Consistently refer to TracerSparsityDetector as detector (#191)

instead of `method`

benchmark/hessian.jl

@@ -9,10 +9,10 @@ Test cases taken from the article:
 > https://www.tandfonline.com/doi/full/10.1080/10556788.2018.1480625
 =#
 
-function hessbench(method)
+function hessbench(detector)
     suite = BenchmarkGroup()
-    suite["ArrowHead"] = hessbench_arrowhead(method)
-    suite["RandomSparsity"] = hessbench_randomsparsity(method)
+    suite["ArrowHead"] = hessbench_arrowhead(detector)
+    suite["RandomSparsity"] = hessbench_randomsparsity(detector)
     return suite
 end
 
@@ -32,7 +32,7 @@ function (ah::ArrowHead)(x::AbstractVector)
     end
 end
 
-function hessbench_arrowhead(method)
+function hessbench_arrowhead(detector)
     suite = BenchmarkGroup()
     # Commented-out cases (N, K) are included in the JuMP paper linked above,
     # but excluded from to accelerate the benchmark suite.
@@ -48,7 +48,7 @@ function hessbench_arrowhead(method)
     ]
         x = rand(N)
         f = ArrowHead(K)
-        suite["N=$N, K=$K"] = @benchmarkable hessian_sparsity($f, $x, $method)
+        suite["N=$N, K=$K"] = @benchmarkable hessian_sparsity($f, $x, $detector)
     end
     return suite
 end
@@ -70,7 +70,7 @@ function (rs::RandomSparsity)(x::AbstractVector)
     end
 end
 
-function hessbench_randomsparsity(method)
+function hessbench_randomsparsity(detector)
     suite = BenchmarkGroup()
     # Commented-out cases (N, K) are included in the JuMP paper linked above,
     # but excluded from to accelerate the benchmark suite.
@@ -86,7 +86,7 @@ function hessbench_randomsparsity(method)
     ]
         x = rand(N)
         f = RandomSparsity(N, K)
-        suite["N=$N, K=$K"] = @benchmarkable hessian_sparsity($f, $x, $method)
+        suite["N=$N, K=$K"] = @benchmarkable hessian_sparsity($f, $x, $detector)
     end
     return suite
 end

benchmark/jacobian.jl

@@ -7,11 +7,11 @@ using SparseArrays: sprand
 using SimpleDiffEq: ODEProblem, solve, SimpleEuler
 using Flux: Conv
 
-function jacbench(method)
+function jacbench(detector)
     suite = BenchmarkGroup()
-    suite["SparseMul"] = jacbench_sparsemul(method)
-    suite["Brusselator"] = jacbench_brusselator(method)
-    suite["Conv"] = jacbench_conv(method)
+    suite["SparseMul"] = jacbench_sparsemul(detector)
+    suite["Brusselator"] = jacbench_brusselator(detector)
+    suite["Conv"] = jacbench_conv(detector)
     return suite
 end
 
@@ -35,48 +35,48 @@ function (ism::IteratedSparseMul)(x::AbstractVector)
     return y
 end
 
-function jacbench_sparsemul(method)
+function jacbench_sparsemul(detector)
     suite = BenchmarkGroup()
     for n in [50], p in [0.01, 0.25], depth in [5]
         x = rand(n)
         f = IteratedSparseMul(; n, p, depth)
         suite["n=$n, p=$p, depth=$depth"] = @benchmarkable jacobian_sparsity(
-            $f, $x, $method
+            $f, $x, $detector
         )
     end
     return suite
 end
 
 ## Brusselator
 
-function jacbench_brusselator(method)
+function jacbench_brusselator(detector)
     suite = BenchmarkGroup()
     for N in (6, 24)
         f! = Brusselator!(N)
         x = rand(N, N, 2)
         y = similar(x)
-        suite["operator"]["N=$N"] = @benchmarkable jacobian_sparsity($f!, $y, $x, $method)
+        suite["operator"]["N=$N"] = @benchmarkable jacobian_sparsity($f!, $y, $x, $detector)
         solver = SimpleEuler()
         prob = ODEProblem(brusselator_2d_loop!, x, (0.0, 1.0), f!.params)
         function brusselator_ode_solve(x)
             return solve(ODEProblem(brusselator_2d_loop!, x, (0.0, 1.0), f!.params), solver; dt=0.5).u[end]
         end
         suite["ODE"]["N=$N"] = @benchmarkable jacobian_sparsity(
-            $brusselator_ode_solve, $x, $method
+            $brusselator_ode_solve, $x, $detector
         )
     end
     return suite
 end
 
 ## Convolution
 
-function jacbench_conv(method)
+function jacbench_conv(detector)
     # TODO: benchmark local sparsity tracers on LeNet-5 CNN
     layer = Conv((5, 5), 3 => 2)
     suite = BenchmarkGroup()
     for N in (28, 128)
         suite["N=$N"] = @benchmarkable jacobian_sparsity(
-            $layer, $(rand(N, N, 3, 1)), $method
+            $layer, $(rand(N, N, 3, 1)), $detector
         )
     end
     return suite

docs/src/user/global_vs_local.md

@@ -24,28 +24,28 @@ These are computed by [`TracerLocalSparsityDetector`](@ref):
 
 ```@repl localvsglobal
 using SparseConnectivityTracer
-method = TracerLocalSparsityDetector();
+detector = TracerLocalSparsityDetector();
 
 f(x) = x[1]*x[2];
 
-jacobian_sparsity(f, [1, 1], method)
-jacobian_sparsity(f, [0, 1], method)
-jacobian_sparsity(f, [1, 0], method)
-jacobian_sparsity(f, [0, 0], method)
+jacobian_sparsity(f, [1, 1], detector)
+jacobian_sparsity(f, [0, 1], detector)
+jacobian_sparsity(f, [1, 0], detector)
+jacobian_sparsity(f, [0, 0], detector)
 ```
 
 In contrast to this, [`TracerSparsityDetector`](@ref) computes a conservative union over all sparsity patterns in $\mathbf{x} \in \mathbb{R}^2$.
 The resulting **global** pattern therefore does not depend on the input.
 All of the following function calls are equivalent:
 
 ```@repl localvsglobal
-method = TracerSparsityDetector();
+detector = TracerSparsityDetector()
 
-jacobian_sparsity(f, [1, 1], method)
-jacobian_sparsity(f, [0, 1], method)
-jacobian_sparsity(f, [1, 0], method)
-jacobian_sparsity(f, [0, 0], method)
-jacobian_sparsity(f, rand(2), method)
+jacobian_sparsity(f, [1, 1], detector)
+jacobian_sparsity(f, [0, 1], detector)
+jacobian_sparsity(f, [1, 0], detector)
+jacobian_sparsity(f, [0, 0], detector)
+jacobian_sparsity(f, rand(2), detector)
 ```
 
 !!! tip "Global vs. Local"

docs/src/user/limitations.md

@@ -24,7 +24,7 @@ it must be written generically enough to accept numbers of type `T<:Real` as (or
 
     ```@example notgeneric
     using SparseConnectivityTracer
-    method = TracerSparsityDetector()
+    detector = TracerSparsityDetector()
 
     relu_bad(x::AbstractFloat) = max(zero(x), x)
     outer_function_bad(xs) = sum(relu_bad, xs)
@@ -39,7 +39,7 @@ it must be written generically enough to accept numbers of type `T<:Real` as (or
 
     outer_function_bad(xs)
 
-    jacobian_sparsity(outer_function_bad, xs, method)
+    jacobian_sparsity(outer_function_bad, xs, detector)
     ```
 
     This is easily fixed by loosening type restrictions or adding an additional methods on `Real`:
@@ -51,7 +51,7 @@ it must be written generically enough to accept numbers of type `T<:Real` as (or
     ```
 
     ```@repl notgeneric
-    jacobian_sparsity(outer_function_good, xs, method)
+    jacobian_sparsity(outer_function_good, xs, detector)
     ```
 
 ## Limited control flow

test/brusselator.jl

@@ -9,18 +9,18 @@ using Test
 # Load definitions of GRADIENT_TRACERS, GRADIENT_PATTERNS, HESSIAN_TRACERS and HESSIAN_PATTERNS
 include("tracers_definitions.jl")
 
-function test_brusselator(method::AbstractSparsityDetector)
+function test_brusselator(detector::AbstractSparsityDetector)
     N = 6
     f! = Brusselator!(N)
     x = rand(N, N, 2)
     y = similar(x)
 
-    J = ADTypes.jacobian_sparsity(f!, y, x, method)
+    J = ADTypes.jacobian_sparsity(f!, y, x, detector)
     @test_reference "references/pattern/jacobian/Brusselator.txt" BitMatrix(J)
 end
 
 @testset "$T" for T in GRADIENT_TRACERS
-    method = TracerSparsityDetector(; gradient_tracer_type=T)
-    test_brusselator(method)
+    detector = TracerSparsityDetector(; gradient_tracer_type=T)
+    test_brusselator(detector)
     yield()
 end

test/ext/test_LogExpFunctions.jl

@@ -28,8 +28,8 @@ lef_1_to_1 = union(lef_1_to_1_pos_input, lef_1_to_1_neg_input)
 lef_2_to_1 = (xlogy, xlog1py, xexpy, logaddexp, logsubexp)
 
 @testset "Jacobian Global" begin
-    method = TracerSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in lef_1_to_1
@@ -44,8 +44,8 @@ lef_2_to_1 = (xlogy, xlog1py, xexpy, logaddexp, logsubexp)
 end
 
 @testset "Jacobian Local" begin
-    method = TracerLocalSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in lef_1_to_1_pos_input
@@ -63,8 +63,8 @@ end
 end
 
 @testset "Hessian Global" begin
-    method = TracerSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in lef_1_to_1
@@ -79,8 +79,8 @@ end
 end
 
 @testset "Hessian Local" begin
-    method = TracerLocalSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in lef_1_to_1_pos_input

test/ext/test_NNlib.jl

@@ -32,17 +32,17 @@ NNLIB_ACTIVATIONS_F = (
 NNLIB_ACTIVATIONS = union(NNLIB_ACTIVATIONS_S, NNLIB_ACTIVATIONS_F)
 
 @testset "Jacobian Global" begin
-    method = TracerSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @testset "$f" for f in NNLIB_ACTIVATIONS
         @test J(f, 1) ≈ [1;;]
     end
 end
 
 @testset "Jacobian Local" begin
-    method = TracerLocalSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @test J(NNlib.relu, -1) ≈ [0;;]
     @test J(NNlib.relu, 1) ≈ [1;;]
@@ -82,8 +82,8 @@ end
 end
 
 @testset "Global Hessian" begin
-    method = TracerSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @testset "First-order differentiable" begin
         @testset "$f" for f in NNLIB_ACTIVATIONS_F
@@ -98,8 +98,8 @@ end
 end
 
 @testset "Local Hessian" begin
-    method = TracerLocalSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @test H(NNlib.relu, -1) ≈ [0;;]
     @test H(NNlib.relu, 1) ≈ [0;;]

test/ext/test_NaNMath.jl

@@ -22,8 +22,8 @@ nan_1_to_1 = (
 )
 
 @testset "Jacobian Global" begin
-    method = TracerSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in nan_1_to_1
@@ -38,8 +38,8 @@ nan_1_to_1 = (
 end
 
 @testset "Jacobian Local" begin
-    method = TracerLocalSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @testset "2-to-1 functions" begin
         @test J(x -> NaNMath.max(x[1], x[2]), [1.0, 2.0, 0.0]) == [0 1 0]
@@ -50,8 +50,8 @@ end
 end
 
 @testset "Hessian Global" begin
-    method = TracerSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @testset "1-to-1 functions" begin
         @testset "$f" for f in nan_1_to_1
@@ -66,8 +66,8 @@ end
 end
 
 @testset "Hessian Local" begin
-    method = TracerLocalSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerLocalSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @testset "2-to-1 functions" begin
         @test H(x -> NaNMath.max(x[1], x[2]), [1.0, 2.0, 0.0]) == zeros(Bool, 3, 3)

test/ext/test_SpecialFunctions.jl

@@ -7,22 +7,22 @@ using Test
 include("../tracers_definitions.jl")
 
 @testset "Jacobian Global" begin
-    method = TracerSparsityDetector()
-    J(f, x) = jacobian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    J(f, x) = jacobian_sparsity(f, x, detector)
 
     @test J(x -> erf(x[1]), rand(2)) == [1 0]
     @test J(x -> beta(x[1], x[2]), rand(3)) == [1 1 0]
 end
 
 # TODO: add tests
 # @testset "Jacobian Local" begin
-#     method = TracerLocalSparsityDetector()
-#     J(f, x) = jacobian_sparsity(f, x, method)
+#     detector = TracerLocalSparsityDetector()
+#     J(f, x) = jacobian_sparsity(f, x, detector)
 # end
 
 @testset "Global Hessian" begin
-    method = TracerSparsityDetector()
-    H(f, x) = hessian_sparsity(f, x, method)
+    detector = TracerSparsityDetector()
+    H(f, x) = hessian_sparsity(f, x, detector)
 
     @test H(x -> erf(x[1]), rand(2)) == [
         1 0
@@ -37,6 +37,6 @@ end
 
 # TODO: add tests
 # @testset "Local Hessian" begin
-#     method = TracerLocalSparsityDetector()
-#     H(f, x) = hessian_sparsity(f, x, method)
+#     detector = TracerLocalSparsityDetector()
+#     H(f, x) = hessian_sparsity(f, x, detector)
 # end