Use SparseConnectivityTracer.jl

Author: amontoison

Project.toml

@@ -3,16 +3,20 @@ uuid = "54578032-b7ea-4c30-94aa-7cbd1cce6c9a"
 version = "0.7.2"
 
 [deps]
+ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"
 ColPack = "ffa27691-3a59-46ab-a8d4-551f45b8d401"
 ForwardDiff = "f6369f11-7733-5829-9624-2563aa707210"
 LinearAlgebra = "37e2e46d-f89d-539d-b4ee-838fcccc9c8e"
 NLPModels = "a4795742-8479-5a88-8948-cc11e1c8c1a6"
 Requires = "ae029012-a4dd-5104-9daa-d747884805df"
 ReverseDiff = "37e2e3b7-166d-5795-8a7a-e32c996b4267"
 SparseArrays = "2f01184e-e22b-5df5-ae63-d93ebab69eaf"
+SparseConnectivityTracer = "9f842d2f-2579-4b1d-911e-f412cf18a3f5"
 
 [compat]
+ADTypes = "1.2.1"
 ColPack = "0.4"
+SparseConnectivityTracer = "0.5"
 ForwardDiff = "0.9.0, 0.10.0"
 NLPModels = "0.18, 0.19, 0.20, 0.21"
 Requires = "1"

README.md

@@ -96,8 +96,6 @@ The following AD packages are supported:
 and as optional dependencies (you must load the package before):
 
 - `Enzyme.jl`;
-- `SparseDiffTools.jl`;
-- `Symbolics.jl`;
 - `Zygote.jl`.
 
 ## Bug reports and discussions

docs/Project.toml

@@ -10,8 +10,6 @@ OptimizationProblems = "5049e819-d29b-5fba-b941-0eee7e64c1c6"
 Percival = "01435c0c-c90d-11e9-3788-63660f8fbccc"
 Plots = "91a5bcdd-55d7-5caf-9e0b-520d859cae80"
 SolverBenchmark = "581a75fa-a23a-52d0-a590-d6201de2218a"
-SymbolicUtils = "d1185830-fcd6-423d-90d6-eec64667417b"
-Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7"
 Zygote = "e88e6eb3-aa80-5325-afca-941959d7151f"
 
 [compat]
@@ -24,6 +22,4 @@ OptimizationProblems = "0.7"
 Percival = "0.7"
 Plots = "1"
 SolverBenchmark = "0.5"
-SymbolicUtils = "=1.5.1"
-Symbolics = "5.3"
 Zygote = "0.6.62"

docs/src/backend.md

@@ -7,14 +7,14 @@ The backend information is in a structure [`ADNLPModels.ADModelBackend`](@ref) i
 
 The functions used internally to define the NLPModel API and the possible backends are defined in the following table:
 
-| Functions | FowardDiff backends | ReverseDiff backends | Zygote backends | Enzyme backend | SparseDiffTools backend | Symbolics backend |
+| Functions | FowardDiff backends | ReverseDiff backends | Zygote backends | Enzyme backend | Sparse backend |
 | ----------- | ----------- | ----------- | ----------- |  ----------- |  ----------- |  ----------- |
-| `gradient` and `gradient!` | `ForwardDiffADGradient`/`GenericForwardDiffADGradient` | `ReverseDiffADGradient`/`GenericReverseDiffADGradient` | `ZygoteADGradient` | `EnzymeADGradient` | -- | -- |
-| `jacobian` | `ForwardDiffADJacobian` | `ReverseDiffADJacobian` | `ZygoteADJacobian` | -- | `SDTSparseADJacobian` | `SparseADJacobian`/`SparseSymbolicsADJacobian` |
-| `hessian` | `ForwardDiffADHessian` | `ReverseDiffADHessian` | `ZygoteADHessian` | -- | -- | `SparseADHessian`/`SparseSymbolicsADHessian` |
-| `Jprod` | `ForwardDiffADJprod`/`GenericForwardDiffADJprod` | `ReverseDiffADJprod`/`GenericReverseDiffADJprod` | `ZygoteADJprod` | -- | `SDTForwardDiffADJprod` | -- | 
-| `Jtprod` | `ForwardDiffADJtprod`/`GenericForwardDiffADJtprod` | `ReverseDiffADJtprod`/`GenericReverseDiffADJtprod` | `ZygoteADJtprod` | -- | -- | -- | 
-| `Hvprod` | `ForwardDiffADHvprod`/`GenericForwardDiffADHvprod` | `ReverseDiffADHvprod`/`GenericReverseDiffADHvprod` | -- | -- | `SDTForwardDiffADHvprod` | -- | 
+| `gradient` and `gradient!` | `ForwardDiffADGradient`/`GenericForwardDiffADGradient` | `ReverseDiffADGradient`/`GenericReverseDiffADGradient` | `ZygoteADGradient` | `EnzymeADGradient` | -- |
+| `jacobian` | `ForwardDiffADJacobian` | `ReverseDiffADJacobian` | `ZygoteADJacobian` | -- | `SparseADJacobian` |
+| `hessian` | `ForwardDiffADHessian` | `ReverseDiffADHessian` | `ZygoteADHessian` | -- | `SparseADHessian` |
+| `Jprod` | `ForwardDiffADJprod`/`GenericForwardDiffADJprod` | `ReverseDiffADJprod`/`GenericReverseDiffADJprod` | `ZygoteADJprod` | -- |
+| `Jtprod` | `ForwardDiffADJtprod`/`GenericForwardDiffADJtprod` | `ReverseDiffADJtprod`/`GenericReverseDiffADJtprod` | `ZygoteADJtprod` | -- |
+| `Hvprod` | `ForwardDiffADHvprod`/`GenericForwardDiffADHvprod` | `ReverseDiffADHvprod`/`GenericReverseDiffADHvprod` | -- |
 | `directional_second_derivative` | `ForwardDiffADGHjvprod` | -- | -- | -- | -- | 
 
 The functions `hess_structure!`, `hess_coord!`, `jac_structure!` and `jac_coord!` defined in `ad.jl` are generic to all the backends for now.
@@ -49,7 +49,7 @@ Thanks to the backends inside `ADNLPModels.jl`, it is easy to change the backend
 
 ```@example adnlp
 nlp = ADNLPModel(f, x0, gradient_backend = ADNLPModels.ReverseDiffADGradient)
-grad(nlp, nlp.meta.x0) # returns the gradient at x0 using `ReverseDiff`
+grad(nlp, nlp.meta.x0)  # returns the gradient at x0 using `ReverseDiff`
 ```
 
 It is also possible to try some new implementation for each function. First, we define a new `ADBackend` structure.
@@ -81,7 +81,7 @@ Finally, we use the homemade backend to compute the gradient.
 
 ```@example adnlp
 nlp = ADNLPModel(sum, ones(3), gradient_backend = NewADGradient)
-grad(nlp, nlp.meta.x0) # returns the gradient at x0 using `NewADGradient`
+grad(nlp, nlp.meta.x0)  # returns the gradient at x0 using `NewADGradient`
 ```
 
 ### Change backend
@@ -104,7 +104,7 @@ set_adbackend!(nlp, adback)
 get_adbackend(nlp)
 ```
 
-The alternative is to use ``set_adbackend!` and pass the new backends via `kwargs`. In the second approach, it is possible to pass either the type of the desired backend or an instance as shown below.
+The alternative is to use `set_adbackend!` and pass the new backends via `kwargs`. In the second approach, it is possible to pass either the type of the desired backend or an instance as shown below.
 
 ```@example adnlp2
 set_adbackend!(

docs/src/performance.md

@@ -84,7 +84,7 @@ v = ones(2)
 It is tempting to define the most generic and efficient `ADNLPModel` from the start.
 
 ```@example ex2
-using ADNLPModels, NLPModels, Symbolics
+using ADNLPModels, NLPModels
 f(x) = (x[1] - x[2])^2
 x0 = ones(2)
 lcon = ucon = ones(1)

docs/src/predefined.md

@@ -49,30 +49,12 @@ get_adbackend(nlp)
 
 ## Hessian and Jacobian computations
 
-It is to be noted that by default the Jacobian and Hessian matrices are dense.
+It is to be noted that by default the Jacobian and Hessian matrices are sparse.
 
 ```@example ex1
-(get_nnzj(nlp), get_nnzh(nlp)) # number of nonzeros elements in the Jacobian and Hessian
+(get_nnzj(nlp), get_nnzh(nlp))  # number of nonzeros elements in the Jacobian and Hessian
 ```
 
-To enable sparse computations of these entries, one needs to first load the package [`Symbolics.jl`](https://github.com/JuliaSymbolics/Symbolics.jl)
-
-```@example ex1
-using Symbolics
-```
-
-and now
-
-```@example ex1
-ADNLPModels.predefined_backend[:optimized][:jacobian_backend]
-```
-
-```@example ex1
-ADNLPModels.predefined_backend[:optimized][:hessian_backend]
-```
-
-Choosing another optimization problem with the optimized backend will compute sparse Jacobian and Hessian matrices.
-
 ```@example ex1
 f(x) = (x[1] - 1)^2
 T = Float64
@@ -92,4 +74,6 @@ x = rand(T, 2)
 jac(nlp, x)
 ```
 
-The package [`Symbolics.jl`](https://github.com/JuliaSymbolics/Symbolics.jl) is used to compute the sparsity pattern of the sparse matrix. The evaluation of the number of directional derivatives needed to evaluate the matrix is done by [`ColPack.jl`](https://github.com/michel2323/ColPack.jl).
+The package [`SparseConnectivityTracer.jl`](https://github.com/adrhill/SparseConnectivityTracer.jl) is used to compute the sparsity pattern of Jacobians and Hessians.
+The evaluation of the number of directional derivatives and the seeds needed to evaluate the compressed Jacobians and Hessians is done by [`ColPack.jl`](https://github.com/exanauts/ColPack.jl).
+We acknowledge Guillaume Dalle (@gdalle), Adrian Hill (@adrhill), and Michel Schanen (@michel2323) for the development of these packages.

docs/src/reference.md

@@ -1,17 +1,17 @@
 # Reference
-​
+
 ## Contents
-​
+
 ```@contents
 Pages = ["reference.md"]
 ```
-​
+
 ## Index
-​
+
 ```@index
 Pages = ["reference.md"]
 ```
-​
+
 ```@autodocs
 Modules = [ADNLPModels]
-```
+```

src/ADNLPModels.jl

@@ -2,8 +2,11 @@ module ADNLPModels
 
 # stdlib
 using LinearAlgebra, SparseArrays
+
 # external
-using ColPack, ForwardDiff, ReverseDiff
+using ADTypes: ADTypes, AbstractSparsityDetector
+using SparseConnectivityTracer, ColPack, ForwardDiff, ReverseDiff
+
 # JSO
 using NLPModels
 using Requires
@@ -16,39 +19,13 @@ const ADModel{T, S} = Union{AbstractADNLPModel{T, S}, AbstractADNLSModel{T, S}}
 include("ad.jl")
 include("ad_api.jl")
 
-"""
-    compute_jacobian_sparsity(c!, cx, x0)
-
-Return a sparse matrix.
-"""
-function compute_jacobian_sparsity(args...)
-  throw(
-    ArgumentError(
-      "Please load Symbolics.jl to enable sparse Jacobian or implement `compute_jacobian_sparsity`.",
-    ),
-  )
-end
-
-"""
-    compute_hessian_sparsity(f, nvar, c!, ncon)
-
-Return a sparse matrix.
-"""
-function compute_hessian_sparsity(args...)
-  throw(
-    ArgumentError(
-      "Please load Symbolics.jl to enable sparse Hessian or implement `compute_hessian_sparsity`.",
-    ),
-  )
-end
-
+include("sparsity_pattern.jl")
 include("sparse_jacobian.jl")
 include("sparse_hessian.jl")
 
 include("forward.jl")
 include("reverse.jl")
 include("enzyme.jl")
-include("sparse_diff_tools.jl")
 include("zygote.jl")
 include("predefined_backend.jl")
 include("nlp.jl")
@@ -181,20 +158,6 @@ function ADNLSModel!(model::AbstractNLSModel; kwargs...)
   end
 end
 
-@init begin
-  @require Symbolics = "0c5d862f-8b57-4792-8d23-62f2024744c7" begin
-    include("sparse_sym.jl")
-
-    predefined_backend[:default][:jacobian_backend] = SparseADJacobian
-    predefined_backend[:default][:jacobian_residual_backend] = SparseADJacobian
-    predefined_backend[:optimized][:jacobian_backend] = SparseADJacobian
-    predefined_backend[:optimized][:jacobian_residual_backend] = SparseADJacobian
-
-    predefined_backend[:default][:hessian_backend] = SparseADHessian
-    predefined_backend[:optimized][:hessian_backend] = SparseReverseADHessian
-  end
-end
-
 export get_adbackend, set_adbackend!
 
 """

src/predefined_backend.jl

@@ -3,13 +3,13 @@ default_backend = Dict(
   :hprod_backend => ForwardDiffADHvprod,
   :jprod_backend => ForwardDiffADJprod,
   :jtprod_backend => ForwardDiffADJtprod,
-  :jacobian_backend => ForwardDiffADJacobian,
-  :hessian_backend => ForwardDiffADHessian,
+  :jacobian_backend => SparseADJacobian, # ForwardDiffADJacobian
+  :hessian_backend => SparseADHessian, # ForwardDiffADHessian
   :ghjvprod_backend => ForwardDiffADGHjvprod,
   :hprod_residual_backend => ForwardDiffADHvprod,
   :jprod_residual_backend => ForwardDiffADJprod,
   :jtprod_residual_backend => ForwardDiffADJtprod,
-  :jacobian_residual_backend => ForwardDiffADJacobian,
+  :jacobian_residual_backend => SparseADJacobian, # ForwardDiffADJacobian,
   :hessian_residual_backend => ForwardDiffADHessian,
 )
 
@@ -18,13 +18,13 @@ optimized = Dict(
   :hprod_backend => ReverseDiffADHvprod,
   :jprod_backend => ForwardDiffADJprod,
   :jtprod_backend => ReverseDiffADJtprod,
-  :jacobian_backend => ForwardDiffADJacobian,
-  :hessian_backend => ForwardDiffADHessian,
+  :jacobian_backend => SparseADJacobian, # ForwardDiffADJacobian
+  :hessian_backend => SparseReverseADHessian, # ForwardDiffADHessian,
   :ghjvprod_backend => ForwardDiffADGHjvprod,
   :hprod_residual_backend => ReverseDiffADHvprod,
   :jprod_residual_backend => ForwardDiffADJprod,
   :jtprod_residual_backend => ReverseDiffADJtprod,
-  :jacobian_residual_backend => ForwardDiffADJacobian,
+  :jacobian_residual_backend => SparseADJacobian, # ForwardDiffADJacobian
   :hessian_residual_backend => ForwardDiffADHessian,
 )