Transformer scaffold layer.
tfm.nlp.layers.TransformerScaffold(
num_attention_heads,
inner_dim=768,
inner_activation=tfm.utils.activations.gelu
,
attention_cls=attention.MultiHeadAttention,
attention_cfg=None,
feedforward_cls=None,
feedforward_cfg=None,
dropout_rate=0.0,
attention_dropout_rate=0.0,
norm_first=False,
norm_epsilon=1e-12,
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 implements the Transformer from "Attention Is All You Need".
(https://arxiv.org/abs/1706.03762), with a customizable attention layer and
feedforward layer option. Users can pass a class to
attention_cls
/feedforward_cls
and associated config to
attention_cfg
/feedforward_cfg
, in which case the scaffold will
instantiate the class with the config, or pass a class instance to
attention_cls
/feedforward_cls
.
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.
|
attention_cls
|
A class to instantiate attention layer, or a layer instance.
|
attention_cfg
|
The config with which to instantiate attention_cls . Ignored
if attention_cls is a layer instance or None. If attention_cls is a
class, but attention_cfg is None, following kwargs will be used to
instantiate the attention instance: {
"num_heads": num_attention_heads,
"key_dim": int(hidden_size // num_attention_heads),
"dropout": attention_dropout_rate,
"name": "self_attention" }, where hidden_size is the input tensor's
last dimension.
|
feedforward_cls
|
A class to instantiate feedforward layer, or a layer
instance. If None, will use the standard feedforward layer as described in
"Attention Is All You Need" paper. If not None, the instantiated
feedforward layer is expected to take the output of attention as input and
its output is this transformer layer's output.
|
feedforward_cfg
|
The config with which to instantiate feedforward_cls .
Ignored if feedforward_cls is a layer instance or is None. If
feedforward_cls is a class, but feedforward_cfg is None, following
kwargs will be used to instantiate the feedforward instance: {
"inner_dim": inner_dim,
"inner_activation": inner_activation,
"dropout": dropout_rate,
"name": "feedforward" }.
|
dropout_rate
|
Dropout probability for the post-attention and output dropout.
|
attention_dropout_rate
|
Dropout probability for within the attention layer.
|
norm_first
|
Whether to normalize inputs to attention and intermediate
dense layers. If set False, output of attention and intermediate dense
layers is normalized.
|
norm_epsilon
|
Epsilon value to initialize normalization layers.
|
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, training=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, 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.
|