862 lines
36 KiB
Python
862 lines
36 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.
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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 should adapt this script on your own causal language modeling task. Pointers for this are left as comments.
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import logging
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import math
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import os
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import sys
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from dataclasses import dataclass, field
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from itertools import chain
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from typing import Optional
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import datasets
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import evaluate
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import numpy as np
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import torch
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from datasets import load_dataset
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import transformers
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from transformers import (
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CONFIG_MAPPING,
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MODEL_FOR_CAUSAL_LM_MAPPING,
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AutoConfig,
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AutoModelForCausalLM,
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AutoTokenizer,
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HfArgumentParser,
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Trainer,
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TrainingArguments,
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default_data_collator,
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is_deepspeed_zero3_enabled,
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is_torch_tpu_available,
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set_seed,
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)
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from transformers.testing_utils import CaptureLogger
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from transformers.trainer_utils import get_last_checkpoint
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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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require_version("datasets>=2.14.0", "To fix: pip install -r examples/pytorch/language-modeling/requirements.txt")
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logger = logging.getLogger(__name__)
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MODEL_CONFIG_CLASSES = list(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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@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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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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torch_dtype: Optional[str] = field(
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default=None,
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metadata={
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"help": (
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"Override the default `torch.dtype` and load the model under this dtype. If `auto` is passed, the "
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"dtype will be automatically derived from the model's weights."
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),
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"choices": ["auto", "bfloat16", "float16", "float32"],
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},
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)
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low_cpu_mem_usage: bool = field(
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default=False,
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metadata={
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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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"set True will benefit LLM loading time and RAM consumption."
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)
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},
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)
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pad_to_multiple_of: bool = field(
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default=False,
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metadata={
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"help": (
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"Whether to pad the embedding layer to a multiple depending on the device. ",
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"For NVIDIA GPUs, this will be a multiple of 8, for TPUs a multiple of 128.",
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)
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},
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)
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attn_implementation: Optional[str] = field(
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default="sdpa", metadata={"help": ("The attention implementation to use. ")}
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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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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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streaming: bool = field(default=False, metadata={"help": "Enable streaming mode"})
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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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fim_rate: Optional[float] = field(
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default=0.5,
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metadata={
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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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)
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fim_spm_rate: Optional[float] = field(
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default=0.5,
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metadata={
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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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)
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truncate_or_pad: Optional[bool] = field(
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default=True,
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metadata={
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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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)
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fim_prefix_token: Optional[str] = field(
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default="<fim_prefix>",
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metadata={"help": ("Fill-in-Middle Prefix token. Defaults to '<fim_prefix>'.")},
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)
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fim_middle_token: Optional[str] = field(
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default="<fim_middle>",
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metadata={"help": ("Fill-in-Middle Middle token. Defaults to '<fim_middle>'.")},
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)
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fim_suffix_token: Optional[str] = field(
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default="<fim_suffix>",
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metadata={"help": ("Fill-in-Middle Suffix token. Defaults to '<fim_suffix>'.")},
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)
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pad_token: Optional[str] = field(
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default="<fim_pad>",
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metadata={
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"help": (
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"Fill-in-Middle Pad token. Used only when 'truncate_or_pad' is set to True. "
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"Defaults to '<fim_pad>'."
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)
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},
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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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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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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.streaming:
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require_version("datasets>=2.0.0", "The streaming feature requires `datasets>=2.0.0`")
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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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def main():
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# See all possible arguments in src/transformers/training_args.py
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# or by passing the --help flag to this script.
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# We now keep distinct sets of args, for a cleaner separation of concerns.
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parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments))
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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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# 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", model_args, data_args)
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# Setup logging
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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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handlers=[logging.StreamHandler(sys.stdout)],
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)
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if training_args.should_log:
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# The default of training_args.log_level is passive, so we set log level at info here to have that default.
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transformers.utils.logging.set_verbosity_info()
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log_level = training_args.get_process_log_level()
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logger.setLevel(log_level)
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datasets.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.set_verbosity(log_level)
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transformers.utils.logging.enable_default_handler()
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transformers.utils.logging.enable_explicit_format()
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# Log on each process the small summary:
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logger.warning(
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f"Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}, "
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+ f"distributed training: {training_args.parallel_mode.value == 'distributed'}, 16-bits training: {training_args.fp16}"
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)
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logger.info(f"Training/evaluation parameters {training_args}")
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# Detecting last checkpoint.
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last_checkpoint = None
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if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir:
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last_checkpoint = get_last_checkpoint(training_args.output_dir)
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if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
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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 overcome."
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)
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elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
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logger.info(
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f"Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change "
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"the `--output_dir` or add `--overwrite_output_dir` to train from scratch."
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)
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# Set seed before initializing model.
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set_seed(training_args.seed)
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# Set a numpy random state for FIM transformations
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np_rng = np.random.RandomState(seed=training_args.seed)
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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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streaming=data_args.streaming,
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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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streaming=data_args.streaming,
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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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streaming=data_args.streaming,
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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.html.
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# Load pretrained model and tokenizer
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#
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# Distributed training:
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# The .from_pretrained methods guarantee that only one local process can concurrently
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# download model & vocab.
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config_kwargs = {
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"cache_dir": model_args.cache_dir,
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"revision": model_args.model_revision,
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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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if model_args.config_name:
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config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs)
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elif model_args.model_name_or_path:
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config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs)
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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.config_overrides is not None:
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logger.info(f"Overriding config: {model_args.config_overrides}")
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config.update_from_string(model_args.config_overrides)
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logger.info(f"New config: {config}")
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tokenizer_kwargs = {
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"cache_dir": model_args.cache_dir,
|
|
"use_fast": model_args.use_fast_tokenizer,
|
|
"revision": model_args.model_revision,
|
|
"token": model_args.token,
|
|
"trust_remote_code": model_args.trust_remote_code,
|
|
}
|
|
if model_args.tokenizer_name:
|
|
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs)
|
|
elif model_args.model_name_or_path:
|
|
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs)
|
|
else:
|
|
raise ValueError(
|
|
"You are instantiating a new tokenizer from scratch. This is not supported by this script. "
|
|
"You can do it from another script, save it, and load it from here, using --tokenizer_name."
|
|
)
|
|
|
|
if model_args.model_name_or_path:
|
|
torch_dtype = (
|
|
model_args.torch_dtype
|
|
if model_args.torch_dtype in ["auto", None]
|
|
else getattr(torch, model_args.torch_dtype)
|
|
)
|
|
model = AutoModelForCausalLM.from_pretrained(
|
|
model_args.model_name_or_path,
|
|
from_tf=bool(".ckpt" in model_args.model_name_or_path),
|
|
config=config,
|
|
cache_dir=model_args.cache_dir,
|
|
revision=model_args.model_revision,
|
|
token=model_args.token,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
torch_dtype=torch_dtype,
|
|
low_cpu_mem_usage=model_args.low_cpu_mem_usage,
|
|
attn_implementation=model_args.attn_implementation,
|
|
)
|
|
|
|
else:
|
|
model = AutoModelForCausalLM.from_config(
|
|
config,
|
|
trust_remote_code=model_args.trust_remote_code,
|
|
attn_implementation=model_args.attn_implementation,
|
|
)
|
|
n_params = sum({p.data_ptr(): p.numel() for p in model.parameters()}.values())
|
|
logger.info(f"Training new model from scratch - Total size={n_params/2**20:.2f}M params")
|
|
|
|
# Add the new FIM tokens to the tokenizer and resize model's vocab embeddings
|
|
special_tokens = [data_args.fim_prefix_token, data_args.fim_middle_token, data_args.fim_suffix_token]
|
|
if data_args.truncate_or_pad:
|
|
special_tokens.append(data_args.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.
|
|
if training_args.do_train:
|
|
column_names = list(raw_datasets["train"].features)
|
|
else:
|
|
column_names = list(raw_datasets["validation"].features)
|
|
text_column_name = "text" if "text" in column_names else column_names[0]
|
|
|
|
# since this will be pickled to avoid _LazyModule error in Hasher force logger loading before tokenize_function
|
|
tok_logger = transformers.utils.logging.get_logger("transformers.tokenization_utils_base")
|
|
|
|
def tokenize_function(examples):
|
|
with CaptureLogger(tok_logger) as cl:
|
|
output = tokenizer(examples[text_column_name])
|
|
# clm-fim input could be much much longer than block_size
|
|
if "Token indices sequence length is longer than the" in cl.out:
|
|
tok_logger.warning(
|
|
"^^^^^^^^^^^^^^^^ Please ignore the warning above - this long input will be chunked into smaller bits"
|
|
" before being passed to the model."
|
|
)
|
|
return output
|
|
|
|
with training_args.main_process_first(desc="dataset map tokenization"):
|
|
if not data_args.streaming:
|
|
tokenized_datasets = raw_datasets.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
remove_columns=column_names,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc="Running tokenizer on dataset",
|
|
)
|
|
else:
|
|
tokenized_datasets = raw_datasets.map(
|
|
tokenize_function,
|
|
batched=True,
|
|
remove_columns=column_names,
|
|
)
|
|
|
|
if data_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 data_args.block_size > tokenizer.model_max_length:
|
|
logger.warning(
|
|
f"The block_size passed ({data_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(data_args.block_size, tokenizer.model_max_length)
|
|
|
|
# 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(data_args.fim_prefix_token)
|
|
middle_tok_id = tokenizer.convert_tokens_to_ids(data_args.fim_middle_token)
|
|
suffix_tok_id = tokenizer.convert_tokens_to_ids(data_args.fim_suffix_token)
|
|
pad_tok_id = None
|
|
|
|
# If truncate_or_pad is on, also get pad token id
|
|
if data_args.truncate_or_pad:
|
|
pad_tok_id = tokenizer.convert_tokens_to_ids(data_args.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, data_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 data_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, data_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 training_args.main_process_first(desc="processing texts together"):
|
|
if not data_args.streaming:
|
|
fim_datasets = tokenized_datasets.map(
|
|
apply_fim,
|
|
batched=True,
|
|
num_proc=data_args.preprocessing_num_workers,
|
|
load_from_cache_file=not data_args.overwrite_cache,
|
|
desc="Performing FIM transformation",
|
|
)
|
|
lm_datasets = fim_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}",
|
|
)
|
|
else:
|
|
fim_datasets = tokenized_datasets.map(
|
|
apply_fim,
|
|
batched=True,
|
|
)
|
|
lm_datasets = fim_datasets.map(
|
|
group_texts,
|
|
batched=True,
|
|
)
|
|
|
|
if training_args.do_train:
|
|
if "train" not in tokenized_datasets:
|
|
raise ValueError("--do_train requires a train dataset")
|
|
train_dataset = lm_datasets["train"]
|
|
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 training_args.do_eval:
|
|
if "validation" not in tokenized_datasets:
|
|
raise ValueError("--do_eval requires a validation dataset")
|
|
eval_dataset = lm_datasets["validation"]
|
|
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))
|
|
|
|
def preprocess_logits_for_metrics(logits, labels):
|
|
if isinstance(logits, tuple):
|
|
# Depending on the model and config, logits may contain extra tensors,
|
|
# like past_key_values, but logits always come first
|
|
logits = logits[0]
|
|
return logits.argmax(dim=-1)
|
|
|
|
metric = evaluate.load("accuracy")
|
|
|
|
def compute_metrics(eval_preds):
|
|
preds, labels = eval_preds
|
|
# preds have the same shape as the labels, after the argmax(-1) has been calculated
|
|
# by preprocess_logits_for_metrics but we need to shift the labels
|
|
labels = labels[:, 1:].reshape(-1)
|
|
preds = preds[:, :-1].reshape(-1)
|
|
return metric.compute(predictions=preds, references=labels)
|
|
|
|
# Initialize our Trainer
|
|
trainer = Trainer(
|
|
model=model,
|
|
args=training_args,
|
|
train_dataset=train_dataset if training_args.do_train else None,
|
|
eval_dataset=eval_dataset if training_args.do_eval else None,
|
|
tokenizer=tokenizer,
|
|
# Data collator will default to DataCollatorWithPadding, so we change it.
|
|
data_collator=default_data_collator,
|
|
compute_metrics=compute_metrics if training_args.do_eval and not is_torch_tpu_available() else None,
|
|
preprocess_logits_for_metrics=(
|
|
preprocess_logits_for_metrics if training_args.do_eval and not is_torch_tpu_available() else None
|
|
),
|
|
)
|
|
|
|
# Training
|
|
if training_args.do_train:
|
|
checkpoint = None
|
|
if training_args.resume_from_checkpoint is not None:
|
|
checkpoint = training_args.resume_from_checkpoint
|
|
elif last_checkpoint is not None:
|
|
checkpoint = last_checkpoint
|
|
train_result = trainer.train(resume_from_checkpoint=checkpoint)
|
|
trainer.save_model() # Saves the tokenizer too for easy upload
|
|
|
|
metrics = train_result.metrics
|
|
|
|
max_train_samples = (
|
|
data_args.max_train_samples if data_args.max_train_samples is not None else len(train_dataset)
|
|
)
|
|
metrics["train_samples"] = min(max_train_samples, len(train_dataset))
|
|
|
|
trainer.log_metrics("train", metrics)
|
|
trainer.save_metrics("train", metrics)
|
|
trainer.save_state()
|
|
|
|
# Evaluation
|
|
if training_args.do_eval:
|
|
logger.info("*** Evaluate ***")
|
|
|
|
metrics = trainer.evaluate()
|
|
|
|
max_eval_samples = data_args.max_eval_samples if data_args.max_eval_samples is not None else len(eval_dataset)
|
|
metrics["eval_samples"] = min(max_eval_samples, len(eval_dataset))
|
|
try:
|
|
perplexity = math.exp(metrics["eval_loss"])
|
|
except OverflowError:
|
|
perplexity = float("inf")
|
|
metrics["perplexity"] = perplexity
|
|
|
|
trainer.log_metrics("eval", metrics)
|
|
trainer.save_metrics("eval", metrics)
|
|
|
|
kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-generation"}
|
|
if data_args.dataset_name is not None:
|
|
kwargs["dataset_tags"] = data_args.dataset_name
|
|
if data_args.dataset_config_name is not None:
|
|
kwargs["dataset_args"] = data_args.dataset_config_name
|
|
kwargs["dataset"] = f"{data_args.dataset_name} {data_args.dataset_config_name}"
|
|
else:
|
|
kwargs["dataset"] = data_args.dataset_name
|
|
|
|
if training_args.push_to_hub:
|
|
trainer.push_to_hub(**kwargs)
|
|
else:
|
|
trainer.create_model_card(**kwargs)
|
|
|
|
|
|
def _mp_fn(index):
|
|
# For xla_spawn (TPUs)
|
|
main()
|
|
|
|
|
|
if __name__ == "__main__":
|
|
main()
|