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Merge pull request #253 from avik-pal/ap/autosparse
New High Level Interface for Sparse Jacobian Computation
2 parents 9c458cd + 713e6ee commit db2f2ae

32 files changed

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‎.gitignore‎

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docs/build/
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docs/site/
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.vscode

‎Project.toml‎

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name = "SparseDiffTools"
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uuid = "47a9eef4-7e08-11e9-0b38-333d64bd3804"
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authors = ["Pankaj Mishra <pankajmishra1511@gmail.com>", "Chris Rackauckas <contact@chrisrackauckas.com>"]
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version = "2.4.1"
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version = "2.5.0"
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[deps]
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ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
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ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
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Graphs = "86223c79-3864-5bf0-83f7-82e725a168b6"
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LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
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PackageExtensionCompat = "65ce6f38-6b18-4e1d-a461-8949797d7930"
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Reexport = "189a3867-3050-52da-a836-e630ba90ab69"
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Requires = "ae029012-a4dd-5104-9daa-d747884805df"
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SciMLOperators = "c0aeaf25-5076-4817-a8d5-81caf7dfa961"
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Setfield = "efcf1570-3423-57d1-acb7-fd33fddbac46"
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SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
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StaticArrayInterface = "0d7ed370-da01-4f52-bd93-41d350b8b718"
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StaticArrays = "90137ffa-7385-5640-81b9-e52037218182"
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Tricks = "410a4b4d-49e4-4fbc-ab6d-cb71b17b3775"
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UnPack = "3a884ed6-31ef-47d7-9d2a-63182c4928ed"
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VertexSafeGraphs = "19fa3120-7c27-5ec5-8db8-b0b0aa330d6f"
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[weakdeps]
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Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
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Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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[extensions]
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SparseDiffToolsEnzymeExt = "Enzyme"
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SparseDiffToolsSymbolicsExt = "Symbolics"
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SparseDiffToolsZygoteExt = "Zygote"
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[compat]
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ADTypes = "0.1"
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ADTypes = "0.2.1"
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Adapt = "3.0"
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ArrayInterface = "7.4.2"
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Compat = "4"
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DataStructures = "0.18"
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Enzyme = "0.11"
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FiniteDiff = "2.8.1"
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ForwardDiff = "0.10"
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Graphs = "1"
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Reexport = "1"
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Requires = "1"
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SciMLOperators = "0.2.11, 0.3"
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Setfield = "1"
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StaticArrayInterface = "1.3"
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StaticArrays = "1"
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Symbolics = "5.5"
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Tricks = "0.1.6"
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UnPack = "1"
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VertexSafeGraphs = "0.2"
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Zygote = "0.6"
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julia = "1.6"
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ArrayInterfaceBlockBandedMatrices = "5331f1e9-51c7-46b0-a9b0-df4434785e0a"
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BandedMatrices = "aae01518-5342-5314-be14-df237901396f"
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BlockBandedMatrices = "ffab5731-97b5-5995-9138-79e8c1846df0"
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Enzyme = "7da242da-08ed-463a-9acd-ee780be4f1d9"
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IterativeSolvers = "42fd0dbc-a981-5370-80f2-aaf504508153"
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Pkg = "44cfe95a-1eb2-52ea-b672-e2afdf69b78f"
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Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
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Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
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[targets]
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test = ["Test", "ArrayInterfaceBandedMatrices", "ArrayInterfaceBlockBandedMatrices", "BandedMatrices", "BlockBandedMatrices", "IterativeSolvers", "Pkg", "Random", "SafeTestsets", "Symbolics", "Zygote", "StaticArrays"]
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test = ["Test", "ArrayInterfaceBandedMatrices", "ArrayInterfaceBlockBandedMatrices", "BandedMatrices", "BlockBandedMatrices", "Enzyme", "IterativeSolvers", "Pkg", "Random", "SafeTestsets", "Symbolics", "Zygote", "StaticArrays"]

‎README.md‎

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end
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```
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## High Level API
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We need to perform the following steps to utilize SparseDiffTools:
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1. Specify a Sparsity Detection Algorithm. There are 3 possible choices currently:
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1. `NoSparsityDetection`: This will ignore any AD choice and compute the dense Jacobian
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2. `JacPrototypeSparsityDetection`: If you already know the sparsity pattern, you can
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specify it as `JacPrototypeSparsityDetection(; jac_prototype=<sparsity pattern>)`.
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3. `SymbolicsSparsityDetection`: This will use `Symbolics.jl` to automatically detect
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the sparsity pattern. (Note that `Symbolics.jl` must be explicitly loaded before
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using this functionality.)
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2. Now choose an AD backend from `ADTypes.jl`:
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1. If using a Non `*Sparse*` type, then we will not use sparsity detection.
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2. All other sparse AD types will internally compute the proper sparsity pattern, and
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try to exploit that.
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3. Now there are 2 options:
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1. Precompute the cache using `sparse_jacobian_cache` and use the `sparse_jacobian` or
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`sparse_jacobian!` functions to compute the Jacobian. This option is recommended if
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you are repeatedly computing the Jacobian for the same function.
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2. Directly use `sparse_jacobian` or `sparse_jacobian!` to compute the Jacobian. This
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option should be used if you are only computing the Jacobian once.
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```julia
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using Symbolics
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sd = SymbolicsSparsityDetection()
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adtype = AutoSparseFiniteDiff()
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x = rand(30)
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y = similar(x)
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# Option 1
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## OOP Function
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cache = sparse_jacobian_cache(adtype, sd, g, x; fx=y) # Passing `fx` is needed if size(y) != size(x)
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J = sparse_jacobian(adtype, cache, g, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, cache, g, x)
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## IIP Function
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cache = sparse_jacobian_cache(adtype, sd, f, y, x)
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J = sparse_jacobian(adtype, cache, f, y, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, cache, f, y, x)
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# Option 2
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## OOP Function
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J = sparse_jacobian(adtype, sd, g, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, sd, g, x)
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## IIP Function
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J = sparse_jacobian(adtype, sd, f, y, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, sd, f, y, x)
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```
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## Lower Level API
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For this function, we know that the sparsity pattern of the Jacobian is a
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`Tridiagonal` matrix. However, if we didn't know the sparsity pattern for
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the Jacobian, we could use the `Symbolics.jacobian_sparsity` function to automatically

‎docs/make.jl‎

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include("pages.jl")
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makedocs(sitename = "SparseDiffTools.jl",
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authors = "Chris Rackauckas",
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modules = [SparseDiffTools],
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clean = true,
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doctest = false,
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format = Documenter.HTML(assets = ["assets/favicon.ico"],
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canonical = "https://docs.sciml.ai/SparseDiffTools/stable/"),
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pages = pages)
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authors = "Chris Rackauckas",
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modules = [SparseDiffTools],
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clean = true,
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doctest = false,
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format = Documenter.HTML(assets = ["assets/favicon.ico"],
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canonical = "https://docs.sciml.ai/SparseDiffTools/stable/"),
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pages = pages)
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deploydocs(repo = "github.com/JuliaDiff/SparseDiffTools.jl.git";
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push_preview = true)
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push_preview = true)

‎docs/src/index.md‎

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end
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```
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## High Level API
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We need to perform the following steps to utilize SparseDiffTools:
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1. Specify a Sparsity Detection Algorithm. There are 3 possible choices currently:
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1. `NoSparsityDetection`: This will ignore any AD choice and compute the dense Jacobian
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2. `JacPrototypeSparsityDetection`: If you already know the sparsity pattern, you can
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specify it as `JacPrototypeSparsityDetection(; jac_prototype=<sparsity pattern>)`.
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3. `SymbolicsSparsityDetection`: This will use `Symbolics.jl` to automatically detect
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the sparsity pattern. (Note that `Symbolics.jl` must be explicitly loaded before
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using this functionality.)
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2. Now choose an AD backend from `ADTypes.jl`:
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1. If using a Non `*Sparse*` type, then we will not use sparsity detection.
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2. All other sparse AD types will internally compute the proper sparsity pattern, and
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try to exploit that.
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3. Now there are 2 options:
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1. Precompute the cache using `sparse_jacobian_cache` and use the `sparse_jacobian` or
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`sparse_jacobian!` functions to compute the Jacobian. This option is recommended if
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you are repeatedly computing the Jacobian for the same function.
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2. Directly use `sparse_jacobian` or `sparse_jacobian!` to compute the Jacobian. This
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option should be used if you are only computing the Jacobian once.
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```julia
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using Symbolics
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sd = SymbolicsSparsityDetection()
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adtype = AutoSparseFiniteDiff()
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x = rand(30)
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y = similar(x)
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# Option 1
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## OOP Function
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cache = sparse_jacobian_cache(adtype, sd, g, x; fx=y) # Passing `fx` is needed if size(y) != size(x)
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J = sparse_jacobian(adtype, cache, g, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, cache, g, x)
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## IIP Function
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cache = sparse_jacobian_cache(adtype, sd, f, y, x)
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J = sparse_jacobian(adtype, cache, f, y, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, cache, f, y, x)
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# Option 2
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## OOP Function
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J = sparse_jacobian(adtype, sd, g, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, sd, g, x)
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## IIP Function
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J = sparse_jacobian(adtype, sd, f, y, x)
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### Or
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J_preallocated = similar(J)
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sparse_jacobian!(J_preallocated, adtype, sd, f, y, x)
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```
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## Lower Level API
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For this function, we know that the sparsity pattern of the Jacobian is a
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`Tridiagonal` matrix. However, if we didn't know the sparsity pattern for
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the Jacobian, we could use the `Symbolics.jacobian_sparsity` function to automatically

‎ext/SparseDiffToolsEnzymeExt.jl‎

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module SparseDiffToolsEnzymeExt
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import ArrayInterface: fast_scalar_indexing
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import SparseDiffTools: __f̂,
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__maybe_copy_x, __jacobian!, __gradient, __gradient!, AutoSparseEnzyme
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# FIXME: For Enzyme we currently assume reverse mode
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import ADTypes: AutoEnzyme
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using Enzyme
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using ForwardDiff
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## Satisfying High-Level Interface for Sparse Jacobians
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function __gradient(::Union{AutoSparseEnzyme, AutoEnzyme}, f, x, cols)
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dx = zero(x)
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autodiff(Reverse, __f̂, Const(f), Duplicated(x, dx), Const(cols))
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return vec(dx)
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end
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function __gradient!(::Union{AutoSparseEnzyme, AutoEnzyme}, f!, fx, x, cols)
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dx = zero(x)
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dfx = zero(fx)
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autodiff(Reverse, __f̂, Active, Const(f!), Duplicated(fx, dfx), Duplicated(x, dx),
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Const(cols))
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return dx
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end
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function __jacobian!(J::AbstractMatrix, ::Union{AutoSparseEnzyme, AutoEnzyme}, f, x)
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J .= jacobian(Reverse, f, x, Val(size(J, 1)))
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return J
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end
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@views function __jacobian!(J, ad::Union{AutoSparseEnzyme, AutoEnzyme}, f!, fx, x)
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# This version is slowish not sure how to do jacobians for inplace functions
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@warn "Current code for computing jacobian for inplace functions in Enzyme is slow." maxlog=1
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dfx = zero(fx)
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dx = zero(x)
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function __f_row_idx(f!, fx, x, row_idx)
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f!(fx, x)
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if fast_scalar_indexing(fx)
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return fx[row_idx]
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else
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# Avoid the GPU Arrays scalar indexing error
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return sum(selectdim(fx, 1, row_idx:row_idx))
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end
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end
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for row_idx in 1:size(J, 1)
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autodiff(Reverse, __f_row_idx, Const(f!), DuplicatedNoNeed(fx, dfx),
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Duplicated(x, dx), Const(row_idx))
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J[row_idx, :] .= dx
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fill!(dfx, 0)
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fill!(dx, 0)
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end
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return J
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end
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__maybe_copy_x(::Union{AutoSparseEnzyme, AutoEnzyme}, x::SubArray) = copy(x)
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end

‎ext/SparseDiffToolsSymbolicsExt.jl‎

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module SparseDiffToolsSymbolicsExt
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using SparseDiffTools, Symbolics
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import SparseDiffTools: AbstractSparseADType
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function (alg::SymbolicsSparsityDetection)(ad::AbstractSparseADType, f, x; fx = nothing,
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kwargs...)
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fx = fx === nothing ? similar(f(x)) : dx
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f!(y, x) = (y .= f(x))
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J = Symbolics.jacobian_sparsity(f!, fx, x)
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_alg = JacPrototypeSparsityDetection(J, alg.alg)
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return _alg(ad, f, x; fx, kwargs...)
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end
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function (alg::SymbolicsSparsityDetection)(ad::AbstractSparseADType, f!, fx, x; kwargs...)
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J = Symbolics.jacobian_sparsity(f!, fx, x)
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_alg = JacPrototypeSparsityDetection(J, alg.alg)
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return _alg(ad, f!, fx, x; kwargs...)
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end
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end

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