[Misc] Add more scoping for improved trace

vllm/v1/core/sched/scheduler.py

@@ -38,6 +38,7 @@
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.spec_decode.metrics import SpecDecodingStats
 from vllm.v1.structured_output import StructuredOutputManager
+from vllm.v1.utils import record_function_or_nullcontext
 
 logger = init_logger(__name__)
 
@@ -259,49 +260,52 @@ def schedule(self) -> SchedulerOutput:
                 continue
 
             # Schedule newly needed KV blocks for the request.
-            while True:
-                new_blocks = self.kv_cache_manager.allocate_slots(
-                    request,
-                    num_new_tokens,
-                    num_lookahead_tokens=self.num_lookahead_tokens,
-                )
-
-                if new_blocks is not None:
-                    # The request can be scheduled.
-                    break
-
-                # The request cannot be scheduled.
-                # Preempt the lowest-priority request.
-                if self.policy == SchedulingPolicy.PRIORITY:
-                    preempted_req = max(
-                        self.running,
-                        key=lambda r: (r.priority, r.arrival_time),
+            with record_function_or_nullcontext("schedule: allocate_slots"):
+                while True:
+                    new_blocks = self.kv_cache_manager.allocate_slots(
+                        request,
+                        num_new_tokens,
+                        num_lookahead_tokens=self.num_lookahead_tokens,
                     )
-                    self.running.remove(preempted_req)
-                    if preempted_req in scheduled_running_reqs:
-                        scheduled_running_reqs.remove(preempted_req)
-                        token_budget += num_scheduled_tokens[preempted_req.request_id]
-                        req_to_new_blocks.pop(preempted_req.request_id)
-                        num_scheduled_tokens.pop(preempted_req.request_id)
-                        req_index -= 1
-                else:
-                    preempted_req = self.running.pop()
 
-                self.kv_cache_manager.free(preempted_req)
-                self.encoder_cache_manager.free(preempted_req)
-                preempted_req.status = RequestStatus.PREEMPTED
-                preempted_req.num_computed_tokens = 0
-                preempted_req.num_preemptions += 1
-                if self.log_stats:
-                    preempted_req.record_event(
-                        EngineCoreEventType.PREEMPTED, scheduled_timestamp
-                    )
+                    if new_blocks is not None:
+                        # The request can be scheduled.
+                        break
 
-                self.waiting.prepend_request(preempted_req)
-                preempted_reqs.append(preempted_req)
-                if preempted_req == request:
-                    # No more request to preempt. Cannot schedule this request.
-                    break
+                    # The request cannot be scheduled.
+                    # Preempt the lowest-priority request.
+                    if self.policy == SchedulingPolicy.PRIORITY:
+                        preempted_req = max(
+                            self.running,
+                            key=lambda r: (r.priority, r.arrival_time),
+                        )
+                        self.running.remove(preempted_req)
+                        if preempted_req in scheduled_running_reqs:
+                            scheduled_running_reqs.remove(preempted_req)
+                            token_budget += num_scheduled_tokens[
+                                preempted_req.request_id
+                            ]
+                            req_to_new_blocks.pop(preempted_req.request_id)
+                            num_scheduled_tokens.pop(preempted_req.request_id)
+                            req_index -= 1
+                    else:
+                        preempted_req = self.running.pop()
+
+                    self.kv_cache_manager.free(preempted_req)
+                    self.encoder_cache_manager.free(preempted_req)
+                    preempted_req.status = RequestStatus.PREEMPTED
+                    preempted_req.num_computed_tokens = 0
+                    preempted_req.num_preemptions += 1
+                    if self.log_stats:
+                        preempted_req.record_event(
+                            EngineCoreEventType.PREEMPTED, scheduled_timestamp
+                        )
+
+                    self.waiting.prepend_request(preempted_req)
+                    preempted_reqs.append(preempted_req)
+                    if preempted_req == request:
+                        # No more request to preempt. Cannot schedule this request.
+                        break
 
             if new_blocks is None:
                 # Cannot schedule this request.
@@ -599,13 +603,14 @@ def schedule(self) -> SchedulerOutput:
         # Get the longest common prefix among all requests in the running queue.
         # This can be potentially used for cascade attention.
         num_common_prefix_blocks = [0] * len(self.kv_cache_config.kv_cache_groups)
-        if self.running:
-            any_request = self.running[0]
-            num_common_prefix_blocks = (
-                self.kv_cache_manager.get_num_common_prefix_blocks(
-                    any_request.request_id
+        with record_function_or_nullcontext("schedule: get_num_common_prefix_blocks"):
+            if self.running:
+                any_request = self.running[0]
+                num_common_prefix_blocks = (
+                    self.kv_cache_manager.get_num_common_prefix_blocks(
+                        any_request.request_id
+                    )
                 )
-            )
 
         # Construct the scheduler output.
         new_reqs_data = [
@@ -614,13 +619,14 @@ def schedule(self) -> SchedulerOutput:
             )
             for req in scheduled_new_reqs
         ]
-        cached_reqs_data = self._make_cached_request_data(
-            scheduled_running_reqs,
-            scheduled_resumed_reqs,
-            num_scheduled_tokens,
-            scheduled_spec_decode_tokens,
-            req_to_new_blocks,
-        )
+        with record_function_or_nullcontext("schedule: make_cached_request_data"):
+            cached_reqs_data = self._make_cached_request_data(
+                scheduled_running_reqs,
+                scheduled_resumed_reqs,
+                num_scheduled_tokens,
+                scheduled_spec_decode_tokens,
+                req_to_new_blocks,
+            )
 
         # Record the request ids that were scheduled in this step.
         self.prev_step_scheduled_req_ids.clear()
@@ -649,8 +655,8 @@ def schedule(self) -> SchedulerOutput:
         if self.connector is not None:
             meta = self.connector.build_connector_meta(scheduler_output)
             scheduler_output.kv_connector_metadata = meta
-
-        self._update_after_schedule(scheduler_output)
+        with record_function_or_nullcontext("schedule: update_after_schedule"):
+            self._update_after_schedule(scheduler_output)
         return scheduler_output
 
     def _update_after_schedule(

vllm/v1/engine/core.py

@@ -61,6 +61,7 @@
 from vllm.v1.request import Request, RequestStatus
 from vllm.v1.serial_utils import MsgpackDecoder, MsgpackEncoder
 from vllm.v1.structured_output import StructuredOutputManager
+from vllm.v1.utils import record_function_or_nullcontext
 from vllm.version import __version__ as VLLM_VERSION
 
 logger = init_logger(__name__)
@@ -315,17 +316,21 @@ def step(self) -> tuple[dict[int, EngineCoreOutputs], bool]:
         # or finished and not yet removed from the batch.
         if not self.scheduler.has_requests():
             return {}, False
-        scheduler_output = self.scheduler.schedule()
-        future = self.model_executor.execute_model(scheduler_output, non_block=True)
-        grammar_output = self.scheduler.get_grammar_bitmask(scheduler_output)
-        with self.log_error_detail(scheduler_output):
-            model_output = future.result()
-            if model_output is None:
-                model_output = self.model_executor.sample_tokens(grammar_output)
-
-        engine_core_outputs = self.scheduler.update_from_output(
-            scheduler_output, model_output
-        )
+        with record_function_or_nullcontext("core step: schedule"):
+            scheduler_output = self.scheduler.schedule()
+
+        with record_function_or_nullcontext("core step: execute_model"):
+            future = self.model_executor.execute_model(scheduler_output, non_block=True)
+            grammar_output = self.scheduler.get_grammar_bitmask(scheduler_output)
+            with self.log_error_detail(scheduler_output):
+                model_output = future.result()
+                if model_output is None:
+                    model_output = self.model_executor.sample_tokens(grammar_output)
+
+        with record_function_or_nullcontext("core step: update_from_output"):
+            engine_core_outputs = self.scheduler.update_from_output(
+                scheduler_output, model_output
+            )
 
         return engine_core_outputs, scheduler_output.total_num_scheduled_tokens > 0
 
@@ -363,32 +368,49 @@ def step_with_batch_queue(
         model_executed = False
         deferred_scheduler_output = None
         if self.scheduler.has_requests():
-            scheduler_output = self.scheduler.schedule()
-            exec_future = self.model_executor.execute_model(
-                scheduler_output, non_block=True
-            )
+            with record_function_or_nullcontext("core step_with_batch_queue: schedule"):
+                scheduler_output = self.scheduler.schedule()
+            with record_function_or_nullcontext(
+                "core step_with_batch_queue: execute_model"
+            ):
+                exec_future = self.model_executor.execute_model(
+                    scheduler_output, non_block=True
+                )
             model_executed = scheduler_output.total_num_scheduled_tokens > 0
 
             if scheduler_output.pending_structured_output_tokens:
-                # We need to defer sampling until we have processed the model output
-                # from the prior step.
-                deferred_scheduler_output = scheduler_output
-                # Block-wait for execute to return (continues running async on the GPU).
-                with self.log_error_detail(scheduler_output):
-                    exec_result = exec_future.result()
-                    assert exec_result is None
+                with record_function_or_nullcontext(
+                    "core step_with_batch_queue: pending_structured_output_tokens"
+                ):
+                    # We need to defer sampling until we have processed the model output
+                    # from the prior step.
+                    deferred_scheduler_output = scheduler_output
+                    # Block-wait for execute to return
+                    # (continues running async on the GPU).
+                    with self.log_error_detail(scheduler_output):
+                        exec_result = exec_future.result()
+                        assert exec_result is None
             else:
-                # We aren't waiting for any tokens, get any grammar output immediately.
-                grammar_output = self.scheduler.get_grammar_bitmask(scheduler_output)
+                with record_function_or_nullcontext(
+                    "core step_with_batch_queue: get_grammar_bitmask"
+                ):
+                    # We aren't waiting for any tokens, get any grammar
+                    # output immediately.
+                    grammar_output = self.scheduler.get_grammar_bitmask(
+                        scheduler_output
+                    )
                 # Block-wait for execute to return (continues running async on the GPU).
                 with self.log_error_detail(scheduler_output):
                     exec_result = exec_future.result()
 
                 if exec_result is None:
-                    # Call sample tokens.
-                    future = self.model_executor.sample_tokens(
-                        grammar_output, non_block=True
-                    )
+                    with record_function_or_nullcontext(
+                        "core step_with_batch_queue: sample_tokens"
+                    ):
+                        # Call sample tokens.
+                        future = self.model_executor.sample_tokens(
+                            grammar_output, non_block=True
+                        )
                 else:
                     # No sampling required (e.g. all requests finished).
                     future = cast(Future[ModelRunnerOutput], exec_future)
@@ -408,27 +430,34 @@ def step_with_batch_queue(
             # only be called when the scheduler contains requests or the queue
             # is non-empty.
             return None, False
-
-        # Block until the next result is available.
-        future, scheduler_output = batch_queue.pop()
-        with self.log_error_detail(scheduler_output):
-            model_output = future.result()
-
-        engine_core_outputs = self.scheduler.update_from_output(
-            scheduler_output, model_output
-        )
+        with record_function_or_nullcontext("core step_with_batch_queue: model_output"):
+            # Block until the next result is available.
+            future, scheduler_output = batch_queue.pop()
+            with self.log_error_detail(scheduler_output):
+                model_output = future.result()
+        with record_function_or_nullcontext(
+            "core step_with_batch_queue: update_from_output"
+        ):
+            engine_core_outputs = self.scheduler.update_from_output(
+                scheduler_output, model_output
+            )
 
         # NOTE(nick): We can either handle the deferred tasks here or save
         # in a field and do it immediately once step_with_batch_queue is
         # re-called. The latter slightly favors TTFT over TPOT/throughput.
         if deferred_scheduler_output:
-            # We now have the tokens needed to compute the bitmask for the
-            # deferred request. Get the bitmask and call sample tokens.
-            grammar_output = self.scheduler.get_grammar_bitmask(
-                deferred_scheduler_output
-            )
-            future = self.model_executor.sample_tokens(grammar_output, non_block=True)
-            batch_queue.appendleft((future, deferred_scheduler_output))
+            with record_function_or_nullcontext(
+                "core step_with_batch_queue: deferred_scheduler_output"
+            ):
+                # We now have the tokens needed to compute the bitmask for the
+                # deferred request. Get the bitmask and call sample tokens.
+                grammar_output = self.scheduler.get_grammar_bitmask(
+                    deferred_scheduler_output
+                )
+                future = self.model_executor.sample_tokens(
+                    grammar_output, non_block=True
+                )
+                batch_queue.appendleft((future, deferred_scheduler_output))
 
         return engine_core_outputs, model_executed
 

vllm/v1/engine/llm_engine.py

@@ -36,6 +36,7 @@
 from vllm.v1.metrics.loggers import StatLoggerFactory, StatLoggerManager
 from vllm.v1.metrics.reader import Metric, get_metrics_snapshot
 from vllm.v1.metrics.stats import IterationStats
+from vllm.v1.utils import record_function_or_nullcontext
 from vllm.v1.worker.worker_base import WorkerBase
 
 logger = init_logger(__name__)
@@ -280,28 +281,32 @@ def step(self) -> list[RequestOutput | PoolingRequestOutput]:
             return []
 
         # 1) Get EngineCoreOutput from the EngineCore.
-        outputs = self.engine_core.get_output()
+        with record_function_or_nullcontext("llm_genine step: get_output"):
+            outputs = self.engine_core.get_output()
 
         # 2) Process EngineCoreOutputs.
-        iteration_stats = IterationStats() if self.log_stats else None
-        processed_outputs = self.output_processor.process_outputs(
-            outputs.outputs,
-            engine_core_timestamp=outputs.timestamp,
-            iteration_stats=iteration_stats,
-        )
-        self.output_processor.update_scheduler_stats(outputs.scheduler_stats)
+        with record_function_or_nullcontext("llm_genine step: process_outputs"):
+            iteration_stats = IterationStats() if self.log_stats else None
+            processed_outputs = self.output_processor.process_outputs(
+                outputs.outputs,
+                engine_core_timestamp=outputs.timestamp,
+                iteration_stats=iteration_stats,
+            )
+            self.output_processor.update_scheduler_stats(outputs.scheduler_stats)
 
         # 3) Abort any reqs that finished due to stop strings.
-        self.engine_core.abort_requests(processed_outputs.reqs_to_abort)
+        with record_function_or_nullcontext("llm_genine step: abort_requests"):
+            self.engine_core.abort_requests(processed_outputs.reqs_to_abort)
 
         # 4) Record stats
-        if self.logger_manager is not None and outputs.scheduler_stats is not None:
-            self.logger_manager.record(
-                scheduler_stats=outputs.scheduler_stats,
-                iteration_stats=iteration_stats,
-                mm_cache_stats=self.processor.stat_mm_cache(),
-            )
-            self.do_log_stats_with_interval()
+        with record_function_or_nullcontext("llm_genine step: record_stats"):
+            if self.logger_manager is not None and outputs.scheduler_stats is not None:
+                self.logger_manager.record(
+                    scheduler_stats=outputs.scheduler_stats,
+                    iteration_stats=iteration_stats,
+                    mm_cache_stats=self.processor.stat_mm_cache(),
+                )
+                self.do_log_stats_with_interval()
 
         return processed_outputs.request_outputs