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c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f c407ed8 8098a3f bf5d82d 8098a3f c407ed8 bf5d82d c407ed8 8098a3f | 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 | from __future__ import annotations
import csv
import math
from pathlib import Path
import torch
from tqdm import trange
from tiny_transformer.config import ModelConfig, TrainConfig
from tiny_transformer.data import TextDataset, split_tokens
from tiny_transformer.model import TinyTransformer
from tiny_transformer.tokenizer import BytePairTokenizer, CharTokenizer, Tokenizer, tokenizer_from_dict
@torch.no_grad()
def estimate_loss(
model: TinyTransformer,
train_data: TextDataset,
val_data: TextDataset,
batch_size: int,
eval_batches: int,
) -> dict[str, float]:
model.eval()
losses: dict[str, float] = {}
for split, dataset in {"train": train_data, "val": val_data}.items():
split_losses = []
for _ in range(eval_batches):
x, y = dataset.get_batch(batch_size)
_, loss = model(x, y)
if loss is None:
raise RuntimeError("Expected a loss during evaluation")
split_losses.append(loss.item())
losses[split] = sum(split_losses) / len(split_losses)
model.train()
return losses
def save_loss_history(history: list[dict[str, float | int]], path: str) -> None:
output = Path(path)
output.parent.mkdir(parents=True, exist_ok=True)
with output.open("w", encoding="utf-8", newline="") as file:
writer = csv.DictWriter(file, fieldnames=["step", "train_loss", "val_loss"])
writer.writeheader()
writer.writerows(history)
def train_from_text(
text: str,
model_config: ModelConfig | None = None,
train_config: TrainConfig | None = None,
device: str = "cpu",
tokenizer_name: str = "char",
bpe_vocab_size: int = 256,
resume_path: str | None = None,
) -> TinyTransformer:
train_config = train_config or TrainConfig()
torch.manual_seed(train_config.seed)
resume_payload = torch.load(resume_path, map_location=device) if resume_path else None
tokenizer = (
tokenizer_from_dict(resume_payload["tokenizer"])
if resume_payload is not None
else train_tokenizer(text, tokenizer_name, bpe_vocab_size)
)
token_ids = tokenizer.encode(text)
train_ids, val_ids = split_tokens(token_ids)
if resume_payload is not None:
model_config = ModelConfig(**resume_payload["model_config"])
elif model_config is None:
model_config = ModelConfig(vocab_size=tokenizer.vocab_size)
else:
model_config = ModelConfig(**{**model_config.to_dict(), "vocab_size": tokenizer.vocab_size})
train_data = TextDataset(train_ids, model_config.block_size, device)
val_data = TextDataset(val_ids, model_config.block_size, device)
model = TinyTransformer(model_config).to(device)
if resume_payload is not None:
model.load_state_dict(resume_payload["model_state"])
optimizer = torch.optim.AdamW(model.parameters(), lr=train_config.learning_rate)
if resume_payload is not None and "optimizer_state" in resume_payload:
optimizer.load_state_dict(resume_payload["optimizer_state"])
start_step = int(resume_payload.get("step", 0)) if resume_payload is not None else 0
device_type = "cuda" if device.startswith("cuda") else "mps" if device == "mps" else "cpu"
amp_enabled = train_config.use_amp and device_type in {"cuda", "mps"}
scaler = torch.amp.GradScaler("cuda", enabled=amp_enabled and device_type == "cuda")
loss_history: list[dict[str, float | int]] = []
progress = trange(start_step, train_config.max_steps, desc="training", leave=True)
for step in progress:
if step % train_config.eval_interval == 0 or step == train_config.max_steps - 1:
losses = estimate_loss(
model, train_data, val_data, train_config.batch_size, train_config.eval_batches
)
progress.set_postfix(train=f"{losses['train']:.3f}", val=f"{losses['val']:.3f}")
loss_history.append(
{
"step": step,
"train_loss": losses["train"],
"val_loss": losses["val"],
}
)
optimizer.zero_grad(set_to_none=True)
for _ in range(train_config.grad_accum_steps):
x, y = train_data.get_batch(train_config.batch_size)
with torch.autocast(device_type=device_type, enabled=amp_enabled):
_, loss = model(x, y)
if loss is None:
raise RuntimeError("Expected a loss during training")
loss = loss / train_config.grad_accum_steps
scaler.scale(loss).backward()
scaler.step(optimizer)
scaler.update()
if train_config.loss_history_path:
save_loss_history(loss_history, train_config.loss_history_path)
save_checkpoint(
model,
tokenizer,
train_config.output_path,
optimizer=optimizer,
step=train_config.max_steps,
train_config=train_config,
)
return model
def train_tokenizer(text: str, tokenizer_name: str, bpe_vocab_size: int) -> Tokenizer:
if tokenizer_name == "char":
return CharTokenizer.train(text)
if tokenizer_name == "bpe":
return BytePairTokenizer.train(text, vocab_size=bpe_vocab_size)
raise ValueError("tokenizer_name must be 'char' or 'bpe'")
def save_checkpoint(
model: TinyTransformer,
tokenizer: Tokenizer,
path: str,
optimizer: torch.optim.Optimizer | None = None,
step: int = 0,
train_config: TrainConfig | None = None,
) -> None:
output = Path(path)
output.parent.mkdir(parents=True, exist_ok=True)
payload = {
"model_config": model.config.to_dict(),
"model_state": model.state_dict(),
"tokenizer": tokenizer.to_dict(),
"step": step,
"parameter_count": model.parameter_count(),
"weight_tied": model.lm_head.weight is model.token_embedding.weight,
}
if optimizer is not None:
payload["optimizer_state"] = optimizer.state_dict()
if train_config is not None:
payload["train_config"] = train_config.to_dict()
torch.save(payload, output)
def load_checkpoint(path: str, device: str = "cpu") -> tuple[TinyTransformer, Tokenizer]:
payload = torch.load(path, map_location=device)
tokenizer = tokenizer_from_dict(payload["tokenizer"])
config = ModelConfig(**payload["model_config"])
model = TinyTransformer(config).to(device)
if not payload.get("weight_tied", False):
# Checkpoints created before weight tying stored independent embedding and
# output matrices. Preserve that behavior so older demos remain compatible.
model.lm_head = torch.nn.Linear(config.n_embd, config.vocab_size).to(device)
model.load_state_dict(payload["model_state"])
model.eval()
return model, tokenizer
def evaluate_checkpoint(
path: str,
text: str,
batch_size: int = 32,
eval_batches: int = 20,
device: str = "cpu",
) -> dict[str, float | int]:
model, tokenizer = load_checkpoint(path, device=device)
token_ids = tokenizer.encode(text)
train_ids, val_ids = split_tokens(token_ids)
train_data = TextDataset(train_ids, model.config.block_size, device)
val_data = TextDataset(val_ids, model.config.block_size, device)
losses = estimate_loss(model, train_data, val_data, batch_size, eval_batches)
return {
"train_loss": losses["train"],
"validation_loss": losses["val"],
"validation_perplexity": math.exp(min(losses["val"], 20)),
"parameter_count": model.parameter_count(),
}
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