler_run_task(
         self,
         hidden_states: torch.Tensor,
-    ) -> torch.Tensor:
-
+        task: PoolingTask,
+    ) -> PoolerOutput:
         num_tokens = hidden_states.shape[0]
         max_num_reqs = self.scheduler_config.max_num_seqs
         num_reqs = min(num_tokens, max_num_reqs)
@@ -2232,37 +2231,55 @@ def _dummy_pooler_run(
 
         hidden_states_list = list(
             torch.split(hidden_states, num_scheduled_tokens_list))
-
         req_num_tokens = num_tokens // num_reqs
 
-        model = cast(VllmModelForPooling, self.model)
-        dummy_task = self.get_supported_pooling_tasks()[0]
-        dummy_pooling_params = PoolingParams(task=dummy_task)
+        dummy_prompt_lens = torch.tensor(
+            [h.shape[0] for h in hidden_states_list],
+            device=self.device,
+        )
+        dummy_token_ids = torch.zeros((num_reqs, req_num_tokens),
+                                      dtype=torch.int32,
+                                      device=self.device)
 
-        to_update = model.pooler.get_pooling_updates(dummy_task)
+        model = cast(VllmModelForPooling, self.model)
+        dummy_pooling_params = PoolingParams(task=task)
+        to_update = model.pooler.get_pooling_updates(task)
         to_update.apply(dummy_pooling_params)
 
         dummy_metadata = PoolingMetadata(
-            prompt_lens=torch.tensor([h.shape[0] for h in hidden_states_list],
-                                     device=self.device),
-            prompt_token_ids=torch.zeros((num_reqs, req_num_tokens),
-                                         dtype=torch.int32,
-                                         device=self.device),
-            pooling_params=[dummy_pooling_params] * num_reqs)
+            prompt_lens=dummy_prompt_lens,
+            prompt_token_ids=dummy_token_ids,
+            pooling_params=[dummy_pooling_params] * num_reqs,
+        )
 
         try:
-            pooler_output = model.pooler(hidden_states=hidden_states_list,
-                                         pooling_metadata=dummy_metadata)
+            return model.pooler(hidden_states=hidden_states_list,
+                                pooling_metadata=dummy_metadata)
         except RuntimeError as e:
             if 'out of memory' in str(e):
                 raise RuntimeError(
-                    "CUDA out of memory occurred when warming up pooler with "
-                    f"{num_reqs} dummy requests. Please try lowering "
-                    "`max_num_seqs` or `gpu_memory_utilization` when "
+                    "CUDA out of memory occurred when warming up pooler "
+                    f"({task=}) with {num_reqs} dummy requests. Please try "
+                    "lowering `max_num_seqs` or `gpu_memory_utilization` when "
                     "initializing the engine.") from e
             else:
                 raise e
-        return pooler_output
+
+    @torch.inference_mode()
+    def _dummy_pooler_run(
+        self,
+        hidden_states: torch.Tensor,
+    ) -> PoolerOutput:
+        # Find the task that has the largest output for subsequent steps
+        output_size = dict[PoolingTask, float]()
+        for task in self.get_supported_pooling_tasks():
+            # Run a full batch with each task to ensure none of them OOMs
+            output = self._dummy_pooler_run_task(hidden_states, task)
+            output_size[task] = output.get_data_nbytes()
+            del output  # Allow GC
+
+        max_task = max(output_size.items(), key=lambda x: x[1])[0]
+        return self._dummy_pooler_run_task(hidden_states, max_task)
 
     def profile_run(self) -> None:
         # Profile with multimodal encoder & encoder cache.