Merge pull request #107 from JuliaDiff/mz/docs

Add two usage examples to documentation

.gitignore

@@ -1,3 +1,5 @@
+dev/
+
 # Files generated by invoking Julia with --code-coverage
 *.jl.cov
 *.jl.*.cov

Project.toml

@@ -1,6 +1,6 @@
 name = "ChainRulesTestUtils"
 uuid = "cdddcdb0-9152-4a09-a978-84456f9df70a"
-version = "0.6.1"
+version = "0.6.2"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

docs/Project.toml

@@ -1,4 +1,5 @@
 [deps]
+ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"
 ChainRulesTestUtils = "cdddcdb0-9152-4a09-a978-84456f9df70a"
 Documenter = "e30172f5-a6a5-5a46-863b-614d45cd2de4"
 

docs/src/index.md

@@ -5,11 +5,136 @@
 
 
 [ChainRulesTestUtils.jl](https://github.com/JuliaDiff/ChainRulesTestUtils.jl) helps you test [`ChainRulesCore.frule`](http://www.juliadiff.org/ChainRulesCore.jl/dev/api.html) and [`ChainRulesCore.rrule`](http://www.juliadiff.org/ChainRulesCore.jl/dev/api.html) methods, when adding rules for your functions in your own packages.
-
 For information about ChainRules, including how to write rules, refer to the general ChainRules Documentation:
 [![](https://img.shields.io/badge/docs-master-blue.svg)](https://JuliaDiff.github.io/ChainRulesCore.jl/dev)
 [![](https://img.shields.io/badge/docs-stable-blue.svg)](https://JuliaDiff.github.io/ChainRulesCore.jl/stable)
 
+## Canonical example
+
+Let's suppose a custom transformation has been defined
+```jldoctest ex; output = false
+function two2three(x1::Float64, x2::Float64)
+    return 1.0, 2.0*x1, 3.0*x2
+end
+
+# output
+two2three (generic function with 1 method)
+```
+along with the `frule`
+```jldoctest ex; output = false
+using ChainRulesCore
+
+function ChainRulesCore.frule((Δf, Δx1, Δx2), ::typeof(two2three), x1, x2)
+    y = two2three(x1, x2)
+    ∂y = Composite{Tuple{Float64, Float64, Float64}}(Zero(), 2.0*Δx1, 3.0*Δx2)
+    return y, ∂y
+end
+# output
+
+```
+and `rrule`
+```jldoctest ex; output = false
+function ChainRulesCore.rrule(::typeof(two2three), x1, x2)
+    y = two2three(x1, x2)
+    function two2three_pullback(Ȳ)
+        return (NO_FIELDS, 2.0*Ȳ[2], 3.0*Ȳ[3])
+    end
+    return y, two2three_pullback
+end
+# output
+
+```
+
+The [`frule_test`](@ref)/[`rrule_test`](@ref) helper function compares the `frule`/`rrule` outputs
+to the gradients obtained by finite differencing.
+They can be used for any type and number of inputs and outputs.
+
+### Testing the `frule`
+
+[`frule_test`](@ref) takes in the function `f` and tuples `(x, ẋ)` for each function argument `x`.
+The call will test the `frule` for function `f` at the point `x` in the domain. Keep
+this in mind when testing discontinuous rules for functions like
+[ReLU](https://en.wikipedia.org/wiki/Rectifier_(neural_networks)), which should ideally
+be tested at both `x` being above and below zero.
+Additionally, choosing `ẋ` in an unfortunate way (e.g. as zeros) could hide
+underlying problems with the defined `frule`.
+
+```jldoctest ex; output = false
+using ChainRulesTestUtils
+
+x1, x2 = (3.33, -7.77)
+ẋ1, ẋ2 = (rand(), rand())
+
+frule_test(two2three, (x1, ẋ1), (x2, ẋ2))
+# output
+Test Summary:                    | Pass  Total
+Tuple{Float64,Float64,Float64}.1 |    1      1
+Test Summary:                    | Pass  Total
+Tuple{Float64,Float64,Float64}.2 |    1      1
+Test Summary:                    | Pass  Total
+Tuple{Float64,Float64,Float64}.3 |    1      1
+Test Passed
+```
+
+### Testing the `rrule`
+
+[`rrule_test`](@ref) takes in the function `f`, sensitivities of the function outputs `ȳ`,
+and tuples `(x, x̄)` for each function argument `x`.
+`x̄` is the accumulated adjoint which can be set arbitrarily.
+The call will test the `rrule` for function `f` at the point `x`, and similarly to
+`frule` some rules should be tested at multiple points in the domain.
+Choosing `ȳ` in an unfortunate way (e.g. as zeros) could hide underlying problems with
+the `rrule`. 
+```jldoctest ex; output = false
+x1, x2 = (3.33, -7.77)
+x̄1, x̄2 = (rand(), rand())
+ȳs = (rand(), rand(), rand())
+
+rrule_test(two2three, ȳs, (x1, x̄1), (x2, x̄2))
+
+# output
+Test Summary:                      |
+Don't thunk only non_zero argument | No tests
+Test.DefaultTestSet("Don't thunk only non_zero argument", Any[], 0, false)
+```
+
+## Scalar example
+
+For functions with a single argument and a single output, such as e.g. ReLU,
+```jldoctest ex; output = false
+function relu(x::Real)
+    return max(0, x)
+end
+
+# output
+relu (generic function with 1 method)
+```
+with the `frule` and `rrule` defined with the help of `@scalar_rule` macro
+```jldoctest ex; output = false
+@scalar_rule relu(x::Real) x <= 0 ? zero(x) : one(x)
+
+# output
+
+```
+
+`test_scalar` function is provided to test both the `frule` and the `rrule` with a single
+call.
+```jldoctest ex; output = false
+test_scalar(relu, 0.5)
+test_scalar(relu, -0.5)
+
+# output
+Test Summary:                 | Pass  Total
+relu at 0.5, with tangent 1.0 |    3      3
+Test Summary:                   | Pass  Total
+relu at 0.5, with cotangent 1.0 |    4      4
+Test Summary:                  | Pass  Total
+relu at -0.5, with tangent 1.0 |    3      3
+Test Summary:                    | Pass  Total
+relu at -0.5, with cotangent 1.0 |    4      4
+```
+
+
 # API Documentation
 
 ```@autodocs

src/iterator.jl

@@ -10,7 +10,7 @@ The iterator wraps another iterator `data`, such as an array, that must have at
 many features implemented as the test iterator and have a `FiniteDifferences.to_vec`
 overload. By default, the iterator it has the same features as `data`.
 
-The optional methods `eltype`, length`, and `size` are automatically defined and forwarded
+The optional methods `eltype`, `length`, and `size` are automatically defined and forwarded
 to `data` if the type arguments indicate that they should be defined.
 """
 struct TestIterator{T,IS,IE}

src/testers.jl

@@ -228,6 +228,7 @@ function frule_test(f, xẋs::Tuple{Any, Any}...; rtol::Real=1e-9, atol::Real=1e
     end
     res = frule((NO_FIELDS, deepcopy(ẋs)...), f, deepcopy(xs)...; deepcopy(fkwargs)...)
     res === nothing && throw(MethodError(frule, typeof((f, xs...))))
+    res isa Tuple || error("The frule should return (y, ∂y), not $res.")
     Ω_ad, dΩ_ad = res
     Ω = f(deepcopy(xs)...; deepcopy(fkwargs)...)
     check_equal(Ω_ad, Ω; isapprox_kwargs...)
@@ -280,6 +281,7 @@ function rrule_test(f, ȳ, xx̄s::Tuple{Any, Any}...; rtol::Real=1e-9, atol::Re
 
     check_inferred && _test_inferred(pullback, ȳ)
     ∂s = pullback(ȳ)
+    ∂s isa Tuple || error("The pullback must return (∂self, ∂args...), not $∂s.")
     ∂self = ∂s[1]
     x̄s_ad = ∂s[2:end]
     @test ∂self === NO_FIELDS  # No internal fields