| |
| """Compile every P100a kernel shape this port can hit, ahead of the first songs. |
| |
| The card compiles kernels per shape and keeps them in TT_METAL_CACHE (data/tt-cache). Without this, the first song |
| of each new length bucket pays that compilation (tens of seconds). Shapes, per model (--model): |
| ENC_BUCKET), and both VAE windows (remote.py RemoteVae) |
| diffrhythm: every DiT sequence bucket of a 96-285 s song (diffrhythm_host.seq_buckets), both CFG branches, and |
| both VAE windows |
| Random inputs: only the shapes matter. Run it again after changing this package or the TT image. |
| """ |
| import argparse |
| import json |
| import time |
|
|
| import torch |
|
|
| from . import diffrhythm_dit, diffrhythm_host as dhost, oobleck_vae |
| from .shapes import VAE_WINDOWS |
|
|
| def warm_vae(vae, report): |
| for window in VAE_WINDOWS: |
| t0 = time.monotonic() |
| audio = vae.decode(torch.zeros(64, window)) |
| rec = {"vae_window": window, "s": round(time.monotonic() - t0, 2), "finite": bool(torch.isfinite(audio).all())} |
| report["vae"].append(rec) |
| print(json.dumps(rec), flush=True) |
|
|
|
|
| def warm_diffrhythm(args): |
| g = torch.Generator().manual_seed(0) |
| started = time.monotonic() |
| dev = diffrhythm_dit.open_device() |
| report = {"seq_buckets": dhost.seq_buckets(), "dit": [], "vae": []} |
| try: |
| dit = diffrhythm_dit.DiffRhythmDiT(dev, dhost.Checkpoint(args.checkpoint or "/checkpoint/cfm_model.pt")) |
| vae = oobleck_vae.OobleckTT(dev, args.vae or "/vae/vae_model.pt") |
| report["load_s"] = round(time.monotonic() - started, 1) |
| warm_vae(vae, report) |
| for seq_pad in report["seq_buckets"]: |
| frames = min(seq_pad, dhost.MAX_FRAMES) |
| t0 = time.monotonic() |
| song = dit.prepare({null: torch.randn(frames, 512, generator=g) for null in (False, True)}, |
| torch.randn(frames, 64, generator=g)) |
| assert song.geo.seq_pad == seq_pad |
| finite = True |
| for null in (False, True): |
| v = dit.forward(torch.randn(frames, 64, generator=g), null, torch.randn(512, generator=g), |
| torch.randn(512, generator=g), song) |
| finite &= bool(torch.isfinite(v).all()) |
| dit.release(song) |
| rec = {"seq_pad": seq_pad, "s": round(time.monotonic() - t0, 2), "finite": finite} |
| report["dit"].append(rec) |
| print(json.dumps(rec), flush=True) |
| finally: |
| diffrhythm_dit.close_device(dev) |
| report["total_s"] = round(time.monotonic() - started, 1) |
| print(json.dumps({k: report[k] for k in ("seq_buckets", "load_s", "total_s")}), flush=True) |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser() |
| parser.add_argument("--checkpoint") |
| parser.add_argument("--vae") |
| args = parser.parse_args() |
| return warm_diffrhythm(args) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
