Add the ability to enable and disable the Profiler via HTTP API. (#1626)

python/sglang/srt/managers/io_struct.py

@@ -20,6 +20,7 @@ processes (TokenizerManager, DetokenizerManager, Controller).
 
 import uuid
 from dataclasses import dataclass
+from enum import Enum
 from typing import Dict, List, Optional, Union
 
 from sglang.srt.managers.schedule_batch import BaseFinishReason
@@ -343,3 +344,8 @@ class UpdateWeightReqOutput:
 class AbortReq:
     # The request id
     rid: str
+
+
+class ProfileReq(Enum):
+    START_PROFILE = 1
+    STOP_PROFILE = 2

python/sglang/srt/managers/scheduler.py

@@ -37,6 +37,7 @@ from sglang.srt.managers.io_struct import (
     BatchEmbeddingOut,
     BatchTokenIDOut,
     FlushCacheReq,
+    ProfileReq,
     TokenizedEmbeddingReqInput,
     TokenizedGenerateReqInput,
     TokenizedRewardReqInput,
@@ -229,6 +230,22 @@ class Scheduler:
         self.new_token_ratio_decay = global_config.new_token_ratio_decay
         self.batch_is_full = False
 
+        if os.getenv("SGLANG_TORCH_PROFILER_DIR", "") == "":
+            self.profiler = None
+        else:
+            self.torch_profiler_trace_dir = os.getenv("SGLANG_TORCH_PROFILER_DIR")
+            logger.info(
+                "Profiling enabled. Traces will be saved to: %s",
+                self.torch_profiler_trace_dir,
+            )
+            self.profiler = torch.profiler.profile(
+                activities=[
+                    torch.profiler.ProfilerActivity.CPU,
+                    torch.profiler.ProfilerActivity.CUDA,
+                ],
+                with_stack=True,
+            )
+
     @torch.inference_mode()
     def event_loop(self):
         while True:
@@ -271,6 +288,11 @@ class Scheduler:
             elif isinstance(recv_req, UpdateWeightReqInput):
                 success, message = self.update_weights(recv_req)
                 self.out_pyobjs.append(UpdateWeightReqOutput(success, message))
+            elif isinstance(recv_req, ProfileReq):
+                if recv_req == ProfileReq.START_PROFILE:
+                    self.start_profile()
+                else:
+                    self.stop_profile()
             else:
                 raise ValueError(f"Invalid request: {recv_req}")
 
@@ -1000,6 +1022,20 @@ class Scheduler:
             logger.error(message)
         return success, message
 
+    def start_profile(self) -> None:
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.start()
+
+    def stop_profile(self) -> None:
+        if self.profiler is None:
+            raise RuntimeError("Profiler is not enabled.")
+        self.profiler.stop()
+        self.profiler.export_chrome_trace(
+            self.torch_profiler_trace_dir + "/" + str(time.time()) + ".trace.json.gz"
+        )
+        logger.info("Profiler is done")
+
 
 def run_scheduler_process(
     server_args: ServerArgs,

python/sglang/srt/managers/tokenizer_manager.py

@@ -46,6 +46,7 @@ from sglang.srt.managers.io_struct import (
     EmbeddingReqInput,
     FlushCacheReq,
     GenerateReqInput,
+    ProfileReq,
     RewardReqInput,
     TokenizedEmbeddingReqInput,
     TokenizedGenerateReqInput,
@@ -512,6 +513,14 @@ class TokenizerManager:
         req = AbortReq(rid)
         self.send_to_scheduler.send_pyobj(req)
 
+    def start_profile(self):
+        req = ProfileReq.START_PROFILE
+        self.send_to_scheduler.send_pyobj(req)
+
+    def stop_profile(self):
+        req = ProfileReq.STOP_PROFILE
+        self.send_to_scheduler.send_pyobj(req)
+
     async def update_weights(
         self, obj: UpdateWeightReqInput, request: Optional[fastapi.Request] = None
     ):

python/sglang/srt/server.py

@@ -145,6 +145,28 @@ async def flush_cache():
     )
 
 
+@app.get("/start_profile")
+@app.post("/start_profile")
+async def start_profile():
+    """Start profiling."""
+    tokenizer_manager.start_profile()
+    return Response(
+        content="Start profiling.\n",
+        status_code=200,
+    )
+
+
+@app.get("/stop_profile")
+@app.post("/stop_profile")
+async def stop_profile():
+    """Stop profiling."""
+    tokenizer_manager.stop_profile()
+    return Response(
+        content="Stop profiling. This will take some time.\n",
+        status_code=200,
+    )
+
+
 @app.post("/update_weights")
 async def update_weights(obj: UpdateWeightReqInput, request: Request):
     """Update the weights inplace without re-launching the server."""