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# coding=utf-8 | |
# Copyright 2020 The HuggingFace Team All rights reserved. | |
# | |
# Licensed under the Apache License, Version 2.0 (the "License"); | |
# you may not use this file except in compliance with the License. | |
# You may obtain a copy of the License at | |
# | |
# http://www.apache.org/licenses/LICENSE-2.0 | |
# | |
# Unless required by applicable law or agreed to in writing, software | |
# distributed under the License is distributed on an "AS IS" BASIS, | |
# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. | |
# See the License for the specific language governing permissions and | |
# limitations under the License. | |
""" | |
Fine-tuning the library models for masked language modeling (BERT, ALBERT, RoBERTa...) with whole word masking on a | |
text file or a dataset. | |
Here is the full list of checkpoints on the hub that can be fine-tuned by this script: | |
https://huggingface.co/models?filter=fill-mask | |
""" | |
# You can also adapt this script on your own masked language modeling task. Pointers for this are left as comments. | |
import json | |
import logging | |
import math | |
import os | |
import sys | |
from dataclasses import dataclass, field | |
from typing import Optional | |
from datasets import Dataset, load_dataset | |
import transformers | |
from transformers import ( | |
CONFIG_MAPPING, | |
MODEL_FOR_MASKED_LM_MAPPING, | |
AutoConfig, | |
AutoModelForMaskedLM, | |
AutoTokenizer, | |
DataCollatorForWholeWordMask, | |
HfArgumentParser, | |
Trainer, | |
TrainingArguments, | |
set_seed, | |
) | |
from transformers.trainer_utils import get_last_checkpoint, is_main_process | |
logger = logging.getLogger(__name__) | |
MODEL_CONFIG_CLASSES = list(MODEL_FOR_MASKED_LM_MAPPING.keys()) | |
MODEL_TYPES = tuple(conf.model_type for conf in MODEL_CONFIG_CLASSES) | |
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_overrides: Optional[str] = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"Override some existing default config settings when a model is trained from scratch. Example: " | |
"n_embd=10,resid_pdrop=0.2,scale_attn_weights=false,summary_type=cls_index" | |
) | |
}, | |
) | |
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 huggingface.co"}, | |
) | |
use_fast_tokenizer: bool = field( | |
default=True, | |
metadata={"help": "Whether to use one of the fast tokenizer (backed by the tokenizers library) or not."}, | |
) | |
model_revision: str = field( | |
default="main", | |
metadata={"help": "The specific model version to use (can be a branch name, tag name or commit id)."}, | |
) | |
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)." | |
) | |
}, | |
) | |
def __post_init__(self): | |
if self.config_overrides is not None and (self.config_name is not None or self.model_name_or_path is not None): | |
raise ValueError( | |
"--config_overrides can't be used in combination with --config_name or --model_name_or_path" | |
) | |
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)."} | |
) | |
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." | |
) | |
}, | |
) | |
def __post_init__(self): | |
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." | |
def add_chinese_references(dataset, ref_file): | |
with open(ref_file, "r", encoding="utf-8") as f: | |
refs = [json.loads(line) for line in f.read().splitlines() if (len(line) > 0 and not line.isspace())] | |
assert len(dataset) == len(refs) | |
dataset_dict = {c: dataset[c] for c in dataset.column_names} | |
dataset_dict["chinese_ref"] = refs | |
return Dataset.from_dict(dataset_dict) | |
def main(): | |
# 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. | |
parser = HfArgumentParser((ModelArguments, DataTrainingArguments, TrainingArguments)) | |
if len(sys.argv) == 2 and sys.argv[1].endswith(".json"): | |
# If we pass only one argument to the script and it's the path to a json file, | |
# let's parse it to get our arguments. | |
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() | |
# Detecting last checkpoint. | |
last_checkpoint = None | |
if os.path.isdir(training_args.output_dir) and training_args.do_train 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: | |
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." | |
) | |
# 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)], | |
) | |
logger.setLevel(logging.INFO if is_main_process(training_args.local_rank) else logging.WARN) | |
# 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}" | |
) | |
# Set the verbosity to info of the Transformers logger (on main process only): | |
if is_main_process(training_args.local_rank): | |
transformers.utils.logging.set_verbosity_info() | |
transformers.utils.logging.enable_default_handler() | |
transformers.utils.logging.enable_explicit_format() | |
logger.info("Training/evaluation parameters %s", training_args) | |
# Set seed before initializing model. | |
set_seed(training_args.seed) | |
# Get the datasets: you can either provide your own CSV/JSON/TXT training and evaluation files (see below) | |
# or just provide the name of one of the public datasets available on the hub at https://huggingface.co/datasets/ | |
# (the dataset will be downloaded automatically from the datasets Hub). | |
# | |
# For CSV/JSON files, this script will use the column called 'text' or the first column if no column called | |
# 'text' is found. You can easily tweak this behavior (see below). | |
# | |
# In distributed training, the load_dataset function guarantee that only one local process can concurrently | |
# download the dataset. | |
if data_args.dataset_name is not None: | |
# Downloading and loading a dataset from the hub. | |
datasets = load_dataset(data_args.dataset_name, data_args.dataset_config_name) | |
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}%]", | |
) | |
datasets["train"] = load_dataset( | |
data_args.dataset_name, | |
data_args.dataset_config_name, | |
split=f"train[{data_args.validation_split_percentage}%:]", | |
) | |
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) | |
# 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. | |
# Load pretrained model and tokenizer | |
# | |
# Distributed training: | |
# The .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
config_kwargs = { | |
"cache_dir": model_args.cache_dir, | |
"revision": model_args.model_revision, | |
"use_auth_token": True if model_args.use_auth_token else None, | |
} | |
if model_args.config_name: | |
config = AutoConfig.from_pretrained(model_args.config_name, **config_kwargs) | |
elif model_args.model_name_or_path: | |
config = AutoConfig.from_pretrained(model_args.model_name_or_path, **config_kwargs) | |
else: | |
config = CONFIG_MAPPING[model_args.model_type]() | |
logger.warning("You are instantiating a new config instance from scratch.") | |
if model_args.config_overrides is not None: | |
logger.info(f"Overriding config: {model_args.config_overrides}") | |
config.update_from_string(model_args.config_overrides) | |
logger.info(f"New config: {config}") | |
tokenizer_kwargs = { | |
"cache_dir": model_args.cache_dir, | |
"use_fast": model_args.use_fast_tokenizer, | |
"revision": model_args.model_revision, | |
"use_auth_token": True if model_args.use_auth_token else None, | |
} | |
if model_args.tokenizer_name: | |
tokenizer = AutoTokenizer.from_pretrained(model_args.tokenizer_name, **tokenizer_kwargs) | |
elif model_args.model_name_or_path: | |
tokenizer = AutoTokenizer.from_pretrained(model_args.model_name_or_path, **tokenizer_kwargs) | |
else: | |
raise ValueError( | |
"You are instantiating a new tokenizer from scratch. This is not supported by this script." | |
"You can do it from another script, save it, and load it from here, using --tokenizer_name." | |
) | |
if model_args.model_name_or_path: | |
model = AutoModelForMaskedLM.from_pretrained( | |
model_args.model_name_or_path, | |
from_tf=bool(".ckpt" in model_args.model_name_or_path), | |
config=config, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
use_auth_token=True if model_args.use_auth_token else None, | |
) | |
else: | |
logger.info("Training new model from scratch") | |
model = AutoModelForMaskedLM.from_config(config) | |
model.resize_token_embeddings(len(tokenizer)) | |
# Preprocessing the datasets. | |
# First we tokenize all the texts. | |
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] | |
padding = "max_length" if data_args.pad_to_max_length else False | |
def tokenize_function(examples): | |
# Remove empty lines | |
examples["text"] = [line for line in examples["text"] if len(line) > 0 and not line.isspace()] | |
return tokenizer(examples["text"], padding=padding, truncation=True, max_length=data_args.max_seq_length) | |
tokenized_datasets = 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, | |
) | |
# Add the chinese references if provided | |
if data_args.train_ref_file is not None: | |
tokenized_datasets["train"] = add_chinese_references(tokenized_datasets["train"], data_args.train_ref_file) | |
if data_args.validation_ref_file is not None: | |
tokenized_datasets["validation"] = add_chinese_references( | |
tokenized_datasets["validation"], data_args.validation_ref_file | |
) | |
# If we have ref files, need to avoid it removed by trainer | |
has_ref = data_args.train_ref_file or data_args.validation_ref_file | |
if has_ref: | |
training_args.remove_unused_columns = False | |
# Data collator | |
# This one will take care of randomly masking the tokens. | |
data_collator = DataCollatorForWholeWordMask(tokenizer=tokenizer, mlm_probability=data_args.mlm_probability) | |
# Initialize our Trainer | |
trainer = Trainer( | |
model=model, | |
args=training_args, | |
train_dataset=tokenized_datasets["train"] if training_args.do_train else None, | |
eval_dataset=tokenized_datasets["validation"] if training_args.do_eval else None, | |
tokenizer=tokenizer, | |
data_collator=data_collator, | |
) | |
# Training | |
if training_args.do_train: | |
if last_checkpoint is not None: | |
checkpoint = last_checkpoint | |
elif model_args.model_name_or_path is not None and os.path.isdir(model_args.model_name_or_path): | |
checkpoint = model_args.model_name_or_path | |
else: | |
checkpoint = None | |
train_result = trainer.train(resume_from_checkpoint=checkpoint) | |
trainer.save_model() # Saves the tokenizer too for easy upload | |
output_train_file = os.path.join(training_args.output_dir, "train_results.txt") | |
if trainer.is_world_process_zero(): | |
with open(output_train_file, "w") as writer: | |
logger.info("***** Train results *****") | |
for key, value in sorted(train_result.metrics.items()): | |
logger.info(f" {key} = {value}") | |
writer.write(f"{key} = {value}\n") | |
# Need to save the state, since Trainer.save_model saves only the tokenizer with the model | |
trainer.state.save_to_json(os.path.join(training_args.output_dir, "trainer_state.json")) | |
# Evaluation | |
results = {} | |
if training_args.do_eval: | |
logger.info("*** Evaluate ***") | |
eval_output = trainer.evaluate() | |
perplexity = math.exp(eval_output["eval_loss"]) | |
results["perplexity"] = perplexity | |
output_eval_file = os.path.join(training_args.output_dir, "eval_results_mlm_wwm.txt") | |
if trainer.is_world_process_zero(): | |
with open(output_eval_file, "w") as writer: | |
logger.info("***** Eval results *****") | |
for key, value in sorted(results.items()): | |
logger.info(f" {key} = {value}") | |
writer.write(f"{key} = {value}\n") | |
return results | |
def _mp_fn(index): | |
# For xla_spawn (TPUs) | |
main() | |
if __name__ == "__main__": | |
main() | |