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#!/usr/bin/env python | |
# coding=utf-8 | |
# Copyright 2020 The HuggingFace Inc. 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. | |
""" Finetuning the library models for sequence classification on GLUE.""" | |
# You can also adapt this script on your own text classification task. Pointers for this are left as comments. | |
import json | |
import logging | |
import os | |
import sys | |
import warnings | |
from dataclasses import dataclass, field | |
from typing import Optional | |
import evaluate | |
import numpy as np | |
import tensorflow as tf | |
from datasets import load_dataset | |
import transformers | |
from transformers import ( | |
AutoConfig, | |
AutoTokenizer, | |
DataCollatorWithPadding, | |
DefaultDataCollator, | |
HfArgumentParser, | |
PretrainedConfig, | |
PushToHubCallback, | |
TFAutoModelForSequenceClassification, | |
TFTrainingArguments, | |
create_optimizer, | |
set_seed, | |
) | |
from transformers.trainer_utils import get_last_checkpoint, is_main_process | |
from transformers.utils import check_min_version, send_example_telemetry | |
# Will error if the minimal version of Transformers is not installed. Remove at your own risks. | |
check_min_version("4.34.0.dev0") | |
task_to_keys = { | |
"cola": ("sentence", None), | |
"mnli": ("premise", "hypothesis"), | |
"mrpc": ("sentence1", "sentence2"), | |
"qnli": ("question", "sentence"), | |
"qqp": ("question1", "question2"), | |
"rte": ("sentence1", "sentence2"), | |
"sst2": ("sentence", None), | |
"stsb": ("sentence1", "sentence2"), | |
"wnli": ("sentence1", "sentence2"), | |
} | |
logger = logging.getLogger(__name__) | |
# region Command-line arguments | |
class DataTrainingArguments: | |
""" | |
Arguments pertaining to what data we are going to input our model for training and eval. | |
Using `HfArgumentParser` we can turn this class | |
into argparse arguments to be able to specify them on | |
the command line. | |
""" | |
task_name: str = field( | |
metadata={"help": "The name of the task to train on: " + ", ".join(task_to_keys.keys())}, | |
) | |
predict_file: str = field( | |
metadata={"help": "A file containing user-supplied examples to make predictions for"}, | |
default=None, | |
) | |
max_seq_length: int = field( | |
default=128, | |
metadata={ | |
"help": ( | |
"The maximum total input sequence length after tokenization. Sequences longer " | |
"than this will be truncated, sequences shorter will be padded." | |
) | |
}, | |
) | |
overwrite_cache: bool = field( | |
default=False, metadata={"help": "Overwrite the cached preprocessed datasets or not."} | |
) | |
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." | |
) | |
}, | |
) | |
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." | |
) | |
}, | |
) | |
def __post_init__(self): | |
self.task_name = self.task_name.lower() | |
if self.task_name not in task_to_keys.keys(): | |
raise ValueError("Unknown task, you should pick one in " + ",".join(task_to_keys.keys())) | |
class ModelArguments: | |
""" | |
Arguments pertaining to which model/config/tokenizer we are going to fine-tune from. | |
""" | |
model_name_or_path: str = field( | |
metadata={"help": "Path to pretrained model or model identifier from huggingface.co/models"} | |
) | |
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)."}, | |
) | |
token: str = field( | |
default=None, | |
metadata={ | |
"help": ( | |
"The token to use as HTTP bearer authorization for remote files. If not specified, will use the token " | |
"generated when running `huggingface-cli login` (stored in `~/.huggingface`)." | |
) | |
}, | |
) | |
use_auth_token: bool = field( | |
default=None, | |
metadata={ | |
"help": "The `use_auth_token` argument is deprecated and will be removed in v4.34. Please use `token`." | |
}, | |
) | |
trust_remote_code: bool = field( | |
default=False, | |
metadata={ | |
"help": ( | |
"Whether or not to allow for custom models defined on the Hub in their own modeling files. This option" | |
"should only be set to `True` for repositories you trust and in which you have read the code, as it will" | |
"execute code present on the Hub on your local machine." | |
) | |
}, | |
) | |
# endregion | |
def main(): | |
# region Argument parsing | |
# 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, TFTrainingArguments)) | |
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() | |
if model_args.use_auth_token is not None: | |
warnings.warn("The `use_auth_token` argument is deprecated and will be removed in v4.34.", FutureWarning) | |
if model_args.token is not None: | |
raise ValueError("`token` and `use_auth_token` are both specified. Please set only the argument `token`.") | |
model_args.token = model_args.use_auth_token | |
# Sending telemetry. Tracking the example usage helps us better allocate resources to maintain them. The | |
# information sent is the one passed as arguments along with your Python/PyTorch versions. | |
send_example_telemetry("run_glue", model_args, data_args, framework="tensorflow") | |
if not (training_args.do_train or training_args.do_eval or training_args.do_predict): | |
exit("Must specify at least one of --do_train, --do_eval or --do_predict!") | |
# endregion | |
# region Checkpoints | |
checkpoint = None | |
if os.path.isdir(training_args.output_dir) and training_args.do_train and not training_args.overwrite_output_dir: | |
checkpoint = get_last_checkpoint(training_args.output_dir) | |
if 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 checkpoint is not None and training_args.resume_from_checkpoint is None: | |
logger.info( | |
f"Checkpoint detected, resuming training at {checkpoint}. To avoid this behavior, change " | |
"the `--output_dir` or add `--overwrite_output_dir` to train from scratch." | |
) | |
# endregion | |
# region 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) | |
# 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(f"Training/evaluation parameters {training_args}") | |
# endregion | |
# region Dataset and labels | |
# Set seed before initializing model. | |
set_seed(training_args.seed) | |
# Downloading and loading a dataset from the hub. In distributed training, the load_dataset function guarantee | |
# that only one local process can concurrently download the dataset. | |
datasets = load_dataset( | |
"glue", | |
data_args.task_name, | |
cache_dir=model_args.cache_dir, | |
token=model_args.token, | |
) | |
# See more about loading any type of standard or custom dataset at | |
# https://huggingface.co/docs/datasets/loading_datasets.html. | |
is_regression = data_args.task_name == "stsb" | |
if not is_regression: | |
label_list = datasets["train"].features["label"].names | |
num_labels = len(label_list) | |
else: | |
num_labels = 1 | |
if data_args.predict_file is not None: | |
logger.info("Preparing user-supplied file for predictions...") | |
data_files = {"data": data_args.predict_file} | |
for key in data_files.keys(): | |
logger.info(f"Loading a local file for {key}: {data_files[key]}") | |
if data_args.predict_file.endswith(".csv"): | |
# Loading a dataset from local csv files | |
user_dataset = load_dataset("csv", data_files=data_files, cache_dir=model_args.cache_dir) | |
else: | |
# Loading a dataset from local json files | |
user_dataset = load_dataset("json", data_files=data_files, cache_dir=model_args.cache_dir) | |
needed_keys = task_to_keys[data_args.task_name] | |
for key in needed_keys: | |
assert key in user_dataset["data"].features, f"Your supplied predict_file is missing the {key} key!" | |
datasets["user_data"] = user_dataset["data"] | |
# endregion | |
# region Load model config and tokenizer | |
# | |
# In distributed training, the .from_pretrained methods guarantee that only one local process can concurrently | |
# download model & vocab. | |
config = AutoConfig.from_pretrained( | |
model_args.config_name if model_args.config_name else model_args.model_name_or_path, | |
num_labels=num_labels, | |
finetuning_task=data_args.task_name, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
) | |
tokenizer = AutoTokenizer.from_pretrained( | |
model_args.tokenizer_name if model_args.tokenizer_name else model_args.model_name_or_path, | |
cache_dir=model_args.cache_dir, | |
use_fast=model_args.use_fast_tokenizer, | |
revision=model_args.model_revision, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
) | |
# endregion | |
# region Dataset preprocessing | |
sentence1_key, sentence2_key = task_to_keys[data_args.task_name] | |
# Padding strategy | |
if data_args.pad_to_max_length: | |
padding = "max_length" | |
else: | |
# We will pad later, dynamically at batch creation, to the max sequence length in each batch | |
padding = False | |
# Some models have set the order of the labels to use, so let's make sure we do use it. | |
label_to_id = None | |
if config.label2id != PretrainedConfig(num_labels=num_labels).label2id and not is_regression: | |
# Some have all caps in their config, some don't. | |
label_name_to_id = {k.lower(): v for k, v in config.label2id.items()} | |
if sorted(label_name_to_id.keys()) == sorted(label_list): | |
label_to_id = {i: int(label_name_to_id[label_list[i]]) for i in range(num_labels)} | |
else: | |
logger.warning( | |
"Your model seems to have been trained with labels, but they don't match the dataset: ", | |
f"model labels: {sorted(label_name_to_id.keys())}, dataset labels: {sorted(label_list)}." | |
"\nIgnoring the model labels as a result.", | |
) | |
label_to_id = {label: i for i, label in enumerate(label_list)} | |
if label_to_id is not None: | |
config.label2id = label_to_id | |
config.id2label = {id: label for label, id in config.label2id.items()} | |
elif data_args.task_name is not None and not is_regression: | |
config.label2id = {l: i for i, l in enumerate(label_list)} | |
config.id2label = {id: label for label, id in config.label2id.items()} | |
if data_args.max_seq_length > tokenizer.model_max_length: | |
logger.warning( | |
f"The max_seq_length passed ({data_args.max_seq_length}) is larger than the maximum length for the" | |
f"model ({tokenizer.model_max_length}). Using max_seq_length={tokenizer.model_max_length}." | |
) | |
max_seq_length = min(data_args.max_seq_length, tokenizer.model_max_length) | |
def preprocess_function(examples): | |
# Tokenize the texts | |
args = ( | |
(examples[sentence1_key],) if sentence2_key is None else (examples[sentence1_key], examples[sentence2_key]) | |
) | |
result = tokenizer(*args, padding=padding, max_length=max_seq_length, truncation=True) | |
return result | |
datasets = datasets.map(preprocess_function, batched=True, load_from_cache_file=not data_args.overwrite_cache) | |
if data_args.pad_to_max_length: | |
data_collator = DefaultDataCollator(return_tensors="np") | |
else: | |
data_collator = DataCollatorWithPadding(tokenizer, return_tensors="np") | |
# endregion | |
# region Metric function | |
metric = evaluate.load("glue", data_args.task_name) | |
def compute_metrics(preds, label_ids): | |
preds = preds["logits"] | |
preds = np.squeeze(preds) if is_regression else np.argmax(preds, axis=1) | |
result = metric.compute(predictions=preds, references=label_ids) | |
if len(result) > 1: | |
result["combined_score"] = np.mean(list(result.values())).item() | |
return result | |
# endregion | |
with training_args.strategy.scope(): | |
# region Load pretrained model | |
if checkpoint is None: | |
model_path = model_args.model_name_or_path | |
else: | |
model_path = checkpoint | |
model = TFAutoModelForSequenceClassification.from_pretrained( | |
model_path, | |
config=config, | |
cache_dir=model_args.cache_dir, | |
revision=model_args.model_revision, | |
token=model_args.token, | |
trust_remote_code=model_args.trust_remote_code, | |
) | |
# endregion | |
# region Convert data to a tf.data.Dataset | |
dataset_options = tf.data.Options() | |
dataset_options.experimental_distribute.auto_shard_policy = tf.data.experimental.AutoShardPolicy.OFF | |
num_replicas = training_args.strategy.num_replicas_in_sync | |
tf_data = {} | |
max_samples = { | |
"train": data_args.max_train_samples, | |
"validation": data_args.max_eval_samples, | |
"validation_matched": data_args.max_eval_samples, | |
"validation_mismatched": data_args.max_eval_samples, | |
"test": data_args.max_predict_samples, | |
"test_matched": data_args.max_predict_samples, | |
"test_mismatched": data_args.max_predict_samples, | |
"user_data": None, | |
} | |
for key in datasets.keys(): | |
if key == "train" or key.startswith("validation"): | |
assert "label" in datasets[key].features, f"Missing labels from {key} data!" | |
if key == "train": | |
shuffle = True | |
batch_size = training_args.per_device_train_batch_size * num_replicas | |
else: | |
shuffle = False | |
batch_size = training_args.per_device_eval_batch_size * num_replicas | |
samples_limit = max_samples[key] | |
dataset = datasets[key] | |
if samples_limit is not None: | |
dataset = dataset.select(range(samples_limit)) | |
# model.prepare_tf_dataset() wraps a Hugging Face dataset in a tf.data.Dataset which is ready to use in | |
# training. This is the recommended way to use a Hugging Face dataset when training with Keras. You can also | |
# use the lower-level dataset.to_tf_dataset() method, but you will have to specify things like column names | |
# yourself if you use this method, whereas they are automatically inferred from the model input names when | |
# using model.prepare_tf_dataset() | |
# For more info see the docs: | |
# https://huggingface.co/docs/transformers/main/en/main_classes/model#transformers.TFPreTrainedModel.prepare_tf_dataset | |
# https://huggingface.co/docs/datasets/main/en/package_reference/main_classes#datasets.Dataset.to_tf_dataset | |
data = model.prepare_tf_dataset( | |
dataset, | |
shuffle=shuffle, | |
batch_size=batch_size, | |
collate_fn=data_collator, | |
tokenizer=tokenizer, | |
) | |
data = data.with_options(dataset_options) | |
tf_data[key] = data | |
# endregion | |
# region Optimizer, loss and compilation | |
if training_args.do_train: | |
num_train_steps = len(tf_data["train"]) * training_args.num_train_epochs | |
if training_args.warmup_steps > 0: | |
num_warmup_steps = training_args.warmup_steps | |
elif training_args.warmup_ratio > 0: | |
num_warmup_steps = int(num_train_steps * training_args.warmup_ratio) | |
else: | |
num_warmup_steps = 0 | |
optimizer, schedule = create_optimizer( | |
init_lr=training_args.learning_rate, | |
num_train_steps=num_train_steps, | |
num_warmup_steps=num_warmup_steps, | |
adam_beta1=training_args.adam_beta1, | |
adam_beta2=training_args.adam_beta2, | |
adam_epsilon=training_args.adam_epsilon, | |
weight_decay_rate=training_args.weight_decay, | |
adam_global_clipnorm=training_args.max_grad_norm, | |
) | |
else: | |
optimizer = "adam" # Just write anything because we won't be using it | |
if is_regression: | |
metrics = [] | |
else: | |
metrics = ["accuracy"] | |
# Transformers models compute the right loss for their task by default when labels are passed, and will | |
# use this for training unless you specify your own loss function in compile(). | |
model.compile(optimizer=optimizer, metrics=metrics, jit_compile=training_args.xla) | |
# endregion | |
# region Preparing push_to_hub and model card | |
push_to_hub_model_id = training_args.push_to_hub_model_id | |
model_name = model_args.model_name_or_path.split("/")[-1] | |
if not push_to_hub_model_id: | |
push_to_hub_model_id = f"{model_name}-finetuned-glue" | |
model_card_kwargs = {"finetuned_from": model_args.model_name_or_path, "tasks": "text-classification"} | |
model_card_kwargs["task_name"] = data_args.task_name | |
if training_args.push_to_hub: | |
callbacks = [ | |
PushToHubCallback( | |
output_dir=training_args.output_dir, | |
hub_model_id=push_to_hub_model_id, | |
hub_token=training_args.push_to_hub_token, | |
tokenizer=tokenizer, | |
**model_card_kwargs, | |
) | |
] | |
else: | |
callbacks = [] | |
# endregion | |
# region Training and validation | |
if training_args.do_train: | |
if training_args.do_eval and not data_args.task_name == "mnli": | |
# Do both evaluation and training in the Keras fit loop, unless the task is MNLI | |
# because MNLI has two validation sets | |
validation_data = tf_data["validation"] | |
else: | |
validation_data = None | |
model.fit( | |
tf_data["train"], | |
validation_data=validation_data, | |
epochs=int(training_args.num_train_epochs), | |
callbacks=callbacks, | |
) | |
# endregion | |
# region Evaluation | |
if training_args.do_eval: | |
# We normally do validation as part of the Keras fit loop, but we run it independently | |
# if there was no fit() step (because we didn't train the model) or if the task is MNLI, | |
# because MNLI has a separate validation-mismatched validation set | |
# In this example, we compute advanced metrics only at the end of training, and only compute | |
# loss and accuracy on the validation set each epoch, but | |
# if you'd like to compute metrics every epoch that are too complex to be written as | |
# standard Keras metrics, you can use our KerasMetricCallback. See | |
# https://huggingface.co/docs/transformers/main/en/main_classes/keras_callbacks | |
logger.info("*** Evaluate ***") | |
# Loop to handle MNLI double evaluation (matched, mis-matched) | |
if data_args.task_name == "mnli": | |
tasks = ["mnli", "mnli-mm"] | |
tf_datasets = [tf_data["validation_matched"], tf_data["validation_mismatched"]] | |
raw_datasets = [datasets["validation_matched"], datasets["validation_mismatched"]] | |
else: | |
tasks = [data_args.task_name] | |
tf_datasets = [tf_data["validation"]] | |
raw_datasets = [datasets["validation"]] | |
for raw_dataset, tf_dataset, task in zip(raw_datasets, tf_datasets, tasks): | |
eval_predictions = model.predict(tf_dataset) | |
eval_metrics = compute_metrics(eval_predictions, raw_dataset["label"]) | |
print(f"Evaluation metrics ({task}):") | |
print(eval_metrics) | |
if training_args.output_dir is not None: | |
output_eval_file = os.path.join(training_args.output_dir, "all_results.json") | |
with open(output_eval_file, "w") as writer: | |
writer.write(json.dumps(eval_metrics)) | |
# endregion | |
# region Prediction | |
if training_args.do_predict or data_args.predict_file: | |
logger.info("*** Predict ***") | |
# Loop to handle MNLI double evaluation (matched, mis-matched) | |
tasks = [] | |
tf_datasets = [] | |
raw_datasets = [] | |
if training_args.do_predict: | |
if data_args.task_name == "mnli": | |
tasks.extend(["mnli", "mnli-mm"]) | |
tf_datasets.extend([tf_data["test_matched"], tf_data["test_mismatched"]]) | |
raw_datasets.extend([datasets["test_matched"], datasets["test_mismatched"]]) | |
else: | |
tasks.append(data_args.task_name) | |
tf_datasets.append(tf_data["test"]) | |
raw_datasets.append(datasets["test"]) | |
if data_args.predict_file: | |
tasks.append("user_data") | |
tf_datasets.append(tf_data["user_data"]) | |
raw_datasets.append(datasets["user_data"]) | |
for raw_dataset, tf_dataset, task in zip(raw_datasets, tf_datasets, tasks): | |
test_predictions = model.predict(tf_dataset) | |
if "label" in raw_dataset: | |
test_metrics = compute_metrics(test_predictions, raw_dataset["label"]) | |
print(f"Test metrics ({task}):") | |
print(test_metrics) | |
if is_regression: | |
predictions_to_write = np.squeeze(test_predictions["logits"]) | |
else: | |
predictions_to_write = np.argmax(test_predictions["logits"], axis=1) | |
output_predict_file = os.path.join(training_args.output_dir, f"predict_results_{task}.txt") | |
with open(output_predict_file, "w") as writer: | |
logger.info(f"***** Writing prediction results for {task} *****") | |
writer.write("index\tprediction\n") | |
for index, item in enumerate(predictions_to_write): | |
if is_regression: | |
writer.write(f"{index}\t{item:3.3f}\n") | |
else: | |
item = model.config.id2label[item] | |
writer.write(f"{index}\t{item}\n") | |
# endregion | |
if training_args.output_dir is not None and not training_args.push_to_hub: | |
# If we're not pushing to hub, at least save a local copy when we're done | |
model.save_pretrained(training_args.output_dir) | |
if __name__ == "__main__": | |
main() | |