Updated internal links with external github links in TF-NLP Data processing

PiperOrigin-RevId: 640215866

Author: tensorflower-gardener

official/nlp/docs/data_processing.md

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+# TF-NLP Data Processing
+
+## Code locations
+
+Open sourced data processing libraries:
+[tensorflow_models/official/nlp/data/](https://github.com/tensorflow/models/tree/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data)
+
+## Preprocess data offline v.s. TFDS
+
+Inside TF-NLP, there are flexible ways to provide training data to the input
+pipeline: 1) using python scripts/beam/flume to process/tokenize the data
+offline; 2) reading the text data directly from
+[TFDS](https://www.tensorflow.org/datasets/api_docs/python/tfds) and using
+[TF.Text](https://www.tensorflow.org/tutorials/tensorflow_text/intro) for
+tokenization and preprocessing inside the tf.data input pipeline.
+
+### Preprocessing scripts
+
+We have implemented data preprocessing for multiple datasets in the following
+python scripts:
+
+*   [create_pretraining_data.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/create_pretraining_data.py)
+
+*   [create_finetuning_data.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/create_finetuning_data.py)
+
+Then, the processed files with `tf.Example` protos inside should be specified to
+the `input_path` argument in
+[`DataConfig`](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/core/config_definitions.py#L28).
+
+### TFDS usages
+
+For convenience and consolidation, we built a common
+[input_reader.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/core/input_reader.py)
+library to standardize input reading, which has built-in pass for TFDS.
+Specifying the arguments in the
+[`DataConfig`](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/core/config_definitions.py#L28),
+`tfds_name`, `tfds_data_dir` and `tfds_split`, will let the tf.data pipeline
+read from the corresponding dataset inside TFDS.
+
+## DataLoaders
+
+To manage multiple datasets and processing functions, we defined the
+[DataLoader](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/data_loader.py)
+class to work with the
+[data loader factory](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/data_loader_factory.py).
+
+Each dataloader defines the tf.data input pipeline inside the `load` method.
+
+```python
+@abc.abstractmethod
+def load(
+    self,
+    input_context: Optional[tf.distribute.InputContext] = None
+) -> tf.data.Dataset:
+```
+
+Then, the `load` method is called inside each NLP task's `build_input` method
+and the trainer wrap that to create distributed datasets.
+
+```python
+def build_inputs(self, params, input_context=None):
+  """Returns tf.data.Dataset for pretraining."""
+  data_loader = YourDataLoader(params)
+  return data_loader.load(input_context)
+```
+
+By default, in the example above, `params` is the `train_data` or
+`validation_data` field of the `task` field of the experiment config. `params`
+is a type of `DataConfig`.
+
+It is important to note that, for TPU training, the entire `load` method will
+run on the TPU workers and it requires that the function does not access
+resources outside, e.g. the task attributes.
+
+To work with raw text features, we need to use the `DataLoader`s handling the
+text data with TF.Text. You can take the following dataloaders as references:
+
+*   [sentence_prediction_dataloader.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/sentence_prediction_dataloader.py)
+    for BERT GLUE fine tuning using TFDS with raw text features.
+
+## Speed up training using TF.data service and dynamic sequence length on TPUs
+
+With TF 2.x, we can enable some types of dynamic shapes on TPUs, thanks to TF
+2.x programing model and TPUStrategy/XLA works.
+
+Depending on the data distribution, we are seeing 50% to 90% speed up on typical
+text data for BERT pretraining applications relative to padded static shape
+inputs.
+
+To enable dynamic sequence, we need to use
+`tf data service` for the global bucketizing over
+sequences. To enable it, you can simply add `--enable_tf_data_service` when you
+start experiments.
+
+To pair with tf data service, we need to use the dataloaders that has the
+bucketizing function implemented. You can take the following dataloaders as
+references:
+
+*   [pretrain_dynamic_dataloader.py](https://github.com/tensorflow/models/blob/28d972a0b30b628cbb7f67a090ea564c3eda99ea/official/nlp/data/pretrain_dynamic_dataloader.py)
+    for BERT pretraining on the tokenized datasets.