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EventReplay: extend beyond aten ops with auto-import and custom initializers #607

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EventReplay: extend beyond aten ops with auto-import and custom initializers #607

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e291628
Expand EventReplay to support custom ops beyond aten::
ajassani Mar 28, 2026
b00976c
Add string arg defaults, op aliases, and module resolution
ajassani Mar 28, 2026
a2bd622
Add custom initializers, auto-import, and updated docs for EventReplay
ajassani Apr 28, 2026
95406f3
Fix bugs, add tests, and improve EventReplay docs
ajassani Apr 28, 2026
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18 changes: 18 additions & 0 deletions TraceLens/EventReplay/__init__.py
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Original file line number Diff line number Diff line change
Expand Up @@ -3,3 +3,21 @@
#
# See LICENSE for license information.
###############################################################################

from .event_replay import EventReplayer
from .custom_inits import (
CustomInit,
PagedAttentionInit,
MoeRoutingInit,
extract_batch_context,
)
from .utils import benchmark_func

__all__ = [
"EventReplayer",
"CustomInit",
"PagedAttentionInit",
"MoeRoutingInit",
"extract_batch_context",
"benchmark_func",
]
21 changes: 15 additions & 6 deletions TraceLens/EventReplay/batched_replay.py
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Expand Up @@ -99,11 +99,17 @@ def _get_args_kwargs_from_ir(
replayed_count = 0
errors = 0

for i, repro_info in enumerate(repro_data_list):
ops_to_replay = repro_data_list
if args.op_filter:
ops_to_replay = [r for r in ops_to_replay if args.op_filter in r["op_name"]]
if args.op_limit:
ops_to_replay = ops_to_replay[: args.op_limit]

for i, repro_info in enumerate(ops_to_replay):

op_name = repro_info["op_name"]
replay_ir = repro_info["replay_ir"]
print(f"\n[{replayed_count + 1}/{len(repro_data_list)}] Replaying: {op_name}")
print(f"\n[{replayed_count + 1}/{len(ops_to_replay)}] Replaying: {op_name}")

# Get the PyTorch operation function
try:
Expand Down Expand Up @@ -151,15 +157,16 @@ def _get_args_kwargs_from_ir(
errors += 1
continue
# --- Benchmark the function ---
mean_time_us = benchmark_func(
metrics = benchmark_func(
lambda: func(*pos_args, **kwargs), args.device, warmup=50, avg_steps=100
)
print(f" Average time taken: {mean_time_us:.2f} microseconds")
mean_time_us = metrics["mean_us"]
print(f" Average time taken: {mean_time_us:.2f} us (median: {metrics['median_us']:.2f} us)")
if "count" in repro_info:
count_workload = repro_info["count"]
total_time_us = mean_time_us * count_workload
print(f" Count in workload: {count_workload}")
print(f" Est time in workload: {total_time_us:.2f} microseconds")
print(f" Est time in workload: {total_time_us:.2f} us")
# --- Optionally sync again ---
if args.device == "cuda":
torch.cuda.synchronize()
Expand Down Expand Up @@ -190,7 +197,9 @@ def _get_args_kwargs_from_ir(
print("\n--- Replay Summary ---")
print(f"Total operations in file: {len(repro_data_list)}")
if args.op_filter:
print(f"Filter applied: '{args.op_filter}'")
print(f"Filter applied: '{args.op_filter}' ({len(ops_to_replay)} matched)")
if args.op_limit:
print(f"Limit applied: {args.op_limit}")
print(f"Attempted replays: {replayed_count}")
print(f"Successful replays: {replayed_count - errors}")
print(f"Errors encountered: {errors}")
Expand Down
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