tfm.nlp.layers.ReuseMultiHeadAttention

MultiHeadAttention layer.

tfm.nlp.layers.ReuseMultiHeadAttention(
    num_heads,
    key_dim,
    value_dim=None,
    dropout=0.0,
    reuse_attention=0,
    use_relative_pe=False,
    pe_max_seq_length=512,
    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
)

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.

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)

Args
`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.
`reuse_attention`	An integer specifying number of heads to reuse. -1 for all heads.
`use_relative_pe`	Whether to use relative position bias.
`max_sequence_length`	Used to set the size of the relative positin encodings.
`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 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.

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 project to the shape specified by `output_shape`.
`attention_scores`	[Optional] multi-head attention coeffients over attention axes.

Methods

`call`

View source

call(
    query,
    value,
    key=None,
    attention_mask=None,
    return_attention_scores=False,
    training=None,
    reuse_attention_scores=None
)

This is where the layer's logic lives.

The call() method may not create state (except in its first invocation, wrapping the creation of variables or other resources in tf.init_scope()). It is recommended to create state in __init__(), or the build() method that is called automatically before call() executes the first time.

Args

Args
`inputs`	Input tensor, or dict/list/tuple of input tensors. The first positional `inputs` argument is subject to special rules: `inputs` must be explicitly passed. A layer cannot have zero arguments, and `inputs` cannot be provided via the default value of a keyword argument. NumPy array or Python scalar values in `inputs` get cast as tensors. Keras mask metadata is only collected from `inputs`. Layers are built (`build(input_shape)` method) using shape info from `inputs` only. `input_spec` compatibility is only checked against `inputs`. Mixed precision input casting is only applied to `inputs`. If a layer has tensor arguments in `args` or `*kwargs`, their casting behavior in mixed precision should be handled manually. The SavedModel input specification is generated using `inputs` only. Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for `inputs` and not for tensors in positional and keyword arguments.
`*args`	Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.
`**kwargs`	Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved: `training`: Boolean scalar tensor of Python boolean indicating whether the `call` is meant for training or inference. `mask`: Boolean input mask. If the layer's `call()` method takes a `mask` argument, its default value will be set to the mask generated for `inputs` by the previous layer (if `input` did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

inputs

Input tensor, or dict/list/tuple of input tensors. The first positional inputs argument is subject to special rules:

inputs must be explicitly passed. A layer cannot have zero arguments, and inputs cannot be provided via the default value of a keyword argument.
NumPy array or Python scalar values in inputs get cast as tensors.
Keras mask metadata is only collected from inputs.
Layers are built (build(input_shape) method) using shape info from inputs only.
input_spec compatibility is only checked against inputs.
Mixed precision input casting is only applied to inputs. If a layer has tensor arguments in *args or **kwargs, their casting behavior in mixed precision should be handled manually.
The SavedModel input specification is generated using inputs only.
Integration with various ecosystem packages like TFMOT, TFLite, TF.js, etc is only supported for inputs and not for tensors in positional and keyword arguments.

*args Additional positional arguments. May contain tensors, although this is not recommended, for the reasons above.

**kwargs Additional keyword arguments. May contain tensors, although this is not recommended, for the reasons above. The following optional keyword arguments are reserved:
training: Boolean scalar tensor of Python boolean indicating whether the call is meant for training or inference.

mask: Boolean input mask. If the layer's call() method takes a mask argument, its default value will be set to the mask generated for inputs by the previous layer (if input did come from a layer that generated a corresponding mask, i.e. if it came from a Keras layer with masking support).

Returns
A tensor or list/tuple of tensors.