Notify the iterator which k-group it is currently pointing to. This does not advance the iterator. Rather, it overrides its internal tracking with co

Notify the iterator which k-group it is currently pointing to. This does not advance the iterator. Rather, it overrides its internal tracking with co

Setup model and optimizer.

Returns the number of elements stored in the host tensor

Stores a fragment to memory at the location pointed to by the iterator

Store a fragment to memory

Store a fragment to memory

Store

Store

Store

Run the decoding with beam search by TensorFlow. Args: memory_tensor: A tf.tensor with shape [batch_size * beam_width, max(memor

Run the bert transformer layer by TensorFlow. Args: input_tensor: A tf.Tensor with shape [batch_size, seq_len, hidden_dimension]

Run the decoding with sampling by TensorFlow. Args: memory_tensor: A tf.tensor with shape [batch_size, max(memory_sequence_lengt

Serializes this instance to a Python dictionary.

Tokenizes a piece of text into its word pieces. This uses a greedy longest-match-first algorithm to perform tokenization using the given voca

Single training step.

Log training information such as losses, timing, ....

Runs basic whitespace cleaning and splitting on a piece of text.

Runs basic whitespace cleaning and splitting on a piece of text.

Runs basic whitespace cleaning and splitting on a piece of text.

Args: x: (B, H, W, C) window_size (int): window size Returns: windows: (num_windows*B, window_size, window_size, C)

Returns a list of the text of each token Args: uncased: lower cases text

Helper to compute the maximum alignment of the epilogue

Sets flags for Unit test

Single source of truth for whether to unroll for `LinearCombinationClamp()`

Single source of truth for whether to unroll for `LinearCombinationClamp()`

Single source of truth for whether to unroll for `LinearCombinationClamp()`

Construct a PredicatedTileAccessIterator with zero threadblock offset

< Layout function

Receive happens between the layers with size [s, b, h].

Build the attention mask and postition ids for the input tokens.

Initialize tokenizer.

Build train, valid, and test datasets.

Compute softmax probability over raw logits.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.

Creates examples for the training and dev sets.