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2c078fc | 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 | #!/usr/bin/env python3
"""
LRF Extended Training — more epochs on CPU with cached latents.
Uses the same proven architecture from v3, just trains longer.
Pushes results to HF Hub.
"""
import math, os, sys, time, json
import torch
import torch.nn as nn
import torch.nn.functional as F
from torch.utils.data import DataLoader, TensorDataset
from einops import rearrange
import numpy as np
DEVICE = 'cuda' if torch.cuda.is_available() else 'cpu'
# Reuse the exact architecture from lrf_v3.py
sys.path.insert(0, '/app')
from lrf_v3 import LRF, FlowScheduler, get_taesd, get_cifar, precompute, save_grid, gen
def main():
OUT = '/app/lrf_extended'
REPO = 'krystv/LatentRecurrentFlow'
os.makedirs(OUT, exist_ok=True)
EPOCHS = 100 # 3x more than v3
BS = 128 # Bigger batch
LR = 5e-4 # Slightly higher LR for faster convergence
print("=" * 60, flush=True)
print(f"LRF Extended Training — {EPOCHS} epochs, bs={BS}", flush=True)
print(f"Device: {DEVICE}", flush=True)
print("=" * 60, flush=True)
# VAE + Data (use cached latents from v3 if available)
print("\n[1] Loading TAESD + CIFAR-10...", flush=True)
vae = get_taesd(DEVICE)
tr, te = get_cifar()
# Check for cached latents from previous run
cache_dir = '/app/lrf_out'
if os.path.exists(f'{cache_dir}/cache_train.pt'):
print(" Using cached latents from v3 run!", flush=True)
tr_lat, tr_lab = precompute(vae, tr, 256, DEVICE, f'{cache_dir}/cache_train.pt')
else:
tr_lat, tr_lab = precompute(vae, tr, 256, DEVICE, f'{OUT}/cache_train.pt')
# Model — use the proven fast config
print("\n[2] Creating model...", flush=True)
cfg = LRF.default()
model = LRF(cfg).to(DEVICE)
print(f" {model.count():,} params", flush=True)
# Try to warm-start from v3 checkpoint
v3_ckpt = '/app/lrf_out/model.pt'
if os.path.exists(v3_ckpt):
print(f" Warm-starting from {v3_ckpt}", flush=True)
ckpt = torch.load(v3_ckpt, map_location=DEVICE, weights_only=False)
model.load_state_dict(ckpt['state'])
prev_losses = ckpt.get('losses', [])
print(f" Previous best loss: {min(prev_losses):.4f}", flush=True)
else:
prev_losses = []
# Train
print(f"\n[3] Training {EPOCHS} epochs...", flush=True)
sched = FlowScheduler()
opt = torch.optim.AdamW(model.parameters(), lr=LR, weight_decay=0.01, betas=(0.9, 0.95))
total_steps = EPOCHS * (len(tr_lat) // BS)
lr_sched = torch.optim.lr_scheduler.CosineAnnealingLR(opt, total_steps, LR * 0.01)
ema = {n: p.clone().detach() for n, p in model.named_parameters()}
losses = list(prev_losses) # Continue loss history
dl = DataLoader(TensorDataset(tr_lat, tr_lab), BS, shuffle=True, drop_last=True, num_workers=0)
best_loss = min(losses) if losses else 999
t0 = time.time()
for ep in range(EPOCHS):
model.train()
el, nb = 0, 0
for lat, lab in dl:
lat, lab = lat.to(DEVICE), lab.to(DEVICE)
B = lat.shape[0]
t = sched.sample_t(B, DEVICE)
eps = torch.randn_like(lat)
zt = sched.add_noise(lat, eps, t)
vp = model.predict_v(zt, t, lab, cfg_drop=0.1)
vt = sched.velocity(lat, eps)
lps = (vp - vt).pow(2).mean([1,2,3])
w = 1.0 / (t * (1-t) + 0.01); w = w / w.mean()
loss = (lps * w).mean()
opt.zero_grad(); loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), 1.0)
opt.step(); lr_sched.step()
with torch.no_grad():
for n, p in model.named_parameters():
ema[n].mul_(0.9995).add_(p, alpha=0.0005)
el += loss.item(); nb += 1
al = el / nb
losses.append(al)
if al < best_loss: best_loss = al
elapsed = time.time() - t0
if (ep+1) % 10 == 0 or ep == 0 or ep == EPOCHS-1:
print(f" Ep {ep+1:3d}/{EPOCHS}: loss={al:.4f} best={best_loss:.4f} "
f"lr={opt.param_groups[0]['lr']:.1e} {elapsed:.0f}s", flush=True)
# Sample every 25 epochs
if (ep+1) % 25 == 0 or ep == EPOCHS-1:
bak = {n: p.clone() for n, p in model.named_parameters()}
with torch.no_grad():
for n, p in model.named_parameters(): p.copy_(ema[n])
model.eval()
samps = gen(model, vae, sched, DEVICE, 16, 20, 2.5)
save_grid(samps, f'{OUT}/ep{ep+1:03d}.png', 4)
with torch.no_grad():
for n, p in model.named_parameters(): p.copy_(bak[n])
# Final EMA
with torch.no_grad():
for n, p in model.named_parameters(): p.copy_(ema[n])
model.eval()
# Final generation
print(f"\n[4] Final generation...", flush=True)
classes = ['airplane','auto','bird','cat','deer','dog','frog','horse','ship','truck']
all_s = []
for ci in range(10):
s = gen(model, vae, sched, DEVICE, 8, 50, 3.0, ci)
all_s.append(s)
print(f" {classes[ci]:10s}: std={s.std():.3f}", flush=True)
save_grid(torch.cat(all_s), f'{OUT}/final.png', 8)
# Save
torch.save({'state': model.state_dict(), 'cfg': cfg, 'losses': losses}, f'{OUT}/model.pt')
# Loss plot
try:
import matplotlib; matplotlib.use('Agg'); import matplotlib.pyplot as plt
plt.figure(figsize=(10,4))
plt.plot(losses, 'b-', alpha=0.7)
if prev_losses:
plt.axvline(x=len(prev_losses), color='r', linestyle='--', alpha=0.5, label='Extended training start')
plt.legend()
plt.xlabel('Epoch'); plt.ylabel('Loss')
plt.title(f'LRF Training (best={best_loss:.4f})')
plt.grid(True, alpha=0.3)
plt.savefig(f'{OUT}/loss.png', dpi=150, bbox_inches='tight'); plt.close()
except: pass
# Push to Hub
print(f"\n[5] Pushing to Hub...", flush=True)
from huggingface_hub import HfApi
api = HfApi()
for f in sorted(os.listdir(OUT)):
fp = os.path.join(OUT, f)
if f.endswith(('.pt', '.png')) and os.path.getsize(fp) < 100_000_000 and 'cache' not in f:
api.upload_file(path_or_fileobj=fp, path_in_repo=f'gpu_trained/{f}',
repo_id=REPO, repo_type='model')
print(f" Uploaded gpu_trained/{f}", flush=True)
# Upload train script
api.upload_file(path_or_fileobj='/app/train_extended.py', path_in_repo='train_gpu.py',
repo_id=REPO, repo_type='model')
print(f" Uploaded train_gpu.py", flush=True)
print(f"\n{'='*60}", flush=True)
print(f"DONE! Best loss: {best_loss:.4f}", flush=True)
print(f"See: https://huggingface.co/{REPO}", flush=True)
print(f"{'='*60}", flush=True)
if __name__ == '__main__':
main()
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