Auto compile Lux models to reactant #665

avik-pal · 2024-05-27T03:11:02Z

Example Usage

This follows the same structure as SimpleChains. User demands a conversion and provides an input prototype.

using Reactant, Lux, Random

model = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax)

reactant_model = ToReactantAdaptor{true}(rand(Float32, 10, 3); force_compile_backward=true)(model)
ps, st = Lux.setup(Random.default_rng(), reactant_model)

x = randn(Float32, 10, 3)

reactant_model(x, ps, st)

Upstream Needs

Patches for Lux integration EnzymeAD/Reactant.jl#7
[WIP] Add a Lux example EnzymeAD/Reactant.jl#5 (for the backward pass)
(Nice to have) Define create_result for NamedTuple EnzymeAD/Reactant.jl#8 for extending the support to LuxCore.apply instead of LuxCore.apply

TODOs

ext/LuxReactantExt.jl

github-actions

Benchmark Results

Benchmark suite	Current: `8ce9707`	Previous: `60c595e`	Ratio
`Dense(2 => 2)/cpu/reverse/ReverseDiff (compiled)/(2, 128)`	`3669.375` ns	`3646.75` ns	`1.01`
`Dense(2 => 2)/cpu/reverse/Zygote/(2, 128)`	`7116.5` ns	`7285` ns	`0.98`
`Dense(2 => 2)/cpu/reverse/Tracker/(2, 128)`	`20819` ns	`21210` ns	`0.98`
`Dense(2 => 2)/cpu/reverse/ReverseDiff/(2, 128)`	`9519.8` ns	`9781.666666666666` ns	`0.97`
`Dense(2 => 2)/cpu/reverse/Flux/(2, 128)`	`8884.25` ns	`9087.2` ns	`0.98`
`Dense(2 => 2)/cpu/reverse/SimpleChains/(2, 128)`	`4494.625` ns	`4453.888888888889` ns	`1.01`
`Dense(2 => 2)/cpu/reverse/Enzyme/(2, 128)`	`1168.6785714285713` ns	`1176.2706766917292` ns	`0.99`
`Dense(2 => 2)/cpu/forward/NamedTuple/(2, 128)`	`1112.012658227848` ns	`1112.28025477707` ns	`1.00`
`Dense(2 => 2)/cpu/forward/ComponentArray/(2, 128)`	`1181.0820895522388` ns	`1189.374074074074` ns	`0.99`
`Dense(2 => 2)/cpu/forward/Flux/(2, 128)`	`1780.4406779661017` ns	`1814.3181818181818` ns	`0.98`
`Dense(2 => 2)/cpu/forward/SimpleChains/(2, 128)`	`179.55460992907803` ns	`179.93324061196105` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/ReverseDiff (compiled)/(20, 128)`	`17252` ns	`17212` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/Zygote/(20, 128)`	`17292` ns	`17463` ns	`0.99`
`Dense(20 => 20)/cpu/reverse/Tracker/(20, 128)`	`36588` ns	`36689` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/ReverseDiff/(20, 128)`	`27992` ns	`28147.5` ns	`0.99`
`Dense(20 => 20)/cpu/reverse/Flux/(20, 128)`	`19677` ns	`20058` ns	`0.98`
`Dense(20 => 20)/cpu/reverse/SimpleChains/(20, 128)`	`16982` ns	`16921` ns	`1.00`
`Dense(20 => 20)/cpu/reverse/Enzyme/(20, 128)`	`4298` ns	`4310.5` ns	`1.00`
`Dense(20 => 20)/cpu/forward/NamedTuple/(20, 128)`	`3867.25` ns	`3867.25` ns	`1`
`Dense(20 => 20)/cpu/forward/ComponentArray/(20, 128)`	`3952.375` ns	`3951.125` ns	`1.00`
`Dense(20 => 20)/cpu/forward/Flux/(20, 128)`	`4775.357142857143` ns	`4787.571428571428` ns	`1.00`
`Dense(20 => 20)/cpu/forward/SimpleChains/(20, 128)`	`1664.2` ns	`1659.1` ns	`1.00`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 3, 128)`	`39058487.5` ns	`38839150` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/reverse/Zygote/(64, 64, 3, 128)`	`58048876.5` ns	`57478179` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/reverse/Tracker/(64, 64, 3, 128)`	`69429268` ns	`68637336` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/reverse/ReverseDiff/(64, 64, 3, 128)`	`88231886` ns	`80248739.5` ns	`1.10`
`Conv((3, 3), 3 => 3)/cpu/reverse/Flux/(64, 64, 3, 128)`	`72608460` ns	`66510498` ns	`1.09`
`Conv((3, 3), 3 => 3)/cpu/reverse/SimpleChains/(64, 64, 3, 128)`	`12052157` ns	`11601127` ns	`1.04`
`Conv((3, 3), 3 => 3)/cpu/reverse/Enzyme/(64, 64, 3, 128)`	`8361274` ns	`8302158.5` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/NamedTuple/(64, 64, 3, 128)`	`7009826` ns	`6958814.5` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/ComponentArray/(64, 64, 3, 128)`	`6996013` ns	`6935871` ns	`1.01`
`Conv((3, 3), 3 => 3)/cpu/forward/Flux/(64, 64, 3, 128)`	`9927628.5` ns	`9886349` ns	`1.00`
`Conv((3, 3), 3 => 3)/cpu/forward/SimpleChains/(64, 64, 3, 128)`	`6387327.5` ns	`6377484` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 16)`	`714625608` ns	`711495815.5` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 64)`	`2809656510` ns	`2802293498` ns	`1.00`
`vgg16/cpu/reverse/Zygote/(32, 32, 3, 2)`	`143359748` ns	`158450926` ns	`0.90`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 16)`	`742274565` ns	`745197995` ns	`1.00`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 64)`	`2535181882` ns	`2536517155` ns	`1.00`
`vgg16/cpu/reverse/Tracker/(32, 32, 3, 2)`	`194702054` ns	`186814591` ns	`1.04`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 16)`	`646623448.5` ns	`698620045` ns	`0.93`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 64)`	`2730166614` ns	`2703329300` ns	`1.01`
`vgg16/cpu/reverse/Flux/(32, 32, 3, 2)`	`119575299` ns	`122294200.5` ns	`0.98`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 16)`	`194591286` ns	`172044480` ns	`1.13`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 64)`	`648527666` ns	`643441503` ns	`1.01`
`vgg16/cpu/forward/NamedTuple/(32, 32, 3, 2)`	`45366635` ns	`45114156` ns	`1.01`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 16)`	`164210512` ns	`163454975.5` ns	`1.00`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 64)`	`636442834` ns	`628139701` ns	`1.01`
`vgg16/cpu/forward/ComponentArray/(32, 32, 3, 2)`	`29909710.5` ns	`29335904` ns	`1.02`
`vgg16/cpu/forward/Flux/(32, 32, 3, 16)`	`186029419` ns	`207955667.5` ns	`0.89`
`vgg16/cpu/forward/Flux/(32, 32, 3, 64)`	`711562847` ns	`722173872` ns	`0.99`
`vgg16/cpu/forward/Flux/(32, 32, 3, 2)`	`35379449` ns	`37423155` ns	`0.95`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 64, 128)`	`1232480179.5` ns	`1242027523.5` ns	`0.99`
`Conv((3, 3), 64 => 64)/cpu/reverse/Zygote/(64, 64, 64, 128)`	`1864117910.5` ns	`1847309072` ns	`1.01`
`Conv((3, 3), 64 => 64)/cpu/reverse/Tracker/(64, 64, 64, 128)`	`2009631582.5` ns	`1988297584` ns	`1.01`
`Conv((3, 3), 64 => 64)/cpu/reverse/ReverseDiff/(64, 64, 64, 128)`	`2333653016.5` ns	`2337208631` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/reverse/Flux/(64, 64, 64, 128)`	`1783708622.5` ns	`1825164998` ns	`0.98`
`Conv((3, 3), 64 => 64)/cpu/reverse/Enzyme/(64, 64, 64, 128)`	`344253318` ns	`347875405.5` ns	`0.99`
`Conv((3, 3), 64 => 64)/cpu/forward/NamedTuple/(64, 64, 64, 128)`	`316840031.5` ns	`318366365` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/forward/ComponentArray/(64, 64, 64, 128)`	`318335263.5` ns	`319738018` ns	`1.00`
`Conv((3, 3), 64 => 64)/cpu/forward/Flux/(64, 64, 64, 128)`	`344631084` ns	`452781616` ns	`0.76`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 1, 128)`	`11976289` ns	`11803413` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Zygote/(64, 64, 1, 128)`	`18037586` ns	`17882962` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Tracker/(64, 64, 1, 128)`	`19230996` ns	`19018033` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/ReverseDiff/(64, 64, 1, 128)`	`23857907` ns	`23755630` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/reverse/Flux/(64, 64, 1, 128)`	`18084016` ns	`17832966.5` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/SimpleChains/(64, 64, 1, 128)`	`1155306.5` ns	`1148767` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/reverse/Enzyme/(64, 64, 1, 128)`	`2520956` ns	`2512938` ns	`1.00`
`Conv((3, 3), 1 => 1)/cpu/forward/NamedTuple/(64, 64, 1, 128)`	`2047653` ns	`2035570` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/ComponentArray/(64, 64, 1, 128)`	`2054475` ns	`2023578.5` ns	`1.02`
`Conv((3, 3), 1 => 1)/cpu/forward/Flux/(64, 64, 1, 128)`	`2070591` ns	`2055760` ns	`1.01`
`Conv((3, 3), 1 => 1)/cpu/forward/SimpleChains/(64, 64, 1, 128)`	`201787` ns	`195727.5` ns	`1.03`
`Dense(200 => 200)/cpu/reverse/ReverseDiff (compiled)/(200, 128)`	`289931.5` ns	`288322` ns	`1.01`
`Dense(200 => 200)/cpu/reverse/Zygote/(200, 128)`	`264083` ns	`262603` ns	`1.01`
`Dense(200 => 200)/cpu/reverse/Tracker/(200, 128)`	`365111` ns	`354936.5` ns	`1.03`
`Dense(200 => 200)/cpu/reverse/ReverseDiff/(200, 128)`	`406990` ns	`400938` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/Flux/(200, 128)`	`272469` ns	`270257` ns	`1.01`
`Dense(200 => 200)/cpu/reverse/SimpleChains/(200, 128)`	`405988` ns	`397421` ns	`1.02`
`Dense(200 => 200)/cpu/reverse/Enzyme/(200, 128)`	`83225.5` ns	`83306` ns	`1.00`
`Dense(200 => 200)/cpu/forward/NamedTuple/(200, 128)`	`80735.5` ns	`80271` ns	`1.01`
`Dense(200 => 200)/cpu/forward/ComponentArray/(200, 128)`	`81221` ns	`80581` ns	`1.01`
`Dense(200 => 200)/cpu/forward/Flux/(200, 128)`	`86432` ns	`85480` ns	`1.01`
`Dense(200 => 200)/cpu/forward/SimpleChains/(200, 128)`	`104584` ns	`104617` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff (compiled)/(64, 64, 16, 128)`	`193678547` ns	`187932820.5` ns	`1.03`
`Conv((3, 3), 16 => 16)/cpu/reverse/Zygote/(64, 64, 16, 128)`	`322350064` ns	`321827872.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/Tracker/(64, 64, 16, 128)`	`390601991` ns	`393773632.5` ns	`0.99`
`Conv((3, 3), 16 => 16)/cpu/reverse/ReverseDiff/(64, 64, 16, 128)`	`455632147` ns	`454117809` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/reverse/Flux/(64, 64, 16, 128)`	`342018490` ns	`366877761` ns	`0.93`
`Conv((3, 3), 16 => 16)/cpu/reverse/SimpleChains/(64, 64, 16, 128)`	`324867768.5` ns	`309426428` ns	`1.05`
`Conv((3, 3), 16 => 16)/cpu/reverse/Enzyme/(64, 64, 16, 128)`	`51185900` ns	`51303991` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/NamedTuple/(64, 64, 16, 128)`	`43468243` ns	`43675671.5` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/ComponentArray/(64, 64, 16, 128)`	`43323005` ns	`43447693` ns	`1.00`
`Conv((3, 3), 16 => 16)/cpu/forward/Flux/(64, 64, 16, 128)`	`59553933.5` ns	`49289683` ns	`1.21`
`Conv((3, 3), 16 => 16)/cpu/forward/SimpleChains/(64, 64, 16, 128)`	`27964827` ns	`28489085` ns	`0.98`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff (compiled)/(2000, 128)`	`18639172.5` ns	`18511523` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Zygote/(2000, 128)`	`19409965` ns	`19373919.5` ns	`1.00`
`Dense(2000 => 2000)/cpu/reverse/Tracker/(2000, 128)`	`23095282.5` ns	`22860858` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/ReverseDiff/(2000, 128)`	`24104485.5` ns	`23821494.5` ns	`1.01`
`Dense(2000 => 2000)/cpu/reverse/Flux/(2000, 128)`	`19543279.5` ns	`19452776.5` ns	`1.00`
`Dense(2000 => 2000)/cpu/reverse/Enzyme/(2000, 128)`	`6497967` ns	`6471809.5` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/NamedTuple/(2000, 128)`	`6517959` ns	`6467840.5` ns	`1.01`
`Dense(2000 => 2000)/cpu/forward/ComponentArray/(2000, 128)`	`6472398.5` ns	`6458192` ns	`1.00`
`Dense(2000 => 2000)/cpu/forward/Flux/(2000, 128)`	`6506948` ns	`6475071.5` ns	`1.00`

This comment was automatically generated by workflow using github-action-benchmark.

codecov · 2024-05-27T04:50:19Z

Codecov Report

Attention: Patch coverage is 0.62500% with 159 lines in your changes missing coverage. Please review.

Project coverage is 80.26%. Comparing base (60c595e) to head (8ce9707).
Report is 579 commits behind head on main.

Files with missing lines	Patch %	Lines
ext/LuxReactantExt/layer.jl	0.00%	101 Missing ⚠️
src/transform/reactant.jl	0.00%	15 Missing ⚠️
ext/LuxReactantExt/utils.jl	0.00%	13 Missing ⚠️
ext/LuxReactantExt/train.jl	0.00%	12 Missing ⚠️
src/layers/extension.jl	0.00%	10 Missing ⚠️
src/contrib/training.jl	0.00%	6 Missing ⚠️
src/utils.jl	0.00%	2 Missing ⚠️

Additional details and impacted files

@@            Coverage Diff             @@
##             main     #665      +/-   ##
==========================================
- Coverage   87.11%   80.26%   -6.85%     
==========================================
  Files          50       55       +5     
  Lines        2515     2671     +156     
==========================================
- Hits         2191     2144      -47     
- Misses        324      527     +203

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wsmoses · 2024-05-27T22:18:54Z

@avik-pal can this be done in a way that reactant compiles the whole update, not just the gradient as separate from the inference pass. Specifically, I expect there to be a substantial perf improvement from doing so -- including the model update actually fully occuring in place.

E.g. the function reactant compiles being something like

function update(model, x, learning_rate)
   grads = gradient(model, x)
   update!(model, grads, learning_rate[1])
   nothing
end

avik-pal · 2024-05-27T22:27:25Z

Not with the layers API. Currently, if we can accelerate just the neural network part, I would consider it a good win. Also, having it like this makes it possible to use regular Julia ops for cases where we can't compile to Reactant, for example, the ODE solves happen in Julia and the neural network is via XLA.

We can add AutoReactant for the training API, where we can compile the entire pass in the first call, and reuse it in subsequent calls (similar to what we do for Enzyme).

avik-pal · 2024-05-27T23:32:56Z

Okay things are working mostly now, we just need a copyto! for TracedRArray

ext/LuxReactantExt.jl

avik-pal · 2024-05-27T23:49:12Z

using Reactant, Lux, Random, ComponentArrays

model = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax)

reactant_model = ToReactantAdaptor{true}(rand(Float32, 10, 3))(model)

Gives me a Pipeline Failed with error: size of operand dimension 0 (3) is not equal to 1 or size of result dimension 0 (2). But the gradient seems to work fine for Enzyme with the XLA compilation.

wsmoses · 2024-05-29T22:52:41Z

@avik-pal you should oopen an issue with the pipeline error on Reactant, once the prereqs are merged

wsmoses · 2024-05-29T22:53:51Z

ext/LuxReactantExt.jl

+@inline __try_similar_structure(x, y) = fmap(__try_similar_structure, x, y)
+
+# Reactant doesn't handle mixed eltypes that well, so we will first try to compile it as
+# a usual julia function. However, if that fails, we will type cast and try to recompile.


we should be able to fix mixed eltypes if you have an mwe by chance

using Reactant, Lux, Random model = Chain(Dense(10, 5, tanh), Dense(5, 2), softmax) reactant_model = ToReactantAdaptor{true}(rand(10, 3))(model)

wsmoses · 2024-05-29T22:56:27Z

ext/LuxReactantExt.jl

+    ps = __try_similar_structure(Lux.__named_tuple(ps), l.concrete_ps)
+    ps = l.adaptor(ps)
+    l.eltype_adaptor !== nothing && (ps = adapt(l.eltype_adaptor, ps))
+    ps = __make_concrete_array(ps)


can this be set up where the ps are already reactant arrays so we don't need to call __make_concrete_array

[hope would be avoiding data shuffling, especially cpu<->gpu of the whole model]

This should not be hit in most cases. I wasn't able to compile a ComponentArrays based version yet (needs a make_tracer overload), so it is a temporary solution.

The correct use of this should hit L163

src/contrib/training.jl

avik-pal · 2024-05-30T02:59:05Z

@wsmoses seems like an incorrect generation?

Module:
module attributes {transform.with_named_sequence} {
  func.func @main(%arg0: tensor<1x5xf32>, %arg1: tensor<1x5xf32>, %arg2: tensor<3x10xf32>, %arg3: tensor<10x5xf32>, %arg4: tensor<10x5xf32>) -> (tensor<1x5xf32>, tensor<f32>, tensor<10x5xf32>) {
    %cst = stablehlo.constant dense<1.000000e+00> : tensor<5x3xf32>
    %cst_0 = stablehlo.constant dense<0.000000e+00> : tensor<f32>
    %cst_1 = arith.constant dense<0.000000e+00> : tensor<5xf32>
    %0 = stablehlo.reshape %arg1 : (tensor<1x5xf32>) -> tensor<5xf32>
    %1 = stablehlo.dot_general %arg4, %arg2, contracting_dims = [0] x [1], precision = [DEFAULT, DEFAULT] : (tensor<10x5xf32>, tensor<3x10xf32>) -> tensor<5x3xf32>
    %2 = stablehlo.broadcast_in_dim %0, dims = [0] : (tensor<5xf32>) -> tensor<5x3xf32>
    %3 = stablehlo.add %1, %2 : tensor<5x3xf32>
    %4 = stablehlo.tanh %3 : tensor<5x3xf32>
    %5 = stablehlo.reduce(%4 init: %cst_0) applies stablehlo.add across dimensions = [0, 1] : (tensor<5x3xf32>, tensor<f32>) -> tensor<f32>
    %6 = stablehlo.multiply %4, %4 : tensor<5x3xf32>
    %7 = stablehlo.subtract %cst, %6 : tensor<5x3xf32>
    %8 = stablehlo.reduce(%7 init: %cst_1) across dimensions = [1] : (tensor<5x3xf32>, tensor<5xf32>) -> tensor<5xf32>
     reducer(%arg5: tensor<5xf32>, %arg6: tensor<5xf32>)  {
      %13 = stablehlo.add %arg5, %arg6 : tensor<5xf32>
      stablehlo.return %13 : tensor<5xf32>
    }
    %9 = stablehlo.dot_general %arg2, %7, contracting_dims = [0] x [1], precision = [DEFAULT, DEFAULT] : (tensor<3x10xf32>, tensor<5x3xf32>) -> tensor<10x5xf32>
    %10 = stablehlo.add %arg3, %9 : tensor<10x5xf32>
    %11 = stablehlo.reshape %8 : (tensor<5xf32>) -> tensor<1x5xf32>
    %12 = stablehlo.add %arg0, %11 : tensor<1x5xf32>
    return %12, %5, %10 : tensor<1x5xf32>, tensor<f32>, tensor<10x5xf32>
  }
}
terminate called after throwing an instance of 'xla::XlaRuntimeError'
  what():  UNKNOWN: <unknown>:0: error: Reduction function must return a scalar or tuple of scalars but returns shape: f32[5]: 
<unknown>:0: note: see current operation: "func.return"(%15, %8, %13) : (tensor<1x5xf32>, tensor<f32>, tensor<10x5xf32>) -> ()

ext/LuxReactantExt/train.jl

wsmoses · 2024-05-31T23:10:16Z

@avik-pal the lux fixes (and named tuple) just landed and were released.

I'll give the reduction error a go shortly, but at minimum we can see what works (and perhaps mark that as expected broken to start with)

avik-pal · 2024-06-01T00:14:35Z

Currently the julia session crashes because of the broken reverse pass, so can't mark it broken

src/transform/reactant.jl

avik-pal · 2024-06-01T06:33:34Z

Can we have a no copy transfer between Julia AbstractArrays and Reactant/XLA Arrays? This makes life simpler to support wrapper types like ComponentArrays.

Also we can keep the parameters as regular Julia arrays which works more nicely with the current optimisers and such

wsmoses · 2024-06-01T06:43:17Z

Not easily as we need to own the data. Similarly the model and ideally the inputs are always kept rarrays here. Also for better performance the optimizers themselves are compiled by reactant

…

On Sat, Jun 1, 2024 at 8:33 AM Avik Pal ***@***.***> wrote: Can we have a no copy transfer between Julia AbstractArrays and Reactant/XLA Arrays? This makes life simpler to support wrapper types like ComponentArrays. Also we can keep the parameters as regular Julia arrays which works more nicely with the current optimisers and such — Reply to this email directly, view it on GitHub <#665 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAJTUXFOCHIUSBR7OQWBYO3ZFFTNJAVCNFSM6AAAAABIKL3C3GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNBTGMZDIMJQGM> . You are receiving this because you were mentioned.Message ID: <LuxDL/Lux. ***@***.***>

avik-pal · 2024-06-01T15:46:31Z

Also for better performance the optimizers themselves are compiled by reactant

Right but I don't think we would be able to compile NeuralODE style models yet right? So having an eager version that can perform operations directly on RArrays seems like a good tradeoff to run part of the model is regular Julia.

I might pull out the AutoReactant code (compiling the training iteration) into a separate PR because that would be easier to merge.

wsmoses · 2024-06-01T15:48:16Z

There’s no reason why we couldn’t in theory, but I don’t think we do right now. Worth testing and opening an issue so we know what to work on though

…

On Sat, Jun 1, 2024 at 5:46 PM Avik Pal ***@***.***> wrote: Also for better performance the optimizers themselves are compiled by reactant Right but I don't think we would be able to compile NeuralODE style models yet right? So having an eager version that can perform operations directly on RArrays seems like a good tradeoff to run part of the model is regular Julia. I might pull out the AutoReactant code (compiling the training iteration) into a separate PR because that would be easier to merge. — Reply to this email directly, view it on GitHub <#665 (comment)>, or unsubscribe <https://github.com/notifications/unsubscribe-auth/AAJTUXBAIIEC7N2TEXAKBB3ZFHUGXAVCNFSM6AAAAABIKL3C3GVHI2DSMVQWIX3LMV43OSLTON2WKQ3PNVWWK3TUHMZDCNBTGQ4TGMJSGM> . You are receiving this because you were mentioned.Message ID: <LuxDL/Lux. ***@***.***>

wsmoses · 2024-06-18T20:07:03Z

@avik-pal fix has landed, can we retry this?

avik-pal · 2024-06-18T23:50:00Z

This one is too broadly scoped, so I will hold it off.

First, I want to finish #673, which compiles the entire training loop and doesn't need to worry about users doing unwanted things to the parameters.

avik-pal · 2024-11-01T19:51:19Z

reworking the partial model compilation logic in #999.

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Patches for Lux integration EnzymeAD/Reactant.jl#7

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Pipeline Error Reactant + Enzyme EnzymeAD/Reactant.jl#14

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avik-pal added 9 commits May 31, 2024 20:42

Special handling for mixed eltypes in Reactant

a7bf7a6

Add checks to validate matching structure

7ea236f

Try converting common parameter types to the compiled type

d186bf6

Accidentally used Boxed value

c632418

Implement a working VJP function

f5a3e58

Move things around

55d4767

Training step function

db3949e

Start impl for Reactant training loop

7ec09d7

More comprehensive compilation options

916e685

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Allow skipping all compilation

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Compile training loop with Reactant #673

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Meta Issue for Reactant Compilation of Lux Models #733

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