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from preprocess import load_datasets, DatasetArguments
from predict import ClassifierArguments, SPONSOR_MATCH_RE
from shared import CustomTokens, device, GeneralArguments, OutputArguments
from model import ModelArguments, get_model, get_tokenizer
import transformers
import logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import datasets
import pickle
from transformers import (
DataCollatorForSeq2Seq,
HfArgumentParser,
Seq2SeqTrainer,
Seq2SeqTrainingArguments
)
from transformers.trainer_utils import get_last_checkpoint
from transformers.utils import check_min_version
from transformers.utils.versions import require_version
from sklearn.linear_model import LogisticRegression
from sklearn.feature_extraction.text import TfidfVectorizer
from utils import re_findall
import re
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version('4.13.0.dev0')
require_version('datasets>=1.8.0',
'To fix: pip install -r requirements.txt')
os.environ['WANDB_DISABLED'] = 'true'
logger = logging.getLogger(__name__)
# Setup logging
logging.basicConfig(
format='%(asctime)s - %(levelname)s - %(name)s - %(message)s',
datefmt='%m/%d/%Y %H:%M:%S',
handlers=[logging.StreamHandler(sys.stdout)],
)
@dataclass
class DataTrainingArguments:
"""
Arguments pertaining to what data we are going to input our model for training and eval.
"""
preprocessing_num_workers: Optional[int] = field(
default=None,
metadata={'help': 'The number of processes to use for the preprocessing.'},
)
# https://github.com/huggingface/transformers/issues/5204
max_source_length: Optional[int] = field(
default=512,
metadata={
'help': 'The maximum total input sequence length after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
},
)
max_target_length: Optional[int] = field(
default=512,
metadata={
'help': 'The maximum total sequence length for target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded.'
},
)
val_max_target_length: Optional[int] = field(
default=None,
metadata={
'help': 'The maximum total sequence length for validation target text after tokenization. Sequences longer '
'than this will be truncated, sequences shorter will be padded. Will default to `max_target_length`.'
'This argument is also used to override the ``max_length`` param of ``model.generate``, which is used '
'during ``evaluate`` and ``predict``.'
},
)
pad_to_max_length: bool = field(
default=False,
metadata={
'help': 'Whether to pad all samples to model maximum sentence length. '
'If False, will pad the samples dynamically when batching to the maximum length in the batch. More '
'efficient on GPU but very bad for TPU.'
},
)
max_train_samples: Optional[int] = field(
default=None,
metadata={
'help': 'For debugging purposes or quicker training, truncate the number of training examples to this value if set.'
},
)
max_eval_samples: Optional[int] = field(
default=None,
metadata={
'help': 'For debugging purposes or quicker training, truncate the number of evaluation examples to this value if set.'
},
)
max_predict_samples: Optional[int] = field(
default=None,
metadata={
'help': 'For debugging purposes or quicker training, truncate the number of prediction examples to this value if set.'
},
)
num_beams: Optional[int] = field(
default=None,
metadata={
'help': 'Number of beams to use for evaluation. This argument will be passed to ``model.generate``, '
'which is used during ``evaluate`` and ``predict``.'
},
)
ignore_pad_token_for_loss: bool = field(
default=True,
metadata={
'help': 'Whether to ignore the tokens corresponding to padded labels in the loss computation or not.'
},
)
source_prefix: Optional[str] = field(
default=CustomTokens.EXTRACT_SEGMENTS_PREFIX.value, metadata={
'help': 'A prefix to add before every source text (useful for T5 models).'}
)
# TODO add vectorizer params
def __post_init__(self):
if self.val_max_target_length is None:
self.val_max_target_length = self.max_target_length
@dataclass
class SequenceTrainingArguments(OutputArguments, Seq2SeqTrainingArguments):
seed: Optional[int] = GeneralArguments.__dataclass_fields__['seed']
num_train_epochs: float = field(
default=1, metadata={'help': 'Total number of training epochs to perform.'})
save_steps: int = field(default=5000, metadata={
'help': 'Save checkpoint every X updates steps.'})
eval_steps: int = field(default=5000, metadata={
'help': 'Run an evaluation every X steps.'})
logging_steps: int = field(default=5000, metadata={
'help': 'Log every X updates steps.'})
skip_train_transformer: bool = field(default=False, metadata={
'help': 'Whether to skip training the transformer.'})
train_classifier: bool = field(default=False, metadata={
'help': 'Whether to run training on the 2nd phase (classifier).'})
# do_eval: bool = field(default=False, metadata={
# 'help': 'Whether to run eval on the dev set.'})
do_predict: bool = field(default=False, metadata={
'help': 'Whether to run predictions on the test set.'})
per_device_train_batch_size: int = field(
default=4, metadata={'help': 'Batch size per GPU/TPU core/CPU for training.'}
)
per_device_eval_batch_size: int = field(
default=4, metadata={'help': 'Batch size per GPU/TPU core/CPU for evaluation.'}
)
# report_to: Optional[List[str]] = field(
# default=None, metadata={"help": "The list of integrations to report the results and logs to."}
# )
evaluation_strategy: str = field(
default='steps',
metadata={
'help': 'The evaluation strategy to use.',
'choices': ['no', 'steps', 'epoch']
},
)
# evaluation_strategy (:obj:`str` or :class:`~transformers.trainer_utils.IntervalStrategy`, `optional`, defaults to :obj:`"no"`):
# The evaluation strategy to adopt during training. Possible values are:
# * :obj:`"no"`: No evaluation is done during training.
# * :obj:`"steps"`: Evaluation is done (and logged) every :obj:`eval_steps`.
# * :obj:`"epoch"`: Evaluation is done at the end of each epoch.
def main():
# reset()
# See all possible arguments in src/transformers/training_args.py
# or by passing the --help flag to this script.
# We now keep distinct sets of args, for a cleaner separation of concerns.
hf_parser = HfArgumentParser((
ModelArguments,
DatasetArguments,
DataTrainingArguments,
SequenceTrainingArguments,
ClassifierArguments
))
model_args, dataset_args, data_training_args, training_args, classifier_args = hf_parser.parse_args_into_dataclasses()
log_level = training_args.get_process_log_level()
logger.setLevel(log_level)
datasets.utils.logging.set_verbosity(log_level)
transformers.utils.logging.set_verbosity(log_level)
transformers.utils.logging.enable_default_handler()
transformers.utils.logging.enable_explicit_format()
# Set seed before initializing model.
# set_seed(training_args.seed)
# Log on each process the small summary:
logger.warning(
f'Process rank: {training_args.local_rank}, device: {training_args.device}, n_gpu: {training_args.n_gpu}'
+ f'distributed training: {bool(training_args.local_rank != -1)}, 16-bits training: {training_args.fp16}'
)
logger.info(f'Training/evaluation parameters {training_args}')
# FP16 https://github.com/huggingface/transformers/issues/9295
# Works:
# https://huggingface.co/docs/transformers/model_doc/t5v1.1
# google/t5-v1_1-small
# google/t5-v1_1-base
# google/t5-v1_1-large
# google/t5-v1_1-xl
# google/t5-v1_1-xxl
# https://huggingface.co/docs/transformers/model_doc/t5
# t5-small
# t5-base
# t5-large
# t5-3b
# t5-11b
# allenai/led-base-16384 - https://github.com/huggingface/transformers/issues/9810
# Further work:
# Multilingual- https://huggingface.co/docs/transformers/model_doc/mt5
# In distributed training, the load_dataset function guarantees that only one local process can concurrently
# download the dataset.
if training_args.skip_train_transformer and not training_args.train_classifier:
print('Nothing to do. Exiting')
return
raw_datasets = load_datasets(dataset_args)
# , cache_dir=model_args.cache_dir
# See more about loading any type of standard or custom dataset (from files, python dict, pandas DataFrame, etc) at
# https://huggingface.co/docs/datasets/loading_datasets.html.
if training_args.train_classifier:
print('Train classifier')
# 1. Vectorize raw data to pass into classifier
# CountVectorizer TfidfVectorizer
# TfidfVectorizer - better (comb of CountVectorizer)
vectorizer = TfidfVectorizer( # CountVectorizer
# lowercase=False,
# stop_words='english', # TODO optimise stop words?
# stop_words=stop_words,
ngram_range=(1, 2), # best so far
# max_features=8000 # remove for higher accuracy?
# max_features=50000
max_features=10000
)
train_test_data = {
'train': {
'X': [],
'y': []
},
'test': {
'X': [],
'y': []
}
}
print('Splitting')
for ds_type in train_test_data:
dataset = raw_datasets[ds_type]
for row in dataset:
# Get matches:
matches = re_findall(SPONSOR_MATCH_RE, row['extracted'])
return # TODO fix
if not matches:
matches = [row['text']]
for match in matches:
train_test_data[ds_type]['X'].append(match)
train_test_data[ds_type]['y'].append(row['sponsor'])
print('Fitting')
_X_train = vectorizer.fit_transform(train_test_data['train']['X'])
_X_test = vectorizer.transform(train_test_data['test']['X'])
y_train = train_test_data['train']['y']
y_test = train_test_data['test']['y']
# 2. Create classifier
classifier = LogisticRegression(max_iter=500)
# 3. Fit data
print('fit classifier')
classifier.fit(_X_train, y_train)
# 4. Measure accuracy
accuracy = classifier.score(_X_test, y_test)
print(f'[LogisticRegression] Accuracy percent:',
round(accuracy*100, 3))
# 5. Save classifier and vectorizer
with open(os.path.join(classifier_args.classifier_dir, classifier_args.classifier_file), 'wb') as fp:
pickle.dump(classifier, fp)
with open(os.path.join(classifier_args.classifier_dir, classifier_args.vectorizer_file), 'wb') as fp:
pickle.dump(vectorizer, fp)
if not training_args.skip_train_transformer:
if data_training_args.source_prefix is None and 't5-' in model_args.model_name_or_path:
logger.warning(
"You're running a t5 model but didn't provide a source prefix, which is the expected, e.g. with `--source_prefix 'summarize: ' `"
)
# Detecting last checkpoint.
last_checkpoint = None
if os.path.isdir(training_args.output_dir) and not training_args.overwrite_output_dir:
last_checkpoint = get_last_checkpoint(training_args.output_dir)
if last_checkpoint is None and len(os.listdir(training_args.output_dir)) > 0:
raise ValueError(
f'Output directory ({training_args.output_dir}) already exists and is not empty. Use --overwrite_output_dir to overcome.'
)
elif last_checkpoint is not None and training_args.resume_from_checkpoint is None:
logger.info(
f'Checkpoint detected, resuming training at {last_checkpoint}. To avoid this behavior, change the `--output_dir` or add `--overwrite_output_dir` to train from scratch.'
)
# Load pretrained model and tokenizer
tokenizer = get_tokenizer(model_args)
model = get_model(model_args)
model.to(device())
model.resize_token_embeddings(len(tokenizer))
if model.config.decoder_start_token_id is None:
raise ValueError(
'Make sure that `config.decoder_start_token_id` is correctly defined')
if hasattr(model.config, 'max_position_embeddings') and model.config.max_position_embeddings < data_training_args.max_source_length:
if model_args.resize_position_embeddings is None:
logger.warning(
f"Increasing the model's number of position embedding vectors from {model.config.max_position_embeddings} to {data_training_args.max_source_length}."
)
model.resize_position_embeddings(
data_training_args.max_source_length)
elif model_args.resize_position_embeddings:
model.resize_position_embeddings(
data_training_args.max_source_length)
else:
raise ValueError(
f'`--max_source_length` is set to {data_training_args.max_source_length}, but the model only has {model.config.max_position_embeddings}'
f' position encodings. Consider either reducing `--max_source_length` to {model.config.max_position_embeddings} or to automatically '
"resize the model's position encodings by passing `--resize_position_embeddings`."
)
# Preprocessing the datasets.
# We need to tokenize inputs and targets.
column_names = raw_datasets['train'].column_names
# Temporarily set max_target_length for training.
max_target_length = data_training_args.max_target_length
padding = 'max_length' if data_training_args.pad_to_max_length else False
if training_args.label_smoothing_factor > 0 and not hasattr(model, 'prepare_decoder_input_ids_from_labels'):
logger.warning(
'label_smoothing is enabled but the `prepare_decoder_input_ids_from_labels` method is not defined for'
f'`{model.__class__.__name__}`. This will lead to loss being calculated twice and will take up more memory'
)
prefix = data_training_args.source_prefix if data_training_args.source_prefix is not None else ''
# https://github.com/huggingface/transformers/issues/5204
def preprocess_function(examples):
inputs = examples['text']
targets = examples['extracted']
inputs = [prefix + inp for inp in inputs]
model_inputs = tokenizer(
inputs, max_length=data_training_args.max_source_length, padding=padding, truncation=True)
# Setup the tokenizer for targets
with tokenizer.as_target_tokenizer():
labels = tokenizer(
targets, max_length=max_target_length, padding=padding, truncation=True)
# If we are padding here, replace all tokenizer.pad_token_id in the labels by -100 when we want to ignore
# padding in the loss.
if padding == 'max_length' and data_training_args.ignore_pad_token_for_loss:
labels['input_ids'] = [
[(l if l != tokenizer.pad_token_id else -100) for l in label] for label in labels['input_ids']
]
model_inputs['labels'] = labels['input_ids']
return model_inputs
def prepare_dataset(dataset, desc):
return dataset.map(
preprocess_function,
batched=True,
num_proc=data_training_args.preprocessing_num_workers,
remove_columns=column_names,
load_from_cache_file=not dataset_args.overwrite_cache,
desc=desc, # tokenizing train dataset
)
# train_dataset # TODO shuffle?
# if training_args.do_train:
if 'train' not in raw_datasets: # TODO do checks above?
raise ValueError('Train dataset missing')
train_dataset = raw_datasets['train']
if data_training_args.max_train_samples is not None:
train_dataset = train_dataset.select(
range(data_training_args.max_train_samples))
with training_args.main_process_first(desc='train dataset map pre-processing'):
train_dataset = prepare_dataset(
train_dataset, desc='Running tokenizer on train dataset')
max_target_length = data_training_args.val_max_target_length
if 'validation' not in raw_datasets:
raise ValueError('Validation dataset missing')
eval_dataset = raw_datasets['validation']
if data_training_args.max_eval_samples is not None:
eval_dataset = eval_dataset.select(
range(data_training_args.max_eval_samples))
with training_args.main_process_first(desc='validation dataset map pre-processing'):
eval_dataset = prepare_dataset(
eval_dataset, desc='Running tokenizer on validation dataset')
if 'test' not in raw_datasets:
raise ValueError('Test dataset missing')
predict_dataset = raw_datasets['test']
if data_training_args.max_predict_samples is not None:
predict_dataset = predict_dataset.select(
range(data_training_args.max_predict_samples))
with training_args.main_process_first(desc='prediction dataset map pre-processing'):
predict_dataset = prepare_dataset(
predict_dataset, desc='Running tokenizer on prediction dataset')
# Data collator
label_pad_token_id = - \
100 if data_training_args.ignore_pad_token_for_loss else tokenizer.pad_token_id
data_collator = DataCollatorForSeq2Seq(
tokenizer,
model=model,
label_pad_token_id=label_pad_token_id,
pad_to_multiple_of=8 if training_args.fp16 else None,
)
# Done processing datasets
# Initialize our Trainer
trainer = Seq2SeqTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
tokenizer=tokenizer,
data_collator=data_collator,
)
# Training
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
try:
train_result = trainer.train(resume_from_checkpoint=checkpoint)
trainer.save_model() # Saves the tokenizer too for easy upload
except KeyboardInterrupt:
print('Saving model')
trainer.save_model(os.path.join(
training_args.output_dir, 'checkpoint-latest')) # TODO use dir
raise
metrics = train_result.metrics
max_train_samples = data_training_args.max_train_samples or 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()
kwargs = {'finetuned_from': model_args.model_name_or_path,
'tasks': 'summarization'}
if training_args.push_to_hub:
trainer.push_to_hub(**kwargs)
else:
trainer.create_model_card(**kwargs)
if __name__ == '__main__':
main()
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