File size: 3,032 Bytes
cfbcb75 | 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 63 64 65 66 67 68 69 70 | #!/usr/bin/env python3
"""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: # exactly as the worker does right after loading (worker.py)
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) # any length in the bucket gives the same shapes
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()
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