tfm.nlp.layers.GatedFeedforward

Gated linear feedforward layer.

tfm.nlp.layers.GatedFeedforward(
    inner_dim=768,
    inner_activation=tfm.utils.activations.gelu,
    dropout=0.0,
    use_gate=True,
    apply_output_layer_norm=True,
    num_blocks=1,
    dropout_position='before_residual',
    kernel_initializer='glorot_uniform',
    bias_initializer='zeros',
    kernel_regularizer=None,
    bias_regularizer=None,
    activity_regularizer=None,
    kernel_constraint=None,
    bias_constraint=None,
    **kwargs
)

This layer follows the paper "GLU Variants Improve Transformer" (https://arxiv.org/abs/2002.05202). In additional, it allows to stack multiple feedforward blocks and specify the position of dropout layer.

Args
`intermediate_size`	Size of the intermediate layer.
`intermediate_activation`	Activation for the intermediate layer.
`dropout`	Dropout probability for the output dropout.
`use_gate`	Whether to use gated linear units. If True, assuming `GELU` as the activation and omitting bias, will apply `GEGLU(x, W, V, W_2) = (GEGLU(xW) * xV)W2`; if False, will follow "Attention Is All You Need" (https://arxiv.org/abs/1706.03762) paper and apply `FFN(x, W, W_2) = GELU(xW_1)W_2.`
`num_blocks`	The number of feedforward blocks to stack. Each block contains a (gated) linear layer and a fully connected layer followed by dropout, layer norm and residual.
`dropout_position`	Where to apply the dropout, the value can be either `before_residual` or `after_residual`. If `before_residual`, will apply `layer_output = layer_norm(dropout(layer_output) + layer_input)`; if `after residual`, will apply `layer_output = dropout(layer_norm(layer_output + layer_input))`.
`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.

Methods

`call`

View source

call(
    inputs
)

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

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.