LuxCore

LuxCore.jl defines the abstract layers for Lux. Allows users to be compatible with the entirely of Lux.jl without having such a heavy dependency. If you are depending on Lux.jl directly, you do not need to depend on LuxCore.jl (all the functionality is exported via Lux.jl).

Index

• LuxCore.AbstractExplicitContainerLayer
• LuxCore.AbstractExplicitLayer
• LuxCore.apply
• LuxCore.check_fmap_condition
• LuxCore.contains_lux_layer
• LuxCore.display_name
• LuxCore.initialparameters
• LuxCore.initialstates
• LuxCore.inputsize
• LuxCore.outputsize
• LuxCore.parameterlength
• LuxCore.replicate
• LuxCore.setup
• LuxCore.statelength
• LuxCore.stateless_apply
• LuxCore.testmode
• LuxCore.trainmode
• LuxCore.update_state

Abstract Types

# LuxCore.AbstractExplicitLayer — Type.

abstract type AbstractExplicitLayer

Abstract Type for all Lux Layers

Users implementing their custom layer, must implement

• initialparameters(rng::AbstractRNG, layer::CustomAbstractExplicitLayer) – This returns a NamedTuple containing the trainable parameters for the layer.

• initialstates(rng::AbstractRNG, layer::CustomAbstractExplicitLayer) – This returns a NamedTuple containing the current state for the layer. For most layers this is typically empty. Layers that would potentially contain this include BatchNorm, LSTM, GRU etc.

Optionally:

• parameterlength(layer::CustomAbstractExplicitLayer) – These can be automatically calculated, but it is recommended that the user defines these.

• statelength(layer::CustomAbstractExplicitLayer) – These can be automatically calculated, but it is recommended that the user defines these.

See also AbstractExplicitContainerLayer

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# LuxCore.AbstractExplicitContainerLayer — Type.

abstract type AbstractExplicitContainerLayer{layers} <: AbstractExplicitLayer

Abstract Container Type for certain Lux Layers. layers is a tuple containing fieldnames for the layer, and constructs the parameters and states using those.

Users implementing their custom layer can extend the same functions as in AbstractExplicitLayer.

Tip

Advanced structure manipulation of these layers post construction is possible via Functors.fmap. For a more flexible interface, we recommend using Lux.Experimental.@layer_map.

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General

# LuxCore.apply — Function.

apply(model, x, ps, st)

In most cases this function simply calls model(x, ps, st). However, it is still recommended to call apply instead of model(x, ps, st) directly. Some of the reasons for this include:

1. For certain types of inputs x, we might want to perform preprocessing before calling model. For eg, if x is an Array of ReverseDiff.TrackedReals this can cause significant regressions in model(x, ps, st) (since it won't hit any of the BLAS dispatches). In those cases, we would automatically convert x to a ReverseDiff.TrackedArray.

2. Certain user defined inputs need to be applied to specific layers but we want the datatype of propagate through all the layers (even unsupported ones). In these cases, we can unpack the input in apply and pass it to the appropriate layer and then repack it before returning. See the Lux manual on Custom Input Types for a motivating example.

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# LuxCore.stateless_apply — Function.

stateless_apply(model, x, ps)

Calls apply and only returns the first argument. This function requires that model has an empty state of NamedTuple(). Behavior of other kinds of models are undefined and it is the responsibility of the user to ensure that the model has an empty state.

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# LuxCore.check_fmap_condition — Function.

check_fmap_condition(cond, tmatch, x) -> Bool

fmaps into the structure x and see if cond is statisfied for any of the leaf elements.

Arguments

• cond - A function that takes a single argument and returns a Bool.

• tmatch - A shortcut to check if x is of type tmatch. Can be disabled by passing nothing.

• x - The structure to check.

Returns

A Boolean Value

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# LuxCore.contains_lux_layer — Function.

contains_lux_layer(l) -> Bool

Check if the structure l is a Lux AbstractExplicitLayer or a container of such a layer.

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# LuxCore.display_name — Function.

display_name(layer::AbstractExplicitLayer)

Printed Name of the layer. If the layer has a field name that is used, else the type name is used.

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# LuxCore.replicate — Function.

replicate(rng::AbstractRNG)

Creates a copy of the rng state depending on its type.

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# LuxCore.setup — Function.

setup(rng::AbstractRNG, layer)

Shorthand for getting the parameters and states of the layer l. Is equivalent to (initialparameters(rng, l), initialstates(rng, l)).

Warning

This function is not pure, it mutates rng.

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Parameters

# LuxCore.initialparameters — Function.

initialparameters(rng::AbstractRNG, layer)

Generate the initial parameters of the layer l.

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# LuxCore.parameterlength — Function.

parameterlength(layer)

Return the total number of parameters of the layer l.

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States

# LuxCore.initialstates — Function.

initialstates(rng::AbstractRNG, layer)

Generate the initial states of the layer l.

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# LuxCore.statelength — Function.

statelength(layer)

Return the total number of states of the layer l.

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# LuxCore.testmode — Function.

testmode(st::NamedTuple)

Make all occurances of training in state st – Val(false).

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# LuxCore.trainmode — Function.

trainmode(st::NamedTuple)

Make all occurances of training in state st – Val(true).

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# LuxCore.update_state — Function.

update_state(st::NamedTuple, key::Symbol, value;
    layer_check=_default_layer_check(key))

Recursively update all occurances of the key in the state st with the value.

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Layer size

Warning

These specifications have been added very recently and most layers currently do not implement them.

# LuxCore.inputsize — Function.

inputsize(layer)

Return the input size of the layer.

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# LuxCore.outputsize — Function.

outputsize(layer, x, rng)

Return the output size of the layer. If outputsize(layer) is defined, that method takes precedence, else we compute the layer output to determine the final size.

The fallback implementation of this function assumes the inputs were batched, i.e., if any of the outputs are Arrays, with ndims(A) > 1, it will return size(A)[1:(end - 1)]. If this behavior is undesirable, provide a custom outputsize(layer, x, rng) implementation).

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