672 lines
29 KiB
Python
Executable File
672 lines
29 KiB
Python
Executable File
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2021 The HuggingFace Inc. team. All rights reserved.
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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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"""
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Fine-tuning the library models for causal language modeling (GPT-2, GPT-Neo...)
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on a text file or a dataset without using HuggingFace Trainer.
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Here is the full list of checkpoints on the hub that can be fine-tuned by this script:
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https://huggingface.co/models?filter=text-generation
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"""
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# You can also adapt this script on your own clm task. Pointers for this are left as comments.
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import json
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# region Imports
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import logging
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import math
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import os
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import random
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import sys
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import warnings
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from dataclasses import dataclass, field
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from itertools import chain
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from pathlib import Path
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from typing import Optional
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import datasets
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import tensorflow as tf
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from datasets import load_dataset
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from sklearn.model_selection import train_test_split
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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CONFIG_NAME,
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TF2_WEIGHTS_NAME,
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TF_MODEL_FOR_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoTokenizer,
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HfArgumentParser,
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PushToHubCallback,
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TFAutoModelForCausalLM,
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TFTrainingArguments,
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create_optimizer,
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set_seed,
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)
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from transformers.utils import send_example_telemetry
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from transformers.utils.versions import require_version
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logger = logging.getLogger(__name__)
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require_version("datasets>=1.8.0", "To fix: pip install -r examples/tensorflow/language-modeling/requirements.txt")
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MODEL_CONFIG_CLASSES = list(TF_MODEL_FOR_CAUSAL_LM_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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# endregion
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# region Command-line arguments
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@dataclass
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class ModelArguments:
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"""
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Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch.
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"""
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model_name_or_path: Optional[str] = field(
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default=None,
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metadata={
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"help": (
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"The model checkpoint for weights initialization. Don't set if you want to train a model from scratch."
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)
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},
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)
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model_type: Optional[str] = field(
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default=None,
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metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)},
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)
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config_overrides: Optional[str] = field(
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default=None,
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metadata={
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"help": (
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"Override some existing default config settings when a model is trained from scratch. Example: "
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"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index"
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)
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},
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)
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config_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"}
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)
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tokenizer_name: Optional[str] = field(
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default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"}
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)
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cache_dir: Optional[str] = field(
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default=None,
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metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
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)
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use_fast_tokenizer: bool = field(
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default=True,
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metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."},
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)
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model_revision: str = field(
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default="main",
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metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."},
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)
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token: str = field(
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default=None,
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metadata={
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"help": (
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"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token "
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"generated when running `huggingface-cli login` (stored in `~/.huggingface`)."
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)
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},
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)
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use_auth_token: bool = field(
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default=None,
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metadata={
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"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead."
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},
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)
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trust_remote_code: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option "
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"should only be set to `True` for repositories you trust and in which you have read the code, as it will "
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"execute code present on the Hub on your local machine."
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)
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},
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)
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def __post_init__(self):
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if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None):
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raise ValueError(
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"--config_overrides can't be used in combination with --config_name or --model_name_or_path"
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)
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@dataclass
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class DataTrainingArguments:
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"""
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Arguments pertaining to what data we are going to input our model for training and eval.
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"""
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dataset_name: Optional[str] = field(
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default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."}
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)
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dataset_config_name: Optional[str] = field(
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default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."}
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)
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train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."})
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validation_file: Optional[str] = field(
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default=None,
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metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."},
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)
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overwrite_cache: bool = field(
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default=False, metadata={"help": "Overwrite the cached training and evaluation sets"}
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)
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validation_split_percentage: Optional[int] = field(
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default=5,
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metadata={
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"help": "The percentage of the train set used as validation set in case there's no validation split"
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},
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)
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block_size: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"Optional input sequence length after tokenization. "
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"The training dataset will be truncated in block of this size for training. "
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"Default to the model max input length for single sentence inputs (take into account special tokens)."
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)
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},
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)
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preprocessing_num_workers: Optional[int] = field(
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default=None,
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metadata={"help": "The number of processes to use for the preprocessing."},
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)
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line_by_line: bool = field(
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default=False,
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metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."},
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)
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max_train_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of training examples to this "
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"value if set."
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)
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},
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)
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max_eval_samples: Optional[int] = field(
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default=None,
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metadata={
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"help": (
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"For debugging purposes or quicker training, truncate the number of evaluation examples to this "
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"value if set."
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)
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},
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)
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keep_linebreaks: bool = field(
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default=True, metadata={"help": "Whether to keep line breaks when using TXT files or not."}
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)
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def __post_init__(self):
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if self.dataset_name is None and self.train_file is None and self.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if self.train_file is not None:
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extension = self.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file."
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if self.validation_file is not None:
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extension = self.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file."
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# endregion
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def main():
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# region Argument Parsing
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TFTrainingArguments))
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if len(sys.argv) == 2 and sys.argv[1].endswith(".json"):
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# If we pass only one argument to the script and it's the path to a json file,
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# let's parse it to get our arguments.
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model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1]))
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else:
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model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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if model_args.use_auth_token is not None:
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warnings.warn(
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"The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token` instead.",
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FutureWarning,
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)
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if model_args.token is not None:
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raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.")
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model_args.token = model_args.use_auth_token
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# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The
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# information sent is the one passed as arguments along with your Python/PyTorch versions.
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send_example_telemetry("run_clm", model_args, data_args, framework="tensorflow")
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# Sanity checks
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if data_args.dataset_name is None and data_args.train_file is None and data_args.validation_file is None:
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raise ValueError("Need either a dataset name or a training/validation file.")
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else:
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if data_args.train_file is not None:
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extension = data_args.train_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, json or txt file."
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if data_args.validation_file is not None:
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extension = data_args.validation_file.split(".")[-1]
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assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, json or txt file."
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if training_args.output_dir is not None:
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training_args.output_dir = Path(training_args.output_dir)
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os.makedirs(training_args.output_dir, exist_ok=True)
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# endregion
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# region Checkpoints
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# Detecting last checkpoint.
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checkpoint = None
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if len(os.listdir(training_args.output_dir)) > 0 and not training_args.overwrite_output_dir:
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config_path = training_args.output_dir / CONFIG_NAME
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weights_path = training_args.output_dir / TF2_WEIGHTS_NAME
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if config_path.is_file() and weights_path.is_file():
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checkpoint = training_args.output_dir
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logger.info(
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f"Checkpoint detected, resuming training from checkpoint in {training_args.output_dir}. To avoid this"
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" behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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else:
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raise ValueError(
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f"Output directory ({training_args.output_dir}) already exists and is not empty. "
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"Use --overwrite_output_dir to continue regardless."
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)
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# endregion
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# region Setup logging
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# accelerator.is_local_main_process is only True for one process per machine.
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logger.setLevel(logging.INFO)
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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# endregion
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# If passed along, set the training seed now.
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if training_args.seed is not None:
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set_seed(training_args.seed)
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# region Load datasets
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# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below)
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# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/
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# (the dataset will be downloaded automatically from the datasets Hub).
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#
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# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called
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# 'text' is found. You can easily tweak this behavior (see below).
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#
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# In distributed training, the load_dataset function guarantee that only one local process can concurrently
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# download the dataset.
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if data_args.dataset_name is not None:
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# Downloading and loading a dataset from the hub.
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raw_datasets = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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raw_datasets["train"] = load_dataset(
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data_args.dataset_name,
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data_args.dataset_config_name,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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)
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else:
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data_files = {}
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dataset_args = {}
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if data_args.train_file is not None:
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data_files["train"] = data_args.train_file
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if data_args.validation_file is not None:
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data_files["validation"] = data_args.validation_file
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extension = (
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data_args.train_file.split(".")[-1]
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if data_args.train_file is not None
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else data_args.validation_file.split(".")[-1]
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)
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if extension == "txt":
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extension = "text"
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dataset_args["keep_linebreaks"] = data_args.keep_linebreaks
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raw_datasets = load_dataset(
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extension,
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data_files=data_files,
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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**dataset_args,
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)
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# If no validation data is there, validation_split_percentage will be used to divide the dataset.
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[:{data_args.validation_split_percentage}%]",
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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**dataset_args,
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)
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raw_datasets["train"] = load_dataset(
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extension,
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data_files=data_files,
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split=f"train[{data_args.validation_split_percentage}%:]",
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cache_dir=model_args.cache_dir,
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token=model_args.token,
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**dataset_args,
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)
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# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
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# https://huggingface.co/docs/datasets/loading_datasets.
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# endregion
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# region Load pretrained model and tokenizer
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#
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# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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if model_args.config_name:
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config = AutoConfig.from_pretrained(
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model_args.config_name,
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token=model_args.token,
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trust_remote_code=model_args.trust_remote_code,
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)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(
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model_args.model_name_or_path, token=model_args.token, trust_remote_code=model_args.trust_remote_code
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)
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else:
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config = CONFIG_MAPPING[model_args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if model_args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.tokenizer_name, token=model_args.token, trust_remote_code=model_args.trust_remote_code
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)
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elif model_args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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model_args.model_name_or_path, token=model_args.token, trust_remote_code=model_args.trust_remote_code
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)
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else:
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raise ValueError(
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"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
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"You can do it from another script, save it, and load it from here, using --tokenizer_name."
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)
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# endregion
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# region Dataset preprocessing
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# First we tokenize all the texts.
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column_names = raw_datasets["train"].column_names
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text_column_name = "text" if "text" in column_names else column_names[0]
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def tokenize_function(examples):
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return tokenizer(examples[text_column_name])
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tokenized_datasets = raw_datasets.map(
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tokenize_function,
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batched=True,
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num_proc=data_args.preprocessing_num_workers,
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remove_columns=column_names,
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load_from_cache_file=not data_args.overwrite_cache,
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desc="Running tokenizer on dataset",
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)
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if data_args.block_size is None:
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block_size = tokenizer.model_max_length
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if block_size > config.max_position_embeddings:
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logger.warning(
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f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
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f"Using block_size={min(1024, config.max_position_embeddings)} instead. You can change that default value by passing --block_size xxx."
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)
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block_size = min(1024, config.max_position_embeddings)
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else:
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if data_args.block_size > tokenizer.model_max_length:
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logger.warning(
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f"The block_size passed ({data_args.block_size}) is larger than the maximum length for the model "
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f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
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)
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block_size = min(data_args.block_size, tokenizer.model_max_length)
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# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
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def group_texts(examples):
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# Concatenate all texts.
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concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
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total_length = len(concatenated_examples[list(examples.keys())[0]])
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# We drop the small remainder, we could add padding if the model supported it instead of this drop, you can
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# customize this part to your needs.
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if total_length >= block_size:
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total_length = (total_length // block_size) * block_size
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# Split by chunks of max_len.
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result = {
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k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
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for k, t in concatenated_examples.items()
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}
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result["labels"] = result["input_ids"].copy()
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return result
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# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
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# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
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# to preprocess.
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#
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# To speed up this part, we use multiprocessing. See the documentation of the map method for more information:
|
|
# https://huggingface.co/docs/datasets/process#map
|
|
|
|
lm_datasets = tokenized_datasets.map(
|
|
group_texts,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc=f"Grouping texts in chunks of {block_size}",
|
|
)
|
|
|
|
train_dataset = lm_datasets["train"]
|
|
if data_args.validation_file is not None:
|
|
eval_dataset = lm_datasets["validation"]
|
|
else:
|
|
logger.info(
|
|
f"Validation file not found: using {data_args.validation_split_percentage}% of the dataset as validation"
|
|
" as provided in data_args"
|
|
)
|
|
train_indices, val_indices = train_test_split(
|
|
list(range(len(train_dataset))), test_size=data_args.validation_split_percentage / 100
|
|
)
|
|
|
|
eval_dataset = train_dataset.select(val_indices)
|
|
train_dataset = train_dataset.select(train_indices)
|
|
|
|
if data_args.max_train_samples is not None:
|
|
max_train_samples = min(len(train_dataset), data_args.max_train_samples)
|
|
train_dataset = train_dataset.select(range(max_train_samples))
|
|
if data_args.max_eval_samples is not None:
|
|
max_eval_samples = min(len(eval_dataset), data_args.max_eval_samples)
|
|
eval_dataset = eval_dataset.select(range(max_eval_samples))
|
|
|
|
# Log a few random samples from the training set:
|
|
for index in random.sample(range(len(train_dataset)), min(3, len(train_dataset))):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
# endregion
|
|
|
|
with training_args.strategy.scope():
|
|
# region Prepare model
|
|
if checkpoint is not None:
|
|
model = TFAutoModelForCausalLM.from_pretrained(
|
|
checkpoint, config=config, token=model_args.token, trust_remote_code=model_args.trust_remote_code
|
|
)
|
|
elif model_args.model_name_or_path:
|
|
model = TFAutoModelForCausalLM.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
config=config,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
)
|
|
else:
|
|
logger.info("Training new model from scratch")
|
|
model = TFAutoModelForCausalLM.from_config(
|
|
config, token=model_args.token, trust_remote_code=model_args.trust_remote_code
|
|
)
|
|
|
|
# We resize the embeddings only when necessary to avoid index errors. If you are creating a model from scratch
|
|
# on a small vocab and want a smaller embedding size, remove this test.
|
|
embeddings = model.get_input_embeddings()
|
|
|
|
# Matt: This is a temporary workaround as we transition our models to exclusively using Keras embeddings.
|
|
# As soon as the transition is complete, all embeddings should be keras.Embeddings layers, and
|
|
# the weights will always be in embeddings.embeddings.
|
|
if hasattr(embeddings, "embeddings"):
|
|
embedding_size = embeddings.embeddings.shape[0]
|
|
else:
|
|
embedding_size = embeddings.weight.shape[0]
|
|
if len(tokenizer) > embedding_size:
|
|
model.resize_token_embeddings(len(tokenizer))
|
|
# endregion
|
|
|
|
# region TF Dataset preparation
|
|
num_replicas = training_args.strategy.num_replicas_in_sync
|
|
options = tf.data.Options()
|
|
options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF
|
|
|
|
# model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in
|
|
# training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also
|
|
# use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names
|
|
# yourself if you use this method, whereas they are automatically inferred from the model input names when
|
|
# using model.prepare_tf_dataset()
|
|
# For more info see the docs:
|
|
# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset
|
|
# https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset
|
|
|
|
tf_train_dataset = model.prepare_tf_dataset(
|
|
train_dataset,
|
|
shuffle=True,
|
|
batch_size=num_replicas * training_args.per_device_train_batch_size,
|
|
).with_options(options)
|
|
|
|
tf_eval_dataset = model.prepare_tf_dataset(
|
|
eval_dataset,
|
|
shuffle=False,
|
|
batch_size=num_replicas * training_args.per_device_eval_batch_size,
|
|
drop_remainder=True,
|
|
).with_options(options)
|
|
# endregion
|
|
|
|
# region Optimizer and loss
|
|
num_train_steps = len(tf_train_dataset) * int(training_args.num_train_epochs)
|
|
if training_args.warmup_steps > 0:
|
|
num_warmup_steps = training_args.warmup_steps
|
|
elif training_args.warmup_ratio > 0:
|
|
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio)
|
|
else:
|
|
num_warmup_steps = 0
|
|
|
|
# Bias and layernorm weights are automatically excluded from the decay
|
|
optimizer, lr_schedule = create_optimizer(
|
|
init_lr=training_args.learning_rate,
|
|
num_train_steps=num_train_steps,
|
|
num_warmup_steps=num_warmup_steps,
|
|
adam_beta1=training_args.adam_beta1,
|
|
adam_beta2=training_args.adam_beta2,
|
|
adam_epsilon=training_args.adam_epsilon,
|
|
weight_decay_rate=training_args.weight_decay,
|
|
adam_global_clipnorm=training_args.max_grad_norm,
|
|
)
|
|
|
|
# Transformers models compute the right loss for their task by default when labels are passed, and will
|
|
# use this for training unless you specify your own loss function in compile().
|
|
model.compile(optimizer=optimizer, jit_compile=training_args.xla)
|
|
# endregion
|
|
|
|
# region Preparing push_to_hub and model card
|
|
push_to_hub_model_id = training_args.push_to_hub_model_id
|
|
model_name = model_args.model_name_or_path.split("/")[-1]
|
|
if not push_to_hub_model_id:
|
|
if data_args.dataset_name is not None:
|
|
push_to_hub_model_id = f"{model_name}-finetuned-{data_args.dataset_name}"
|
|
else:
|
|
push_to_hub_model_id = f"{model_name}-finetuned-clm"
|
|
|
|
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
|
|
if data_args.dataset_name is not None:
|
|
model_card_kwargs["dataset_tags"] = data_args.dataset_name
|
|
if data_args.dataset_config_name is not None:
|
|
model_card_kwargs["dataset_args"] = data_args.dataset_config_name
|
|
model_card_kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
|
else:
|
|
model_card_kwargs["dataset"] = data_args.dataset_name
|
|
|
|
if training_args.push_to_hub:
|
|
callbacks = [
|
|
PushToHubCallback(
|
|
output_dir=training_args.output_dir,
|
|
hub_model_id=push_to_hub_model_id,
|
|
hub_token=training_args.push_to_hub_token,
|
|
tokenizer=tokenizer,
|
|
**model_card_kwargs,
|
|
)
|
|
]
|
|
else:
|
|
callbacks = []
|
|
# endregion
|
|
|
|
# region Training and validation
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num Epochs = {training_args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {training_args.per_device_train_batch_size}")
|
|
logger.info(f" Total train batch size = {training_args.per_device_train_batch_size * num_replicas}")
|
|
|
|
# For long training runs, you may wish to use the PushToHub() callback here to save intermediate checkpoints
|
|
# to the Hugging Face Hub rather than just pushing the finished model.
|
|
# See https://huggingface.co/docs/transformers/main_classes/keras_callbacks#transformers.PushToHubCallback
|
|
|
|
history = model.fit(
|
|
tf_train_dataset,
|
|
validation_data=tf_eval_dataset,
|
|
epochs=int(training_args.num_train_epochs),
|
|
callbacks=callbacks,
|
|
)
|
|
train_loss = history.history["loss"][-1]
|
|
try:
|
|
train_perplexity = math.exp(train_loss)
|
|
except OverflowError:
|
|
train_perplexity = math.inf
|
|
logger.info(f" Final train loss: {train_loss:.3f}")
|
|
logger.info(f" Final train perplexity: {train_perplexity:.3f}")
|
|
validation_loss = history.history["val_loss"][-1]
|
|
try:
|
|
validation_perplexity = math.exp(validation_loss)
|
|
except OverflowError:
|
|
validation_perplexity = math.inf
|
|
logger.info(f" Final validation loss: {validation_loss:.3f}")
|
|
logger.info(f" Final validation perplexity: {validation_perplexity:.3f}")
|
|
|
|
if training_args.output_dir is not None:
|
|
output_eval_file = os.path.join(training_args.output_dir, "all_results.json")
|
|
results_dict = {}
|
|
results_dict["train_loss"] = train_loss
|
|
results_dict["train_perplexity"] = train_perplexity
|
|
results_dict["eval_loss"] = validation_loss
|
|
results_dict["eval_perplexity"] = validation_perplexity
|
|
with open(output_eval_file, "w") as writer:
|
|
writer.write(json.dumps(results_dict))
|
|
# endregion
|
|
|
|
if training_args.output_dir is not None and not training_args.push_to_hub:
|
|
# If we're not pushing to hub, at least save a local copy when we're done
|
|
model.save_pretrained(training_args.output_dir)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|