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.

- inputs
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.

- 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.

- ...
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.