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46 lines
1.9 KiB
Python
46 lines
1.9 KiB
Python
### Distributed LLM Generation
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from tensorrt_llm import SamplingParams
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from tensorrt_llm._tensorrt_engine import LLM
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def main():
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# model could accept HF model name or a path to local HF model.
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llm = LLM(
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model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
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# Enable 2-way tensor parallelism
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tensor_parallel_size=2
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# Enable 2-way pipeline parallelism if needed
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# pipeline_parallel_size=2
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# Enable 2-way expert parallelism for MoE model's expert weights
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# moe_expert_parallel_size=2
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# Enable 2-way tensor parallelism for MoE model's expert weights
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# moe_tensor_parallel_size=2
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)
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# Sample prompts.
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prompts = [
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"Hello, my name is",
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"The president of the United States is",
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"The capital of France is",
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"The future of AI is",
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]
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# Create a sampling params.
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sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
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for output in llm.generate(prompts, sampling_params):
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print(
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f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
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)
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# Got output like
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# Prompt: 'Hello, my name is', Generated text: '\n\nJane Smith. I am a student pursuing my degree in Computer Science at [university]. I enjoy learning new things, especially technology and programming'
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# Prompt: 'The president of the United States is', Generated text: 'likely to nominate a new Supreme Court justice to fill the seat vacated by the death of Antonin Scalia. The Senate should vote to confirm the'
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# Prompt: 'The capital of France is', Generated text: 'Paris.'
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# Prompt: 'The future of AI is', Generated text: 'an exciting time for us. We are constantly researching, developing, and improving our platform to create the most advanced and efficient model available. We are'
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# The entry point of the program need to be protected for spawning processes.
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if __name__ == '__main__':
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main()
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