[compile_trace] Add compile time Kineto trace

tritonbench/components/compile_time/__init__.py

@@ -1 +1,5 @@
-from .trace import do_compile_time_in_task, fbcode_do_compile_time_in_task  # noqa F401
+from .trace import (  # noqa F401
+    do_compile_kineto_trace_in_task,
+    do_compile_time_in_task,
+    fbcode_do_compile_time_in_task,
+)

tritonbench/components/compile_time/trace.py

@@ -1,11 +1,26 @@
-from typing import Callable, Dict
+import random
+import string
+from datetime import datetime
+from functools import partial
+from typing import Callable, Dict, Optional
 
 import torch
+import torch.profiler as profiler
 from tritonbench.utils.env_utils import fresh_triton_cache, is_fbcode
 
 if is_fbcode():
     from triton.fb.triton_util import triton_add_listener, TritonHook
 
+    from .fb.run_utils import trace_handler
+
+DEFAULT_PROFILE_OPTS = {
+    "record_shapes": True,
+    "profile_memory": True,
+    "with_stack": True,
+    "with_flops": True,
+    "with_modules": True,
+}
+
 
 def fbcode_do_compile_time_in_task(fn: Callable) -> Dict[str, float]:
     # not yet getting results that make sense to me
@@ -37,3 +52,41 @@ def do_compile_time_in_task(fn: Callable) -> float:
         torch.cuda.synchronize()  # Wait for the events to be recorded!
     latency_with_compile = start_event.elapsed_time(end_event)
     return latency_with_compile
+
+
+def do_compile_kineto_trace_in_task(
+    fn: Callable,
+    profile_opts: Optional[Dict[str, bool]] = None,
+    output_dir: Optional[str] = None,
+) -> Optional[str]:
+    """Profile compilation stage using Kineto."""
+    activity_groups = [
+        profiler.ProfilerActivity.CUDA,
+        profiler.ProfilerActivity.CPU,
+    ]
+    if not profile_opts:
+        profile_opts = DEFAULT_PROFILE_OPTS
+    prefix = f"tritonbench_{fn._name}"
+    name = f"{prefix}_{datetime.now().strftime('%Y%m%d_%H%M%S')}_{''.join(random.choices(string.digits, k=10))}.json"
+    with fresh_triton_cache():
+        with profiler.profile(
+            schedule=profiler.schedule(wait=0, warmup=0, active=1, repeat=1),
+            activities=activity_groups,
+            record_shapes=profile_opts["record_shapes"],
+            profile_memory=profile_opts["profile_memory"],
+            with_stack=profile_opts["with_stack"],
+            with_flops=profile_opts["with_flops"],
+            with_modules=profile_opts["with_modules"],
+            on_trace_ready=(
+                partial(trace_handler, name)
+                if is_fbcode()
+                else profiler.tensorboard_trace_handler(output_dir)
+            ),
+        ) as prof:
+            fn()
+            prof.step()
+    print(f"output dir: {output_dir}")
+    if not hasattr(torch.version, "git_version"):
+        return f"https://www.internalfb.com/intern/perfdoctor/trace_view?filepath=tree/traces/test/{name}.gz&bucket=pyper_traces"
+    else:
+        return output_dir

tritonbench/utils/triton_op.py

@@ -199,6 +199,8 @@ class BenchmarkOperatorMetrics:
     compile_time: Optional[float] = None
     # stage breakdown of compile times
     compile_time_by_stage: Optional[Dict[str, float]] = None
+    # compile time with kineto trace
+    compile_trace: Optional[str] = None
     # ncu trace file
     ncu_trace: Optional[str] = None
     # ncu replay file
@@ -1145,6 +1147,10 @@ def _init_extra_metrics() -> Dict[str, Any]:
                 metrics.compile_time = compile_time
                 if compile_time_by_stage:
                     metrics.compile_time_by_stage = compile_time_by_stage
+            if "compile_trace" in self.required_metrics:
+                metrics.compile_trace = self.compile_time(
+                    input_id, fn_name, metrics, kineto_trace=True
+                )
             if "ncu_trace" in self.required_metrics:
                 metrics.ncu_trace = self.ncu_trace(input_id, fn_name)
             # Collect NCU metrics if any required metrics match the ncu analyzer
@@ -1236,6 +1242,29 @@ def _init_extra_metrics() -> Dict[str, Any]:
                 metrics.all_configs = self.all_configs(fn)
             if "kernel_source_hash" in self.required_metrics:
                 metrics.kernel_source_hash = self.kernel_hash(fn)
+            if "_compile_time_kineto_trace_in_task" in self.required_metrics:
+                assert (
+                    self.required_metrics == ["_compile_time_kineto_trace_in_task"]
+                    and len(self._only) == 1
+                    and (self._input_id is not None)
+                ), (
+                    "_compile_time_kineto_trace_in_task must be measured by itself. "
+                    f"required_metrics: {self.required_metrics}, _only: {self._only}, _input_id: {self._input_id}"
+                )
+                from tritonbench.components.compile_time import (
+                    do_compile_kineto_trace_in_task,
+                )
+
+                kineto_trace_output_dir = self.get_temp_path("kineto_trace")
+                kineto_trace_output_dir.mkdir(parents=True, exist_ok=True)
+                metrics.extra_metrics["_compile_time_kineto_trace"] = (
+                    do_compile_kineto_trace_in_task(
+                        fn, output_dir=str(kineto_trace_output_dir)
+                    )
+                )
+                self._compile_time_kineto_trace = metrics.extra_metrics[
+                    "_compile_time_kineto_trace"
+                ]
             if "_compile_time_in_task" in self.required_metrics:
                 assert (
                     self.required_metrics == ["_compile_time_in_task"]
@@ -1591,8 +1620,12 @@ def kineto_trace(self, input_id: int, fn: Callable) -> str:
         )
 
     def compile_time(
-        self, input_id: int, fn_name: str, metrics: BenchmarkOperatorMetrics
-    ) -> float:
+        self,
+        input_id: int,
+        fn_name: str,
+        metrics: BenchmarkOperatorMetrics,
+        kineto_trace: bool = False,
+    ) -> Union[float, str]:
         # We need to spawn a subprocess when user wants to measure the compile time
         # of multiple sample inputs and backends.
         from tritonbench.operators.op_task import OpTask
@@ -1611,12 +1644,20 @@ def compile_time(
                 "--input-id",
                 str(input_id),
                 "--metrics",
-                "_compile_time_in_task",
+                (
+                    "_compile_time_in_task"
+                    if not kineto_trace
+                    else "_compile_time_kineto_trace_in_task"
+                ),
             ]
         )
         op_task = OpTask(name=self.name)
         op_task.make_operator_instance(args=op_task_args)
         op_task.run()
+        if kineto_trace:
+            kineto_trace_loc = op_task.get_attribute("_compile_time_kineto_trace")
+            del op_task
+            return kineto_trace_loc
         if op_task.get_attribute("triton_hook_latency") is not None:
             compiled_time = op_task.get_attribute("triton_hook_latency")
             compile_time_by_stage = op_task.get_attribute("compile_time_by_stage")