[Examples] TPU-based training of a language model using TensorFlow (#21657)

* add: tokenizer training script for TF TPU LM training.

* add: script for preparing the TFRecord shards.

* add: sequence of execution to readme.

* remove limit from the tfrecord shard name.

* Add initial train_model.py

* Add basic training arguments and model init

* Get up to the point of writing the data collator

* Pushing progress so far!

* Complete first draft of model training code

* feat: grouping of texts efficiently.

Co-authored-by: Matt <rocketknight1@gmail.com>

* Add proper masking collator and get training loop working

* fix: things.

* Read sample counts from filenames

* Read sample counts from filenames

* Draft README

* Improve TPU warning

* Use distribute instead of distribute.experimental

* Apply suggestions from code review

Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

* Modularize loading and add MLM probability as arg

* minor refactoring to better use the cli args.

* readme fillup.

* include tpu and inference sections in the readme.

* table of contents.

* parallelize maps.

* polish readme.

* change script name to run_mlm.py

* address PR feedback (round I).

---------

Co-authored-by: Matt <rocketknight1@gmail.com>
Co-authored-by: Matt <Rocketknight1@users.noreply.github.com>

examples/tensorflow/language-modeling-tpu/README.md

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+# Training a masked language model end-to-end from scratch on TPUs
+
+In this example, we're going to demonstrate how to train a TensorFlow model from 🤗 Transformers from scratch. If you're interested in some background theory on training Hugging Face models with TensorFlow on TPU, please check out our 
+[tutorial doc](https://huggingface.co/docs/transformers/main/perf_train_tpu_tf) on this topic!
+If you're interested in smaller-scale TPU training from a pre-trained checkpoint, you can also check out the  [TPU fine-tuning example](https://colab.research.google.com/github/huggingface/notebooks/blob/main/examples/tpu_training-tf.ipynb).
+
+This example will demonstrate pre-training language models at the 100M-1B parameter scale, similar to BERT or GPT-2. More concretely, we will show how to train a [RoBERTa](https://huggingface.co/docs/transformers/model_doc/roberta) (base model) from scratch on the [WikiText dataset (v1)](https://huggingface.co/datasets/wikitext).
+
+We've tried to ensure that all the practices we show you here are scalable, though - with relatively few changes, the code could be scaled up to much larger models. 
+
+Google's gargantuan [PaLM model](https://arxiv.org/abs/2204.02311), with
+over 500B parameters, is a good example of how far you can go with pure TPU training, though gathering the dataset and the budget to train at that scale is not an easy task!
+
+### Table of contents 
+
+- [Setting up a TPU-VM](#setting-up-a-tpu-vm)
+- [Training a tokenizer](#training-a-tokenizer)
+- [Preparing the dataset](#preparing-the-dataset)
+- [Training the model](#training-the-model)
+- [Inference](#inference)
+
+## Setting up a TPU-VM
+
+Since this example focuses on using TPUs, the first step is to set up access to TPU hardware. For this example, we chose to use a TPU v3-8 VM. Follow [this guide](https://cloud.google.com/tpu/docs/run-calculation-tensorflow) to quickly create a TPU VM with TensorFlow pre-installed. 
+
+> 💡 **Note**: You don't need a TPU-enabled hardware for tokenizer training and TFRecord shard preparation.
+
+## Training a tokenizer
+
+To train a language model from scratch, the first step is to tokenize text. In most Hugging Face examples, we begin from a pre-trained model and use its tokenizer. However, in this example, we're going to train a tokenizer from scratch as well. The script for this is `train_unigram.py`. An example command is:
+
+```bash 
+python train_unigram.py --batch_size 1000 --vocab_size 25000 --export_to_hub
+```
+
+The script will automatically load the `train` split of the WikiText dataset and train a [Unigram tokenizer](https://huggingface.co/course/chapter6/7?fw=pt) on it.
+
+> 💡 **Note**: In order for `export_to_hub` to work, you must authenticate yourself with the `huggingface-cli`. Run `huggingface-cli login` and follow the on-screen instructions.
+
+## Preparing the dataset
+
+The next step is to prepare the dataset. This consists of loading a text dataset from the Hugging Face Hub, tokenizing it and grouping it into chunks of a fixed length ready for training. The script for this is `prepare_tfrecord_shards.py`.
+
+The reason we create TFRecord output files from this step is that these files work well with [`tf.data` pipelines](https://www.tensorflow.org/guide/data_performance). This makes them very suitable for scalable TPU training - the dataset can easily be sharded and read in parallel just by tweaking a few parameters in the pipeline. An example command is:
+
+```bash
+python prepare_tfrecord_shards.py \
+  --tokenizer_name_or_path tf-tpu/unigram-tokenizer-wikitext \
+  --shard_size 5000  \
+  --split test 
+  --max_length 128 \
+  --output_dir gs://tf-tpu-training-resources
+```
+
+**Notes**:
+
+* While running the above script, you need to specify the `split` accordingly. The example command above will only filter the `test` split of the dataset. 
+* If you append `gs://` in your `output_dir` the TFRecord shards will be directly serialized to a Google Cloud Storage (GCS) bucket. Ensure that you have already [created the GCS bucket](https://cloud.google.com/storage/docs). 
+* If you're using a TPU node, you must stream data from a GCS bucket. Otherwise, if you're using a TPU VM,you can store the data locally. You may need to [attach](https://cloud.google.com/tpu/docs/setup-persistent-disk) a persistent storage to the VM. 
+* Additional CLI arguments are also supported. We encourage you to run `python prepare_tfrecord_shards.py -h` to know more about them.
+
+## Training the model
+
+Once that's done, the model is ready for training. By default, training takes place on TPU, but you can use the `--no_tpu` flag to train on CPU for testing purposes. An example command is:
+
+```bash
+python3 run_mlm.py \
+  --train_dataset gs://tf-tpu-training-resources/train/ \
+  --eval_dataset gs://tf-tpu-training-resources/validation/ \
+  --tokenizer tf-tpu/unigram-tokenizer-wikitext \
+  --output_dir trained_model  
+```
+
+If you had specified a `hub_model_id` while launching training, then your model will be pushed to a model repository on the Hugging Face Hub. You can find such an example repository here:
+[tf-tpu/roberta-base-epochs-500-no-wd](https://huggingface.co/tf-tpu/roberta-base-epochs-500-no-wd).
+
+## Inference
+
+Once the model is trained, you can use 🤗 Pipelines to perform inference:
+
+```python
+from transformers import pipeline
+
+model_id = "tf-tpu/roberta-base-epochs-500-no-wd"
+unmasker = pipeline("fill-mask", model=model_id, framework="tf")
+unmasker("Goal of my life is to [MASK].")
+
+[{'score': 0.1003185287117958,
+  'token': 52,
+  'token_str': 'be',
+  'sequence': 'Goal of my life is to be.'},
+ {'score': 0.032648514956235886,
+  'token': 5,
+  'token_str': '',
+  'sequence': 'Goal of my life is to .'},
+ {'score': 0.02152673341333866,
+  'token': 138,
+  'token_str': 'work',
+  'sequence': 'Goal of my life is to work.'},
+ {'score': 0.019547373056411743,
+  'token': 984,
+  'token_str': 'act',
+  'sequence': 'Goal of my life is to act.'},
+ {'score': 0.01939118467271328,
+  'token': 73,
+  'token_str': 'have',
+  'sequence': 'Goal of my life is to have.'}]
+```
+
+You can also try out inference using the [Inference Widget](https://huggingface.co/tf-tpu/roberta-base-epochs-500-no-wd?text=Goal+of+my+life+is+to+%5BMASK%5D.) from the model page.

examples/tensorflow/language-modeling-tpu/prepare_tfrecord_shards.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 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.
+
+"""Script for preparing TFRecord shards for pre-tokenized examples."""
+
+import argparse
+import logging
+import os
+
+import datasets
+import tensorflow as tf
+
+from transformers import AutoTokenizer
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(
+        description="Prepare TFRecord shards from pre-tokenized samples of the wikitext dataset."
+    )
+    parser.add_argument(
+        "--tokenizer_name_or_path",
+        type=str,
+        default="sayakpaul/unigram-tokenizer-wikitext",
+        help="Tokenizer identifier. Can be a local filepath or a Hub identifier.",
+    )
+    parser.add_argument(
+        "--shard_size",
+        type=int,
+        default=1000,
+        help="Number of entries to go in a single shard.",
+    )
+    parser.add_argument("--split", type=str, default="train", choices=["train", "test", "validation"])
+    parser.add_argument(
+        "--limit",
+        default=None,
+        type=int,
+        help="Limit the number of shards (used for debugging).",
+    )
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=512,
+        help="Maximum sequence length. For training on TPUs, it helps to have a maximum"
+        " sequence length that is a multiple of 8.",
+    )
+    parser.add_argument(
+        "--output_dir",
+        default="tf-tpu",
+        type=str,
+        help="Output directory where the TFRecord shards will be saved. If the"
+        " path is appended with `gs://` ('gs://tf-tpu', for example) then the TFRecord"
+        " shards will be directly saved to a Google Cloud Storage bucket.",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def tokenize_function(tokenizer):
+    def fn(examples):
+        return tokenizer(examples["text"])
+
+    return fn
+
+
+def get_serialized_examples(tokenized_data):
+    records = []
+    for i in range(len(tokenized_data["input_ids"])):
+        features = {
+            "input_ids": tf.train.Feature(int64_list=tf.train.Int64List(value=tokenized_data["input_ids"][i])),
+            "attention_mask": tf.train.Feature(
+                int64_list=tf.train.Int64List(value=tokenized_data["attention_mask"][i])
+            ),
+        }
+        features = tf.train.Features(feature=features)
+        example = tf.train.Example(features=features)
+        record_bytes = example.SerializeToString()
+        records.append(record_bytes)
+    return records
+
+
+def main(args):
+    wikitext = datasets.load_dataset("wikitext", "wikitext-103-raw-v1", split=args.split)
+
+    if args.limit is not None:
+        max_samples = min(len(wikitext), args.limit)
+        wikitext = wikitext.select(range(max_samples))
+        print(f"Limiting the dataset to {args.limit} entries.")
+
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer_name_or_path)
+
+    # Handle output directory creation.
+    # For serializing into a Google Cloud Storage Bucket, one needs to first
+    # create a bucket.
+    if "gs" not in args.output_dir:
+        if not os.path.exists(args.output_dir):
+            os.makedirs(args.output_dir)
+        split_dir = os.path.join(args.output_dir, args.split)
+        if not os.path.exists(split_dir):
+            os.makedirs(split_dir)
+    else:
+        split_dir = os.path.join(args.output_dir, args.split)
+
+    # Tokenize the whole dataset at once.
+    tokenize_fn = tokenize_function(tokenizer)
+    wikitext_tokenized = wikitext.map(tokenize_fn, batched=True, num_proc=4, remove_columns=["text"])
+
+    # We need to concatenate all our texts together, and then split the result
+    # into chunks of a fixed size, which we will call block_size. To do this, we
+    # will use the map method again, with the option batched=True. When we use batched=True,
+    # the function we pass to map() will be passed multiple inputs at once, allowing us
+    # to group them into more or fewer examples than we had in the input.
+    # This allows us to create our new fixed-length samples. The advantage of this
+    # method is that we don't lose a whole lot of content from the dataset compared to the
+    # case where we simply tokenize with a pre-defined max_length.
+
+    def group_texts(examples):
+        # Concatenate all texts.
+        concatenated_examples = {k: sum(examples[k], []) for k in examples.keys()}
+        total_length = len(concatenated_examples[list(examples.keys())[0]])
+        # We drop the small remainder, though you could add padding instead if the model supports it
+        # In this, as in all things, we advise you to follow your heart 🫀
+        total_length = (total_length // args.max_length) * args.max_length
+        # Split by chunks of max_len.
+        result = {
+            k: [t[i : i + args.max_length] for i in range(0, total_length, args.max_length)]
+            for k, t in concatenated_examples.items()
+        }
+        return result
+
+    grouped_dataset = wikitext_tokenized.map(group_texts, batched=True, batch_size=1000, num_proc=4)
+
+    shard_count = 0
+    total_records = 0
+    for shard in range(0, len(grouped_dataset), args.shard_size):
+        dataset_snapshot = grouped_dataset[shard : shard + args.shard_size]
+        records_containing = len(dataset_snapshot["input_ids"])
+        filename = os.path.join(split_dir, f"wikitext-{shard_count}-{records_containing}.tfrecord")
+        serialized_examples = get_serialized_examples(dataset_snapshot)
+
+        with tf.io.TFRecordWriter(filename) as out_file:
+            for i in range(len(serialized_examples)):
+                example = serialized_examples[i]
+                out_file.write(example)
+            print("Wrote file {} containing {} records".format(filename, records_containing))
+
+        shard_count += 1
+        total_records += records_containing
+
+    with open(f"split-{args.split}-records-count.txt", "w") as f:
+        print(f"Total {args.split} records: {total_records}", file=f)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

examples/tensorflow/language-modeling-tpu/requirements.txt

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+transformers==4.26.1
+datasets==2.9.0
+tokenizers==0.13.2

examples/tensorflow/language-modeling-tpu/run_mlm.py

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+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 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.
+
+"""Script for training a masked language model on TPU."""
+
+import argparse
+import logging
+import os
+import re
+
+import tensorflow as tf
+
+from transformers import (
+    AutoConfig,
+    AutoTokenizer,
+    DataCollatorForLanguageModeling,
+    PushToHubCallback,
+    TFAutoModelForMaskedLM,
+    create_optimizer,
+)
+
+
+logger = logging.getLogger(__name__)
+
+AUTO = tf.data.AUTOTUNE
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Train a masked language model on TPU.")
+    parser.add_argument(
+        "--pretrained_model_config",
+        type=str,
+        default="roberta-base",
+        help="The model config to use. Note that we don't copy the model's weights, only the config!",
+    )
+    parser.add_argument(
+        "--tokenizer",
+        type=str,
+        default="unigram-tokenizer-wikitext",
+        help="The name of the tokenizer to load. We use the pretrained tokenizer to initialize the model's vocab size.",
+    )
+
+    parser.add_argument(
+        "--per_replica_batch_size",
+        type=int,
+        default=8,
+        help="Batch size per TPU core.",
+    )
+
+    parser.add_argument(
+        "--no_tpu",
+        action="store_true",
+        help="If set, run on CPU and don't try to initialize a TPU. Useful for debugging on non-TPU instances.",
+    )
+
+    parser.add_argument(
+        "--tpu_name",
+        type=str,
+        help="Name of TPU resource to initialize. Should be blank on Colab, and 'local' on TPU VMs.",
+        default="local",
+    )
+
+    parser.add_argument(
+        "--tpu_zone",
+        type=str,
+        help="Google cloud zone that TPU resource is located in. Only used for non-Colab TPU nodes.",
+    )
+
+    parser.add_argument(
+        "--gcp_project", type=str, help="Google cloud project name. Only used for non-Colab TPU nodes."
+    )
+
+    parser.add_argument(
+        "--bfloat16",
+        action="store_true",
+        help="Use mixed-precision bfloat16 for training. This is the recommended lower-precision format for TPU.",
+    )
+
+    parser.add_argument(
+        "--train_dataset",
+        type=str,
+        help="Path to training dataset to load. If the path begins with `gs://`"
+        " then the dataset will be loaded from a Google Cloud Storage bucket.",
+    )
+
+    parser.add_argument(
+        "--shuffle_buffer_size",
+        type=int,
+        default=2**18,  # Default corresponds to a 1GB buffer for seq_len 512
+        help="Size of the shuffle buffer (in samples)",
+    )
+
+    parser.add_argument(
+        "--eval_dataset",
+        type=str,
+        help="Path to evaluation dataset to load. If the path begins with `gs://`"
+        " then the dataset will be loaded from a Google Cloud Storage bucket.",
+    )
+
+    parser.add_argument(
+        "--num_epochs",
+        type=int,
+        default=1,
+        help="Number of epochs to train for.",
+    )
+
+    parser.add_argument(
+        "--learning_rate",
+        type=float,
+        default=1e-4,
+        help="Learning rate to use for training.",
+    )
+
+    parser.add_argument(
+        "--weight_decay_rate",
+        type=float,
+        default=1e-3,
+        help="Weight decay rate to use for training.",
+    )
+
+    parser.add_argument(
+        "--max_length",
+        type=int,
+        default=512,
+        help="Maximum length of tokenized sequences. Should match the setting used in prepare_tfrecord_shards.py",
+    )
+
+    parser.add_argument(
+        "--mlm_probability",
+        type=float,
+        default=0.15,
+        help="Fraction of tokens to mask during training.",
+    )
+
+    parser.add_argument("--output_dir", type=str, required=True, help="Path to save model checkpoints to.")
+    parser.add_argument("--hub_model_id", type=str, help="Model ID to upload to on the Hugging Face Hub.")
+
+    args = parser.parse_args()
+    return args
+
+
+def initialize_tpu(args):
+    try:
+        if args.tpu_name:
+            tpu = tf.distribute.cluster_resolver.TPUClusterResolver(
+                args.tpu_name, zone=args.tpu_zone, project=args.gcp_project
+            )
+        else:
+            tpu = tf.distribute.cluster_resolver.TPUClusterResolver()
+    except ValueError:
+        raise RuntimeError(
+            "Couldn't connect to TPU! Most likely you need to specify --tpu_name, --tpu_zone, or "
+            "--gcp_project. When running on a TPU VM, use --tpu_name local."
+        )
+
+    tf.config.experimental_connect_to_cluster(tpu)
+    tf.tpu.experimental.initialize_tpu_system(tpu)
+
+    return tpu
+
+
+def count_samples(file_list):
+    num_samples = 0
+    for file in file_list:
+        filename = file.split("/")[-1]
+        sample_count = re.search(r"-\d+-(\d+)\.tfrecord", filename).group(1)
+        sample_count = int(sample_count)
+        num_samples += sample_count
+
+    return num_samples
+
+
+def prepare_dataset(records, decode_fn, mask_fn, batch_size, shuffle, shuffle_buffer_size=None):
+    num_samples = count_samples(records)
+    dataset = tf.data.Dataset.from_tensor_slices(records)
+    if shuffle:
+        dataset = dataset.shuffle(len(dataset))
+    dataset = tf.data.TFRecordDataset(dataset, num_parallel_reads=AUTO)
+    # TF can't infer the total sample count because it doesn't read all the records yet, so we assert it here
+    dataset = dataset.apply(tf.data.experimental.assert_cardinality(num_samples))
+    dataset = dataset.map(decode_fn, num_parallel_calls=AUTO)
+    if shuffle:
+        assert shuffle_buffer_size is not None
+        dataset = dataset.shuffle(args.shuffle_buffer_size)
+    dataset = dataset.batch(batch_size, drop_remainder=True)
+    dataset = dataset.map(mask_fn, num_parallel_calls=AUTO)
+    dataset = dataset.prefetch(AUTO)
+    return dataset
+
+
+def main(args):
+    if not args.no_tpu:
+        tpu = initialize_tpu(args)
+        strategy = tf.distribute.TPUStrategy(tpu)
+    else:
+        strategy = tf.distribute.OneDeviceStrategy(device="/gpu:0")
+
+    if args.bfloat16:
+        tf.keras.mixed_precision.set_global_policy("mixed_bfloat16")
+
+    tokenizer = AutoTokenizer.from_pretrained(args.tokenizer)
+    config = AutoConfig.from_pretrained(args.pretrained_model_config)
+    config.vocab_size = tokenizer.vocab_size
+
+    training_records = tf.io.gfile.glob(os.path.join(args.train_dataset, "*.tfrecord"))
+    if not training_records:
+        raise ValueError(f"No .tfrecord files found in {args.train_dataset}.")
+    eval_records = tf.io.gfile.glob(os.path.join(args.eval_dataset, "*.tfrecord"))
+    if not eval_records:
+        raise ValueError(f"No .tfrecord files found in {args.eval_dataset}.")
+
+    num_train_samples = count_samples(training_records)
+
+    steps_per_epoch = num_train_samples // (args.per_replica_batch_size * strategy.num_replicas_in_sync)
+    total_train_steps = steps_per_epoch * args.num_epochs
+
+    with strategy.scope():
+        model = TFAutoModelForMaskedLM.from_config(config)
+        model(model.dummy_inputs)  # Pass some dummy inputs through the model to ensure all the weights are built
+        optimizer, schedule = create_optimizer(
+            num_train_steps=total_train_steps,
+            num_warmup_steps=total_train_steps // 20,
+            init_lr=args.learning_rate,
+            weight_decay_rate=args.weight_decay_rate,
+            # TODO Add the other Adam parameters?
+        )
+        model.compile(optimizer=optimizer, metrics=["accuracy"])
+
+    def decode_fn(example):
+        features = {
+            "input_ids": tf.io.FixedLenFeature(dtype=tf.int64, shape=(args.max_length,)),
+            "attention_mask": tf.io.FixedLenFeature(dtype=tf.int64, shape=(args.max_length,)),
+        }
+        return tf.io.parse_single_example(example, features)
+
+    # Many of the data collators in Transformers are TF-compilable when return_tensors == "tf", so we can
+    # use their methods in our data pipeline.
+    data_collator = DataCollatorForLanguageModeling(
+        tokenizer=tokenizer, mlm_probability=args.mlm_probability, mlm=True, return_tensors="tf"
+    )
+
+    def mask_with_collator(batch):
+        # TF really needs an isin() function
+        special_tokens_mask = (
+            ~tf.cast(batch["attention_mask"], tf.bool)
+            | (batch["input_ids"] == tokenizer.cls_token_id)
+            | (batch["input_ids"] == tokenizer.sep_token_id)
+        )
+        batch["input_ids"], batch["labels"] = data_collator.tf_mask_tokens(
+            batch["input_ids"],
+            vocab_size=len(tokenizer),
+            mask_token_id=tokenizer.mask_token_id,
+            special_tokens_mask=special_tokens_mask,
+        )
+        return batch
+
+    batch_size = args.per_replica_batch_size * strategy.num_replicas_in_sync
+
+    train_dataset = prepare_dataset(
+        training_records,
+        decode_fn=decode_fn,
+        mask_fn=mask_with_collator,
+        batch_size=batch_size,
+        shuffle=True,
+        shuffle_buffer_size=args.shuffle_buffer_size,
+    )
+
+    eval_dataset = prepare_dataset(
+        eval_records,
+        decode_fn=decode_fn,
+        mask_fn=mask_with_collator,
+        batch_size=batch_size,
+        shuffle=False,
+    )
+
+    callbacks = []
+    if args.hub_model_id:
+        callbacks.append(
+            PushToHubCallback(output_dir=args.output_dir, hub_model_id=args.hub_model_id, tokenizer=tokenizer)
+        )
+
+    model.fit(
+        train_dataset,
+        validation_data=eval_dataset,
+        epochs=args.num_epochs,
+        callbacks=callbacks,
+    )
+
+    model.save_pretrained(args.output_dir)
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

examples/tensorflow/language-modeling-tpu/train_unigram.py

@@ -0,0 +1,119 @@
+#!/usr/bin/env python
+# coding=utf-8
+# Copyright 2023 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.
+
+"""Script for training a Unigram tokenizer."""
+
+import argparse
+import logging
+
+import datasets
+from tokenizers import Tokenizer, decoders, normalizers, pre_tokenizers, processors
+from tokenizers.models import Unigram
+from tokenizers.trainers import UnigramTrainer
+
+from transformers import AlbertTokenizerFast
+
+
+logger = logging.getLogger(__name__)
+
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Train a unigram tokenizer on the wikitext dataset.")
+    parser.add_argument(
+        "--dataset_name",
+        type=str,
+        default="wikitext",
+        help="Name of the training. Explore datasets at: hf.co/datasets.",
+    )
+    parser.add_argument(
+        "--dataset_config", type=str, default="wikitext-103-raw-v1", help="Configuration name of the dataset."
+    )
+    parser.add_argument(
+        "--batch_size",
+        type=int,
+        default=1000,
+        help="Batch size during training.",
+    )
+    parser.add_argument(
+        "--vocab_size",
+        type=int,
+        default=10048,
+        help="Size of the desired vocabulary.",
+    )
+    parser.add_argument(
+        "--limit",
+        default=None,
+        type=int,
+        help="Limit the number of shards (used for debugging).",
+    )
+    parser.add_argument(
+        "--export_to_hub",
+        action="store_true",
+    )
+
+    args = parser.parse_args()
+    return args
+
+
+def main(args):
+    wikitext = datasets.load_dataset(args.dataset_name, args.dataset_config, split="train")
+
+    if args.limit is not None:
+        max_train_samples = min(len(wikitext), args.limit)
+        wikitext = wikitext.select(range(max_train_samples))
+        logger.info(f"Limiting the dataset to {args.limit} entries.")
+
+    def batch_iterator():
+        for i in range(0, len(wikitext), args.batch_size):
+            yield wikitext[i : i + args.batch_size]["text"]
+
+    # Prepare the tokenizer.
+    tokenizer = Tokenizer(Unigram())
+    tokenizer.normalizer = normalizers.Sequence([normalizers.Replace("``", '"'), normalizers.Replace("''", '"')])
+    tokenizer.pre_tokenizer = pre_tokenizers.Metaspace()
+
+    # Prepare the trainer.
+    trainer = UnigramTrainer(
+        unk_token="<unk>",
+        special_tokens=["[CLS]", "[SEP]", "<unk>", "<pad>", "[MASK]"],
+        vocab_size=args.vocab_size,
+    )
+
+    logger.info("Training the tokenizer.")
+    tokenizer.train_from_iterator(batch_iterator(), trainer=trainer)
+    logger.info("Tokenizer training complete!")
+
+    cls_token_id = tokenizer.token_to_id("[CLS]")
+    sep_token_id = tokenizer.token_to_id("[SEP]")
+    tokenizer.post_processor = processors.TemplateProcessing(
+        single="[CLS]:0 $A:0 [SEP]:0",
+        pair="[CLS]:0 $A:0 [SEP]:0 $B:1 [SEP]:1",
+        special_tokens=[
+            ("[CLS]", cls_token_id),
+            ("[SEP]", sep_token_id),
+        ],
+    )
+    tokenizer.decoder = decoders.Metaspace()
+
+    if args.export_to_hub:
+        logger.info("Exporting the trained tokenzier to Hub.")
+        new_tokenizer = AlbertTokenizerFast(tokenizer_object=tokenizer)
+        new_tokenizer.push_to_hub("unigram-tokenizer-wikitext")
+
+
+if __name__ == "__main__":
+    args = parse_args()
+    main(args)

src/transformers/data/data_collator.py

@@ -664,6 +664,8 @@ class DataCollatorForLanguageModeling(DataCollatorMixin):
         """
         import tensorflow as tf
 
+        mask_token_id = tf.cast(mask_token_id, inputs.dtype)
+
         input_shape = tf.shape(inputs)
         # 1 for a special token, 0 for a normal token in the special tokens mask
         # We sample a few tokens in each sequence for MLM training (with probability `self.mlm_probability`)
@@ -677,8 +679,9 @@ class DataCollatorForLanguageModeling(DataCollatorMixin):
         inputs = tf.where(indices_replaced, mask_token_id, inputs)
 
         # 10% of the time, we replace masked input tokens with random word
-        indices_random = self.tf_bernoulli(input_shape, 0.5) & masked_indices & ~indices_replaced
+        indices_random = self.tf_bernoulli(input_shape, 0.1) & masked_indices & ~indices_replaced
-        random_words = tf.random.uniform(input_shape, maxval=vocab_size, dtype=tf.int64)
+        random_words = tf.random.uniform(input_shape, maxval=vocab_size, dtype=inputs.dtype)
+
         inputs = tf.where(indices_random, random_words, inputs)
 
         # The rest of the time (10% of the time) we keep the masked input tokens unchanged