Getting Started
Installation
Install Julia v1.10 or above. Lux.jl is available through the Julia package manager. You can enter it by pressing ] in the REPL and then typing add Lux. Alternatively, you can also do
julia
import Pkg
Pkg.add("Lux")Update to v1
If you are using a pre-v1 version of Lux.jl, please see the Updating to v1 section for instructions on how to update.
Quickstart
Pre-Requisites
You need to install Optimisers and Zygote if not done already. Pkg.add(["Optimisers", "Zygote"])
julia
using Lux, Random, Optimisers, Zygote
# using LuxCUDA, AMDGPU, Metal, oneAPI # Optional packages for GPU supportWe take randomness very seriously
julia
# Seeding
rng = Random.default_rng()
Random.seed!(rng, 0)Random.TaskLocalRNG()Build the model
julia
# Construct the layer
model = Chain(Dense(128, 256, tanh), Chain(Dense(256, 1, tanh), Dense(1, 10)))Chain(
layer_1 = Dense(128 => 256, tanh), # 33_024 parameters
layer_2 = Chain(
layer_1 = Dense(256 => 1, tanh), # 257 parameters
layer_2 = Dense(1 => 10), # 20 parameters
),
) # Total: 33_301 parameters,
# plus 0 states.Models don't hold parameters and states so initialize them. From there on, we can just use our standard AD and Optimisers API. However, here we will show how to use Lux's Training API that provides an uniform API over all supported AD systems.
julia
# Get the device determined by Lux
dev = gpu_device()
# Parameter and State Variables
ps, st = Lux.setup(rng, model) |> dev
# Dummy Input
x = rand(rng, Float32, 128, 2) |> dev
# Run the model
y, st = Lux.apply(model, x, ps, st)
# Gradients
## First construct a TrainState
train_state = Lux.Training.TrainState(model, ps, st, Adam(0.0001f0))
## We can compute the gradients using Training.compute_gradients
gs, loss, stats, train_state = Lux.Training.compute_gradients(
AutoZygote(), MSELoss(),
(x, dev(rand(rng, Float32, 10, 2))), train_state
)
## Optimization
train_state = Training.apply_gradients!(train_state, gs) # or Training.apply_gradients (no `!` at the end)
# Both these steps can be combined into a single call
gs, loss, stats, train_state = Training.single_train_step!(
AutoZygote(), MSELoss(),
(x, dev(rand(rng, Float32, 10, 2))), train_state
)((layer_1 = (weight = Float32[0.0017983615 0.006062332 … 0.0053392933 0.0056276177; 0.0011292367 0.0041270256 … 0.003585879 0.0038155357; … ; -0.0008762945 -0.0031371699 … -0.0027350332 -0.0029033197; 0.0011154839 0.002197485 … 0.0021741025 0.0021157824], bias = Float32[0.006656272, 0.004425203, 0.0028994146, -0.0116051175, 0.0031301186, 0.0037318026, 0.0136483535, 0.013969757, -0.015173428, -0.005173992 … -0.0018621369, -0.0015270555, -0.007873881, -0.0076395273, -0.0022123815, 0.0039605754, 0.0034407252, -0.0045406874, -0.003383829, 0.0029306945]), layer_2 = (layer_1 = (weight = Float32[0.04993449 0.03202845 … -0.059382 0.07701616], bias = Float32[0.08797912]), layer_2 = (weight = Float32[-0.094527975; -0.11476975; … ; -0.016841749; -0.0698748;;], bias = Float32[-0.21608135, -0.26255828, -0.23534852, -0.21524015, -0.055711076, -0.20314303, -0.1895644, 0.03666526, -0.03937737, -0.15905891]))), 0.8455785f0, NamedTuple(), Lux.Training.TrainState{Nothing, Nothing, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}}, Nothing}, @NamedTuple{layer_1::@NamedTuple{weight::Matrix{Float32}, bias::Vector{Float32}}, layer_2::@NamedTuple{layer_1::@NamedTuple{weight::Matrix{Float32}, bias::Vector{Float32}}, layer_2::@NamedTuple{weight::Matrix{Float32}, bias::Vector{Float32}}}}, @NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{layer_1::@NamedTuple{}, layer_2::@NamedTuple{}}}, Adam{Float32, Tuple{Float64, Float64}, Float64}, @NamedTuple{layer_1::@NamedTuple{weight::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Matrix{Float32}, Matrix{Float32}, Tuple{Float32, Float32}}}, bias::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Vector{Float32}, Vector{Float32}, Tuple{Float32, Float32}}}}, layer_2::@NamedTuple{layer_1::@NamedTuple{weight::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Matrix{Float32}, Matrix{Float32}, Tuple{Float32, Float32}}}, bias::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Vector{Float32}, Vector{Float32}, Tuple{Float32, Float32}}}}, layer_2::@NamedTuple{weight::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Matrix{Float32}, Matrix{Float32}, Tuple{Float32, Float32}}}, bias::Optimisers.Leaf{Adam{Float32, Tuple{Float64, Float64}, Float64}, Tuple{Vector{Float32}, Vector{Float32}, Tuple{Float32, Float32}}}}}}}(nothing, nothing, Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}}, Nothing}((layer_1 = Dense(128 => 256, tanh), layer_2 = Chain{@NamedTuple{layer_1::Dense{typeof(tanh), Int64, Int64, Nothing, Nothing, Static.True}, layer_2::Dense{typeof(identity), Int64, Int64, Nothing, Nothing, Static.True}}, Nothing}((layer_1 = Dense(256 => 1, tanh), layer_2 = Dense(1 => 10)), nothing)), nothing), (layer_1 = (weight = Float32[-0.22542597 0.22379348 … 0.1997513 -0.018708104; -0.023026714 0.15451026 … -0.065325744 0.18120264; … ; 0.038037397 -0.07125516 … -0.03306083 0.039138064; -0.18810266 -0.09693537 … -0.18102062 0.019230088], bias = Float32[0.030937059, -0.060276944, 0.084569596, 0.00040024254, -0.065509446, -0.08527214, -0.026523968, 0.06347208, 0.042247728, 0.027705256 … -0.06052852, 0.03504307, -0.028244259, 0.06788022, 0.0027464977, -0.06942153, 0.0064240773, 0.0141069945, -0.029283267, 0.01174226]), layer_2 = (layer_1 = (weight = Float32[0.12008221 0.06026435 … -0.070576 0.1577647], bias = Float32[0.026844418]), layer_2 = (weight = Float32[0.5345728; -0.28288874; … ; -0.32983455; -0.45298168;;], bias = Float32[-0.59751064, -0.7033041, -0.8457602, -0.53789175, -0.31473723, 0.17461234, -0.82945836, 0.67841595, 0.35837248, -0.14941788]))), (layer_1 = NamedTuple(), layer_2 = (layer_1 = NamedTuple(), layer_2 = NamedTuple())), Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (layer_1 = (weight = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[0.000926728 0.000860063 … 0.00110328 0.000908301; 0.000480834 0.000574605 … 0.000665883 0.000584197; … ; -0.000391039 -0.000438617 … -0.000520651 -0.000449867; 0.00106235 0.000365587 … 0.000813131 0.000495484], Float32[7.20343f-8 4.46976f-8 … 6.84867f-8 4.63952f-8; 1.79691f-8 2.02649f-8 … 2.45046f-8 1.96227f-8; … ; 1.21215f-8 1.17657f-8 … 1.50136f-8 1.15681f-8; 1.12738f-7 7.45199f-9 … 4.8495f-8 1.44173f-8], (0.729, 0.997003))), bias = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[0.00169459, 0.000977637, 0.00103866, -0.00234933, 0.000659175, 0.000868318, 0.00303222, 0.00271383, -0.00326585, -0.0014993 … -0.000480712, -0.000501535, -0.00174489, -0.00160158, -0.000470662, 0.00127967, 0.000618911, -0.00103705, -0.000773079, 0.00146704], Float32[1.74884f-7, 5.48983f-8, 7.75433f-8, 3.08981f-7, 2.45763f-8, 4.41623f-8, 5.29156f-7, 4.09021f-7, 6.07287f-7, 1.45678f-7 … 1.4164f-8, 1.73391f-8, 1.7507f-7, 1.44894f-7, 1.25673f-8, 1.1198f-7, 2.11545f-8, 6.25338f-8, 3.4755f-8, 1.78565f-7], (0.729, 0.997003)))), layer_2 = (layer_1 = (weight = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[0.00443555 0.00163654 … -0.0124978 0.0123434], Float32[2.53181f-6 1.32838f-6 … 8.83289f-6 8.58873f-6], (0.729, 0.997003))), bias = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[0.0191175], Float32[2.08743f-5], (0.729, 0.997003)))), layer_2 = (weight = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[-0.0172084; -0.0213176; … ; -0.00376332; -0.0116419;;], Float32[1.63537f-5; 2.51152f-5; … ; 8.16783f-7; 7.55419f-6;;], (0.729, 0.997003))), bias = Leaf(Adam(eta=0.0001, beta=(0.9, 0.999), epsilon=1.0e-8), (Float32[-0.0365001, -0.045083, -0.0507623, -0.0390298, -0.0242259, -0.0404982, -0.0358925, 0.0114351, -0.00803444, -0.0248332], Float32[7.40417f-5, 0.000112652, 0.000146818, 8.41229f-5, 4.60234f-5, 9.15105f-5, 7.13093f-5, 8.78741f-6, 3.62043f-6, 3.51285f-5], (0.729, 0.997003)))))), 2))Defining Custom Layers
We can train our model using the above code, but let's go ahead and see how to use Reactant. Reactant is a julia frontend that generates MLIR and then compiles it using XLA (after running fancy optimizations). It is the current recommended way to train large models in Lux. For more details on using Reactant, see the manual.
julia
using Lux, Random, Optimisers, Reactant, Enzyme
using Printf # For pretty printing
dev = reactant_device()(::ReactantDevice{Missing, Missing, Missing}) (generic function with 1 method)We will define a custom MLP using the @compact macro. The macro takes in a list of parameters, layers and states, and a function defining the forward pass of the neural network.
julia
n_in = 1
n_out = 1
nlayers = 3
model = @compact(
w1=Dense(n_in => 32),
w2=[Dense(32 => 32) for i in 1:nlayers],
w3=Dense(32 => 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 => 32), # 64 parameters
w2 = NamedTuple(
1 = Dense(32 => 32), # 1_056 parameters
2 = Dense(32 => 32), # 1_056 parameters
3 = Dense(32 => 32), # 1_056 parameters
),
w3 = Dense(32 => 1), # 33 parameters
act = relu,
) do x
embed = act(w1(x))
for w = w2
embed = act(w(embed))
end
out = w3(embed)
return out
end # Total: 3_265 parameters,
# plus 1 states.We can initialize the model and train it with the same code as before!
julia
rng = Random.default_rng()
Random.seed!(rng, 0)
ps, st = Lux.setup(rng, model) |> dev
x = rand(rng, Float32, n_in, 32) |> dev
@jit model(x, ps, st) # 1×32 Matrix and updated state as output.
x_data = reshape(collect(-2.0f0:0.1f0:2.0f0), 1, :)
y_data = 2 .* x_data .- x_data .^ 3
x_data, y_data = dev(x_data), dev(y_data)
function train_model!(model, ps, st, x_data, y_data)
train_state = Lux.Training.TrainState(model, ps, st, Adam(0.001f0))
for iter in 1:1000
_, loss, _, train_state = Lux.Training.single_train_step!(
AutoEnzyme(), MSELoss(),
(x_data, y_data), train_state
)
if iter % 100 == 1 || iter == 1000
@printf "Iteration: %04d \t Loss: %10.9g\n" iter loss
end
end
return model, ps, st
end
train_model!(model, ps, st, x_data, y_data)2025-03-31 21:19:46.007792: I external/xla/xla/service/service.cc:152] XLA service 0x8f1e4e0 initialized for platform CUDA (this does not guarantee that XLA will be used). Devices:
2025-03-31 21:19:46.008144: I external/xla/xla/service/service.cc:160] StreamExecutor device (0): NVIDIA A100-PCIE-40GB MIG 1g.5gb, Compute Capability 8.0
WARNING: All log messages before absl::InitializeLog() is called are written to STDERR
I0000 00:00:1743455986.010133 3613320 se_gpu_pjrt_client.cc:1040] Using BFC allocator.
I0000 00:00:1743455986.010499 3613320 gpu_helpers.cc:136] XLA backend allocating 3825205248 bytes on device 0 for BFCAllocator.
I0000 00:00:1743455986.010729 3613320 gpu_helpers.cc:177] XLA backend will use up to 1275068416 bytes on device 0 for CollectiveBFCAllocator.
I0000 00:00:1743455986.026884 3613320 cuda_dnn.cc:529] Loaded cuDNN version 90400
E0000 00:00:1743456223.407888 3613320 buffer_comparator.cc:156] Difference at 16: 0, expected 18.4532
E0000 00:00:1743456223.408902 3613320 buffer_comparator.cc:156] Difference at 17: 0, expected 16.1701
E0000 00:00:1743456223.408916 3613320 buffer_comparator.cc:156] Difference at 18: 0, expected 18.5372
E0000 00:00:1743456223.408924 3613320 buffer_comparator.cc:156] Difference at 19: 0, expected 17.7684
E0000 00:00:1743456223.408930 3613320 buffer_comparator.cc:156] Difference at 20: 0, expected 17.8078
E0000 00:00:1743456223.408937 3613320 buffer_comparator.cc:156] Difference at 21: 0, expected 17.412
E0000 00:00:1743456223.408944 3613320 buffer_comparator.cc:156] Difference at 22: 0, expected 18.0425
E0000 00:00:1743456223.408950 3613320 buffer_comparator.cc:156] Difference at 23: 0, expected 17.7822
E0000 00:00:1743456223.408957 3613320 buffer_comparator.cc:156] Difference at 24: 0, expected 16.8692
E0000 00:00:1743456223.408964 3613320 buffer_comparator.cc:156] Difference at 25: 0, expected 19.6248
2025-03-31 21:23:43.408981: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.412279 3613320 buffer_comparator.cc:156] Difference at 16: 0, expected 18.4532
E0000 00:00:1743456223.412306 3613320 buffer_comparator.cc:156] Difference at 17: 0, expected 16.1701
E0000 00:00:1743456223.412314 3613320 buffer_comparator.cc:156] Difference at 18: 0, expected 18.5372
E0000 00:00:1743456223.412321 3613320 buffer_comparator.cc:156] Difference at 19: 0, expected 17.7684
E0000 00:00:1743456223.412328 3613320 buffer_comparator.cc:156] Difference at 20: 0, expected 17.8078
E0000 00:00:1743456223.412335 3613320 buffer_comparator.cc:156] Difference at 21: 0, expected 17.412
E0000 00:00:1743456223.412341 3613320 buffer_comparator.cc:156] Difference at 22: 0, expected 18.0425
E0000 00:00:1743456223.412348 3613320 buffer_comparator.cc:156] Difference at 23: 0, expected 17.7822
E0000 00:00:1743456223.412354 3613320 buffer_comparator.cc:156] Difference at 24: 0, expected 16.8692
E0000 00:00:1743456223.412361 3613320 buffer_comparator.cc:156] Difference at 25: 0, expected 19.6248
2025-03-31 21:23:43.412372: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.415607 3613320 buffer_comparator.cc:156] Difference at 656: 0, expected 15.8892
E0000 00:00:1743456223.415618 3613320 buffer_comparator.cc:156] Difference at 657: 0, expected 15.1292
E0000 00:00:1743456223.415622 3613320 buffer_comparator.cc:156] Difference at 658: 0, expected 14.0499
E0000 00:00:1743456223.415625 3613320 buffer_comparator.cc:156] Difference at 659: 0, expected 13.8377
E0000 00:00:1743456223.415628 3613320 buffer_comparator.cc:156] Difference at 660: 0, expected 13.7353
E0000 00:00:1743456223.415631 3613320 buffer_comparator.cc:156] Difference at 661: 0, expected 15.7468
E0000 00:00:1743456223.415634 3613320 buffer_comparator.cc:156] Difference at 662: 0, expected 14.9101
E0000 00:00:1743456223.415637 3613320 buffer_comparator.cc:156] Difference at 663: 0, expected 14.8135
E0000 00:00:1743456223.415640 3613320 buffer_comparator.cc:156] Difference at 664: 0, expected 13.6403
E0000 00:00:1743456223.415643 3613320 buffer_comparator.cc:156] Difference at 665: 0, expected 15.8348
2025-03-31 21:23:43.415648: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.418594 3613320 buffer_comparator.cc:156] Difference at 672: 0, expected 16.0696
E0000 00:00:1743456223.418605 3613320 buffer_comparator.cc:156] Difference at 673: 0, expected 14.3019
E0000 00:00:1743456223.418609 3613320 buffer_comparator.cc:156] Difference at 674: 0, expected 15.5573
E0000 00:00:1743456223.418612 3613320 buffer_comparator.cc:156] Difference at 675: 0, expected 14.6242
E0000 00:00:1743456223.418615 3613320 buffer_comparator.cc:156] Difference at 676: 0, expected 14.8486
E0000 00:00:1743456223.418620 3613320 buffer_comparator.cc:156] Difference at 677: 0, expected 14.7699
E0000 00:00:1743456223.418623 3613320 buffer_comparator.cc:156] Difference at 678: 0, expected 15.1617
E0000 00:00:1743456223.418626 3613320 buffer_comparator.cc:156] Difference at 679: 0, expected 14.9394
E0000 00:00:1743456223.418629 3613320 buffer_comparator.cc:156] Difference at 680: 0, expected 13.4678
E0000 00:00:1743456223.418632 3613320 buffer_comparator.cc:156] Difference at 681: 0, expected 16.1851
2025-03-31 21:23:43.418636: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.421587 3613320 buffer_comparator.cc:156] Difference at 688: 0, expected 15.1187
E0000 00:00:1743456223.421598 3613320 buffer_comparator.cc:156] Difference at 689: 0, expected 14.6251
E0000 00:00:1743456223.421601 3613320 buffer_comparator.cc:156] Difference at 690: 0, expected 14.2005
E0000 00:00:1743456223.421604 3613320 buffer_comparator.cc:156] Difference at 691: 0, expected 15.1561
E0000 00:00:1743456223.421607 3613320 buffer_comparator.cc:156] Difference at 692: 0, expected 15.4235
E0000 00:00:1743456223.421610 3613320 buffer_comparator.cc:156] Difference at 693: 0, expected 14.1331
E0000 00:00:1743456223.421613 3613320 buffer_comparator.cc:156] Difference at 694: 0, expected 14.4063
E0000 00:00:1743456223.421616 3613320 buffer_comparator.cc:156] Difference at 695: 0, expected 14.0259
E0000 00:00:1743456223.421619 3613320 buffer_comparator.cc:156] Difference at 696: 0, expected 15.0279
E0000 00:00:1743456223.421622 3613320 buffer_comparator.cc:156] Difference at 729: 0, expected 14.5946
2025-03-31 21:23:43.421627: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.424576 3613320 buffer_comparator.cc:156] Difference at 688: 0, expected 15.1187
E0000 00:00:1743456223.424588 3613320 buffer_comparator.cc:156] Difference at 689: 0, expected 14.6251
E0000 00:00:1743456223.424591 3613320 buffer_comparator.cc:156] Difference at 690: 0, expected 14.2005
E0000 00:00:1743456223.424594 3613320 buffer_comparator.cc:156] Difference at 691: 0, expected 15.1561
E0000 00:00:1743456223.424597 3613320 buffer_comparator.cc:156] Difference at 692: 0, expected 15.4235
E0000 00:00:1743456223.424600 3613320 buffer_comparator.cc:156] Difference at 693: 0, expected 14.1331
E0000 00:00:1743456223.424603 3613320 buffer_comparator.cc:156] Difference at 694: 0, expected 14.4063
E0000 00:00:1743456223.424606 3613320 buffer_comparator.cc:156] Difference at 695: 0, expected 14.0259
E0000 00:00:1743456223.424609 3613320 buffer_comparator.cc:156] Difference at 696: 0, expected 15.0279
E0000 00:00:1743456223.424612 3613320 buffer_comparator.cc:156] Difference at 729: 0, expected 14.5946
2025-03-31 21:23:43.424617: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.427560 3613320 buffer_comparator.cc:156] Difference at 688: 0, expected 15.1187
E0000 00:00:1743456223.427571 3613320 buffer_comparator.cc:156] Difference at 689: 0, expected 14.6251
E0000 00:00:1743456223.427575 3613320 buffer_comparator.cc:156] Difference at 690: 0, expected 14.2005
E0000 00:00:1743456223.427578 3613320 buffer_comparator.cc:156] Difference at 691: 0, expected 15.1561
E0000 00:00:1743456223.427581 3613320 buffer_comparator.cc:156] Difference at 692: 0, expected 15.4235
E0000 00:00:1743456223.427584 3613320 buffer_comparator.cc:156] Difference at 693: 0, expected 14.1331
E0000 00:00:1743456223.427587 3613320 buffer_comparator.cc:156] Difference at 694: 0, expected 14.4063
E0000 00:00:1743456223.427590 3613320 buffer_comparator.cc:156] Difference at 695: 0, expected 14.0259
E0000 00:00:1743456223.427593 3613320 buffer_comparator.cc:156] Difference at 696: 0, expected 15.0279
E0000 00:00:1743456223.427597 3613320 buffer_comparator.cc:156] Difference at 729: 0, expected 14.5946
2025-03-31 21:23:43.427602: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.439163 3613320 buffer_comparator.cc:156] Difference at 16: -nan, expected 11.328
E0000 00:00:1743456223.439175 3613320 buffer_comparator.cc:156] Difference at 17: -nan, expected 8.55983
E0000 00:00:1743456223.439178 3613320 buffer_comparator.cc:156] Difference at 18: -nan, expected 10.4588
E0000 00:00:1743456223.439181 3613320 buffer_comparator.cc:156] Difference at 19: -nan, expected 8.81169
E0000 00:00:1743456223.439184 3613320 buffer_comparator.cc:156] Difference at 20: -nan, expected 8.98138
E0000 00:00:1743456223.439187 3613320 buffer_comparator.cc:156] Difference at 21: -nan, expected 9.49466
E0000 00:00:1743456223.439190 3613320 buffer_comparator.cc:156] Difference at 22: -nan, expected 8.4604
E0000 00:00:1743456223.439192 3613320 buffer_comparator.cc:156] Difference at 23: -nan, expected 9.78691
E0000 00:00:1743456223.439195 3613320 buffer_comparator.cc:156] Difference at 24: -nan, expected 8.15491
E0000 00:00:1743456223.439198 3613320 buffer_comparator.cc:156] Difference at 25: -nan, expected 13.0125
2025-03-31 21:23:43.439203: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.441337 3613320 buffer_comparator.cc:156] Difference at 16: -nan, expected 11.328
E0000 00:00:1743456223.441348 3613320 buffer_comparator.cc:156] Difference at 17: -nan, expected 8.55983
E0000 00:00:1743456223.441351 3613320 buffer_comparator.cc:156] Difference at 18: -nan, expected 10.4588
E0000 00:00:1743456223.441354 3613320 buffer_comparator.cc:156] Difference at 19: -nan, expected 8.81169
E0000 00:00:1743456223.441357 3613320 buffer_comparator.cc:156] Difference at 20: -nan, expected 8.98138
E0000 00:00:1743456223.441360 3613320 buffer_comparator.cc:156] Difference at 21: -nan, expected 9.49466
E0000 00:00:1743456223.441363 3613320 buffer_comparator.cc:156] Difference at 22: -nan, expected 8.4604
E0000 00:00:1743456223.441365 3613320 buffer_comparator.cc:156] Difference at 23: -nan, expected 9.78691
E0000 00:00:1743456223.441368 3613320 buffer_comparator.cc:156] Difference at 24: -nan, expected 8.15491
E0000 00:00:1743456223.441371 3613320 buffer_comparator.cc:156] Difference at 25: -nan, expected 13.0125
2025-03-31 21:23:43.441375: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.443509 3613320 buffer_comparator.cc:156] Difference at 656: -nan, expected 8.69665
E0000 00:00:1743456223.443520 3613320 buffer_comparator.cc:156] Difference at 657: -nan, expected 7.68202
E0000 00:00:1743456223.443523 3613320 buffer_comparator.cc:156] Difference at 658: -nan, expected 7.88703
E0000 00:00:1743456223.443527 3613320 buffer_comparator.cc:156] Difference at 659: -nan, expected 7.16689
E0000 00:00:1743456223.443530 3613320 buffer_comparator.cc:156] Difference at 660: -nan, expected 6.63868
E0000 00:00:1743456223.443533 3613320 buffer_comparator.cc:156] Difference at 661: -nan, expected 8.39542
E0000 00:00:1743456223.443537 3613320 buffer_comparator.cc:156] Difference at 662: -nan, expected 7.00635
E0000 00:00:1743456223.443539 3613320 buffer_comparator.cc:156] Difference at 663: -nan, expected 7.06674
E0000 00:00:1743456223.443542 3613320 buffer_comparator.cc:156] Difference at 664: -nan, expected 6.11613
E0000 00:00:1743456223.443545 3613320 buffer_comparator.cc:156] Difference at 665: -nan, expected 8.63651
2025-03-31 21:23:43.443551: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.445682 3613320 buffer_comparator.cc:156] Difference at 672: -nan, expected 8.61244
E0000 00:00:1743456223.445693 3613320 buffer_comparator.cc:156] Difference at 673: -nan, expected 6.1493
E0000 00:00:1743456223.445696 3613320 buffer_comparator.cc:156] Difference at 674: -nan, expected 8.90756
E0000 00:00:1743456223.445699 3613320 buffer_comparator.cc:156] Difference at 675: -nan, expected 7.1184
E0000 00:00:1743456223.445702 3613320 buffer_comparator.cc:156] Difference at 676: -nan, expected 8.03527
E0000 00:00:1743456223.445704 3613320 buffer_comparator.cc:156] Difference at 677: -nan, expected 7.44864
E0000 00:00:1743456223.445707 3613320 buffer_comparator.cc:156] Difference at 678: -nan, expected 7.35203
E0000 00:00:1743456223.445710 3613320 buffer_comparator.cc:156] Difference at 679: -nan, expected 7.89603
E0000 00:00:1743456223.445712 3613320 buffer_comparator.cc:156] Difference at 680: -nan, expected 7.3266
E0000 00:00:1743456223.445715 3613320 buffer_comparator.cc:156] Difference at 681: -nan, expected 9.7807
2025-03-31 21:23:43.445720: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.447849 3613320 buffer_comparator.cc:156] Difference at 688: -nan, expected 7.86868
E0000 00:00:1743456223.447859 3613320 buffer_comparator.cc:156] Difference at 689: -nan, expected 7.33715
E0000 00:00:1743456223.447863 3613320 buffer_comparator.cc:156] Difference at 690: -nan, expected 6.05665
E0000 00:00:1743456223.447865 3613320 buffer_comparator.cc:156] Difference at 691: -nan, expected 7.16547
E0000 00:00:1743456223.447868 3613320 buffer_comparator.cc:156] Difference at 692: -nan, expected 8.27916
E0000 00:00:1743456223.447871 3613320 buffer_comparator.cc:156] Difference at 693: -nan, expected 5.80258
E0000 00:00:1743456223.447873 3613320 buffer_comparator.cc:156] Difference at 694: -nan, expected 6.06621
E0000 00:00:1743456223.447876 3613320 buffer_comparator.cc:156] Difference at 695: -nan, expected 7.00273
E0000 00:00:1743456223.447879 3613320 buffer_comparator.cc:156] Difference at 696: -nan, expected 7.92525
E0000 00:00:1743456223.447882 3613320 buffer_comparator.cc:156] Difference at 729: -nan, expected 7.66068
2025-03-31 21:23:43.447886: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.450012 3613320 buffer_comparator.cc:156] Difference at 688: -nan, expected 7.86868
E0000 00:00:1743456223.450023 3613320 buffer_comparator.cc:156] Difference at 689: -nan, expected 7.33715
E0000 00:00:1743456223.450026 3613320 buffer_comparator.cc:156] Difference at 690: -nan, expected 6.05665
E0000 00:00:1743456223.450029 3613320 buffer_comparator.cc:156] Difference at 691: -nan, expected 7.16547
E0000 00:00:1743456223.450031 3613320 buffer_comparator.cc:156] Difference at 692: -nan, expected 8.27916
E0000 00:00:1743456223.450034 3613320 buffer_comparator.cc:156] Difference at 693: -nan, expected 5.80258
E0000 00:00:1743456223.450037 3613320 buffer_comparator.cc:156] Difference at 694: -nan, expected 6.06621
E0000 00:00:1743456223.450040 3613320 buffer_comparator.cc:156] Difference at 695: -nan, expected 7.00273
E0000 00:00:1743456223.450042 3613320 buffer_comparator.cc:156] Difference at 696: -nan, expected 7.92525
E0000 00:00:1743456223.450045 3613320 buffer_comparator.cc:156] Difference at 729: -nan, expected 7.66068
2025-03-31 21:23:43.450064: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.452190 3613320 buffer_comparator.cc:156] Difference at 688: -nan, expected 7.86868
E0000 00:00:1743456223.452201 3613320 buffer_comparator.cc:156] Difference at 689: -nan, expected 7.33715
E0000 00:00:1743456223.452206 3613320 buffer_comparator.cc:156] Difference at 690: -nan, expected 6.05665
E0000 00:00:1743456223.452208 3613320 buffer_comparator.cc:156] Difference at 691: -nan, expected 7.16547
E0000 00:00:1743456223.452211 3613320 buffer_comparator.cc:156] Difference at 692: -nan, expected 8.27916
E0000 00:00:1743456223.452214 3613320 buffer_comparator.cc:156] Difference at 693: -nan, expected 5.80258
E0000 00:00:1743456223.452216 3613320 buffer_comparator.cc:156] Difference at 694: -nan, expected 6.06621
E0000 00:00:1743456223.452219 3613320 buffer_comparator.cc:156] Difference at 695: -nan, expected 7.00273
E0000 00:00:1743456223.452222 3613320 buffer_comparator.cc:156] Difference at 696: -nan, expected 7.92525
E0000 00:00:1743456223.452225 3613320 buffer_comparator.cc:156] Difference at 729: -nan, expected 7.66068
2025-03-31 21:23:43.452229: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.462325 3613320 buffer_comparator.cc:156] Difference at 16: -nan, expected 29.4863
E0000 00:00:1743456223.462337 3613320 buffer_comparator.cc:156] Difference at 17: -nan, expected 25.4275
E0000 00:00:1743456223.462340 3613320 buffer_comparator.cc:156] Difference at 18: -nan, expected 29.498
E0000 00:00:1743456223.462343 3613320 buffer_comparator.cc:156] Difference at 19: -nan, expected 24.9024
E0000 00:00:1743456223.462346 3613320 buffer_comparator.cc:156] Difference at 20: -nan, expected 31.8883
E0000 00:00:1743456223.462349 3613320 buffer_comparator.cc:156] Difference at 21: -nan, expected 30.5795
E0000 00:00:1743456223.462352 3613320 buffer_comparator.cc:156] Difference at 22: -nan, expected 26.1755
E0000 00:00:1743456223.462355 3613320 buffer_comparator.cc:156] Difference at 23: -nan, expected 30.0282
E0000 00:00:1743456223.462358 3613320 buffer_comparator.cc:156] Difference at 24: -nan, expected 25.7237
E0000 00:00:1743456223.462360 3613320 buffer_comparator.cc:156] Difference at 25: -nan, expected 25.7191
2025-03-31 21:23:43.462365: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.465304 3613320 buffer_comparator.cc:156] Difference at 16: -nan, expected 29.4863
E0000 00:00:1743456223.465315 3613320 buffer_comparator.cc:156] Difference at 17: -nan, expected 25.4275
E0000 00:00:1743456223.465319 3613320 buffer_comparator.cc:156] Difference at 18: -nan, expected 29.498
E0000 00:00:1743456223.465322 3613320 buffer_comparator.cc:156] Difference at 19: -nan, expected 24.9024
E0000 00:00:1743456223.465324 3613320 buffer_comparator.cc:156] Difference at 20: -nan, expected 31.8883
E0000 00:00:1743456223.465327 3613320 buffer_comparator.cc:156] Difference at 21: -nan, expected 30.5795
E0000 00:00:1743456223.465330 3613320 buffer_comparator.cc:156] Difference at 22: -nan, expected 26.1755
E0000 00:00:1743456223.465333 3613320 buffer_comparator.cc:156] Difference at 23: -nan, expected 30.0282
E0000 00:00:1743456223.465336 3613320 buffer_comparator.cc:156] Difference at 24: -nan, expected 25.7237
E0000 00:00:1743456223.465338 3613320 buffer_comparator.cc:156] Difference at 25: -nan, expected 25.7191
2025-03-31 21:23:43.465343: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.468281 3613320 buffer_comparator.cc:156] Difference at 512: -nan, expected 13.9275
E0000 00:00:1743456223.468292 3613320 buffer_comparator.cc:156] Difference at 513: -nan, expected 12.9447
E0000 00:00:1743456223.468296 3613320 buffer_comparator.cc:156] Difference at 514: -nan, expected 13.899
E0000 00:00:1743456223.468299 3613320 buffer_comparator.cc:156] Difference at 515: -nan, expected 14.1578
E0000 00:00:1743456223.468302 3613320 buffer_comparator.cc:156] Difference at 516: -nan, expected 15.4892
E0000 00:00:1743456223.468306 3613320 buffer_comparator.cc:156] Difference at 517: -nan, expected 16.545
E0000 00:00:1743456223.468309 3613320 buffer_comparator.cc:156] Difference at 518: -nan, expected 17.8581
E0000 00:00:1743456223.468312 3613320 buffer_comparator.cc:156] Difference at 519: -nan, expected 13.0536
E0000 00:00:1743456223.468315 3613320 buffer_comparator.cc:156] Difference at 520: -nan, expected 16.1329
E0000 00:00:1743456223.468317 3613320 buffer_comparator.cc:156] Difference at 521: -nan, expected 14.5245
2025-03-31 21:23:43.468322: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
E0000 00:00:1743456223.471262 3613320 buffer_comparator.cc:156] Difference at 528: -nan, expected 17.5032
E0000 00:00:1743456223.471273 3613320 buffer_comparator.cc:156] Difference at 529: -nan, expected 15.1785
E0000 00:00:1743456223.471277 3613320 buffer_comparator.cc:156] Difference at 530: -nan, expected 15.9473
E0000 00:00:1743456223.471280 3613320 buffer_comparator.cc:156] Difference at 531: -nan, expected 14.437
E0000 00:00:1743456223.471283 3613320 buffer_comparator.cc:156] Difference at 532: -nan, expected 17.9637
E0000 00:00:1743456223.471286 3613320 buffer_comparator.cc:156] Difference at 533: -nan, expected 17.3157
E0000 00:00:1743456223.471288 3613320 buffer_comparator.cc:156] Difference at 534: -nan, expected 15.7802
E0000 00:00:1743456223.471291 3613320 buffer_comparator.cc:156] Difference at 535: -nan, expected 17.6887
E0000 00:00:1743456223.471294 3613320 buffer_comparator.cc:156] Difference at 536: -nan, expected 15.1881
E0000 00:00:1743456223.471297 3613320 buffer_comparator.cc:156] Difference at 537: -nan, expected 14.4224
2025-03-31 21:23:43.471302: E external/xla/xla/service/gpu/autotuning/gemm_fusion_autotuner.cc:1137] Results do not match the reference. This is likely a bug/unexpected loss of precision.
Iteration: 0001 Loss: 2.08073235
Iteration: 0101 Loss: 0.142443746
Iteration: 0201 Loss: 0.00498719513
Iteration: 0301 Loss: 0.00116170431
Iteration: 0401 Loss: 0.000497820089
Iteration: 0501 Loss: 0.00027494994
Iteration: 0601 Loss: 0.000180605697
Iteration: 0701 Loss: 0.000201430521
Iteration: 0801 Loss: 0.000379697973
Iteration: 0901 Loss: 0.000106757798
Iteration: 1000 Loss: 0.000231371887Training with Optimization.jl
If you are coming from the SciML ecosystem and want to use Optimization.jl, please refer to the Optimization.jl Tutorial.
Additional Packages
LuxDL hosts various packages that provide additional functionality for Lux.jl. All packages mentioned in this documentation are available via the Julia General Registry.
You can install all those packages via import Pkg; Pkg.add(<package name>).
XLA (CPU/GPU/TPU) Support
Lux.jl supports XLA compilation for CPU, GPU, and TPU using Reactant.jl.
GPU Support
GPU Support for Lux.jl requires loading additional packages:
LuxCUDA.jlfor CUDA support.AMDGPU.jlfor AMDGPU support.Metal.jlfor Apple Metal support.oneAPI.jlfor oneAPI support.