HairBSDF, sample_f, cosTheta

Average Error: 0.2 → 0.2

Time: 13.6s

Precision: binary32

Cost: 10080

\[\left(10^{-5} \leq u \land u \leq 1\right) \land \left(0 \leq v \land v \leq 109.746574\right)\]

Math

\[1 + v \cdot \log \left(u + \left(1 - u\right) \cdot e^{\frac{-2}{v}}\right) \]

↓

\[1 + v \cdot \left(2 \cdot \log \left(\sqrt{u + \left(1 - u\right) \cdot e^{\frac{-2}{v}}}\right)\right) \]

FPCore

(FPCore (u v)
 :precision binary32
 (+ 1.0 (* v (log (+ u (* (- 1.0 u) (exp (/ -2.0 v))))))))

↓

(FPCore (u v)
 :precision binary32
 (+ 1.0 (* v (* 2.0 (log (sqrt (+ u (* (- 1.0 u) (exp (/ -2.0 v))))))))))

C

float code(float u, float v) {
	return 1.0f + (v * logf((u + ((1.0f - u) * expf((-2.0f / v))))));
}

↓

float code(float u, float v) {
	return 1.0f + (v * (2.0f * logf(sqrtf((u + ((1.0f - u) * expf((-2.0f / v))))))));
}

Fortran

real(4) function code(u, v)
    real(4), intent (in) :: u
    real(4), intent (in) :: v
    code = 1.0e0 + (v * log((u + ((1.0e0 - u) * exp(((-2.0e0) / v))))))
end function

↓

real(4) function code(u, v)
    real(4), intent (in) :: u
    real(4), intent (in) :: v
    code = 1.0e0 + (v * (2.0e0 * log(sqrt((u + ((1.0e0 - u) * exp(((-2.0e0) / v))))))))
end function

Julia

function code(u, v)
	return Float32(Float32(1.0) + Float32(v * log(Float32(u + Float32(Float32(Float32(1.0) - u) * exp(Float32(Float32(-2.0) / v)))))))
end

↓

function code(u, v)
	return Float32(Float32(1.0) + Float32(v * Float32(Float32(2.0) * log(sqrt(Float32(u + Float32(Float32(Float32(1.0) - u) * exp(Float32(Float32(-2.0) / v)))))))))
end

MATLAB

function tmp = code(u, v)
	tmp = single(1.0) + (v * log((u + ((single(1.0) - u) * exp((single(-2.0) / v))))));
end

↓

function tmp = code(u, v)
	tmp = single(1.0) + (v * (single(2.0) * log(sqrt((u + ((single(1.0) - u) * exp((single(-2.0) / v))))))));
end

Derivation

1. Initial program 0.2

   \[1 + v \cdot \log \left(u + \left(1 - u\right) \cdot e^{\frac{-2}{v}}\right) \]

2. Applied egg-rr 0.2

   \[\leadsto 1 + v \cdot \color{blue}{\left(\log \left(\sqrt{\mathsf{fma}\left(1 - u, e^{\frac{-2}{v}}, u\right)}\right) + \log \left(\sqrt{\mathsf{fma}\left(1 - u, e^{\frac{-2}{v}}, u\right)}\right)\right)} \]

3. Simplified 0.2

   \[\leadsto 1 + v \cdot \color{blue}{\left(2 \cdot \log \left(\sqrt{\mathsf{fma}\left(1 - u, e^{\frac{-2}{v}}, u\right)}\right)\right)} \]
   Proof
   (*.f32 2 (log.f32 (sqrt.f32 (fma.f32 (-.f32 1 u) (exp.f32 (/.f32 -2 v)) u)))): 0 points increase in error, 0 points decrease in error
   (Rewrite<= count-2_binary32 (+.f32 (log.f32 (sqrt.f32 (fma.f32 (-.f32 1 u) (exp.f32 (/.f32 -2 v)) u))) (log.f32 (sqrt.f32 (fma.f32 (-.f32 1 u) (exp.f32 (/.f32 -2 v)) u))))): 0 points increase in error, 0 points decrease in error

4. Taylor expanded in v around 0 0.2

   \[\leadsto 1 + v \cdot \left(2 \cdot \color{blue}{\log \left(\sqrt{\left(1 - u\right) \cdot e^{\frac{-2}{v}} + u}\right)}\right) \]

5. Final simplification 0.2

   \[\leadsto 1 + v \cdot \left(2 \cdot \log \left(\sqrt{u + \left(1 - u\right) \cdot e^{\frac{-2}{v}}}\right)\right) \]

Alternatives

Alternative 1
Error	0.2
Cost	9952

\[1 + v \cdot \log \left(\mathsf{fma}\left(1 - u, e^{\frac{-2}{v}}, u\right)\right) \]

Alternative 2
Error	0.2
Cost	6816

\[1 + v \cdot \log \left(u + \left(1 - u\right) \cdot e^{\frac{-2}{v}}\right) \]

Alternative 3
Error	2.9
Cost	3748

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;1 + \left(-2 + v \cdot \left(u \cdot \left(\frac{1}{e^{\frac{-2}{v}}} + -1\right)\right)\right)\\ \end{array} \]

Alternative 4
Error	2.9
Cost	3684

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;1 + v \cdot \left(u \cdot \mathsf{expm1}\left(\frac{2}{v}\right) + \frac{-2}{v}\right)\\ \end{array} \]

Alternative 5
Error	2.9
Cost	3556

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;v \cdot \left(u \cdot \mathsf{expm1}\left(\frac{2}{v}\right)\right) + -1\\ \end{array} \]

Alternative 6
Error	2.9
Cost	484

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;u \cdot \left(2 + \frac{2 + \frac{1.3333333333333333}{v}}{v}\right) + -1\\ \end{array} \]

Alternative 7
Error	3.0
Cost	356

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;2 \cdot \left(u + \frac{u}{v}\right) + -1\\ \end{array} \]

Alternative 8
Error	3.2
Cost	228

\[\begin{array}{l} \mathbf{if}\;v \leq 0.20000000298023224:\\ \;\;\;\;1\\ \mathbf{else}:\\ \;\;\;\;2 \cdot u + -1\\ \end{array} \]

Alternative 9
Error	30.1
Cost	32

\[-1 \]

Alternative 10
Error	4.2
Cost	32

\[1 \]

Reproduce

herbie shell --seed 2022338 
(FPCore (u v)
  :name "HairBSDF, sample_f, cosTheta"
  :precision binary32
  :pre (and (and (<= 1e-5 u) (<= u 1.0)) (and (<= 0.0 v) (<= v 109.746574)))
  (+ 1.0 (* v (log (+ u (* (- 1.0 u) (exp (/ -2.0 v))))))))