Make PyTorch profiler gzip and CUDA time dump configurable (#29568)

Signed-off-by: Yifei Zhang <yifei.zhang1992@outlook.com>

docs/contributing/profiling.md

@@ -11,6 +11,8 @@ We support tracing vLLM workers using the `torch.profiler` module. You can enabl
 - `VLLM_TORCH_PROFILER_WITH_PROFILE_MEMORY=1` to record memory, off by default
 - `VLLM_TORCH_PROFILER_WITH_STACK=1` to enable recording stack information, on by default
 - `VLLM_TORCH_PROFILER_WITH_FLOPS=1` to enable recording FLOPs, off by default
+- `VLLM_TORCH_PROFILER_USE_GZIP=0` to disable gzip-compressing profiling files, on by default
+- `VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL=0` to disable dumping and printing the aggregated CUDA self time table, on by default
 
 The OpenAI server also needs to be started with the `VLLM_TORCH_PROFILER_DIR` environment variable set.
 

vllm/envs.py

@@ -100,6 +100,8 @@ if TYPE_CHECKING:
     VLLM_TORCH_PROFILER_WITH_FLOPS: bool = False
     VLLM_PROFILER_DELAY_ITERS: int = 0
     VLLM_PROFILER_MAX_ITERS: int = 0
+    VLLM_TORCH_PROFILER_USE_GZIP: bool = True
+    VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL: bool = True
     VLLM_USE_TRITON_AWQ: bool = False
     VLLM_ALLOW_RUNTIME_LORA_UPDATING: bool = False
     VLLM_SKIP_P2P_CHECK: bool = False
@@ -890,6 +892,17 @@ environment_variables: dict[str, Callable[[], Any]] = {
     # Maximum number of iterations to profile when using the torch/torch CUDA profiler.
     # If set to 0, will not limit the number of iterations.
     "VLLM_PROFILER_MAX_ITERS": lambda: int(os.getenv("VLLM_PROFILER_MAX_ITERS", "0")),
+    # Control whether torch profiler gzip-compresses profiling files.
+    # Set VLLM_TORCH_PROFILER_USE_GZIP=0 to disable gzip (enabled by default).
+    "VLLM_TORCH_PROFILER_USE_GZIP": lambda: bool(
+        os.getenv("VLLM_TORCH_PROFILER_USE_GZIP", "1") != "0"
+    ),
+    # Control whether torch profiler dumps the self_cuda_time_total table.
+    # Set VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL=0 to disable dumping
+    # (enabled by default).
+    "VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL": lambda: bool(
+        os.getenv("VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL", "1") != "0"
+    ),
     # If set, vLLM will use Triton implementations of AWQ.
     "VLLM_USE_TRITON_AWQ": lambda: bool(int(os.getenv("VLLM_USE_TRITON_AWQ", "0"))),
     # If set, allow loading or unloading lora adapters in runtime,

vllm/profiler/gpu_profiler.py

@@ -162,7 +162,9 @@ class TorchProfilerWrapper(WorkerProfiler):
             with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
             with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
             on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                torch_profiler_trace_dir, worker_name=worker_name, use_gzip=True
+                torch_profiler_trace_dir,
+                worker_name=worker_name,
+                use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
             ),
         )
 
@@ -174,6 +176,7 @@ class TorchProfilerWrapper(WorkerProfiler):
     def _stop(self) -> None:
         self.profiler.stop()
 
+        if envs.VLLM_TORCH_PROFILER_DUMP_CUDA_TIME_TOTAL:
             rank = self.local_rank
             profiler_dir = envs.VLLM_TORCH_PROFILER_DIR
             profiler_out_file = f"{profiler_dir}/profiler_out_{rank}.txt"

vllm/v1/engine/async_llm.py

@@ -190,7 +190,9 @@ class AsyncLLM(EngineClient):
                 ],
                 with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                 on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    envs.VLLM_TORCH_PROFILER_DIR, worker_name=worker_name, use_gzip=True
+                    envs.VLLM_TORCH_PROFILER_DIR,
+                    worker_name=worker_name,
+                    use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
                 ),
             )
         else:

vllm/v1/worker/xpu_worker.py

@@ -64,7 +64,9 @@ class XPUWorker(Worker):
                 with_stack=envs.VLLM_TORCH_PROFILER_WITH_STACK,
                 with_flops=envs.VLLM_TORCH_PROFILER_WITH_FLOPS,
                 on_trace_ready=torch.profiler.tensorboard_trace_handler(
-                    torch_profiler_trace_dir, worker_name=worker_name, use_gzip=True
+                    torch_profiler_trace_dir,
+                    worker_name=worker_name,
+                    use_gzip=envs.VLLM_TORCH_PROFILER_USE_GZIP,
                 ),
             )
         else: