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https://github.com/NVIDIA/TensorRT-LLM.git
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9212e9a740
feat(scaffolding): make sampling_params only setable by controller Signed-off-by: Zhenhuan Chen <chenzhh3671@gmail.com>
105 lines
3.5 KiB
Python
105 lines
3.5 KiB
Python
import argparse
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from stream_generation_controller import NativeStreamGenerationController
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from tensorrt_llm.scaffolding import ScaffoldingLlm, TRTLLMWorker
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from tensorrt_llm.scaffolding.contrib import (StreamGenerationTask,
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stream_generation_handler)
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def parse_arguments():
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parser = argparse.ArgumentParser()
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# .e.g. 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_type', type=str, default='original')
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args = parser.parse_args()
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return args
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def test(prompts, proposer_worker):
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prototype_controller = NativeStreamGenerationController(
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sampling_params={"temperature": 0.9})
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llm = ScaffoldingLlm(
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prototype_controller,
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{NativeStreamGenerationController.WorkerTag.STREAM: 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'test main shutting down...')
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llm.shutdown()
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print(f'test worker shutting down...')
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proposer_worker.shutdown()
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print(f'test main shut down done')
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def test_step(prompts, proposer_worker):
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prototype_controller = NativeStreamGenerationController()
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prototype_controller.set_stream_step(20)
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llm = ScaffoldingLlm(
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prototype_controller,
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{NativeStreamGenerationController.WorkerTag.STREAM: 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'test step main shutting down...')
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llm.shutdown()
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print(f'test step worker shutting down...')
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proposer_worker.shutdown()
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print(f'test step main shut down done')
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def test_cancel(prompts, proposer_worker):
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prototype_controller = NativeStreamGenerationController()
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prototype_controller.set_output_threshold(200)
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llm = ScaffoldingLlm(
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prototype_controller,
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{NativeStreamGenerationController.WorkerTag.STREAM: 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'test cancel main shutting down...')
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llm.shutdown()
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print(f'test cancel worker shutting down...')
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proposer_worker.shutdown()
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print(f'test cancel main shut down done')
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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(
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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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)
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print(f'main llm worker init done')
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llm_worker.register_task_handler(StreamGenerationTask,
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stream_generation_handler)
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if args.run_type == 'original':
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test(prompts, llm_worker)
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elif args.run_type == 'step':
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test_step(prompts, llm_worker)
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elif args.run_type == 'cancel':
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test_cancel(prompts, llm_worker)
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if __name__ == "__main__":
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main()
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