mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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110 lines
3.9 KiB
Python
110 lines
3.9 KiB
Python
# SPDX-FileCopyrightText: Copyright (c) 2022-2024 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 logging
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from typing import Literal, Optional, Tuple
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import click
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from pydantic import BaseModel, field_validator
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from transformers import AutoTokenizer
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from utils.prepare_real_data import dataset
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from utils.prepare_synthetic_data import token_norm_dist
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from utils.utils import get_req_time_interval
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class RootArgs(BaseModel):
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tokenizer: str
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output: str
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request_rate: float
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mean_time_bet_reqs: float
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time_delay_dist: Literal["constant", "exponential_dist"]
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random_seed: int
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task_id: int
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rand_task_id: Optional[Tuple[int, int]]
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@field_validator('tokenizer')
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def get_tokenizer(cls, v: str):
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try:
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tokenizer = AutoTokenizer.from_pretrained(v, padding_side='left')
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except EnvironmentError as e:
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raise ValueError(
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f"Cannot find a tokenizer from the given string because of {e}\nPlease set tokenizer to the directory that contains the tokenizer, or set to a model name in HuggingFace."
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)
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tokenizer.pad_token = tokenizer.eos_token
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return tokenizer
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@click.group()
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@click.option(
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"--tokenizer",
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required=True,
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type=str,
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help=
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"Tokenizer dir for the model run by gptManagerBenchmark, or the model name from HuggingFace."
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)
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@click.option("--output",
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type=str,
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help="Output json filename.",
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default="preprocessed_dataset.json")
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@click.option(
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"--request-rate",
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type=float,
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help="# of reqs/sec. -1 indicates Speed of Light/Zero-delay injection rate",
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default=-1.0)
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@click.option("--time-delay-dist",
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type=click.Choice(["constant", "exponential_dist"]),
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help="Distribution of the time delay.",
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default="exponential_dist")
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@click.option("--random-seed",
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required=False,
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type=int,
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help="random seed for exponential delays and token_ids",
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default=420)
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@click.option("--task-id", type=int, default=-1, help="LoRA task id")
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@click.option("--rand-task-id",
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type=int,
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default=None,
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nargs=2,
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help="Random LoRA Tasks")
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@click.option("--log-level",
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default="info",
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type=click.Choice(['info', 'debug']),
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help="Logging level.")
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@click.pass_context
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def cli(ctx, **kwargs):
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"""This script generates dataset input for gptManagerBenchmark."""
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if kwargs['log_level'] == 'info':
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logging.basicConfig(level=logging.INFO)
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elif kwargs['log_level'] == 'debug':
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logging.basicConfig(level=logging.DEBUG)
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else:
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raise ValueError(f"Unsupported logging level {kwargs['log_level']}")
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ctx.obj = RootArgs(tokenizer=kwargs['tokenizer'],
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output=kwargs['output'],
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request_rate=kwargs['request_rate'],
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mean_time_bet_reqs=get_req_time_interval(
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kwargs['request_rate']),
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time_delay_dist=kwargs['time_delay_dist'],
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random_seed=kwargs['random_seed'],
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task_id=kwargs['task_id'],
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rand_task_id=kwargs['rand_task_id'])
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cli.add_command(dataset)
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cli.add_command(token_norm_dist)
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if __name__ == "__main__":
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cli()
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