MultiHeadAttention layer

[source]

MultiHeadAttention class

tf.keras.layers.MultiHeadAttention(
    num_heads,
    key_dim,
    value_dim=None,
    dropout=0.0,
    use_bias=True,
    output_shape=None,
    attention_axes=None,
    kernel_initializer="glorot_uniform",
    bias_initializer="zeros",
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)

MultiHeadAttention layer.

This is an implementation of multi-headed attention as described in the paper "Attention is all you Need" (Vaswani et al., 2017). If query, key, value are the same, then this is self-attention. Each timestep in query attends to the corresponding sequence in key, and returns a fixed-width vector.

This layer first projects query, key and value. These are (effectively) a list of tensors of length num_attention_heads, where the corresponding shapes are (batch_size, <query dimensions>, key_dim), (batch_size, <key/value dimensions>, key_dim), (batch_size, <key/value dimensions>, value_dim).

Then, the query and key tensors are dot-producted and scaled. These are softmaxed to obtain attention probabilities. The value tensors are then interpolated by these probabilities, then concatenated back to a single tensor.

Finally, the result tensor with the last dimension as value_dim can take an linear projection and return.

When using MultiHeadAttention inside a custom Layer, the custom Layer must implement build() and call MultiHeadAttention's _build_from_signature(). This enables weights to be restored correctly when the model is loaded. TODO(b/172609172): link to documentation about calling custom build functions when used in a custom Layer.

Examples

Performs 1D cross-attention over two sequence inputs with an attention mask. Returns the additional attention weights over heads.

>>> layer = MultiHeadAttention(num_heads=2, key_dim=2)
>>> target = tf.keras.Input(shape=[8, 16])
>>> source = tf.keras.Input(shape=[4, 16])
>>> output_tensor, weights = layer(target, source,
...                                return_attention_scores=True)
>>> print(output_tensor.shape)
(None, 8, 16)
>>> print(weights.shape)
(None, 2, 8, 4)

Performs 2D self-attention over a 5D input tensor on axes 2 and 3.

>>> layer = MultiHeadAttention(
...     num_heads=2, key_dim=2, attention_axes=(2, 3))
>>> input_tensor = tf.keras.Input(shape=[5, 3, 4, 16])
>>> output_tensor = layer(input_tensor, input_tensor)
>>> print(output_tensor.shape)
(None, 5, 3, 4, 16)

Arguments

• num_heads: Number of attention heads.
• key_dim: Size of each attention head for query and key.
• value_dim: Size of each attention head for value.
• dropout: Dropout probability.
• use_bias: Boolean, whether the dense layers use bias vectors/matrices.
• output_shape: The expected shape of an output tensor, besides the batch and sequence dims. If not specified, projects back to the key feature dim.
• attention_axes: axes over which the attention is applied. None means attention over all axes, but batch, heads, and features.
• kernel_initializer: Initializer for dense layer kernels.
• bias_initializer: Initializer for dense layer biases.
• kernel_regularizer: Regularizer for dense layer kernels.
• bias_regularizer: Regularizer for dense layer biases.
• activity_regularizer: Regularizer for dense layer activity.
• kernel_constraint: Constraint for dense layer kernels.
• bias_constraint: Constraint for dense layer kernels.

Call arguments

• query: Query Tensor of shape (B, T, dim).
• value: Value Tensor of shape (B, S, dim).
• key: Optional key Tensor of shape (B, S, dim). If not given, will use value for both key and value, which is the most common case.
• attention_mask: a boolean mask of shape (B, T, S), that prevents attention to certain positions. The boolean mask specifies which query elements can attend to which key elements, 1 indicates attention and 0 indicates no attention. Broadcasting can happen for the missing batch dimensions and the head dimension.
• return_attention_scores: A boolean to indicate whether the output should be (attention_output, attention_scores) if True, or attention_output if False. Defaults to False.
• training: Python boolean indicating whether the layer should behave in training mode (adding dropout) or in inference mode (no dropout). Defaults to either using the training mode of the parent layer/model, or False (inference) if there is no parent layer.
• use_causal_mask: A boolean to indicate whether to apply a causal mask to prevent tokens from attending to future tokens (e.g., used in a decoder Transformer).

Returns

• attention_output: The result of the computation, of shape (B, T, E), where T is for target sequence shapes and E is the query input last dimension if output_shape is None. Otherwise, the multi-head outputs are projected to the shape specified by output_shape.
• attention_scores: [Optional] multi-head attention coefficients over attention axes.