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58ba6df | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | """In-process A/B of the served call: blocking vs non-blocking trace replay (TtHamer.nonblocking_replay),
interleaved in blocks so host-load drift hits both equally. usage: ab_replay.py [N]"""
import os, sys, time, json, statistics
from pathlib import Path
_REPO = Path(__file__).resolve().parents[1]
sys.path.insert(0, str(_REPO))
os.environ.setdefault("TT_FUSED_HASH", "0")
import torch
from hamer import Hamer, build_from_reference, load_hamer_checkpoint
from hamer.preprocess import preprocess
N = int(sys.argv[1]) if len(sys.argv) > 1 else 400
hf = Path.home() / ".cache/huggingface/hub/models--changh95--hamer-weights/snapshots/e8217b46d54b4e8afaaf0dd6efb6b62341c97d32"
ref = Hamer().eval(); load_hamer_checkpoint(ref, hf / "hamer.ckpt", hf / "mano_mean_params.npz")
media = _REPO.parent / "media"
x, _ = preprocess(media / "sample1.jpg", bbox_xyxy=(20, 170, 295, 260), is_right=True)
tt = build_from_reference(ref, device_id=0, strict=True)
res = {True: [], False: []}
with torch.no_grad():
tt(x); tt(x)
o = {m: None for m in res}
for blk in range(N // 20):
for mode in (True, False) if blk % 2 == 0 else (False, True):
tt.nonblocking_replay = mode
for _ in range(10):
t0 = time.perf_counter(); out = tt(x); res[mode].append((time.perf_counter() - t0) * 1e3)
o[mode] = out
print("identical outputs:", torch.equal(o[True], o[False]))
for m, v in res.items():
print(f"AB nonblocking={m}: median {statistics.median(v):.3f} ms min {min(v):.3f} p90 {sorted(v)[int(0.9 * len(v))]:.3f} n={len(v)}")
tt.close()
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