Use cuda event wait and synchronization instead of busy waiting (#2089)

python/sglang/srt/managers/schedule_batch.py

@@ -1063,7 +1063,7 @@ class ScheduleBatch:
             out_cache_loc=self.out_cache_loc,
             return_logprob=self.return_logprob,
             decoding_reqs=self.decoding_reqs,
-            sampling_info=dataclasses.replace(self.sampling_info),
+            sampling_info=self.sampling_info,
         )
 
     def __str__(self):

python/sglang/srt/managers/scheduler.py

@@ -387,9 +387,6 @@ class Scheduler:
             batch = self.get_next_batch_to_run()
             self.cur_batch = batch
             if batch:
-                # We need a stream synchronization here. Otherwise, there will be cuda illegal memory access errors.
-                _ = batch.seq_lens[0].item()
-
                 result = self.run_batch(batch)
                 result_queue.append((batch.copy(), result))
 

python/sglang/srt/managers/tp_worker.py

@@ -142,12 +142,12 @@ class TpModelWorker:
     def forward_batch_generation(
         self,
         model_worker_batch: ModelWorkerBatch,
-        launch_event: Optional[threading.Event] = None,
+        launch_done: Optional[threading.Event] = None,
     ):
         forward_batch = ForwardBatch.init_new(model_worker_batch, self.model_runner)
         logits_output = self.model_runner.forward(forward_batch)
-        if launch_event:
-            launch_event.set()
+        if launch_done:
+            launch_done.set()
         next_token_ids = self.model_runner.sample(logits_output, model_worker_batch)
         return logits_output, next_token_ids
 

python/sglang/srt/managers/tp_worker_overlap_thread.py

@@ -96,19 +96,22 @@ class TpModelWorkerClient:
     @torch.no_grad()
     def forward_thread_func_(self):
         while True:
-            model_worker_batch, future_token_ids_ct = self.input_queue.get()
+            model_worker_batch, future_token_ids_ct, compute_info_done = (
+                self.input_queue.get()
+            )
             if not model_worker_batch:
                 break
-            self.launch_event = threading.Event()
-            copy_event = torch.cuda.Event()
+            self.launch_done = threading.Event()
+            copy_done = torch.cuda.Event()
 
             # Resolve future tokens in the input
             input_ids = model_worker_batch.input_ids
             resolve_future_token_ids(input_ids, self.future_token_ids_map)
 
             # Run forward
+            compute_info_done.wait()
             logits_output, next_token_ids = self.worker.forward_batch_generation(
-                model_worker_batch, self.launch_event
+                model_worker_batch, self.launch_done
             )
 
             # Update the future token ids map
@@ -133,15 +136,14 @@ class TpModelWorkerClient:
                         )
                     )
             next_token_ids = next_token_ids.to("cpu", non_blocking=True)
-            copy_event.record()
+            copy_done.record()
 
-            self.output_queue.put((copy_event, logits_output, next_token_ids))
+            self.output_queue.put((copy_done, logits_output, next_token_ids))
 
     def resolve_batch_result(self, bid: int):
-        copy_event, logits_output, next_token_ids = self.output_queue.get()
-        while not copy_event.query():
-            time.sleep(1e-5)
-        self.launch_event.wait()
+        copy_done, logits_output, next_token_ids = self.output_queue.get()
+        copy_done.synchronize()
+        self.launch_done.wait()
 
         if logits_output.next_token_logprobs is not None:
             logits_output.next_token_logprobs = (
@@ -162,7 +164,11 @@ class TpModelWorkerClient:
         model_worker_batch.sampling_info = dataclasses.replace(
             model_worker_batch.sampling_info
         )
-        self.input_queue.put((model_worker_batch, self.future_token_ids_ct))
+        compute_info_done = torch.cuda.Event()
+        compute_info_done.record()
+        self.input_queue.put(
+            (model_worker_batch, self.future_token_ids_ct, compute_info_done)
+        )
 
         # Allocate output future objects
         bs = len(model_worker_batch.seq_lens)

test/srt/test_large_max_new_tokens.py

@@ -38,7 +38,7 @@ class TestLargeMaxNewTokens(unittest.TestCase):
             api_key=cls.api_key,
             other_args=(
                 "--max-total-token",
-                "1024",
+                "1536",
                 "--context-len",
                 "8192",
                 "--decode-log-interval",

test/srt/test_srt_engine.py

@@ -29,7 +29,7 @@ class TestSRTEngine(unittest.TestCase):
 
         sampling_params = {"temperature": 0, "max_new_tokens": 8}
 
-        engine = sgl.Engine(model_path=model_path, random_seed=42, log_level="error")
+        engine = sgl.Engine(model_path=model_path, random_seed=42)
         out1 = engine.generate(prompt, sampling_params)["text"]
         engine.shutdown()
 
@@ -51,7 +51,7 @@ class TestSRTEngine(unittest.TestCase):
 
         sampling_params = {"temperature": 0, "max_new_tokens": 8}
 
-        engine = sgl.Engine(model_path=model_path, random_seed=42, log_level="error")
+        engine = sgl.Engine(model_path=model_path, random_seed=42)
         engine.generate(prompt, sampling_params)
         engine.generate(prompt, sampling_params)
         engine.shutdown()
@@ -74,7 +74,6 @@ class TestSRTEngine(unittest.TestCase):
         # Create an LLM.
         llm = sgl.Engine(
             model_path=DEFAULT_SMALL_MODEL_NAME_FOR_TEST,
-            log_level="error",
         )
 
         # 1. sync + non streaming
@@ -118,7 +117,9 @@ class TestSRTEngine(unittest.TestCase):
         prompt = "The capital of UK is"
 
         model_path = DEFAULT_SMALL_MODEL_NAME_FOR_TEST
-        engine = sgl.Engine(model_path=model_path, random_seed=42, log_level="error")
+        engine = sgl.Engine(
+            model_path=model_path, random_seed=42, disable_radix_cache=True
+        )
         sampling_params = {"temperature": 0, "max_new_tokens": 8}
         out1 = engine.generate(prompt, sampling_params)["text"]
 
@@ -141,9 +142,7 @@ class TestSRTEngine(unittest.TestCase):
         prompt = "Today is a sunny day and I like"
         model_path = DEFAULT_SMALL_EMBEDDING_MODEL_NAME_FOR_TEST
 
-        engine = sgl.Engine(
-            model_path=model_path, is_embedding=True, random_seed=42, log_level="error"
-        )
+        engine = sgl.Engine(model_path=model_path, is_embedding=True, random_seed=42)
         out1 = torch.tensor(engine.encode(prompt)["embedding"])
         engine.shutdown()