Fix docstring for value_and_pullback_function

Project.toml

@@ -1,7 +1,7 @@
 name = "AbstractDifferentiation"
 uuid = "c29ec348-61ec-40c8-8164-b8c60e9d9f3d"
 authors = ["Mohamed Tarek <mohamed82008@gmail.com> and contributors"]
-version = "0.6.0"
+version = "0.6.1"
 
 [deps]
 ChainRulesCore = "d360d2e6-b24c-11e9-a2a3-2a2ae2dbcce4"

src/AbstractDifferentiation.jl

@@ -226,44 +226,48 @@ The pushfoward function `pf` accepts as input a `Tuple` of tangents, one for eac
 If `xs` consists of a single element, `pf` can also accept a single tangent instead of a 1-tuple.
 """
 function pushforward_function(ab::AbstractBackend, f, xs...)
-    return (ds) -> begin
-        return jacobian(
-            lowest(ab),
-            (xds...,) -> begin
-                if ds isa Tuple
-                    @assert length(xs) == length(ds)
-                    newxs = xs .+ ds .* xds
-                    return f(newxs...)
-                else
-                    newx = only(xs) + ds * only(xds)
-                    return f(newx)
-                end
-            end,
-            _zero.(xs, ds)...,
-        )
+    function pf(ds)
+        function pf_aux(xds...)
+            if ds isa Tuple
+                @assert length(xs) == length(ds)
+                newxs = xs .+ ds .* xds
+                return f(newxs...)
+            else
+                newx = only(xs) + ds * only(xds)
+                return f(newx)
+            end
+        end,
+        return jacobian(lowest(ab), pf_aux, _zero.(xs, ds)...)
     end
+    return pf
 end
 
 """
     AD.value_and_pushforward_function(ab::AD.AbstractBackend, f, xs...)
     
-Return a function that, given tangents `ts`, computes the tuple `(v, p)` of the function value `v = f(xs...)` and the output `p` of the pushforward function `AD.pushforward_function(ab, f, xs...)` applied to `ts`.
+Return a function `vpf` which, given tangents `ts`, computes the tuple `(v, p) = vpf(ts)` composed of
+    
+- the function value `v = f(xs...)`
+- the pushforward value `p = pf(ts)` given by the pushforward function `pf = AD.pushforward_function(ab, f, xs...)` applied to `ts`.
 
 See also [`AbstractDifferentiation.pushforward_function`](@ref).
+
+!!! warning
+    This name should be understood as "(value and pushforward) function", and thus is not aligned with the reverse mode counterpart [`AbstractDifferentiation.value_and_pullback_function`](@ref).
 """
 function value_and_pushforward_function(ab::AbstractBackend, f, xs...)
     n = length(xs)
     value = f(xs...)
-    pf_function = pushforward_function(lowest(ab), f, xs...)
+    pf = pushforward_function(lowest(ab), f, xs...)
 
-    return ds -> begin
+    function vpf(ds)
         if !(ds isa Tuple)
             ds = (ds,)
         end
         @assert length(ds) == n
-        pf = pf_function(ds)
-        return value, pf
+        return value, pf(ds)
     end
+    return vpf
 end
 
 _zero(::Number, d::Number) = zero(d)
@@ -291,21 +295,24 @@ The pullback function `pb` accepts as input a `Tuple` of cotangents, one for eac
 If `f` has a single output, `pb` can also accept a single input instead of a 1-tuple.
 """
 function pullback_function(ab::AbstractBackend, f, xs...)
-    _, pbf = value_and_pullback_function(ab, f, xs...)
-    return pbf
+    _, pb = value_and_pullback_function(ab, f, xs...)
+    return pb
 end
 
 """
     AD.value_and_pullback_function(ab::AD.AbstractBackend, f, xs...)
 
-Return a function that, given cotangents `ts`, computes the tuple `(v, p)` of the function value `v = f(xs...)` and the output `p` of the pullback function `AD.pullback_function(ab, f, xs...)` applied to `ts`.
+Return a tuple `(v, pb)` of the function value `v = f(xs...)` and the pullback function `pb = AD.pullback_function(ab, f, xs...)`.
 
 See also [`AbstractDifferentiation.pullback_function`](@ref).
+
+!!! warning
+    This name should be understood as "value and (pullback function)", and thus is not aligned with the forward mode counterpart [`AbstractDifferentiation.value_and_pushforward_function`](@ref).
 """
 function value_and_pullback_function(ab::AbstractBackend, f, xs...)
     value = f(xs...)
-    function pullback_function(ws)
-        function pullback_gradient_function(_xs...)
+    function pb(ws)
+        function pb_aux(_xs...)
             vs = f(_xs...)
             if ws isa Tuple
                 @assert length(vs) == length(ws)
@@ -314,9 +321,9 @@ function value_and_pullback_function(ab::AbstractBackend, f, xs...)
                 return _dot(vs, ws)
             end
         end
-        return gradient(lowest(ab), pullback_gradient_function, xs...)
+        return gradient(lowest(ab), pb_aux, xs...)
     end
-    return value, pullback_function
+    return value, pb
 end
 
 struct LazyDerivative{B,F,X}