From 73a532651a2a6a6ade9d29bb51f85d10d829ee2e Mon Sep 17 00:00:00 2001 From: Matt Date: Thu, 10 Jun 2021 14:14:37 +0100 Subject: [PATCH] New TF GLUE example (#12028) * 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> --- .../text-classification/run_glue.py | 557 ++++++++++++++++++ .../run_text_classification.py | 5 +- .../text-classification/run_tf_glue.py | 265 --------- 3 files changed, 558 insertions(+), 269 deletions(-) create mode 100644 examples/tensorflow/text-classification/run_glue.py delete mode 100755 examples/tensorflow/text-classification/run_tf_glue.py diff --git a/examples/tensorflow/text-classification/run_glue.py b/examples/tensorflow/text-classification/run_glue.py new file mode 100644 index 0000000000..13146702c2 --- /dev/null +++ b/examples/tensorflow/text-classification/run_glue.py @@ -0,0 +1,557 @@ +#!/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() diff --git a/examples/tensorflow/text-classification/run_text_classification.py b/examples/tensorflow/text-classification/run_text_classification.py index 32e020d7bf..27324f59d4 100644 --- a/examples/tensorflow/text-classification/run_text_classification.py +++ b/examples/tensorflow/text-classification/run_text_classification.py @@ -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, diff --git a/examples/tensorflow/text-classification/run_tf_glue.py b/examples/tensorflow/text-classification/run_tf_glue.py deleted file mode 100755 index 5b6df337e9..0000000000 --- a/examples/tensorflow/text-classification/run_tf_glue.py +++ /dev/null @@ -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()