914 lines
38 KiB
Python
914 lines
38 KiB
Python
#!/usr/bin/env python
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# coding=utf-8
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# Copyright 2024 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 using
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Fill-in-the middle (FIM) objective 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 fim causal language modeling task. Pointers for this are left as comments.
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import argparse
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import json
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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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from itertools import chain
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from pathlib import Path
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import datasets
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import numpy as np
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import torch
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from accelerate import Accelerator, DistributedType
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from accelerate.logging import get_logger
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from accelerate.utils import set_seed
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from datasets import load_dataset
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from huggingface_hub import Repository, create_repo
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from torch.utils.data import DataLoader
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from tqdm.auto import tqdm
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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MODEL_MAPPING,
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AutoConfig,
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AutoModelForCausalLM,
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AutoTokenizer,
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SchedulerType,
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default_data_collator,
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get_scheduler,
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is_deepspeed_zero3_enabled,
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is_torch_tpu_available,
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)
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from transformers.utils import check_min_version, send_example_telemetry
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from transformers.utils.versions import require_version
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# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
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check_min_version("4.42.0.dev0")
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logger = get_logger(__name__)
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require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
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MODEL_CONFIG_CLASSES = list(MODEL_MAPPING.keys())
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES)
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def parse_args():
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parser = argparse.ArgumentParser(
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description="Finetune a transformers model on a causal language modeling task using fill-in-the middle objective"
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)
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parser.add_argument(
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"--dataset_name",
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type=str,
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default=None,
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help="The name of the dataset to use (via the datasets library).",
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)
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parser.add_argument(
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"--dataset_config_name",
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type=str,
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default=None,
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help="The configuration name of the dataset to use (via the datasets library).",
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)
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parser.add_argument(
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"--train_file", type=str, default=None, help="A csv, txt or a json file containing the training data."
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)
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parser.add_argument(
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"--validation_file", type=str, default=None, help="A csv, txt or a json file containing the validation data."
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)
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parser.add_argument(
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"--validation_split_percentage",
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default=5,
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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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parser.add_argument(
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"--model_name_or_path",
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type=str,
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help="Path to pretrained model or model identifier from huggingface.co/models.",
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required=False,
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)
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parser.add_argument(
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"--config_name",
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type=str,
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default=None,
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help="Pretrained config name or path if not the same as model_name",
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)
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parser.add_argument(
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"--tokenizer_name",
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type=str,
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default=None,
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help="Pretrained tokenizer name or path if not the same as model_name",
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)
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parser.add_argument(
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"--use_slow_tokenizer",
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action="store_true",
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help="If passed, will use a slow tokenizer (not backed by the 🤗 Tokenizers library).",
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)
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parser.add_argument(
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"--per_device_train_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the training dataloader.",
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)
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parser.add_argument(
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"--per_device_eval_batch_size",
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type=int,
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default=8,
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help="Batch size (per device) for the evaluation dataloader.",
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)
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parser.add_argument(
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"--learning_rate",
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type=float,
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default=5e-5,
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help="Initial learning rate (after the potential warmup period) to use.",
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)
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parser.add_argument("--weight_decay", type=float, default=0.0, help="Weight decay to use.")
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parser.add_argument("--num_train_epochs", type=int, default=3, help="Total number of training epochs to perform.")
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parser.add_argument(
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"--max_train_steps",
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type=int,
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default=None,
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help="Total number of training steps to perform. If provided, overrides num_train_epochs.",
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)
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parser.add_argument(
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"--gradient_accumulation_steps",
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type=int,
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default=1,
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help="Number of updates steps to accumulate before performing a backward/update pass.",
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)
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parser.add_argument(
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"--lr_scheduler_type",
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type=SchedulerType,
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default="linear",
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help="The scheduler type to use.",
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choices=["linear", "cosine", "cosine_with_restarts", "polynomial", "constant", "constant_with_warmup"],
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)
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parser.add_argument(
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"--num_warmup_steps", type=int, default=0, help="Number of steps for the warmup in the lr scheduler."
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)
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parser.add_argument("--output_dir", type=str, default=None, help="Where to store the final model.")
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parser.add_argument("--seed", type=int, default=42, help="A seed for reproducible training.")
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parser.add_argument(
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"--model_type",
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type=str,
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default=None,
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help="Model type to use if training from scratch.",
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choices=MODEL_TYPES,
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)
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parser.add_argument(
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"--block_size",
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type=int,
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default=None,
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help=(
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"Optional input sequence length after tokenization. The training dataset will be truncated in block of"
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" this size for training. Default to the model max input length for single sentence inputs (take into"
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" account special tokens)."
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),
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)
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parser.add_argument(
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"--fim_rate",
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type=float,
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default=0.5,
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help=(
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" Optional probability with which the FIM transformation is applied to the example."
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" Default is 0.5. A rate of 1.0 means every example will undergo FIM transformation,"
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" while a rate of 0.0 means no example will."
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),
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)
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parser.add_argument(
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"--fim_spm_rate",
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type=float,
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default=0.5,
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help=(
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"Within the examples undergoing FIM transformation, this rate determines the probability"
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" of applying the Sentence Permutation Mode (SPM)."
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" Default is 0.5. A rate of 1.0 means all FIM transformations will use SPM,"
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" while a rate of 0.0 means none will."
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),
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)
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parser.add_argument(
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"--truncate_or_pad",
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type=bool,
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default=True,
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help=(
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"Indicates whether the transformed example should be truncated or padded to maintain"
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" the same length as the original example."
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" Default is True. If False, the function will not truncate or pad the examples."
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),
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)
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parser.add_argument(
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"--fim_prefix_token",
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type=str,
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default="<fim_prefix>",
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help="Fill-in-Middle Prefix token. Defaults to '<fim_prefix>'.",
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)
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parser.add_argument(
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"--fim_middle_token",
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type=str,
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default="<fim_middle>",
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help="Fill-in-Middle Middle token. Defaults to '<fim_middle>'.",
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)
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parser.add_argument(
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"--fim_suffix_token",
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type=str,
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default="<fim_suffix>",
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help="Fill-in-Middle Middle token. Defaults to '<fim_suffix>'.",
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)
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parser.add_argument(
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"--fim_pad_token",
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type=str,
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default="<fim_pad>",
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help=(
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"Fill-in-Middle Pad token. Used only when 'truncate_or_pad' is set to True." " Defaults to '<fim_pad>'."
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),
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)
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parser.add_argument(
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"--preprocessing_num_workers",
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type=int,
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default=None,
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help="The number of processes to use for the preprocessing.",
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)
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parser.add_argument(
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"--overwrite_cache", action="store_true", help="Overwrite the cached training and evaluation sets"
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)
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parser.add_argument(
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"--no_keep_linebreaks", action="store_true", help="Do not keep line breaks when using TXT files."
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)
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parser.add_argument("--push_to_hub", action="store_true", help="Whether or not to push the model to the Hub.")
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parser.add_argument(
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"--hub_model_id", type=str, help="The name of the repository to keep in sync with the local `output_dir`."
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)
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parser.add_argument("--hub_token", type=str, help="The token to use to push to the Model Hub.")
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parser.add_argument(
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"--trust_remote_code",
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type=bool,
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default=False,
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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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parser.add_argument(
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"--checkpointing_steps",
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type=str,
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default=None,
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help="Whether the various states should be saved at the end of every n steps, or 'epoch' for each epoch.",
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)
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parser.add_argument(
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"--resume_from_checkpoint",
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type=str,
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default=None,
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help="If the training should continue from a checkpoint folder.",
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)
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parser.add_argument(
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"--with_tracking",
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action="store_true",
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help="Whether to enable experiment trackers for logging.",
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)
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parser.add_argument(
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"--report_to",
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type=str,
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default="all",
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help=(
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'The integration to report the results and logs to. Supported platforms are `"tensorboard"`,'
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' `"wandb"`, `"comet_ml"` and `"clearml"`. Use `"all"` (default) to report to all integrations. '
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"Only applicable when `--with_tracking` is passed."
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),
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)
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parser.add_argument(
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"--low_cpu_mem_usage",
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action="store_true",
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help=(
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"It is an option to create the model as an empty shell, then only materialize its parameters when the pretrained weights are loaded. "
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"If passed, LLM loading time and RAM consumption will be benefited."
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),
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)
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args = parser.parse_args()
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# Sanity checks
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if args.dataset_name is None and args.train_file is None and 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 args.train_file is not None:
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extension = args.train_file.split(".")[-1]
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if extension not in ["csv", "json", "txt"]:
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raise ValueError("`train_file` should be a csv, json or txt file.")
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if args.validation_file is not None:
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extension = args.validation_file.split(".")[-1]
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if extension not in ["csv", "json", "txt"]:
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raise ValueError("`validation_file` should be a csv, json or txt file.")
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if args.push_to_hub:
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if args.output_dir is None:
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raise ValueError("Need an `output_dir` to create a repo when `--push_to_hub` is passed.")
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return args
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def main():
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args = parse_args()
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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_fim_no_trainer", args)
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# Initialize the accelerator. We will let the accelerator handle device placement for us in this example.
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# If we're using tracking, we also need to initialize it here and it will by default pick up all supported trackers
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# in the environment
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accelerator_log_kwargs = {}
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if args.with_tracking:
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accelerator_log_kwargs["log_with"] = args.report_to
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accelerator_log_kwargs["project_dir"] = args.output_dir
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accelerator = Accelerator(gradient_accumulation_steps=args.gradient_accumulation_steps, **accelerator_log_kwargs)
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# Make one log on every process with the configuration for debugging.
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logging.basicConfig(
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format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
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datefmt="%m/%d/%Y %H:%M:%S",
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level=logging.INFO,
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)
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logger.info(accelerator.state, main_process_only=False)
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if accelerator.is_local_main_process:
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datasets.utils.logging.set_verbosity_warning()
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transformers.utils.logging.set_verbosity_info()
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else:
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datasets.utils.logging.set_verbosity_error()
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transformers.utils.logging.set_verbosity_error()
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# If passed along, set the training seed now.
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if args.seed is not None:
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set_seed(args.seed)
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# Set a numpy random state for FIM transformations
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np_rng = np.random.RandomState(seed=args.seed)
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else:
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# Still set a random state for FIM transformations
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np_rng = np.random.RandomState(seed=42)
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# Handle the repository creation
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if accelerator.is_main_process:
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if args.push_to_hub:
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# Retrieve of infer repo_name
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repo_name = args.hub_model_id
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if repo_name is None:
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repo_name = Path(args.output_dir).absolute().name
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# Create repo and retrieve repo_id
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repo_id = create_repo(repo_name, exist_ok=True, token=args.hub_token).repo_id
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# Clone repo locally
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repo = Repository(args.output_dir, clone_from=repo_id, token=args.hub_token)
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with open(os.path.join(args.output_dir, ".gitignore"), "w+") as gitignore:
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if "step_*" not in gitignore:
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gitignore.write("step_*\n")
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if "epoch_*" not in gitignore:
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gitignore.write("epoch_*\n")
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elif args.output_dir is not None:
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os.makedirs(args.output_dir, exist_ok=True)
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accelerator.wait_for_everyone()
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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 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(args.dataset_name, args.dataset_config_name)
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if "validation" not in raw_datasets.keys():
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raw_datasets["validation"] = load_dataset(
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args.dataset_name,
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args.dataset_config_name,
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split=f"train[:{args.validation_split_percentage}%]",
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)
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raw_datasets["train"] = load_dataset(
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args.dataset_name,
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args.dataset_config_name,
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split=f"train[{args.validation_split_percentage}%:]",
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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 args.train_file is not None:
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data_files["train"] = args.train_file
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if args.validation_file is not None:
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data_files["validation"] = args.validation_file
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extension = args.train_file.split(".")[-1]
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if extension == "txt":
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extension = "text"
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dataset_args["keep_linebreaks"] = not args.no_keep_linebreaks
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raw_datasets = load_dataset(extension, data_files=data_files, **dataset_args)
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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[:{args.validation_split_percentage}%]",
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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[{args.validation_split_percentage}%:]",
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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.html.
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# 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 args.config_name:
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config = AutoConfig.from_pretrained(
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args.config_name,
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trust_remote_code=args.trust_remote_code,
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)
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elif args.model_name_or_path:
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config = AutoConfig.from_pretrained(
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args.model_name_or_path,
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trust_remote_code=args.trust_remote_code,
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)
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else:
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config = CONFIG_MAPPING[args.model_type]()
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logger.warning("You are instantiating a new config instance from scratch.")
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if args.tokenizer_name:
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tokenizer = AutoTokenizer.from_pretrained(
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args.tokenizer_name, use_fast=not args.use_slow_tokenizer, trust_remote_code=args.trust_remote_code
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)
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elif args.model_name_or_path:
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tokenizer = AutoTokenizer.from_pretrained(
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args.model_name_or_path, use_fast=not args.use_slow_tokenizer, trust_remote_code=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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if args.model_name_or_path:
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model = AutoModelForCausalLM.from_pretrained(
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args.model_name_or_path,
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from_tf=bool(".ckpt" in args.model_name_or_path),
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config=config,
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low_cpu_mem_usage=args.low_cpu_mem_usage,
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trust_remote_code=args.trust_remote_code,
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|
)
|
|
else:
|
|
logger.info("Training new model from scratch")
|
|
model = AutoModelForCausalLM.from_config(config, trust_remote_code=args.trust_remote_code)
|
|
|
|
# Add the new FIM tokens to the tokenizer and resize model's vocab embeddings
|
|
special_tokens = [args.fim_prefix_token, args.fim_middle_token, args.fim_suffix_token]
|
|
if args.truncate_or_pad:
|
|
special_tokens.append(args.fim_pad_token)
|
|
|
|
# Get the factor by which the embedding layer should be padded based on the device
|
|
pad_factor = 1
|
|
if torch.cuda.is_availble():
|
|
pad_factor = 8
|
|
|
|
elif is_torch_tpu_available():
|
|
pad_factor = 128
|
|
|
|
# Add the new tokens to the tokenizer
|
|
tokenizer.add_tokens(special_tokens)
|
|
original_embeddings = model.get_input_embeddings()
|
|
|
|
if is_deepspeed_zero3_enabled():
|
|
import deepspeed
|
|
|
|
with deepspeed.zero.GatheredParameters(original_embeddings.weight, modifier_rank=0):
|
|
# Get the pre-expansion embeddings of the model and resize the embedding layer
|
|
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
|
|
embeddings = model.get_input_embeddings()
|
|
|
|
# Sample the embeddings for the new tokens from a multivariate normal distribution
|
|
# We do this so that the new embeddings are close to the original embeddings and not necessarily zero
|
|
# More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
|
|
mean = original_embeddings.mean(dim=0)
|
|
n = original_embeddings.size()[0]
|
|
sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n
|
|
dist = torch.distributions.multivariate_normal.MultivariateNormal(
|
|
mean,
|
|
covariance_matrix=1e-5 * sigma,
|
|
)
|
|
new_token_embeddings = torch.stack(
|
|
tuple((dist.sample() for _ in range(len(special_tokens)))),
|
|
dim=0,
|
|
)
|
|
else:
|
|
original_embeddings = model.get_input_embeddings()
|
|
# Get the pre-expansion embeddings of the model and resize the embedding layer
|
|
model.resize_token_embeddings(len(tokenizer), pad_to_multiple_of=pad_factor)
|
|
embeddings = model.get_input_embeddings()
|
|
|
|
# Sample the embeddings for the new tokens from a multivariate normal distribution
|
|
# We do this so that the new embeddings are close to the original embeddings and not necessarily zero
|
|
# More on this: https://nlp.stanford.edu/~johnhew/vocab-expansion.html
|
|
mean = original_embeddings.mean(dim=0)
|
|
n = original_embeddings.size()[0]
|
|
sigma = ((original_embeddings - mean).T @ (original_embeddings - mean)) / n
|
|
dist = torch.distributions.multivariate_normal.MultivariateNormal(
|
|
mean,
|
|
covariance_matrix=1e-5 * sigma,
|
|
)
|
|
new_token_embeddings = torch.stack(
|
|
tuple((dist.sample() for _ in range(len(special_tokens)))),
|
|
dim=0,
|
|
)
|
|
|
|
if is_deepspeed_zero3_enabled():
|
|
import deepspeed
|
|
|
|
with deepspeed.zero.GatheredParameters(embeddings.weight, modifier_rank=0):
|
|
# Set the new tokens' embeddings to the newly sampled embeddings
|
|
embeddings.weight.data[-len(special_tokens) :] = new_token_embeddings
|
|
else:
|
|
# Set the new tokens' embeddings to the newly sampled embeddings
|
|
embeddings.weight.data[-len(special_tokens) :] = new_token_embeddings
|
|
|
|
# Update the model's embeddings with the new embeddings
|
|
model.set_input_embeddings(embeddings)
|
|
|
|
logger.info("Added special tokens to the tokenizer and resized model's embedding layer")
|
|
|
|
# Preprocessing the datasets.
|
|
# First we tokenize all the texts.
|
|
column_names = raw_datasets["train"].column_names
|
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
|
|
|
def tokenize_function(examples):
|
|
return tokenizer(examples[text_column_name])
|
|
|
|
with accelerator.main_process_first():
|
|
tokenized_datasets = raw_datasets.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
|
|
if args.block_size is None:
|
|
block_size = tokenizer.model_max_length
|
|
if block_size > config.max_position_embeddings:
|
|
logger.warning(
|
|
f"The tokenizer picked seems to have a very large `model_max_length` ({tokenizer.model_max_length}). "
|
|
f"Using block_size={min(1024, config.max_position_embeddings)} instead. You can change that default value by passing --block_size xxx."
|
|
)
|
|
block_size = min(1024, config.max_position_embeddings)
|
|
else:
|
|
if args.block_size > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The block_size passed ({args.block_size}) is larger than the maximum length for the model "
|
|
f"({tokenizer.model_max_length}). Using block_size={tokenizer.model_max_length}."
|
|
)
|
|
block_size = min(args.block_size, tokenizer.model_max_length)
|
|
|
|
# Main data processing function that will concatenate all texts from our dataset and generate chunks of block_size.
|
|
def group_texts(examples):
|
|
# Concatenate all texts.
|
|
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()}
|
|
total_length = len(concatenated_examples[list(examples.keys())[0]])
|
|
# We drop the small remainder, and if the total_length < block_size we exclude this batch and return an empty dict.
|
|
# We could add padding if the model supported it instead of this drop, you can customize this part to your needs.
|
|
total_length = (total_length // block_size) * block_size
|
|
# Split by chunks of max_len.
|
|
result = {
|
|
k: [t[i : i + block_size] for i in range(0, total_length, block_size)]
|
|
for k, t in concatenated_examples.items()
|
|
}
|
|
result["labels"] = result["input_ids"].copy()
|
|
return result
|
|
|
|
# Get the FIM-specific token ids
|
|
prefix_tok_id = tokenizer.convert_tokens_to_ids(args.fim_prefix_token)
|
|
middle_tok_id = tokenizer.convert_tokens_to_ids(args.fim_middle_token)
|
|
suffix_tok_id = tokenizer.convert_tokens_to_ids(args.fim_suffix_token)
|
|
pad_tok_id = None
|
|
|
|
# If truncate_or_pad is on, also get pad token id
|
|
if args.truncate_or_pad:
|
|
pad_tok_id = tokenizer.convert_tokens_to_ids(args.fim_pad_token)
|
|
|
|
# The two functions below perform the FIM transformation on the data (either PSM or SPM or PSM+SPM)
|
|
# Don't call fim_transform directly in .map()
|
|
# Adapted from https://github.com/loubnabnl/santacoder-finetuning/blob/main/fim.py#L22C13-L83
|
|
def fim_transform(example):
|
|
"""
|
|
This function performs FIM transformation on a single example (list of tokens)
|
|
"""
|
|
if np_rng.binomial(1, args.fim_rate):
|
|
boundaries = sorted(np_rng.randint(low=0, high=len(example) + 1, size=2))
|
|
|
|
prefix = example[: boundaries[0]]
|
|
middle = example[boundaries[0] : boundaries[1]]
|
|
suffix = example[boundaries[1] :]
|
|
|
|
if args.truncate_or_pad:
|
|
total_length = len(prefix) + len(middle) + len(suffix) + 3
|
|
diff = total_length - len(example)
|
|
if diff > 0:
|
|
suffix = suffix[: max(0, len(suffix) - diff)]
|
|
elif diff < 0:
|
|
suffix.extend([pad_tok_id] * (-diff))
|
|
|
|
if np_rng.binomial(1, args.fim_spm_rate):
|
|
# Apply Suffix-Prefix-Middle (SPM) transformation
|
|
transformed_example = [prefix_tok_id, suffix_tok_id] + suffix + [middle_tok_id] + prefix + middle
|
|
else:
|
|
# Apply Prefix-Suffix-Middle (PSM) transformation
|
|
transformed_example = [prefix_tok_id] + prefix + [suffix_tok_id] + suffix + [middle_tok_id] + middle
|
|
else:
|
|
transformed_example = example
|
|
|
|
return transformed_example
|
|
|
|
# Below function is the one you are supposed to call in the .map() function
|
|
def apply_fim(examples):
|
|
"""
|
|
Apply FIM transformation to a batch of examples
|
|
"""
|
|
fim_transform_ids = [fim_transform(ids) for ids in examples["input_ids"]]
|
|
examples["input_ids"] = fim_transform_ids
|
|
examples["labels"] = fim_transform_ids
|
|
# If your application requires custom attention mask, please adjust this function's below line.
|
|
# Since FIM transformation increases the number of tokens in input_ids and labels
|
|
# but leaves the number of tokens unchanged in attention_masks which would cause problems
|
|
examples["attention_mask"] = [[1] * len(mask) for mask in examples["input_ids"]]
|
|
return examples
|
|
|
|
# Note that with `batched=True`, this map processes 1,000 texts together, so group_texts throws away a remainder
|
|
# for each of those groups of 1,000 texts. You can adjust that batch_size here but a higher value might be slower
|
|
# to preprocess.
|
|
#
|
|
# 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
|
|
|
|
# FIM transformations are only supposed to be applied before group_texts processing otherwise some sentences will
|
|
# have 3-4 more tokens than others due to probabilistic addition of FIM-specific tokens which will raise errors
|
|
with accelerator.main_process_first():
|
|
fim_datasets = tokenized_datasets.map(
|
|
apply_fim,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc="Performing FIM transformation",
|
|
)
|
|
lm_datasets = fim_datasets.map(
|
|
group_texts,
|
|
batched=True,
|
|
num_proc=args.preprocessing_num_workers,
|
|
load_from_cache_file=not args.overwrite_cache,
|
|
desc=f"Grouping texts in chunks of {block_size}",
|
|
)
|
|
|
|
train_dataset = lm_datasets["train"]
|
|
eval_dataset = lm_datasets["validation"]
|
|
|
|
# Log a few random samples from the training set:
|
|
for index in random.sample(range(len(train_dataset)), 3):
|
|
logger.info(f"Sample {index} of the training set: {train_dataset[index]}.")
|
|
|
|
# DataLoaders creation:
|
|
train_dataloader = DataLoader(
|
|
train_dataset, shuffle=True, collate_fn=default_data_collator, batch_size=args.per_device_train_batch_size
|
|
)
|
|
eval_dataloader = DataLoader(
|
|
eval_dataset, collate_fn=default_data_collator, batch_size=args.per_device_eval_batch_size
|
|
)
|
|
|
|
# Optimizer
|
|
# Split weights in two groups, one with weight decay and the other not.
|
|
no_decay = ["bias", "layer_norm.weight"]
|
|
optimizer_grouped_parameters = [
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if not any(nd in n for nd in no_decay)],
|
|
"weight_decay": args.weight_decay,
|
|
},
|
|
{
|
|
"params": [p for n, p in model.named_parameters() if any(nd in n for nd in no_decay)],
|
|
"weight_decay": 0.0,
|
|
},
|
|
]
|
|
optimizer = torch.optim.AdamW(optimizer_grouped_parameters, lr=args.learning_rate)
|
|
|
|
# Scheduler and math around the number of training steps.
|
|
overrode_max_train_steps = False
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if args.max_train_steps is None:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
overrode_max_train_steps = True
|
|
|
|
lr_scheduler = get_scheduler(
|
|
name=args.lr_scheduler_type,
|
|
optimizer=optimizer,
|
|
num_warmup_steps=args.num_warmup_steps * args.gradient_accumulation_steps,
|
|
num_training_steps=args.max_train_steps * args.gradient_accumulation_steps,
|
|
)
|
|
|
|
# Prepare everything with our `accelerator`.
|
|
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler = accelerator.prepare(
|
|
model, optimizer, train_dataloader, eval_dataloader, lr_scheduler
|
|
)
|
|
|
|
# On TPU, the tie weights in our model have been disconnected, so we need to restore the ties.
|
|
if accelerator.distributed_type == DistributedType.TPU:
|
|
model.tie_weights()
|
|
|
|
# We need to recalculate our total training steps as the size of the training dataloader may have changed.
|
|
num_update_steps_per_epoch = math.ceil(len(train_dataloader) / args.gradient_accumulation_steps)
|
|
if overrode_max_train_steps:
|
|
args.max_train_steps = args.num_train_epochs * num_update_steps_per_epoch
|
|
# Afterwards we recalculate our number of training epochs
|
|
args.num_train_epochs = math.ceil(args.max_train_steps / num_update_steps_per_epoch)
|
|
|
|
# Figure out how many steps we should save the Accelerator states
|
|
checkpointing_steps = args.checkpointing_steps
|
|
if checkpointing_steps is not None and checkpointing_steps.isdigit():
|
|
checkpointing_steps = int(checkpointing_steps)
|
|
|
|
# We need to initialize the trackers we use, and also store our configuration.
|
|
# The trackers initializes automatically on the main process.
|
|
if args.with_tracking:
|
|
experiment_config = vars(args)
|
|
# TensorBoard cannot log Enums, need the raw value
|
|
experiment_config["lr_scheduler_type"] = experiment_config["lr_scheduler_type"].value
|
|
accelerator.init_trackers("fim_no_trainer", experiment_config)
|
|
|
|
# Train!
|
|
total_batch_size = args.per_device_train_batch_size * accelerator.num_processes * args.gradient_accumulation_steps
|
|
|
|
logger.info("***** Running training *****")
|
|
logger.info(f" Num examples = {len(train_dataset)}")
|
|
logger.info(f" Num Epochs = {args.num_train_epochs}")
|
|
logger.info(f" Instantaneous batch size per device = {args.per_device_train_batch_size}")
|
|
logger.info(f" Total train batch size (w. parallel, distributed & accumulation) = {total_batch_size}")
|
|
logger.info(f" Gradient Accumulation steps = {args.gradient_accumulation_steps}")
|
|
logger.info(f" Total optimization steps = {args.max_train_steps}")
|
|
# Only show the progress bar once on each machine.
|
|
progress_bar = tqdm(range(args.max_train_steps), disable=not accelerator.is_local_main_process)
|
|
completed_steps = 0
|
|
starting_epoch = 0
|
|
|
|
# Potentially load in the weights and states from a previous save
|
|
if args.resume_from_checkpoint:
|
|
if args.resume_from_checkpoint is not None or args.resume_from_checkpoint != "":
|
|
checkpoint_path = args.resume_from_checkpoint
|
|
path = os.path.basename(args.resume_from_checkpoint)
|
|
else:
|
|
# Get the most recent checkpoint
|
|
dirs = [f.name for f in os.scandir(os.getcwd()) if f.is_dir()]
|
|
dirs.sort(key=os.path.getctime)
|
|
path = dirs[-1] # Sorts folders by date modified, most recent checkpoint is the last
|
|
checkpoint_path = path
|
|
path = os.path.basename(checkpoint_path)
|
|
|
|
accelerator.print(f"Resumed from checkpoint: {checkpoint_path}")
|
|
accelerator.load_state(checkpoint_path)
|
|
# Extract `epoch_{i}` or `step_{i}`
|
|
training_difference = os.path.splitext(path)[0]
|
|
|
|
if "epoch" in training_difference:
|
|
starting_epoch = int(training_difference.replace("epoch_", "")) + 1
|
|
resume_step = None
|
|
completed_steps = starting_epoch * num_update_steps_per_epoch
|
|
else:
|
|
# need to multiply `gradient_accumulation_steps` to reflect real steps
|
|
resume_step = int(training_difference.replace("step_", "")) * args.gradient_accumulation_steps
|
|
starting_epoch = resume_step // len(train_dataloader)
|
|
completed_steps = resume_step // args.gradient_accumulation_steps
|
|
resume_step -= starting_epoch * len(train_dataloader)
|
|
|
|
# update the progress_bar if load from checkpoint
|
|
progress_bar.update(completed_steps)
|
|
|
|
for epoch in range(starting_epoch, args.num_train_epochs):
|
|
model.train()
|
|
if args.with_tracking:
|
|
total_loss = 0
|
|
if args.resume_from_checkpoint and epoch == starting_epoch and resume_step is not None:
|
|
# We skip the first `n` batches in the dataloader when resuming from a checkpoint
|
|
active_dataloader = accelerator.skip_first_batches(train_dataloader, resume_step)
|
|
else:
|
|
active_dataloader = train_dataloader
|
|
for step, batch in enumerate(active_dataloader):
|
|
with accelerator.accumulate(model):
|
|
outputs = model(**batch)
|
|
loss = outputs.loss
|
|
# We keep track of the loss at each epoch
|
|
if args.with_tracking:
|
|
total_loss += loss.detach().float()
|
|
accelerator.backward(loss)
|
|
optimizer.step()
|
|
lr_scheduler.step()
|
|
optimizer.zero_grad()
|
|
|
|
# Checks if the accelerator has performed an optimization step behind the scenes
|
|
if accelerator.sync_gradients:
|
|
progress_bar.update(1)
|
|
completed_steps += 1
|
|
|
|
if isinstance(checkpointing_steps, int):
|
|
if completed_steps % checkpointing_steps == 0:
|
|
output_dir = f"step_{completed_steps}"
|
|
if args.output_dir is not None:
|
|
output_dir = os.path.join(args.output_dir, output_dir)
|
|
accelerator.save_state(output_dir)
|
|
if completed_steps >= args.max_train_steps:
|
|
break
|
|
|
|
model.eval()
|
|
losses = []
|
|
for step, batch in enumerate(eval_dataloader):
|
|
with torch.no_grad():
|
|
outputs = model(**batch)
|
|
|
|
loss = outputs.loss
|
|
losses.append(accelerator.gather_for_metrics(loss.repeat(args.per_device_eval_batch_size)))
|
|
|
|
losses = torch.cat(losses)
|
|
try:
|
|
eval_loss = torch.mean(losses)
|
|
perplexity = math.exp(eval_loss)
|
|
except OverflowError:
|
|
perplexity = float("inf")
|
|
|
|
logger.info(f"epoch {epoch}: perplexity: {perplexity} eval_loss: {eval_loss}")
|
|
|
|
if args.with_tracking:
|
|
accelerator.log(
|
|
{
|
|
"perplexity": perplexity,
|
|
"eval_loss": eval_loss,
|
|
"train_loss": total_loss.item() / len(train_dataloader),
|
|
"epoch": epoch,
|
|
"step": completed_steps,
|
|
},
|
|
step=completed_steps,
|
|
)
|
|
|
|
if args.push_to_hub and epoch < args.num_train_epochs - 1:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(
|
|
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
|
|
)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
|
|
repo.push_to_hub(
|
|
commit_message=f"Training in progress epoch {epoch}", blocking=False, auto_lfs_prune=True
|
|
)
|
|
|
|
if args.checkpointing_steps == "epoch":
|
|
output_dir = f"epoch_{epoch}"
|
|
if args.output_dir is not None:
|
|
output_dir = os.path.join(args.output_dir, output_dir)
|
|
accelerator.save_state(output_dir)
|
|
|
|
if args.with_tracking:
|
|
accelerator.end_training()
|
|
|
|
if args.output_dir is not None:
|
|
accelerator.wait_for_everyone()
|
|
unwrapped_model = accelerator.unwrap_model(model)
|
|
unwrapped_model.save_pretrained(
|
|
args.output_dir, is_main_process=accelerator.is_main_process, save_function=accelerator.save
|
|
)
|
|
if accelerator.is_main_process:
|
|
tokenizer.save_pretrained(args.output_dir)
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if args.push_to_hub:
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repo.push_to_hub(commit_message="End of training", auto_lfs_prune=True)
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|
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with open(os.path.join(args.output_dir, "all_results.json"), "w") as f:
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json.dump({"perplexity": perplexity}, f)
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|
|
|
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if __name__ == "__main__":
|
|
main()
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