forked from EngineX-Ascend/enginex-ascend-910-vllm
53 lines
1.6 KiB
Python
53 lines
1.6 KiB
Python
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import os
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import time
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from vllm import LLM, SamplingParams
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os.environ["VLLM_USE_MODELSCOPE"] = "True"
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os.environ["VLLM_WORKER_MULTIPROC_METHOD"] = "spawn"
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# enable dual-batch overlap for vllm ascend
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os.environ["VLLM_ASCEND_ENABLE_DBO"] = "1"
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# Sample prompts.
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prompts = ["The president of the United States is"] * 41
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# Create a sampling params object.
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sampling_params = SamplingParams(max_tokens=100, temperature=0.0)
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def main():
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# Create an LLM.
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llm = LLM(model="deepseek-ai/DeepSeek-V3-Lite-base-latest-w8a8-dynamic",
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enforce_eager=True,
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tensor_parallel_size=2,
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max_model_len=4096,
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trust_remote_code=True,
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enable_expert_parallel=True,
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additional_config={
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"torchair_graph_config": {
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"enabled": False
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},
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"ascend_scheduler_config": {
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"enabled": True
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},
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})
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# Generate texts from the prompts. The output is a list of RequestOutput
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# objects that contain the prompt, generated text, and other information.
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outputs = llm.generate(prompts, sampling_params)
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# Print the outputs.
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print("-" * 50)
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for output in outputs:
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prompt = output.prompt
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generated_text = output.outputs[0].text
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print(f"Prompt: {prompt!r}\nGenerated text: {generated_text!r}")
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print("-" * 50)
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# Add a buffer to wait for profiler in the background process
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# (in case MP is on) to finish writing profiling output.
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time.sleep(10)
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if __name__ == "__main__":
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main()
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