Distributed LLM Generation#
Source NVIDIA/TensorRT-LLM.
1from tensorrt_llm import LLM, SamplingParams
2
3
4def main():
5 # model could accept HF model name or a path to local HF model.
6 llm = LLM(
7 model="TinyLlama/TinyLlama-1.1B-Chat-v1.0",
8 # Enable 2-way tensor parallelism
9 tensor_parallel_size=2
10 # Enable 2-way pipeline parallelism if needed
11 # pipeline_parallel_size=2
12 # Enable 2-way expert parallelism for MoE model's expert weights
13 # moe_expert_parallel_size=2
14 # Enable 2-way tensor parallelism for MoE model's expert weights
15 # moe_tensor_parallel_size=2
16 )
17
18 # Sample prompts.
19 prompts = [
20 "Hello, my name is",
21 "The president of the United States is",
22 "The capital of France is",
23 "The future of AI is",
24 ]
25
26 # Create a sampling params.
27 sampling_params = SamplingParams(temperature=0.8, top_p=0.95)
28
29 for output in llm.generate(prompts, sampling_params):
30 print(
31 f"Prompt: {output.prompt!r}, Generated text: {output.outputs[0].text!r}"
32 )
33
34 # Got output like
35 # 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'
36 # 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'
37 # Prompt: 'The capital of France is', Generated text: 'Paris.'
38 # 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'
39
40
41# The entry point of the program need to be protected for spawning processes.
42if __name__ == '__main__':
43 main()