mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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119 lines
4.0 KiB
Python
119 lines
4.0 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 Optional, Tuple
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import click
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from pydantic import BaseModel, model_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, token_unif_dist
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class RootArgs(BaseModel):
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tokenizer: str
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output: str
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random_seed: int
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task_id: int
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std_out: bool
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trust_remote_code: bool = False
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rand_task_id: Optional[Tuple[int, int]]
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lora_dir: Optional[str] = None
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@model_validator(mode='after')
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def validate_tokenizer(self):
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try:
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tokenizer = AutoTokenizer.from_pretrained(
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self.tokenizer,
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padding_side='left',
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trust_remote_code=self.trust_remote_code)
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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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self.tokenizer = tokenizer
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return self
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@click.group(deprecated=True)
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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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"--stdout",
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is_flag=True,
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help="Print output to stdout with a JSON dataset entry on each line.",
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default=False)
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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 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("--lora-dir",
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type=str,
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default=None,
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help="Directory containing LoRA adapters")
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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.option("--trust-remote-code",
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is_flag=True,
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default=False,
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envvar="TRUST_REMOTE_CODE",
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help="Trust remote code.")
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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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std_out=kwargs['stdout'],
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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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lora_dir=kwargs['lora_dir'],
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trust_remote_code=kwargs['trust_remote_code'])
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cli.add_command(dataset)
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cli.add_command(token_norm_dist)
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cli.add_command(token_unif_dist)
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if __name__ == "__main__":
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cli()
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