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""" |
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Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a |
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text file or a dataset. |
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|
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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=fill-mask |
|
""" |
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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 sys |
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import time |
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from dataclasses import asdict, dataclass, field |
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from enum import Enum |
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from itertools import chain |
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|
|
|
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from pathlib import Path |
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from typing import Dict, List, Optional, Tuple |
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|
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import flax |
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import jax |
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import jax.numpy as jnp |
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import numpy as np |
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import optax |
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from datasets import load_dataset |
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from flax import jax_utils, traverse_util |
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from flax.jax_utils import pad_shard_unpad |
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from flax.training import train_state |
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from flax.training.common_utils import get_metrics, onehot, shard |
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from huggingface_hub import HfApi |
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from tqdm import tqdm |
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|
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from transformers import ( |
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CONFIG_MAPPING, |
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FLAX_MODEL_FOR_MASKED_LM_MAPPING, |
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AutoConfig, |
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AutoTokenizer, |
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FlaxAutoModelForMaskedLM, |
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HfArgumentParser, |
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PreTrainedTokenizerBase, |
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TensorType, |
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is_tensorboard_available, |
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set_seed, |
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) |
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from transformers.utils import send_example_telemetry |
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|
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|
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MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) |
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MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) |
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|
|
|
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@dataclass |
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class TrainingArguments: |
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output_dir: str = field( |
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metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, |
|
) |
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overwrite_output_dir: bool = field( |
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default=False, |
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metadata={ |
|
"help": ( |
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"Overwrite the content of the output directory. " |
|
"Use this to continue training if output_dir points to a checkpoint directory." |
|
) |
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}, |
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) |
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do_train: bool = field(default=False, metadata={"help": "Whether to run training."}) |
|
do_eval: bool = field(default=False, metadata={"help": "Whether to run eval on the dev set."}) |
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per_device_train_batch_size: int = field( |
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default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} |
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) |
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per_device_eval_batch_size: int = field( |
|
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} |
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) |
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learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) |
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weight_decay: float = field(default=0.0, metadata={"help": "Weight decay for AdamW if we apply some."}) |
|
adam_beta1: float = field(default=0.9, metadata={"help": "Beta1 for AdamW optimizer"}) |
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adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) |
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adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) |
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adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) |
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num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) |
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warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) |
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logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) |
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save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) |
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eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) |
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seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) |
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push_to_hub: bool = field( |
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default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} |
|
) |
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hub_model_id: str = field( |
|
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} |
|
) |
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hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) |
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gradient_checkpointing: bool = field( |
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default=False, |
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metadata={ |
|
"help": "If True, use gradient checkpointing to save memory at the expense of slower backward pass." |
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}, |
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) |
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|
|
def __post_init__(self): |
|
if self.output_dir is not None: |
|
self.output_dir = os.path.expanduser(self.output_dir) |
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|
|
def to_dict(self): |
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""" |
|
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates |
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the token values by removing their value. |
|
""" |
|
d = asdict(self) |
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for k, v in d.items(): |
|
if isinstance(v, Enum): |
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d[k] = v.value |
|
if isinstance(v, list) and len(v) > 0 and isinstance(v[0], Enum): |
|
d[k] = [x.value for x in v] |
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if k.endswith("_token"): |
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d[k] = f"<{k.upper()}>" |
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return d |
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|
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|
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@dataclass |
|
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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|
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model_name_or_path: Optional[str] = field( |
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default=None, |
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metadata={ |
|
"help": ( |
|
"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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) |
|
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, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} |
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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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) |
|
dtype: Optional[str] = field( |
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default="float32", |
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metadata={ |
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"help": ( |
|
"Floating-point format in which the model weights should be initialized and trained. Choose one of" |
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" `[float32, float16, bfloat16]`." |
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) |
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}, |
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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 to trust the execution of code from datasets/models defined on the Hub." |
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" This option should only be set to `True` for repositories you trust and in which you have read the" |
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" code, as it will 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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|
|
|
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@dataclass |
|
class DataTrainingArguments: |
|
""" |
|
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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) |
|
dataset_config_name: Optional[str] = field( |
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} |
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) |
|
train_file: Optional[str] = field(default=None, metadata={"help": "The input training data file (a text file)."}) |
|
validation_file: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "An optional input evaluation data file to evaluate the perplexity on (a text file)."}, |
|
) |
|
train_ref_file: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "An optional input train ref data file for whole word masking in Chinese."}, |
|
) |
|
validation_ref_file: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "An optional input validation ref data file for whole word masking in Chinese."}, |
|
) |
|
overwrite_cache: bool = field( |
|
default=False, metadata={"help": "Overwrite the cached training and evaluation sets"} |
|
) |
|
validation_split_percentage: Optional[int] = field( |
|
default=5, |
|
metadata={ |
|
"help": "The percentage of the train set used as validation set in case there's no validation split" |
|
}, |
|
) |
|
max_seq_length: Optional[int] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"The maximum total input sequence length after tokenization. Sequences longer " |
|
"than this will be truncated. Default to the max input length of the model." |
|
) |
|
}, |
|
) |
|
preprocessing_num_workers: Optional[int] = field( |
|
default=None, |
|
metadata={"help": "The number of processes to use for the preprocessing."}, |
|
) |
|
mlm_probability: float = field( |
|
default=0.15, metadata={"help": "Ratio of tokens to mask for masked language modeling loss"} |
|
) |
|
pad_to_max_length: bool = field( |
|
default=False, |
|
metadata={ |
|
"help": ( |
|
"Whether to pad all samples to `max_seq_length`. " |
|
"If False, will pad the samples dynamically when batching to the maximum length in the batch." |
|
) |
|
}, |
|
) |
|
line_by_line: bool = field( |
|
default=False, |
|
metadata={"help": "Whether distinct lines of text in the dataset are to be handled as distinct sequences."}, |
|
) |
|
|
|
def __post_init__(self): |
|
if self.dataset_name is None and self.train_file is None and self.validation_file is None: |
|
raise ValueError("Need either a dataset name or a training/validation file.") |
|
else: |
|
if self.train_file is not None: |
|
extension = self.train_file.split(".")[-1] |
|
assert extension in ["csv", "json", "txt"], "`train_file` should be a csv, a json or a txt file." |
|
if self.validation_file is not None: |
|
extension = self.validation_file.split(".")[-1] |
|
assert extension in ["csv", "json", "txt"], "`validation_file` should be a csv, a json or a txt file." |
|
|
|
|
|
@flax.struct.dataclass |
|
class FlaxDataCollatorForLanguageModeling: |
|
""" |
|
Data collator used for language modeling. Inputs are dynamically padded to the maximum length of a batch if they |
|
are not all of the same length. |
|
|
|
Args: |
|
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): |
|
The tokenizer used for encoding the data. |
|
mlm_probability (:obj:`float`, `optional`, defaults to 0.15): |
|
The probability with which to (randomly) mask tokens in the input. |
|
|
|
.. note:: |
|
|
|
For best performance, this data collator should be used with a dataset having items that are dictionaries or |
|
BatchEncoding, with the :obj:`"special_tokens_mask"` key, as returned by a |
|
:class:`~transformers.PreTrainedTokenizer` or a :class:`~transformers.PreTrainedTokenizerFast` with the |
|
argument :obj:`return_special_tokens_mask=True`. |
|
""" |
|
|
|
tokenizer: PreTrainedTokenizerBase |
|
mlm_probability: float = 0.15 |
|
|
|
def __post_init__(self): |
|
if self.tokenizer.mask_token is None: |
|
raise ValueError( |
|
"This tokenizer does not have a mask token which is necessary for masked language modeling. " |
|
"You should pass `mlm=False` to train on causal language modeling instead." |
|
) |
|
|
|
def __call__(self, examples: List[Dict[str, np.ndarray]], pad_to_multiple_of: int) -> Dict[str, np.ndarray]: |
|
|
|
batch = self.tokenizer.pad(examples, pad_to_multiple_of=pad_to_multiple_of, return_tensors=TensorType.NUMPY) |
|
|
|
|
|
special_tokens_mask = batch.pop("special_tokens_mask", None) |
|
|
|
batch["input_ids"], batch["labels"] = self.mask_tokens( |
|
batch["input_ids"], special_tokens_mask=special_tokens_mask |
|
) |
|
return batch |
|
|
|
def mask_tokens( |
|
self, inputs: np.ndarray, special_tokens_mask: Optional[np.ndarray] |
|
) -> Tuple[np.ndarray, np.ndarray]: |
|
""" |
|
Prepare masked tokens inputs/labels for masked language modeling: 80% MASK, 10% random, 10% original. |
|
""" |
|
labels = inputs.copy() |
|
|
|
probability_matrix = np.full(labels.shape, self.mlm_probability) |
|
special_tokens_mask = special_tokens_mask.astype("bool") |
|
|
|
probability_matrix[special_tokens_mask] = 0.0 |
|
masked_indices = np.random.binomial(1, probability_matrix).astype("bool") |
|
labels[~masked_indices] = -100 |
|
|
|
|
|
indices_replaced = np.random.binomial(1, np.full(labels.shape, 0.8)).astype("bool") & masked_indices |
|
inputs[indices_replaced] = self.tokenizer.convert_tokens_to_ids(self.tokenizer.mask_token) |
|
|
|
|
|
indices_random = np.random.binomial(1, np.full(labels.shape, 0.5)).astype("bool") |
|
indices_random &= masked_indices & ~indices_replaced |
|
|
|
random_words = np.random.randint(self.tokenizer.vocab_size, size=labels.shape, dtype="i4") |
|
inputs[indices_random] = random_words[indices_random] |
|
|
|
|
|
return inputs, labels |
|
|
|
|
|
def generate_batch_splits(samples_idx: np.ndarray, batch_size: int, drop_last=True) -> np.ndarray: |
|
"""Generate batches of data for a specified batch size from sample indices. If the dataset size is not divisible by |
|
the batch size and `drop_last` is `True`, the last incomplete batch is dropped. Else, it is returned.""" |
|
num_samples = len(samples_idx) |
|
if drop_last: |
|
samples_to_remove = num_samples % batch_size |
|
if samples_to_remove != 0: |
|
samples_idx = samples_idx[:-samples_to_remove] |
|
sections_split = num_samples // batch_size |
|
samples_idx = samples_idx.reshape((sections_split, batch_size)) |
|
else: |
|
sections_split = math.ceil(num_samples / batch_size) |
|
samples_idx = np.array_split(samples_idx, sections_split) |
|
return samples_idx |
|
|
|
|
|
def write_train_metric(summary_writer, train_metrics, train_time, step): |
|
summary_writer.scalar("train_time", train_time, step) |
|
|
|
train_metrics = get_metrics(train_metrics) |
|
for key, vals in train_metrics.items(): |
|
tag = f"train_{key}" |
|
for i, val in enumerate(vals): |
|
summary_writer.scalar(tag, val, step - len(vals) + i + 1) |
|
|
|
|
|
def write_eval_metric(summary_writer, eval_metrics, step): |
|
for metric_name, value in eval_metrics.items(): |
|
summary_writer.scalar(f"eval_{metric_name}", value, step) |
|
|
|
|
|
def main(): |
|
|
|
|
|
|
|
|
|
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) |
|
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): |
|
|
|
|
|
model_args, data_args, training_args = parser.parse_json_file(json_file=os.path.abspath(sys.argv[1])) |
|
else: |
|
model_args, data_args, training_args = parser.parse_args_into_dataclasses() |
|
|
|
|
|
|
|
send_example_telemetry("run_mlm", model_args, data_args, framework="flax") |
|
|
|
if ( |
|
os.path.exists(training_args.output_dir) |
|
and os.listdir(training_args.output_dir) |
|
and training_args.do_train |
|
and not training_args.overwrite_output_dir |
|
): |
|
raise ValueError( |
|
f"Output directory ({training_args.output_dir}) already exists and is not empty. " |
|
"Use --overwrite_output_dir to overcome." |
|
) |
|
|
|
|
|
logging.basicConfig( |
|
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s", |
|
level=logging.INFO, |
|
datefmt="[%X]", |
|
) |
|
|
|
|
|
logger = logging.getLogger(__name__) |
|
|
|
|
|
logger.info(f"Training/evaluation parameters {training_args}") |
|
|
|
|
|
set_seed(training_args.seed) |
|
|
|
|
|
if training_args.push_to_hub: |
|
|
|
repo_name = training_args.hub_model_id |
|
if repo_name is None: |
|
repo_name = Path(training_args.output_dir).absolute().name |
|
|
|
api = HfApi() |
|
repo_id = api.create_repo(repo_name, exist_ok=True, token=training_args.hub_token).repo_id |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if data_args.dataset_name is not None: |
|
|
|
datasets = load_dataset( |
|
data_args.dataset_name, |
|
data_args.dataset_config_name, |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
|
|
if "validation" not in datasets.keys(): |
|
datasets["validation"] = load_dataset( |
|
data_args.dataset_name, |
|
data_args.dataset_config_name, |
|
split=f"train[:{data_args.validation_split_percentage}%]", |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
datasets["train"] = load_dataset( |
|
data_args.dataset_name, |
|
data_args.dataset_config_name, |
|
split=f"train[{data_args.validation_split_percentage}%:]", |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
else: |
|
data_files = {} |
|
if data_args.train_file is not None: |
|
data_files["train"] = data_args.train_file |
|
extension = data_args.train_file.split(".")[-1] |
|
if data_args.validation_file is not None: |
|
data_files["validation"] = data_args.validation_file |
|
extension = data_args.validation_file.split(".")[-1] |
|
if extension == "txt": |
|
extension = "text" |
|
datasets = load_dataset( |
|
extension, |
|
data_files=data_files, |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
) |
|
|
|
if "validation" not in datasets.keys(): |
|
datasets["validation"] = load_dataset( |
|
extension, |
|
data_files=data_files, |
|
split=f"train[:{data_args.validation_split_percentage}%]", |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
) |
|
datasets["train"] = load_dataset( |
|
extension, |
|
data_files=data_files, |
|
split=f"train[{data_args.validation_split_percentage}%:]", |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
num_proc=data_args.preprocessing_num_workers, |
|
) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if model_args.config_name: |
|
config = AutoConfig.from_pretrained( |
|
model_args.config_name, |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
elif model_args.model_name_or_path: |
|
config = AutoConfig.from_pretrained( |
|
model_args.model_name_or_path, |
|
cache_dir=model_args.cache_dir, |
|
token=model_args.token, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
else: |
|
config = CONFIG_MAPPING[model_args.model_type]() |
|
logger.warning("You are instantiating a new config instance from scratch.") |
|
|
|
if model_args.tokenizer_name: |
|
tokenizer = AutoTokenizer.from_pretrained( |
|
model_args.tokenizer_name, |
|
cache_dir=model_args.cache_dir, |
|
use_fast=model_args.use_fast_tokenizer, |
|
token=model_args.token, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
elif model_args.model_name_or_path: |
|
tokenizer = AutoTokenizer.from_pretrained( |
|
model_args.model_name_or_path, |
|
cache_dir=model_args.cache_dir, |
|
use_fast=model_args.use_fast_tokenizer, |
|
token=model_args.token, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
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 training_args.do_train: |
|
column_names = datasets["train"].column_names |
|
else: |
|
column_names = datasets["validation"].column_names |
|
text_column_name = "text" if "text" in column_names else column_names[0] |
|
|
|
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) |
|
|
|
if data_args.line_by_line: |
|
|
|
padding = "max_length" if data_args.pad_to_max_length else False |
|
|
|
def tokenize_function(examples): |
|
|
|
examples = [line for line in examples if len(line) > 0 and not line.isspace()] |
|
return tokenizer( |
|
examples, |
|
return_special_tokens_mask=True, |
|
padding=padding, |
|
truncation=True, |
|
max_length=max_seq_length, |
|
) |
|
|
|
tokenized_datasets = datasets.map( |
|
tokenize_function, |
|
input_columns=[text_column_name], |
|
batched=True, |
|
num_proc=data_args.preprocessing_num_workers, |
|
remove_columns=column_names, |
|
load_from_cache_file=not data_args.overwrite_cache, |
|
) |
|
|
|
else: |
|
|
|
|
|
|
|
def tokenize_function(examples): |
|
return tokenizer(examples[text_column_name], return_special_tokens_mask=True) |
|
|
|
tokenized_datasets = 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, |
|
) |
|
|
|
|
|
|
|
def group_texts(examples): |
|
|
|
concatenated_examples = {k: list(chain(*examples[k])) for k in examples.keys()} |
|
total_length = len(concatenated_examples[list(examples.keys())[0]]) |
|
|
|
|
|
if total_length >= max_seq_length: |
|
total_length = (total_length // max_seq_length) * max_seq_length |
|
|
|
result = { |
|
k: [t[i : i + max_seq_length] for i in range(0, total_length, max_seq_length)] |
|
for k, t in concatenated_examples.items() |
|
} |
|
return result |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
tokenized_datasets = tokenized_datasets.map( |
|
group_texts, |
|
batched=True, |
|
num_proc=data_args.preprocessing_num_workers, |
|
load_from_cache_file=not data_args.overwrite_cache, |
|
) |
|
|
|
|
|
has_tensorboard = is_tensorboard_available() |
|
if has_tensorboard and jax.process_index() == 0: |
|
try: |
|
from flax.metrics.tensorboard import SummaryWriter |
|
|
|
summary_writer = SummaryWriter(log_dir=Path(training_args.output_dir)) |
|
except ImportError as ie: |
|
has_tensorboard = False |
|
logger.warning( |
|
f"Unable to display metrics through TensorBoard because some package are not installed: {ie}" |
|
) |
|
else: |
|
logger.warning( |
|
"Unable to display metrics through TensorBoard because the package is not installed: " |
|
"Please run pip install tensorboard to enable." |
|
) |
|
|
|
|
|
|
|
data_collator = FlaxDataCollatorForLanguageModeling(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) |
|
|
|
|
|
rng = jax.random.PRNGKey(training_args.seed) |
|
dropout_rngs = jax.random.split(rng, jax.local_device_count()) |
|
|
|
if model_args.model_name_or_path: |
|
model = FlaxAutoModelForMaskedLM.from_pretrained( |
|
model_args.model_name_or_path, |
|
config=config, |
|
seed=training_args.seed, |
|
dtype=getattr(jnp, model_args.dtype), |
|
token=model_args.token, |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
else: |
|
model = FlaxAutoModelForMaskedLM.from_config( |
|
config, |
|
seed=training_args.seed, |
|
dtype=getattr(jnp, model_args.dtype), |
|
trust_remote_code=model_args.trust_remote_code, |
|
) |
|
|
|
if training_args.gradient_checkpointing: |
|
model.enable_gradient_checkpointing() |
|
|
|
|
|
num_epochs = int(training_args.num_train_epochs) |
|
|
|
|
|
local_device_count = jax.local_device_count() |
|
|
|
|
|
train_batch_size = training_args.per_device_train_batch_size * local_device_count |
|
per_device_eval_batch_size = training_args.per_device_eval_batch_size |
|
eval_batch_size = per_device_eval_batch_size * local_device_count |
|
|
|
|
|
global_train_batch_size = train_batch_size * jax.process_count() |
|
global_eval_batch_size = eval_batch_size * jax.process_count() |
|
|
|
|
|
logger.info(f"Per-process train batch size: {train_batch_size}") |
|
logger.info(f"Global train batch size: {global_train_batch_size}") |
|
logger.info(f"Per-process eval batch size: {per_device_eval_batch_size}") |
|
logger.info(f"Global eval batch size: {global_eval_batch_size}") |
|
|
|
num_train_steps = (len(tokenized_datasets["train"]) // (train_batch_size * jax.process_count())) * num_epochs |
|
logger.info(f"Number of training steps: {num_train_steps}") |
|
|
|
|
|
warmup_fn = optax.linear_schedule( |
|
init_value=0.0, end_value=training_args.learning_rate, transition_steps=training_args.warmup_steps |
|
) |
|
decay_fn = optax.linear_schedule( |
|
init_value=training_args.learning_rate, |
|
end_value=0, |
|
transition_steps=num_train_steps - training_args.warmup_steps, |
|
) |
|
linear_decay_lr_schedule_fn = optax.join_schedules( |
|
schedules=[warmup_fn, decay_fn], boundaries=[training_args.warmup_steps] |
|
) |
|
|
|
|
|
|
|
|
|
|
|
def decay_mask_fn(params): |
|
flat_params = traverse_util.flatten_dict(params) |
|
|
|
layer_norm_candidates = ["layernorm", "layer_norm", "ln"] |
|
layer_norm_named_params = { |
|
layer[-2:] |
|
for layer_norm_name in layer_norm_candidates |
|
for layer in flat_params.keys() |
|
if layer_norm_name in "".join(layer).lower() |
|
} |
|
flat_mask = {path: (path[-1] != "bias" and path[-2:] not in layer_norm_named_params) for path in flat_params} |
|
return traverse_util.unflatten_dict(flat_mask) |
|
|
|
|
|
if training_args.adafactor: |
|
|
|
|
|
optimizer = optax.adafactor( |
|
learning_rate=linear_decay_lr_schedule_fn, |
|
) |
|
else: |
|
optimizer = optax.adamw( |
|
learning_rate=linear_decay_lr_schedule_fn, |
|
b1=training_args.adam_beta1, |
|
b2=training_args.adam_beta2, |
|
eps=training_args.adam_epsilon, |
|
weight_decay=training_args.weight_decay, |
|
mask=decay_mask_fn, |
|
) |
|
|
|
|
|
state = train_state.TrainState.create(apply_fn=model.__call__, params=model.params, tx=optimizer) |
|
|
|
def train_step(state, batch, dropout_rng): |
|
dropout_rng, new_dropout_rng = jax.random.split(dropout_rng) |
|
|
|
def loss_fn(params): |
|
labels = batch.pop("labels") |
|
logits = state.apply_fn(**batch, params=params, dropout_rng=dropout_rng, train=True)[0] |
|
|
|
|
|
label_mask = jnp.where(labels != -100, 1.0, 0.0) |
|
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask |
|
|
|
|
|
loss = loss.sum() |
|
num_labels = label_mask.sum() |
|
|
|
return loss, num_labels |
|
|
|
grad_fn = jax.value_and_grad(loss_fn, has_aux=True) |
|
(loss, num_labels), grad = grad_fn(state.params) |
|
|
|
|
|
num_labels = jax.lax.psum(num_labels, "batch") |
|
|
|
|
|
loss = jax.lax.psum(loss, "batch") / num_labels |
|
|
|
|
|
grad = jax.lax.psum(grad, "batch") |
|
grad = jax.tree_util.tree_map(lambda x: x / num_labels, grad) |
|
new_state = state.apply_gradients(grads=grad) |
|
|
|
metrics = {"loss": loss, "learning_rate": linear_decay_lr_schedule_fn(state.step)} |
|
|
|
return new_state, metrics, new_dropout_rng |
|
|
|
|
|
|
|
p_train_step = jax.pmap(train_step, "batch") |
|
|
|
|
|
def eval_step(params, batch): |
|
labels = batch.pop("labels") |
|
|
|
logits = model(**batch, params=params, train=False)[0] |
|
|
|
|
|
label_mask = jnp.where(labels!=-100, 1.0, 0.0) |
|
loss = optax.softmax_cross_entropy(logits, onehot(labels, logits.shape[-1])) * label_mask |
|
|
|
|
|
accuracy = jnp.equal(jnp.argmax(logits, axis=-1), labels) * label_mask |
|
|
|
|
|
metrics = {"loss": loss.sum(), "accuracy": accuracy.sum(), "normalizer": label_mask.sum()} |
|
metrics = jax.lax.psum(metrics, axis_name="batch") |
|
|
|
return metrics |
|
|
|
p_eval_step = jax.pmap(eval_step, "batch", donate_argnums=(0,)) |
|
|
|
|
|
state = jax_utils.replicate(state) |
|
|
|
train_time = 0 |
|
epochs = tqdm(range(num_epochs), desc=f"Epoch ... (1/{num_epochs})", position=0) |
|
for epoch in epochs: |
|
|
|
testsample = [tokenized_datasets["train"][0]] |
|
testbatch = data_collator(testsample, pad_to_multiple_of=16) |
|
print("Labels in a sample batch:", testbatch["labels"]) |
|
|
|
|
|
train_start = time.time() |
|
train_metrics = [] |
|
|
|
|
|
rng, input_rng = jax.random.split(rng) |
|
|
|
|
|
num_train_samples = len(tokenized_datasets["train"]) |
|
|
|
train_samples_idx = np.arange(num_train_samples) |
|
train_samples_idx = np.random.permutation(train_samples_idx) |
|
|
|
train_samples_idx = np.array_split(train_samples_idx, jax.process_count())[jax.process_index()] |
|
train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size, drop_last=True) |
|
|
|
|
|
for step, batch_idx in enumerate(tqdm(train_batch_idx, desc="Training...", position=1)): |
|
samples = [tokenized_datasets["train"][int(idx)] for idx in batch_idx] |
|
model_inputs = data_collator(samples, pad_to_multiple_of=16) |
|
|
|
|
|
|
|
|
|
|
|
model_inputs = shard(model_inputs.data) |
|
state, train_metric, dropout_rngs = p_train_step(state, model_inputs, dropout_rngs) |
|
train_metrics.append(train_metric) |
|
|
|
cur_step = epoch * (num_train_samples // (train_batch_size * jax.process_count())) + step |
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0: |
|
|
|
|
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0: |
|
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) |
|
total_norm = 0 |
|
for p in jax.tree_util.tree_leaves(params): |
|
total_norm += np.sum(np.square(p)) |
|
total_norm = np.sqrt(total_norm) |
|
print("Parameter norm:", total_norm) |
|
|
|
|
|
train_metric = jax_utils.unreplicate(train_metric) |
|
train_time += time.time() - train_start |
|
if has_tensorboard and jax.process_index() == 0: |
|
write_train_metric(summary_writer, train_metrics, train_time, cur_step) |
|
|
|
epochs.write( |
|
f"Step... ({cur_step} | Loss: {train_metric['loss']}, Learning Rate:" |
|
f" {train_metric['learning_rate']})" |
|
) |
|
|
|
train_metrics = [] |
|
|
|
if cur_step % training_args.eval_steps == 0 and cur_step > 0: |
|
|
|
num_eval_samples = len(tokenized_datasets["validation"]) |
|
|
|
eval_samples_idx = np.arange(num_eval_samples) |
|
|
|
eval_samples_idx = np.array_split(eval_samples_idx, jax.process_count())[jax.process_index()] |
|
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False) |
|
|
|
eval_metrics = [] |
|
for i, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): |
|
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] |
|
model_inputs = data_collator(samples, pad_to_multiple_of=16) |
|
|
|
|
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)( |
|
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size |
|
) |
|
eval_metrics.append(metrics) |
|
|
|
|
|
eval_metrics = get_metrics(eval_metrics) |
|
eval_metrics = jax.tree_util.tree_map(jnp.sum, eval_metrics) |
|
eval_normalizer = eval_metrics.pop("normalizer") |
|
eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) |
|
|
|
|
|
epochs.desc = f"Step... ({cur_step} | Loss: {eval_metrics['loss']}, Acc: {eval_metrics['accuracy']})" |
|
|
|
|
|
if has_tensorboard and jax.process_index() == 0: |
|
write_eval_metric(summary_writer, eval_metrics, cur_step) |
|
|
|
if cur_step % training_args.save_steps == 0 and cur_step > 0: |
|
|
|
if jax.process_index() == 0: |
|
params = jax.device_get(jax.tree_util.tree_map(lambda x: x[0], state.params)) |
|
model.save_pretrained(training_args.output_dir, params=params) |
|
tokenizer.save_pretrained(training_args.output_dir) |
|
if training_args.push_to_hub: |
|
api.upload_folder( |
|
commit_message=f"Saving weights and logs of step {cur_step}", |
|
folder_path=training_args.output_dir, |
|
repo_id=repo_id, |
|
repo_type="model", |
|
token=training_args.hub_token, |
|
) |
|
|
|
if training_args.do_eval: |
|
num_eval_samples = len(tokenized_datasets["validation"]) |
|
|
|
eval_samples_idx = np.arange(num_eval_samples) |
|
eval_samples_idx = np.array_split(eval_samples_idx, jax.process_count())[jax.process_index()] |
|
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size, drop_last=False) |
|
|
|
eval_metrics = [] |
|
for _, batch_idx in enumerate(tqdm(eval_batch_idx, desc="Evaluating ...", position=2)): |
|
samples = [tokenized_datasets["validation"][int(idx)] for idx in batch_idx] |
|
model_inputs = data_collator(samples, pad_to_multiple_of=16) |
|
|
|
|
|
metrics = pad_shard_unpad(p_eval_step, static_return=True)( |
|
state.params, model_inputs.data, min_device_batch=per_device_eval_batch_size |
|
) |
|
eval_metrics.append(metrics) |
|
|
|
|
|
eval_metrics = get_metrics(eval_metrics) |
|
eval_metrics = jax.tree_util.tree_map(lambda metric: jnp.sum(metric).item(), eval_metrics) |
|
eval_normalizer = eval_metrics.pop("normalizer") |
|
eval_metrics = jax.tree_util.tree_map(lambda x: x / eval_normalizer, eval_metrics) |
|
|
|
try: |
|
perplexity = math.exp(eval_metrics["loss"]) |
|
except OverflowError: |
|
perplexity = float("inf") |
|
eval_metrics["perplexity"] = perplexity |
|
|
|
if jax.process_index() == 0: |
|
eval_metrics = {f"eval_{metric_name}": value for metric_name, value in eval_metrics.items()} |
|
path = os.path.join(training_args.output_dir, "eval_results.json") |
|
with open(path, "w") as f: |
|
json.dump(eval_metrics, f, indent=4, sort_keys=True) |
|
|
|
|
|
if __name__ == "__main__": |
|
main() |
|
|