cerebras.modelzoo.data.nlp.transformer.TransformerDynamicDataProcessor.TransformerDynamicDataProcessor#

class cerebras.modelzoo.data.nlp.transformer.TransformerDynamicDataProcessor.TransformerDynamicDataProcessor[source]#

Bases: cerebras.modelzoo.data.nlp.t5.T5DynamicDataProcessor.T5DynamicDataProcessor

Reads text files containing the input text tokens. :param str src_vocab_file: Path to file containing tokens of vocabulary, one token per line. :param str src_data_dir: Path to directory containing the output of preprocess.sh, with all the files of tokenized data. :param int batch_size: Number of sequences per batch. Note that it is different between systems. :param bool shuffle, optional: If true the data will be shuffled before passing into the model. Recommended for training. Can be set to False for debugging. :param int shuffle_seed, optional: Sets random seed for the order of data shuffling. Allows for reproducibility while still shuffling data. :param int shuffle_buffer: Size of buffer used to store data before shuffling :param int extra_ids, optional: Number of sentinel tokens for T5 objective :param int src_max_sequence_length, optional: Largest possible sequence length for the input. If longer it will be truncated. All other sequences padded to this length. :param int tgt_max_sequence_length, optional: Largest possible sequence length for the labels. If longer it will be truncated. All other sequences padded to this length. :param int num_workers, optional: Number of processes that move data to the accelerator system, so that the system doesn’t process data faster than it receives it. :param bool drop_last, optional: If the last batch is not the full size, i.e. the dataset could not divide evenly into the batch-size, do not use the last batch. :param int prefetch_factor, optional: Number of batch loaded in advance by each worker. :param bool persistent_workers, optional: If set, workers will not be shutdown after going through the dataset once. :param bool do_lower, optional: If set, will lowercase all tokens in vocabulary. T5’s vocabulary is cased so this is not recommended. :param list buckets, optional: A list of boundaries for sequence lengths to bucket together in order to speed up VTS/VSL. :param bool dynamic_loss_weight, optional: If set, will divide the loss for a token by the length of the sequence that the token comes from. :param bool pack_sequences, optional: If set, will concatenate sequences so that computation is performed on real data rather than padding :param int num_documents_to_concatenate, optional: Specifies how many documents to pack together :param str oov_token, optional: Token for out-of-vocabulary words/sub-words :param str sos_token, optional: Token for start-of-sequence :param str eos_token, optional: Token for end-of-sequence :param str pad_token, optional: Token for padding :param int labels_pad_id, optional: Can set specific padding for labels :param int input_pad_id, optional: Can set specific padding for inputs :param bool mixed_precision, optional: If set, will use float16 rather than float32 when possible

Methods

`create_dataloader`	Classmethod to create the dataloader object.
`element_length_fn`	Takes a single sample and returns the sequence length of that sample to be used for VTS bucketing.
`get_meta_data`	Read data from meta files.
`get_single_item`	Iterating over the data to construct input features.
`load_buffer`	Generator to read the data in chunks of size of data_buffer.

__init__(params)[source]#

get_meta_data(data_dir)[source]#: Read data from meta files. :param str data_dir: Path to the input directory. :return: Processed meta data.

load_buffer()[source]#: Generator to read the data in chunks of size of data_buffer. We read data from both source and target input datasets to prepare features for side by side translation task. :returns: Yields the data stored in the data_buffer.

get_single_item()[source]#

Iterating over the data to construct input features.

Returns

A dict with training features: * np.array[int.32] input_ids: Numpy array with encoder input token indices.

Shape: (src_max_sequence_length).

np.array[int.32] decoder_input_ids: Numpy array with decoder input token indices.
Shape: (tgt_max_sequence_length).
np.array[int.32] attention_mask: Numpy array with attention mask for encoder.
Shape: (src_max_sequence_length).
np.array[int.32] decoder_attention_mask: Numpy array with attention mask for decoder.
Shape: (tgt_max_sequence_length).
np.array[int.32] labels: Numpy array with labels for teacher forcing mode.
Shape: (tgt_max_sequence_length).

element_length_fn(features)[source]#: Takes a single sample and returns the sequence length of that sample to be used for VTS bucketing.

__call__(*args: Any, **kwargs: Any) → Any#: Call self as a function.

static __new__(cls, *args: Any, **kwargs: Any) → Any#

create_dataloader()#: Classmethod to create the dataloader object.

cerebras.modelzoo.data.nlp.transformer.TransformerDynamicDataProcessor

cerebras.modelzoo.data.vision