Support GPU execution time breakdown by forward mode metrics (#15396)

python/sglang/srt/environ.py

@@ -365,6 +365,9 @@ class Envs:
     # Numa
     SGLANG_NUMA_BIND_V2 = EnvBool(True)
 
+    # Metrics
+    SGLANG_ENABLE_METRICS_DEVICE_TIMER = EnvBool(False)
+
     # fmt: on
 
 

python/sglang/srt/managers/scheduler.py

@@ -2105,10 +2105,11 @@ class Scheduler(
                 with self.forward_stream_ctx:
                     self.forward_stream.wait_stream(self.default_stream)
                     self.future_map.resolve_future(model_worker_batch)
-                    batch_result = self.model_worker.forward_batch_generation(
-                        model_worker_batch
-                        # here pp is not compatible with overlap
-                    )
+                    with self.record_forward_metrics(batch):
+                        batch_result = self.model_worker.forward_batch_generation(
+                            model_worker_batch
+                            # here pp is not compatible with overlap
+                        )
                     # FIXME(lsyin): maybe move this to forward_batch_generation
                     batch_result.copy_done = self.device_module.Event()
                     if batch_result.delay_sample_func is None:
@@ -2144,9 +2145,10 @@ class Scheduler(
                     if self.spec_algorithm.is_none()
                     else {}
                 )
-                batch_result = self.model_worker.forward_batch_generation(
-                    worker_batch_or_batch, **kwargs
-                )
+                with self.record_forward_metrics(batch):
+                    batch_result = self.model_worker.forward_batch_generation(
+                        worker_batch_or_batch, **kwargs
+                    )
                 future_indices_or_next_token_ids = batch_result.next_token_ids
                 self.update_cache_from_scheduler(batch, batch_result)
 

python/sglang/srt/managers/scheduler_metrics_mixin.py

@@ -3,6 +3,7 @@ from __future__ import annotations
 import logging
 import time
 from collections import defaultdict
+from contextlib import contextmanager
 from typing import TYPE_CHECKING, List, Optional
 
 from sglang.srt.disaggregation.kv_events import EventPublisherFactory, KVEventBatch
@@ -13,6 +14,7 @@ from sglang.srt.managers.schedule_policy import PrefillAdder
 from sglang.srt.managers.scheduler import Req, ScheduleBatch
 from sglang.srt.metrics.collector import SchedulerMetricsCollector, SchedulerStats
 from sglang.srt.utils import get_bool_env_var
+from sglang.srt.utils.device_timer import DeviceTimer
 
 if TYPE_CHECKING:
     from sglang.srt.managers.scheduler import Scheduler
@@ -21,6 +23,7 @@ logger = logging.getLogger(__name__)
 
 RECORD_STEP_TIME = get_bool_env_var("SGLANG_RECORD_STEP_TIME")
 LOG_FORWARD_ITERS = envs.SGLANG_LOG_FORWARD_ITERS.get()
+ENABLE_METRICS_DEVICE_TIMER = envs.SGLANG_ENABLE_METRICS_DEVICE_TIMER.get()
 
 
 class KvMetrics:
@@ -80,6 +83,11 @@ class SchedulerMetricsMixin:
                 labels["dp_rank"] = dp_rank
             self.metrics_collector = SchedulerMetricsCollector(labels=labels)
 
+            if ENABLE_METRICS_DEVICE_TIMER:
+                self.forward_pass_device_timer = DeviceTimer(
+                    reporter=self.metrics_collector.increment_gpu_execution_seconds
+                )
+
         if self.enable_kv_cache_events:
             self.init_kv_events(self.server_args.kv_events_config)
 
@@ -455,3 +463,13 @@ class SchedulerMetricsMixin:
             num_waiting_reqs=num_waiting_reqs,
             num_tokens=num_tokens,
         )
+
+    @contextmanager
+    def record_forward_metrics(self: Scheduler, batch):
+        if not (self.enable_metrics and ENABLE_METRICS_DEVICE_TIMER):
+            yield
+            return
+
+        category = "forward_" + batch.forward_mode.name.lower()
+        with self.forward_pass_device_timer.wrap(category=category):
+            yield

python/sglang/srt/metrics/collector.py

@@ -12,6 +12,7 @@
 # limitations under the License.
 # ==============================================================================
 """Utilities for Prometheus Metrics Collection."""
+import logging
 import os
 import time
 from dataclasses import dataclass, field
@@ -25,6 +26,9 @@ from sglang.srt.utils import get_bool_env_var
 SGLANG_TEST_REQUEST_TIME_STATS = get_bool_env_var("SGLANG_TEST_REQUEST_TIME_STATS")
 
 
+logger = logging.getLogger(__name__)
+
+
 def get_histogram_conf_from_env(env_var_name: str) -> Optional[List[float]]:
     """
     Get the histogram configuration from the environment variable.
@@ -660,6 +664,12 @@ class SchedulerMetricsCollector:
             labelnames=labels.keys(),
         )
 
+        self.gpu_execution_seconds_total = Counter(
+            name="sglang:gpu_execution_seconds_total",
+            documentation="Total time that GPU is busy executing a workload.",
+            labelnames=list(labels.keys()) + ["category"],
+        )
+
     def _log_gauge(self, gauge, data: Union[int, float]) -> None:
         # Convenience function for logging to gauge.
         gauge.labels(**self.labels).set(data)
@@ -699,6 +709,10 @@ class SchedulerMetricsCollector:
         )
         self.realtime_decode_tokens_total.labels(**self.labels).inc(decode_tokens)
 
+    def increment_gpu_execution_seconds(self, category: str, t: float):
+        logger.debug(f"GPU execution seconds: {category=} {t=:.3f}")
+        self.gpu_execution_seconds_total.labels(**self.labels, category=category).inc(t)
+
     def log_stats(self, stats: SchedulerStats) -> None:
         self._log_gauge(self.num_running_reqs, stats.num_running_reqs)
         self._log_gauge(self.num_used_tokens, stats.num_used_tokens)

python/sglang/srt/utils/device_timer.py

@@ -0,0 +1,54 @@
+from collections import deque
+from contextlib import contextmanager
+from dataclasses import dataclass
+from typing import Callable, Deque, Optional
+
+import torch
+
+
+class DeviceTimer:
+    def __init__(self, reporter: Callable[[str, float], None]):
+        self._intervals: Deque[_TimingInterval] = deque()
+        self._reporter = reporter
+
+    @contextmanager
+    def wrap(self, category: str):
+        self._intervals.append(_TimingInterval.create())
+        try:
+            yield
+        finally:
+            self._intervals[-1].end(category=category)
+            self._report()
+
+    def _report(self):
+        while len(self._intervals) > 0:
+            interval = self._intervals[0]
+            if not interval.end_event.query():
+                break
+
+            self._intervals.popleft()
+            self._reporter(interval.category, interval.elapsed_time() / 1000.0)
+
+
+@dataclass
+class _TimingInterval:
+    start_event: torch.cuda.Event
+    end_event: Optional[torch.cuda.Event] = None
+    category: Optional[str] = None
+
+    @staticmethod
+    def create():
+        start_event = torch.cuda.Event(enable_timing=True)
+        start_event.record()
+        return _TimingInterval(start_event=start_event)
+
+    def end(self, category: str):
+        end_event = torch.cuda.Event(enable_timing=True)
+        end_event.record()
+
+        assert self.end_event is None
+        self.end_event = end_event
+        self.category = category
+
+    def elapsed_time(self) -> float:
+        return self.start_event.elapsed_time(self.end_event)