| from __future__ import annotations |
|
|
| import argparse |
| import json |
|
|
| from transformers import LlamaForCausalLM |
|
|
| from kawk50m.cli import configure_utf8_stdio |
| from kawk50m.config import build_llama_config, count_unique_parameters, load_yaml |
|
|
|
|
| def main() -> None: |
| configure_utf8_stdio() |
| parser = argparse.ArgumentParser(description="Inspect the configured model size.") |
| parser.add_argument("--config", default="configs/model_50m.yaml") |
| args = parser.parse_args() |
|
|
| config, expected = build_llama_config(load_yaml(args.config)) |
| model = LlamaForCausalLM(config) |
| unique_parameters = count_unique_parameters(model) |
| embedding_parameters = config.vocab_size * config.hidden_size |
| result = { |
| "parameters": unique_parameters, |
| "parameters_millions": round(unique_parameters / 1_000_000, 6), |
| "embedding_parameters": embedding_parameters, |
| "transformer_and_norm_parameters": unique_parameters - embedding_parameters, |
| "layers": config.num_hidden_layers, |
| "hidden_size": config.hidden_size, |
| "vocab_size": config.vocab_size, |
| "attention_heads": config.num_attention_heads, |
| "kv_heads": config.num_key_value_heads, |
| "context_length": config.max_position_embeddings, |
| "expected_parameters": expected, |
| } |
| print(json.dumps(result, ensure_ascii=False, indent=2)) |
| if expected is not None and unique_parameters != expected: |
| raise RuntimeError( |
| f"Parameter count mismatch: expected={expected}, actual={unique_parameters}" |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
|
|
