code wiki / _hdl_build / nx_nofloat_scale_ffn_gate.nx
nx_nofloat_scale_ffn_gate.nx
buildroot/runtime/_hdl_build/nx_nofloat_scale_ffn_gate.nx
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nx_nofloat_scale_ffn_gate.nx -- R4 FINAL test: the COMPLETE transformer block (attention + FFN) on the richer
grammar. All prior scale attempts were ATTENTION-ONLY (no FFN) -- but a real transformer block has an FFN, the
per-token nonlinear map that can turn a token's category into a SHARP output distribution. Honesty requires
testing the complete architecture before concluding the floor is unreachable. Strongest single shot = FFN +
mini-batch averaging (lowest-noise training). Richer 4-cat grammar (vocab 16), dm=32, ffn=64.
floor = avg(ln2,ln4,ln5,ln5)=1324 milli-nats (ppl 3.76); uniform = ln(16)=2773; attention-only plateaued ~2135.
T1 held-out CE << uniform. T2 held-out CE ~= floor (near-OPTIMAL -> the FFN was the missing piece -> R4 lands).
If T2 fails too, the tractable levers (optimizer x6 + complete architecture) are EXHAUSTED -> R4-FULL genuinely
needs compute-scale (operator-gated), the honest end of the road. expect_exit: 0 Sovereign: nofloat_autograd.
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
| 13 | const Q16: i64 = 65536 |
| 14 | const UNIFORM_MNAT: i64 = 2773 |
| 15 | const FLOOR_MNAT: i64 = 1324 |
functions
| 18 | 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 |
| 19 | func lcg(st: *i64) -> i64 { st[0]=(st[0]*1103515245 + 12345) & 2147483647; return (st[0] >> 15) } called by 1: make_stream4 |
| 20 | func make_stream4(S: *i64, tgt: *i64, P: i64, st: *i64) -> i64 |
| 28 | func clm_ffn(tape: *i64, vals: *i64, st: *i64, W: *i64, ids: *i64, tgt: *i64, T: i64, dm: i64, ffn: i64, V: i64, scale: i64, lv: *i64) -> i64 called by 2: do_train_batcheval_ce calls 11: nfa_leafnfa_embednfa_rmsnorm_rowsnfa_matmulnfa_ropenfa_matmul_nt+5 |
| 62 | func do_train_batch(tape: *i64, vals: *i64, grads: *i64, st: *i64, W: *i64, WN: *i64, gacc: *i64, S: *i64, tgt: *i64, P: i64, dm: i64, ffn: i64, V: i64, scale: i64, lv: *i64, gb: *i64, outer: i64, B: i64, lr: i64, sdat: *i64) -> i64 |
| 82 | func eval_ce(tape: *i64, vals: *i64, st: *i64, W: *i64, S: *i64, tgt: *i64, P: i64, dm: i64, ffn: i64, V: i64, scale: i64, lv: *i64, N: i64, sdat: *i64) -> i64 |
| 89 | func main() -> i64 |