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https://github.com/NVIDIA/TensorRT-LLM.git
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74df12bbaa
* fix formula Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> * update doc Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> * fix Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> * 1st version Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> * polish Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> * fix Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com> --------- Signed-off-by: Enwei Zhu <21126786+syuoni@users.noreply.github.com>
88 lines
3.3 KiB
Python
88 lines
3.3 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2025 NVIDIA CORPORATION & AFFILIATES. All rights reserved.
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# SPDX-License-Identifier: Apache-2.0
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import random
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from abc import ABC, abstractmethod
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from typing import Any, Iterable, List, Optional
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import numpy as np
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import torch
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from tqdm import tqdm
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import tensorrt_llm.profiler as profiler
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from ..llmapi import RequestOutput
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from ..logger import logger
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from ..sampling_params import SamplingParams
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class Evaluator(ABC):
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def __init__(self,
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random_seed: int = 0,
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apply_chat_template: bool = False,
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system_prompt: Optional[str] = None):
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random.seed(random_seed)
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np.random.seed(random_seed)
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torch.manual_seed(random_seed)
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self.apply_chat_template = apply_chat_template
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self.system_prompt = system_prompt
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@abstractmethod
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def generate_samples(self) -> Iterable[tuple]:
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raise NotImplementedError()
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@abstractmethod
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def compute_score(self, outputs: List[RequestOutput], references: List[str],
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*auxiliaries) -> float:
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raise NotImplementedError()
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def do_apply_chat_template(self, llm: Any, prompt: str) -> str:
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messages = [{"role": "user", "content": prompt}]
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if self.system_prompt is not None:
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messages = [{
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"role": "system",
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"content": self.system_prompt
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}] + messages
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return llm.tokenizer.apply_chat_template(messages,
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tokenize=False,
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add_generation_prompt=True)
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def evaluate(self,
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llm: Any,
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sampling_params: Optional[SamplingParams] = None) -> float:
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profiler.start("trtllm exec")
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outputs, references, auxiliaries = [], [], []
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for prompt, reference, *aux in tqdm(self.generate_samples(),
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desc="Submitting requests"):
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if self.apply_chat_template:
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prompt = self.do_apply_chat_template(llm, prompt)
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output = llm.generate_async(prompt, sampling_params)
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outputs.append(output)
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references.append(reference)
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auxiliaries.append(aux)
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for output in tqdm(outputs, desc="Fetching responses"):
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output.result()
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profiler.stop("trtllm exec")
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elapsed_time = profiler.elapsed_time_in_sec("trtllm exec")
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logger.info(f"TRTLLM execution time: {elapsed_time:.3f} seconds.")
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profiler.reset("trtllm exec")
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score = self.compute_score(outputs, references, *zip(*auxiliaries))
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return score
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@staticmethod
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def command(ctx, *args, **kwargs) -> None:
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raise NotImplementedError()
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