Friendly errors on array vs. scalar mistakes (#489)

* friendly errors for output type f(x) * friendly errors on input type * change error type, fix jacobian * add tests * spelling + wording Co-authored-by: Michael Abbott <me@escbook>
JuliaDiff · Dec 28, 2020 · ec28978 · ec28978
1 parent c6f52b2
commit ec28978
Show file tree

Hide file tree

Showing 6 changed files with 32 additions and 0 deletions.
diff --git a/src/derivative.jl b/src/derivative.jl
@@ -69,6 +69,8 @@ Set `check` to `Val{false}()` to disable tag checking. This can lead to perturba
     return result
 end
 
+derivative(f, x::AbstractArray) = throw(DimensionMismatch("derivative(f, x) expects that x is a real number. Perhaps you meant gradient(f, x)?"))
+
 #####################
 # result extraction #
 #####################

diff --git a/src/gradient.jl b/src/gradient.jl
@@ -49,6 +49,8 @@ end
 @inline gradient!(result::Union{AbstractArray,DiffResult}, f, x::StaticArray, cfg::GradientConfig) = gradient!(result, f, x)
 @inline gradient!(result::Union{AbstractArray,DiffResult}, f, x::StaticArray, cfg::GradientConfig, ::Val) = gradient!(result, f, x)
 
+gradient(f, x::Real) = throw(DimensionMismatch("gradient(f, x) expects that x is an array. Perhaps you meant derivative(f, x)?"))
+
 #####################
 # result extraction #
 #####################
@@ -91,12 +93,18 @@ function extract_gradient_chunk!(::Type{T}, result::DiffResult, dual, index, chu
     return result
 end
 
+extract_gradient_chunk!(::Type, result, dual::AbstractArray, index, chunksize) = throw(GRAD_ERROR)
+extract_gradient_chunk!(::Type, result::DiffResult, dual::AbstractArray, index, chunksize) = throw(GRAD_ERROR)
+
+const GRAD_ERROR = DimensionMismatch("gradient(f, x) expects that f(x) is a real number. Perhaps you meant jacobian(f, x)?")
+
 ###############
 # vector mode #
 ###############
 
 function vector_mode_gradient(f::F, x, cfg::GradientConfig{T}) where {T, F}
     ydual = vector_mode_dual_eval(f, x, cfg)
+    ydual isa Real || throw(GRAD_ERROR)
     result = similar(x, valtype(ydual))
     return extract_gradient!(T, result, ydual)
 end

diff --git a/src/jacobian.jl b/src/jacobian.jl
@@ -90,6 +90,8 @@ end
 @inline jacobian!(result::Union{AbstractArray,DiffResult}, f, x::StaticArray, cfg::JacobianConfig) = jacobian!(result, f, x)
 @inline jacobian!(result::Union{AbstractArray,DiffResult}, f, x::StaticArray, cfg::JacobianConfig, ::Val) = jacobian!(result, f, x)
 
+jacobian(f, x::Real) = throw(DimensionMismatch("jacobian(f, x) expects that x is an array. Perhaps you meant derivative(f, x)?"))
+
 #####################
 # result extraction #
 #####################
@@ -143,6 +145,7 @@ reshape_jacobian(result::DiffResult, ydual, xdual) = reshape_jacobian(DiffResult
 
 function vector_mode_jacobian(f::F, x, cfg::JacobianConfig{T,V,N}) where {F,T,V,N}
     ydual = vector_mode_dual_eval(f, x, cfg)
+    ydual isa AbstractArray || throw(JACOBIAN_ERROR)
     result = similar(ydual, valtype(eltype(ydual)), length(ydual), N)
     extract_jacobian!(T, result, ydual, N)
     extract_value!(T, result, ydual)
@@ -194,6 +197,8 @@ end
     return result
 end
 
+const JACOBIAN_ERROR = DimensionMismatch("jacobian(f, x) expexts that f(x) is an array. Perhaps you meant gradient(f, x)?")
+
 # chunk mode #
 #------------#
 
@@ -216,6 +221,7 @@ function jacobian_chunk_mode_expr(work_array_definition::Expr, compute_ydual::Ex
         # do first chunk manually to calculate output type
         seed!(xdual, x, 1, seeds)
         $(compute_ydual)
+        ydual isa AbstractArray || throw(JACOBIAN_ERROR)
         $(result_definition)
         out_reshaped = reshape_jacobian(result, ydual, xdual)
         extract_jacobian_chunk!(T, out_reshaped, ydual, 1, N)

diff --git a/test/DerivativeTest.jl b/test/DerivativeTest.jl
@@ -96,4 +96,8 @@ end
     @test (x -> ForwardDiff.derivative(y -> x^y,  1.5))(0.0) === 0.0
 end
 
+@testset "dimension error for derivative" begin
+    @test_throws DimensionMismatch ForwardDiff.derivative(sum, fill(2pi, 3))
+end
+
 end # module
diff --git a/test/GradientTest.jl b/test/GradientTest.jl
@@ -156,4 +156,10 @@ end
     @test isempty(g_grad_const(zeros(Float64, 0)))
 end
 
+@testset "dimension errors for gradient" begin
+    @test_throws DimensionMismatch ForwardDiff.gradient(identity, 2pi) # input
+    @test_throws DimensionMismatch ForwardDiff.gradient(identity, fill(2pi, 2)) # vector_mode_gradient
+    @test_throws DimensionMismatch ForwardDiff.gradient(identity, fill(2pi, 10^6)) # chunk_mode_gradient
+end
+
 end # module
diff --git a/test/JacobianTest.jl b/test/JacobianTest.jl
@@ -225,4 +225,10 @@ for T in (StaticArrays.SArray, StaticArrays.MArray)
     @test DiffResults.jacobian(sresult3) == DiffResults.jacobian(result)
 end
 
+@testset "dimension errors for jacobian" begin
+    @test_throws DimensionMismatch ForwardDiff.jacobian(identity, 2pi) # input
+    @test_throws DimensionMismatch ForwardDiff.jacobian(sum, fill(2pi, 2)) # vector_mode_jacobian
+    @test_throws DimensionMismatch ForwardDiff.jacobian(sum, fill(2pi, 10^6)) # chunk_mode_jacobian
+end
+
 end # module