mirror of
https://github.com/NVIDIA/TensorRT-LLM.git
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94 lines
3.3 KiB
Python
94 lines
3.3 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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from pathlib import Path
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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 tensorrt_llm.bench.dataset.prepare_real_data import real_dataset
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from tensorrt_llm.bench.dataset.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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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, padding_side="left", trust_remote_code=self.trust_remote_code
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)
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except EnvironmentError as e:
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raise ValueError(
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"Cannot find a tokenizer from the given string because of "
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f"{e}\nPlease set tokenizer to the directory that contains "
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"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(name="prepare-dataset")
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@click.option(
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"--output", type=str, help="Output json filename.", default="preprocessed_dataset.json"
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)
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@click.option(
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"--random-seed", required=False, type=int, help="random seed for token_ids", default=420
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)
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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", type=int, default=None, nargs=2, help="Random LoRA Tasks")
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@click.option("--lora-dir", type=str, default=None, help="Directory containing LoRA adapters")
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@click.option(
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"--log-level", default="info", type=click.Choice(["info", "debug"]), help="Logging level."
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)
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@click.option(
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"--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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)
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@click.pass_context
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def prepare_dataset(ctx, **kwargs):
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"""Prepare dataset for benchmarking with trtllm-bench."""
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model = ctx.obj.model or ctx.obj.checkpoint_path
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output_path = Path(kwargs["output"])
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output_path.parent.mkdir(parents=True, exist_ok=True)
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ctx.obj = RootArgs(
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tokenizer=model,
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output=kwargs["output"],
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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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)
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prepare_dataset.add_command(real_dataset)
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prepare_dataset.add_command(token_norm_dist)
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prepare_dataset.add_command(token_unif_dist)
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