Perf and Pre-Allocation

Project.toml

@@ -1,7 +1,7 @@
 name = "Mooncake"
 uuid = "da2b9cff-9c12-43a0-ae48-6db2b0edb7d6"
 authors = ["Will Tebbutt, Hong Ge, and contributors"]
-version = "0.4.50"
+version = "0.4.51"
 
 [deps]
 ADTypes = "47edcb42-4c32-4615-8424-f2b9edc5f35b"

src/fwds_rvs_data.jl

@@ -784,7 +784,7 @@ tangent_type(::Type{NoFData}, ::Type{R}) where {R<:IEEEFloat} = R
 tangent_type(::Type{F}, ::Type{NoRData}) where {F<:Array} = F
 
 # Tuples
-function tangent_type(::Type{F}, ::Type{R}) where {F<:Tuple,R<:Tuple}
+@generated function tangent_type(::Type{F}, ::Type{R}) where {F<:Tuple,R<:Tuple}
     return Tuple{tuple_map(tangent_type, Tuple(F.parameters), Tuple(R.parameters))...}
 end
 function tangent_type(::Type{NoFData}, ::Type{R}) where {R<:Tuple}

src/interface.jl

@@ -1,5 +1,5 @@
 """
-    __value_and_pullback!!(rule, ȳ, f::CoDual, x::CoDual...)
+    __value_and_pullback!!(rule, ȳ, f::CoDual, x::CoDual...; y_cache=nothing)
 
 *Note:* this is not part of the public Mooncake.jl interface, and may change without warning.
 
@@ -8,13 +8,15 @@
 if calling this function multiple times with different values of `x`, should be careful to
 ensure that you zero-out the tangent fields of `x` each time.
 """
-function __value_and_pullback!!(rule::R, ȳ::T, fx::Vararg{CoDual,N}) where {R,N,T}
+function __value_and_pullback!!(
+    rule::R, ȳ::T, fx::Vararg{CoDual,N}; y_cache=nothing
+) where {R,N,T}
     fx_fwds = tuple_map(to_fwds, fx)
     __verify_sig(rule, fx_fwds)
     out, pb!! = rule(fx_fwds...)
     @assert _typeof(tangent(out)) == fdata_type(T)
     increment!!(tangent(out), fdata(ȳ))
-    v = copy(primal(out))
+    v = y_cache === nothing ? copy(primal(out)) : _copy!!(y_cache, primal(out))
     return v, tuple_map((f, r) -> tangent(fdata(tangent(f)), r), fx, pb!!(rdata(ȳ)))
 end
 
@@ -38,6 +40,15 @@
     msg::String
 end
 
+function throw_val_and_grad_ret_type_error(y)
+    throw(
+        ValueAndGradientReturnTypeError(
+            "When calling __value_and_gradient!!, return value of primal must be a " *
+            "subtype of IEEEFloat. Instead, found value of type $(typeof(y)).",
+        ),
+    )
+end
+
 """
     __value_and_gradient!!(rule, f::CoDual, x::CoDual...)
 
@@ -72,17 +83,7 @@
     __verify_sig(rule, fx_fwds)
     out, pb!! = rule(fx_fwds...)
     y = primal(out)
-    if !(y isa IEEEFloat)
-        throw(
-            ValueAndGradientReturnTypeError(
-                "When calling __value_and_gradient!!, return value of primal must be a " *
-                "subtype of IEEEFloat. Instead, found value of type $(typeof(y)).",
-            ),
-        )
-    end
-    @assert y isa IEEEFloat
-    @assert tangent(out) isa NoFData
-
+    y isa IEEEFloat || throw_val_and_grad_ret_type_error(y)
     return y, tuple_map((f, r) -> tangent(fdata(tangent(f)), r), fx, pb!!(one(y)))
 end
 
@@ -126,8 +127,8 @@
 `Float64`.
 
 *Note:* There are lots of subtle ways to mis-use `value_and_pullback!!`, so we generally
-recommend using [`value_and_gradient!!`](@ref) (this function) where possible. The docstring for
-`value_and_pullback!!` is useful for understanding this function though.
+recommend using `Mooncake.value_and_gradient!!` (this function) where possible. The
+docstring for `value_and_pullback!!` is useful for understanding this function though.
 
 An example:
 ```jldoctest
@@ -163,3 +164,96 @@
         end
     end
 end
+
+struct Cache{Trule,Ty_cache,Ttangents<:Tuple}
+    rule::Trule
+    y_cache::Ty_cache
+    tangents::Ttangents
+end
+
+_copy!!(dst, src) = copy!(dst, src)
+_copy!!(::Number, src::Number) = src
+
+"""
+    prepare_pullback_cache(f, x...)
+
+WARNING: experimental functionality. Interface subject to change without warning!
+
+Returns a `cache` which can be passed to `value_and_gradient!!`. See the docstring for
+`Mooncake.value_and_gradient!!` for more info.
+"""
+function prepare_pullback_cache(fx...; kwargs...)
+
+    # Take a copy before mutating.
+    fx = deepcopy(fx)
+
+    # Construct rule and tangents.
+    rule = build_rrule(get_interpreter(), Tuple{map(_typeof, fx)...}; kwargs...)
+    tangents = map(zero_tangent, fx)
+
+    # Run the rule forwards -- this should do a decent chunk of pre-allocation.
+    y, _ = rule(map((x, dx) -> CoDual(x, fdata(dx)), fx, tangents)...)
+
+    # Construct cache for output. Check that `copy!`ing appears to work.
+    y_cache = copy(primal(y))
+    return Cache(rule, _copy!!(y_cache, primal(y)), tangents)
+end
+
+"""
+    value_and_pullback!!(cache::Cache, ȳ, f, x...)
+
+WARNING: experimental functionality. Interface subject to change without warning!
+
+Like other methods of `value_and_pullback!!`, but makes use of the `cache` object in order
+to avoid having to re-allocate various tangent objects repeatedly.
+
+You must ensure that `f` and `x` are the same types and sizes as those used to construct
+`cache`.
+
+Warning: any mutable components of values returned by `value_and_gradient!!` will be mutated
+if you run this function again with different arguments. Therefore, if you need to keep the
+values returned by this function around over multiple calls to this function with the same
+`cache`, you should take a copy of them before calling again.
+"""
+function value_and_pullback!!(cache::Cache, ȳ, f::F, x::Vararg{Any,N}) where {F,N}
+    tangents = tuple_map(set_to_zero!!, cache.tangents)
+    coduals = tuple_map(CoDual, (f, x...), tangents)
+    return __value_and_pullback!!(cache.rule, ȳ, coduals...; y_cache=cache.y_cache)
+end
+
+"""
+    prepare_gradient_cache(f, x...)
+
+WARNING: experimental functionality. Interface subject to change without warning!
+
+Returns a `cache` which can be passed to `value_and_gradient!!`. See the docstring for
+`Mooncake.value_and_gradient!!` for more info.
+"""
+function prepare_gradient_cache(fx...; kwargs...)
+    rule = build_rrule(fx...; kwargs...)
+    tangents = map(zero_tangent, fx)
+    y, _ = rule(map((x, dx) -> CoDual(x, fdata(dx)), fx, tangents)...)
+    primal(y) isa IEEEFloat || throw_val_and_grad_ret_type_error(primal(y))
+    return Cache(rule, nothing, tangents)
+end
+
+"""
+    value_and_gradient!!(cache::Cache, fx::Vararg{Any, N}) where {N}
+
+WARNING: experimental functionality. Interface subject to change without warning!
+
+Like other methods of `value_and_gradient!!`, but makes use of the `cache` object in order
+to avoid having to re-allocate various tangent objects repeatedly.
+
+You must ensure that `f` and `x` are the same types and sizes as those used to construct
+`cache`.
+
+Warning: any mutable components of values returned by `value_and_gradient!!` will be mutated
+if you run this function again with different arguments. Therefore, if you need to keep the
+values returned by this function around over multiple calls to this function with the same
+`cache`, you should take a copy of them before calling again.
+"""
+function value_and_gradient!!(cache::Cache, f::F, x::Vararg{Any,N}) where {F,N}
+    coduals = tuple_map(CoDual, (f, x...), tuple_map(set_to_zero!!, cache.tangents))
+    return __value_and_gradient!!(cache.rule, coduals...)
+end

src/tangents.jl

@@ -623,7 +623,7 @@ Set `x` to its zero element (`x` should be a tangent, so the zero must exist).
 """
 set_to_zero!!(::NoTangent) = NoTangent()
 set_to_zero!!(x::Base.IEEEFloat) = zero(x)
-set_to_zero!!(x::Union{Tuple,NamedTuple}) = map(set_to_zero!!, x)
+set_to_zero!!(x::Union{Tuple,NamedTuple}) = tuple_map(set_to_zero!!, x)
 function set_to_zero!!(x::T) where {T<:PossiblyUninitTangent}
     return is_init(x) ? T(set_to_zero!!(val(x))) : x
 end

test/interface.jl

@@ -1,3 +1,9 @@
+function count_allocs(fargs::P) where {P<:Tuple}
+    f, args... = fargs
+    f(args...) # warmup
+    return @allocations f(args...)
+end
+
 @testset "interface" begin
     @testset "$(typeof((f, x...)))" for (ȳ, f, x...) in Any[
         (1.0, (x, y) -> x * y + sin(x) * cos(y), 5.0, 4.0),
@@ -28,6 +34,7 @@
             ((x, y) -> x + sin(y), randn(Float64), randn(Float64)),
             ((x, y) -> x + sin(y), randn(Float32), randn(Float32)),
             ((x...) -> x[1] + x[2], randn(Float64), randn(Float64)),
+            (sum, randn(10)),
         ]
             rule = build_rrule(fargs...)
             f, args... = fargs
@@ -36,12 +43,47 @@
             for (arg, darg) in zip(fargs, dfargs)
                 @test tangent_type(typeof(arg)) == typeof(darg)
             end
+
+            cache = Mooncake.prepare_gradient_cache(fargs...)
+            _v, _dfargs = value_and_gradient!!(cache, fargs...)
+            @test _v == v
+            for (arg, darg) in zip(fargs, _dfargs)
+                @test tangent_type(typeof(arg)) == typeof(darg)
+            end
+            @test count_allocs((value_and_gradient!!, cache, fargs...)) == 0
         end
 
         rule = build_rrule(identity, (5.0, 4.0))
         @test_throws(
             Mooncake.ValueAndGradientReturnTypeError,
             value_and_gradient!!(rule, identity, (5.0, 4.0)),
         )
+        @test_throws(
+            Mooncake.ValueAndGradientReturnTypeError,
+            Mooncake.prepare_gradient_cache(identity, (5.0, 4.0)),
+        )
+    end
+    @testset "value_and_pullback!!" begin
+        @testset "($(typeof(fargs))" for (ȳ, fargs...) in Any[
+            (randn(10), identity, randn(10)),
+            (randn(), sin, randn(Float64)),
+            (randn(), sum, randn(Float64)),
+        ]
+            rule = build_rrule(fargs...)
+            f, args... = fargs
+            v, dfargs = value_and_pullback!!(rule, ȳ, fargs...)
+            @test v == f(args...)
+            for (arg, darg) in zip(fargs, dfargs)
+                @test tangent_type(typeof(arg)) == typeof(darg)
+            end
+
+            cache = Mooncake.prepare_pullback_cache(fargs...)
+            _v, _dfargs = value_and_pullback!!(cache, ȳ, fargs...)
+            @test _v == v
+            for (arg, darg) in zip(fargs, _dfargs)
+                @test tangent_type(typeof(arg)) == typeof(darg)
+            end
+            @test count_allocs((value_and_pullback!!, cache, ȳ, fargs...)) == 0
+        end
     end
 end