Merge pull request #22 from cheunglei/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()

example.py

@@ -3,27 +3,32 @@ from nanovllm import LLM, SamplingParams
 from transformers import AutoTokenizer
 
 
-path = os.path.expanduser("~/huggingface/Qwen3-0.6B/")
-tokenizer = AutoTokenizer.from_pretrained(path)
-llm = LLM(path, enforce_eager=True)
+def main():
+    path = os.path.expanduser("~/huggingface/Qwen3-0.6B/")
+    tokenizer = AutoTokenizer.from_pretrained(path)
+    llm = LLM(path, enforce_eager=True, tensor_parallel_size=1)
 
-sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
-prompts = [
-    "introduce yourself",
-    "list all prime numbers within 100",
-]
-prompts = [
-    tokenizer.apply_chat_template(
-        [{"role": "user", "content": prompt}],
-        tokenize=False,
-        add_generation_prompt=True,
-        enable_thinking=True
-    )
-    for prompt in prompts
-]
-outputs = llm.generate(prompts, sampling_params)
+    sampling_params = SamplingParams(temperature=0.6, max_tokens=256)
+    prompts = [
+        "introduce yourself",
+        "list all prime numbers within 100",
+    ]
+    prompts = [
+        tokenizer.apply_chat_template(
+            [{"role": "user", "content": prompt}],
+            tokenize=False,
+            add_generation_prompt=True,
+            enable_thinking=True
+        )
+        for prompt in prompts
+    ]
+    outputs = llm.generate(prompts, sampling_params)
 
-for prompt, output in zip(prompts, outputs):
-    print("\n")
-    print(f"Prompt: {prompt!r}")
-    print(f"Completion: {output['text']!r}")
+    for prompt, output in zip(prompts, outputs):
+        print("\n")
+        print(f"Prompt: {prompt!r}")
+        print(f"Completion: {output['text']!r}")
+
+
+if __name__ == "__main__":
+    main()

nanovllm/engine/llm_engine.py

@@ -20,9 +20,10 @@ class LLMEngine:
         config = Config(model, **config_kwargs)
         self.ps = []
         self.events = []
+        ctx = mp.get_context("spawn")
         for i in range(1, config.tensor_parallel_size):
-            event = mp.Event()
-            process = mp.Process(target=ModelRunner, args=(config, i, event))
+            event = ctx.Event()
+            process = ctx.Process(target=ModelRunner, args=(config, i, event))
             process.start()
             self.ps.append(process)
             self.events.append(event)