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Script to execute SLM inference and training benchmarks.
"""
import sys
import os
sys.path.insert(0, os.path.abspath(os.path.join(os.path.dirname(__file__), "..")))
from slm.config.model_config import ModelConfig
from slm.model.transformer_lm import SLMForCausalLM
from slm.evaluation.benchmark import benchmark_inference, benchmark_training_step
from slm.utils.logger import get_logger
from slm.utils.utils import get_device
logger = get_logger("slm.scripts.benchmark")
def run() -> None:
device = get_device("auto")
logger.info(f"Starting hardware benchmark on device={device}...")
# Benchmark Nano model
nano_cfg = ModelConfig(vocab_size=2000, d_model=128, n_heads=4, n_layers=2, d_ff=512)
nano_model = SLMForCausalLM(nano_cfg)
nano_inf = benchmark_inference(nano_model, batch_size=4, seq_len=128, device=device)
logger.info("=== Nano Model Inference Benchmark ===")
logger.info(f"Avg Latency: {nano_inf['avg_latency_ms']} ms")
logger.info(f"Throughput: {nano_inf['tokens_per_sec']} tokens/sec")
# Benchmark Standard model
std_cfg = ModelConfig(vocab_size=32000, d_model=512, n_heads=8, n_layers=8, d_ff=2048)
std_model = SLMForCausalLM(std_cfg)
std_inf = benchmark_inference(std_model, batch_size=2, seq_len=256, device=device)
logger.info("=== Standard Model Inference Benchmark ===")
logger.info(f"Avg Latency: {std_inf['avg_latency_ms']} ms")
logger.info(f"Throughput: {std_inf['tokens_per_sec']} tokens/sec")
if __name__ == "__main__":
run()
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