mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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125 lines
4.8 KiB
Python
125 lines
4.8 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 json
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from typing import Iterable, List, Optional, Union
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import click
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import datasets
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import numpy as np
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from .. import LLM as PyTorchLLM
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from .._tensorrt_engine import LLM
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from ..llmapi import RequestOutput
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from ..logger import logger
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from ..sampling_params import GuidedDecodingParams, SamplingParams
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from .interface import Evaluator
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class JsonModeEval(Evaluator):
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def __init__(self,
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dataset_path: Optional[str] = None,
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num_samples: Optional[int] = None,
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random_seed: int = 0,
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apply_chat_template: bool = True,
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system_prompt: Optional[str] = None):
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if not apply_chat_template:
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raise ValueError(
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f"{self.__class__.__name__} requires apply_chat_template=True.")
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super().__init__(random_seed=random_seed,
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apply_chat_template=apply_chat_template,
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system_prompt=system_prompt)
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if dataset_path is None:
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dataset_path = "NousResearch/json-mode-eval"
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self.data = datasets.load_dataset(dataset_path,
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split="train",
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trust_remote_code=True)
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self.data = self.data.shuffle(random_seed)
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if num_samples is None:
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self.num_samples = self.data.num_rows
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else:
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self.num_samples = min(num_samples, self.data.num_rows)
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def generate_samples(self) -> Iterable[tuple]:
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for i, sample in enumerate(self.data):
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if i >= self.num_samples:
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break
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sampling_args = {
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"guided_decoding": GuidedDecodingParams(json=sample["schema"])
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}
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yield sample["prompt"], sampling_args, sample["completion"]
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def compute_score(self, outputs: List[RequestOutput],
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references: List[str]) -> float:
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all_corrections = []
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for output, ref in zip(outputs, references):
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try:
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output_json = json.loads(output.outputs[0].text)
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except json.JSONDecodeError:
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all_corrections.append(False)
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continue
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ref_json = json.loads(ref)
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all_corrections.append(output_json == ref_json)
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acc = np.mean(all_corrections) * 100
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logger.info(
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f"JSON Mode Eval accuracy: {acc:.2f} ({len(all_corrections)})")
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return acc
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@click.command("json_mode_eval")
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@click.option("--dataset_path",
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type=str,
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default=None,
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help="The path to JSON Mode Eval dataset. "
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"If unspecified, the dataset is downloaded from HF hub.")
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@click.option(
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"--num_samples",
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type=int,
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default=None,
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help="Number of samples to run the evaluation; None means full dataset."
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)
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@click.option("--random_seed",
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type=int,
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default=0,
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help="Random seed for dataset processing.")
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@click.option("--system_prompt",
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type=str,
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default=None,
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help="System prompt.")
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@click.option("--max_input_length",
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type=int,
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default=1024,
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help="Maximum prompt length.")
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@click.option("--max_output_length",
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type=int,
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default=512,
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help="Maximum generation length.")
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@click.pass_context
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@staticmethod
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def command(ctx, dataset_path: Optional[str], num_samples: int,
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random_seed: int, system_prompt: Optional[str],
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max_input_length: int, max_output_length: int) -> None:
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llm: Union[LLM, PyTorchLLM] = ctx.obj
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sampling_params = SamplingParams(
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max_tokens=max_output_length,
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truncate_prompt_tokens=max_input_length)
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evaluator = JsonModeEval(dataset_path,
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num_samples=num_samples,
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random_seed=random_seed,
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apply_chat_template=True,
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system_prompt=system_prompt)
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evaluator.evaluate(llm, sampling_params)
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llm.shutdown()
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