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Sparse Mixer encoder network.
tfm.nlp.networks.SparseMixer(
vocab_size: int,
hidden_size: int = 512,
num_layers: int = 14,
moe_layers: Sequence[int] = (5, 6, 7, 8),
attention_layers: Sequence[int] = (10, 11, 12, 13),
num_experts: int = 16,
train_capacity_factor: float = 1.0,
eval_capacity_factor: float = 1.0,
examples_per_group: float = 1.0,
mixing_mechanism: tfm.nlp.layers.MixingMechanism
= tfm.nlp.layers.MixingMechanism.LINEAR
,
use_fft: bool = False,
num_attention_heads: int = 8,
max_sequence_length: int = 512,
type_vocab_size: int = 16,
inner_dim: int = 2048,
inner_activation: _Activation = _approx_gelu,
output_dropout: float = 0.1,
attention_dropout: float = 0.1,
initializer: _Initializer = tf.keras.initializers.TruncatedNormal(stddev=0.02),
output_range: Optional[int] = None,
embedding_width: Optional[int] = None,
embedding_layer: Optional[tf.keras.layers.Layer] = None,
norm_first: bool = False,
with_dense_inputs: bool = False,
export_metrics: bool = True,
**kwargs
)
Based on "Sparse Mixers: Combining MoE and Mixing to build a more efficient BERT". Sparse Mixer is an efficient encoder network that replaces typical Transformer encoder blocks with a combination of linear mixing and sparsely activated Mixture-of-Experts (MoE) sublayers.
This implementation defaults to the canonical Sparse Mixer Base model. To use
the "Fast Sparse Mixer" configuration, set *_capacity_factor
=0.5. This
yields a sparser and faster variant of the canonical Sparse Mixer model, in
which each expert processes roughly 50% less tokens.
Notes:
- The underlying MoeLayer uses the Keras add_loss() and add_metric() APIs to propagate auxiliary MoE losses and metrics. Any model using this network, should collect these losses and, if desired, metrics.
- The input length is fixed to 'max_sequence_length' to accomodate the mixing mechanisms.
Args | |
---|---|
vocab_size
|
The size of the token vocabulary. |
hidden_size
|
The size of the transformer hidden layers. |
num_layers
|
The number of transformer layers. |
moe_layers
|
Specifies which layers, if any, should be sparsely activated Mixture-of-Experts (MoE) layers. The remaining [0, num_layers) setminus moe_layers will use the vanilla MLP sublayers. Defaults to placing MoE layers in the middle of the model. |
attention_layers
|
Specifies which layers, if any, should be attention layers
in the encoder. The remaining [0, num_layers) setminus attention_layers
will use the specified mixing_mechanism . If using attention layers, a
good rule of thumb is to place them in the final few layers.
|
num_experts
|
Number of experts. Experts are themselves MLP modules, with the
same inner_dim and inner_activation as the vanilla MLP sublayers.
|
train_capacity_factor
|
Scaling factor to increase the expert token capacity during training. See layers.MoeLayer for further details. The "Fast Sparse Mixer" increases model sparsity (and speed) by using a capacity factor of 0.5. |
eval_capacity_factor
|
As above, but used during evaluation. |
max_group_size
|
The total number of tokens on each device is subdivided into groups of this size. Router computations are then performed on a per-group basis. See layers.MoeLayer for further details. |
mixing_mechanism
|
Type of mixing mechanism used in place of self-attention layers. Defaults to 'Linear' mixing. |
use_fft
|
Only used for spectral mixing mechanisms. Determines whether to use Fast Fourier Transform (True) or the Discrete Fourier Transform (DFT) matrix (False; default) to compute the Fourier Transform. See layers.FourierTransformLayer or layers.HartleyTransformLayer for advice. |
num_attention_heads
|
The number of attention heads for each transformer. The hidden size must be divisible by the number of attention heads. |
max_sequence_length
|
The only sequence length that this encoder can consume. This determines the variable shape for positional embeddings and the size of the mixing matrices. |
type_vocab_size
|
The number of types that the 'type_ids' input can take. |
inner_dim
|
The output dimension of the first Dense layer in a two-layer feedforward network for each transformer. |
inner_activation
|
The activation for the first Dense layer in a two-layer feedforward network for each transformer. |
output_dropout
|
Dropout probability for the post-attention and output dropout. |
attention_dropout
|
The dropout rate to use for the attention layers within the transformer layers. |
initializer
|
The initializer to use for all weights in this encoder. |
output_range
|
The sequence output range, [0, output_range), by slicing the
target sequence of the last transformer layer. None means the entire
target sequence will attend to the source sequence, which yields the full
output.
|
embedding_width
|
The width of the word embeddings. If the embedding width is not equal to hidden size, embedding parameters will be factorized into two matrices in the shape of ['vocab_size', 'embedding_width'] and 'embedding_width', 'hidden_size'. |
embedding_layer
|
An optional Layer instance which will be called to generate embeddings for the input word IDs. |
norm_first
|
Whether to normalize inputs to attention and intermediate dense layers. If set False, output of attention and intermediate dense layers is normalized. |
with_dense_inputs
|
Whether to accept dense embeddings as the input. |
export_metrics
|
Whether to export metrics using Keras add_metric API. |
Attributes | |
---|---|
activity_regularizer
|
Optional regularizer function for the output of this layer. |
compute_dtype
|
The dtype of the layer's computations.
This is equivalent to Layers automatically cast their inputs to the compute dtype, which
causes computations and the output to be in the compute dtype as well.
This is done by the base Layer class in Layers often perform certain internal computations in higher precision
when |
dtype
|
The dtype of the layer weights.
This is equivalent to |
dtype_policy
|
The dtype policy associated with this layer.
This is an instance of a |
dynamic
|
Whether the layer is dynamic (eager-only); set in the constructor. |
input
|
Retrieves the input tensor(s) of a layer.
Only applicable if the layer has exactly one input, i.e. if it is connected to one incoming layer. |
input_spec
|
InputSpec instance(s) describing the input format for this layer.
When you create a layer subclass, you can set
Now, if you try to call the layer on an input that isn't rank 4
(for instance, an input of shape
Input checks that can be specified via
For more information, see |
losses
|
List of losses added using the add_loss() API.
Variable regularization tensors are created when this property is
accessed, so it is eager safe: accessing
|
metrics
|
List of metrics attached to the layer. |
non_trainable_weights
|
List of all non-trainable weights tracked by this layer.
Non-trainable weights are not updated during training. They are
expected to be updated manually in |
output
|
Retrieves the output tensor(s) of a layer.
Only applicable if the layer has exactly one output, i.e. if it is connected to one incoming layer. |
pooler_layer
|
The pooler dense layer after the transformer layers. |
supports_masking
|
Whether this layer supports computing a mask using compute_mask .
|
trainable
|
|
trainable_weights
|
List of all trainable weights tracked by this layer.
Trainable weights are updated via gradient descent during training. |
transformer_layers
|
List of Transformer layers in the encoder. |
variable_dtype
|
Alias of Layer.dtype , the dtype of the weights.
|
weights
|
Returns the list of all layer variables/weights. |
Methods
add_loss
add_loss(
losses, **kwargs
)
Add loss tensor(s), potentially dependent on layer inputs.
Some losses (for instance, activity regularization losses) may be
dependent on the inputs passed when calling a layer. Hence, when reusing
the same layer on different inputs a
and b
, some entries in
layer.losses
may be dependent on a
and some on b
. This method
automatically keeps track of dependencies.
This method can be used inside a subclassed layer or model's call
function, in which case losses
should be a Tensor or list of Tensors.
Example:
class MyLayer(tf.keras.layers.Layer):
def call(self, inputs):
self.add_loss(tf.abs(tf.reduce_mean(inputs)))
return inputs
The same code works in distributed training: the input to add_loss()
is treated like a regularization loss and averaged across replicas
by the training loop (both built-in Model.fit()
and compliant custom
training loops).
The add_loss
method can also be called directly on a Functional Model
during construction. In this case, any loss Tensors passed to this Model
must be symbolic and be able to be traced back to the model's Input
s.
These losses become part of the model's topology and are tracked in
get_config
.
Example:
inputs = tf.keras.Input(shape=(10,))
x = tf.keras.layers.Dense(10)(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Activity regularization.
model.add_loss(tf.abs(tf.reduce_mean(x)))
If this is not the case for your loss (if, for example, your loss
references a Variable
of one of the model's layers), you can wrap your
loss in a zero-argument lambda. These losses are not tracked as part of
the model's topology since they can't be serialized.
Example:
inputs = tf.keras.Input(shape=(10,))
d = tf.keras.layers.Dense(10)
x = d(inputs)
outputs = tf.keras.layers.Dense(1)(x)
model = tf.keras.Model(inputs, outputs)
# Weight regularization.
model.add_loss(lambda: tf.reduce_mean(d.kernel))
Args | |
---|---|
losses
|
Loss tensor, or list/tuple of tensors. Rather than tensors, losses may also be zero-argument callables which create a loss tensor. |
**kwargs
|
Used for backwards compatibility only. |
build
build(
input_shape
)
Creates the variables of the layer (for subclass implementers).
This is a method that implementers of subclasses of Layer
or Model
can override if they need a state-creation step in-between
layer instantiation and layer call. It is invoked automatically before
the first execution of call()
.
This is typically used to create the weights of Layer
subclasses
(at the discretion of the subclass implementer).
Args | |
---|---|
input_shape
|
Instance of TensorShape , or list of instances of
TensorShape if the layer expects a list of inputs
(one instance per input).
|
build_from_config
build_from_config(
config
)
Builds the layer's states with the supplied config dict.
By default, this method calls the build(config["input_shape"])
method,
which creates weights based on the layer's input shape in the supplied
config. If your config contains other information needed to load the
layer's state, you should override this method.
Args | |
---|---|
config
|
Dict containing the input shape associated with this layer. |
compute_mask
compute_mask(
inputs, mask=None
)
Computes an output mask tensor.
Args | |
---|---|
inputs
|
Tensor or list of tensors. |
mask
|
Tensor or list of tensors. |
Returns | |
---|---|
None or a tensor (or list of tensors, one per output tensor of the layer). |
compute_output_shape
compute_output_shape(
input_shape
)
Computes the output shape of the layer.
This method will cause the layer's state to be built, if that has not happened before. This requires that the layer will later be used with inputs that match the input shape provided here.
Args | |
---|---|
input_shape
|
Shape tuple (tuple of integers) or tf.TensorShape ,
or structure of shape tuples / tf.TensorShape instances
(one per output tensor of the layer).
Shape tuples can include None for free dimensions,
instead of an integer.
|
Returns | |
---|---|
A tf.TensorShape instance
or structure of tf.TensorShape instances.
|
count_params
count_params()
Count the total number of scalars composing the weights.
Returns | |
---|---|
An integer count. |
Raises | |
---|---|
ValueError
|
if the layer isn't yet built (in which case its weights aren't yet defined). |
from_config
@classmethod
from_config( config, custom_objects=None )
Creates a layer from its config.
This method is the reverse of get_config
,
capable of instantiating the same layer from the config
dictionary. It does not handle layer connectivity
(handled by Network), nor weights (handled by set_weights
).
Args | |
---|---|
config
|
A Python dictionary, typically the output of get_config. |
Returns | |
---|---|
A layer instance. |
get_build_config
get_build_config()
Returns a dictionary with the layer's input shape.
This method returns a config dict that can be used by
build_from_config(config)
to create all states (e.g. Variables and
Lookup tables) needed by the layer.
By default, the config only contains the input shape that the layer was built with. If you're writing a custom layer that creates state in an unusual way, you should override this method to make sure this state is already created when Keras attempts to load its value upon model loading.
Returns | |
---|---|
A dict containing the input shape associated with the layer. |
get_config
get_config()
Returns the config of the layer.
A layer config is a Python dictionary (serializable) containing the configuration of a layer. The same layer can be reinstantiated later (without its trained weights) from this configuration.
The config of a layer does not include connectivity
information, nor the layer class name. These are handled
by Network
(one layer of abstraction above).
Note that get_config()
does not guarantee to return a fresh copy of
dict every time it is called. The callers should make a copy of the
returned dict if they want to modify it.
Returns | |
---|---|
Python dictionary. |
get_embedding_layer
get_embedding_layer()
get_embedding_table
get_embedding_table()
get_weights
get_weights()
Returns the current weights of the layer, as NumPy arrays.
The weights of a layer represent the state of the layer. This function returns both trainable and non-trainable weight values associated with this layer as a list of NumPy arrays, which can in turn be used to load state into similarly parameterized layers.
For example, a Dense
layer returns a list of two values: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Returns | |
---|---|
Weights values as a list of NumPy arrays. |
load_own_variables
load_own_variables(
store
)
Loads the state of the layer.
You can override this method to take full control of how the state of
the layer is loaded upon calling keras.models.load_model()
.
Args | |
---|---|
store
|
Dict from which the state of the model will be loaded. |
save_own_variables
save_own_variables(
store
)
Saves the state of the layer.
You can override this method to take full control of how the state of
the layer is saved upon calling model.save()
.
Args | |
---|---|
store
|
Dict where the state of the model will be saved. |
set_weights
set_weights(
weights
)
Sets the weights of the layer, from NumPy arrays.
The weights of a layer represent the state of the layer. This function sets the weight values from numpy arrays. The weight values should be passed in the order they are created by the layer. Note that the layer's weights must be instantiated before calling this function, by calling the layer.
For example, a Dense
layer returns a list of two values: the kernel
matrix and the bias vector. These can be used to set the weights of
another Dense
layer:
layer_a = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(1.))
a_out = layer_a(tf.convert_to_tensor([[1., 2., 3.]]))
layer_a.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
layer_b = tf.keras.layers.Dense(1,
kernel_initializer=tf.constant_initializer(2.))
b_out = layer_b(tf.convert_to_tensor([[10., 20., 30.]]))
layer_b.get_weights()
[array([[2.],
[2.],
[2.]], dtype=float32), array([0.], dtype=float32)]
layer_b.set_weights(layer_a.get_weights())
layer_b.get_weights()
[array([[1.],
[1.],
[1.]], dtype=float32), array([0.], dtype=float32)]
Args | |
---|---|
weights
|
a list of NumPy arrays. The number
of arrays and their shape must match
number of the dimensions of the weights
of the layer (i.e. it should match the
output of get_weights ).
|
Raises | |
---|---|
ValueError
|
If the provided weights list does not match the layer's specifications. |
__call__
__call__(
*args, **kwargs
)
Wraps call
, applying pre- and post-processing steps.
Args | |
---|---|
*args
|
Positional arguments to be passed to self.call .
|
**kwargs
|
Keyword arguments to be passed to self.call .
|
Returns | |
---|---|
Output tensor(s). |
Note | |
---|---|
|
Raises | |
---|---|
ValueError
|
if the layer's call method returns None (an invalid
value).
|
RuntimeError
|
if super().__init__() was not called in the
constructor.
|