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Oops, forgot .JuliaFormatter.toml this time
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@Saransh-cpp
Saransh-cpp committed Oct 6, 2022
1 parent 1d40095 commit 5ea7255
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9 changes: 9 additions & 0 deletions .JuliaFormatter.toml
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@@ -0,0 +1,9 @@
style = "sciml"
whitespace_in_kwargs = true
format_docstrings = true
always_for_in = true
join_lines_based_on_source = true
separate_kwargs_with_semicolon = true
always_use_return = true
margin = 92
indent = 4
108 changes: 54 additions & 54 deletions docs/make.jl
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@@ -1,61 +1,61 @@
using Documenter,
Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore
Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote, ChainRulesCore

DocMeta.setdocmeta!(Flux, :DocTestSetup, :(using Flux); recursive = true)

makedocs(;
modules = [
Flux,
NNlib,
Functors,
MLUtils,
BSON,
Optimisers,
OneHotArrays,
Zygote,
ChainRulesCore,
Base,
],
doctest = false,
sitename = "Flux",
# strict = [:cross_references,],
pages = [
"Home" => "index.md",
"Building Models" => [
"Overview" => "models/overview.md",
"Basics" => "models/basics.md",
"Recurrence" => "models/recurrence.md",
"Layer Reference" => "models/layers.md",
"Loss Functions" => "models/losses.md",
"Regularisation" => "models/regularisation.md",
"Custom Layers" => "models/advanced.md",
"NNlib.jl" => "models/nnlib.md",
"Activation Functions" => "models/activation.md",
],
"Handling Data" =>
["MLUtils.jl" => "data/mlutils.md", "OneHotArrays.jl" => "data/onehot.md"],
"Training Models" => [
"Optimisers" => "training/optimisers.md",
"Training" => "training/training.md",
"Callback Helpers" => "training/callbacks.md",
"Zygote.jl" => "training/zygote.md",
],
"GPU Support" => "gpu.md",
"Model Tools" => [
"Saving & Loading" => "saving.md",
"Shape Inference" => "outputsize.md",
"Weight Initialisation" => "utilities.md",
"Functors.jl" => "models/functors.md",
],
"Performance Tips" => "performance.md",
"Flux's Ecosystem" => "ecosystem.md",
],
format = Documenter.HTML(;
sidebar_sitename = false,
analytics = "UA-36890222-9",
assets = ["assets/flux.css"],
prettyurls = get(ENV, "CI", nothing) == "true",
),
)
modules = [
Flux,
NNlib,
Functors,
MLUtils,
BSON,
Optimisers,
OneHotArrays,
Zygote,
ChainRulesCore,
Base,
],
doctest = false,
sitename = "Flux",
# strict = [:cross_references,],
pages = [
"Home" => "index.md",
"Building Models" => [
"Overview" => "models/overview.md",
"Basics" => "models/basics.md",
"Recurrence" => "models/recurrence.md",
"Layer Reference" => "models/layers.md",
"Loss Functions" => "models/losses.md",
"Regularisation" => "models/regularisation.md",
"Custom Layers" => "models/advanced.md",
"NNlib.jl" => "models/nnlib.md",
"Activation Functions" => "models/activation.md",
],
"Handling Data" => [
"MLUtils.jl" => "data/mlutils.md",
"OneHotArrays.jl" => "data/onehot.md",
],
"Training Models" => [
"Optimisers" => "training/optimisers.md",
"Training" => "training/training.md",
"Callback Helpers" => "training/callbacks.md",
"Zygote.jl" => "training/zygote.md",
],
"GPU Support" => "gpu.md",
"Model Tools" => [
"Saving & Loading" => "saving.md",
"Shape Inference" => "outputsize.md",
"Weight Initialisation" => "utilities.md",
"Functors.jl" => "models/functors.md",
],
"Performance Tips" => "performance.md",
"Flux's Ecosystem" => "ecosystem.md",
],
format = Documenter.HTML(;
sidebar_sitename = false,
analytics = "UA-36890222-9",
assets = ["assets/flux.css"],
prettyurls = get(ENV, "CI", nothing) == "true"))

deploydocs(; repo = "github.com/FluxML/Flux.jl.git", target = "build", push_preview = true)
6 changes: 3 additions & 3 deletions perf/bench_utils.jl
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Expand Up @@ -24,19 +24,19 @@ function run_benchmark(model, x; cuda = true)
fw(model, x)
GC.gc()
CUDA.reclaim() #warmup
@btime CUDA.@sync(fw($model, $x)) teardown = (GC.gc(); CUDA.reclaim())
@btime CUDA.@sync(fw($model, $x)) teardown=(GC.gc(); CUDA.reclaim())

println(" backward")
bw(back)
GC.gc()
CUDA.reclaim() #warmup
@btime CUDA.@sync(bw($back)) teardown = (GC.gc(); CUDA.reclaim())
@btime CUDA.@sync(bw($back)) teardown=(GC.gc(); CUDA.reclaim())

println(" forw and back")
fwbw(model, ps, x)
GC.gc()
CUDA.reclaim() #warmup
@btime CUDA.@sync(fwbw($model, $ps, $x)) teardown = (GC.gc(); CUDA.reclaim())
@btime CUDA.@sync(fwbw($model, $ps, $x)) teardown=(GC.gc(); CUDA.reclaim())
else
println(" forward")
fw(model, x) #warmup
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2 changes: 1 addition & 1 deletion perf/recurrent.jl
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Expand Up @@ -51,7 +51,7 @@ end

for rnn_type in [Flux.RNN, Flux.GRU, Flux.LSTM]
rnn_benchmark_sweep(rnn_type) do n, ts
return [randn(Float32, n, n) for _ = 1:ts], "Vec"
return [randn(Float32, n, n) for _ in 1:ts], "Vec"
end
end

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76 changes: 37 additions & 39 deletions perf/vgg.jl
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Expand Up @@ -6,45 +6,43 @@ using CUDA
using Zygote: pullback

function vgg16()
return Chain(
Conv((3, 3), 3 => 64, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(64),
Conv((3, 3), 64 => 64, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(64),
MaxPool((2, 2)),
Conv((3, 3), 64 => 128, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(128),
Conv((3, 3), 128 => 128, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(128),
MaxPool((2, 2)),
Conv((3, 3), 128 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
Conv((3, 3), 256 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
Conv((3, 3), 256 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
MaxPool((2, 2)),
Conv((3, 3), 256 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
MaxPool((2, 2)),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
MaxPool((2, 2)),
flatten,
Dense(512, 4096, relu),
Dropout(0.5),
Dense(4096, 4096, relu),
Dropout(0.5),
Dense(4096, 10),
)
return Chain(Conv((3, 3), 3 => 64, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(64),
Conv((3, 3), 64 => 64, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(64),
MaxPool((2, 2)),
Conv((3, 3), 64 => 128, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(128),
Conv((3, 3), 128 => 128, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(128),
MaxPool((2, 2)),
Conv((3, 3), 128 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
Conv((3, 3), 256 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
Conv((3, 3), 256 => 256, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(256),
MaxPool((2, 2)),
Conv((3, 3), 256 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
MaxPool((2, 2)),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
Conv((3, 3), 512 => 512, relu; pad = (1, 1), stride = (1, 1)),
BatchNorm(512),
MaxPool((2, 2)),
flatten,
Dense(512, 4096, relu),
Dropout(0.5),
Dense(4096, 4096, relu),
Dropout(0.5),
Dense(4096, 10))
end

let model = vgg16(), x = rand(Float32, 32, 32, 3, 64)
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116 changes: 56 additions & 60 deletions src/Flux.jl
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Expand Up @@ -15,84 +15,80 @@ export gradient

# Pirate error to catch a common mistake. (Internal function `base` because overloading `update!` is more likely to give ambiguities.)
function Optimisers.base(dx::Zygote.Grads)
return error(
"Optimisers.jl cannot be used with Zygote.jl's implicit gradients, `Params` & `Grads`",
)
return error("Optimisers.jl cannot be used with Zygote.jl's implicit gradients, `Params` & `Grads`")
end

export Chain,
Dense,
Maxout,
SkipConnection,
Parallel,
PairwiseFusion,
RNN,
LSTM,
GRU,
GRUv3,
SamePad,
Conv,
CrossCor,
ConvTranspose,
DepthwiseConv,
AdaptiveMaxPool,
AdaptiveMeanPool,
GlobalMaxPool,
GlobalMeanPool,
MaxPool,
MeanPool,
Dropout,
AlphaDropout,
LayerNorm,
BatchNorm,
InstanceNorm,
GroupNorm,
Upsample,
PixelShuffle,
fmap,
cpu,
gpu,
f32,
f64,
testmode!,
trainmode!
Dense,
Maxout,
SkipConnection,
Parallel,
PairwiseFusion,
RNN,
LSTM,
GRU,
GRUv3,
SamePad,
Conv,
CrossCor,
ConvTranspose,
DepthwiseConv,
AdaptiveMaxPool,
AdaptiveMeanPool,
GlobalMaxPool,
GlobalMeanPool,
MaxPool,
MeanPool,
Dropout,
AlphaDropout,
LayerNorm,
BatchNorm,
InstanceNorm,
GroupNorm,
Upsample,
PixelShuffle,
fmap,
cpu,
gpu,
f32,
f64,
testmode!,
trainmode!

include("optimise/Optimise.jl")
using .Optimise
using .Optimise: @epochs
using .Optimise: skip
export Descent,
Adam,
Momentum,
Nesterov,
RMSProp,
AdaGrad,
AdaMax,
AdaDelta,
AMSGrad,
NAdam,
OAdam,
AdamW,
RAdam,
AdaBelief,
InvDecay,
ExpDecay,
WeightDecay,
ClipValue,
ClipNorm
Adam,
Momentum,
Nesterov,
RMSProp,
AdaGrad,
AdaMax,
AdaDelta,
AMSGrad,
NAdam,
OAdam,
AdamW,
RAdam,
AdaBelief,
InvDecay,
ExpDecay,
WeightDecay,
ClipValue,
ClipNorm

using CUDA
const use_cuda = Ref{Union{Nothing,Bool}}(nothing)
const use_cuda = Ref{Union{Nothing, Bool}}(nothing)

using Adapt, Functors, OneHotArrays
include("utils.jl")
include("functor.jl")

# Pirate error to catch a common mistake.
function Functors.functor(::Type{<:MLUtils.DataLoader}, x)
return error(
"`DataLoader` does not support Functors.jl, thus functions like `Flux.gpu` will not act on its contents.",
)
return error("`DataLoader` does not support Functors.jl, thus functions like `Flux.gpu` will not act on its contents.")
end

include("layers/stateless.jl")
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