tfm.nlp.layers.ReZeroTransformer

Transformer layer with ReZero.

tfm.nlp.layers.ReZeroTransformer(
    num_attention_heads,
    inner_dim=768,
    inner_activation=tfm.utils.activations.gelu,
    dropout_rate=0.0,
    attention_dropout_rate=0.0,
    output_range=None,
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    use_layer_norm=False,
    share_rezero=True,
    **kwargs
)

This layer implements the Transformer from "Attention Is All You Need". (https://arxiv.org/abs/1706.03762). The residual connection implements the ReZero method. (https://arxiv.org/abs/2003.04887)

Args
num_attention_heads	Number of attention heads.
inner_dim	The output dimension of the first Dense layer in a two-layer feedforward network.
inner_activation	The activation for the first Dense layer in a two-layer feedforward network.
dropout_rate	Dropout probability for the post-attention and output dropout.
attention_dropout_rate	Dropout probability for within the attention layer.
output_range	the sequence output range, [0, output_range) by slicing the target sequence. None means the target sequence is not sliced.
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.
use_layer_norm	If add layer_norm on top of the ReZero.
share_rezero	If attention layer and FFN layer share the same alpha.

Methods

call

View source

call(
    inputs, output_range: Optional[tf.Tensor] = None
) -> tf.Tensor

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, including tf.Variable instances and nested Layer instances, in __init__(), or in the build() method that is called automatically before call() executes for the first time.

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).

Returns
A tensor or list/tuple of tensors.

reset_rezero

View source

reset_rezero()