File size: 2,657 Bytes
91ac3cc | 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 | from __future__ import annotations
import argparse
import json
import math
from pathlib import Path
import torch
from transformers import AutoTokenizer
from kawk50m.config import build_llama_config, load_yaml
from kawk50m.sft_dataset import SFTPackedDataset
def main() -> None:
parser = argparse.ArgumentParser(description="Validate an SFT run before GPU use.")
parser.add_argument("--config", required=True)
parser.add_argument("--init-from", required=True)
args = parser.parse_args()
config = load_yaml(args.config)
data = config["data"]
training = config["training"]
tokenizer = AutoTokenizer.from_pretrained(
config["tokenizer_dir"], use_fast=False, legacy=True
)
model_config, expected_parameters = build_llama_config(
load_yaml(config["model_config"]),
tokenizer_vocab_size=len(tokenizer),
)
sequence_length = int(data["sequence_length"])
train = SFTPackedDataset(data["train_prefixes"], sequence_length)
validation = SFTPackedDataset(data["validation_prefixes"], sequence_length)
batch_size = int(training["per_device_batch_size"])
accumulation = int(training["gradient_accumulation_steps"])
epochs = float(training["epochs"])
sequences_per_step = batch_size * accumulation
max_steps = math.ceil(len(train) * epochs / sequences_per_step)
init_path = Path(args.init_from)
errors: list[str] = []
if not (init_path / "model" / "model.safetensors").is_file():
errors.append("Initial checkpoint model.safetensors is missing")
if sequence_length > model_config.max_position_embeddings:
errors.append("SFT sequence length exceeds model context length")
result = {
"tokenizer_vocab_size": len(tokenizer),
"expected_parameters": expected_parameters,
"sequence_length": sequence_length,
"train_sequences": len(train),
"validation_sequences": len(validation),
"epochs": epochs,
"sequences_per_step": sequences_per_step,
"max_steps": max_steps,
"planned_capacity_tokens": max_steps * sequences_per_step * sequence_length,
"init_from": str(init_path.resolve()),
"gpu": {
"available": torch.cuda.is_available(),
"bf16_supported": (
torch.cuda.is_bf16_supported() if torch.cuda.is_available() else False
),
"name": torch.cuda.get_device_name(0) if torch.cuda.is_available() else None,
},
"errors": errors,
}
print(json.dumps(result, ensure_ascii=False, indent=2))
if errors:
raise SystemExit(1)
if __name__ == "__main__":
main()
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