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""" |
|
Pretraining the library models for denoising language modeling on a text file or a dataset. |
|
Here is the full list of checkpoints on the hub that can be pretrained by this script: |
|
https://huggingface.co/models?filter=bart |
|
""" |
|
|
|
|
|
import json |
|
import logging |
|
import math |
|
import os |
|
import sys |
|
import time |
|
from dataclasses import asdict, dataclass, field |
|
from enum import Enum |
|
from itertools import chain |
|
from pathlib import Path |
|
from typing import Dict, List, Optional |
|
|
|
import nltk |
|
import numpy as np |
|
from datasets import load_dataset, load_from_disk |
|
from tqdm import tqdm |
|
|
|
import flax |
|
import jax |
|
import jax.numpy as jnp |
|
import optax |
|
from flax import jax_utils, traverse_util |
|
from flax.jax_utils import pad_shard_unpad |
|
from flax.training import train_state |
|
from flax.training.common_utils import get_metrics, onehot, shard |
|
from huggingface_hub import Repository, create_repo |
|
from transformers import ( |
|
CONFIG_MAPPING, |
|
FLAX_MODEL_FOR_MASKED_LM_MAPPING, |
|
AutoTokenizer, |
|
BartConfig, |
|
BatchEncoding, |
|
FlaxBartForConditionalGeneration, |
|
HfArgumentParser, |
|
PreTrainedTokenizerBase, |
|
is_tensorboard_available, |
|
set_seed, |
|
) |
|
from transformers.models.bart.modeling_flax_bart import shift_tokens_right |
|
from transformers.utils import get_full_repo_name, send_example_telemetry |
|
|
|
|
|
MODEL_CONFIG_CLASSES = list(FLAX_MODEL_FOR_MASKED_LM_MAPPING.keys()) |
|
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) |
|
|
|
|
|
@dataclass |
|
class TrainingArguments: |
|
output_dir: str = field( |
|
metadata={"help": "The output directory where the model predictions and checkpoints will be written."}, |
|
) |
|
overwrite_output_dir: bool = field( |
|
default=False, |
|
metadata={ |
|
"help": ( |
|
"Overwrite the content of the output directory. " |
|
"Use this to continue training if output_dir points to a checkpoint directory." |
|
) |
|
}, |
|
) |
|
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."}) |
|
per_device_train_batch_size: int = field( |
|
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for training."} |
|
) |
|
per_device_eval_batch_size: int = field( |
|
default=8, metadata={"help": "Batch size per GPU/TPU core/CPU for evaluation."} |
|
) |
|
learning_rate: float = field(default=5e-5, metadata={"help": "The initial learning rate for AdamW."}) |
|
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"}) |
|
adam_beta2: float = field(default=0.999, metadata={"help": "Beta2 for AdamW optimizer"}) |
|
adam_epsilon: float = field(default=1e-8, metadata={"help": "Epsilon for AdamW optimizer."}) |
|
adafactor: bool = field(default=False, metadata={"help": "Whether or not to replace AdamW by Adafactor."}) |
|
num_train_epochs: float = field(default=3.0, metadata={"help": "Total number of training epochs to perform."}) |
|
warmup_steps: int = field(default=0, metadata={"help": "Linear warmup over warmup_steps."}) |
|
logging_steps: int = field(default=500, metadata={"help": "Log every X updates steps."}) |
|
save_steps: int = field(default=500, metadata={"help": "Save checkpoint every X updates steps."}) |
|
eval_steps: int = field(default=None, metadata={"help": "Run an evaluation every X steps."}) |
|
seed: int = field(default=42, metadata={"help": "Random seed that will be set at the beginning of training."}) |
|
push_to_hub: bool = field( |
|
default=False, metadata={"help": "Whether or not to upload the trained model to the model hub after training."} |
|
) |
|
hub_model_id: str = field( |
|
default=None, metadata={"help": "The name of the repository to keep in sync with the local `output_dir`."} |
|
) |
|
hub_token: str = field(default=None, metadata={"help": "The token to use to push to the Model Hub."}) |
|
|
|
def __post_init__(self): |
|
if self.output_dir is not None: |
|
self.output_dir = os.path.expanduser(self.output_dir) |
|
|
|
def to_dict(self): |
|
""" |
|
Serializes this instance while replace `Enum` by their values (for JSON serialization support). It obfuscates |
|
the token values by removing their value. |
|
""" |
|
d = asdict(self) |
|
for k, v in d.items(): |
|
if isinstance(v, Enum): |
|
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] |
|
if k.endswith("_token"): |
|
d[k] = f"<{k.upper()}>" |
|
return d |
|
|
|
|
|
@dataclass |
|
class ModelArguments: |
|
""" |
|
Arguments pertaining to which model/config/tokenizer we are going to fine-tune, or train from scratch. |
|
""" |
|
|
|
model_name_or_path: Optional[str] = field( |
|
default=None, |
|
metadata={ |
|
"help": ( |
|
"The model checkpoint for weights initialization.Don't set if you want to train a model from scratch." |
|
) |
|
}, |
|
) |
|
model_type: Optional[str] = field( |
|
default=None, |
|
metadata={"help": "If training from scratch, pass a model type from the list: " + ", ".join(MODEL_TYPES)}, |
|
) |
|
config_name: Optional[str] = field( |
|
default=None, metadata={"help": "Pretrained config name or path if not the same as model_name"} |
|
) |
|
tokenizer_name: Optional[str] = field( |
|
default=None, metadata={"help": "Pretrained tokenizer name or path if not the same as model_name"} |
|
) |
|
cache_dir: Optional[str] = field( |
|
default=None, metadata={"help": "Where do you want to store the pretrained models downloaded from s3"} |
|
) |
|
use_fast_tokenizer: bool = field( |
|
default=True, |
|
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, |
|
) |
|
dtype: Optional[str] = field( |
|
default="float32", |
|
metadata={ |
|
"help": ( |
|
"Floating-point format in which the model weights should be initialized and trained. Choose one of" |
|
" `[float32, float16, bfloat16]`." |
|
) |
|
}, |
|
) |
|
use_auth_token: bool = field( |
|
default=False, |
|
metadata={ |
|
"help": ( |
|
"Will use the token generated when running `huggingface-cli login` (necessary to use this script " |
|
"with private models)." |
|
) |
|
}, |
|
) |
|
|
|
|
|
@dataclass |
|
class DataTrainingArguments: |
|
""" |
|
Arguments pertaining to what data we are going to input our model for training and eval. |
|
""" |
|
|
|
dataset_name: Optional[str] = field( |
|
default=None, metadata={"help": "The name of the dataset to use (via the datasets library)."} |
|
) |
|
dataset_config_name: Optional[str] = field( |
|
default=None, metadata={"help": "The configuration name of the dataset to use (via the datasets library)."} |
|
) |
|
dataset_filepath: Optional[str] = field( |
|
default=None, metadata={"help": "Filepath to locally saved HF Dataset (with 'dataset.save_to_disk' method) to use for training"} |
|
) |
|
tokenized_dataset_filepath: Optional[str] = field( |
|
default=None, metadata={"help": "Filepath to locally saved pre-tokenized HF Dataset (with 'dataset.save_to_disk' method) to use for training"} |
|
) |
|
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 and masking. 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=0.3, metadata={"help": "Ratio of tokens to mask for span masked language modeling loss"} |
|
) |
|
permute_sentence_ratio: float = field( |
|
default=1.0, metadata={"help": "Ratio of sentences to be permuted in each document"} |
|
) |
|
poisson_lambda: float = field( |
|
default=3.5, metadata={"help": "Mean of Poisson distribution used to generate span-lengths to be masked"} |
|
) |
|
|
|
def __post_init__(self): |
|
if self.dataset_name is None and self.dataset_filepath is None and self.tokenized_dataset_filepath 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 FlaxDataCollatorForBartDenoisingLM: |
|
""" |
|
Data collator used for BART denoising language modeling. The code is largely copied from |
|
`<https://github.com/morganmcg1/rotobart/blob/main/data_collator.py#L223>`__. |
|
For more information on how BART denoising language modeling works, one can take a look |
|
at the `official paper <https://arxiv.org/pdf/1910.13461.pdf>`__ |
|
or the `official code for preprocessing <https://github.com/facebookresearch/fairseq/blob/main/fairseq/data/denoising_dataset.py>`__ . |
|
Args: |
|
tokenizer (:class:`~transformers.PreTrainedTokenizer` or :class:`~transformers.PreTrainedTokenizerFast`): |
|
The tokenizer used for encoding the data |
|
mask_ratio (:obj:`float`): |
|
The probability with which to (randomly) mask tokens in the input |
|
poisson_lambda (:obj:`float`): |
|
Mean parameter of Poisson distribution used to generate span-lengths to be masked |
|
permute_sentence_ratio (:obj:`float`): |
|
Ratio of sentences to be permuted in each document |
|
decoder_start_token_id: (:obj:`int): |
|
The decoder start token id of the model |
|
""" |
|
|
|
tokenizer: PreTrainedTokenizerBase |
|
decoder_start_token_id: int |
|
mask_ratio: float = 0.3 |
|
poisson_lambda: float = 3.0 |
|
permute_sentence_ratio: float = 1.0 |
|
|
|
def __post_init__(self): |
|
if self.tokenizer.mask_token is None or self.tokenizer.eos_token is None: |
|
raise ValueError( |
|
"This tokenizer does not have a mask token or eos token token which is necessary for denoising" |
|
" language modeling. " |
|
) |
|
|
|
def __call__(self, examples: List[Dict[str, List[int]]]) -> BatchEncoding: |
|
|
|
batch = BatchEncoding( |
|
{k: np.array([examples[i][k] for i in range(len(examples))]) for k, v in examples[0].items()} |
|
) |
|
batch["labels"] = batch["input_ids"].copy() |
|
batch["decoder_input_ids"] = shift_tokens_right( |
|
batch["labels"], self.tokenizer.pad_token_id, self.decoder_start_token_id |
|
) |
|
|
|
do_permute = False |
|
if self.permute_sentence_ratio > 0.0: |
|
batch["input_ids"] = self.permute_sentences(batch["input_ids"]) |
|
do_permute = True |
|
|
|
|
|
if self.mask_ratio: |
|
batch["input_ids"], batch["labels"] = self.span_mask_tokens( |
|
batch["input_ids"], batch["labels"], do_permute |
|
) |
|
|
|
|
|
batch["attention_mask"] = (batch["input_ids"] != self.tokenizer.pad_token_id).astype(int) |
|
batch["decoder_attention_mask"] = (batch["decoder_input_ids"] != self.tokenizer.pad_token_id).astype(int) |
|
return batch |
|
|
|
def permute_sentences(self, input_ids): |
|
""" |
|
Shuffle sentences in each document. |
|
""" |
|
results = input_ids.copy() |
|
|
|
|
|
end_sentence_mask = input_ids == self.tokenizer.pad_token_id |
|
sentence_ends = np.argwhere(end_sentence_mask) |
|
sentence_ends[:, 1] += 1 |
|
example_has_multiple_sentences, num_sentences = np.unique(sentence_ends[:, 0], return_counts=True) |
|
num_sentences_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_sentences)} |
|
|
|
num_to_permute = np.ceil(num_sentences * self.permute_sentence_ratio).astype(int) |
|
num_to_permute_map = { |
|
sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, num_to_permute) |
|
} |
|
|
|
sentence_ends = np.split(sentence_ends[:, 1], np.unique(sentence_ends[:, 0], return_index=True)[1][1:]) |
|
sentence_ends_map = {sent_idx: count for sent_idx, count in zip(example_has_multiple_sentences, sentence_ends)} |
|
|
|
for i in range(input_ids.shape[0]): |
|
if i not in example_has_multiple_sentences: |
|
continue |
|
substitutions = np.random.permutation(num_sentences_map[i])[: num_to_permute_map[i]] |
|
ordering = np.arange(0, num_sentences_map[i]) |
|
ordering[substitutions] = substitutions[np.random.permutation(num_to_permute_map[i])] |
|
|
|
|
|
index = 0 |
|
for j in ordering: |
|
sentence = input_ids[i, (sentence_ends_map[i][j - 1] if j > 0 else 0) : sentence_ends_map[i][j]] |
|
results[i, index : index + sentence.shape[0]] = sentence |
|
index += sentence.shape[0] |
|
return results |
|
|
|
def span_mask_tokens(self, input_ids, labels, do_permute): |
|
""" |
|
Sampling text spans with span lengths drawn from a Poisson distribution and masking them. |
|
""" |
|
special_tokens_mask_labels = [ |
|
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in labels.tolist() |
|
] |
|
special_tokens_mask_inputs = [ |
|
self.tokenizer.get_special_tokens_mask(val, already_has_special_tokens=True) for val in input_ids.tolist() |
|
] |
|
special_tokens_mask_labels = np.array(special_tokens_mask_labels, dtype=bool) |
|
special_tokens_mask_inputs = np.array(special_tokens_mask_inputs, dtype=bool) |
|
|
|
|
|
is_token_mask = ~(input_ids == self.tokenizer.pad_token_id) & ~special_tokens_mask_inputs |
|
num_tokens_to_mask = int(math.ceil(is_token_mask.astype(float).sum() * self.mask_ratio)) |
|
if num_tokens_to_mask == 0: |
|
return input_ids, labels |
|
|
|
|
|
span_lengths = np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,)) |
|
while np.cumsum(span_lengths, 0)[-1] < num_tokens_to_mask: |
|
span_lengths = np.concatenate( |
|
[span_lengths, np.random.poisson(lam=self.poisson_lambda, size=(num_tokens_to_mask,))] |
|
) |
|
|
|
|
|
|
|
|
|
span_lengths = span_lengths[span_lengths > 0] |
|
|
|
|
|
cutoff_idx = np.argmin(np.abs(np.cumsum(span_lengths, 0) - num_tokens_to_mask)) + 1 |
|
span_lengths = span_lengths[:cutoff_idx] |
|
|
|
|
|
token_indices = np.argwhere(is_token_mask == 1) |
|
span_starts = np.random.permutation(token_indices.shape[0])[: span_lengths.shape[0]] |
|
|
|
masked_indices = np.array(token_indices[span_starts]) |
|
mask = np.full_like(input_ids, fill_value=False) |
|
|
|
|
|
for mi in masked_indices: |
|
mask[tuple(mi)] = True |
|
span_lengths -= 1 |
|
|
|
|
|
max_index = input_ids.shape[1] - 1 |
|
remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) |
|
while np.any(remaining): |
|
masked_indices[remaining, 1] += 1 |
|
for mi in masked_indices: |
|
mask[tuple(mi)] = True |
|
span_lengths -= 1 |
|
remaining = (span_lengths > 0) & (masked_indices[:, 1] < max_index) |
|
|
|
|
|
mask[np.where(special_tokens_mask_inputs)] = False |
|
input_ids[np.where(mask)] = self.tokenizer.mask_token_id |
|
if not do_permute: |
|
labels[np.where(mask == 0)] = -100 |
|
else: |
|
labels[np.where(special_tokens_mask_labels)] = -100 |
|
|
|
|
|
to_remove = (mask == 1) & np.roll((mask == 1), 1, 1) |
|
new_input_ids = np.full_like(input_ids, fill_value=self.tokenizer.pad_token_id) |
|
for i, example in enumerate(input_ids): |
|
new_example = example[~to_remove[i]] |
|
new_input_ids[i, : new_example.shape[0]] = new_example |
|
|
|
return new_input_ids, 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_bart_dlm", 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: |
|
if training_args.hub_model_id is None: |
|
repo_name = get_full_repo_name( |
|
Path(training_args.output_dir).absolute().name, token=training_args.hub_token |
|
) |
|
else: |
|
repo_name = training_args.hub_model_id |
|
create_repo(repo_name, exist_ok=True, token=training_args.hub_token) |
|
repo = Repository(training_args.output_dir, clone_from=repo_name, token=training_args.hub_token) |
|
|
|
|
|
|
|
|
|
|
|
|
|
|
|
if not data_args.tokenized_dataset_filepath: |
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
|
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
elif data_args.dataset_filepath is not None: |
|
|
|
datasets = load_from_disk( |
|
data_args.dataset_filepath |
|
) |
|
if "validation" not in datasets.keys(): |
|
datasets = datasets.train_test_split( |
|
test_size=data_args.validation_split_percentage/100, shuffle=True, seed=training_args.seed) |
|
datasets["validation"] = datasets["test"] |
|
keys_to_remove = set(datasets.keys()) - \ |
|
set(["train", "validation"]) |
|
for key in keys_to_remove: |
|
del datasets[key] |
|
else: |
|
data_files = {} |
|
if data_args.train_file is not None: |
|
data_files["train"] = data_args.train_file |
|
if data_args.validation_file is not None: |
|
data_files["validation"] = data_args.validation_file |
|
extension = data_args.train_file.split(".")[-1] |
|
if extension == "txt": |
|
extension = "text" |
|
datasets = load_dataset( |
|
extension, |
|
data_files=data_files, |
|
cache_dir=model_args.cache_dir, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
|
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
datasets["train"] = load_dataset( |
|
extension, |
|
data_files=data_files, |
|
split=f"train[{data_args.validation_split_percentage}%:]", |
|
cache_dir=model_args.cache_dir, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
|
|
|
|
print(datasets) |
|
|
|
|
|
|
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
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, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
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.config_name: |
|
config = BartConfig.from_pretrained( |
|
model_args.config_name, |
|
cache_dir=model_args.cache_dir, |
|
vocab_size=len(tokenizer), |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
elif model_args.model_name_or_path: |
|
config = BartConfig.from_pretrained( |
|
model_args.model_name_or_path, |
|
cache_dir=model_args.cache_dir, |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
else: |
|
config = CONFIG_MAPPING[model_args.model_type]() |
|
logger.warning("You are instantiating a new config instance from scratch.") |
|
|
|
if not data_args.tokenized_dataset_filepath: |
|
|
|
|
|
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) |
|
|
|
|
|
nltk.download("punkt") |
|
sentence_tokenizer = nltk.data.load("tokenizers/punkt/finnish.pickle") |
|
|
|
def sentence_split_function(example): |
|
sents = sentence_tokenizer.tokenize(example["text"]) |
|
|
|
new_text = tokenizer.bos_token + f"{tokenizer.pad_token}".join(sents) + tokenizer.eos_token |
|
return {"text": new_text} |
|
|
|
split_datasets = datasets.map( |
|
sentence_split_function, |
|
batched=False, |
|
num_proc=data_args.preprocessing_num_workers, |
|
remove_columns=column_names, |
|
load_from_cache_file=not data_args.overwrite_cache, |
|
) |
|
|
|
|
|
|
|
def tokenize_function(examples): |
|
return tokenizer(examples[text_column_name], add_special_tokens=False, return_attention_mask=False) |
|
|
|
tokenized_datasets = split_datasets.map( |
|
tokenize_function, |
|
batched=True, |
|
num_proc=data_args.preprocessing_num_workers, |
|
remove_columns=text_column_name, |
|
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, |
|
) |
|
|
|
tokenized_datasets.save_to_disk("/researchdisk/lm_training_dataset_tokenized") |
|
else: |
|
tokenized_datasets = load_from_disk(data_args.tokenized_dataset_filepath) |
|
|
|
|
|
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." |
|
) |
|
|
|
|
|
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 = FlaxBartForConditionalGeneration.from_pretrained( |
|
model_args.model_name_or_path, |
|
config=config, |
|
seed=training_args.seed, |
|
dtype=getattr(jnp, model_args.dtype), |
|
use_auth_token=True if model_args.use_auth_token else None, |
|
) |
|
else: |
|
config.vocab_size = len(tokenizer) |
|
model = FlaxBartForConditionalGeneration( |
|
config, |
|
seed=training_args.seed, |
|
dtype=getattr(jnp, model_args.dtype), |
|
) |
|
|
|
|
|
|
|
data_collator = FlaxDataCollatorForBartDenoisingLM( |
|
tokenizer=tokenizer, |
|
decoder_start_token_id=model.config.decoder_start_token_id, |
|
mask_ratio=data_args.mlm_probability, |
|
poisson_lambda=data_args.poisson_lambda, |
|
permute_sentence_ratio=data_args.permute_sentence_ratio, |
|
) |
|
|
|
|
|
num_epochs = int(training_args.num_train_epochs) |
|
train_batch_size = int(training_args.per_device_train_batch_size) * jax.device_count() |
|
per_device_eval_batch_size = int(training_args.per_device_eval_batch_size) |
|
eval_batch_size = per_device_eval_batch_size * jax.device_count() |
|
|
|
num_train_steps = len(tokenized_datasets["train"]) // train_batch_size * num_epochs |
|
|
|
|
|
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 = set( |
|
[ |
|
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, |
|
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 > 0, 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") |
|
loss = jax.tree_util.tree_map(lambda x: x / num_labels, loss) |
|
|
|
|
|
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", donate_argnums=(0,)) |
|
|
|
|
|
def eval_step(params, batch): |
|
labels = batch.pop("labels") |
|
|
|
logits = model(**batch, params=params, train=False)[0] |
|
|
|
|
|
label_mask = jnp.where(labels > 0, 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="Epoch ... ", position=0) |
|
for epoch in epochs: |
|
|
|
train_start = time.time() |
|
train_metrics = [] |
|
|
|
|
|
rng, input_rng = jax.random.split(rng) |
|
|
|
|
|
num_train_samples = len(tokenized_datasets["train"]) |
|
|
|
train_samples_idx = np.random.permutation(np.arange(num_train_samples)) |
|
train_batch_idx = generate_batch_splits(train_samples_idx, train_batch_size) |
|
|
|
|
|
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) |
|
|
|
|
|
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) + step |
|
|
|
if cur_step % training_args.logging_steps == 0 and cur_step > 0: |
|
|
|
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_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) |
|
|
|
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) |
|
|
|
|
|
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) |
|
|
|
try: |
|
perplexity = math.exp(eval_metrics["loss"]) |
|
except OverflowError: |
|
perplexity = float("inf") |
|
eval_metrics["perplexity"] = perplexity |
|
|
|
|
|
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: |
|
repo.push_to_hub(commit_message=f"Saving weights and logs of step {cur_step}", blocking=False) |
|
|
|
|
|
if training_args.do_eval: |
|
num_eval_samples = len(tokenized_datasets["validation"]) |
|
|
|
eval_samples_idx = np.arange(num_eval_samples) |
|
eval_batch_idx = generate_batch_splits(eval_samples_idx, eval_batch_size) |
|
|
|
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) |
|
|
|
|
|
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() |