| 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() |
|
|
