mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
synced 2026-01-14 06:27:45 +08:00
84 lines
2.7 KiB
Python
84 lines
2.7 KiB
Python
import argparse
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import asyncio
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from tensorrt_llm.scaffolding import (NativeGenerationController,
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ScaffoldingLlm, TRTLLMWorker)
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def parse_arguments():
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parser = argparse.ArgumentParser()
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# .e.g. /home/scratch.trt_llm_data/llm-models/DeepSeek-R1/DeepSeek-R1-Distill-Qwen-7B
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parser.add_argument(
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'--model_dir',
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type=str,
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required=True,
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help="Path to the directory containing the generation model")
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parser.add_argument('--run_async', action='store_true')
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args = parser.parse_args()
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return args
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def test_sync(prompts, proposer_worker):
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prototype_controller = NativeGenerationController()
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llm = ScaffoldingLlm(
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prototype_controller,
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{NativeGenerationController.WorkerTag.GENERATION: proposer_worker},
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)
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results = llm.generate(prompts)
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for result in results:
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print(result.output.output_str)
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print(f'main shutting down...')
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llm.shutdown()
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print(f'worker shutting down...')
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proposer_worker.shutdown()
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print(f'main shut down done')
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def test_async(prompt, proposer_worker):
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async def test_async_func(prompt, proposer_worker):
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prototype_controller = NativeGenerationController()
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llm = ScaffoldingLlm(
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prototype_controller,
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{NativeGenerationController.WorkerTag.GENERATION: proposer_worker},
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)
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future = llm.generate_async(prompt)
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result = await future.aresult()
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print(result.output.output_str)
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print(f'main shutting down...')
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llm.shutdown()
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print(f'worker shutting down...')
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proposer_worker.shutdown()
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print(f'main shut down done')
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asyncio.run(test_async_func(prompt, proposer_worker))
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def main():
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args = parse_arguments()
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prompts = [
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"Natalia sold clips to 48 of her friends in April, and then she sold half as many clips in May. How many clips did Natalia sell altogether in April and May?\r\n\r\n",
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"There exist real numbers $x$ and $y$, both greater than 1, such that $\\log_x\\left(y^x\\right)=\\log_y\\left(x^{4y}\\right)=10$. Find $xy$.",
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"Find the largest possible real part of \\[(75+117i)z+\\frac{96+144i}{z}\\]where $z$ is a complex number with $|z|=4$.",
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]
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llm_worker = TRTLLMWorker.init_with_new_llm(args.model_dir,
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backend="pytorch",
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max_batch_size=32,
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max_num_tokens=4096,
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temperature=0.9)
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if args.run_async:
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test_async(prompts[0], llm_worker)
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else:
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test_sync(prompts, llm_worker)
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if __name__ == "__main__":
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main()
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