This is an implementation of multi-headed attention based on "Attention is all you Need". 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.
layer_multi_head_attention(
inputs,
num_heads,
key_dim,
value_dim = NULL,
dropout = 0,
use_bias = TRUE,
output_shape = NULL,
attention_axes = NULL,
kernel_initializer = "glorot_uniform",
bias_initializer = "zeros",
kernel_regularizer = NULL,
bias_regularizer = NULL,
activity_regularizer = NULL,
kernel_constraint = NULL,
bias_constraint = NULL,
...
)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 project to the
shape specified by output_shape.
attention_scores: (Optional) multi-head attention coeffients over attention axes.
List of the following tensors:
query: Query Tensor of shape [batch_size, Tq, dim].
value: Value Tensor of shape [batch_size, Tv, dim].
key: Optional key Tensor of shape [batch_size, Tv, dim]. If not
given, will use value for both key and value, which is the most common
case.
Number of attention heads.
Size of each attention head for query and key.
Size of each attention head for value.
Dropout probability.
Boolean, whether the dense layers use bias vectors/matrices.
The expected shape of an output tensor, besides the batch and sequence dims. If not specified, projects back to the key feature dim.
axes over which the attention is applied. None means attention over all axes, but batch, heads, and features.
Initializer for dense layer kernels.
Initializer for dense layer biases.
Regularizer for dense layer kernels.
Regularizer for dense layer biases.
Regularizer for dense layer activity.
Constraint for dense layer kernels.
Constraint for dense layer kernels.
Other arguments passed to the layer. Eg, name, training.
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.
return_attention_scores: A boolean to indicate whether the output should be attention output if TRUE, or (attention_output, attention_scores) 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.
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, , key_dim], [batch_size, , key_dim], [batch_size, , 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.