mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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6eee15900e
Signed-off-by: fredw (generated by with_the_same_user script) <20514172+WeiHaocheng@users.noreply.github.com>
104 lines
3.4 KiB
Python
104 lines
3.4 KiB
Python
import argparse
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import json
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from tensorrt_llm.scaffolding import (MajorityVoteController,
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NativeGenerationController,
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ScaffoldingLlm, TRTLLMWorker,
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extract_answer_from_boxed)
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def parse_arguments():
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parser = argparse.ArgumentParser()
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parser.add_argument(
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'--generation_dir',
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type=str,
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default=
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"/home/scratch.trt_llm_data/llm-models/DeepSeek-R1/DeepSeek-R1-Distill-Qwen-7B"
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)
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# https://github.com/QwenLM/Qwen2.5-Math/blob/main/evaluation/data/aime24/test.jsonl
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parser.add_argument('--jsonl_file', type=str, default='./test.jsonl')
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parser.add_argument('--threshold', type=float, default=None)
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parser.add_argument('--sample_num', type=int, default=10)
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args = parser.parse_args()
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return args
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def load_test_file(jsonl_file: str):
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data = []
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with open(jsonl_file, "r", encoding="utf-8") as file:
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for line in file:
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if line.strip():
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data.append(json.loads(line))
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return data
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def main():
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args = parse_arguments()
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workers = {}
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llm_worker = TRTLLMWorker.init_with_new_llm(args.generation_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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prototype_generation_controller = NativeGenerationController(
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custom_sampling_params={
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"max_tokens": 4096,
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"top_p": 0.9,
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})
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workers[NativeGenerationController.WorkerTag.GENERATION] = llm_worker
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prototype_majority_vote_controller = MajorityVoteController(
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generation_controller=prototype_generation_controller,
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default_sample_num=args.sample_num,
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)
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llm = ScaffoldingLlm(
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prototype_majority_vote_controller,
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workers=workers,
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)
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test_dataset = load_test_file(args.jsonl_file)
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total_count = 0
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correct_count = 0
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controller_name = "MajorityVoteController"
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prompts = []
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for i in range(len(test_dataset)):
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test_case = test_dataset[i]
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prompts.append(test_case["problem"])
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results = llm.generate(prompts)
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for i in range(len(results)):
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result = results[i]
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test_case = test_dataset[i]
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ref_answer = int(test_case["answer"])
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result.result()
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output = result.output
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extracted_answer = extract_answer_from_boxed(output.output_str)
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try:
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# print(f"[QUESTION]:\n{prompt}\n\n[OUTPUT]\n\n{output.output_str}\n\n")
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answer = int(extracted_answer)
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print(f'Answer={answer}, reference={ref_answer}')
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if answer == ref_answer:
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correct_count += 1
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except:
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print(f'extracted_answer={extracted_answer}, not integer.')
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total_count += 1
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print(
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f'Controller {controller_name} Accuracy: {correct_count} out of {total_count}'
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)
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if args.threshold is not None:
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assert correct_count >= args.threshold * total_count, \
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f'Accuracy check failed with {correct_count}/{total_count} < {args.threshold}'
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print(f'Accuracy check passed with threshold={args.threshold}')
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print(f'main shutting down...')
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llm.shutdown()
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llm_worker.shutdown()
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print(f'main shut down done')
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if __name__ == '__main__':
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main()
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