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Enable scalar/broadcast operation for LazyPropagation #167

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Enable scalar/broadcast operation for LazyPropagation #167

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4c1ef4d
add scalar
ziyiyin97 Jan 3, 2023
2252837
add a value to store F*q
ziyiyin97 Jan 6, 2023
f045df7
fix reshape judivector in ad
ziyiyin97 Jan 6, 2023
8594806
length for lazy prop
ziyiyin97 Jan 6, 2023
6c693a2
fix a few issues raised by dr. louboutin
ziyiyin97 Jan 6, 2023
171170f
fix reshape
ziyiyin97 Jan 8, 2023
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1 change: 1 addition & 0 deletions src/TimeModeling/LinearOperators/lazy.jl
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Expand Up @@ -130,6 +130,7 @@ end
size(jA::jAdjoint) = (jA.op.n, jA.op.m)
display(P::jAdjoint) = println("Adjoint($(P.op))")
display(P::judiProjection{D}) where D = println("JUDI projection operator $(repr(P.n)) -> $(repr(P.m))")
display(P::judiWavelet{T}) where T = println("JUDI wavelet injected at every grid point")
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That's not quite true, it's just a container for a single time trace there is nothing about "everywhere in space" in it.


############################################################################################################################
# Indexing
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10 changes: 9 additions & 1 deletion src/TimeModeling/Types/abstract.jl
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Expand Up @@ -73,7 +73,15 @@ vec(x::judiMultiSourceVector) = vcat(vec.(x.data)...)

time_sampling(ms::judiMultiSourceVector) = [1 for i=1:ms.nsrc]

reshape(ms::judiMultiSourceVector, dims::Dims{N}) where N = reshape(vec(ms), dims)
function reshape(ms::judiMultiSourceVector, dims::Dims{N}) where N
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This is for

failing example 
using JUDI
using Flux
using ArgParse, Test, Printf, Aqua
using SegyIO, LinearAlgebra, Distributed, JOLI
using TimerOutputs: TimerOutputs, @timeit

Flux.Random.seed!(2022)

### Model
tti = false
viscoacoustic = false

nsrc = 1
dt = 1f0
include(joinpath(JUDIPATH, "../test/seismic_utils.jl"))
model, model0, dm = setup_model(tti, viscoacoustic, 4)
m, m0 = model.m.data, model0.m.data
q, srcGeometry, recGeometry, f0 = setup_geom(model; nsrc=nsrc, dt=dt)

# Common op
Pr = judiProjection(recGeometry)
Ps = judiProjection(srcGeometry)

opt = Options(sum_padding=true, f0=f0)
A_inv = judiModeling(model; options=opt)
A_inv0 = judiModeling(model0; options=opt)

# Operators
F = Pr*A_inv*adjoint(Ps)
J = judiJacobian(F,q)
dm = vec(m-m0)

gs_inv = gradient(q -> norm(J(q)*dm), q)
ERROR: LoadError: DimensionMismatch: new dimensions (1,) must be consistent with array size 1501
Stacktrace:
  [1] (::Base.var"#throw_dmrsa#289")(dims::Tuple{Int64}, len::Int64)
    @ Base ./reshapedarray.jl:41
  [2] reshape
    @ ./reshapedarray.jl:45 [inlined]
  [3] reshape
    @ ~/.julia/dev/JUDI/src/TimeModeling/Types/abstract.jl:76 [inlined]
  [4] reshape(parent::judiVector{Float32, Matrix{Float32}}, shp::Tuple{Base.OneTo{Int64}})
    @ Base ./reshapedarray.jl:111
  [5] (::ChainRulesCore.ProjectTo{AbstractArray, NamedTuple{(:element, :axes), Tuple{ChainRulesCore.ProjectTo{Float32, NamedTuple{(), Tuple{}}}, Tuple{Base.OneTo{Int64}}}}})(dx::JUDI.LazyPropagation)
    @ JUDI ~/.julia/dev/JUDI/src/rrules.jl:142
  [6] _project
    @ ~/.julia/packages/Zygote/SmJK6/src/compiler/chainrules.jl:184 [inlined]
  [7] map(f::typeof(Zygote._project), t::Tuple{judiVector{Float32, Matrix{Float32}}}, s::Tuple{JUDI.LazyPropagation})
    @ Base ./tuple.jl:246
  [8] gradient(f::Function, args::judiVector{Float32, Matrix{Float32}})
    @ Zygote ~/.julia/packages/Zygote/SmJK6/src/compiler/interface.jl:98
  [9] top-level scope
    @ ~/.julia/dev/JUDI/test/MFE.jl:33
 [10] include(fname::String)
    @ Base.MainInclude ./client.jl:476
 [11] top-level scope
    @ REPL[1]:1

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It wasn't failing before what changed?

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The example above fails on master branch

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To be more specific:

julia> gs_inv = gradient(() -> norm(J(q)*dm), Flux.params(q))
Operator `born` ran in 0.75 s
Grads(...)

this doesn't fail but

julia> gs_inv = gradient(q -> norm(J(q)*dm), q)
Operator `born` ran in 0.72 s
Operator `born` ran in 0.73 s
ERROR: DimensionMismatch: new dimensions (1,) must be consistent with array size 1501
Stacktrace:
  [1] (::Base.var"#throw_dmrsa#289")(dims::Tuple{Int64}, len::Int64)
    @ Base ./reshapedarray.jl:41
  [2] reshape
    @ ./reshapedarray.jl:45 [inlined]
  [3] reshape
    @ ~/.julia/dev/JUDI/src/TimeModeling/Types/abstract.jl:76 [inlined]
  [4] reshape(parent::judiVector{Float32, Matrix{Float32}}, shp::Tuple{Base.OneTo{Int64}})
    @ Base ./reshapedarray.jl:111
  [5] (::ChainRulesCore.ProjectTo{AbstractArray, NamedTuple{(:element, :axes), Tuple{ChainRulesCore.ProjectTo{Float32, NamedTuple{(), Tuple{}}}, Tuple{Base.OneTo{Int64}}}}})(dx::JUDI.LazyPropagation)
    @ JUDI ~/.julia/dev/JUDI/src/rrules.jl:142
  [6] _project
    @ ~/.julia/packages/Zygote/SmJK6/src/compiler/chainrules.jl:184 [inlined]
  [7] map(f::typeof(Zygote._project), t::Tuple{judiVector{Float32, Matrix{Float32}}}, s::Tuple{JUDI.LazyPropagation})
    @ Base ./tuple.jl:246
  [8] gradient(f::Function, args::judiVector{Float32, Matrix{Float32}})
    @ Zygote ~/.julia/packages/Zygote/SmJK6/src/compiler/interface.jl:98
  [9] top-level scope
    @ REPL[25]:1
 [10] top-level scope
    @ ~/.julia/packages/CUDA/DfvRa/src/initialization.jl:52

this fail

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Hum ok, then split into Dims{N} and Dims{1} and just an if/else these try/catch are really a bad idea anywhere near Zygote

try
return reshape(vec(ms), dims)
catch e
@assert dims[1] == ms.nsrc ### during AD, size(ms::judiVector) = ms.nsrc
return ms
end
end

############################################################################################################################
# Linear algebra `*`
(msv::judiMultiSourceVector{mT})(x::AbstractVector{T}) where {mT, T<:Number} = x
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31 changes: 28 additions & 3 deletions src/rrules.jl
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Expand Up @@ -18,14 +18,19 @@ Parameters
* `F`: the JUDI propgator
* `q`: The source to compute F*q
"""
struct LazyPropagation
mutable struct LazyPropagation
post::Function
F::judiPropagator
q
val # store F * q
end

eval_prop(F::LazyPropagation) = F.post(F.F * F.q)
function eval_prop(F::LazyPropagation)
isnothing(F.val) && (F.val = F.F * F.q)
return F.post(F.val)
end
Base.collect(F::LazyPropagation) = eval_prop(F)
LazyPropagation(post, F::judiPropagator, q) = LazyPropagation(post, F, q, nothing)
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::Function

LazyPropagation(F::judiPropagator, q) = LazyPropagation(identity, F, q)
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COnstructor LazyPropagation(identity, F, q, nothing) extra function call not needed


# Only a few arithmetic operation are supported
Expand All @@ -45,15 +50,35 @@ for op in [:+, :-, :*, :/]
end
end

for op in [:*, :/]
@eval begin
$(op)(F::LazyPropagation, y::T) where T <: Number = LazyPropagation(F.post, F.F, $(op)(F.q, y), isnothing(F.val) ? nothing : $(op)(F.val, y))
$(op)(y::T, F::LazyPropagation) where T <: Number = LazyPropagation(F.post, F.F, $(op)(y, F.q), isnothing(F.val) ? nothing : $(op)(y, F.val))
broadcasted(::typeof($op), F::LazyPropagation, y::T) where T <: Number = LazyPropagation(F.post, F.F, broadcasted($(op), F.q, y), isnothing(F.val) ? nothing : broadcasted($(op), F.val, y))
broadcasted(::typeof($op), y::T, F::LazyPropagation) where T <: Number = LazyPropagation(F.post, F.F, broadcasted($(op), y, F.q), isnothing(F.val) ? nothing : broadcasted($(op), y, F.val))
end
end

for op in [:+, :-]
@eval begin
$(op)(F::LazyPropagation, y::T) where T <: Number = $(op)(eval_prop(F), y)
$(op)(y::T, F::LazyPropagation) where T <: Number = $(op)(y, eval_prop(F))
broadcasted(::typeof($op), F::LazyPropagation, y::T) where T <: Number = broadcasted($(op), eval_prop(F), y)
broadcasted(::typeof($op), y::T, F::LazyPropagation) where T <: Number = broadcasted($(op), y, eval_prop(F))
end
end

broadcasted(::typeof(^), y::LazyPropagation, p::Real) = eval_prop(y).^(p)
*(F::judiPropagator, q::LazyPropagation) = F*eval_prop(q)

reshape(F::LazyPropagation, dims...) = LazyPropagation(x->reshape(x, dims...), F.F, Q.q)
reshape(F::LazyPropagation, dims...) = LazyPropagation(x->reshape(x, dims...), F.F, F.q, F.val)
vec(F::LazyPropagation) = LazyPropagation(vec, F.F, F.q, F.val)
copyto!(x::AbstractArray, F::LazyPropagation) = copyto!(x, eval_prop(F))
dot(x::AbstractArray, F::LazyPropagation) = dot(x, eval_prop(F))
dot(F::LazyPropagation, x::AbstractArray) = dot(x, F)
norm(F::LazyPropagation, p::Real=2) = norm(eval_prop(F), p)
adjoint(F::JUDI.LazyPropagation) = F
length(F::JUDI.LazyPropagation) = size(F.F, 1)

############################ Two params rules ############################################
function rrule(F::judiPropagator{T, O}, m::AbstractArray{T}, q::AbstractArray{T}) where {T, O}
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2 changes: 1 addition & 1 deletion test/test_rrules.jl
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Expand Up @@ -32,7 +32,7 @@ perturb(x::judiVector) = judiVector(x.geometry, [randx(x.data[i]) for i=1:x.nsrc
reverse(x::judiVector) = judiVector(x.geometry, [x.data[i][end:-1:1, :] for i=1:x.nsrc])

misfit_objective_2p(d_obs, q0, m0, F) = .5f0*norm(F(m0, q0) - d_obs)^2
misfit_objective_1p(d_obs, q0, m0, F) = .5f0*norm(F(m0)*q0 - d_obs)^2
misfit_objective_1p(d_obs, q0, m0, F) = .5f0*norm(F(1f0*m0)*q0 - d_obs)^2

function loss(misfit, d_obs, q0, m0, F)
local ϕ
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