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scripts/eval_checkpoint.py
Report loss/perplexity on a small held-out token memmap or text file.
Usage:
PYTHONPATH=. python scripts/eval_checkpoint.py \\
--config configs/model/brain_0_3_35m.yaml \\
--checkpoint path/to/ckpt.pt \\
--tokens data/val.tokens.uint16 \\
--ctx 512 --batches 20
"""
from __future__ import annotations
import argparse
from pathlib import Path
import numpy as np
import torch
from brain.core.config import BrainConfig
from brain.eval.metrics import perplexity_from_loss
from brain.model.brain_model import BrainForCausalLM
def main() -> None:
p = argparse.ArgumentParser()
p.add_argument("--config", required=True)
p.add_argument("--checkpoint", required=True)
p.add_argument("--tokens", required=True, help="uint16 memmap of token ids")
p.add_argument("--ctx", type=int, default=512)
p.add_argument("--batches", type=int, default=20)
p.add_argument("--batch-size", type=int, default=4)
args = p.parse_args()
cfg = BrainConfig.from_yaml(args.config)
try:
model = BrainForCausalLM.load_checkpoint(args.checkpoint)
except Exception:
model = BrainForCausalLM(cfg)
payload = torch.load(args.checkpoint, map_location="cpu")
state = payload.get("model_state_dict", payload)
model.load_state_dict(state, strict=False)
model.eval()
device = "cuda" if torch.cuda.is_available() else "cpu"
model.to(device)
n = Path(args.tokens).stat().st_size // 2
tokens = np.memmap(args.tokens, dtype=np.uint16, mode="r", shape=(n,))
losses = []
with torch.no_grad():
for _ in range(args.batches):
starts = np.random.randint(0, n - args.ctx - 1, size=args.batch_size)
x = np.stack([tokens[s : s + args.ctx] for s in starts]).astype(np.int64)
x_t = torch.from_numpy(x).to(device)
out = model(x_t, labels=x_t)
loss = out["loss"] if isinstance(out, dict) else out.loss
losses.append(float(loss))
mean_loss = sum(losses) / len(losses)
print(f"mean_loss={mean_loss:.4f} ppl={perplexity_from_loss(mean_loss):.2f}")
if __name__ == "__main__":
main()
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