| """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))) |
|
|
