mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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ff3b741045
* feat: adding multimodal (only image for now) support in trtllm-bench Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * fix: add in load_dataset() calls to maintain the v2.19.2 behavior Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * re-adding prompt_token_ids and using that for prompt_len Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * updating the datasets version in examples as well Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * api changes are not needed Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * moving datasets requirement and removing a missed api change Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * addressing review comments Signed-off-by: Rakib Hasan <rhasan@nvidia.com> * refactoring the quickstart example Signed-off-by: Rakib Hasan <rhasan@nvidia.com> --------- Signed-off-by: Rakib Hasan <rhasan@nvidia.com>
98 lines
4.2 KiB
Python
98 lines
4.2 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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from typing import Iterable, List, Optional, Union
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import click
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import datasets
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import evaluate
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from .._torch import LLM as PyTorchLLM
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from ..llmapi import LLM, RequestOutput
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from ..logger import logger
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from ..sampling_params import SamplingParams
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from .interface import Evaluator
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class CnnDailymail(Evaluator):
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def __init__(self,
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dataset_path: str = "ccdv/cnn_dailymail",
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num_samples: int = None,
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random_seed: int = 0,
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rouge_path: str = "rouge",
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apply_chat_template: bool = False,
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system_prompt: Optional[str] = None):
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super().__init__(apply_chat_template=apply_chat_template,
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system_prompt=system_prompt)
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self.data = datasets.load_dataset(dataset_path,
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"3.0.0",
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split="test",
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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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self.rouge = evaluate.load(rouge_path)
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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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prompt = sample["article"] + " TL;DR:"
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prompt = prompt.strip().replace(" n't", "n't")
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yield prompt, sample["highlights"]
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def compute_score(self, outputs: List[RequestOutput],
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references: List[str]) -> float:
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for beam_idx in range(len(outputs[0].outputs)):
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metrics = self.rouge.compute(
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predictions=[output.outputs[0].text for output in outputs],
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references=references)
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logger.info(f"Beam {beam_idx} rouge scores:")
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for key in metrics.keys():
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logger.info(f"\t{key}: {metrics[key]*100:.3f}")
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if beam_idx == 0:
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rouge1 = metrics["rouge1"] * 100
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return rouge1
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@click.command("cnn_dailymail")
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@click.option("--dataset_path", type=str, default="ccdv/cnn_dailymail")
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@click.option("--num_samples", type=int, default=None)
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@click.option("--random_seed", type=int, default=0)
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@click.option("--rouge_path", type=str, default="rouge")
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@click.option("--max_input_length", type=int, default=924)
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@click.option("--max_output_length", type=int, default=100)
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@click.option("--check_accuracy", is_flag=True, default=False)
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@click.option("--accuracy_threshold", type=float, default=15)
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@click.pass_context
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@staticmethod
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def command(ctx, dataset_path: str, num_samples: int, random_seed: int,
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rouge_path: str, max_input_length: int, max_output_length: int,
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check_accuracy: bool, accuracy_threshold: float) -> 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 = CnnDailymail(dataset_path,
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num_samples=num_samples,
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random_seed=random_seed,
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rouge_path=rouge_path)
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accuracy = evaluator.evaluate(llm, sampling_params)
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llm.shutdown()
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if check_accuracy:
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assert accuracy >= accuracy_threshold, f"Expected accuracy >= {accuracy_threshold}, but got {accuracy}"
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