Ok some progress report here. I have currently a
library(linear) in the working which is only a few
lines of code, but it provides vectors and matrixes.
One can use the library to define matrix exponentiation:
matexp(M, 1, M) :- !.
matexp(M, N, R) :- N mod 2 =:= 0, !,
I is N // 2,
matexp(M, I, H),
matmul(H, H, R).
matexp(M, N, R) :-
I is N-1,
matexp(M, I, H),
matmul(H, M, R).
And then do fancy stuff like answering the question
what are the last 8 digits of fibonacci(1000000):
?- time((fib(1000000, _X), Y is _X mod 10^8)).
% Zeit 28 ms, GC 0 ms, Lips 88857, Uhr 16.03.2025 22:48
Y = 42546875
The 28 ms execution time are not bad, since modulo was not
integrated into matexp/3, making it to compute the full
fibonacci(1000000) before taking the modulo. Not sure whether
JavaScript bigint is faster or slower than GMP ?
So what can we do with library(linear) besides implementing
eval/3 and back/3 ? We can finally update a neural network
and do this iteratively. Using a very simple random pick
to choose some training data sample:
update([V], _, [V]) :- !.
update([V,M|L], [_,M3|R], [V,M4|S]) :-
maplist(maplist(compose(add,mul(0.1))), M3, M, M4),
update(L, R, S).
iter(0, _, N, N) :- !.
iter(I, Z, N, M) :-
random(R), K is floor(R*4)+1,
call_nth(data(Z, X, Y), K),
eval(N, X, U),
back(U, Y, V),
update(U, V, W),
J is I-1,
iter(J, Z, W, M).
Disclaimer: This is only a proof of concept. It mostlikely
doesn’t have all the finess of Python torch.autograd. Also
it uses a very simple update of the weights via μ Δwij with
μ = 0.1. But you can already use it to learn an AND
or to learn an XOR.
Mild Shock schrieb:
Somehow I shied away from implementing call/n for
my new Prolog system. I thought my new Prolog system
has only monomorphic caches , I will never be able to
replicate what I did for my old Prolog system with
arity polymorphic caches. This changed when I had
the idea to dynamically add a cache for the duration
of a higher order loop such as maplist/n, foldl/n etc…
So this is the new implementation of maplist/3:
% maplist(+Closure, +List, -List)
maplist(C, L, R) :-
sys_callable_cacheable(C, D),
sys_maplist(L, D, R).
% sys_maplist(+List, +Closure, -List)
sys_maplist([], _, []).
sys_maplist([X|L], C, [Y|R]) :-
call(C, X, Y),
sys_maplist(L, C, R).
Its similar as the SWI-Prolog implementation in that
it reorders the arguments for better first argument
indexing. But the new thing is sys_callable_cacheable/1,
which prepares the closure to be more efficiently
called. The invocation of the closure is already
quite fast since call/3 is implemented natively,
but the cache adds an itch more speed. Here some
measurements that I did:
/* SWI-Prolog 9.3.20 */
?- findall(X,between(1,1000,X),L), time((between(1,1000,_),
maplist(succ,L,_),fail; true)), fail.
% 2,003,000 inferences, 0.078 CPU in 0.094 seconds
/* Scryer Prolog 0.9.4-350 */
?- findall(X,between(1,1000,X),L), time((between(1,1000,_),
maplist(succ,L,_),fail; true)), fail.
% CPU time: 0.318s, 3_007_105 inferences
/* Dogelog Player 1.3.1 */
?- findall(X,between(1,1000,X),L), time((between(1,1000,_),
maplist(succ,L,_),fail; true)), fail.
% Zeit 342 ms, GC 0 ms, Lips 11713646, Uhr 10.03.2025 09:18
/* realla Prolog 2.64.6-2 */
?- findall(X,between(1,1000,X),L), time((between(1,1000,_),
maplist(succ,L,_),fail; true)), fail.
% Time elapsed 1.694s, 15004003 Inferences, 8.855 MLips
Not surprisingly SWI-Prolog is fastest. What was
a little surprise is that Scryer Prolog can do it quite
fast, possibly since they heavily use maplist/n all
over the place, they came up with things like '$fast_call'
etc.. in their call/n implementation. Trealla Prolog is
a little bit disappointing at the moment.
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