[bugfix] torch profiler bug for single gpu with GPUExecutor

examples/offline_inference_with_profiler.py

@@ -16,7 +16,7 @@
 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
 
 # Create an LLM.
-llm = LLM(model="facebook/opt-125m")
+llm = LLM(model="facebook/opt-125m", tensor_parallel_size=1)
 
 llm.start_profile()
 

vllm/engine/async_llm_engine.py

@@ -16,6 +16,7 @@
                                     PromptComponents, SchedulerOutputState)
 from vllm.engine.metrics_types import StatLoggerBase
 from vllm.executor.executor_base import ExecutorAsyncBase
+from vllm.executor.gpu_executor import GPUExecutorAsync
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import (EncoderDecoderLLMInputs, LLMInputs, PromptInputs,
                          SingletonPromptInputs)
@@ -1156,7 +1157,17 @@ def remove_logger(self, logger_name: str) -> None:
         self.engine.remove_logger(logger_name=logger_name)
 
     async def start_profile(self) -> None:
-        self.engine.model_executor._run_workers("start_profile")
+        # using type instead of isinstance to check to avoid capturing
+        # inherited classes
+        if type(self.engine.model_executor) == GPUExecutorAsync:
+            self.engine.model_executor.start_profile()
+        else:
+            self.engine.model_executor._run_workers("start_profile")
 
     async def stop_profile(self) -> None:
-        self.engine.model_executor._run_workers("stop_profile")
+        # using type instead of isinstance to check to avoid capturing
+        # inherited classes
+        if type(self.engine.model_executor) == GPUExecutorAsync:
+            self.engine.model_executor.stop_profile()
+        else:
+            self.engine.model_executor._run_workers("stop_profile")

vllm/engine/llm_engine.py

@@ -26,6 +26,7 @@
 from vllm.engine.output_processor.stop_checker import StopChecker
 from vllm.engine.output_processor.util import create_output_by_sequence_group
 from vllm.executor.executor_base import ExecutorBase
+from vllm.executor.gpu_executor import GPUExecutor
 from vllm.executor.ray_utils import initialize_ray_cluster
 from vllm.inputs import (INPUT_REGISTRY, EncoderDecoderLLMInputs,
                          InputRegistry, LLMInputs, PromptInputs,
@@ -1964,10 +1965,20 @@ def check_health(self) -> None:
         self.model_executor.check_health()
 
     def start_profile(self) -> None:
-        self.model_executor.start_profile()
+        # using type instead of isinstance to check to avoid capturing
+        # inherited classes (MultiprocessingGPUExecutor)
+        if type(self.model_executor) == GPUExecutor:
+            self.model_executor.start_profile()
+        else:
+            self.model_executor._run_workers("start_profile")
 
     def stop_profile(self) -> None:
-        self.model_executor.stop_profile()
+        # using type instead of isinstance to check to avoid capturing
+        # inherited classes (MultiprocessingGPUExecutor)
+        if type(self.model_executor) == GPUExecutor:
+            self.model_executor.stop_profile()
+        else:
+            self.model_executor._run_workers("stop_profile")
 
     def is_tracing_enabled(self) -> bool:
         return self.tracer is not None