initial sketch
This commit is contained in:
Mike J Innes
Dhairya Gandhi
parent
53be49b102
commit
a2d2d068aa
@ -19,8 +19,8 @@ export Tracker, TrackedArray, TrackedVector, TrackedMatrix, param
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include("optimise/Optimise.jl")
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using .Optimise
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using .Optimise: @epochs
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export SGD, ADAM, ADAMW, AdaMax, Momentum, Nesterov,
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RMSProp, ADAGrad, ADADelta, AMSGrad, NADAM
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export SGD, Descent, ADAM, AdaMax, Momentum, Nesterov,
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RMSProp, ADAGrad, ADADelta, AMSGrad
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include("utils.jl")
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include("onehot.jl")
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@ -1,23 +1,9 @@
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module Optimise
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export train!,
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SGD, ADAM, ADAMW, AdaMax, Momentum, Nesterov,
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RMSProp, ADAGrad, ADADelta, AMSGrad, NADAM, stop, StopException
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struct Param{T}
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x::T
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Δ::T
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end
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Param(x::AbstractArray) = Param(x, zero(x))
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SGD, Descent, ADAM, AdaMax, Momentum, Nesterov, RMSProp, ADAGrad, ADADelta, AMSGrad
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include("optimisers.jl")
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include("interface.jl")
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include("train.jl")
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using Flux.Tracker: TrackedArray
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Param(x::TrackedArray) = Param(x.data, x.grad)
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# Base.convert(::Type{Param}, x::TrackedArray) = Param(x.data, x.grad)
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end
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end
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@ -1,110 +0,0 @@
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call(f, xs...) = f(xs...)
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# note for optimisers: set to zero
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# p.Δ at the end of the weights update
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function optimiser(ps, fs...)
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ps = [Param(p) for p in ps]
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fs = map(ps) do p
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os = map(f -> f(p), fs)
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() -> foreach(call, os)
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end
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() -> foreach(call, fs)
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end
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"""
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SGD(params, η = 0.1; decay = 0)
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Classic gradient descent optimiser with learning rate `η`.
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For each parameter `p` and its gradient `δp`, this runs `p -= η*δp`.
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Supports inverse decaying learning rate if the `decay` argument is provided.
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"""
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SGD(ps, η = 0.1; decay = 0) =
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optimiser(ps, p -> invdecay(p, decay), p -> descent(p,η))
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"""
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Momentum(params, η = 0.01; ρ = 0.9, decay = 0)
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SGD with learning rate `η`, momentum `ρ` and optional learning rate inverse decay.
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"""
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Momentum(ps, η = 0.01; ρ = 0.9, decay = 0) =
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optimiser(ps, p->invdecay(p,decay), p->momentum(p, ρ, η), p->descent(p,1))
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"""
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Nesterov(params, η = 0.01; ρ = 0.9, decay = 0)
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SGD with learning rate `η`, Nesterov momentum `ρ` and optional learning rate inverse decay.
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"""
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Nesterov(ps, η = 0.01; ρ = 0.9, decay = 0) =
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optimiser(ps, p->invdecay(p,decay), p->nesterov(p, ρ, η), p->descent(p,1))
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"""
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RMSProp(params, η = 0.001; ρ = 0.9, ϵ = 1e-8, decay = 0)
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[RMSProp](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
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optimiser. Parameters other than learning rate don't need tuning. Often a good
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choice for recurrent networks.
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"""
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RMSProp(ps, η = 0.001; ρ = 0.9, ϵ = 1e-8, decay = 0) =
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optimiser(ps, p->rmsprop(p; η=η, ρ=ρ, ϵ=ϵ), p->invdecay(p,decay), p->descent(p,1))
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"""
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ADAM(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
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"""
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ADAM(ps, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0) =
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optimiser(ps, p->adam(p; η=η, β1=β1, β2=β2, ϵ=ϵ), p->invdecay(p,decay), p->descent(p,1))
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"""
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ADAMW((params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[ADAMW](https://arxiv.org/abs/1711.05101) fixing weight decay regularization in Adam.
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"""
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ADAMW(ps, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0) =
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optimiser(ps, p->adam(p; η=η, β1=β1, β2=β2, ϵ=ϵ), p->descentweightdecay(p,1,decay))
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"""
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AdaMax(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[AdaMax](https://arxiv.org/abs/1412.6980v9) optimiser. Variant of ADAM based on
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the ∞-norm.
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"""
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AdaMax(ps, η = 0.002; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0) =
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optimiser(ps, p->adamax(p; η=η, β1=β1, β2=β2, ϵ=ϵ), p->invdecay(p,decay), p->descent(p,1))
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"""
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ADAGrad(params, η = 0.01; ϵ = 1e-8, decay = 0)
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[ADAGrad](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) optimiser.
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Parameters don't need tuning.
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"""
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ADAGrad(ps, η = 0.01; ϵ = 1e-8, decay = 0) =
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optimiser(ps, p->adagrad(p; η=η, ϵ=ϵ), p->invdecay(p,decay), p->descent(p,1))
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"""
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ADADelta(params; ρ = 0.9, ϵ = 1e-8, decay = 0)
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[ADADelta](http://arxiv.org/abs/1212.5701) optimiser. Parameters don't need
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tuning.
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"""
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ADADelta(ps; ρ = 0.9, ϵ = 1e-8, decay = 0) =
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optimiser(ps, p->adadelta(p; ρ=ρ, ϵ=ϵ), p->descent(p,1))
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"""
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AMSGrad(params; η = 0.001, β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) optimiser. Parameters don't need
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tuning.
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"""
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AMSGrad(ps, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0) =
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optimiser(ps, p -> amsgrad(p; η = η, β1 = β1, β2 = β2, ϵ = ϵ), p -> invdecay(p, decay), p -> descent(p, 1))
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"""
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NADAM(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[NADAM](https://openreview.net/pdf?id=OM0jvwB8jIp57ZJjtNEZ) optimiser. Parameters other
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than learning rate don't need tuning.
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"""
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NADAM(ps, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0) =
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optimiser(ps, p->nadam(p; η=η, β1=β1, β2=β2, ϵ=ϵ), p->invdecay(p,decay), p->descent(p,1))
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@ -1,130 +1,139 @@
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function descent(p::Param, η::Real)
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function ()
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@. p.x -= η * p.Δ
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@. p.Δ = 0
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end
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using Flux
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using Base: @get!
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const ϵ = 1e-8
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# TODO: should use weak refs
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"""
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Descent(η)
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Classic gradient descent optimiser with learning rate `η`.
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For each parameter `p` and its gradient `δp`, this runs `p -= η*δp`.
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"""
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mutable struct Descent
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eta::Float64
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end
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# Ref: https://arxiv.org/abs/1711.05101.pdf
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function descentweightdecay(p::Param, η::Real, γ::Real)
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function ()
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@. p.x = p.x - η * (p.Δ + γ * p.x)
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@. p.Δ = 0
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end
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function update!(o::Descent, x, Δ)
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Δ .*= o.eta
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end
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function momentum(p::Param, ρ, η)
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v = zero(p.x)
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function ()
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@. v = ρ * v - η * p.Δ
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@. p.Δ = -v
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end
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"""
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Momentum(params, η = 0.01; ρ = 0.9, decay = 0)
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Gradient descent with learning rate `η` and momentum `ρ`.
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"""
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mutable struct Momentum
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eta::Float64
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rho::Float64
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velocity::ObjectIdDict
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end
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# Ref. https://arxiv.org/pdf/1212.0901.pdf
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function nesterov(p::Param, ρ, η)
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v = zero(p.x)
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function ()
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d = @. ρ^2 * v - (1+ρ) * η * p.Δ
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@. v = ρ*v - η*p.Δ
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@. p.Δ = -d
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end
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Momentum(η, ρ = 0.9) = Momentum(η, ρ, ObjectIdDict())
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function update!(o::Momentum, x, Δ)
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η, ρ = o.eta, o.rho
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v = @get!(o.velocity, x, zero(x))::typeof(x)
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@. v = ρ * v - η * Δ
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@. Δ = -v
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end
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function rmsprop(p::Param; η::Real = 0.001, ρ::Real = 0.9, ϵ::Real = 1e-8)
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acc = zero(p.x)
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function ()
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@. acc = ρ * acc + (1 - ρ) * p.Δ^2
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@. p.Δ *= η / √(acc + ϵ)
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end
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"""
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Nesterov(eta, ρ = 0.9)
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Gradient descent with learning rate `η` and Nesterov momentum `ρ`.
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"""
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mutable struct Nesterov
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eta::Float64
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rho::Float64
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velocity::ObjectIdDict
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end
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function adagrad(p::Param; η::Real = 0.01, ϵ::Real = 1e-8)
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acc = zero(p.x) .+ ϵ
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function ()
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@. acc += p.Δ^2
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@. p.Δ *= η / √(acc + ϵ)
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end
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Nesterov(η, ρ = 0.9) = Nesterov(η, ρ, ObjectIdDict())
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function update!(o::Nesterov, x, Δ)
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η, ρ = o.eta, o.rho
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v = @get!(o.velocity, x, zero(x))::typeof(x)
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d = @. ρ^2 * v - (1+ρ) * η * Δ
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@. v = ρ*v - η*Δ
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@. Δ = -d
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end
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function adadelta(p::Param; ρ::Real = 0.9, ϵ::Real = 1e-8)
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acc = zero(p.x)
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Δacc = zero(p.x)
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function ()
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@. acc = ρ * acc + (1 - ρ) * p.Δ^2
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@. p.Δ *= √(Δacc + ϵ) / √(acc + ϵ)
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@. Δacc = ρ * Δacc + (1 - ρ) * p.Δ^2
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end
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"""
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RMSProp(η = 0.001, ρ = 0.9)
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[RMSProp](http://www.cs.toronto.edu/~tijmen/csc321/slides/lecture_slides_lec6.pdf)
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optimiser. Parameters other than learning rate don't need tuning. Often a good
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choice for recurrent networks.
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"""
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mutable struct RMSProp
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eta::Float64
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rho::Float64
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acc::ObjectIdDict
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end
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function adam(p::Param; η::Real = 0.001, β1::Real = 0.9, β2::Real = 0.999, ϵ::Real = 1e-8)
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mt = zero(p.x)
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vt = zero(p.x)
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β1p, β2p = β1, β2
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function ()
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@. mt = β1 * mt + (1 - β1) * p.Δ
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@. vt = β2 * vt + (1 - β2) * p.Δ^2
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@. p.Δ = mt / (1 - β1p) / √(vt / (1 - β2p) + ϵ) * η
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β1p *= β1
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β2p *= β2
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end
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RMSProp(η = 0.001, ρ = 0.9) = RMSProp(η, ρ, ObjectIdDict())
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function update!(o::RMSProp, x, Δ)
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η, ρ = o.eta, o.rho
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acc = @get!(o.acc, x, zero(x))::typeof(x)
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@. acc = ρ * acc + (1 - ρ) * Δ^2
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@. Δ *= η / (√acc + ϵ)
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end
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function adamax(p::Param; η::Real = 0.002, β1::Real = 0.9, β2::Real = 0.999, ϵ::Real = 1e-8)
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mt = zero(p.x)
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ut = zero(p.x)
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β1p = β1
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function ()
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@. mt = β1 * mt + (1 - β1) * p.Δ
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@. ut = max(β2 * ut, abs(p.Δ))
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@. p.Δ = (η/(1 - β1p)) * mt/(ut + ϵ)
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β1p *= β1
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end
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"""
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ADAM(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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[ADAM](https://arxiv.org/abs/1412.6980v8) optimiser.
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"""
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mutable struct ADAM
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eta::Float64
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beta::Tuple{Float64,Float64}
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state::ObjectIdDict
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end
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function amsgrad(p::Param; η::Real = 0.001, β1::Real = 0.9, β2::Real = 0.999, ϵ::Real = 1e-8)
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mt = zero(p.x)
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vt = zero(p.x) .+ ϵ
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v̂t = zero(p.x) .+ ϵ
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function ()
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@. mt = β1 * mt + (1 - β1) * p.Δ
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@. vt = β2 * vt + (1 - β2) * p.Δ ^ 2
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@. v̂t = max.(v̂t, vt)
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@. p.Δ = η * mt / √v̂t
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end
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ADAM(η = 0.001, β = (0.9, 0.999)) = ADAM(η, β, ObjectIdDict())
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function update!(o::ADAM, x, Δ)
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η, β = o.eta, o.beta
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mt, vt, βp = @get!(o.state, x, (zero(x), zero(x), β))
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@. mt = β[1] * mt + (1 - β[1]) * Δ
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@. vt = β[2] * vt + (1 - β[2]) * Δ^2
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@. Δ = mt / (1 - βp[1]) / (√(vt / (1 - βp[2])) + ϵ) * η
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o.state[x] = (mt, vt, βp .* β)
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end
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function nadam(p::Param; η::Real = 0.001, β1::Real = 0.9, β2::Real = 0.999, ϵ::Real = 1e-8)
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mt = zero(p.x)
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vt = zero(p.x)
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β1p, β2p = β1, β2
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function ()
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@. mt = β1 * mt + (1 - β1) * p.Δ
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@. vt = β2 * vt + (1 - β2) * p.Δ^2
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@. p.Δ = (β1 * mt / (1 - β1 * β1p) + (1 - β1) * p.Δ / (1 - β1p)) / √(vt * β2 / (1 - β2p) + ϵ) * η
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β1p *= β1
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β2p *= β2
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end
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end
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# """
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# AdaMax(params, η = 0.001; β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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#
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# [AdaMax](https://arxiv.org/abs/1412.6980v9) optimiser. Variant of ADAM based on
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# the ∞-norm.
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# """
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clip(p::Param, thresh::Real) = () -> clamp!(p.Δ, -thresh, thresh)
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# """
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# ADAGrad(params, η = 0.01; ϵ = 1e-8, decay = 0)
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#
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# [ADAGrad](http://www.jmlr.org/papers/volume12/duchi11a/duchi11a.pdf) optimiser.
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# Parameters don't need tuning.
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# """
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function expdecay(p::Param, γ::Real)
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if γ != 0
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return () -> p.Δ .+= γ .* p.x
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else
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return () -> nothing
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end
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end
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# """
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# ADADelta(params; ρ = 0.9, ϵ = 1e-8, decay = 0)
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#
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# [ADADelta](http://arxiv.org/abs/1212.5701) optimiser. Parameters don't need
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# tuning.
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# """
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function invdecay(p::Param, γ::Real)
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if γ != 0
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n = 0
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return () -> begin
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p.Δ .*= 1 / (1 + γ * n)
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n += 1
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end
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else
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return () -> nothing
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end
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end
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# """
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# AMSGrad(params; η = 0.001, β1 = 0.9, β2 = 0.999, ϵ = 1e-08, decay = 0)
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#
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# [AMSGrad](https://openreview.net/forum?id=ryQu7f-RZ) optimiser. Parameters don't need
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# tuning.
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# """
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# struct Optimiser
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# os::Vector{Any}
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# end
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# TODO: decay
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@ -1,7 +1,16 @@
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using Juno
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using Flux.Tracker: back!
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import Base.depwarn
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using Flux.Tracker: data, grad, back!
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function update!(opt, xs)
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for x in xs
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x, Δ = data(x), grad(x)
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update!(opt, x, Δ)
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x .-= Δ
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Δ .= 0
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end
|
||||
end
|
||||
|
||||
# Callback niceties
|
||||
runall(f) = f
|
||||
runall(fs::AbstractVector) = () -> foreach(call, fs)
|
||||
|
||||
|
||||
@ -1,27 +1,23 @@
|
||||
module Tracker
|
||||
|
||||
using MacroTools
|
||||
using MacroTools: @q, @forward
|
||||
|
||||
import Base: ==
|
||||
|
||||
export TrackedArray, TrackedVector, TrackedMatrix, Params, param, back!
|
||||
export TrackedArray, TrackedVector, TrackedMatrix, param, back!
|
||||
|
||||
tracker(x) = nothing
|
||||
|
||||
istracked(x) = tracker(x) ≠ nothing
|
||||
isleaf(x) = !istracked(x) || isleaf(tracker(x))
|
||||
data(x) = istracked(x) ? data(tracker(x)) : x
|
||||
grad(x) = grad(tracker(x))
|
||||
grad(::Nothing) = nothing
|
||||
data(x) = x
|
||||
|
||||
struct Call{F,As<:Tuple}
|
||||
func::F
|
||||
args::As
|
||||
end
|
||||
|
||||
Call(f::F, args::T) where {F,T} = Call{F,T}(f, args)
|
||||
Call() = Call(nothing, ())
|
||||
Call(f, args...) = Call{typeof(f),typeof(args)}(f, args)
|
||||
|
||||
# When deserialising, the object_id changes
|
||||
a::Call == b::Call = a.func == b.func && a.args == b.args
|
||||
@ -32,86 +28,37 @@ mutable struct Tracked{T}
|
||||
ref::UInt32
|
||||
f::Call
|
||||
isleaf::Bool
|
||||
data::T
|
||||
grad::T
|
||||
Tracked{T}(f::Call) where T = new(0, f, false)
|
||||
Tracked{T}(f::Call, grad::T) where T = new(0, f, false, grad)
|
||||
Tracked{T}(f::Call{Nothing}, grad::T) where T = new(0, f, true, grad)
|
||||
Tracked{T}(f::Call, data::T) where T = new(0, f, false, data)
|
||||
Tracked{T}(f::Call, data::T, grad::T) where T = new(0, f, false, data, grad)
|
||||
Tracked{T}(f::Call{Void}, data::T, grad::T) where T = new(0, f, true, data, grad)
|
||||
end
|
||||
|
||||
Tracked(f::Call, x) = Tracked{typeof(x)}(f, x)
|
||||
Tracked(f::Call, x, Δ) = Tracked{typeof(x)}(f, x, Δ)
|
||||
|
||||
track(f::Call, x) = Tracked(f, x)
|
||||
track(f::Call) = track(f, f())
|
||||
track(f, xs...) = track(Call(f, xs...))
|
||||
|
||||
istracked(x::Tracked) = true
|
||||
isleaf(x::Tracked) = x.f == Call()
|
||||
isleaf(x::Tracked) = x.f == Call(nothing)
|
||||
data(x::Tracked) = x.data
|
||||
grad(x::Tracked) = x.grad
|
||||
|
||||
track(f::Call, x) = Tracked{typeof(x)}(f)
|
||||
|
||||
function _forward end
|
||||
|
||||
function track(f::F, xs...; kw...) where F
|
||||
y, back = _forward(f, xs...; kw...)
|
||||
track(Call(back, tracker.(xs)), y)
|
||||
end
|
||||
|
||||
macro grad(ex)
|
||||
@capture(shortdef(ex), (name_(args__) = body_) |
|
||||
(name_(args__) where {T__} = body_)) || error("Need a function definition")
|
||||
T == nothing && (T = [])
|
||||
isexpr(name, :(::)) || (name = :(::typeof($name)))
|
||||
insert!(args, 1+isexpr(args[1], :parameters) , name)
|
||||
@q(Tracker._forward($(args...)) where $(T...) = $body) |> esc
|
||||
end
|
||||
|
||||
function update!(x, Δ)
|
||||
x.data .+= data(Δ)
|
||||
tracker(x).grad .= 0
|
||||
return x
|
||||
end
|
||||
|
||||
include("idset.jl")
|
||||
include("back.jl")
|
||||
include("scalar.jl")
|
||||
include("array.jl")
|
||||
include("numeric.jl")
|
||||
|
||||
"""
|
||||
hook(f, x) -> x′
|
||||
|
||||
Hook into gradient backpropagation. `x` is unmodified, but when backpropagating
|
||||
`f` will be applied to the incoming gradient. For example, `hook(-, x)` will reverse
|
||||
the sign of the gradient applied to `x`."""
|
||||
hook(f, x) = istracked(x) ? track(hook, f, x) : x
|
||||
@grad hook(f, x) = data(x), Δ -> (nothing, f(Δ))
|
||||
|
||||
"""
|
||||
checkpoint(f, args...)
|
||||
|
||||
Behaves like `f(args...)`, but avoids storing the intermediate values needed for
|
||||
calculating gradients. Instead, `f(args...)` will be called again during the
|
||||
backward pass. This can be used to save memory in larger models.
|
||||
"""
|
||||
checkpoint(f, args...) = track(checkpoint, f, args...)
|
||||
|
||||
@grad function checkpoint(f, args...)
|
||||
data(f(args...)), function (Δ)
|
||||
y, back = forward(f, args...)
|
||||
(nothing, back(Δ)...)
|
||||
end
|
||||
end
|
||||
|
||||
nobacksies(f, x) = track(nobacksies, f, x)
|
||||
nobacksies(f, xs::Tuple) = map(x -> nobacksies(f, x), xs)
|
||||
@grad nobacksies(f, x) = data(x), Δ -> error("Nested AD not defined for $f")
|
||||
|
||||
param(x::Number) = TrackedReal(float(x))
|
||||
param(xs::AbstractArray) = TrackedArray(float.(xs))
|
||||
|
||||
@grad identity(x) = data(x), Δ -> (Δ,)
|
||||
param(x::TrackedReal) = track(identity, x)
|
||||
param(x::TrackedArray) = track(identity, x)
|
||||
|
||||
import NNlib.cudata
|
||||
import Adapt.adapt
|
||||
|
||||
cudata(x::TrackedArray) = data(x)
|
||||
adapt(T, xs::TrackedArray) = param(adapt(T, data(xs)))
|
||||
|
||||
end
|
||||
end
|
||||
Loading…
Reference in New Issue
Block a user