| import argparse |
| import torch |
| from math import log2 |
| from text_size import calculate_text_size_per_token |
| from likelihood import calculate_negative_log_likelihood |
| from flops import gqa_model_theoretical_flops, mla_model_theoretical_flops, qwen3_5_theoretical_flops |
|
|
| def calculate_information_capacity( |
| model_path: str, |
| data_path: str, |
| max_sample_length: int = 1024, |
| batch_size: int = 1, |
| numerator_bias: float = None, |
| attention_mechanism: str = None, |
| ) -> float: |
| if attention_mechanism is None: |
| if "deepseek" in model_path.lower(): attention_mechanism = "mla" |
| elif "qwen3.5" in model_path.lower(): attention_mechanism = "qwen3_5" |
| else: attention_mechanism = "gqa" |
| else: |
| attention_mechanism = attention_mechanism.lower() |
| if attention_mechanism != "gqa" and attention_mechanism != "mla" and attention_mechanism != "qwen3_5": |
| raise NotImplementedError("attention_mechanism argument should be either gqa or mla") |
| |
| if numerator_bias is None: |
| if "mixed_text.jsonl" in data_path: numerator_bias = -24 |
| elif "Ch-FineWeb-Edu.jsonl" in data_path: numerator_bias = -18.7 |
| else: numerator_bias = -27 |
| print(f"numerator_bias is not designated, default to {numerator_bias} based on the data_path") |
| |
| ts_results = calculate_text_size_per_token(data_path, model_path, target_token_length=max_sample_length) |
| avg_ts = ts_results["mean_text_size"] |
| for k, v in ts_results.items(): print(f"{k}: {v}") |
|
|
| nlls = calculate_negative_log_likelihood(model_path, data_path, max_sample_length, batch_size=batch_size, num_samples=ts_results["total_valid_lines"]) |
| avg_nll = torch.nanmean(nlls).item() |
| print(f"Average negative log-likelihood: {avg_nll}") |
|
|
| cfg_path = model_path + "/config.json" |
| if attention_mechanism == "gqa": |
| flops_results = gqa_model_theoretical_flops(cfg_path, gen_len=max_sample_length) |
| elif attention_mechanism == "mla": |
| flops_results = mla_model_theoretical_flops(cfg_path, gen_len=max_sample_length) |
| elif attention_mechanism == "qwen3_5": |
| flops_results = qwen3_5_theoretical_flops(cfg_path, gen_len=max_sample_length) |
| per_token_flops = flops_results["decode_total_TFLOPs"] * 1e12 / max_sample_length |
| for k, v in flops_results.items(): print(f"{k}: {v}") |
| |
| ic = (avg_ts - avg_nll + numerator_bias) / log2(per_token_flops) |
| print(f"\nInformation capacity: {ic}") |
|
|
| return ic |
|
|
|
|
| def main(): |
| parser = argparse.ArgumentParser( |
| description="Compute the information capacity of a language model." |
| ) |
| parser.add_argument("-m", "--model_path", type=str, required=True, help="Path to the model directory.") |
| parser.add_argument("-d", "--data_path", type=str, required=True, help="Path to the dataset (JSONL format).") |
| parser.add_argument("-l", "--max_sample_length", type=int, default=1024, help="Maximum token length for each sample.") |
| parser.add_argument("-b", "--batch_size", type=int, default=1, help="Batch size for evaluation.") |
| parser.add_argument("-n", "--numerator_bias", type=float, default=None, |
| help="Optional numerator bias. If not set, inferred automatically.") |
| parser.add_argument("-a", "--attention_mechanism", type=str, choices=["gqa", "mla"], default=None, |
| help="Specify attention mechanism ('gqa' or 'mla'). If not set, inferred automatically.") |
|
|
| args = parser.parse_args() |
|
|
| calculate_information_capacity( |
| model_path=args.model_path, |
| data_path=args.data_path, |
| max_sample_length=args.max_sample_length, |
| batch_size=args.batch_size, |
| numerator_bias=args.numerator_bias, |
| attention_mechanism=args.attention_mechanism, |
| ) |
|
|
|
|
| if __name__ == "__main__": |
| main() |
