File size: 5,097 Bytes
faa037a | 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 | import torch, sys, os, time, json, re
sys.path.insert(0, '/workspace/fractus-cte')
os.chdir('/workspace/fractus-cte')
from fractus.continuous_engine import ContinuousThoughtEngine
import torch.nn.functional as F
GPU = int(os.environ.get('GPU_ID', '0'))
LB_COEF = float(os.environ.get('LB_COEF', '0.02'))
GATE_TEMP = float(os.environ.get('GATE_TEMP', '2.5'))
LR = float(os.environ.get('LR', '5e-4')) # fine phase
EMA_BETA = float(os.environ.get('EMA_BETA', '0.98'))
torch.manual_seed(42 + GPU)
device = torch.device('cuda:0')
TARGET = dict(d_model=1280, n_heads=20, d_head=64, n_levels=2, n_oscillators=16,
coupling_rank=8, n_experts=128, top_k=2, expert_d_ff=2048, siren_rank=64, n_layers=16)
ckpt_path = f'checkpoints/fractus_1b_gpu{GPU}.pt'
print(f'GPU {GPU}: STAGE2-FINE resume {ckpt_path} LR={LR}', flush=True)
ck = torch.load(ckpt_path, map_location='cpu', weights_only=False)
sd = ck.get('model_state', ck)
clean = {(k[10:] if k.startswith('_orig_mod.') else k): v for k,v in sd.items()}
eng = ContinuousThoughtEngine(vocab_size=50257, **{k: TARGET[k] for k in TARGET})
own = eng.state_dict()
for k,v in clean.items():
if k in own and own[k].shape == v.shape:
own[k] = v
elif k in own and v.dim()>=1 and own[k].dim()>=1 and v.shape[0]>own[k].shape[0] and v.shape[1:]==own[k].shape[1:]:
own[k] = v[:own[k].shape[0]].contiguous()
eng.load_state_dict(own, strict=False)
with torch.no_grad():
for blk in eng.blocks:
blk.moe.temperature = GATE_TEMP
om = blk.kuramoto.omega
if float(om.detach().std()) < 0.08:
om.mul_(4.0)
om.add_(torch.randn_like(om) * 0.01)
om.clamp_(-0.5, 0.5)
eng = eng.to(device)
eng.reset_thought(batch_size=2)
print(f'GPU {GPU}: temp={GATE_TEMP} lb={LB_COEF} omega_std={float(eng.blocks[0].kuramoto.omega.detach().std()):.4f}', flush=True)
eng = torch.compile(eng, mode='reduce-overhead')
opt = torch.optim.SGD(eng.parameters(), lr=LR, momentum=0.9)
tokens = torch.load(f'data/shard_gpu{GPU}.pt', weights_only=False).to(torch.int64)
B, seq_len = 2, 128
step_tokens = B * seq_len
start_token = int(os.environ.get('START_TOKEN', '-1'))
if start_token < 0:
# prefer manifest
man_path = '/workspace/RESUME_MANIFEST_FINE.json'
if os.path.exists(man_path):
man = json.load(open(man_path))
start_token = int(man['gpus'][str(GPU)]['start_token'])
else:
for logp in [f'/workspace/stage2_gpu{GPU}.log', f'/workspace/stage2_fine_gpu{GPU}.log']:
if os.path.exists(logp):
for line in reversed(open(logp).read().strip().splitlines()):
m = re.search(r'(\d[\d,]*)\s+loss=', line)
if m:
start_token = (int(m.group(1).replace(',','')) // step_tokens) * step_tokens
break
if start_token >= 0:
break
if start_token < 0:
start_token = 0
print(f'GPU {GPU}: RESUME start_token={start_token}', flush=True)
t0 = time.time()
ema_ce = None
ema_lb = None
total_n = 0
tokens_this_session = 0
for start in range(start_token, len(tokens) - step_tokens - seq_len - 1, step_tokens):
chunk = tokens[start:start+step_tokens].view(B, seq_len).to(device)
target = tokens[start+1:start+step_tokens+1].view(B, seq_len).to(device)
with torch.autocast('cuda', dtype=torch.bfloat16):
out = eng.tick_chunk_train(chunk)
logits, lb = out if isinstance(out, tuple) else (out, eng.last_lb_loss)
ce = F.cross_entropy(logits.reshape(-1, logits.size(-1)), target.reshape(-1))
loss = ce + LB_COEF * lb
opt.zero_grad()
loss.backward()
torch.nn.utils.clip_grad_norm_(eng.parameters(), 1.0)
opt.step()
ce_v = float(ce.item())
lb_v = float(lb.detach().item()) if torch.is_tensor(lb) else float(lb)
ema_ce = ce_v if ema_ce is None else (EMA_BETA * ema_ce + (1 - EMA_BETA) * ce_v)
ema_lb = lb_v if ema_lb is None else (EMA_BETA * ema_lb + (1 - EMA_BETA) * lb_v)
total_n += 1
tokens_this_session += step_tokens
if total_n % 50 == 0:
processed = start + step_tokens
elapsed = max(time.time() - t0, 1e-6)
real_tps = tokens_this_session / elapsed
mem = torch.cuda.max_memory_allocated() / 1e9
print(
f'GPU {GPU}: {processed:>12,} ce={ce_v:.3f} ema={ema_ce:.3f} lb={lb_v:.3f} '
f'{real_tps:.0f} tok/s mem={mem:.1f}GB [fine LR={LR}]',
flush=True,
)
if total_n % 1000 == 0:
torch.save(
{
'model_state': eng.state_dict(),
'config': {
**TARGET,
'gpu': GPU,
'stage2_dense_ce': True,
'fine_phase': True,
'lr': LR,
'gate_temp': GATE_TEMP,
'lb_coef': LB_COEF,
},
},
f'checkpoints/fractus_1b_gpu{GPU}.pt',
)
print(f'GPU {GPU}: checkpoint saved [fine]', flush=True)
print(f'GPU {GPU}: DONE fine phase', flush=True)
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