layer_multi_head_attention
MultiHeadAttention layer
Description
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.
Usage
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,
... )
Arguments
Arguments | Description |
---|---|
inputs | a list of inputs first should be the query tensor, the second the value tensor |
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 . |
Details
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.
Section
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.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.
Value
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.