# Layers

Layers are the fundamental building blocks for NLP models. They can be used to
assemble new layers, networks, or models.

*   [DenseEinsum](dense_einsum.py) implements a feedforward network using
    tf.einsum. This layer contains the einsum op, the associated weight, and the
    logic required to generate the einsum expression for the given
    initialization parameters.

*   [MultiHeadAttention](attention.py) implements an optionally masked attention
    between two tensors, from_tensor and to_tensor, as described in
    ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762). If
    `from_tensor` and `to_tensor` are the same, then this is self-attention.

*   [CachedAttention](attention.py) implements an attention layer with cache
    used for auto-agressive decoding.

*   [TalkingHeadsAttention](talking_heads_attention.py) implements the talking
    heads attention, as decribed in
    ["Talking-Heads Attention"](https://arxiv.org/abs/2003.02436).

*   [Transformer](transformer.py) implements an optionally masked transformer as
    described in
    ["Attention Is All You Need"](https://arxiv.org/abs/1706.03762).

*   [ReZeroTransformer](rezero_transformer.py) implements Transformer with
    ReZero described in
    ["ReZero is All You Need: Fast Convergence at Large Depth"](https://arxiv.org/abs/2003.04887).

*   [OnDeviceEmbedding](on_device_embedding.py) implements efficient embedding
    lookups designed for TPU-based models.

*   [PositionalEmbedding](position_embedding.py) creates a positional embedding
    as described in ["BERT: Pre-training of Deep Bidirectional Transformers for
    Language Understanding"](https://arxiv.org/abs/1810.04805).

*   [SelfAttentionMask](self_attention_mask.py) creates a 3D attention mask from
    a 2D tensor mask.

*   [MaskedSoftmax](masked_softmax.py) implements a softmax with an optional
    masking input. If no mask is provided to this layer, it performs a standard
    softmax; however, if a mask tensor is applied (which should be 1 in
    positions where the data should be allowed through, and 0 where the data
    should be masked), the output will have masked positions set to
    approximately zero.

*   [ClassificationHead](cls_head.py) A pooling head over a sequence of
    embeddings, commonly used by classification tasks.
