use spawn

bench.py

@@ -5,23 +5,28 @@ from nanovllm import LLM, SamplingParams
 # from vllm import LLM, SamplingParams
 
 
-seed(0)
-num_seqs = 256
-max_input_len = 1024
-max_ouput_len = 1024
+def main():
+    seed(0)
+    num_seqs = 256
+    max_input_len = 1024
+    max_ouput_len = 1024
 
-path = os.path.expanduser("~/huggingface/Qwen3-0.6B/")
-llm = LLM(path, enforce_eager=False, max_model_len=4096)
+    path = os.path.expanduser("~/huggingface/Qwen3-0.6B/")
+    llm = LLM(path, enforce_eager=False, max_model_len=4096)
 
-prompt_token_ids = [[randint(0, 10000) for _ in range(randint(100, max_input_len))] for _ in range(num_seqs)]
-sampling_params = [SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=randint(100, max_ouput_len)) for _ in range(num_seqs)]
-# uncomment the following line for vllm
-# prompt_token_ids = [dict(prompt_token_ids=p) for p in prompt_token_ids]
+    prompt_token_ids = [[randint(0, 10000) for _ in range(randint(100, max_input_len))] for _ in range(num_seqs)]
+    sampling_params = [SamplingParams(temperature=0.6, ignore_eos=True, max_tokens=randint(100, max_ouput_len)) for _ in range(num_seqs)]
+    # uncomment the following line for vllm
+    # prompt_token_ids = [dict(prompt_token_ids=p) for p in prompt_token_ids]
 
-llm.generate(["Benchmark: "], SamplingParams())
-t = time.time()
-llm.generate(prompt_token_ids, sampling_params)
-t = (time.time() - t)
-total_tokens = sum(sp.max_tokens for sp in sampling_params)
-throughput = total_tokens / t
-print(f"Total: {total_tokens}tok, Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+    llm.generate(["Benchmark: "], SamplingParams())
+    t = time.time()
+    llm.generate(prompt_token_ids, sampling_params)
+    t = (time.time() - t)
+    total_tokens = sum(sp.max_tokens for sp in sampling_params)
+    throughput = total_tokens / t
+    print(f"Total: {total_tokens}tok, Time: {t:.2f}s, Throughput: {throughput:.2f}tok/s")
+
+
+if __name__ == "__main__":
+    main()