File size: 2,589 Bytes
5dd27d0 | 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 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 | """Time the browser-free path end to end: engine step + native GPU render, per condition frame.
usage: bench_render.py [frames] [compile 0|1]
GPU time comes from CUDA events around render(); wall time includes the engine
round trip and a synchronize, i.e. "frame ready for the proxy encoder".
"""
import json
import sys
import time
import torch
from engine_client import Engine
from native_render import NativeRenderer
FRAMES = int(sys.argv[1]) if len(sys.argv) > 1 else 480
COMPILE = (sys.argv[2] if len(sys.argv) > 2 else '1') == '1'
engine = Engine()
renderer = NativeRenderer(engine.scene, engine.scene_arrays, compile=COMPILE)
rows = []
for i in range(FRAMES):
target = round((i + 1) * 60 / 24)
events = [{'action': 'enter', 'phase': 'down'}, {'action': 'enter', 'phase': 'up'}] if i == 5 else []
if 40 <= i < 440 and i % 24 == 0:
events.append({'action': 'w', 'phase': 'down' if (i // 24) % 2 == 0 else 'up'})
if 80 <= i < 440 and i % 37 == 0:
events += [{'action': 'j', 'phase': 'down'}, {'action': 'j', 'phase': 'up'}]
capture = i % 4 == 0
t0 = time.perf_counter()
header, frame = engine.advance(target, events, capture=capture, state=False)
t1 = time.perf_counter()
if not capture:
rows.append(dict(capture=False, engine=(t1 - t0) * 1e3))
continue
start, end = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
start.record()
image = renderer.render(frame)
end.record()
t2 = time.perf_counter()
end.synchronize()
t3 = time.perf_counter()
rows.append(dict(capture=True, engine=(t1 - t0) * 1e3, submit=(t2 - t1) * 1e3, ready=(t3 - t1) * 1e3,
gpu=start.elapsed_time(end), total=(t3 - t0) * 1e3))
engine.close()
def stat(xs):
xs = sorted(xs)
return dict(n=len(xs), median=round(xs[len(xs) // 2], 3), p95=round(xs[int(len(xs) * .95)], 3), max=round(xs[-1], 3))
caps = [r for r in rows if r['capture']][10:] # first renders include compilation
sims = [r for r in rows if not r['capture']][30:]
print(json.dumps(dict(
compile=COMPILE, image=list(image.shape), dtype=str(image.dtype), device=str(image.device),
sim_frame_ms=stat([r['engine'] for r in sims]),
condition_frame_engine_ms=stat([r['engine'] for r in caps]),
render_submit_ms=stat([r['submit'] for r in caps]),
render_gpu_ms=stat([r['gpu'] for r in caps]),
render_ready_ms=stat([r['ready'] for r in caps]),
condition_frame_total_ms=stat([r['total'] for r in caps]),
gpu_memory_mb=round(torch.cuda.max_memory_allocated() / 2**20))))
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