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{-# LANGUAGE DataKinds #-}
{-# LANGUAGE OverloadedLabels #-}
{-# LANGUAGE TypeApplications #-}
module Bench.GMM where
import AST
import Data
import Language
type R = TScal TF64
type I64 = TScal TI64
type TVec = TArr (S Z)
type TMat = TArr (S (S Z))
-- N, D, K: integers > 0
-- alpha, M, Q, L: the active parameters
-- X: inactive data
-- m: integer
-- k1: 1/2 N D log(2 pi)
-- k2: 1/2 gamma^2
-- k3: K * (n' D (log(gamma) - 1/2 log(2)) - log MultiGamma(1/2 n', D))
-- where n' = D + m + 1
--
-- Inputs from the file are: N, D, K, alpha, M, Q, L, gamma, m.
--
-- See:
-- - "A benchmark of selected algorithmic differentiation tools on some problems
-- in computer vision and machine learning". Optim. Methods Softw. 33(4-6):
-- 889-906 (2018).
-- <https://www.tandfonline.com/doi/full/10.1080/10556788.2018.1435651>
-- <https://github.com/microsoft/ADBench>
-- - 2021 Tom Smeding: “Reverse Automatic Differentiation for Accelerate”.
-- Master thesis at Utrecht University.
-- <https://studenttheses.uu.nl/bitstream/handle/20.500.12932/38958/report.pdf?sequence=1&isAllowed=y>
-- <https://tomsmeding.com/f/master.pdf>
objective :: Ex [R, R, R, I64, TMat R, TMat R, TMat R, TMat R, TVec R, I64, I64, I64] R
objective = fromNamed $
lambda #N $ lambda #D $ lambda #K $
lambda #alpha $ lambda #M $ lambda #Q $ lambda #L $
lambda #X $ lambda #m $
lambda #k1 $ lambda #k2 $ lambda #k3 $
body $
let -- We have:
-- sum (exp (x - max(x)))
-- = sum (exp x / exp (max(x)))
-- = sum (exp x) / exp (max(x))
-- Hence:
-- sum (exp x) = sum (exp (x - max(x))) * exp (max(x)) (*)
--
-- So:
-- d/dxi log (sum (exp x))
-- = 1/(sum (exp x)) * d/dxi sum (exp x)
-- = 1/(sum (exp x)) * sum (d/dxi exp x)
-- = 1/(sum (exp x)) * exp xi
-- = exp xi / sum (exp x)
-- (by (*))
-- = exp xi / (sum (exp (x - max(x))) * exp (max(x)))
-- = exp (xi - max(x)) / sum (exp (x - max(x)))
logsumexp' = lambda @(TVec R) #vec $ body $
custom (#_ :-> #v :->
let_ #m (idx0 (maximum1i #v)) $
log (idx0 (sum1i (map_ (#x :-> exp (#x - #m)) #v))) + #m)
(#_ :-> #v :->
let_ #m (idx0 (maximum1i #v)) $
let_ #ex (map_ (#x :-> exp (#x - #m)) #v) $
let_ #s (idx0 (sum1i #ex)) $
pair (log #s + #m)
(pair #ex #s))
(#tape :-> #d :->
map_ (#exi :-> #exi / snd_ #tape * #d) (fst_ #tape))
nil #vec
logsumexp v = inline logsumexp' (SNil .$ v)
mulmatvec = lambda @(TMat R) #mat $ lambda @(TVec R) #vec $ body $
let_ #hei (snd_ (fst_ (shape #mat))) $
let_ #wid (snd_ (shape #mat)) $
build1 #hei $ #i :->
idx0 (sum1i (build1 #wid $ #j :->
#mat ! pair (pair nil #i) #j * #vec ! pair nil #j))
m *@ v = inline mulmatvec (SNil .$ m .$ v)
subvec = lambda @(TVec R) #a $ lambda @(TVec R) #b $ body $
build1 (snd_ (shape #a)) $ #i :-> #a ! pair nil #i - #b ! pair nil #i
a .- b = inline subvec (SNil .$ a .$ b)
matrow = lambda @(TMat R) #mat $ lambda @TIx #i $ body $
build1 (snd_ (shape #mat)) (#j :-> #mat ! pair (pair nil #i) #j)
m .! i = inline matrow (SNil .$ m .$ i)
normsq' = lambda @(TVec R) #vec $ body $
idx0 (sum1i (build (SS SZ) (shape #vec) (#i :-> let_ #x (#vec ! #i) $ #x * #x)))
normsq v = inline normsq' (SNil .$ v)
qmat' = lambda @(TVec R) #q $ lambda @(TVec R) #l $ body $
_
qmat q l = inline qmat' (SNil .$ q .$ l)
in - #k1
+ idx0 (sum1i (build1 #N $ #i :->
logsumexp (build1 #K $ #k :->
#alpha ! pair nil #k
+ idx0 (sum1i (#Q .! #k))
- 0.5 * normsq (qmat (#Q .! #k) (#L .! #k) *@ ((#X .! #i) .- (#M .! #k))))))
- toFloat_ #N * logsumexp #alpha
+ idx0 (sum1i (build1 #K $ #k :->
#k2 * (normsq (map_ (#x :-> exp #x) (#Q .! #k)) + normsq (#L .! #k))
- toFloat_ #m * idx0 (sum1i (#Q .! #k))))
- #k3
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