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lumi/Manifest.toml

@@ -1,6 +1,6 @@
 # This file is machine-generated - editing it directly is not advised
 
-julia_version = "1.10.1"
+julia_version = "1.10.5"
 manifest_format = "2.0"
 project_hash = "e6029301621baa2fecc22ebba39262d01d08f0c8"
 
@@ -359,7 +359,7 @@ weakdeps = ["Dates", "LinearAlgebra"]
 [[deps.CompilerSupportLibraries_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "e66e0078-7015-5450-92f7-15fbd957f2ae"
-version = "1.1.0+0"
+version = "1.1.1+0"
 
 [[deps.CompositionsBase]]
 git-tree-sha1 = "802bb88cd69dfd1509f6670416bd4434015693ad"
@@ -2296,7 +2296,7 @@ version = "0.15.2+0"
 [[deps.libblastrampoline_jll]]
 deps = ["Artifacts", "Libdl"]
 uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
-version = "5.8.0+1"
+version = "5.11.0+0"
 
 [[deps.libfdk_aac_jll]]
 deps = ["Artifacts", "JLLWrappers", "Libdl"]

lumi/pyproject.toml

@@ -1,6 +1,6 @@
 [tool.poetry]
 name = "luminescent"
-version = "0.1.58"
+version = "0.1.59"
 description = "Lumi FDTD by Luminescent AI is a GPU and automatic differentiation (AD) compatible FDTD package for simulation and inverse design in semiconductor photonics. Create layout in gdsfactory. Then use the Lumi Python plugin for calculating sparams or perform inverse design on Lumi's Julia backend."
 authors = ["Paul Shen"]
 license = "MIT"

lumi/src/luminescent/gplugins/luminescent/run.jl

@@ -14,7 +14,7 @@ sol = gfrun(path)
 # # Pkg.resolve()
 
 # using Pkg
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Porcupine.jl"
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\ArrayPadding.jl"
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Jello.jl"
-# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Luminescent.jl"
+# pkg"add https://github.com/paulxshen/Luminescent.jl,Porcupine,Jello,ArrayPadding;up"
+
+# using Pkg
+# pkg"dev C:\Users\pxshe\OneDrive\Desktop\Porcupine.jl;dev C:\Users\pxshe\OneDrive\Desktop\ArrayPadding.jl; dev C:\Users\pxshe\OneDrive\Desktop\Jello.jl;dev C:\Users\pxshe\OneDrive\Desktop\Luminescent.jl;up"

lumi/src/straight_test.py

@@ -8,23 +8,23 @@
 
 c = gf.components.straight(.5)
 
-sol = lumi.write_sparams(
-    # c, wavelengths=[1.55], keys=["2,1"],
-    # bbox_layer=[LAYER.WAFER, LAYER.SLAB90],
-    c, wavelengths=[1.55], keys=["o2@1,o1@1"],
-    bbox_layer=LAYER.WAFER,
-    dx=0.1, approx_2D=True, gpu="CUDA",)
-# dx=0.1, approx_2D=False, gpu="CUDA",)  # dtype="16", dev=True,)
-lumi.show_solution()
-pp.pprint(sol)
-raise ValueError("stop here")
+# sol = lumi.write_sparams(
+#     # c, wavelengths=[1.55], keys=["2,1"],
+#     # bbox_layer=[LAYER.WAFER, LAYER.SLAB90],
+#     c, wavelengths=[1.55], keys=["o2@1,o1@1"],
+#     bbox_layer=LAYER.WAFER,
+#     dx=0.1, approx_2D=True, gpu="CUDA",)
+# # dx=0.1, approx_2D=False, gpu="CUDA",)  # dtype="16", dev=True,)
+# lumi.show_solution()
+# pp.pprint(sol)
+# raise ValueError("stop here")
 
 sleep(1)
 for (approx_2D, gpu, dtype, wavelengths) in itertools.product(
     [True, False],
     [None, "CUDA"],
-    #  ["f32"]
-    ["f32", "f16"],
+    ["f32"],
+    # ["f32", "f16"],
         [[1.55], ]):
     lumi.write_sparams(c, wavelengths=wavelengths, keys=["2,1"], dx=0.1,
                        bbox_layer=LAYER.WAFER,

lumi/src/tiny_test.py

@@ -13,35 +13,29 @@
     }}}
 # c.show()
 
-prob = lumi.inverse_design_problem(
-    c, targets,
-    # bbox_layer=LAYER.WAFER,
-    # lmin=0.2, dx=0.1, iters=2, eta=10., approx_2D=False, compression=True)  # gpu="CUDA", dev=True)
-    lmin=0.2, dx=0.1, iters=2, approx_2D=True, compression=False)  # gpu="CUDA", dev=True)
-# lmin=0.2, dx=0.1, iters=2, eta=10., approx_2D=True, gpu="CUDA", dev=True)
-sol = lumi.solve(prob, )
-# sol = lumi.finetune(2)
-sol = lumi.load_solution()
-c = lumi.apply_design(c, sol)
-c.show()
-# raise ValueError("stop here")
-
-# for (approx_2D, gpu, dtype, ) in itertools.product(
-#     [True,],
-#     [None, "CUDA"],
-#     # [None, ],
-#     ["f32"],
-#     # ["f32", "f16"],
-# ):
-#     prob = lumi.inverse_design_problem(
-#         c, targets=targets,
-#         bbox_layer=LAYER.WAFER,
-#         lmin=0.2, dx=0.1, iters=2, eta=1., approx_2D=approx_2D, gpu=gpu, dtype=dtype, run=False)
-#     sol = lumi.solve(prob, run=False)
-#     sleep(1)
-# path="precompile_execution")
-# lmin=0.2, dx=0.1, iters=2, eta=10., approx_2D=True, gpu="CUDA")
+# prob = lumi.inverse_design_problem(
+#     c, targets,
+#     # bbox_layer=LAYER.WAFER,
+#     # lmin=0.2, dx=0.1, iters=2, eta=10., approx_2D=False, compression=True)  # gpu="CUDA", dev=True)
+#     lmin=0.2, dx=0.1, iters=2, approx_2D=True, compression=False)  # gpu="CUDA", dev=True)
+# # lmin=0.2, dx=0.1, iters=2, eta=10., approx_2D=True, gpu="CUDA", dev=True)
+# sol = lumi.solve(prob, )
+# # sol = lumi.finetune(2)
+# sol = lumi.load_solution()
+# c = lumi.apply_design(c, sol)
+# c.show()
+# # raise ValueError("stop here")
 
-# lumi.show_solution()
-# print("post optim tparams:")
-# pprint(sol["tparams"])
+for (approx_2D, gpu, dtype, ) in itertools.product(
+    [True,],
+    # [None, "CUDA"],
+    [None, ],
+    ["f32"],
+    # ["f32", "f16"],
+):
+    prob = lumi.inverse_design_problem(
+        c, targets,
+        bbox_layer=LAYER.WAFER,
+        lmin=0.2, dx=0.1, iters=2, approx_2D=approx_2D, gpu=gpu, dtype=dtype, run=False)
+    sol = lumi.solve(prob, run=False)
+    sleep(1)

src/geometry.jl

@@ -27,51 +27,6 @@ function apply_subpixel_averaging(sas, gs)
     sas = ignore_derivatives() do
         sas
     end
-    # if length(sas) == 6
-    #     return (;
-    #         ϵxx=_apply_subpixel_averaging(sas.ϵxx, gs.ϵ),
-    #         ϵyy=_apply_subpixel_averaging(sas.ϵyy, gs.ϵ),
-    #         μzz=_apply_subpixel_averaging(sas.μzz, gs.μ),
-    #         σxx=_apply_subpixel_averaging(sas.σxx, gs.σ),
-    #         σyy=_apply_subpixel_averaging(sas.σyy, gs.σ),
-    #         mzz=_apply_subpixel_averaging(sas.mzz, gs.m),)
-    # end
-    # (; ϵxx=_apply_subpixel_averaging(sas.ϵxx, gs.ϵ),
-    #     ϵyy=_apply_subpixel_averaging(sas.ϵyy, gs.ϵ),
-    #     ϵzz=_apply_subpixel_averaging(sas.ϵzz, gs.ϵ),
-    #     μxx=_apply_subpixel_averaging(sas.μxx, gs.μ),
-    #     μyy=_apply_subpixel_averaging(sas.μyy, gs.μ),
-    #     μzz=_apply_subpixel_averaging(sas.μzz, gs.μ),
-    #     σxx=_apply_subpixel_averaging(sas.σxx, gs.σ),
-    #     σyy=_apply_subpixel_averaging(sas.σyy, gs.σ),
-    #     σzz=_apply_subpixel_averaging(sas.σzz, gs.σ), mxx=_apply_subpixel_averaging(sas.mxx, gs.m),
-    #     myy=_apply_subpixel_averaging(sas.myy, gs.m),
-    #     mzz=_apply_subpixel_averaging(sas.mzz, gs.m),)
-
-    # gks = ignore_derivatives() do
-    #     getindex.(keys(sas), 1)
-    # end
-
-    # namedtuple([k => begin
-    #     # sa = sas[k]
-    #     reduce(enumerate(eachcol(sa.v)), init=a) do a, (i, (l, r))
-    #         d = ndims(a)
-    #         select = i .== (1:d) |> Tuple
-    #         if l == -1
-    #             a = pad(a, :replicate, select, 0)
-    #         end
-    #         if r == 1
-    #             a = pad(a, :replicate, 0, select)
-    #         end
-    #         if l == -1 == r
-    #             a = (2selectdim(a, i, 2:(size(a, i))) - diff(a, dims=i)) / 2
-    #         elseif l == 1 == r
-    #             a = (2selectdim(a, i, 1:(size(a, i)-1)) + diff(a, dims=i)) / 2
-    #         end
-    #         a
-    #     end
-    # end for ((k, sa), a) = zip(pairs(sas), getindex.((gs,), gks))])
-    # end for k = keys(sas)])
     namedtuple([k => _apply_subpixel_averaging(sas[k], values(gs)[findfirst(keys(gs)) do gk
         startswith(string(k), string(gk))
     end]) for k = keys(sas)])
@@ -87,22 +42,7 @@ function _apply_geometry_padding(p::AbstractVector{<:OutPad}, a)
 end
 
 function apply_geometry_padding(gps, gs)
-    # (;
-    #     ϵ=_apply_geometry_padding(gps.ϵ, gs.ϵ),
-    #     μ=_apply_geometry_padding(gps.μ, gs.μ),
-    #     σ=_apply_geometry_padding(gps.σ, gs.σ),
-    #     m=_apply_geometry_padding(gps.m, gs.m),)
-    # global _gps = gps
-    # global _gs = gs
-    # dict([(k, apply(gps[k], gs[k])) for k = Porcupine.keys(gs)])
     namedtuple([k => _apply_geometry_padding(gps[k], gs[k]) for k = keys(gs)])
-    # dict([k => _apply_geometry_padding(gps[k], gs[k]) for k = keys(gs)])
-    # namedtuple(r)
-    # v = Porcupine.values(gs)
-    # u = ignore_derivatives() do
-    #     getindex.((gps,), keys(gs))
-    # end
-    # namedtuple([k => apply(u, v) for (k, u, v) = zip(Porcupine.keys(gs), u, v)])
 end
 
 function _apply_field_padding(p::AbstractVector{<:InPad}, a::AbstractArray; nonzero_only=false)

src/gf.jl

@@ -5,7 +5,6 @@ Optimisers.maywrite(::CUDA.CUSPARSE.CuSparseMatrixCSC{Float32,Int32}) = true
 Optimisers.maywrite(::CUDA.CUSPARSE.CuSparseMatrixCSC{Float16,Int32}) = true
 Optimisers.maywrite(::SparseArrays.SparseMatrixCSC{Float32,Int32}) = true
 Optimisers.maywrite(::SparseArrays.SparseMatrixCSC{Float16,Int32}) = true
-# using Porcupine: keys, values, first
 
 function julia_main()::Cint
     if !isempty(ARGS)
@@ -14,7 +13,6 @@ function julia_main()::Cint
     end
     return 0
 end
-# add Dates, DataStructures, JSON, Images, BSON,Flux,CUDA
 function lastrun(s=nothing, path=joinpath(pwd(), "lumi_runs"))
     l = filter(isdir, readdir(path, join=true))
     sort!(l)
@@ -32,21 +30,17 @@ function lastrun(s=nothing, path=joinpath(pwd(), "lumi_runs"))
     end
 end
 
-function write_sparams(runs, run_probs, g, path, origin, dx,
+function write_sparams(runs, run_probs, geometry, path, origin, dx,
     designs=nothing, design_config=nothing, models=nothing;
     img=nothing, autodiff=false, compression=false, verbose=false, perturb=nothing, with=false, ls=nothing, kw...)
     F = run_probs[1].F
-    geometry = make_geometry(models, origin, dx, g, designs, design_config; F, perturb)
+    geometry = make_geometry(models, origin, dx, geometry, designs, design_config; F, perturb)
 
-    i = 1
-
-
-    global sols = [
+    sols = [
         begin
-            # prob[:geometry] = geometry
-            # prob = merge(prob, (; geometry,))
+            prob[:geometry] = geometry
             #@debug typeof(prob.u0.E.Ex), typeof(prob.geometry.ϵ)
-            sol = solve(prob, geometry; autodiff, ls, compression, verbose)
+            sol = solve(prob; autodiff, ls, compression, verbose)
 
             ignore() do
                 if !isnothing(img)
@@ -62,7 +56,7 @@ function write_sparams(runs, run_probs, g, path, origin, dx,
                 end
             end
             sol
-        end for prob in run_probs
+        end for (i, prob) in enumerate(run_probs)
         # end for (i, prob) in enumerate(run_probs)
     ]
     # return sols[1].forward_mode_powers[1][1][1]
@@ -190,7 +184,6 @@ end
 # global virgin, stop, best, best0, sparams0
 function gfrun(path; kw...)
     println("setting up simulation...")
-    # do something based on ARGS?
     PROB_PATH = joinpath(path, "prob.bson")
     SOL_PATH = joinpath(path, "sol.json")
 
@@ -199,9 +192,7 @@ function gfrun(path; kw...)
     tol = 1e-3
     dosave = false
     verbose = false
-    #=
-    We load design layout which includes a 2d device of device waveguide geometry as well as variables with locations of ports, sources, design regions and material properties.
-    =#
+
     @load PROB_PATH name dtype margin zmargin source_margin Courant port_source_offset portsides runs ports dx components study mode_solutions eps_2D eps_3D mode_height zmin thickness zcore gpu_backend d magic
     F = Float32
     if contains(dtype, "16")
@@ -438,7 +429,6 @@ function gfrun(path; kw...)
                     verbose,)
             end for (i, (run, sources, monitors)) in enumerate(zip(runs, runs_sources, runs_monitors))
         ]
-    const g0 = run_probs[1].geometry
 
     if !isempty(gpu_backend)
         println("using $gpu_backend backend.")
@@ -456,6 +446,7 @@ function gfrun(path; kw...)
     else
         println("using CPU backend.")
     end
+    g0 = run_probs[1].geometry
 
     virgin = true
     # error()
@@ -504,7 +495,7 @@ function gfrun(path; kw...)
 
         for i = 1:iters
             # for i = 1:20
-            global virgin, stop, best, best0, sparams0
+            # global virgin, stop, best, best0, sparams0
             global img = if virgin
                 virgin = false
                 "before.png"
@@ -519,6 +510,7 @@ function gfrun(path; kw...)
                         F, img, autodiff, compression, ls)
                     l = 0
                     for k = keys(targets)
+                        print("losses ")
                         y = targets[k]
                         err = -
                         if :phasediff == k
@@ -551,7 +543,7 @@ function gfrun(path; kw...)
                         print("$(k): $_l ")
                         l += _l
                     end
-                    println("\n$i loss $l\n")
+                    println("\n($i) weighted total loss $l\n")
                     l
                 end
             elseif "phase_shifter" == preset.name

src/main.jl

@@ -1,17 +1,12 @@
 using Humanize: digitsep
 using UnPack, LinearAlgebra, Statistics, Random, Jello, Functors, DataStructures, GPUArraysCore
-# using Meshes: Sphere
 using ArrayPadding: left, right
 using Zygote
 using CairoMakie
-# Random.seed!(1)ImageIO,ImageTransformations,
 
 using Porcupine: keys, values, fmap
 using Porcupine
 using ArrayPadding
-# include("../../Porcupine.jl/src/main.jl")
-# include("../../ArrayPadding.jl/src/main.jl")
-# include("../../Jello.jl/src/main.jl")
 include("constants.jl")
 include("utils.jl")
 include("modes.jl")
@@ -33,7 +28,7 @@ using Dates, DataStructures, JSON, Images, BSON, Flux, CUDA, GPUArraysCore
 using Flux: mae, Adam
 using Zygote: withgradient, Buffer, ignore_derivatives
 using BSON: @save, @load, load
+include("gf.jl")
 # using AbbreviatedStackTraces
 
-# include("gf.jl")
 # include("main.jl")

src/solve.jl

@@ -17,12 +17,11 @@ function f2(((u, mf), p, (dx, dt, field_padding, source_instances, autodiff), (T
     ((u, mf), p, (dx, dt, field_padding, source_instances, autodiff), (T, monitor_instances))
 end
 
-function solve(prob, geometry; autodiff=false, compression=false, ls=nothing, verbose=false, kwargs...)
+function solve(prob, ; autodiff=false, compression=false, ls=nothing, verbose=false, kwargs...)
     # global _prob = prob
-    @unpack dx, dt, u0, field_padding, geometry_padding, subpixel_averaging, source_instances, monitor_instances, transient_duration, F, polarization, steady_state_duration, d, n = prob
+    @unpack dx, dt, u0, geometry, field_padding, geometry_padding, subpixel_averaging, source_instances, monitor_instances, transient_duration, F, polarization, steady_state_duration, d, n = prob
 
     p = apply_geometry_padding(geometry_padding, geometry)
-    # global asdffsd = p
     p = apply_subpixel_averaging(subpixel_averaging, p)
     # return p.ϵxx |> sum