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Run JuliaFormatter again
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@Saransh-cpp
Saransh-cpp committed Oct 6, 2022
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113 changes: 56 additions & 57 deletions docs/make.jl
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@@ -1,62 +1,61 @@
using Documenter, Flux, NNlib, Functors, MLUtils, BSON, Optimisers, OneHotArrays, Zygote,
ChainRulesCore
using Documenter,
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)
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 _ in 1:ts], "Vec"
return [randn(Float32, n, n) for _ = 1:ts], "Vec"
end
end

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76 changes: 39 additions & 37 deletions perf/vgg.jl
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Expand Up @@ -6,43 +6,45 @@ 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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78 changes: 63 additions & 15 deletions src/Flux.jl
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Expand Up @@ -15,37 +15,85 @@ 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, Embedding, 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!
export Chain,
Dense,
Embedding,
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
export Descent,
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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38 changes: 30 additions & 8 deletions src/cuda/cudnn.jl
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@@ -1,17 +1,39 @@
import NNlibCUDA: batchnorm, ∇batchnorm

function (BN::Flux.BatchNorm)(x::Union{CuArray{T, 2}, CuArray{T, 4}, CuArray{T, 5}},
cache = nothing) where {T <: Union{Float32, Float64}}
function (BN::Flux.BatchNorm)(
x::Union{CuArray{T,2},CuArray{T,4},CuArray{T,5}},
cache = nothing,
) where {T<:Union{Float32,Float64}}
@assert BN.affine "BatchNorm: only affine=true supported on gpu"
@assert BN.track_stats "BatchNorm: only track_stats=true supported on gpu"
@assert length(BN.β)==size(x, ndims(x) - 1) "BatchNorm: input has wrong number of channels"
return BN.λ.(batchnorm(BN.γ, BN.β, x, BN.μ, BN.σ², BN.momentum;
cache = cache, alpha = 1, beta = 0, eps = BN.ϵ,
training = Flux._isactive(BN)))
@assert length(BN.β) == size(x, ndims(x) - 1) "BatchNorm: input has wrong number of channels"
return BN.λ.(
batchnorm(
BN.γ,
BN.β,
x,
BN.μ,
BN.σ²,
BN.momentum;
cache = cache,
alpha = 1,
beta = 0,
eps = BN.ϵ,
training = Flux._isactive(BN),
)
)
end

function ChainRulesCore.rrule(::typeof(batchnorm), g, b, x, running_mean, running_var,
momentum; kw...)
function ChainRulesCore.rrule(
::typeof(batchnorm),
g,
b,
x,
running_mean,
running_var,
momentum;
kw...,
)
y = batchnorm(g, b, x, running_mean, running_var, momentum; kw...)
function batchnorm_pullback(Δ)
grad = ∇batchnorm(g, b, x, unthunk(Δ), running_mean, running_var, momentum; kw...)
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