Utilities

Training API

Helper Functions making it easier to train Lux.jl models.

Training is meant to be simple and provide extremely basic functionality. We provide basic building blocks which can be seamlessly composed to create complex training pipelines.

Lux.Training.TrainState Type

TrainState

Training State containing:

• model: Lux model.

• parameters: Trainable Variables of the model.

• states: Non-trainable Variables of the model.

• optimizer: Optimizer from Optimisers.jl.

• optimizer_state: Optimizer State.

• step: Number of updates of the parameters made.

Internal fields:

• cache: Cached values. Implementations are free to use this for whatever they want.

• objective_function: Objective function might be cached.

Warning

Constructing this object directly shouldn't be considered a stable API. Use the version with the Optimisers API.

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Lux.Training.compute_gradients Function

compute_gradients(ad::AbstractADType, objective_function::Function, data,
    ts::TrainState)

Compute the gradients of the objective function wrt parameters stored in ts.

Backends & AD Packages

Supported Backends	Packages Needed
AutoZygote	Zygote.jl
AutoReverseDiff(; compile)	ReverseDiff.jl
AutoTracker	Tracker.jl
AutoEnzyme	Enzyme.jl

Arguments

• ad: Backend (from ADTypes.jl) used to compute the gradients.

• objective_function: Objective function. The function must take 4 inputs – model, parameters, states and data. The function must return 3 values – loss, updated_state, and any computed statistics.

• data: Data used to compute the gradients.

• ts: Current Training State. See TrainState.

Return

A 4-Tuple containing:

• grads: Computed Gradients.

• loss: Loss from the objective function.

• stats: Any computed statistics from the objective function.

• ts: Updated Training State.

Known Limitations

• AutoReverseDiff(; compile=true) is not supported for Lux models with non-empty state st. Additionally the returned stats must be empty (NamedTuple()). We catch these issues in most cases and throw an error.

Aliased Gradients

grads returned by this function might be aliased by the implementation of the gradient backend. For example, if you cache the grads from step i, the new gradients returned in step i + 1 might be aliased by the old gradients. If you want to prevent this, simply use copy(grads) or deepcopy(grads) to make a copy of the gradients.

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Lux.Training.apply_gradients Function

apply_gradients(ts::TrainState, grads)

Update the parameters stored in ts using the gradients grads.

Arguments

• ts: TrainState object.

• grads: Gradients of the loss function wrt ts.params.

Returns

Updated TrainState object.

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Lux.Training.apply_gradients! Function

apply_gradients!(ts::TrainState, grads)

Update the parameters stored in ts using the gradients grads. This is an inplace version of apply_gradients.

Arguments

• ts: TrainState object.

• grads: Gradients of the loss function wrt ts.params.

Returns

Updated TrainState object.

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Lux.Training.single_train_step Function

single_train_step(backend, obj_fn::F, data, ts::TrainState)

Perform a single training step. Computes the gradients using compute_gradients and updates the parameters using apply_gradients. All backends supported via compute_gradients are supported here.

In most cases you should use single_train_step! instead of this function.

Return

Returned values are the same as compute_gradients.

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Lux.Training.single_train_step! Function

single_train_step!(backend, obj_fn::F, data, ts::TrainState)

Perform a single training step. Computes the gradients using compute_gradients and updates the parameters using apply_gradients!. All backends supported via compute_gradients are supported here.

Return

Returned values are the same as compute_gradients. Note that despite the !, only the parameters in ts are updated inplace. Users should be using the returned ts object for further training steps, else there is no caching and performance will be suboptimal (and absolutely terrible for backends like AutoReactant).

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Loss Functions

Loss Functions Objects take 2 forms of inputs:

1. ŷ and y where ŷ is the predicted output and y is the target output.

2. model, ps, st, (x, y) where model is the model, ps are the parameters, st are the states and (x, y) are the input and target pair. Then it returns the loss, updated states, and an empty named tuple. This makes them compatible with the Training API.

Warning

When using ChainRules.jl compatible AD (like Zygote), we only compute the gradients wrt the inputs and drop any gradients wrt the targets.

Lux.GenericLossFunction Type

GenericLossFunction(loss_fn; agg = mean)

Takes any function loss_fn that maps 2 number inputs to a single number output. Additionally, array inputs are efficiently broadcasted and aggregated using agg.

julia> mseloss = GenericLossFunction((ŷ, y) -> abs2(ŷ - y));

julia> y_model = [1.1, 1.9, 3.1];

julia> mseloss(y_model, 1:3) ≈ 0.01
true

Special Note

This function takes any of the LossFunctions.jl public functions into the Lux Losses API with efficient aggregation.

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Lux.BinaryCrossEntropyLoss Type

BinaryCrossEntropyLoss(; agg = mean, epsilon = nothing,
    label_smoothing::Union{Nothing, Real}=nothing,
    logits::Union{Bool, Val}=Val(false))

Binary Cross Entropy Loss with optional label smoothing and fused logit computation.

Returns the binary cross entropy loss computed as:

• If logits is either false or Val(false):

$$\text{agg}(-\tilde{y}\ast \log (\hat{y}+ϵ)-(1-\tilde{y})\ast \log (1-\hat{y}+ϵ))$$

• If logits is true or Val(true):

$$\text{agg}((1-\tilde{y})\ast \hat{y}-log\sigma (\hat{y}))$$

The value of $\tilde{y}$ is computed using label smoothing. If label_smoothing is nothing, then no label smoothing is applied. If label_smoothing is a real number $\in [0,1]$, then the value of $\tilde{y}$ is:

$$\tilde{y}=(1-\alpha )\ast y+\alpha \ast 0.5$$

where $\alpha$ is the value of label_smoothing.

Extended Help

Example

julia> bce = BinaryCrossEntropyLoss();

julia> y_bin = Bool[1, 0, 1];

julia> y_model = Float32[2, -1, pi]
3-element Vector{Float32}:
  2.0
 -1.0
  3.1415927

julia> logitbce = BinaryCrossEntropyLoss(; logits=Val(true));

julia> logitbce(y_model, y_bin) ≈ 0.160832f0
true

julia> bce(sigmoid.(y_model), y_bin) ≈ 0.16083185f0
true

julia> bce_ls = BinaryCrossEntropyLoss(label_smoothing=0.1);

julia> bce_ls(sigmoid.(y_model), y_bin) > bce(sigmoid.(y_model), y_bin)
true

julia> logitbce_ls = BinaryCrossEntropyLoss(label_smoothing=0.1, logits=Val(true));

julia> logitbce_ls(y_model, y_bin) > logitbce(y_model, y_bin)
true

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Lux.BinaryFocalLoss Type

BinaryFocalLoss(; gamma = 2, agg = mean, epsilon = nothing)

Return the binary focal loss [1]. The model input, $\hat{y}$, is expected to be normalized (i.e. softmax output).

For $\gamma =0$ this is equivalent to BinaryCrossEntropyLoss.

Example

julia> y = [0  1  0
            1  0  1];

julia> ŷ = [0.268941  0.5  0.268941
            0.731059  0.5  0.731059];

julia> BinaryFocalLoss()(ŷ, y) ≈ 0.0728675615927385
true

julia> BinaryFocalLoss(gamma=0)(ŷ, y) ≈ BinaryCrossEntropyLoss()(ŷ, y)
true

References

[1] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.

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Lux.CrossEntropyLoss Type

CrossEntropyLoss(;
    agg=mean, epsilon=nothing, dims=1, logits::Union{Bool, Val}=Val(false),
    label_smoothing::Union{Nothing, Real}=nothing
)

Return the cross entropy loss which is used in multi-class classification tasks. The input, $\hat{y}$, is expected to be normalized (i.e. softmax output) if logits is false or Val(false).

The loss is calculated as:

$$\text{agg}(-\sum \tilde{y}\log (\hat{y}+ϵ))$$

where $ϵ$ is added for numerical stability. The value of $\tilde{y}$ is computed using label smoothing. If label_smoothing is nothing, then no label smoothing is applied. If label_smoothing is a real number $\in [0,1]$, then the value of $\tilde{y}$ is calculated as:

$$\tilde{y}=(1-\alpha )\ast y+\alpha \ast \text{size along dim}$$

where $\alpha$ is the value of label_smoothing.

Extended Help

Example

julia> y = [1  0  0  0  1
            0  1  0  1  0
            0  0  1  0  0]
3×5 Matrix{Int64}:
 1  0  0  0  1
 0  1  0  1  0
 0  0  1  0  0

julia> y_model = softmax(reshape(-7:7, 3, 5) .* 1f0)
3×5 Matrix{Float32}:
 0.0900306  0.0900306  0.0900306  0.0900306  0.0900306
 0.244728   0.244728   0.244728   0.244728   0.244728
 0.665241   0.665241   0.665241   0.665241   0.665241

julia> CrossEntropyLoss()(y_model, y) ≈ 1.6076053f0
true

julia> 5 * 1.6076053f0 ≈ CrossEntropyLoss(; agg=sum)(y_model, y)
true

julia> CrossEntropyLoss(label_smoothing=0.15)(y_model, y) ≈ 1.5776052f0
true

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Lux.DiceCoeffLoss Type

DiceCoeffLoss(; smooth = true, agg = mean)

Return the Dice Coefficient loss [1] which is used in segmentation tasks. The dice coefficient is similar to the F1_score. Loss calculated as:

$$agg(1-\frac{2\sum y\hat{y}+\alpha }{\sum y^2+\sum {\hat{y}}^2+\alpha })$$

where $\alpha$ is the smoothing factor (smooth).

Example

julia> y_pred = [1.1, 2.1, 3.1];

julia> DiceCoeffLoss()(y_pred, 1:3)  ≈ 0.000992391663909964
true

julia> 1 - DiceCoeffLoss()(y_pred, 1:3)  ≈ 0.99900760833609
true

julia> DiceCoeffLoss()(reshape(y_pred, 3, 1), reshape(1:3, 3, 1)) ≈ 0.000992391663909964
true

References

[1] Milletari, Fausto, Nassir Navab, and Seyed-Ahmad Ahmadi. "V-net: Fully convolutional neural networks for volumetric medical image segmentation." 2016 fourth international conference on 3D vision (3DV). Ieee, 2016.

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Lux.FocalLoss Type

FocalLoss(; gamma = 2, dims = 1, agg = mean, epsilon = nothing)

Return the focal loss [1] which can be used in classification tasks with highly imbalanced classes. It down-weights well-classified examples and focuses on hard examples. The input, $\hat{y}$, is expected to be normalized (i.e. softmax output).

The modulating factor $\gamma$, controls the down-weighting strength. For $\gamma =0$ this is equivalent to CrossEntropyLoss.

Example

julia> y = [1  0  0  0  1
            0  1  0  1  0
            0  0  1  0  0]
3×5 Matrix{Int64}:
 1  0  0  0  1
 0  1  0  1  0
 0  0  1  0  0

julia> ŷ = softmax(reshape(-7:7, 3, 5) .* 1f0)
3×5 Matrix{Float32}:
 0.0900306  0.0900306  0.0900306  0.0900306  0.0900306
 0.244728   0.244728   0.244728   0.244728   0.244728
 0.665241   0.665241   0.665241   0.665241   0.665241

julia> FocalLoss()(ŷ, y) ≈ 1.1277556f0
true

References

[1] Lin, Tsung-Yi, et al. "Focal loss for dense object detection." Proceedings of the IEEE international conference on computer vision. 2017.

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Lux.HingeLoss Function

HingeLoss(; agg = mean)

Return the hinge loss loss given the prediction ŷ and true labels y (containing 1 or -1); calculated as:

$$\text{agg}(max(0,1-y\hat{y}))$$

Usually used with classifiers like Support Vector Machines.

Example

julia> loss = HingeLoss();

julia> y_true = [1, -1, 1, 1];

julia> y_pred = [0.1, 0.3, 1, 1.5];

julia> loss(y_pred, y_true) ≈ 0.55
true

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Lux.HuberLoss Function

HuberLoss(; delta = 1, agg = mean)

Returns the Huber loss, calculated as:

$$L=\{\begin{array}{ll}0.5\ast |y-\hat{y}{|}^2& \text{if }|y-\hat{y}|\le \delta \\ \delta \ast (|y-\hat{y}|-0.5\ast \delta )& \text{otherwise}\end{array}$$

where $\delta$ is the delta parameter.

Example

julia> y_model = [1.1, 2.1, 3.1];

julia> HuberLoss()(y_model, 1:3) ≈ 0.005000000000000009
true

julia> HuberLoss(delta=0.05)(y_model, 1:3) ≈ 0.003750000000000005
true

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Lux.KLDivergenceLoss Type

KLDivergenceLoss(; dims = 1, agg = mean, epsilon = nothing, label_smoothing = nothing)

Return the Kullback-Leibler Divergence loss between the predicted distribution $\hat{y}$ and the true distribution $y$:

The KL divergence is a measure of how much one probability distribution is different from the other. It is always non-negative, and zero only when both the distributions are equal.

For epsilon and label_smoothing, see CrossEntropyLoss.

Example

julia> p1 = [1 0; 0 1]
2×2 Matrix{Int64}:
 1  0
 0  1

julia> p2 = fill(0.5, 2, 2)
2×2 Matrix{Float64}:
 0.5  0.5
 0.5  0.5

julia> KLDivergenceLoss()(p2, p1) ≈ log(2)
true

julia> KLDivergenceLoss(; agg=sum)(p2, p1) ≈ 2 * log(2)
true

julia> KLDivergenceLoss(; epsilon=0)(p2, p2)
0.0

julia> KLDivergenceLoss(; epsilon=0)(p1, p2)
Inf

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Lux.MAELoss Function

MAELoss(; agg = mean)

Returns the loss corresponding to mean absolute error:

$$\text{agg}\left(|\hat{y}-y|\right)$$

Example

julia> loss = MAELoss();

julia> y_model = [1.1, 1.9, 3.1];

julia> loss(y_model, 1:3) ≈ 0.1
true

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Lux.MSELoss Function

MSELoss(; agg = mean)

Returns the loss corresponding to mean squared error:

$$\text{agg}\left({(\hat{y}-y)}^2\right)$$

Example

julia> loss = MSELoss();

julia> y_model = [1.1, 1.9, 3.1];

julia> loss(y_model, 1:3) ≈ 0.01
true

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Lux.MSLELoss Function

MSLELoss(; agg = mean, epsilon = nothing)

Returns the loss corresponding to mean squared logarithmic error:

$$\text{agg}\left({(\log (\hat{y}+ϵ)-\log (y+ϵ))}^2\right)$$

epsilon is added to both y and ŷ to prevent taking the logarithm of zero. If epsilon is nothing, then we set it to eps(<type of y and ŷ>).

Example

julia> loss = MSLELoss();

julia> loss(Float32[1.1, 2.2, 3.3], 1:3) ≈ 0.009084041f0
true

julia> loss(Float32[0.9, 1.8, 2.7], 1:3) ≈ 0.011100831f0
true

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Lux.PoissonLoss Function

PoissonLoss(; agg = mean, epsilon = nothing)

Return how much the predicted distribution $\hat{y}$ diverges from the expected Poisson distribution $y$, calculated as:

$$\text{agg}(\hat{y}-y\ast \log (\hat{y}))$$

Example

julia> y_model = [1, 3, 3];  # data should only take integral values

julia> PoissonLoss()(y_model, 1:3) ≈ 0.502312852219817
true

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Lux.SiameseContrastiveLoss Function

SiameseContrastiveLoss(; margin = true, agg = mean)

Return the contrastive loss [1] which can be useful for training Siamese Networks. It is given by:

$$\text{agg}((1-y){\hat{y}}^2+y\ast max(0,\text{margin}-\hat{y}{)}^2)$$

Specify margin to set the baseline for distance at which pairs are dissimilar.

Example

julia> ŷ = [0.5, 1.5, 2.5];

julia> SiameseContrastiveLoss()(ŷ, 1:3) ≈ -4.833333333333333
true

julia> SiameseContrastiveLoss(margin=2)(ŷ, 1:3) ≈ -4.0
true

References

[1] Hadsell, Raia, Sumit Chopra, and Yann LeCun. "Dimensionality reduction by learning an invariant mapping." 2006 IEEE computer society conference on computer vision and pattern recognition (CVPR'06). Vol. 2. IEEE, 2006.

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Lux.SquaredHingeLoss Function

SquaredHingeLoss(; agg = mean)

Return the squared hinge loss loss given the prediction ŷ and true labels y (containing 1 or -1); calculated as:

$$\text{agg}(max(0,1-y\hat{y}{)}^2)$$

Usually used with classifiers like Support Vector Machines.

Example

julia> loss = SquaredHingeLoss();

julia> y_true = [1, -1, 1, 1];

julia> y_pred = [0.1, 0.3, 1, 1.5];

julia> loss(y_pred, y_true) ≈ 0.625
true

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LuxOps Module

Lux.LuxOps Module

LuxOps

This module is a part of Lux.jl. It contains operations that are useful in DL context. Additionally certain operations here alias Base functions to behave more sensibly with GPUArrays.

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Lux.LuxOps.eachslice Function

eachslice(x, dims::Val)

Same as Base.eachslice but doesn't produce a SubArray for the slices if x is a GPUArray.

Additional dispatches for RNN helpers are also provided for TimeLastIndex and BatchLastIndex.

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Lux.LuxOps.foldl_init Function

foldl_init(op, x)
foldl_init(op, x, init)

Exactly same as foldl(op, x; init) in the forward pass. But, gives gradients wrt init in the backward pass.

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Lux.LuxOps.getproperty Function

getproperty(x, ::Val{v})
getproperty(x, ::StaticSymbol{v})

Similar to Base.getproperty but requires a Val (or Static.StaticSymbol). Additionally, if v is not present in x, then nothing is returned.

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Lux.LuxOps.xlogx Function

xlogx(x::Number)

Return x * log(x) for x ≥ 0, handling x == 0 by taking the limit from above, to get zero.

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Lux.LuxOps.xlogy Function

xlogy(x::Number, y::Number)

Return x * log(y) for y > 0, and zero when x == 0.

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Lux.LuxOps.istraining Function

istraining(::Val{training})
istraining(::StaticBool)
istraining(::Bool)
istraining(st::NamedTuple)

Returns true if training is true or if st contains a training field with value true. Else returns false.

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Lux.LuxOps.multigate Function

multigate(x::AbstractArray, ::Val{N})

Split up x into N equally sized chunks (along dimension 1).

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Recursive Operations

Lux.recursive_map Function

recursive_map(f, x, args...)

Similar to fmap(f, args...) but with restricted support for the notion of "leaf" types. However, this allows for more efficient and type stable implementations of recursive operations.

How this works?

For the following types it directly defines recursion rules:

1. AbstractArray: If eltype is isbitstype, then f is applied to the array, else we recurse on the array.

2. Tuple/NamedTuple: We recurse on the values.

3. Number/Val/Nothing: We directly apply f.

4. For all other types, we recurse on the fields using Functors.fmap.

Note

In most cases, users should gravitate towards Functors.fmap if it is being used outside of hot loops. Even for other cases, it is always recommended to verify the correctness of this implementation for specific usecases.

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Lux.recursive_add!! Function

recursive_add!!(x, y)

Recursively add the leaves of two nested structures x and y. In Functor language, this is equivalent to doing fmap(+, x, y), but this implementation uses type stable code for common cases.

Any leaves of x that are arrays and allow in-place addition will be modified in place.

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Lux.recursive_copyto! Function

recursive_copyto!(x, y)

Recursively copy the leaves of two nested structures x and y. In Functor language, this is equivalent to doing fmap(copyto!, x, y), but this implementation uses type stable code for common cases. Note that any immutable leaf will lead to an error.

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Lux.recursive_eltype Function

recursive_eltype(x, unwrap_ad_types = Val(false))

Recursively determine the element type of a nested structure x. This is equivalent to doing fmap(Lux.Utils.eltype, x), but this implementation uses type stable code for common cases.

For ambiguous inputs like nothing and Val types we return Bool as the eltype.

If unwrap_ad_types is set to Val(true) then for tracing and operator overloading based ADs (ForwardDiff, ReverseDiff, Tracker), this function will return the eltype of the unwrapped value.

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Lux.recursive_make_zero Function

recursive_make_zero(x)

Recursively create a zero value for a nested structure x. This is equivalent to doing fmap(zero, x), but this implementation uses type stable code for common cases.

See also Lux.recursive_make_zero!!.

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Lux.recursive_make_zero!! Function

recursive_make_zero!!(x)

Recursively create a zero value for a nested structure x. Leaves that can be mutated with in-place zeroing will be modified in place.

See also Lux.recursive_make_zero for fully out-of-place version.

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Updating Floating Point Precision

By default, Lux uses Float32 for all parameters and states. To update the precision simply pass the parameters / states / arrays into one of the following functions.

Lux.f16 Function

f16(m)

Converts the eltype of m floating point values to Float16. Recurses into structs marked with Functors.@functor.

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Lux.f32 Function

f32(m)

Converts the eltype of m floating point values to Float32. Recurses into structs marked with Functors.@functor.

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Lux.f64 Function

f64(m)

Converts the eltype of m floating point values to Float64. Recurses into structs marked with Functors.@functor.

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Element Type Matching

Lux.match_eltype Function

match_eltype(layer, ps, st, args...)

Helper function to "maybe" (see below) match the element type of args... with the element type of the layer's parameters and states. This is useful for debugging purposes, to track down accidental type-promotions inside Lux layers.

Extended Help

Controlling the Behavior via Preferences

Behavior of this function is controlled via the eltype_mismatch_handling preference. The following options are supported:

• "none": This is the default behavior. In this case, this function is a no-op, i.e., it simply returns args....

• "warn": This option will issue a warning if the element type of args... does not match the element type of the layer's parameters and states. The warning will contain information about the layer and the element type mismatch.

• "convert": This option is same as "warn", but it will also convert the element type of args... to match the element type of the layer's parameters and states (for the cases listed below).

• "error": Same as "warn", but instead of issuing a warning, it will throw an error.

Warning

We print the warning for type-mismatch only once.

Element Type Conversions

For "convert" only the following conversions are done:

Element Type of parameters/states	Element Type of args...	Converted to
Float64	Integer	Float64
Float32	Float64	Float32
Float32	Integer	Float32
Float16	Float64	Float16
Float16	Float32	Float16
Float16	Integer	Float16

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Stateful Layer

Lux.StatefulLuxLayer Type

StatefulLuxLayer{FT}(model, ps, st)

Warning

This is not a Lux.AbstractLuxLayer

A convenience wrapper over Lux layers which stores the parameters and states internally. This is meant to be used in internal implementation of layers.

Usecases

• Internal implementation of @compact heavily uses this layer.

• In SciML codebases where propagating state might involving Boxing. For a motivating example, see the Neural ODE tutorial.

• Facilitates Nested AD support in Lux. For more details on this feature, see the Nested AD Manual Page.

Static Parameters

• If FT = true then the type of the state is fixed, i.e., typeof(last(model(x, ps, st))) == st.

• If FT = false then type of the state might change. Note that while this works in all cases, it will introduce type instability.

Arguments

• model: A Lux layer

• ps: The parameters of the layer. This can be set to nothing, if the user provides the parameters on function call

• st: The state of the layer

Inputs

• x: The input to the layer

• ps: The parameters of the layer. Optional, defaults to s.ps

Outputs

• y: The output of the layer

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Compact Layer

Lux.@compact Macro

@compact(kw...) do x
    ...
    @return y # optional (but recommended for best performance)
end
@compact(kw...) do x, p
    ...
    @return y # optional (but recommended for best performance)
end
@compact(forward::Function; name=nothing, dispatch=nothing, parameters...)

Creates a layer by specifying some parameters, in the form of keywords, and (usually as a do block) a function for the forward pass. You may think of @compact as a specialized let block creating local variables that are trainable in Lux. Declared variable names may be used within the body of the forward function. Note that unlike typical Lux models, the forward function doesn't need to explicitly manage states.

Defining the version with p allows you to access the parameters in the forward pass. This is useful when using it with SciML tools which require passing in the parameters explicitly.

Reserved Kwargs:

1. name: The name of the layer.

2. dispatch: The constructed layer has the type Lux.CompactLuxLayer{dispatch} which can be used for custom dispatches.

Tip

Check the Lux tutorials for more examples of using @compact.

If you are passing in kwargs by splatting them, they will be passed as is to the function body. This means if your splatted kwargs contain a lux layer that won't be registered in the CompactLuxLayer.

Special Syntax

• @return: This macro doesn't really exist, but is used to return a value from the @compact block. Without the presence of this macro, we need to rely on closures which can lead to performance penalties in the reverse pass.

  • Having statements after the last @return macro might lead to incorrect code.

  • Don't do things like @return return x. This will generate non-sensical code like <new var> = return x. Essentially, @return <expr> supports any expression, that can be assigned to a variable.

  • Since this macro doesn't "exist", it cannot be imported as using Lux: @return. Simply use it in code, and @compact will understand it.

• @init_fn: Provide a function that will be used to initialize the layer's parameters or state. See the docs of @init_fn for more details.

• @non_trainable: Mark a value as non-trainable. This bypasses the regular checks and places the value into the state of the layer. See the docs of @non_trainable for more details.

Extended Help

Examples

Here is a linear model:

julia> using Lux, Random

julia> r = @compact(w=ones(3)) do x
           @return w .* x
       end
@compact(
    w = 3-element Vector{Float64},
) do x
    return w .* x
end       # Total: 3 parameters,
          #        plus 0 states.

julia> ps, st = Lux.setup(Xoshiro(0), r);

julia> r([1, 2, 3], ps, st)  # x is set to [1, 1, 1].
([1.0, 2.0, 3.0], NamedTuple())

Here is a linear model with bias and activation:

julia> d_in = 5
5

julia> d_out = 3
3

julia> d = @compact(W=ones(d_out, d_in), b=zeros(d_out), act=relu) do x
           y = W * x
           @return act.(y .+ b)
       end
@compact(
    W = 3×5 Matrix{Float64},
    b = 3-element Vector{Float64},
    act = relu,
) do x
    y = W * x
    return act.(y .+ b)
end       # Total: 18 parameters,
          #        plus 1 states.

julia> ps, st = Lux.setup(Xoshiro(0), d);

julia> d(ones(5, 2), ps, st)[1] # 3×2 Matrix as output.
3×2 Matrix{Float64}:
 5.0  5.0
 5.0  5.0
 5.0  5.0

julia> ps_dense = (; weight=ps.W, bias=ps.b);

julia> first(d([1, 2, 3, 4, 5], ps, st)) ≈
       first(Dense(d_in => d_out, relu)([1, 2, 3, 4, 5], ps_dense, NamedTuple())) # Equivalent to a dense layer
true

Finally, here is a simple MLP. We can train this model just like any Lux model:

julia> n_in = 1;

julia> n_out = 1;

julia> nlayers = 3;

julia> model = @compact(w1=Dense(n_in, 128),
           w2=[Dense(128, 128) for i in 1:nlayers], w3=Dense(128, n_out), act=relu) do x
           embed = act.(w1(x))
           for w in w2
               embed = act.(w(embed))
           end
           out = w3(embed)
           @return out
       end
@compact(
    w1 = Dense(1 => 128),               # 256 parameters
    w2 = NamedTuple(
        1 = Dense(128 => 128),          # 16_512 parameters
        2 = Dense(128 => 128),          # 16_512 parameters
        3 = Dense(128 => 128),          # 16_512 parameters
    ),
    w3 = Dense(128 => 1),               # 129 parameters
    act = relu,
) do x
    embed = act.(w1(x))
    for w = w2
        embed = act.(w(embed))
    end
    out = w3(embed)
    return out
end       # Total: 49_921 parameters,
          #        plus 1 states.

julia> ps, st = Lux.setup(Xoshiro(0), model);

julia> size(first(model(randn(n_in, 32), ps, st)))  # 1×32 Matrix as output.
(1, 32)

julia> using Optimisers, Zygote

julia> x_data = collect(-2.0f0:0.1f0:2.0f0)';

julia> y_data = 2 .* x_data .- x_data .^ 3;

julia> optim = Optimisers.setup(Adam(), ps);

julia> loss_initial = sum(abs2, first(model(x_data, ps, st)) .- y_data);

julia> for epoch in 1:1000
           loss, gs = Zygote.withgradient(
               ps -> sum(abs2, first(model(x_data, ps, st)) .- y_data), ps)
           Optimisers.update!(optim, ps, gs[1])
       end;

julia> loss_final = sum(abs2, first(model(x_data, ps, st)) .- y_data);

julia> loss_initial > loss_final
true

You may also specify a name for the model, which will be used instead of the default printout, which gives a verbatim representation of the code used to construct the model:

julia> model = @compact(w=rand(3), name="Linear(3 => 1)") do x
           @return sum(w .* x)
       end
Linear(3 => 1)      # 3 parameters

This can be useful when using @compact to hierarchically construct complex models to be used inside a Chain.

Type Stability

If your input function f is type-stable but the generated model is not type stable, it should be treated as a bug. We will appreciate issues if you find such cases.

Parameter Count

Array Parameter don't print the number of parameters on the side. However, they do account for the total number of parameters printed at the bottom.

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Lux.@init_fn Macro

@init_fn(fn, kind::Symbol = :parameter)

Create an initializer function for a parameter or state to be used for in a Compact Lux Layer created using @compact.

Arguments

• fn: The function to be used for initializing the parameter or state. This only takes a single argument rng.

• kind: If set to :parameter, the initializer function will be used to initialize the parameters of the layer. If set to :state, the initializer function will be used to initialize the states of the layer.

Examples

julia> using Lux, Random

julia> r = @compact(w=@init_fn(rng->randn32(rng, 3, 2)),
           b=@init_fn(rng->randn32(rng, 3), :state)) do x
           @return w * x .+ b
       end;

julia> ps, st = Lux.setup(Xoshiro(0), r);

julia> size(ps.w)
(3, 2)

julia> size(st.b)
(3,)

julia> size(r([1, 2], ps, st)[1])
(3,)

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Lux.@non_trainable Macro

@non_trainable(x)

Mark a value as non-trainable. This bypasses the regular checks and places the value into the state of the layer.

Arguments

• x: The value to be marked as non-trainable.

Examples

julia> using Lux, Random

julia> r = @compact(w=ones(3), w_fixed=@non_trainable(rand(3))) do x
           @return sum(w .* x .+ w_fixed)
       end;

julia> ps, st = Lux.setup(Xoshiro(0), r);

julia> size(ps.w)
(3,)

julia> size(st.w_fixed)
(3,)

julia> res, st_ = r([1, 2, 3], ps, st);

julia> st_.w_fixed == st.w_fixed
true

julia> res isa Number
true

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Miscellaneous

Lux.set_dispatch_doctor_preferences! Function

set_dispatch_doctor_preferences!(mode::String)
set_dispatch_doctor_preferences!(; luxcore::String="disable", luxlib::String="disable")

Set the dispatch doctor preference for LuxCore and LuxLib packages.

mode can be "disable", "warn", or "error". For details on the different modes, see the DispatchDoctor.jl documentation.

If the preferences are already set, then no action is taken. Otherwise the preference is set. For changes to take effect, the Julia session must be restarted.

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