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

 % 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

 }

 @_transparent
+@differentiable(


Suggested change

@differentiable(

// SWIFT_ENABLE_TENSORFLOW

@differentiable(

done in 27ca6a2

rxwei · 2019-10-07T16:18:55Z

stdlib/public/Platform/tgmath.swift.gyb

 }

 @_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 _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

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

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vguerra commented Oct 7, 2019

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rxwei commented Oct 7, 2019

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rxwei commented Oct 7, 2019

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rxwei commented Oct 7, 2019

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rxwei commented Oct 7, 2019

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rxwei commented Oct 8, 2019

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vguerra commented Oct 6, 2019 •

edited

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