code wiki / _hdl_build / nx_fnet_mlm_gate.nx
nx_fnet_mlm_gate.nx source
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1// nx_fnet_mlm_gate.nx -- GATE for MODEL-002: a tiny MASKED LANGUAGE MODEL on the FNet mixer. FNet's Fourier
2// mix is BIDIRECTIONAL (every position sees every other), so its native LM objective is masked-LM (BERT-style),
3// NOT causal next-token (which would let the model see the answer). Here: one position of a 4-token palindrome
4// [a,b,b,a] is replaced by a MASK token; the model fills it in -- which requires MIXING the mirror position into
5// the masked one (a pure bag-of-words model cannot). No attention anywhere.
6// tokens(+MASK) -> EMBED(trained) -> FNET mix -> relu FFN -> softmax-CE over the vocab at the masked slot
7//
8// G_train masked-token accuracy >= 7/8 AND final loss < first loss.
9// G_repro bit-exact: train twice -> identical accuracy + final-loss bits.
10//
11// Evidence -> knowledge/status/fnet_mlm.log (FNETMLMGATE authored=organ ... verdict=GREEN). license_tier: ORIGINAL
12import "nx_autograd_tensor.nx"
13import "nx_syscalls.nx"
14
15const MV: i64 = 5 // input vocab: tokens 0..3 + MASK=4
16const MN: i64 = 4 // sequence length
17const MD: i64 = 4 // d_model
18const MH: i64 = 8 // FFN hidden
19const MC: i64 = 4 // output vocab (predict token 0..3)
20const MND: i64 = 16 // MN*MD
21
22const ML_LOG: *u8 = "knowledge/status/fnet_mlm.log"
23
24func ml_w(fd: i64, s: *u8) -> i64 { var n: i64 = 0; while s[n] != (0 as u8) { n = n + 1 } sys_write(fd, s, n); return 0 }
25func ml_wn(fd: i64, v: i64) -> i64 {
26 let bb: *u8 = sys_mmap(28); var m: i64 = v
27 if m < 0 { m = 0 - m; sys_write(fd, "-" as *u8, 1) }
28 let t: *u8 = sys_mmap(28); var k: i64 = 0
29 if m == 0 { t[0] = 48; k = 1 }
30 while m > 0 { t[k] = (48 + (m % 10)) as u8; m = m / 10; k = k + 1 }
31 var i: i64 = 0
32 while i < k { bb[i] = t[k - 1 - i]; i = i + 1 }
33 sys_write(fd, bb, k); return 0
34}
35
36func ml_embed(E: *i64, seq: *i64, soff: i64, xout: *i64) -> i64 {
37 var i: i64 = 0
38 while i < MN {
39 let tok: i64 = seq[soff + i]
40 var j: i64 = 0
41 while j < MD { xout[i * MD + j] = E[tok * MD + j]; j = j + 1 }
42 i = i + 1
43 }
44 return 0
45}
46
47func ml_fwd(tape: *i64, vals: *i64, st: *i64, x: *i64, nW1: i64, nb1: i64, nW2: i64, nb2: i64) -> i64 {
48 let xl: i64 = ta_leaf(tape, vals, st, MN, MD, x, 0)
49 let m: i64 = ta_fnet(tape, vals, st, xl)
50 let h: i64 = ta_relu(tape, vals, st, ta_vadd(tape, vals, st, ta_matvec(tape, vals, st, nW1, m), nb1))
51 return ta_vadd(tape, vals, st, ta_matvec(tape, vals, st, nW2, h), nb2)
52}
53
54func ml_build(tape: *i64, vals: *i64, st: *i64, E: *i64, W1: *i64, b1: *i64, W2: *i64, b2: *i64,
55 seqs: *i64, labels: *i64, c8: *i64, wb: *i64, xl8: *i64) -> i64 {
56 st[0] = 0; st[1] = 0
57 let nW1: i64 = ta_leaf(tape, vals, st, MH, MND, W1, 0)
58 let nb1: i64 = ta_leaf(tape, vals, st, MH, 1, b1, 0)
59 let nW2: i64 = ta_leaf(tape, vals, st, MC, MH, W2, 0)
60 let nb2: i64 = ta_leaf(tape, vals, st, MC, 1, b2, 0)
61 wb[0] = nW1; wb[1] = nb1; wb[2] = nW2; wb[3] = nb2
62 let x: *i64 = (sys_mmap(MND * 8)) as *i64
63 let th: *i64 = (sys_mmap(MC * 8)) as *i64
64 var sumn: i64 = 0 - 1
65 var s: i64 = 0
66 while s < 8 {
67 ml_embed(E, seqs, s * MN, x)
68 let xl: i64 = ta_leaf(tape, vals, st, MN, MD, x, 0)
69 xl8[s] = xl
70 let m: i64 = ta_fnet(tape, vals, st, xl)
71 let h: i64 = ta_relu(tape, vals, st, ta_vadd(tape, vals, st, ta_matvec(tape, vals, st, nW1, m), nb1))
72 let lo: i64 = ta_vadd(tape, vals, st, ta_matvec(tape, vals, st, nW2, h), nb2)
73 var j: i64 = 0
74 while j < MC { th[j] = TA_F32_ZERO; j = j + 1 }
75 th[labels[s]] = TA_F32_ONE
76 let tgt: i64 = ta_leaf(tape, vals, st, MC, 1, th, 0)
77 let ls: i64 = ta_softce(tape, vals, st, lo, tgt)
78 if s == 0 { sumn = ls } else { sumn = ta_vadd(tape, vals, st, sumn, ls) }
79 s = s + 1
80 }
81 let inv8: i64 = ta_leaf(tape, vals, st, 1, 1, c8, 0)
82 return ta_matvec(tape, vals, st, inv8, sumn)
83}
84
85func ml_predict(tape: *i64, vals: *i64, st: *i64, E: *i64, W1: *i64, b1: *i64, W2: *i64, b2: *i64, seqs: *i64, s: i64) -> i64 {
86 st[0] = 0; st[1] = 0
87 let nW1: i64 = ta_leaf(tape, vals, st, MH, MND, W1, 0)
88 let nb1: i64 = ta_leaf(tape, vals, st, MH, 1, b1, 0)
89 let nW2: i64 = ta_leaf(tape, vals, st, MC, MH, W2, 0)
90 let nb2: i64 = ta_leaf(tape, vals, st, MC, 1, b2, 0)
91 let x: *i64 = (sys_mmap(MND * 8)) as *i64
92 ml_embed(E, seqs, s * MN, x)
93 let lo: i64 = ml_fwd(tape, vals, st, x, nW1, nb1, nW2, nb2)
94 var best: i64 = 0
95 var bestv: i64 = ta_val(tape, vals, lo, 0)
96 var c: i64 = 1
97 while c < MC {
98 let v: i64 = ta_val(tape, vals, lo, c)
99 if nx_f32_gt(v, bestv) == 1 { bestv = v; best = c }
100 c = c + 1
101 }
102 return best
103}
104
105func ml_adamw(p: *i64, m: *i64, v: *i64, g: *i64, n: i64, lr: i64, beta1: i64, beta2: i64, om1: i64, om2: i64, eps: i64, c1: i64, c2: i64) -> i64 {
106 var i: i64 = 0
107 while i < n {
108 let gi: i64 = g[i]
109 m[i] = nx_f32_add(nx_f32_mul(beta1, m[i]), nx_f32_mul(om1, gi))
110 v[i] = nx_f32_add(nx_f32_mul(beta2, v[i]), nx_f32_mul(om2, nx_f32_mul(gi, gi)))
111 p[i] = nx_f32_sub(p[i], nx_f32_mul(lr, nx_f32_div(nx_f32_div(m[i], c1), nx_f32_add(nx_f32_sqrt(nx_f32_div(v[i], c2)), eps))))
112 i = i + 1
113 }
114 return 0
115}
116
117func ml_train(tape: *i64, vals: *i64, grads: *i64, st: *i64, E: *i64, W1: *i64, b1: *i64, W2: *i64, b2: *i64,
118 seqs: *i64, labels: *i64, epochs: i64, lf: *i64, ll: *i64) -> i64 {
119 ta_det_init(E, MV * MD, 5)
120 ta_det_init(W1, MH * MND, 3)
121 ta_det_init(W2, MC * MH, 7)
122 var z: i64 = 0
123 while z < MH { b1[z] = TA_F32_ZERO; z = z + 1 }
124 z = 0
125 while z < MC { b2[z] = TA_F32_ZERO; z = z + 1 }
126 let mE: *i64 = (sys_mmap(MV * MD * 8)) as *i64; let vE: *i64 = (sys_mmap(MV * MD * 8)) as *i64
127 let mW1: *i64 = (sys_mmap(MH * MND * 8)) as *i64; let vW1: *i64 = (sys_mmap(MH * MND * 8)) as *i64
128 let mb1: *i64 = (sys_mmap(MH * 8)) as *i64; let vb1: *i64 = (sys_mmap(MH * 8)) as *i64
129 let mW2: *i64 = (sys_mmap(MC * MH * 8)) as *i64; let vW2: *i64 = (sys_mmap(MC * MH * 8)) as *i64
130 let mb2: *i64 = (sys_mmap(MC * 8)) as *i64; let vb2: *i64 = (sys_mmap(MC * 8)) as *i64
131 z = 0
132 while z < MV * MD { mE[z] = TA_F32_ZERO; vE[z] = TA_F32_ZERO; z = z + 1 }
133 z = 0
134 while z < MH * MND { mW1[z] = TA_F32_ZERO; vW1[z] = TA_F32_ZERO; z = z + 1 }
135 z = 0
136 while z < MH { mb1[z] = TA_F32_ZERO; vb1[z] = TA_F32_ZERO; z = z + 1 }
137 z = 0
138 while z < MC * MH { mW2[z] = TA_F32_ZERO; vW2[z] = TA_F32_ZERO; z = z + 1 }
139 z = 0
140 while z < MC { mb2[z] = TA_F32_ZERO; vb2[z] = TA_F32_ZERO; z = z + 1 }
141 let beta1: i64 = ta_constf(9, 10); let beta2: i64 = ta_constf(999, 1000)
142 let om1: i64 = ta_constf(1, 10); let om2: i64 = ta_constf(1, 1000)
143 let lr: i64 = ta_constf(1, 50); let eps: i64 = ta_constf(1, 100000000)
144 var b1t: i64 = TA_F32_ONE; var b2t: i64 = TA_F32_ONE
145 let c8: *i64 = (sys_mmap(8)) as *i64; c8[0] = ta_constf(1, 8)
146 let wb: *i64 = (sys_mmap(4 * 8)) as *i64
147 let xl8: *i64 = (sys_mmap(8 * 8)) as *i64
148 let gW1: *i64 = (sys_mmap(MH * MND * 8)) as *i64
149 let gb1: *i64 = (sys_mmap(MH * 8)) as *i64
150 let gW2: *i64 = (sys_mmap(MC * MH * 8)) as *i64
151 let gb2: *i64 = (sys_mmap(MC * 8)) as *i64
152 let dE: *i64 = (sys_mmap(MV * MD * 8)) as *i64
153 var ep: i64 = 0
154 while ep < epochs {
155 let loss: i64 = ml_build(tape, vals, st, E, W1, b1, W2, b2, seqs, labels, c8, wb, xl8)
156 ta_backward(tape, vals, grads, st[0], loss)
157 if ep == 0 { *lf = ta_val(tape, vals, loss, 0) }
158 *ll = ta_val(tape, vals, loss, 0)
159 var i: i64 = 0
160 while i < MH * MND { gW1[i] = ta_grad(tape, grads, wb[0], i); i = i + 1 }
161 i = 0
162 while i < MH { gb1[i] = ta_grad(tape, grads, wb[1], i); i = i + 1 }
163 i = 0
164 while i < MC * MH { gW2[i] = ta_grad(tape, grads, wb[2], i); i = i + 1 }
165 i = 0
166 while i < MC { gb2[i] = ta_grad(tape, grads, wb[3], i); i = i + 1 }
167 i = 0
168 while i < MV * MD { dE[i] = TA_F32_ZERO; i = i + 1 }
169 var s: i64 = 0
170 while s < 8 {
171 var pos: i64 = 0
172 while pos < MN {
173 let tok: i64 = seqs[s * MN + pos]
174 var j: i64 = 0
175 while j < MD {
176 dE[tok * MD + j] = nx_f32_add(dE[tok * MD + j], ta_grad(tape, grads, xl8[s], pos * MD + j))
177 j = j + 1
178 }
179 pos = pos + 1
180 }
181 s = s + 1
182 }
183 b1t = nx_f32_mul(b1t, beta1); b2t = nx_f32_mul(b2t, beta2)
184 let c1: i64 = nx_f32_sub(TA_F32_ONE, b1t); let c2: i64 = nx_f32_sub(TA_F32_ONE, b2t)
185 ml_adamw(E, mE, vE, dE, MV * MD, lr, beta1, beta2, om1, om2, eps, c1, c2)
186 ml_adamw(W1, mW1, vW1, gW1, MH * MND, lr, beta1, beta2, om1, om2, eps, c1, c2)
187 ml_adamw(b1, mb1, vb1, gb1, MH, lr, beta1, beta2, om1, om2, eps, c1, c2)
188 ml_adamw(W2, mW2, vW2, gW2, MC * MH, lr, beta1, beta2, om1, om2, eps, c1, c2)
189 ml_adamw(b2, mb2, vb2, gb2, MC, lr, beta1, beta2, om1, om2, eps, c1, c2)
190 ep = ep + 1
191 }
192 return 0
193}
194
195func main() -> i64 {
196 var ok: i64 = 1
197 let tape: *i64 = (sys_mmap(2048 * 7 * 8)) as *i64
198 let vals: *i64 = (sys_mmap(8192 * 8)) as *i64
199 let grads: *i64 = (sys_mmap(8192 * 8)) as *i64
200 let st: *i64 = (sys_mmap(2 * 8)) as *i64
201
202 // 8 masked palindromes [a,b,b,a] (MASK=4 at one position); label = the true masked token (its mirror).
203 let seqs: *i64 = (sys_mmap(32 * 8)) as *i64
204 seqs[0]=0; seqs[1]=1; seqs[2]=1; seqs[3]=4
205 seqs[4]=4; seqs[5]=2; seqs[6]=2; seqs[7]=1
206 seqs[8]=2; seqs[9]=4; seqs[10]=3; seqs[11]=2
207 seqs[12]=3; seqs[13]=0; seqs[14]=4; seqs[15]=3
208 seqs[16]=0; seqs[17]=2; seqs[18]=2; seqs[19]=4
209 seqs[20]=4; seqs[21]=3; seqs[22]=3; seqs[23]=1
210 seqs[24]=2; seqs[25]=1; seqs[26]=4; seqs[27]=2
211 seqs[28]=3; seqs[29]=4; seqs[30]=0; seqs[31]=3
212 let labels: *i64 = (sys_mmap(8 * 8)) as *i64
213 labels[0]=0; labels[1]=1; labels[2]=3; labels[3]=0; labels[4]=0; labels[5]=1; labels[6]=1; labels[7]=0
214
215 let E: *i64 = (sys_mmap(MV * MD * 8)) as *i64
216 let W1: *i64 = (sys_mmap(MH * MND * 8)) as *i64
217 let b1: *i64 = (sys_mmap(MH * 8)) as *i64
218 let W2: *i64 = (sys_mmap(MC * MH * 8)) as *i64
219 let b2: *i64 = (sys_mmap(MC * 8)) as *i64
220 let lf: *i64 = (sys_mmap(8)) as *i64
221 let ll: *i64 = (sys_mmap(8)) as *i64
222 ml_train(tape, vals, grads, st, E, W1, b1, W2, b2, seqs, labels, 2000, lf, ll)
223 var acc: i64 = 0
224 var s: i64 = 0
225 while s < 8 {
226 if ml_predict(tape, vals, st, E, W1, b1, W2, b2, seqs, s) == labels[s] { acc = acc + 1 }
227 s = s + 1
228 }
229 var trainPass: i64 = 1
230 if acc < 7 { trainPass = 0 }
231 if nx_f32_lt(*ll, *lf) != 1 { trainPass = 0 }
232 if trainPass != 1 { ok = 0 }
233
234 let E2: *i64 = (sys_mmap(MV * MD * 8)) as *i64
235 let W1b: *i64 = (sys_mmap(MH * MND * 8)) as *i64
236 let b1b: *i64 = (sys_mmap(MH * 8)) as *i64
237 let W2b: *i64 = (sys_mmap(MC * MH * 8)) as *i64
238 let b2b: *i64 = (sys_mmap(MC * 8)) as *i64
239 let lf2: *i64 = (sys_mmap(8)) as *i64
240 let ll2: *i64 = (sys_mmap(8)) as *i64
241 ml_train(tape, vals, grads, st, E2, W1b, b1b, W2b, b2b, seqs, labels, 2000, lf2, ll2)
242 var acc2: i64 = 0
243 s = 0
244 while s < 8 {
245 if ml_predict(tape, vals, st, E2, W1b, b1b, W2b, b2b, seqs, s) == labels[s] { acc2 = acc2 + 1 }
246 s = s + 1
247 }
248 var reproPass: i64 = 1
249 if acc2 != acc { reproPass = 0 }
250 if *ll2 != *ll { reproPass = 0 }
251 if reproPass != 1 { ok = 0 }
252
253 var fdi: i64 = 1
254 while fdi >= 0 {
255 var out: i64 = 1
256 if fdi == 0 { out = sys_openat_append(ML_LOG, 420) }
257 if out >= 0 {
258 ml_w(out, "FNETMLMGATE authored=organ model=masked-LM embed+fnet-mix+relu-ffn+softmaxCE task=palindrome-fill no-attention" as *u8)
259 ml_w(out, " | masked_token_accuracy=" as *u8); ml_wn(out, acc); ml_w(out, "/8" as *u8)
260 ml_w(out, " loss_first_milli=" as *u8); ml_wn(out, ta_f32_to_milli(*lf))
261 ml_w(out, " loss_last_milli=" as *u8); ml_wn(out, ta_f32_to_milli(*ll))
262 ml_w(out, " | bitexact_repro=" as *u8); ml_wn(out, reproPass)
263 if ok == 1 { ml_w(out, " verdict=GREEN\n" as *u8) } else { ml_w(out, " verdict=RED\n" as *u8) }
264 if fdi == 0 { sys_close(out) }
265 }
266 fdi = fdi - 1
267 }
268 if ok == 1 { return 0 }
269 return 1
270}