seqio 0.1.0rc0

SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models.

SeqIO: Task-based datasets, preprocessing, and evaluation for sequence models.

SeqIO is a library for processing sequential data to be fed into downstream sequence models. It uses tf.data.Dataset to create data pipelines but requires minimal use of TensorFlow.

SeqIO is a refactor of the t5.data library used (in conjunction with the Mesh Tensorflow Transformer implementation) to train the T5 models introduced in Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer.

If you have used t5.data in the past and want to know how SeqIO differs, please read this section.

Usage Tutorial

Defining a Task

The most important class in SeqIO is the Task. It is an abstraction that combines:

• a raw data source
• one or more preprocessing steps
• a vocabulary to tokenize/detokenize each preprocessed feature for the model
• a postprocessor to convert detokenized model outputs into a format for evaluation
• one or more metrics to evaluate with

Oftentimes a Task lines up with a common benchmark. In this tutorial we will create a task definition for the closed-book, open-domain version of TriviaQA, defining various parts as we go. In the end, our Task will look like this:

seqio.TaskRegistry.add(
    "trivia_qa_open",
    source=seqio.TfdsDataSource(
      tfds_name="trivia_qa/unfiltered.nocontext:1.1.0",
      splits={
          "train": "train[:90%]",
          "validation": "train[90%:]",
          "test": "validation"
      }),
    preprocessors=[
        tqa_open_preprocessor,
        seqio.tokenize,
        seqio.append_eos,
    ],
    output_features={
        "inputs": seqio.Feature(
           seqio.SentencePieceVocabulary("/path/to/inputs/vocab"),
           add_eos=False, dtype=tf.int32
        ),
        "targets": seqio.Feature(
           seqio.SentencePieceVocabulary("/path/to/targets/vocab"),
           add_eos=True, dtype=tf.int32
        ),
    },
    postprocess_fn=tqa_open_postprocessor,
    metric_fns=[tqa_metric])

We typically add the Task to the global registry when we define it (as shown above) to make it easier to use with model configs and flags. Thus, it must have a unique string name ("trivia_qa_open" in this case). Note, however, that you may also instantiate a seqio.Task directly without adding it to the registry, if desired.

We'll now break down each part of the task definition.

Data Source

Data sources are the first step in your pipeline, providing a way to load raw data in many format as a tf.data.Dataset. All data sources are subclasses of the DataSource base class and are defined in dataset_providers,

Existing implementations include:

• TfdsDataSource for loading examples from TensorFlow Datasets.
• TextLineDataset for loading examples from text files (e.g., tsv).
• TFExampleDataSource for loading tf.train.Example protos from a file (e.g. a TFRecord file.)
• FunctionDataSource for providing an custom function that returns a tf.data.Dataset.

In our example, we are using the TfdsDataSource. We specify the name of the TriviaQA dataset in TFDS ("trivia_qa"), the specific config that excludes the context for the open domain setting ("unfiltered.nocontext"), and the version number ("1.1.0"). We also override the default splits to match what is commonly used for the open domain setting. Specifically, we set our "test" split to be the TFDS "validation" split, and create a small pseudo-"validation" set by taking examples out of the TFDS "train" split.

Output Features

The output_features field expects a dictionary that maps string feature names to seqio.Feature objects. This defines what the Task is expected to produce in its output examples. The output examples may contain additional fields, but they must contain these fields in the specified format or exceptions will be raised.

Each Feature includes:

• A vocabulary, which must subclass seqio.Vocabulary, to specify how the feature can be tokenized and detokenized. You may use seqio.PassThroughVocabulary if tokenization is not necessary.
• add_eos, which specifies whether the feature should end with the vocabulary's EOS token.
• The output dtype which must be a tf.dtypes.DType.

Note: specifying these options on Feature does not by itself ensure the proper transformations are applied -- you must also include then necessary preprocessors.

The tasks used in T5 all produce "inputs" and "targets" features to be consumed by the text-to-text model. For a decoder-only language model, only a single feature (e.g., "targets") would be necessary. Nevertheless, SeqIO is flexible enough to generate arbitrary output features what will be converted into model features by the FeatureConverter later in the pipeline.

Preprocessors

Preprocessors are functions that transform one tf.data.Dataset into a new tf.data.Dataset. Typically this involves executing a map over the given dataset. The preprocessors provided to the Task will be executed sequentially.

As an example, let's look at the previously undefined tqa_open_preprocessor from the "trivia_qa_open" example above.

def trivia_qa_open(
    dataset: tf.data.Dataset,
    prefix:str = "trivia_qa question: "
  ) -> tf.data.Dataset:
  """Convert TriviaQA dataset to open domain qa examples.

  The function takes the trivia_qa TFDS dataset and emits examples of the
  form:
  {
    "inputs": "trivia_qa question: What are the names of the Olsen Twins?"
    "targets": "Mary-Kate and Ashley",
    "answers": ["Mary-Kate and Ashley", "Ashley and Mary-Kate"]
  }

  Args:
    dataset: a tf.data.Dataset to process.
    prefix: str, prefix to prepend to the inputs.

  Returns:
    a tf.data.Dataset
  """
  def tqa_map(ex):
    """Map TriviaQA example to text-to-text example."""
    return {
        "inputs": prefix + ex["question"],
        "targets": ex["answer"]["value"],
        "answers": ex["answer"]["aliases"],
    }

  return dataset.map(tqa_map, num_parallel_calls=tf.data.experimental.AUTOTUNE)

A few important notes:

1. When instantiating a Task, the preprocessor functions can have the following arguments: dataset, output_features, and sequence_length. The first (positional) dataset argument is always required. If an argument named output_features is provided, the output feature mapping will be passed to the preprocessor. If sequence_length is provided, a mapping from feature name to its maximum final sequence length (provided by the caller will be passed -- any sequences that are too long after preprocessing will be automatically truncated. If a preprocessor function does have other arguments, they must have default values or be bound (e.g., with functools.partial) before instantiating the Task.

2. Mapping functions operate on and return tf.Tensors using TensorFlow operations, although it is possible to take advantage of automatic AutoGraph conversion for numpy or use tf.py_function to wrap arbitrary Python code. See tf.data.Dataset documentation for more details.

3. When calling map, it is important to always set num_parallel_calls=tf.data.experimental.AUTOTUNE to avoid creating a bottleneck. The seqio.map_over_dataset decorator helps enforce this as follows:

def trivia_qa_open(
  dataset: tf.data.Dataset,
  prefix: str = "trivia_qa question: "
) -> tf.data.Dataset:

  @seqio.map_over_dataset
  def tqa_map(ex: Mapping[str, tf.Tensor]) -> Mapping[str, tf.Tensor]:
    """Map TriviaQA example to text-to-text example."""
    return {
        "inputs": prefix + ex["question"],
        "targets": ex["answer"]["value"],
        "answers": ex["answer"]["aliases"],
    }

return tqa_map(dataset)

1. Stochastic operations must be stateless if deterministic pipelines are needed. To get (optionally deterministic) seeds for these operations, use the seqio.map_over_dataset(num_seeds=n) decorator. For example:

def random_chunk(
  dataset: tf.data.Dataset,
  sequence_length: Mapping[str, int]
) -> tf.data.Dataset:
"""Takes a random chunk out of each feature the size of `sequence_length`."""

  @seqio.map_over_dataset(num_seeds=1)
  def take_chunk(
      ex: Mapping[str, tf.Tensor],
      seed
  ) -> Mapping[str, tf.Tensor]:
    new_ex = {}
    for k, v in ex.items():
      if k in sequence_length:
        length = sequence_length[k]
        start_idx = tf.random.stateless_uniform(
           (), seed, 0, tf.size(v) - (length + 1))
        new_ex[k] = v[start_idx:start_idx+length]
      else:
        new_ex[k] = v
    return new_ex

return take_chunk(dataset)

If num_seeds > 1, the arg will instead be called seeds and will contain a sequence of seeds.

In our "trivia_qa_open" task, we also use the predefined preprocessors seqio.tokenize and seqio.append_eos. The former uses each Feature.vocabulary to tokenize it, and the the latter appends Feature.vocabulary.eos_id to the feature if the Feaure.add_eos is True. See preprocessors.py for their implementations and other useful preprocessors.

Postprocessor

During evaluation, the model outputs are first detokenized using the output feature vocabulary. Before passing these predictions to the metric functions, they can be run through a Python postprocessing function, alongside the full input example. Similarly, the raw targets are run through this function before being passed to the metrics. Since the postprocess function is used on both the model output and the targets, it is passed an is_target boolean in case the behavior should be different. It is also passed the fully preprocessed example, including fields that were excluded from output_features.

As an example, lets look at the previously undefined tqa_open_postprocessor.

def tqa_open_postprocessor(output_or_target, example=None, is_target=False):
  """Returns output as answer, or all answers if the full example is provided."""
  if is_target:
    return [a.decode("utf-8") for a in example["answers"]]
  else:
    return output_or_target.decode("utf-8")

When processing the target, we ignore output_or_target (equivalent to example["targets"]) since it is just selecting a single answer in trivia_qa_open. Instead, we extract the full list of answers from the example and convert them from bytes to text. When handling the model output, we simply convert it to text from detokenized bytes.

Metrics

Metrics are functions that are passed (by the Evaluator) the fully-materialized list of postprocessed model outputs (or scores) and targets and return a mapping from string names to Metric objects containing their values. These are most commonly floating-point scalars, but may also be text, images, audio, histograms, etc (see evaluation.py for the full list).

The first argument of a metric function must always be called targets. If the second argument of a metric function is called predictions, it will be passed the decoded and detokenized model prediction. If it is called scores, it will be passed a list of log-likelihood scores for each example.

If multiple metric functions are provided, they will all be used and their returned mappings merged.

Prediction Metrics

Prediction metrics are computed using the postprocessed targets and model outputs (predictions). The args must be named targets and predictions.

Let's look at the previously undefined tqa_metric prediction metric:

def tqa_metric(
  targets: Sequence[Sequence[str]],
  predictions: Sequence[str]
) -> Mapping[str, seqio.Metric]:
  """Computes official TriviaQA metrics.

  Args:
    targets: list of lists of strings
    predictions: list of strings

  Returns:
    dict with score_key: squad score across all targets and predictions
  """

  if len(targets) != len(predictions):
    raise ValueError("Number of targets and predictions must match.")

  def _normalize_answer(text):
    """Lower text and remove punctuation, articles and extra whitespace."""
    # Remove articles.
    text = re.sub(r"\b(a|an|the)\b", " ", s)
    # Remove punctuation.
    for punc in string.punctuation:
      text = text.replace(punc, '')
    # Normalize white space
    text = " ".join(s.split())
    return text

  # Normalize answers before comparing.
  targets = [[_normalize_answer(t) for t in u] for u in targets]
  predictions = [_normalize_answer(p) for p in predictions]

  em = np.mean([
      max(pred == gt for gt in ground_truths)
      for pred, ground_truths in zip(predictions, targets)
  ])
  return {
      "exact_match": seqio.evaluation.Scalar(em),
  }

Score Metrics

Score metrics are computed using the postprocessed targets and their log-likelihood scores according to the model. The args must be named targets and scores.

def perplexity(targets: Sequence[str], scores: Sequence[int]):
  return {
    "perplexity": seqio.evaluation.Scalar(np.exp(np.mean(scores)))
  }

Defining a Mixture

Once you have multiple Tasks added to the TaskRegistry, you can define Mixtures that will combine the examples from them according to some specified rate. Examples will then be sampled from each task in proportion to its rate.

As an example, Multilingual T5 uses a Mixture of per-language Tasks with tail languages up-weighted in the mixture.

There are 3 ways to specify the tasks and their rates:

1. Provide a rate along with each task's name (rates are normalized before sampling):

seqio.MixtureRegistry.add(
  "mix1",
  [("task1", 1), ("task2", 7)]
)

1. Provide a constant default rate for some or all tasks, which will be used when only the name is provided. The example below will produce identical mixing rates as the previous one.

seqio.MixtureRegistry.add(
  "mix1",
  [("task1", 0.5), "task2"],
  default_rate=3.5
)

1. Provide a function that generates the rate for each task at runtime. The example below uses the provided seqio.mixing_rate_num_examples, which uses the number of examples (computed during offline caching) as the rate for each task.

seqio.MixtureRegistry.add(
  "mix2",
  ["task1", "task2"],
  default_rate=seqio.mixing_rate_num_examples
)

You can also include Mixtures in your Mixture! For example, the following task would contain 1/24 (from "mix1") + 1/3 "task1", 7/24 (from "mix1") of "task2", and 1/3 "task3".

seqio.MixtureRegistry.add(
  "mix3",
  ["mix1", task1", "task3"],
  default_rate=1
)

Getting a Preprocessed Dataset

Now that your Task (and/or Mixture) is defined, its primary functionality is to use it to generate a dataset.

You may first need to use seqio.get_mixture_or_task(mixture_or_task_name) to access your dataset provider from the registry.

After that, you can call get_dataset to build the tf.data.Dataset. For example:

dataset = seqio.get_mixture_or_task("mix1").get_dataset(
    sequence_length={"inputs": 256, targets": 128},
    dataset_split="train",
    shuffle=True,
    num_epochs=1,
    shard_info=seqio.ShardInfo(index=0, num_shards=10),
    use_cached=False,
    seed=42
)

# Print the first 5 examples.
for _, ex in zip(range(5), dataset.as_numpy_iterator()):
  print(ex)

Some notes on a few the arguments:

• sequence_length: An optional mapping from feature name to maximum length. Will be passed to the preprocessors with a sequence_length argument. If not None, the final example features will be truncated if they exceed the specified length. Note that this value may be required to be set if any of the preprocessors use the sequence_length argument and do not handle the None case.
• num_epochs: The number of times to repeat the source dataset. Preprocessing will be re-applied with new seeds to enable new samples from stochastic steps. Note that if the CacheDatasetPlaceholder is included (see below) preprocessing is only re-applied after that step.
• shard_info: An optional sharding specification for loading a deterministic subset of the dataset. Loading will be most efficient if the number of shards evenly divides the number of shards in the raw data source.
• use_cached: Specifies whether to load from a pre-cached task for increased performance or to do the preprocessing on-the-fly. See the following section for details on how to cache your task, which must be done before this can be set to True.
• seed: An optional seed to use for deterministic shuffling and (stateless) stochastic ops. These operations will still be pseudorandom but will be reproducible with the same seed. Set to None if determinism is not desired.

(Optional) Offline Caching

For improved performance at load time and avoid redundant computations for commonly used tasks, you can pre-cache your Task with all or part of the preprocessing done in advance of training.

The first step to doing so is to add a seqio.CacheDatasetPlaceholder(required=False) as one of the steps in your preprocessing pipeline. All steps before the placeholder will be cached offline and all steps after will be executed on the fly at load time. You may set required=True if you want get_dataset to fail unless use_cached=True.

Caveats:

• Any stochastic operations that you wish to be re-run when num_epochs > 1 or with a different seed should go after the placeholder since only a single sample will be cached.
• Any preprocessing steps that use the sequence_length argument must come after the seqio.CacheDatasetPlaceholder preproessor since this is only known at runtime, or an exception will be raised. If you wish to cache for a specific sequence length, you can use seqio.experimental.add_fully_cached_task.

Once your Task is registered, you can run cache_tasks_main to execute the offline preprocessing, providing it with the module containing your task definitions via the --module_import flag. For very large datasets, it's recommended you run this Apache Beam script on a distributed framework like Google Cloud DataFlow.

Finally, you are ready to load the cached version of your Task (or Mixture) containing it. You will need to add the path to the directory you passed to --output_cache_dir via seqio.add_global_cache_dirs(["/my/cache/dir"]). Now when you call task_or_mixture.get_dataset(..., use_cached=True), the data will be loaded from the cache directory instead of the raw data source.

FeatureConverter

Given the Task's preprocessed output features, the FeatureConverter determines how they will be seen by the model...

TODO(hwchung)

Evaluator

TODO(hwchung)

Differences from t5.data

The original t5 library introduced and implemented the t5.data.Task abstraction for specifying preprocessing and evaluation metrics for text-to-text tasks. When creating a task, users specify a source dataset of raw text, some preprocessing steps, a vocabulary for tokenization, and evaluation metrics. The fully-specified Task can then be used to pre-train or fine-tune a encoder-decoder transformer model. However, the design included many baked-in assumptions about the types of tasks users could specify.

SeqIO removes some of the constraints of this abstraction:

• Inputs and outputs are no longer required to be strings (e.g., it may be images or audio).
• Architectures other than the original encoder-decoder are supported (e.g., decoder-only languaged models like GPT or encoder-only models like BERT).
• Users can control at which stage of the pipeline offline caching occurs.
• Users can control when and where EOS tokens are added.

Furthermore, SeqIO has been made more modular with respect to the Mesh TensorFlow Transformer. This allows it to be used with other model implementations with more consistency and much less code duplication.