File size: 1,729 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 | """CUDA-graph rendering must equal eager rendering bit for bit; and how long a frame takes."""
import json, statistics, time
import torch
from engine_client import Engine
from native_render import NativeRenderer
engine = Engine()
frames = []
for i in range(400):
ev = [{'action': 'enter', 'phase': 'down'}, {'action': 'enter', 'phase': 'up'}] if i == 5 else []
if 40 <= i < 380 and i % 24 == 0:
ev.append({'action': 'w', 'phase': 'down' if (i // 24) % 2 == 0 else 'up'})
if 60 <= i < 380 and i % 37 == 0:
ev += [{'action': 'j', 'phase': 'down'}, {'action': 'j', 'phase': 'up'}]
header, scene = engine.advance(round((i + 1) * 2.5), ev, capture=i % 4 == 0, state=False)
if i % 4 == 0:
frames.append({k: v.copy() for k, v in scene.items()})
engine.close()
eager = NativeRenderer(engine.scene, engine.scene_arrays, graphs=False)
graph = NativeRenderer(engine.scene, engine.scene_arrays, graphs=True)
same = sum(int(torch.equal(eager.render(f), graph.render(f))) for f in frames[10:])
rows = {}
for name, r in (('eager', eager), ('graphs', graph)):
cpu, gpu = [], []
for f in frames[10:]:
torch.cuda.synchronize()
s, e = torch.cuda.Event(enable_timing=True), torch.cuda.Event(enable_timing=True)
t = time.perf_counter(); s.record(); r.render(f); e.record(); submit = time.perf_counter() - t
e.synchronize(); cpu.append(submit * 1e3); gpu.append(s.elapsed_time(e))
rows[name] = dict(submit_ms=round(statistics.median(cpu), 3), ready_ms=round(statistics.median(gpu), 3),
ready_p95_ms=round(sorted(gpu)[int(len(gpu) * .95)], 3))
print(json.dumps(dict(frames=len(frames) - 10, identical=f'{same}/{len(frames) - 10}', **rows)))
|