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
|
"""Train a tiny char-level GPT on tinyshakespeare with softmax or sigmoid attn.
Logs train/val loss to runs/<run_name>/log.jsonl (one JSON per line).
No checkpoints are saved (disk-conscious).
"""
import argparse
import json
import math
import os
import pickle
import time
from pathlib import Path
import numpy as np
import torch
from model import GPT, GPTConfig
def get_batch(split: str, data_dir: Path, block_size: int, batch_size: int, device: str):
fn = "train.bin" if split == "train" else "val.bin"
data = np.memmap(data_dir / fn, dtype=np.uint16, mode="r")
ix = torch.randint(len(data) - block_size - 1, (batch_size,))
x = torch.stack([torch.from_numpy(data[i : i + block_size].astype(np.int64)) for i in ix])
y = torch.stack([torch.from_numpy(data[i + 1 : i + 1 + block_size].astype(np.int64)) for i in ix])
return x.to(device, non_blocking=True), y.to(device, non_blocking=True)
@torch.no_grad()
def estimate_loss(model, data_dir, block_size, batch_size, device, eval_iters):
out = {}
model.eval()
for split in ("train", "val"):
losses = torch.zeros(eval_iters)
for k in range(eval_iters):
X, Y = get_batch(split, data_dir, block_size, batch_size, device)
_, loss = model(X, Y)
losses[k] = loss.item()
out[split] = losses.mean().item()
model.train()
return out
def lr_schedule(it, warmup_iters, lr_decay_iters, max_lr, min_lr):
if it < warmup_iters:
return max_lr * (it + 1) / (warmup_iters + 1)
if it > lr_decay_iters:
return min_lr
decay_ratio = (it - warmup_iters) / (lr_decay_iters - warmup_iters)
coeff = 0.5 * (1.0 + math.cos(math.pi * decay_ratio))
return min_lr + coeff * (max_lr - min_lr)
def main():
p = argparse.ArgumentParser()
p.add_argument("--run_name", type=str, required=True)
p.add_argument("--attn_mode", type=str, default="softmax", choices=["softmax", "sigmoid"])
p.add_argument("--sigmoid_bias_mode", type=str, default="neg_log_n",
choices=["zero", "neg_log_n", "learned"])
p.add_argument("--seed", type=int, default=1337)
p.add_argument("--data_dir", type=str, default="data/shakespeare_char")
p.add_argument("--out_dir", type=str, default="runs")
p.add_argument("--block_size", type=int, default=256)
p.add_argument("--batch_size", type=int, default=64)
p.add_argument("--n_layer", type=int, default=6)
p.add_argument("--n_head", type=int, default=6)
p.add_argument("--n_embd", type=int, default=384)
p.add_argument("--dropout", type=float, default=0.2)
p.add_argument("--max_iters", type=int, default=5000)
p.add_argument("--warmup_iters", type=int, default=100)
p.add_argument("--lr_decay_iters", type=int, default=5000)
p.add_argument("--max_lr", type=float, default=1e-3)
p.add_argument("--min_lr", type=float, default=1e-4)
p.add_argument("--weight_decay", type=float, default=0.1)
p.add_argument("--beta1", type=float, default=0.9)
p.add_argument("--beta2", type=float, default=0.99)
p.add_argument("--grad_clip", type=float, default=1.0)
p.add_argument("--eval_interval", type=int, default=250)
p.add_argument("--eval_iters", type=int, default=100)
p.add_argument("--log_interval", type=int, default=50)
p.add_argument("--dtype", type=str, default="bfloat16", choices=["float32", "bfloat16", "float16"])
args = p.parse_args()
torch.manual_seed(args.seed)
torch.cuda.manual_seed_all(args.seed)
device = "cuda" if torch.cuda.is_available() else "cpu"
data_dir = Path(args.data_dir)
with open(data_dir / "meta.pkl", "rb") as f:
meta = pickle.load(f)
vocab_size = meta["vocab_size"]
run_dir = Path(args.out_dir) / args.run_name
run_dir.mkdir(parents=True, exist_ok=True)
log_path = run_dir / "log.jsonl"
cfg_path = run_dir / "config.json"
with open(cfg_path, "w") as f:
json.dump(vars(args) | {"vocab_size": vocab_size}, f, indent=2)
cfg = GPTConfig(
block_size=args.block_size,
vocab_size=vocab_size,
n_layer=args.n_layer,
n_head=args.n_head,
n_embd=args.n_embd,
dropout=args.dropout,
attn_mode=args.attn_mode,
sigmoid_bias_mode=args.sigmoid_bias_mode,
)
model = GPT(cfg).to(device)
n_params = model.num_params()
# AdamW with weight-decay-free for 1D params (ln, embeddings, biases, sig_bias)
decay_params, nodecay_params = [], []
for n, pr in model.named_parameters():
if not pr.requires_grad:
continue
if pr.dim() >= 2:
decay_params.append(pr)
else:
nodecay_params.append(pr)
optimizer = torch.optim.AdamW(
[
{"params": decay_params, "weight_decay": args.weight_decay},
{"params": nodecay_params, "weight_decay": 0.0},
],
lr=args.max_lr,
betas=(args.beta1, args.beta2),
fused=(device == "cuda"),
)
dtype_map = {"float32": torch.float32, "bfloat16": torch.bfloat16, "float16": torch.float16}
amp_dtype = dtype_map[args.dtype]
scaler = torch.amp.GradScaler("cuda", enabled=(args.dtype == "float16"))
t0 = time.time()
def log(record: dict):
record["t"] = time.time() - t0
with open(log_path, "a") as f:
f.write(json.dumps(record) + "\n")
log({"event": "start", "params": n_params, "config": vars(args) | {"vocab_size": vocab_size}})
print(f"[{args.run_name}] params={n_params/1e6:.2f}M device={device} dtype={args.dtype}")
model.train()
for it in range(args.max_iters + 1):
lr = lr_schedule(it, args.warmup_iters, args.lr_decay_iters, args.max_lr, args.min_lr)
for g in optimizer.param_groups:
g["lr"] = lr
if it % args.eval_interval == 0 or it == args.max_iters:
losses = estimate_loss(model, data_dir, args.block_size, args.batch_size, device, args.eval_iters)
log({"event": "eval", "iter": it, "train_loss": losses["train"], "val_loss": losses["val"], "lr": lr})
print(f"[{args.run_name}] iter {it:5d} train {losses['train']:.4f} val {losses['val']:.4f} lr {lr:.4g}")
if it == args.max_iters:
break
X, Y = get_batch("train", data_dir, args.block_size, args.batch_size, device)
with torch.amp.autocast(device_type="cuda", dtype=amp_dtype, enabled=(device == "cuda")):
_, loss = model(X, Y)
optimizer.zero_grad(set_to_none=True)
if args.dtype == "float16":
scaler.scale(loss).backward()
scaler.unscale_(optimizer)
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
scaler.step(optimizer)
scaler.update()
else:
loss.backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), args.grad_clip)
optimizer.step()
if it % args.log_interval == 0:
log({"event": "step", "iter": it, "train_loss": loss.item(), "lr": lr})
log({"event": "done", "iter": args.max_iters})
print(f"[{args.run_name}] done in {time.time()-t0:.1f}s")
if __name__ == "__main__":
main()
|
