File size: 8,211 Bytes
79cd361 | 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 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 | from __future__ import annotations
import argparse
import copy
import json
import math
import os
import time
import numpy as np
import torch
import torch.nn.functional as F
from safetensors.torch import save_file
from audio_dit import AudioDiT
def cosine_lr(step, total, base, floor, warmup):
if step < warmup:
return base * (step + 1) / max(1, warmup)
t = (step - warmup) / max(1, total - warmup)
return floor + 0.5 * (base - floor) * (1 + math.cos(math.pi * t))
def load_split(data, split, dev):
meta = json.load(open(f"{data}/{split}_meta.json"))
mel = np.load(f"{data}/{split}_mel.npy")
seq = np.load(f"{data}/{split}_text_seq.npy")
pool = np.load(f"{data}/{split}_text_pool.npy")
ci = np.array(meta["pair_clip_idx"], dtype=np.int64)
return (torch.from_numpy(mel).to(dev), torch.from_numpy(seq).to(dev),
torch.from_numpy(pool).to(dev), torch.from_numpy(ci).to(dev), meta)
def main():
ap = argparse.ArgumentParser()
ap.add_argument("--data", default="/root/data")
ap.add_argument("--out", default="/root/runs/audio_v1")
ap.add_argument("--steps", type=int, default=90000)
ap.add_argument("--batch-size", type=int, default=128)
ap.add_argument("--lr", type=float, default=2e-4)
ap.add_argument("--min-lr", type=float, default=1e-6)
ap.add_argument("--warmup", type=int, default=500)
ap.add_argument("--cfg-dropout", type=float, default=0.1)
ap.add_argument("--ema", type=float, default=0.9999)
ap.add_argument("--val-every", type=int, default=2000)
ap.add_argument("--val-batch", type=int, default=256)
ap.add_argument("--log-every", type=int, default=200)
ap.add_argument("--save-every", type=int, default=5000)
ap.add_argument("--resume", default="")
ap.add_argument("--seed", type=int, default=0)
args = ap.parse_args()
dev = "cuda"
os.makedirs(args.out, exist_ok=True)
torch.manual_seed(args.seed)
torch.backends.cuda.matmul.allow_tf32 = True
torch.backends.cudnn.allow_tf32 = True
mel_t, seq_t, pool_t, ci_t, meta = load_split(args.data, "development", dev)
vmel_t, vseq_t, vpool_t, vci_t, vmeta = load_split(args.data, "validation", dev)
null_seq = torch.from_numpy(np.load(f"{args.data}/null_seq.npy")).to(dev).float()
null_pool = torch.from_numpy(np.load(f"{args.data}/null_pool.npy")).to(dev).float()
N = ci_t.shape[0]
x_res, y_res = meta["x_res"], meta["y_res"]
def mel_batch(mel_tensor, ci_tensor, idx):
return (mel_tensor[ci_tensor[idx]].float() / 127.5 - 1.0).unsqueeze(1)
vg = torch.Generator(device=dev).manual_seed(1234)
n_val = min(args.val_batch, vci_t.shape[0])
vidx = torch.arange(n_val, device=dev)
vx1 = mel_batch(vmel_t, vci_t, vidx)
vs = vseq_t[vidx].float()
vp = vpool_t[vidx].float()
vx0 = torch.randn(vx1.shape, device=dev, generator=vg)
vt = torch.sigmoid(torch.randn(vx1.shape[0], device=dev, generator=vg))
@torch.no_grad()
def val_loss():
ema.eval()
tb = vt.view(-1, 1, 1, 1)
xt = (1 - tb) * vx0 + tb * vx1
with torch.autocast("cuda", dtype=torch.bfloat16):
v = ema(xt, vt, vs, vp)
return F.mse_loss(v.float(), vx1 - vx0).item()
model = AudioDiT(x_res=x_res, y_res=y_res,
text_seq_dim=meta["seq_dim"], text_pool_dim=meta["pool_dim"]).to(dev)
ema = copy.deepcopy(model).eval()
for p in ema.parameters():
p.requires_grad_(False)
opt = torch.optim.AdamW(model.parameters(), lr=args.lr, betas=(0.9, 0.99), weight_decay=0.0)
start = 0
best_val = float("inf")
if args.resume and os.path.exists(args.resume):
ck = torch.load(args.resume, map_location=dev)
model.load_state_dict(ck["model"])
ema.load_state_dict(ck["ema"])
opt.load_state_dict(ck["opt"])
start = ck["step"] + 1
best_val = ck.get("best_val", float("inf"))
B = args.batch_size
gen = torch.Generator(device=dev).manual_seed(args.seed)
epochs = args.steps * B / N
print(f"[train] {N} train pairs ({meta['n_clips']} clips) + {n_val} val, "
f"{model.num_params()/1e6:.2f}M params, grid {model.grid_h}x{model.grid_w} "
f"({model.grid_h*model.grid_w} tokens)", flush=True)
print(f"[train] {args.steps} steps x batch {B} = {epochs:.0f} epochs, resume from {start}", flush=True)
logf = open(f"{args.out}/log.jsonl", "a")
running, t0 = 0.0, time.time()
for step in range(start, args.steps):
idx = torch.randint(0, N, (B,), device=dev, generator=gen)
x1 = mel_batch(mel_t, ci_t, idx)
s = seq_t[idx].float()
p = pool_t[idx].float()
drop = torch.rand(B, device=dev, generator=gen) < args.cfg_dropout
s = torch.where(drop[:, None, None], null_seq, s)
p = torch.where(drop[:, None], null_pool, p)
x0 = torch.randn(x1.shape, device=dev, generator=gen)
u = torch.randn(B, device=dev, generator=gen)
t = torch.sigmoid(u)
tb = t.view(-1, 1, 1, 1)
xt = (1 - tb) * x0 + tb * x1
target = x1 - x0
lr = cosine_lr(step, args.steps, args.lr, args.min_lr, args.warmup)
for g in opt.param_groups:
g["lr"] = lr
with torch.autocast("cuda", dtype=torch.bfloat16):
v = model(xt, t, s, p)
loss = F.mse_loss(v.float(), target)
opt.zero_grad(set_to_none=True)
loss.backward()
gn = torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step()
with torch.no_grad():
d = 1 - args.ema
for pe, pm in zip(ema.parameters(), model.parameters()):
pe.mul_(args.ema).add_(pm.detach(), alpha=d)
running += loss.item()
if (step + 1) % args.log_every == 0:
avg = running / args.log_every
el = time.time() - t0
sps = args.log_every / el
eta = (args.steps - step - 1) / sps / 3600
print(f"[s{step+1:06d}] loss={avg:.5f} lr={lr:.2e} gnorm={gn:.2f} "
f"{sps:.2f} steps/s eta={eta:.2f}h", flush=True)
logf.write(json.dumps({"step": step + 1, "loss": avg, "lr": lr,
"gnorm": float(gn), "steps_per_s": sps}) + "\n")
logf.flush()
running, t0 = 0.0, time.time()
if (step + 1) % args.val_every == 0 or step + 1 == args.steps:
vl = val_loss()
tag = ""
if vl < best_val:
best_val = vl
save_file({k: v.contiguous() for k, v in ema.state_dict().items()},
f"{args.out}/model_best.safetensors")
json.dump({"step": step + 1, "val_loss": vl}, open(f"{args.out}/best.json", "w"))
tag = " (new best, saved model_best.safetensors)"
print(f"[s{step+1:06d}] val_loss={vl:.5f} (ema, {n_val} held out){tag}", flush=True)
logf.write(json.dumps({"step": step + 1, "val_loss": vl}) + "\n")
logf.flush()
t0 = time.time()
if (step + 1) % args.save_every == 0 or step + 1 == args.steps:
torch.save({"model": model.state_dict(), "ema": ema.state_dict(),
"opt": opt.state_dict(), "step": step, "best_val": best_val},
f"{args.out}/ckpt.pt")
save_file({k: v.contiguous() for k, v in ema.state_dict().items()},
f"{args.out}/model.safetensors")
print(f"[s{step+1:06d}] saved", flush=True)
json.dump({"dit": {"x_res": x_res, "y_res": y_res, "patch": 16, "dim": 384, "depth": 12,
"heads": 6, "text_seq_dim": meta["seq_dim"], "text_pool_dim": meta["pool_dim"]},
"mel": {"sample_rate": meta["sample_rate"], "n_fft": meta["n_fft"],
"hop_length": meta["hop_length"], "top_db": meta["top_db"]},
"steps": args.steps, "batch_size": args.batch_size,
"train_pairs": N, "train_clips": meta["n_clips"], "best_val": best_val},
open(f"{args.out}/config.json", "w"), indent=2)
print("TRAINDONE", flush=True)
if __name__ == "__main__":
main()
|