code wiki / _hdl_build / nx_nofloat_grammar_gate.nx
nx_nofloat_grammar_gate.nx
buildroot/runtime/_hdl_build/nx_nofloat_grammar_gate.nx
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nx_nofloat_grammar_gate.nx -- CAP-NF-GRAMMAR: the AFFORDABLE "landing" step the sovereign researcher
identified (knowledge/research/2026-06-23-nofloat-affordable-land-roadmap.md, R1, grounded in TinyStories
arXiv 2305.07759 + Textbooks arXiv 2306.11644: a tiny model is COHERENT iff the DOMAIN is constrained).
Instead of memorizing one fixed sentence, the model learns a small GRAMMAR and GENERATES coherent NOVEL
sentences. Grammar: a stream of [ARTICLE, NOUN, VERB] triples. Vocab 8: articles {the=0,a=1}, nouns
{cat=2,dog=3,bird=4}, verbs {ran=5,sat=6,ate=7}. EVERY training stream is fresh-random members -> the model
cannot memorize sentences; it must learn the CATEGORY GRAMMAR (article->noun->verb->article ...).
T1 GENERATES coherent grammar: from a seed, free argmax-decode produces a stream whose every token is in
the grammatically-correct category for its position (>= 90%, vs ~33% chance) = coherent NOVEL generation.
T2 learned the grammar on HELD-OUT streams: teacher-forced next-token category accuracy >= 90% (>> chance).
Pure integer Q16, attention-only block, 3 clm_fwd sites in separate helpers (nx_cc-lean). Sovereign.
HONEST: a CONSTRAINED grammar domain (the affordable landing per the research), not open prose. expect_exit: 0
dependencies 3 imports · 0 importers
imports: nx_nofloat_autograd.nxnx_syscalls.nxnx_gate_emit_lib.nx
imported by: nobody (leaf or entry point)
call flow from main pre-order; caps 40 nodes / depth 6 declared; ↻ = already shown
structs
| none |
consts
| 16 | const Q16: i64 = 65536 |
functions
| 19 | func dini(a: *i64, n: i64, sd: i64) -> i64 { var i: i64=0; while i<n { a[i]=(((i*7+sd*13+1)%11)-5)*13107; i=i+1 } return 0 } called by 1: main |
| 20 | func lcg(st: *i64) -> i64 { st[0]=(st[0]*1103515245 + 12345) & 2147483647; return (st[0] >> 15) } called by 1: make_stream |
| 21 | func cat_of(tok: i64) -> i64 { if tok<2 { return 0 } if tok<5 { return 1 } return 2 } |
| 23 | func make_stream(S: *i64, tgt: *i64, P: i64, st: *i64) -> i64 |
| 31 | func clm_fwd(tape: *i64, vals: *i64, st: *i64, W: *i64, ids: *i64, tgt: *i64, T: i64, dm: i64, V: i64, scale: i64, leaves: *i64) -> i64 called by 3: do_traineval_tfgen_coherent calls 10: nfa_leafnfa_embednfa_rmsnorm_rowsnfa_matmulnfa_ropenfa_matmul_nt+4 |
| 59 | func step_all(tape: *i64, grads: *i64, W: *i64, WN: *i64, leaves: *i64, nW: i64, lr: i64, clip: i64, gb: *i64) -> i64 |
| 64 | func amx(tape: *i64, vals: *i64, logn: i64, r: i64, V: i64) -> i64 { let o: i64=tape[7*logn+5]; var b: i64=0; var bv: i64=vals[o+r*V]; var j: i64=1; while j<V { if vals[o+r*V+j]>bv { bv=vals[o+r*V+j]; b=j } j=j+1 } return b } |
| 66 | func do_train(tape: *i64, vals: *i64, grads: *i64, st: *i64, W: *i64, WN: *i64, S: *i64, tgt: *i64, P: i64, dm: i64, V: i64, scale: i64, leaves: *i64, gb: *i64, steps: i64, sdat: *i64) -> i64 |
| 72 | func eval_tf(tape: *i64, vals: *i64, st: *i64, W: *i64, S: *i64, tgt: *i64, P: i64, dm: i64, V: i64, scale: i64, leaves: *i64, N: i64, sdat: *i64, totp: *i64) -> i64 |
| 85 | func gen_coherent(tape: *i64, vals: *i64, st: *i64, W: *i64, gen: *i64, P: i64, dm: i64, V: i64, scale: i64, leaves: *i64, outtok: *i64) -> i64 |
| 100 | func main() -> i64 |