Spaces:
Running on Zero
Running on Zero
File size: 9,590 Bytes
f37be5a | 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 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 193 194 195 196 197 198 199 200 201 202 203 204 205 206 207 208 209 210 211 212 213 214 215 216 217 218 219 220 221 222 223 224 225 226 227 228 229 230 231 232 233 234 235 236 237 238 239 240 241 242 243 244 245 246 247 248 249 250 251 252 253 254 255 256 257 258 259 260 261 262 263 264 265 266 267 268 269 270 271 272 273 274 275 276 277 278 279 280 281 282 283 284 285 286 287 288 289 290 291 | from __future__ import annotations
import json
import math
from dataclasses import replace
from pathlib import Path
from typing import Any
import torch
from src.data.anchor_synthetic import load_anchor_synthetic
from src.data.openwebmath_bpe import load_openwebmath_bpe
from src.data.shakespeare import load_shakespeare
from src.data.the_stack_bpe import load_the_stack_bpe
from src.data.tinystories_bpe import load_tinystories_bpe
from src.data.wikitext_bpe import load_wikitext_bpe
from src.model.testformer import TestFormerLM
from src.model.testformer_config import TestFormerConfig, build_testformer_config
def _default_learning_rate(cfg: TestFormerConfig) -> float:
if cfg.d_model <= 384:
return 3.0e-4
if cfg.d_model <= 640:
return 2.0e-4
return 1.5e-4
def _make_cosine_warmup_scheduler(
optimizer: torch.optim.Optimizer,
total_steps: int,
warmup_fraction: float,
) -> torch.optim.lr_scheduler.LambdaLR:
warmup_steps = max(1, int(total_steps * warmup_fraction))
def lr_lambda(current_step: int) -> float:
if current_step < warmup_steps:
return float(current_step + 1) / float(warmup_steps)
progress = (current_step - warmup_steps) / max(1, total_steps - warmup_steps)
return 0.5 * (1.0 + math.cos(math.pi * progress))
return torch.optim.lr_scheduler.LambdaLR(optimizer, lr_lambda=lr_lambda)
def load_testformer_dataset(
dataset: str,
seq_len: int,
device: str,
data_dir: str = "data_cache",
the_stack_repo: str = "bigcode/the-stack-smol-xs",
the_stack_lang: str = "python",
the_stack_bytes: int = 8_000_000,
the_stack_vocab_size: int = 4096,
tinystories_repo: str = "roneneldan/TinyStories",
tinystories_bytes: int = 16_000_000,
tinystories_vocab_size: int = 4096,
openwebmath_repo: str = "open-web-math/open-web-math",
openwebmath_bytes: int = 200_000,
openwebmath_vocab_size: int = 256,
wikitext_repo: str = "wikitext",
wikitext_config_name: str = "wikitext-2-raw-v1",
wikitext_bytes: int = 2_000_000,
wikitext_vocab_size: int = 4096,
) -> tuple[Any, Any]:
if dataset == "anchor-synthetic":
return load_anchor_synthetic(seq_len=24, device=device)
if dataset == "shakespeare":
return load_shakespeare(seq_len=seq_len, device=device, data_dir=data_dir)
if dataset == "the-stack-bpe":
return load_the_stack_bpe(
seq_len=seq_len,
device=device,
data_dir=data_dir,
repo_id=the_stack_repo,
lang=the_stack_lang,
target_bytes=the_stack_bytes,
vocab_size=the_stack_vocab_size,
)
if dataset == "tinystories-bpe":
return load_tinystories_bpe(
seq_len=seq_len,
device=device,
data_dir=data_dir,
repo_id=tinystories_repo,
target_bytes=tinystories_bytes,
vocab_size=tinystories_vocab_size,
)
if dataset == "openwebmath-bpe":
return load_openwebmath_bpe(
seq_len=seq_len,
device=device,
data_dir=data_dir,
repo_id=openwebmath_repo,
target_bytes=openwebmath_bytes,
vocab_size=openwebmath_vocab_size,
)
if dataset == "wikitext-bpe":
return load_wikitext_bpe(
seq_len=seq_len,
device=device,
data_dir=data_dir,
repo_id=wikitext_repo,
config_name=wikitext_config_name,
target_bytes=wikitext_bytes,
vocab_size=wikitext_vocab_size,
)
raise ValueError(f"Unknown TestFormer dataset: {dataset}")
def evaluate_testformer(
model: TestFormerLM,
dataset: Any,
batch_size: int,
device: str,
max_batches: int = 5,
) -> dict[str, float]:
model.eval()
total_loss = 0.0
total_tokens = 0
with torch.no_grad():
for _ in range(max_batches):
x, y = dataset.get_batch(batch_size)
x = x.to(device)
y = y.to(device)
out = model(x, y)
total_loss += float(out["loss"].item()) * y.numel()
total_tokens += y.numel()
mean_loss = total_loss / max(1, total_tokens)
return {
"loss": mean_loss,
"bpb": mean_loss / math.log(2.0),
}
def train_testformer(
cfg: TestFormerConfig,
dataset: str = "anchor-synthetic",
device: str = "cpu",
data_dir: str = "data_cache",
steps: int = 100,
batch_size: int = 16,
eval_every: int = 20,
eval_batches: int = 5,
learning_rate: float | None = None,
weight_decay: float = 0.1,
beta1: float = 0.9,
beta2: float = 0.95,
grad_clip: float = 1.0,
warmup_fraction: float = 0.02,
the_stack_repo: str = "bigcode/the-stack-smol-xs",
the_stack_lang: str = "python",
the_stack_bytes: int = 8_000_000,
the_stack_vocab_size: int = 4096,
tinystories_repo: str = "roneneldan/TinyStories",
tinystories_bytes: int = 16_000_000,
tinystories_vocab_size: int = 4096,
openwebmath_repo: str = "open-web-math/open-web-math",
openwebmath_bytes: int = 200_000,
openwebmath_vocab_size: int = 256,
wikitext_repo: str = "wikitext",
wikitext_config_name: str = "wikitext-2-raw-v1",
wikitext_bytes: int = 2_000_000,
wikitext_vocab_size: int = 4096,
) -> tuple[TestFormerLM, list[dict[str, float]], Any, Any]:
train_data, val_data = load_testformer_dataset(
dataset=dataset,
seq_len=cfg.max_seq_len,
device=device,
data_dir=data_dir,
the_stack_repo=the_stack_repo,
the_stack_lang=the_stack_lang,
the_stack_bytes=the_stack_bytes,
the_stack_vocab_size=the_stack_vocab_size,
tinystories_repo=tinystories_repo,
tinystories_bytes=tinystories_bytes,
tinystories_vocab_size=tinystories_vocab_size,
openwebmath_repo=openwebmath_repo,
openwebmath_bytes=openwebmath_bytes,
openwebmath_vocab_size=openwebmath_vocab_size,
wikitext_repo=wikitext_repo,
wikitext_config_name=wikitext_config_name,
wikitext_bytes=wikitext_bytes,
wikitext_vocab_size=wikitext_vocab_size,
)
effective_cfg = replace(
cfg,
vocab_size=train_data.vocab_size,
max_seq_len=getattr(train_data, "seq_len", cfg.max_seq_len),
)
model = TestFormerLM(effective_cfg).to(device)
optimizer = torch.optim.AdamW(
model.parameters(),
lr=learning_rate or _default_learning_rate(effective_cfg),
betas=(beta1, beta2),
weight_decay=weight_decay,
)
scheduler = _make_cosine_warmup_scheduler(
optimizer=optimizer,
total_steps=max(steps, 1),
warmup_fraction=warmup_fraction,
)
history: list[dict[str, float]] = []
for step in range(steps):
model.train()
x, y = train_data.get_batch(batch_size)
x = x.to(device)
y = y.to(device)
out = model(x, y)
optimizer.zero_grad()
out["loss"].backward()
torch.nn.utils.clip_grad_norm_(model.parameters(), grad_clip)
optimizer.step()
scheduler.step()
if (step + 1) % eval_every == 0 or step == steps - 1:
metrics = evaluate_testformer(
model=model,
dataset=val_data,
batch_size=batch_size,
device=device,
max_batches=eval_batches,
)
history.append(
{
"step": float(step + 1),
"train_loss": float(out["loss"].item()),
"train_bpb": float(out["loss"].item() / math.log(2.0)),
"val_loss": metrics["loss"],
"val_bpb": metrics["bpb"],
"lr": float(optimizer.param_groups[0]["lr"]),
}
)
model.training_history = history
return model, history, train_data, val_data
def summarize_testformer_result(
preset_name: str,
motif_name: str,
dataset_name: str,
model: TestFormerLM,
history: list[dict[str, float]],
) -> dict[str, Any]:
last = history[-1] if history else {}
return {
"preset": preset_name,
"motif": motif_name,
"dataset": dataset_name,
"parameters": model.parameter_count(),
"body_parameters": model.body_parameter_count(),
"d_model": model.cfg.d_model,
"n_layers": model.cfg.n_layers,
"n_heads": model.cfg.n_heads,
"d_ff": model.cfg.d_ff,
"qk_dim": model.cfg.qk_dim,
"v_dim": model.cfg.v_dim,
"final_train_loss": last.get("train_loss"),
"final_val_loss": last.get("val_loss"),
"final_val_bpb": last.get("val_bpb"),
"history": history,
}
def save_testformer_json(payload: Any, path: str | Path) -> None:
path = Path(path)
if path.parent:
path.parent.mkdir(parents=True, exist_ok=True)
path.write_text(json.dumps(payload, indent=2), encoding="utf-8")
def build_runtime_testformer_config(
preset_name: str,
motif_name: str,
vocab_size: int = 32000,
seq_len: int | None = None,
resid_dropout: float = 0.0,
attn_dropout: float = 0.0,
emb_dropout: float = 0.0,
) -> TestFormerConfig:
return build_testformer_config(
preset_name=preset_name,
motif_name=motif_name,
vocab_size=vocab_size,
max_seq_len=seq_len,
resid_dropout=resid_dropout,
attn_dropout=attn_dropout,
emb_dropout=emb_dropout,
)
|