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New TF GLUE example (#12028)

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* Pushing partially-complete new GLUE example

* First draft of the new TF GLUE example! Needs a little more testing to be sure but it's almost ready.

* Fix to the fit() call

* Bugfixes, making sure TPU and multi-GPU support is ready

* Remove logger line that depends on Pytorch

* Style pass

* Deleting old TF GLUE example

* Include label2id and id2label in the saved model config

* Don't clobber the existing model.config.label2id

* Style fixes

* Update examples/tensorflow/text-classification/run_glue.py

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>

Co-authored-by: Sylvain Gugger <35901082+sgugger@users.noreply.github.com>
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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 logging
import os
import sys
from dataclasses import dataclass, field
from typing import Optional
import numpy as np
import tensorflow as tf
from datasets import load_dataset, load_metric
import transformers
from transformers import (
AutoConfig,
AutoTokenizer,
HfArgumentParser,
PretrainedConfig,
TFAutoModelForSequenceClassification,
TFTrainingArguments,
set_seed,
)
from transformers.trainer_utils import get_last_checkpoint, is_main_process
from transformers.utils import check_min_version
# region Helper functions
def convert_dataset_for_tensorflow(
dataset, non_label_column_names, batch_size, dataset_mode="variable_batch", shuffle=True, drop_remainder=True
):
"""Converts a Hugging Face dataset to a Tensorflow Dataset. The dataset_mode controls whether we pad all batches
to the maximum sequence length, or whether we only pad to the maximum length within that batch. The former
is most useful when training on TPU, as a new graph compilation is required for each sequence length.
"""
def densify_ragged_batch(features, label=None):
features = {
feature: ragged_tensor.to_tensor(shape=batch_shape[feature]) for feature, ragged_tensor in features.items()
}
if label is None:
return features
else:
return features, label
feature_keys = list(set(dataset.features.keys()) - set(non_label_column_names + ["label"]))
if dataset_mode == "variable_batch":
batch_shape = {key: None for key in feature_keys}
data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
elif dataset_mode == "constant_batch":
data = {key: tf.ragged.constant(dataset[key]) for key in feature_keys}
batch_shape = {
key: tf.concat(([batch_size], ragged_tensor.bounding_shape()[1:]), axis=0)
for key, ragged_tensor in data.items()
}
else:
raise ValueError("Unknown dataset mode!")
if "label" in dataset.features:
labels = tf.convert_to_tensor(np.array(dataset["label"]))
tf_dataset = tf.data.Dataset.from_tensor_slices((data, labels))
else:
tf_dataset = tf.data.Dataset.from_tensor_slices(data)
if shuffle:
tf_dataset = tf_dataset.shuffle(buffer_size=len(dataset))
tf_dataset = tf_dataset.batch(batch_size=batch_size, drop_remainder=drop_remainder).map(densify_ragged_batch)
return tf_dataset
class SavePretrainedCallback(tf.keras.callbacks.Callback):
# Hugging Face models have a save_pretrained() method that saves both the weights and the necessary
# metadata to allow them to be loaded as a pretrained model in future. This is a simple Keras callback
# that saves the model with this method after each epoch.
def __init__(self, output_dir, **kwargs):
super().__init__()
self.output_dir = output_dir
def on_epoch_end(self, epoch, logs=None):
self.model.save_pretrained(self.output_dir)
# endregion
# Will error if the minimal version of Transformers is not installed. Remove at your own risks.
check_min_version("4.6.0.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
@dataclass
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()))
@dataclass
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)."},
)
use_auth_token: bool = field(
default=False,
metadata={
"help": "Will use the token generated when running `transformers-cli login` (necessary to use this script "
"with private models)."
},
)
# 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 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)
# 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,
use_auth_token=True if model_args.use_auth_token else None,
)
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,
use_auth_token=True if model_args.use_auth_token else None,
)
# endregion
# region Dataset preprocessing
sentence1_key, sentence2_key = task_to_keys[data_args.task_name]
non_label_column_names = [name for name in datasets["train"].column_names if name != "label"]
# 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 list(sorted(label_name_to_id.keys())) == list(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: {list(sorted(label_name_to_id.keys()))}, dataset labels: {list(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()}
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)
# endregion
# region Metric function
metric = load_metric("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,
use_auth_token=True if model_args.use_auth_token else None,
)
# endregion
# region Optimizer, loss and compilation
optimizer = tf.keras.optimizers.Adam(
learning_rate=training_args.learning_rate,
beta_1=training_args.adam_beta1,
beta_2=training_args.adam_beta2,
epsilon=training_args.adam_epsilon,
clipnorm=training_args.max_grad_norm,
)
if is_regression:
loss_fn = tf.keras.losses.MeanSquaredError()
metrics = []
else:
loss_fn = tf.keras.losses.SparseCategoricalCrossentropy(from_logits=True)
metrics = ["accuracy"]
model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)
# endregion
# region Convert data to a tf.data.Dataset
tf_data = dict()
if isinstance(training_args.strategy, tf.distribute.TPUStrategy) or data_args.pad_to_max_length:
logger.info("Padding all batches to max length because argument was set or we're on TPU.")
dataset_mode = "constant_batch"
else:
dataset_mode = "variable_batch"
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
drop_remainder = True # Saves us worrying about scaling gradients for the last batch
else:
shuffle = False
batch_size = training_args.per_device_eval_batch_size
drop_remainder = False
samples_limit = max_samples[key]
dataset = datasets[key]
if samples_limit is not None:
dataset = dataset.select(range(samples_limit))
data = convert_dataset_for_tensorflow(
dataset,
non_label_column_names,
batch_size=batch_size,
dataset_mode=dataset_mode,
drop_remainder=drop_remainder,
shuffle=shuffle,
)
tf_data[key] = data
# endregion
# region Training and validation
if training_args.do_train:
callbacks = [SavePretrainedCallback(output_dir=training_args.output_dir)]
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
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)
# 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 __name__ == "__main__":
main()

5
examples/tensorflow/text-classification/run_text_classification.py
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@ -205,7 +205,6 @@ class ModelArguments:
"with private models)."
},
)
tpu: Optional[str] = field(default=None, metadata={"help": "Name of the TPU resource to use, if available"})
# endregion
@ -439,10 +438,8 @@ def main():
model.compile(optimizer=optimizer, loss=loss_fn, metrics=metrics)
# endregion
# region Convert data to TF format
# region Convert data to a tf.data.Dataset
# Convert data to a tf.keras.utils.Sequence object for training if we're not using a TPU
# For TPU, convert to a tf.data.Dataset
tf_data = dict()
max_samples = {
"train": data_args.max_train_samples,

265
examples/tensorflow/text-classification/run_tf_glue.py
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@ -1,265 +0,0 @@
#!/usr/bin/env python
# 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 sequence classification."""
import logging
import os
from dataclasses import dataclass, field
from enum import Enum
from typing import Dict, Optional
import numpy as np
import tensorflow as tf
import tensorflow_datasets as tfds
from transformers import (
AutoConfig,
AutoTokenizer,
EvalPrediction,
HfArgumentParser,
PreTrainedTokenizer,
TFAutoModelForSequenceClassification,
TFTrainer,
TFTrainingArguments,
glue_compute_metrics,
glue_convert_examples_to_features,
glue_output_modes,
glue_processors,
glue_tasks_num_labels,
)
from transformers.utils import logging as hf_logging
hf_logging.set_verbosity_info()
hf_logging.enable_default_handler()
hf_logging.enable_explicit_format()
class Split(Enum):
train = "train"
dev = "validation"
test = "test"
def get_tfds(
task_name: str,
tokenizer: PreTrainedTokenizer,
max_seq_length: Optional[int] = None,
mode: Split = Split.train,
data_dir: str = None,
):
if task_name == "mnli-mm" and mode == Split.dev:
tfds_name = "mnli_mismatched"
elif task_name == "mnli-mm" and mode == Split.train:
tfds_name = "mnli"
elif task_name == "mnli" and mode == Split.dev:
tfds_name = "mnli_matched"
elif task_name == "sst-2":
tfds_name = "sst2"
elif task_name == "sts-b":
tfds_name = "stsb"
else:
tfds_name = task_name
ds, info = tfds.load("glue/" + tfds_name, split=mode.value, with_info=True, data_dir=data_dir)
ds = glue_convert_examples_to_features(ds, tokenizer, max_seq_length, task_name)
ds = ds.apply(tf.data.experimental.assert_cardinality(info.splits[mode.value].num_examples))
return ds
logger = logging.getLogger(__name__)
@dataclass
class GlueDataTrainingArguments:
"""
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(glue_processors.keys())})
data_dir: Optional[str] = field(default=None, metadata={"help": "The input/output data dir for TFDS."})
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 training and evaluation sets"}
)
def __post_init__(self):
self.task_name = self.task_name.lower()
@dataclass
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"}
)
use_fast: bool = field(default=False, metadata={"help": "Set this flag to use fast tokenization."})
# If you want to tweak more attributes on your tokenizer, you should do it in a distinct script,
# or just modify its tokenizer_config.json.
cache_dir: Optional[str] = field(
default=None,
metadata={"help": "Where do you want to store the pretrained models downloaded from huggingface.co"},
)
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, GlueDataTrainingArguments, TFTrainingArguments))
model_args, data_args, training_args = parser.parse_args_into_dataclasses()
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."
)
# Setup logging
logging.basicConfig(
format="%(asctime)s - %(levelname)s - %(name)s - %(message)s",
datefmt="%m/%d/%Y %H:%M:%S",
level=logging.INFO,
)
logger.info(
f"n_replicas: {training_args.n_replicas}, distributed training: {bool(training_args.n_replicas > 1)}, "
f"16-bits training: {training_args.fp16}",
)
logger.info(f"Training/evaluation parameters {training_args}")
try:
num_labels = glue_tasks_num_labels["mnli" if data_args.task_name == "mnli-mm" else data_args.task_name]
output_mode = glue_output_modes[data_args.task_name]
except KeyError:
raise ValueError(f"Task not found: {data_args.task_name}")
# Load pretrained model and tokenizer
#
# 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,
)
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,
)
with training_args.strategy.scope():
model = TFAutoModelForSequenceClassification.from_pretrained(
model_args.model_name_or_path,
from_pt=bool(".bin" in model_args.model_name_or_path),
config=config,
cache_dir=model_args.cache_dir,
)
# Get datasets
train_dataset = (
get_tfds(
task_name=data_args.task_name,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
data_dir=data_args.data_dir,
)
if training_args.do_train
else None
)
eval_dataset = (
get_tfds(
task_name=data_args.task_name,
tokenizer=tokenizer,
max_seq_length=data_args.max_seq_length,
mode=Split.dev,
data_dir=data_args.data_dir,
)
if training_args.do_eval
else None
)
def compute_metrics(p: EvalPrediction) -> Dict:
if output_mode == "classification":
preds = np.argmax(p.predictions, axis=1)
elif output_mode == "regression":
preds = np.squeeze(p.predictions)
return glue_compute_metrics(data_args.task_name, preds, p.label_ids)
# Initialize our Trainer
trainer = TFTrainer(
model=model,
args=training_args,
train_dataset=train_dataset,
eval_dataset=eval_dataset,
compute_metrics=compute_metrics,
)
# Training
if training_args.do_train:
trainer.train()
trainer.save_model()
tokenizer.save_pretrained(training_args.output_dir)
# Evaluation
results = {}
if training_args.do_eval:
logger.info("*** Evaluate ***")
result = trainer.evaluate()
output_eval_file = os.path.join(training_args.output_dir, "eval_results.txt")
with open(output_eval_file, "w") as writer:
logger.info("***** Eval results *****")
for key, value in result.items():
logger.info(f" {key} = {value}")
writer.write(f"{key} = {value}\n")
results.update(result)
return results
if __name__ == "__main__":
main()