| """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:] |
| 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)))) |
|
|
