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[TF-812] Defines remaining derivatives for tgmath functions. #27953

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[TF-812] Defines remaining derivatives for tgmath functions. #27953

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86 changes: 86 additions & 0 deletions stdlib/public/Platform/tgmath.swift.gyb
View file
Open in desktop
Original file line number Diff line number Diff line change
Expand Up @@ -25,6 +25,7 @@ public func fabs<T: FloatingPoint>(_ x: T) -> T {
vjp: _vjpSqrt
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func sqrt<T: FloatingPoint>(_ x: T) -> T {
return x.squareRoot()
}
Expand All @@ -36,36 +37,75 @@ public func sqrt<T: FloatingPoint>(_ x: T) -> T {
vjp: _vjpFma
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func fma<T: FloatingPoint>(_ x: T, _ y: T, _ z: T) -> T {
return z.addingProduct(x, y)
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
wrt: (x, y),
vjp: _vjpRemainder
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func remainder<T: FloatingPoint>(_ x: T, _ y: T) -> T {
return x.remainder(dividingBy: y)
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
wrt: (x, y),
vjp: _vjpFmod
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func fmod<T: FloatingPoint>(_ x: T, _ y: T) -> T {
return x.truncatingRemainder(dividingBy: y)
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
vjp: _vjpCeil
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func ceil<T: FloatingPoint>(_ x: T) -> T {
return x.rounded(.up)
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
vjp: _vjpFloor
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func floor<T: FloatingPoint>(_ x: T) -> T {
return x.rounded(.down)
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
vjp: _vjpRound
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func round<T: FloatingPoint>(_ x: T) -> T {
return x.rounded()
}

@_transparent
// SWIFT_ENABLE_TENSORFLOW
@differentiable(
vjp: _vjpTrunc
where T : Differentiable & FloatingPoint, T == T.TangentVector
)
// SWIFT_ENABLE_TENSORFLOW END
public func trunc<T: FloatingPoint>(_ x: T) -> T {
return x.rounded(.towardZero)
}
Expand Down Expand Up @@ -111,6 +151,51 @@ func _vjpFma<T: FloatingPoint & Differentiable> (
return (fma(x, y, z), { v in (v * y, v * x, v) })
}

@usableFromInline
func _vjpRemainder<T: FloatingPoint & Differentiable> (
_ x: T,
_ y: T
) -> (T, (T) -> (T, T)) where T == T.TangentVector {
return (remainder(x, y), { v in (v, v) })
}

@usableFromInline
func _vjpFmod<T: FloatingPoint & Differentiable> (
_ x: T,
_ y: T
) -> (T, (T) -> (T, T)) where T == T.TangentVector {
return (fmod(x, y), { v in (v, v) })
}

@usableFromInline
func _vjpCeil<T: FloatingPoint & Differentiable> (
_ x: T
) -> (T, (T) -> T) where T == T.TangentVector {
return (ceil(x), { v in 0 })
}

@usableFromInline
func _vjpFloor<T: FloatingPoint & Differentiable> (
_ x: T
) -> (T, (T) -> T) where T == T.TangentVector {
return (floor(x), { v in 0 })
}

@usableFromInline
func _vjpRound<T: FloatingPoint & Differentiable> (
_ x: T
) -> (T, (T) -> T) where T == T.TangentVector {
return (round(x), { v in 0 })
}

@usableFromInline
func _vjpTrunc<T: FloatingPoint & Differentiable> (
_ x: T
) -> (T, (T) -> T) where T == T.TangentVector {
return (trunc(x), { v in 0 })
}
// SWIFT_ENABLE_TENSORFLOW END

%for T in ['Float','Double']:
@available(swift, deprecated: 4.2, renamed: "scalbn")
@_transparent
Expand Down Expand Up @@ -233,6 +318,7 @@ func _vjpErf(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
func _vjpErfc(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
return (erfc(x), { v in v * -${T}(M_2_SQRTPI) * exp(-x * x) })
}
// SWIFT_ENABLE_TENSORFLOW END
% if T == 'Float80':
#endif
% end
Expand Down
10 changes: 10 additions & 0 deletions test/stdlib/tgmath.swift.gyb
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Original file line number Diff line number Diff line change
Expand Up @@ -273,6 +273,16 @@ MathTests.test("gradient_${T}") {
expectEqualWithTolerance(5.0, fmaGrad.0, ulps: 16)
expectEqualWithTolerance(4.0, fmaGrad.1, ulps: 16)
expectEqualWithTolerance(1.0, fmaGrad.2, ulps: 16)
let remainderGrad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, in: { x, y in remainder(x, y) })
expectEqualWithTolerance(1.0, remainderGrad.0, ulps: 16)
expectEqualWithTolerance(1.0, remainderGrad.1, ulps: 16)
let fmodGrad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, in: { x, y in fmod(x, y) })
expectEqualWithTolerance(1.0, fmodGrad.0, ulps: 16)
expectEqualWithTolerance(1.0, fmodGrad.1, ulps: 16)
expectEqualWithTolerance(0.0, gradient(at: 2.0 as ${T}, in: { ceil($0) }), ulps: 16)
expectEqualWithTolerance(0.0, gradient(at: 2.0 as ${T}, in: { floor($0) }), ulps: 16)
expectEqualWithTolerance(0.0, gradient(at: 2.0 as ${T}, in: { round($0) }), ulps: 16)
expectEqualWithTolerance(0.0, gradient(at: 2.0 as ${T}, in: { trunc($0) }), ulps: 16)
}
%end

Expand Down