Debugging Lux Models

Debugging DNNs can be very painful. Especially with the gigantic stacktraces for Lux, it is even harder to pin-point to which particular layer errored out. This page describes some useful tools that ship with Lux, that can help you debug your models.

TL;DR

Simply wrap your model with Lux.Experimental.@debug_mode!!

Don't Forget

Remember to use the non Debug mode model after you finish debugging. Debug mode models are way slower.

Let us construct a model which has an obviously incorrect dimension. In this example, you will see how easy it is to pin-point the problematic layer.

Incorrect Model Specification: Dimension Mismatch Problems

using Lux, Random

model = Chain(Dense(1 => 16, relu), Chain(Dense(16 => 3), Dense(1 => 1)),
    BatchNorm(1); disable_optimizations=true)

model_debug = Lux.Experimental.@debug_mode model

Chain(
    layer_1 = DebugLayer(
        layer = Dense(1 => 16, relu),   # 32 parameters
    ),
    layer_2 = Chain(
        layer_1 = DebugLayer(
            layer = Dense(16 => 3),     # 51 parameters
        ),
        layer_2 = DebugLayer(
            layer = Dense(1 => 1),      # 2 parameters
        ),
    ),
    layer_3 = DebugLayer(
        layer = BatchNorm(1, affine=true, track_stats=true),  # 2 parameters, plus 3
    ),
)         # Total: 87 parameters,
          #        plus 3 states.

Note that we can use the parameters and states for model itself in model_debug, no need to make any changes. If you ran the original model this is the kind of error you would see:

rng = Xoshiro(0)

ps, st = Lux.setup(rng, model)
x = randn(rng, Float32, 1, 2)

try
    model(x, ps, st)
catch e
    println(e)
end

DimensionMismatch("A has dimensions (1,1) but B has dimensions (3,2)")

Ofcourse, this error will come with a detailed stacktrace, but it is still not very useful. Now let's try using the debug mode model:

try
    model_debug(x, ps, st)
catch e
    println(e)
end

[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: Dense(1 => 16, relu) at location model.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (16, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (16, 2)
[ Info: Running Layer: Dense(16 => 3) at location model.layers.layer_2.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (3, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (3, 2)
[ Info: Running Layer: Dense(1 => 1) at location model.layers.layer_2.layers.layer_2!
┌ Error: Layer Dense(1 => 1) failed!! This layer is present at location model.layers.layer_2.layers.layer_2
└ @ Lux.Experimental /var/lib/buildkite-agent/builds/gpuci-2/julialang/lux-dot-jl/src/contrib/debug.jl:107
DimensionMismatch("A has dimensions (1,1) but B has dimensions (3,2)")

See now we know that model.layers.layer_2.layers.layer_2 is the problematic layer. Let us fix that layer and see what happens:

model = Chain(Dense(1 => 16, relu),
    Chain(Dense(16 => 3),  
    Chain(Dense(16 => 1),  
        Dense(1 => 1)),
    BatchNorm(1); disable_optimizations=true)

model_fixed = Chain(Dense(1 => 16, relu), Chain(Dense(16 => 1), Dense(1 => 1)),
    BatchNorm(1); disable_optimizations=true)

ps, st = Lux.setup(rng, model_fixed)

model_fixed(x, ps, st)

(Float32[0.9986285 -0.9986285], (layer_1 = NamedTuple(), layer_2 = (layer_1 = NamedTuple(), layer_2 = NamedTuple()), layer_3 = (running_mean = Float32[0.00066049065], running_var = Float32[0.90072757], training = Val{true}())))

Voila!! We have tracked down and fixed the problem.

Tracking down NaNs

Have you encountered those pesky little NaNs in your training? They are very hard to track down. We will create an artificially simulate NaNs in our model and see how we can track the offending layer.

We can set nan_check to :forward, :backward or :both to check for NaNs in the debug model. (or even disable it by setting it to :none)

model = Chain(Dense(1 => 16, relu), Chain(Dense(16 => 1), Dense(1 => 1)),
    BatchNorm(1); disable_optimizations=true)

ps, st = Lux.setup(rng, model)

model_debug = Lux.Experimental.@debug_mode model nan_check=:both

Chain(
    layer_1 = DebugLayer(
        layer = Dense(1 => 16, relu),   # 32 parameters
    ),
    layer_2 = Chain(
        layer_1 = DebugLayer(
            layer = Dense(16 => 1),     # 17 parameters
        ),
        layer_2 = DebugLayer(
            layer = Dense(1 => 1),      # 2 parameters
        ),
    ),
    layer_3 = DebugLayer(
        layer = BatchNorm(1, affine=true, track_stats=true),  # 2 parameters, plus 3
    ),
)         # Total: 53 parameters,
          #        plus 3 states.

Let us set a value in the parameter to NaN:

ps.layer_2.layer_2.weight[1, 1] = NaN

NaN

Now let us run the model

model(x, ps, st)

(Float32[NaN NaN], (layer_1 = NamedTuple(), layer_2 = (layer_1 = NamedTuple(), layer_2 = NamedTuple()), layer_3 = (running_mean = Float32[NaN], running_var = Float32[NaN], training = Val{true}())))

Ah as expected our output is NaN. But is is not very clear how to track where the first NaN occurred. Let's run the debug model and check:

try
    model_debug(x, ps, st)
catch e
    println(e)
end

[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: Dense(1 => 16, relu) at location model.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (16, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (16, 2)
[ Info: Running Layer: Dense(16 => 1) at location model.layers.layer_2.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (1, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: Dense(1 => 1) at location model.layers.layer_2.layers.layer_2!
DomainError((weight = Float32[NaN;;], bias = Float32[0.0;;]), "NaNs detected in parameters of layer Dense(1 => 1) at location model.layers.layer_2.layers.layer_2")

And we have figured it out! The first NaN occurred in the parameters of model.layers.layer_2.layers.layer_2! But what if NaN occurs in the reverse pass! Let us define a custom layer and introduce a fake NaN in the backward pass.

using ChainRulesCore, Zygote

const CRC = ChainRulesCore

offending_layer(x) = 2 .* x

offending_layer (generic function with 1 method)

model = Chain(Dense(1 => 16, relu), Chain(Dense(16 => 1), offending_layer),
    BatchNorm(1); disable_optimizations=true)

ps, st = Lux.setup(rng, model)

model(x, ps, st)

(Float32[-0.9999676 0.9999676], (layer_1 = NamedTuple(), layer_2 = (layer_1 = NamedTuple(), layer_2 = NamedTuple()), layer_3 = (running_mean = Float32[-0.090743795], running_var = Float32[0.930855], training = Val{true}())))

Let us define a custom backward pass to introduce some NaNs:

function CRC.rrule(::typeof(offending_layer), x)
    y = offending_layer(x)
    function ∇offending_layer(Δ)
        Δ[1] = NaN
        return NoTangent(), Δ
    end
    return y, ∇offending_layer
end

Let us compute the gradient of the layer now:

Zygote.gradient(ps -> sum(first(model(x, ps, st))), ps)

((layer_1 = (weight = Float32[NaN; NaN; … ; NaN; NaN;;], bias = Float32[NaN; NaN; … ; NaN; NaN;;]), layer_2 = (layer_1 = (weight = Float32[NaN NaN … NaN NaN], bias = Float32[NaN;;]), layer_2 = nothing), layer_3 = (scale = Float32[0.0], bias = Fill(2.0f0, 1))),)

Oh no!! A NaN is present in the gradient of ps. Let us run the debug model and see where the NaN occurred:

model_debug = Lux.Experimental.@debug_mode model nan_check=:both

try
    Zygote.gradient(ps -> sum(first(model_debug(x, ps, st))), ps)
catch e
    println(e)
end

[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: Dense(1 => 16, relu) at location model.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (16, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (16, 2)
[ Info: Running Layer: Dense(16 => 1) at location model.layers.layer_2.layers.layer_1!
[ Info: Output Type: Matrix{Float32} | Output Structure: (1, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: WrappedFunction(offending_layer) at location model.layers.layer_2.layers.layer_2!
[ Info: Output Type: Matrix{Float32} | Output Structure: (1, 2)
[ Info: Input Type: Matrix{Float32} | Input Structure: (1, 2)
[ Info: Running Layer: BatchNorm(1, affine=true, track_stats=true) at location model.layers.layer_3!
[ Info: Output Type: Matrix{Float32} | Output Structure: (1, 2)
DomainError(Float32[NaN 0.0], "NaNs detected in pullback output for WrappedFunction(offending_layer) at location model.layers.layer_2.layers.layer_2!")

And there you go our debug layer prints that the problem is in WrappedFunction(offending_layer) at location model.layers.layer_2.layers.layer_2! Once we fix the pullback of the layer, we will fix the NaNs.

Conclusion

In this manual section, we have discussed tracking down errors in Lux models. We have covered tracking incorrect model specifications and NaNs in forward and backward passes. However, remember that this is an Experimental feature, and there might be edge cases that don't work correctly. If you find any such cases, please open an issue on GitHub!