Defines derivatives for remaining tgmath math functions. #27559

vguerra · 2019-10-06T21:01:01Z

Inverse trigonometric functions.
Exponents and logarithms.
Hyperbolic functions.
Error functions ( erf, erfc ).
Free generic functions: sqrt, fma.

Partially resolves TF-812.

* Inverse trigonometric functions. * Exponents and logarithms. * Hyperbolic functions. * Error functions ( erf, erfc ).

rxwei · 2019-10-07T00:06:55Z

test/stdlib/tgmath.swift.gyb

@@ -248,10 +248,26 @@ MathTests.test("${T}") {
 % for T in ['Float', 'Float80']:
 MathTests.test("gradient_${T}") {
  expectEqualWithTolerance(7.3890560989306502274, gradient(at: 2.0 as ${T}, in: exp), ulps:16)
+  expectEqualWithTolerance(2.772588722239781145, gradient(at: 2.0 as ${T}, in: exp2), ulps:16)


Suggested change

expectEqualWithTolerance(2.772588722239781145, gradient(at: 2.0 as ${T}, in: exp2), ulps:16)

expectEqualWithTolerance(2.772588722239781145, gradient(at: 2.0 as ${T}, in: exp2), ulps: 16)

Same for all other occurrences.

fixed on c92e34b

rxwei · 2019-10-07T00:07:25Z

stdlib/public/Platform/tgmath.swift.gyb

+
+@usableFromInline
+func _vjpLog2(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
+    return (log2(x), { v in v / (${T}(M_LN2) * x)})


Indent by 2.

fixed on c92e34b

with respective tests.

vguerra · 2019-10-07T12:45:58Z

I pushed 62c019b which implements derivates for sqrt and fma... thanks @rxwei for your explanation on the forums

rxwei · 2019-10-07T16:18:51Z

stdlib/public/Platform/tgmath.swift.gyb

 public func sqrt<T: FloatingPoint>(_ x: T) -> T {
  return x.squareRoot()
 }

 @_transparent
+@differentiable(


Suggested change

@differentiable(

// SWIFT_ENABLE_TENSORFLOW

@differentiable(

done in 27ca6a2

rxwei · 2019-10-07T16:18:55Z

stdlib/public/Platform/tgmath.swift.gyb

@@ -20,11 +20,20 @@ public func fabs<T: FloatingPoint>(_ x: T) -> T {
 }

 @_transparent
+@differentiable(


Suggested change

@differentiable(

// SWIFT_ENABLE_TENSORFLOW

@differentiable(

done in 27ca6a2

rxwei · 2019-10-07T17:55:26Z

@swift-ci please test tensorflow

rxwei · 2019-10-07T17:56:16Z

@swift-ci please test tensorflow

rxwei · 2019-10-07T17:56:22Z

@swift-ci please test tensorflow

rxwei · 2019-10-07T19:25:21Z

stdlib/public/Platform/tgmath.swift.gyb

+  _ z: T
+) -> (T, (T) -> (T, T, T)) where T == T.TangentVector {
+  return (fma(x, y, z),
+    {v in return (v * y, v * x, v)})


Move this to the end of the previous line.

Add a space after each { before each }. Same for all other occurrences.

Omit return when a closure has a single expression.

done in 6b1ebf2

rxwei · 2019-10-07T19:25:36Z

stdlib/public/Platform/tgmath.swift.gyb

+  _ x: T
+) -> (T, (T) -> T) where T == T.TangentVector {
+  let value = x.squareRoot()
+  return (value, {v in (1 / 2) * ( 1 / value) * v})


Suggested change

return (value, {v in (1 / 2) * ( 1 / value) * v})

return (value, { v in v / (2 * value) })

done in 6b1ebf2

rxwei · 2019-10-07T19:27:33Z

stdlib/public/Platform/tgmath.swift.gyb

 @usableFromInline
 func _vjpLog(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
  return (log(x), { v in v / x })
 }

+@usableFromInline
+func _vjpLog10(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
+  return (log10(x), { v in v * ${T}(M_LOG10E) / x})


Suggested change

return (log10(x), { v in v * ${T}(M_LOG10E) / x})

return (log10(x), { v in v * ${T}(M_LOG10E) / x })

done in 6b1ebf2

rxwei · 2019-10-07T19:27:42Z

stdlib/public/Platform/tgmath.swift.gyb

+
+@usableFromInline
+func _vjpLog2(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
+  return (log2(x), { v in v / (${T}(M_LN2) * x)})


Suggested change

return (log2(x), { v in v / (${T}(M_LN2) * x)})

return (log2(x), { v in v / (${T}(M_LN2) * x) })

done in 6b1ebf2

rxwei · 2019-10-07T19:28:04Z

stdlib/public/Platform/tgmath.swift.gyb

+
+@usableFromInline
+func _vjpLog1p(_ x: ${T}) -> (${T}, (${T}) -> ${T}) {
+  return (log1p(x), { v in v / ( x + 1) })


Suggested change

return (log1p(x), { v in v / ( x + 1) })

return (log1p(x), { v in v / (x + 1) })

done in 6b1ebf2

rxwei · 2019-10-07T19:28:20Z

test/stdlib/tgmath.swift.gyb

+  expectEqualWithTolerance(0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erf), ulps: 16)
+  expectEqualWithTolerance(-0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erfc), ulps: 16)
+  expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: {x in sqrt(x)}), ulps: 16)
+  let fma_grad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})


Suggested change

let fma_grad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})

let fmaGrad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: { x, y, z in fma(x, y, z) })

done in 6b1ebf2

rxwei · 2019-10-07T19:28:34Z

test/stdlib/tgmath.swift.gyb

+  expectEqualWithTolerance(1.3333333333333333334, gradient(at: 0.5 as ${T}, in: atanh), ulps: 16)
+  expectEqualWithTolerance(0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erf), ulps: 16)
+  expectEqualWithTolerance(-0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erfc), ulps: 16)
+  expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: {x in sqrt(x)}), ulps: 16)


Suggested change

expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: {x in sqrt(x)}), ulps: 16)

expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: { sqrt($0) }), ulps: 16)

done in 6b1ebf2

rxwei · 2019-10-07T19:28:53Z

test/stdlib/tgmath.swift.gyb

+  expectEqualWithTolerance(0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erf), ulps: 16)
+  expectEqualWithTolerance(-0.020666985354092053575, gradient(at: 2.0 as ${T}, in: erfc), ulps: 16)
+  expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: {x in sqrt(x)}), ulps: 16)
+  let fma_grad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})


Always use camel case.

Suggested change

let fma_grad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})

let fmaGrad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})

done in 6b1ebf2

rxwei · 2019-10-07T19:30:59Z

@stephentyrone Could you help review the usage of math constants in these derivatives?

rxwei · 2019-10-08T21:19:07Z

@swift-ci please test tensorflow

rxwei · 2019-10-08T22:13:41Z

@swift-ci please test tensorflow

Defines derivatives for remaning tgmath math functions.

79e0fea

* Inverse trigonometric functions. * Exponents and logarithms. * Hyperbolic functions. * Error functions ( erf, erfc ).

rxwei reviewed Oct 7, 2019

View reviewed changes

vguerra added 2 commits October 7, 2019 10:09

addressing feedback on formating

c92e34b

Adding derivatives for generic functions: sqrt and fma.

62c019b

with respective tests.

rxwei approved these changes Oct 7, 2019

View reviewed changes

adding SWIFT_ENABLE_TENSORFLOW comments where needed

27ca6a2

rxwei reviewed Oct 7, 2019

View reviewed changes

rxwei requested changes Oct 7, 2019

View reviewed changes

rxwei requested a review from stephentyrone October 7, 2019 19:30

rxwei changed the title ~~Defines derivatives for remaning tgmath math functions.~~ Defines derivatives for remaining tgmath math functions. Oct 7, 2019

addressing formating issues

6b1ebf2

rxwei approved these changes Oct 8, 2019

View reviewed changes

rxwei merged commit c76bde9 into swiftlang:tensorflow Oct 9, 2019

vguerra deleted the TF-812 branch October 30, 2019 12:00

	expectEqualWithTolerance(2.772588722239781145, gradient(at: 2.0 as ${T}, in: exp2), ulps:16)
	expectEqualWithTolerance(2.772588722239781145, gradient(at: 2.0 as ${T}, in: exp2), ulps: 16)

	return (value, {v in (1 / 2) * ( 1 / value) * v})
	return (value, { v in v / (2 * value) })

	return (log10(x), { v in v * ${T}(M_LOG10E) / x})
	return (log10(x), { v in v * ${T}(M_LOG10E) / x })

	return (log2(x), { v in v / (${T}(M_LN2) * x)})
	return (log2(x), { v in v / (${T}(M_LN2) * x) })

	return (log1p(x), { v in v / ( x + 1) })
	return (log1p(x), { v in v / (x + 1) })

	let fma_grad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: {x, y, z in fma(x, y, z)})
	let fmaGrad = gradient(at: 4.0 as ${T}, 5.0 as ${T}, 6.0 as ${T}, in: { x, y, z in fma(x, y, z) })

	expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: {x in sqrt(x)}), ulps: 16)
	expectEqualWithTolerance(0.35355339059327376222, gradient(at: 2.0 as ${T}, in: { sqrt($0) }), ulps: 16)

Defines derivatives for remaining tgmath math functions. #27559

Defines derivatives for remaining tgmath math functions. #27559

Uh oh!

Conversation

vguerra commented Oct 6, 2019 • edited Loading Uh oh! There was an error while loading. Please reload this page.

Uh oh!

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

vguerra commented Oct 7, 2019

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

rxwei commented Oct 7, 2019

Uh oh!

rxwei commented Oct 7, 2019

Uh oh!

rxwei commented Oct 7, 2019

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

Choose a reason for hiding this comment

Uh oh!

rxwei commented Oct 7, 2019

Uh oh!

rxwei commented Oct 8, 2019

Uh oh!

rxwei commented Oct 8, 2019

Uh oh!

Uh oh!

vguerra commented Oct 6, 2019 •

edited

Loading