1 # bend-ml-tensor-array: tensors over a flat Array, with the shape in the type. 2 # 3 # import bend-ml-tensor-array@0.1.18.0/main.bend as TA 4 # 5 # Mat stores r*c numbers in an Array, row by row (index i*c + j). The 6 # dimensions are erased type parameters: the same guarantee as bend-ml-tensor 7 # (a product with wrong dimensions does not compile), but ~50x faster than lists: 8 # Array.get/set by index, and products in parallel blocks of rows. 9 # 10 # An Array is affine (a single owner): every operation that reads an operand returns it 11 # together with the result (as Array.get does), in records MM, AR, ... Use Mat.clone 12 # when you need two copies. 13 # 14 # What is NOT proved: the invariant "the Array has capacity >= r*c" holds because 15 # the constructors (Mat.zeros, Mat.of_list) allocate the right size; and F32 is 16 # validated by tests against PyTorch (reference/test_tensor_array.py), not by proof. 17 18 import Base 19 20 # --------------------------------------------------------------------- 21 # helper types 22 # --------------------------------------------------------------------- 23 24 type R is Type: 25 R{a: Array, b: Array, x: F32} 26 27 type G is Type: 28 G{a: Array, b: Array, c: Array} 29 30 # A dot product with four partial sums. Each step adds its product to a0 and rotates the sums 31 # (a1, a2, a3, a0), so each sum gets every fourth product and the processor can work on the next 32 # addition before the last one has finished; at the end the sums are added in pairs. The order of 33 # the additions is fixed, so the result is the same on every run and every thread count, but it is 34 # not the order of a single running sum (NOTES.md, exp. 17). 35 def dotr(k: Nat, +ia: U32, +ib: U32, +sa: U32, +sb: U32, a0: F32, a1: F32, a2: F32, a3: F32, ra: Array & F32, rb: Array & F32) -> R: 36 match k ra rb: 37 case 0n Tuple{a, x} Tuple{b, y}: 38 R{a, b, ((a0 + a1 : F32) + (a2 + a3 : F32) : F32)} 39 case 1n+p Tuple{a, +x} Tuple{b, +y}: 40 dotr(p, (ia + sa : U32), (ib + sb : U32), sa, sb, a1, a2, a3, (a0 + (x * y : F32) : F32), Array.get(F32, a, (ia + sa : U32)), Array.get(F32, b, (ib + sb : U32))) 41 42 def dotg(k: Nat, +ia: U32, +ib: U32, +sa: U32, +sb: U32, acc: F32, ra: Array & F32, rb: Array & F32) -> R: 43 dotr(k, ia, ib, sa, sb, acc, 0.0, 0.0, 0.0, ra, rb) 44 45 # writes one cell of C: c[idx] = x, or c[idx] = c[idx] + x when ac (an accumulating product, 0.1.10.0). x is computed 46 # exactly as without ac and added once, so C + A·B has the bits of adding a separately computed A·B to C. 47 48 def cwr_add(+idx: U32, +x: F32, r: Array & F32) -> Array: 49 match r: 50 case Tuple{c, +v}: 51 Array.set(F32, c, idx, (v + x : F32)) 52 53 def cwr(ac: Bool, +idx: U32, +x: F32, c: Array) -> Array: 54 match ac: 55 case True{}: 56 cwr_add(idx, x, Array.get(F32, c, idx)) 57 case False{}: 58 Array.set(F32, c, idx, x) 59 60 # row i of C, columns j.. ; r is the product of the current column (already computed) 61 62 def gcols(+ac: Bool, mleft: Nat, +kn: Nat, +ia0: U32, +ib0: U32, +sa: U32, +sb: U32, +bcol: U32, +cidx: U32, c: Array, r: R) -> G: 63 match mleft r: 64 case 0n R{a, b, +x}: 65 G{a, b, cwr(ac, cidx, x, c)} 66 case 1n+p R{a, b, +x}: 67 gcols(ac, p, kn, ia0, (ib0 + bcol : U32), sa, sb, bcol, (cidx + 1 : U32), cwr(ac, cidx, x, c), dotg(kn, ia0, (ib0 + bcol : U32), sa, sb, 0.0, Array.get(F32, a, ia0), Array.get(F32, b, (ib0 + bcol : U32)))) 68 69 def grows(+ac: Bool, nleft: Nat, +mm1: Nat, +kn: Nat, +i: U32, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, +mu: U32, g: G) -> G: 70 match nleft g: 71 case 0n G{a, b, c}: 72 G{a, b, c} 73 case 1n+p G{a, b, c}: 74 grows(ac, p, mm1, kn, (i + 1 : U32), arow, sa, sb, bcol, mu, gcols(ac, mm1, kn, (i * arow : U32), 0, sa, sb, bcol, (i * mu : U32), c, dotg(kn, (i * arow : U32), 0, sa, sb, 0.0, Array.get(F32, a, (i * arow : U32)), Array.get(F32, b, 0)))) 75 76 # C (n x m) = A · B with k steps, or C + A · B when ac; c is the output array (with capacity n*m) 77 78 def gemm_ac(+ac: Bool, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 79 grows(ac, n, Nat.sub(m, 1n), k, 0, arow, sa, sb, bcol, U32.from_nat(m), G{a, b, c}) 80 81 def gemm(+n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 82 gemm_ac(False{}, n, m, k, arow, sa, sb, bcol, a, b, c) 83 84 85 # --------------------------------------------------------------------- 86 # parallel gemm: 2^d blocks of rows of C, each with its own copy of A and B, 87 # returning lists that are written into C at the end 88 # --------------------------------------------------------------------- 89 90 type S3 is Type: 91 S3{a: Array, b: Array, out: List<&2, F32>} 92 93 def lcols(mleft: Nat, +kn: Nat, +ia0: U32, +ib0: U32, +sa: U32, +sb: U32, +bcol: U32, out: List<&2, F32>, r: R) -> S3: 94 match mleft r: 95 case 0n R{a, b, +x}: 96 S3{a, b, x <> out} 97 case 1n+p R{a, b, +x}: 98 lcols(p, kn, ia0, (ib0 + bcol : U32), sa, sb, bcol, x <> out, dotg(kn, ia0, (ib0 + bcol : U32), sa, sb, 0.0, Array.get(F32, a, ia0), Array.get(F32, b, (ib0 + bcol : U32)))) 99 100 def lrows(nleft: Nat, +mm1: Nat, +kn: Nat, +i: U32, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, st: S3) -> S3: 101 match nleft st: 102 case 0n S3{a, b, out}: 103 S3{a, b, out} 104 case 1n+p S3{a, b, out}: 105 lrows(p, mm1, kn, (i + 1 : U32), arow, sa, sb, bcol, lcols(mm1, kn, (i * arow : U32), 0, sa, sb, bcol, out, dotg(kn, (i * arow : U32), 0, sa, sb, 0.0, Array.get(F32, a, (i * arow : U32)), Array.get(F32, b, 0)))) 106 107 def lblock_fin(st: S3) -> List<&2, F32>: 108 match st: 109 case S3{a, b, out}: 110 List.reverse(&2, F32, out) 111 112 def lblock(cnt: Nat, +mm1: Nat, +kn: Nat, +i: U32, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array) -> List<&2, F32>: 113 lblock_fin(lrows(cnt, mm1, kn, i, arow, sa, sb, bcol, S3{a, b, Nil{}})) 114 115 def lpar(d: Nat, +cnt: Nat, +mm1: Nat, +kn: Nat, +i: U32, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, ra: Array & Array, rb: Array & Array) -> List<&2, F32>: 116 match d ra rb: 117 case 0n Tuple{a, a2} Tuple{b, b2}: 118 lblock(cnt, mm1, kn, i, arow, sa, sb, bcol, a, b) 119 case 1n++q Tuple{a, a2} Tuple{b, b2}: 120 x y = lpar(q, Nat.div(cnt, 2n), mm1, kn, i, arow, sa, sb, bcol, Array.clone(F32, a), Array.clone(F32, b)) lpar(q, Nat.sub(cnt, Nat.div(cnt, 2n)), mm1, kn, (i + U32.from_nat(Nat.div(cnt, 2n)) : U32), arow, sa, sb, bcol, Array.clone(F32, a2), Array.clone(F32, b2)) 121 List.append(&2, F32, x, y) 122 123 def write_l(xs: List<&2, F32>, +idx: U32, c: Array) -> Array: 124 match xs: 125 case Nil{}: 126 c 127 case Con{h, t}: 128 write_l(t, (idx + 1 : U32), Array.set(F32, c, idx, h)) 129 130 # c[idx..] = list, or c[idx..] += list when ac 131 132 def write_la(+ac: Bool, xs: List<&2, F32>, +idx: U32, c: Array) -> Array: 133 match xs: 134 case Nil{}: 135 c 136 case Con{+h, t}: 137 write_la(ac, t, (idx + 1 : U32), cwr(ac, idx, h, c)) 138 139 def gp3(+ac: Bool, a: Array, b: Array, c: Array, xs: List<&2, F32>) -> G: 140 G{a, b, write_la(ac, xs, 0, c)} 141 142 def gp2(d: Nat, +n: Nat, +mm1: Nat, +kn: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, c: Array, pa: Array & Array, pb: Array & Array) -> G: 143 match pa pb: 144 case Tuple{a1, a2} Tuple{b1, b2}: 145 gp3(False{}, a1, b1, c, lpar(d, n, mm1, kn, 0, arow, sa, sb, bcol, Array.clone(F32, a2), Array.clone(F32, b2))) 146 147 # C (n x m) = A · B, with 2^d blocks of rows in parallel; returns A, B (intact) and C 148 149 def gemm_par(d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 150 gp2(d, n, Nat.sub(m, 1n), k, arow, sa, sb, bcol, c, Array.clone(F32, a), Array.clone(F32, b)) 151 152 153 # --------------------------------------------------------------------- 154 # parallel gemm by COLUMNS, for n = 1 (matrix · vector): each block computes a 155 # range of columns of C, with its own copy of the operands 156 # --------------------------------------------------------------------- 157 158 def cblock_fin(st: S3) -> List<&2, F32>: 159 match st: 160 case S3{a, b, out}: 161 List.reverse(&2, F32, out) 162 163 # cnt columns starting at j0 (cnt >= 1); row 0 of A is at ia0 164 165 def cblock_go(cnt1: Nat, +kn: Nat, +ia0: U32, +j0: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array) -> List<&2, F32>: 166 cblock_fin(lcols(cnt1, kn, ia0, (j0 * bcol : U32), sa, sb, bcol, Nil{}, dotg(kn, ia0, (j0 * bcol : U32), sa, sb, 0.0, Array.get(F32, a, ia0), Array.get(F32, b, (j0 * bcol : U32))))) 167 168 def cblock(cnt: Nat, +kn: Nat, +ia0: U32, +j0: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array) -> List<&2, F32>: 169 match cnt: 170 case 0n: 171 Nil{} 172 case 1n+p: 173 cblock_go(p, kn, ia0, j0, sa, sb, bcol, a, b) 174 175 def cpar(d: Nat, +cnt: Nat, +kn: Nat, +ia0: U32, +j0: U32, +sa: U32, +sb: U32, +bcol: U32, ra: Array & Array, rb: Array & Array) -> List<&2, F32>: 176 match d ra rb: 177 case 0n Tuple{a, a2} Tuple{b, b2}: 178 cblock(cnt, kn, ia0, j0, sa, sb, bcol, a, b) 179 case 1n++q Tuple{a, a2} Tuple{b, b2}: 180 x y = cpar(q, Nat.div(cnt, 2n), kn, ia0, j0, sa, sb, bcol, Array.clone(F32, a), Array.clone(F32, b)) cpar(q, Nat.sub(cnt, Nat.div(cnt, 2n)), kn, ia0, (j0 + U32.from_nat(Nat.div(cnt, 2n)) : U32), sa, sb, bcol, Array.clone(F32, a2), Array.clone(F32, b2)) 181 List.append(&2, F32, x, y) 182 183 def cp2(+ac: Bool, d: Nat, +m: Nat, +kn: Nat, +sa: U32, +sb: U32, +bcol: U32, c: Array, pa: Array & Array, pb: Array & Array) -> G: 184 match pa pb: 185 case Tuple{a1, a2} Tuple{b1, b2}: 186 gp3(ac, a1, b1, c, cpar(d, m, kn, 0, 0, sa, sb, bcol, Array.clone(F32, a2), Array.clone(F32, b2))) 187 188 # C (1 x m) = A (1 x k) · B, with 2^d blocks of columns in parallel. Since 0.1.12.0 no product calls it (one row runs 189 # the scalar loop, see gemm_any3): it clones both operands at every node. Kept for callers of earlier versions. 190 191 def gemm_par_cols(+ac: Bool, d: Nat, +m: Nat, +k: Nat, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 192 cp2(ac, d, m, k, sa, sb, bcol, c, Array.clone(F32, a), Array.clone(F32, b)) 193 194 # --------------------------------------------------------------------- 195 # element-wise operations (in place, by index) 196 # --------------------------------------------------------------------- 197 198 # --------------------------------------------------------------------- 199 # gemm: C[i][j] = sum_p A[i*arow + p*sa] * B[p*sb + j*bcol] 200 # (nn: arow=K, sa=1, sb=M, bcol=1 | nt: B stored m x k: sb=1, bcol=K | tn: A stored k x n: arow=1, sa=N) 201 # --------------------------------------------------------------------- 202 203 # acc += A[ia + t*sa] * B[ib + t*sb] for k steps; ra, rb already carry the first pair 204 205 type P2 is Type: 206 P2{a: Array, b: Array} 207 208 type AL is Type: 209 AL{c: Array, l: List<&2, F32>} 210 211 # list of m elements starting at idx (reads with Array.get, returning the array) 212 213 def read_l(mleft: Nat, +idx: U32, acc: List<&2, F32>, rc: Array & F32) -> AL: 214 match mleft rc: 215 case 0n Tuple{c, +v}: 216 AL{c, List.reverse(&2, F32, v <> acc)} 217 case 1n+p Tuple{c, +v}: 218 read_l(p, (idx + 1 : U32), v <> acc, Array.get(F32, c, (idx + 1 : U32))) 219 220 # writes the list starting at idx 221 222 def addl(xs: List<&2, F32>, +idx: U32, rc: Array & F32) -> Array: 223 match xs rc: 224 case Nil{} Tuple{c, v}: 225 c 226 case Con{+h, t} Tuple{c, +v}: 227 addl(t, (idx + 1 : U32), Array.get(F32, Array.set(F32, c, idx, (v + h : F32)), (idx + 1 : U32))) 228 229 # adds a bias (a list of m) to each of the n rows of c (n x m) 230 231 def bias_rows(nleft: Nat, +i: U32, +mu: U32, +bl: List<&2, F32>, c: Array) -> Array: 232 match nleft: 233 case 0n: 234 c 235 case 1n+p: 236 bias_rows(p, (i + 1 : U32), mu, bl, addl(bl, (i * mu : U32), Array.get(F32, c, (i * mu : U32)))) 237 238 # in-place relu on the first n elements 239 240 def relu_l(nleft: Nat, +t: U32, rc: Array & F32) -> Array: 241 match nleft rc: 242 case 0n Tuple{c, v}: 243 c 244 case 1n+p Tuple{c, +v}: 245 relu_l(p, (t + 1 : U32), Array.get(F32, Array.set(F32, c, t, F32.max(v, 0.0)), (t + 1 : U32))) 246 247 def pos2(b: Bool) -> F32: 248 match b: 249 case True{}: 250 1.0 251 case False{}: 252 0.0 253 254 def pos(+y: F32) -> F32: 255 pos2(F32.is_gt(y, 0.0)) 256 257 # d[t] *= (h[t] > 0): the relu gradient; returns both arrays 258 259 def mask_l(nleft: Nat, +t: U32, rd: Array & F32, rh: Array & F32) -> P2: 260 match nleft rd rh: 261 case 0n Tuple{d, x} Tuple{h, y}: 262 P2{d, h} 263 case 1n+p Tuple{d, +x} Tuple{h, +y}: 264 mask_l(p, (t + 1 : U32), Array.get(F32, Array.set(F32, d, t, (x * pos(y) : F32)), (t + 1 : U32)), Array.get(F32, h, (t + 1 : U32))) 265 266 # w[t] -= lr * dw[t] 267 268 def maxl2(xs: List<&2, F32>, cur: F32) -> F32: 269 match xs: 270 case Nil{}: 271 cur 272 case Con{+h, t}: 273 maxl2(t, F32.max(cur, h)) 274 275 def maxl(xs: List<&2, F32>) -> F32: 276 match xs: 277 case Nil{}: 278 0.0 279 case Con{+h, t}: 280 maxl2(t, h) 281 282 def expl(xs: List<&2, F32>, +m: F32) -> List<&2, F32>: 283 match xs: 284 case Nil{}: 285 Nil{} 286 case Con{+h, t}: 287 F32.exp((h - m : F32)) <> expl(t, m) 288 289 def suml(xs: List<&2, F32>, acc: F32) -> F32: 290 match xs: 291 case Nil{}: 292 acc 293 case Con{+h, t}: 294 suml(t, (acc + h : F32)) 295 296 def divl(xs: List<&2, F32>, +s: F32) -> List<&2, F32>: 297 match xs: 298 case Nil{}: 299 Nil{} 300 case Con{+h, t}: 301 (h / s : F32) <> divl(t, s) 302 303 def softmax2(+es: List<&2, F32>) -> List<&2, F32>: 304 divl(es, suml(es, 0.0)) 305 306 def softmax(+xs: List<&2, F32>) -> List<&2, F32>: 307 softmax2(expl(xs, maxl(xs))) 308 309 # p[label] 310 311 def sgd_l(nleft: Nat, +t: U32, +lr: F32, rw: Array & F32, rd: Array & F32) -> P2: 312 match nleft rw rd: 313 case 0n Tuple{w, x} Tuple{d, y}: 314 P2{w, d} 315 case 1n+p Tuple{w, +x} Tuple{d, +y}: 316 sgd_l(p, (t + 1 : U32), lr, Array.get(F32, Array.set(F32, w, t, (x - (lr * y : F32) : F32)), (t + 1 : U32)), Array.get(F32, d, (t + 1 : U32))) 317 318 # --------------------------------------------------------------------- 319 # softmax + cross-entropy per row (lists of 10 elements: negligible cost) 320 # --------------------------------------------------------------------- 321 322 def pick(ps: List<&2, F32>, n: Nat) -> F32: 323 match ps n: 324 case Con{+h, t} 0n: 325 h 326 case Con{h, t} 1n+q: 327 pick(t, q) 328 case Nil{} _: 329 0.0 330 331 # (p - onehot(label)) / n 332 333 def hit(b: Bool) -> F32: 334 match b: 335 case True{}: 336 1.0 337 case False{}: 338 0.0 339 340 def grad_l(ps: List<&2, F32>, +label: Nat, +j: Nat, +nf: F32) -> List<&2, F32>: 341 match ps: 342 case Nil{}: 343 Nil{} 344 case Con{+h, t}: 345 (((h - hit(Nat.is_eq(label, j)) : F32) / nf) : F32) <> grad_l(t, label, 1n+j, nf) 346 347 type L2 is Type: 348 L2{loss: F32, c: Array} 349 350 def ce_row2(+ps: List<&2, F32>, +label: Nat, +nf: F32, +idx: U32, loss: F32, c: Array) -> L2: 351 L2{(loss - F32.log(F32.max(pick(ps, label), 0.000000000001)) : F32), write_l(grad_l(ps, label, 0n, nf), idx, c)} 352 353 def ce_row(al: AL, +label: Nat, +nf: F32, +idx: U32, loss: F32) -> L2: 354 match al: 355 case AL{c, l}: 356 ce_row2(softmax(l), label, nf, idx, loss, c) 357 358 # walks the rows of z2 (n x m): r carries the accumulated loss and the array; at the end the array has become the gradient 359 360 type Mat<-r: Nat, -c: Nat> is Type: 361 Mat{d: Array} 362 363 def ce_rows(labels: List<&2, Nat>, +mm1: Nat, +mu: U32, +nf: F32, +i: U32, r: L2) -> L2: 364 match labels r: 365 case Nil{} L2{loss, c}: 366 L2{loss, c} 367 case Con{+y, t} L2{loss, c}: 368 ce_rows(t, mm1, mu, nf, (i + 1 : U32), ce_row(read_l(mm1, (i * mu : U32), Nil{}, Array.get(F32, c, (i * mu : U32))), y, nf, (i * mu : U32), loss)) 369 370 # ---- the same rows over the array itself: no lists ---- 371 # The list version above reads each row into a list and walks it five times. This one walks the row in place with tail 372 # loops, with the same operations in the same order (the max from the first cell, exp(x - max) summed from +0.0 left to 373 # right, p = e / s, the loss term read before the row is overwritten, (p - onehot) / n written back), so the same bits; 374 # MetalBend measured 3.4x fewer cycles on 64 rows of 49,152 (2026-10-25). The list version stays as the reference 375 # tests/ce_array.bend compares it with. 376 377 type AF is Type: 378 AF{c: Array, x: F32} 379 380 # the max of k + 1 cells from idx, from m (the first cell's value, already read in r) 381 def cea_max(k: Nat, +idx: U32, +m: F32, r: Array & F32) -> AF: 382 match k r: 383 case 0n Tuple{c, +x}: 384 AF{c, F32.max(m, x)} 385 case 1n+p Tuple{c, +x}: 386 cea_max(p, (idx + 1 : U32), F32.max(m, x), Array.get(F32, c, (idx + 1 : U32))) 387 388 # cells idx..idx + k become exp(cell - m); returns their sum from s, added left to right 389 def cea_exp(k: Nat, +idx: U32, +m: F32, +s: F32, r: Array & F32) -> AF: 390 match k r: 391 case 0n Tuple{c, +x}: 392 +e = F32.exp((x - m : F32)) 393 AF{Array.set(F32, c, idx, e), (s + e : F32)} 394 case 1n+p Tuple{c, +x}: 395 +e = F32.exp((x - m : F32)) 396 cea_exp(p, (idx + 1 : U32), m, (s + e : F32), Array.get(F32, Array.set(F32, c, idx, e), (idx + 1 : U32))) 397 398 # cell idx (column j) becomes ((e / s) - onehot) / nf, for k + 1 cells 399 def cea_grad(k: Nat, +idx: U32, +j: Nat, +label: Nat, +s: F32, +nf: F32, r: Array & F32) -> Array: 400 match k r: 401 case 0n Tuple{c, +x}: 402 Array.set(F32, c, idx, ((((x / s : F32) - hit(Nat.is_eq(label, j)) : F32) / nf) : F32)) 403 case 1n+p Tuple{c, +x}: 404 cea_grad(p, (idx + 1 : U32), 1n+j, label, s, nf, Array.get(F32, Array.set(F32, c, idx, ((((x / s : F32) - hit(Nat.is_eq(label, j)) : F32) / nf) : F32)), (idx + 1 : U32))) 405 406 def cea_p2(+s: F32, r: Array & F32) -> AF: 407 match r: 408 case Tuple{c, +e}: 409 AF{c, (e / s : F32)} 410 411 # p[label] = e[label] / s, or 0.0 for a label past the row (pick's Nil case) 412 def cea_p(inside: Bool, +o: U32, +label: Nat, +s: F32, c: Array) -> AF: 413 match inside: 414 case True{}: 415 cea_p2(s, Array.get(F32, c, (o + U32.from_nat(label) : U32))) 416 case False{}: 417 AF{c, 0.0} 418 419 def cea_row3(+mm1: Nat, +o: U32, +label: Nat, +s: F32, +nf: F32, +loss: F32, q: AF) -> L2: 420 match q: 421 case AF{c, +p}: 422 L2{(loss - F32.log(F32.max(p, 0.000000000001)) : F32), cea_grad(mm1, o, 0n, label, s, nf, Array.get(F32, c, o))} 423 424 def cea_row2(+mm1: Nat, +o: U32, +label: Nat, +nf: F32, +loss: F32, q: AF) -> L2: 425 match q: 426 case AF{c, +s}: 427 cea_row3(mm1, o, label, s, nf, loss, cea_p(Nat.is_le(label, mm1), o, label, s, c)) 428 429 def cea_row1(+mm1: Nat, +o: U32, +label: Nat, +nf: F32, +loss: F32, q: AF) -> L2: 430 match q: 431 case AF{c, +m}: 432 cea_row2(mm1, o, label, nf, loss, cea_exp(mm1, o, m, 0.0, Array.get(F32, c, o))) 433 434 # a row's max starts from its first cell (maxl), then folds over the k + 1 cells from it 435 def cea_row0(+mm1: Nat, +o: U32, +label: Nat, +nf: F32, +loss: F32, r: Array & F32) -> L2: 436 match r: 437 case Tuple{c, +x0}: 438 cea_row1(mm1, o, label, nf, loss, cea_max(mm1, o, x0, Array.get(F32, c, o))) 439 440 # one row per label, in order, as ce_rows 441 def cea_rows(labels: List<&2, Nat>, +mm1: Nat, +mu: U32, +nf: F32, +i: U32, r: L2) -> L2: 442 match labels r: 443 case Nil{} L2{loss, c}: 444 L2{loss, c} 445 case Con{+y, t} L2{+loss, c}: 446 cea_rows(t, mm1, mu, nf, (i + 1 : U32), cea_row0(mm1, (i * mu : U32), y, nf, loss, Array.get(F32, c, (i * mu : U32)))) 447 448 # ---- helper: hits per row ---- 449 450 type CR is Type: 451 CR{c: Array, n: Nat} 452 453 def gt_head(xs: List<&2, F32>, +v: F32) -> Bool: 454 match xs: 455 case Nil{}: 456 False{} 457 case Con{+h, t}: 458 F32.is_gt(h, v) 459 460 def argmax2(xs: List<&2, F32>, g: Bool, +i: Nat, +bi: Nat, +bv: F32) -> Nat: 461 match xs g: 462 case Nil{} _: 463 bi 464 case Con{+h, +t} True{}: 465 argmax2(t, gt_head(t, h), 1n+i, i, h) 466 case Con{h, +t} False{}: 467 argmax2(t, gt_head(t, bv), 1n+i, bi, bv) 468 469 def argmax(xs: List<&2, F32>) -> Nat: 470 match xs: 471 case Nil{}: 472 0n 473 case Con{+h, +t}: 474 argmax2(t, gt_head(t, h), 1n, 0n, h) 475 476 def one(b: Bool) -> Nat: 477 match b: 478 case True{}: 479 1n 480 case False{}: 481 0n 482 483 def count_rows3(+y: Nat, n: Nat, al: AL) -> CR: 484 match al: 485 case AL{c, l}: 486 CR{c, Nat.add(n, one(Nat.is_eq(argmax(l), y)))} 487 488 def count_rows(labels: List<&2, Nat>, +mm1: Nat, +mu: U32, +i: U32, r: CR) -> CR: 489 match labels r: 490 case Nil{} CR{c, n}: 491 CR{c, n} 492 case Con{+y, t} CR{c, n}: 493 count_rows(t, mm1, mu, (i + 1 : U32), count_rows3(y, n, read_l(mm1, (i * mu : U32), Nil{}, Array.get(F32, c, (i * mu : U32))))) 494 495 496 # ===================================================================== 497 # Typed API: Mat (the dimensions enter the type) 498 # ===================================================================== 499 500 # ===================================================================== 501 # Array capacity: a PROVED depth for the number of slots 502 # ===================================================================== 503 # 504 # cap_depth(n) is the d such that an Array with 2^d slots holds n elements. It is computed by 505 # structural recursion on n (not with U32.log2) so that the law cap_ok below can prove 506 # `n <= 2^cap_depth(n)`. What stays trusted is only that Array.new(T, d, v) allocates 2^d slots. 507 508 # a type chosen by a Bool: rewriting through it refutes True == False 509 def BD(b: Bool, t: Type, f: Type) -> Type: 510 match b: 511 case True{}: 512 t 513 case False{}: 514 f 515 516 # a <= b, by direct recursion 517 def leb(a: Nat, b: Nat) -> Bool: 518 match a b: 519 case 0n _: 520 True{} 521 case 1n+p 0n: 522 False{} 523 case 1n+p 1n+q: 524 leb(p, q) 525 526 def pow2(d: Nat) -> Nat: 527 match d: 528 case 0n: 529 1n 530 case 1n++q: 531 Nat.add(pow2(q), pow2(q)) 532 533 # smallest power of two >= n, by structural recursion on n: returns (depth, 2^depth) 534 def cap_pick(ok: Bool, d: Nat, +p: Nat) -> Nat & Nat: 535 match ok: 536 case True{}: 537 (d, p) 538 case False{}: 539 (1n+d, Nat.add(p, p)) 540 541 def cap_step(r: Nat & Nat, +n: Nat) -> Nat & Nat: 542 match r: 543 case Tuple{+d, +p}: 544 cap_pick(leb(n, p), d, p) 545 546 def cap_pair(n: Nat) -> Nat & Nat: 547 match n: 548 case 0n: 549 (0n, 1n) 550 case 1n++m: 551 cap_step(cap_pair(m), 1n+m) 552 553 def fst_nat(r: Nat & Nat) -> Nat: 554 match r: 555 case Tuple{d, p}: 556 d 557 558 def snd_nat(r: Nat & Nat) -> Nat: 559 match r: 560 case Tuple{d, p}: 561 p 562 563 # leb is Base's Nat.is_le, case for case. leb walks its arguments one step at a time in the compiled program; Base's 564 # comparison is one native comparison there (0.1.13.0). 565 def le_leb(a: Nat, b: Nat) -> {Nat.is_le(a, b) == leb(a, b) : Bool}: 566 match a b: 567 case 0n 0n: 568 {==} 569 case 0n 1n+q: 570 {==} 571 case 1n+p 0n: 572 {==} 573 case 1n+p 1n+q: 574 le_leb(p, q) 575 576 def leb_of(+a: Nat, +b: Nat, e: {True{} == Nat.is_le(a, b) : Bool}) -> {True{} == leb(a, b) : Bool}: 577 %le_leb(a, b) : {True{} == _ : Bool} 578 e 579 580 # the same pair by doubling (0.1.10.0): p doubles until n <= p, log2(n) steps in a tail loop, where cap_pair takes n 581 # steps of a recursion that is not a tail call, each with a comparison of up to n steps. Its fuel is n, which always 582 # suffices (2^n >= n); if it ran out, the answer would be cap_pair's, so the law below needs no arithmetic about how 583 # many doublings there are. ok is Nat.is_le(n, p), computed by the caller (match only inspects a parameter); with leb 584 # it was n steps in all, 152 ms for n = 786,432 on one GPU lane (0.1.13.0). 585 def cap_dbl(f: Nat, ok: Bool, +n: Nat, +d: Nat, +p: Nat) -> Nat & Nat: 586 match f ok: 587 case _ True{}: 588 (d, p) 589 case 0n False{}: 590 cap_pair(n) 591 case 1n+g False{}: 592 cap_dbl(g, Nat.is_le(n, Nat.add(p, p)), n, 1n+d, Nat.add(p, p)) 593 594 # the depth d such that an Array with 2^d slots holds n elements (the smallest such d) 595 def cap_depth(+n: Nat) -> Nat: 596 fst_nat(cap_dbl(n, Nat.is_le(n, 1n), n, 0n, 1n)) 597 598 # ---- lemmas ---- 599 600 # a <= b -> a <= 1 + b 601 def leb_succ(a: Nat, b: Nat, e: {True{} == leb(a, b) : Bool}) -> {True{} == leb(a, 1n+b) : Bool}: 602 match a b: 603 case 0n _: 604 {==} 605 case 1n+p 0n: 606 %e : BD(_, Unit, {True{} == leb(1n+p, 1n) : Bool}) 607 Unit{} 608 case 1n+p 1n+q: 609 leb_succ(p, q, e) 610 611 # a <= b -> a <= c + b 612 def leb_up_l(+a: Nat, +b: Nat, c: Nat, +e: {True{} == leb(a, b) : Bool}) -> {True{} == leb(a, Nat.add(c, b)) : Bool}: 613 match c: 614 case 0n: 615 e 616 case 1n++c2: 617 leb_succ(a, Nat.add(c2, b), leb_up_l(a, b, c2, e)) 618 619 # a <= b -> a <= b + c 620 def leb_up(a: Nat, b: Nat, c: Nat, e: {True{} == leb(a, b) : Bool}) -> {True{} == leb(a, Nat.add(b, c)) : Bool}: 621 match a b: 622 case 0n _: 623 {==} 624 case 1n+p 0n: 625 %e : BD(_, Unit, {True{} == leb(1n+p, Nat.add(0n, c)) : Bool}) 626 Unit{} 627 case 1n+p 1n+q: 628 leb_up(p, q, c, e) 629 630 # a <= b and c <= d -> a + c <= b + d 631 def leb_add(a: Nat, b: Nat, c: Nat, d: Nat, e1: {True{} == leb(a, b) : Bool}, e2: {True{} == leb(c, d) : Bool}) -> {True{} == leb(Nat.add(a, c), Nat.add(b, d)) : Bool}: 632 match a b: 633 case 0n _: 634 leb_up_l(c, d, b, e2) 635 case 1n+p 0n: 636 %e1 : BD(_, Unit, {True{} == leb(Nat.add(1n+p, c), Nat.add(0n, d)) : Bool}) 637 Unit{} 638 case 1n+p 1n+q: 639 leb_add(p, q, c, d, e1, e2) 640 641 # 1 <= 2^d 642 def pow2_pos(d: Nat) -> {True{} == leb(1n, pow2(d)) : Bool}: 643 match d: 644 case 0n: 645 {==} 646 case 1n++q: 647 leb_up(1n, pow2(q), pow2(q), pow2_pos(q)) 648 649 # 1 <= p when p = 2^d 650 def pos_p(d: Nat, p: Nat, i1: {p == pow2(d) : Nat}) -> {True{} == leb(1n, p) : Bool}: 651 %Equal.sym(Nat, p, pow2(d), i1) : {True{} == leb(1n, _) : Bool} 652 pow2_pos(d) 653 654 # p + p == 2^(1+d) when p == 2^d 655 def step_eq(d: Nat, p: Nat, i1: {p == pow2(d) : Nat}) -> {Nat.add(p, p) == pow2(1n+d) : Nat}: 656 %i1 : {Nat.add(p, p) == Nat.add(_, _) : Nat} 657 {==} 658 659 # one step: given the invariant for m, the invariant for 1+m holds whichever way the comparison goes 660 def step_ok(ok: Bool, +d: Nat, +p: Nat, +m: Nat, +e: {ok == leb(1n+m, p) : Bool}, +i1: {p == pow2(d) : Nat}, +i2: {True{} == leb(m, p) : Bool}) -> {snd_nat(cap_pick(ok, d, p)) == pow2(fst_nat(cap_pick(ok, d, p))) : Nat} & {True{} == leb(1n+m, snd_nat(cap_pick(ok, d, p))) : Bool}: 661 match ok: 662 case True{}: 663 (i1, e) 664 case False{}: 665 (step_eq(d, p, i1), leb_add(1n, p, m, p, pos_p(d, p, i1), i2)) 666 667 668 # the invariant for cap_step on a pair, opened 669 def step_pair_ok(+m: Nat, r: Nat & Nat, i1: {snd_nat(r) == pow2(fst_nat(r)) : Nat}, i2: {True{} == leb(m, snd_nat(r)) : Bool}) -> {snd_nat(cap_step(r, 1n+m)) == pow2(fst_nat(cap_step(r, 1n+m))) : Nat} & {True{} == leb(1n+m, snd_nat(cap_step(r, 1n+m))) : Bool}: 670 match r: 671 case Tuple{+d, +p}: 672 step_ok(leb(1n+m, p), d, p, m, {==}, i1, i2) 673 674 def cap_succ(+m: Nat, ih: {snd_nat(cap_pair(m)) == pow2(fst_nat(cap_pair(m))) : Nat} & {True{} == leb(m, snd_nat(cap_pair(m))) : Bool}) -> {snd_nat(cap_pair(1n+m)) == pow2(fst_nat(cap_pair(1n+m))) : Nat} & {True{} == leb(1n+m, snd_nat(cap_pair(1n+m))) : Bool}: 675 match ih: 676 case Tuple{i1, i2}: 677 step_pair_ok(m, cap_pair(m), i1, i2) 678 679 # the invariant of cap_pair: the second component is 2^(first) and is >= n 680 def cap_inv(n: Nat) -> {snd_nat(cap_pair(n)) == pow2(fst_nat(cap_pair(n))) : Nat} & {True{} == leb(n, snd_nat(cap_pair(n))) : Bool}: 681 match n: 682 case 0n: 683 ({==}, {==}) 684 case 1n++m: 685 cap_succ(m, cap_inv(m)) 686 687 # the invariant of cap_dbl: whichever way it ends (n <= p found, or the fuel out and cap_pair's answer), the second 688 # component is 2^(first) and is >= n 689 def dbl_inv(f: Nat, ok: Bool, +n: Nat, +d: Nat, +p: Nat, e: {ok == Nat.is_le(n, p) : Bool}, i1: {p == pow2(d) : Nat}) -> {snd_nat(cap_dbl(f, ok, n, d, p)) == pow2(fst_nat(cap_dbl(f, ok, n, d, p))) : Nat} & {True{} == leb(n, snd_nat(cap_dbl(f, ok, n, d, p))) : Bool}: 690 match f ok: 691 case _ True{}: 692 (i1, leb_of(n, p, e)) 693 case 0n False{}: 694 cap_inv(n) 695 case 1n+g False{}: 696 dbl_inv(g, Nat.is_le(n, Nat.add(p, p)), n, 1n+d, Nat.add(p, p), {==}, step_eq(d, p, i1)) 697 698 def dbl_final(n: Nat, pr: {snd_nat(cap_dbl(n, Nat.is_le(n, 1n), n, 0n, 1n)) == pow2(fst_nat(cap_dbl(n, Nat.is_le(n, 1n), n, 0n, 1n))) : Nat} & {True{} == leb(n, snd_nat(cap_dbl(n, Nat.is_le(n, 1n), n, 0n, 1n))) : Bool}) -> {True{} == leb(n, pow2(cap_depth(n))) : Bool}: 699 match pr: 700 case Tuple{i1, i2}: 701 %i1 : {True{} == leb(n, _) : Bool} 702 i2 703 704 # LAW (cap_ok): an Array with 2^cap_depth(n) slots always has room for n elements. 705 law cap_ok: 706 for +n: Nat 707 {True{} == leb(n, pow2(cap_depth(n))) : Bool} 708 709 def cap_ok(n): 710 dbl_final(n, dbl_inv(n, Nat.is_le(n, 1n), n, 0n, 1n, {==}, {==})) 711 712 # r x c of zeros, with the exact capacity (smallest power of 2) 713 714 def Mat.zeros(+r: Nat, +c: Nat) -> Mat: 715 Mat{Array.new(F32, cap_depth(Nat.mul(r, c)), 0.0)} 716 717 def Mat.fill(+r: Nat, +c: Nat, +v: F32) -> Mat: 718 Mat{Array.new(F32, cap_depth(Nat.mul(r, c)), v)} 719 720 def fill_list(xs: List<&2, F32>, +i: U32, a: Array) -> Array: 721 match xs: 722 case Nil{}: 723 a 724 case Con{h, t}: 725 fill_list(t, (i + 1 : U32), Array.set(F32, a, i, h)) 726 727 def len_is(xs: List<&2, F32>, n: Nat) -> Bool: 728 match xs n: 729 case Nil{} 0n: 730 True{} 731 case Con{h, t} 1n+p: 732 len_is(t, p) 733 case _ _: 734 False{} 735 736 def Mat.of_list2(-r: Nat, -c: Nat, +rr: Nat, +cc: Nat, ok: Bool, xs: List<&2, F32>) -> Maybe<&1, Mat>: 737 match ok: 738 case True{}: 739 Some{Mat{fill_list(xs, 0, Array.new(F32, cap_depth(Nat.mul(rr, cc)), 0.0))}} 740 case False{}: 741 None{} 742 743 # Mat from r*c numbers in row order; None if the size does not match 744 745 def Mat.of_list(+r: Nat, +c: Nat, +xs: List<&2, F32>) -> Maybe<&1, Mat>: 746 Mat.of_list2(r, c, r, c, len_is(xs, Nat.mul(r, c)), xs) 747 748 # reads the first n numbers, returning the matrix 749 750 # Mat without checking the list size (a short list leaves the rest at zero, a long one is cut 751 # by the capacity): only for when the size is known by construction. Prefer Mat.of_list. 752 753 def Mat.from_list(+r: Nat, +c: Nat, xs: List<&2, F32>) -> Mat: 754 Mat{fill_list(xs, 0, Array.new(F32, cap_depth(Nat.mul(r, c)), 0.0))} 755 756 # writes the numbers of xs into m starting at flat index i (row-major); numbers past the 757 # capacity are dropped. For loading a large matrix block by block without building one big list. 758 def Mat.fill_at(-r: Nat, -c: Nat, +i: U32, xs: List<&2, F32>, m: Mat) -> Mat: 759 match m: 760 case Mat{d}: 761 Mat{fill_list(xs, i, d)} 762 763 type ML<-r: Nat, -c: Nat> is Type: 764 ML{m: Mat, l: List<&2, F32>} 765 766 def Mat.to_list2(-r: Nat, -c: Nat, al: AL) -> ML: 767 match al: 768 case AL{d, l}: 769 ML{Mat{d}, l} 770 771 def ML.len(-r: Nat, -c: Nat, ml: ML) -> Nat: 772 match ml: 773 case ML{m, l}: 774 List.length(&2, F32, l) 775 776 # n numbers from index 0, in row order; n = 0 gives the empty list (a Mat with no numbers) 777 def Mat.to_list_count(-r: Nat, -c: Nat, d: Array, n: Nat) -> ML: 778 match n: 779 case 0n: 780 ML{Mat{d}, Nil{}} 781 case 1n+p: 782 Mat.to_list2(r, c, read_l(p, 0, Nil{}, Array.get(F32, d, 0))) 783 784 # two independent copies 785 786 def Mat.to_list(+r: Nat, +c: Nat, m: Mat) -> ML: 787 match m: 788 case Mat{d}: 789 Mat.to_list_count(r, c, d, Nat.mul(r, c)) 790 791 type MM2<-r: Nat, -c: Nat> is Type: 792 MM2{a: Mat, b: Mat} 793 794 def Mat.clone2(-r: Nat, -c: Nat, p: Array & Array) -> MM2: 795 match p: 796 case Tuple{x, y}: 797 MM2{Mat{x}, Mat{y}} 798 799 # ---- products: C (n x m) = A · B, with 2^par blocks of rows in parallel ---- 800 801 # C = A(n x k) · B(k x m) 802 803 def Mat.clone(-r: Nat, -c: Nat, m: Mat) -> MM2: 804 match m: 805 case Mat{d}: 806 Mat.clone2(r, c, Array.clone(F32, d)) 807 808 type MMul<-n: Nat, -k: Nat, -m: Nat> is Type: 809 MMul{a: Mat, b: Mat, c: Mat} 810 811 # C = A(n x k) · Bᵀ, with B stored m x k 812 813 type MMulNT<-n: Nat, -k: Nat, -m: Nat> is Type: 814 MMulNT{a: Mat, b: Mat, c: Mat} 815 816 # C = Aᵀ · B, with A stored k x n 817 818 type MMulTN<-n: Nat, -k: Nat, -m: Nat> is Type: 819 MMulTN{a: Mat, b: Mat, c: Mat} 820 821 # --------------------------------------------------------------------- 822 # gemm in tiles of 8 (0.1.8.0): for products with 2 or more rows, A's rows and B's columns are packed once into cells of 823 # 8 F32 (a row of k numbers is ceil(k/8) cells, zeros after the last), so the inner loop reads 8 numbers per array 824 # access instead of one. The order of the additions is that of `dotr`: product p of a dot goes to partial sum p mod 4, 825 # in order, and the four sums are combined as dotr combines them after k steps (its rotation depends on k mod 4). The 826 # padding adds +0 to a sum that starts at +0, which changes nothing, so the results are the same bits as the scalar 827 # loop's. Products with one row (matrix · vector) keep the scalar loop: packing B would cost as much as the product. 828 # --------------------------------------------------------------------- 829 830 type T8 is Data: 831 T8{v0: F32, v1: F32, v2: F32, v3: F32, v4: F32, v5: F32, v6: F32, v7: F32} 832 833 def t8_zero() -> T8: 834 T8{0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0, 0.0} 835 836 def pick0(b: Bool, +v: F32) -> F32: 837 match b: 838 case True{}: 839 v 840 case False{}: 841 0.0 842 843 type PK is Type: 844 PK{a: Array, t: Array} 845 846 # one cell: the 8 numbers of a at idx, idx + st, ..., those at positions q0 + q >= lim (past the end of the line) as 0 847 def pk_g7(+v0: F32, +v1: F32, +v2: F32, +v3: F32, +v4: F32, +v5: F32, +v6: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 848 match r: 849 case Tuple{a, +v}: 850 PK{a, Array.set(T8, t, o, T8{v0, v1, v2, v3, v4, v5, v6, pick0(U32.is_lt((q0 + 7 : U32), lim), v)})} 851 852 def pk_g6(+v0: F32, +v1: F32, +v2: F32, +v3: F32, +v4: F32, +v5: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 853 match r: 854 case Tuple{a, +v}: 855 pk_g7(v0, v1, v2, v3, v4, v5, pick0(U32.is_lt((q0 + 6 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 7 : U32) : U32))) 856 857 def pk_g5(+v0: F32, +v1: F32, +v2: F32, +v3: F32, +v4: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 858 match r: 859 case Tuple{a, +v}: 860 pk_g6(v0, v1, v2, v3, v4, pick0(U32.is_lt((q0 + 5 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 6 : U32) : U32))) 861 862 def pk_g4(+v0: F32, +v1: F32, +v2: F32, +v3: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 863 match r: 864 case Tuple{a, +v}: 865 pk_g5(v0, v1, v2, v3, pick0(U32.is_lt((q0 + 4 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 5 : U32) : U32))) 866 867 def pk_g3(+v0: F32, +v1: F32, +v2: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 868 match r: 869 case Tuple{a, +v}: 870 pk_g4(v0, v1, v2, pick0(U32.is_lt((q0 + 3 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 4 : U32) : U32))) 871 872 def pk_g2(+v0: F32, +v1: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 873 match r: 874 case Tuple{a, +v}: 875 pk_g3(v0, v1, pick0(U32.is_lt((q0 + 2 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 3 : U32) : U32))) 876 877 def pk_g1(+v0: F32, +q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 878 match r: 879 case Tuple{a, +v}: 880 pk_g2(v0, pick0(U32.is_lt((q0 + 1 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 2 : U32) : U32))) 881 882 def pk_g0(+q0: U32, +lim: U32, +idx: U32, +st: U32, +o: U32, t: Array, r: Array & F32) -> PK: 883 match r: 884 case Tuple{a, +v}: 885 pk_g1(pick0(U32.is_lt((q0 + 0 : U32), lim), v), q0, lim, idx, st, o, t, Array.get(F32, a, (idx + (st * 1 : U32) : U32))) 886 887 # the cells of one line, cell o.. of t; q0 counts the line's numbers before the cell 888 def pk_line(tl: Nat, +q0: U32, +lim: U32, +base: U32, +st: U32, +o: U32, r: PK) -> PK: 889 match tl r: 890 case 0n r: 891 r 892 case 1n+q PK{a, t}: 893 pk_line(q, (q0 + 8 : U32), lim, (base + (st * 8 : U32) : U32), st, (o + 1 : U32), pk_g0(q0, lim, base, st, o, t, Array.get(F32, a, base))) 894 895 # lines count lines; source line i starts at i * lstep (in a), and goes to cell row o (cells o * tku .. in t) 896 def pk_lines(cnt: Nat, +tk: Nat, +tku: U32, +ku: U32, +i: U32, +o: U32, +lstep: U32, +st: U32, r: PK) -> PK: 897 match cnt: 898 case 0n: 899 r 900 case 1n+p: 901 pk_lines(p, tk, tku, ku, (i + 1 : U32), (o + 1 : U32), lstep, st, pk_line(tk, 0, ku, (i * lstep : U32), st, (o * tku : U32), r)) 902 903 def pk_pack(+cnt: Nat, +tk: Nat, +k: Nat, +lstep: U32, +st: U32, a: Array) -> PK: 904 pk_lines(cnt, tk, U32.from_nat(tk), U32.from_nat(k), 0, 0, lstep, st, PK{a, Array.new(T8, cap_depth(Nat.mul(cnt, tk)), t8_zero())}) 905 906 # the dot of two lines of cells: tk cells from ia (in a) and ib (in b); ra, rb hold the arrays and the cells at ia, ib, 907 # already read. Product q of a cell goes to sum q mod 4, the first four before the last four, as dotr adds them. 908 def t8_dot(tk: Nat, +ia: U32, +ib: U32, s0: F32, s1: F32, s2: F32, s3: F32, ra: Array & T8, rb: Array & T8) -> TD: 909 match tk ra rb: 910 case 0n Tuple{a, x} Tuple{b, y}: 911 TD{a, b, s0, s1, s2, s3} 912 case 1n+p Tuple{a, T8{x0, x1, x2, x3, x4, x5, x6, x7}} Tuple{b, T8{y0, y1, y2, y3, y4, y5, y6, y7}}: 913 t8_dot(p, (ia + 1 : U32), (ib + 1 : U32), ((s0 + (x0 * y0 : F32) : F32) + (x4 * y4 : F32) : F32), ((s1 + (x1 * y1 : F32) : F32) + (x5 * y5 : F32) : F32), ((s2 + (x2 * y2 : F32) : F32) + (x6 * y6 : F32) : F32), ((s3 + (x3 * y3 : F32) : F32) + (x7 * y7 : F32) : F32), Array.get(T8, a, (ia + 1 : U32)), Array.get(T8, b, (ib + 1 : U32))) 914 915 type TD is Type: 916 TD{a: Array, b: Array, s0: F32, s1: F32, s2: F32, s3: F32} 917 918 # dotr's final sum after k products: the sums sit rotated by k mod 4 when its loop ends 919 def t8_comb(+rot: Nat, +s0: F32, +s1: F32, +s2: F32, +s3: F32) -> F32: 920 match rot: 921 case 0n: 922 ((s0 + s1 : F32) + (s2 + s3 : F32) : F32) 923 case 1n: 924 ((s1 + s2 : F32) + (s3 + s0 : F32) : F32) 925 case 2n: 926 ((s2 + s3 : F32) + (s0 + s1 : F32) : F32) 927 case _: 928 ((s3 + s0 : F32) + (s1 + s2 : F32) : F32) 929 930 type TR is Type: 931 TR{a: Array, b: Array, x: F32} 932 933 def t8_fin(+rot: Nat, d: TD) -> TR: 934 match d: 935 case TD{a, b, +s0, +s1, +s2, +s3}: 936 TR{a, b, t8_comb(rot, s0, s1, s2, s3)} 937 938 def t8_cell(+tk: Nat, +tku: U32, +rot: Nat, +i: U32, +j: U32, a: Array, b: Array) -> TR: 939 t8_fin(rot, t8_dot(tk, (i * tku : U32), (j * tku : U32), 0.0, 0.0, 0.0, 0.0, Array.get(T8, a, (i * tku : U32)), Array.get(T8, b, (j * tku : U32)))) 940 941 type TG is Type: 942 TG{a: Array, b: Array, c: Array} 943 944 def t8_set(+ac: Bool, +idx: U32, c: Array, r: TR) -> TG: 945 match r: 946 case TR{a, b, +x}: 947 TG{a, b, cwr(ac, idx, x, c)} 948 949 def t8_cols(+ac: Bool, mleft: Nat, +tk: Nat, +tku: U32, +rot: Nat, +i: U32, +j: U32, +cidx: U32, g: TG) -> TG: 950 match mleft g: 951 case 0n g: 952 g 953 case 1n+p TG{a, b, c}: 954 t8_cols(ac, p, tk, tku, rot, i, (j + 1 : U32), (cidx + 1 : U32), t8_set(ac, cidx, c, t8_cell(tk, tku, rot, i, j, a, b))) 955 956 def t8_rows(+ac: Bool, nleft: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, +i: U32, +mu: U32, g: TG) -> TG: 957 match nleft: 958 case 0n: 959 g 960 case 1n+p: 961 t8_rows(ac, p, m, tk, tku, rot, (i + 1 : U32), mu, t8_cols(ac, m, tk, tku, rot, i, 0, (i * mu : U32), g)) 962 963 # rows i0 .. i0+cnt-1 of C as a list, in order (the parallel version's leaves) 964 type TL is Type: 965 TL{a: Array, b: Array, out: List<&2, F32>} 966 967 # cells j.. of row i, m - j of them; r is cell j, already computed 968 def t8_lcols(mleft: Nat, +tk: Nat, +tku: U32, +rot: Nat, +i: U32, +j: U32, out: List<&2, F32>, r: TR) -> TL: 969 match mleft r: 970 case 0n TR{a, b, +x}: 971 TL{a, b, x <> out} 972 case 1n+p TR{a, b, +x}: 973 t8_lcols(p, tk, tku, rot, i, (j + 1 : U32), x <> out, t8_cell(tk, tku, rot, i, (j + 1 : U32), a, b)) 974 975 def t8_lrows(nleft: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, +i: U32, l: TL) -> TL: 976 match nleft l: 977 case 0n l: 978 l 979 case 1n+p TL{a, b, out}: 980 t8_lrows(p, m, tk, tku, rot, (i + 1 : U32), t8_lcols(Nat.sub(m, 1n), tk, tku, rot, i, 0, out, t8_cell(tk, tku, rot, i, 0, a, b))) 981 982 def t8_lfin(l: TL) -> List<&2, F32>: 983 match l: 984 case TL{a, b, out}: 985 List.reverse(&2, F32, out) 986 987 type PKK is Type: 988 PKK{a: Array, b: Array, ta: Array, tb: Array} 989 990 def t8_pk2(pa: PK, pb: PK) -> PKK: 991 match pa pb: 992 case PK{a, ta} PK{b, tb}: 993 PKK{a, b, ta, tb} 994 995 def t8_g(a: Array, b: Array, g: TG) -> G: 996 match g: 997 case TG{ta, tb, c}: 998 G{a, b, c} 999 1000 def t8_out_seq(+ac: Bool, +n: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, c: Array, p: PKK) -> G: 1001 match p: 1002 case PKK{a, b, ta, tb}: 1003 t8_g(a, b, t8_rows(ac, n, m, tk, tku, rot, 0, U32.from_nat(m), TG{ta, tb, c})) 1004 1005 # ---- the parallel path (0.1.9.0): the larger operand is packed into one piece per block, so a block owns its piece; 1006 # the other operand is copied once per node of the fork tree (2^d - 1 copies), instead of both at every node ---- 1007 1008 type PT is Type: 1009 PLeaf{t: Array} 1010 PNode{l: PT, r: PT} 1011 1012 type PKT is Type: 1013 PKT{a: Array, t: PT} 1014 1015 def pkt_leaf(r: PK) -> PKT: 1016 match r: 1017 case PK{a, t}: 1018 PKT{a, PLeaf{t}} 1019 1020 def pkt_then(r: PKT, k: Array -> PT -> PKT) -> PKT: 1021 match r: 1022 case PKT{a, t}: 1023 k(a, t) 1024 1025 # lines i .. i+cnt-1 of a, packed into 2^d pieces (halves of the line count, as the fork tree splits them) 1026 def pk_tree(+d: Nat, +cnt: Nat, +i: U32, +tk: Nat, +tku: U32, +ku: U32, +lstep: U32, +st: U32, a: Array) -> PKT: 1027 match d: 1028 case 0n: 1029 pkt_leaf(pk_lines(cnt, tk, tku, ku, i, 0, lstep, st, PK{a, Array.new(T8, cap_depth(Nat.mul(cnt, tk)), t8_zero())})) 1030 case 1n+q: 1031 pkt_then(pk_tree(q, Nat.div(cnt, 2n), i, tk, tku, ku, lstep, st, a), a2 => l => pkt_then(pk_tree(q, Nat.sub(cnt, Nat.div(cnt, 2n)), (i + U32.from_nat(Nat.div(cnt, 2n)) : U32), tk, tku, ku, lstep, st, a2), a3 => r => PKT{a3, PNode{l, r}})) 1032 1033 def t8_with2(-R: Type, p: Array & Array, k: Array -> Array -> R) -> R: 1034 match p: 1035 case Tuple{x, y}: 1036 k(x, y) 1037 1038 # split by rows of C: the pieces are A's rows, B is shared; the result is C's rows in order 1039 def t8_rpar(t: PT, +cnt: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, b: Array) -> List<&2, F32>: 1040 match t: 1041 case PLeaf{a}: 1042 t8_lfin(t8_lrows(cnt, m, tk, tku, rot, 0, TL{a, b, Nil{}})) 1043 case PNode{l, r}: 1044 t8_with2(List<&2, F32>, Array.clone(T8, b), b1 => b2 => ( 1045 x y = t8_rpar(l, Nat.div(cnt, 2n), m, tk, tku, rot, b1) t8_rpar(r, Nat.sub(cnt, Nat.div(cnt, 2n)), m, tk, tku, rot, b2) 1046 List.append(&2, F32, x, y))) 1047 1048 # a block of C's columns j0 .. j0+w-1 for every row, row by row 1049 type CB is Data: 1050 CB{j0: U32, w: Nat, xs: List<&2, F32>} 1051 1052 # split by columns of C: the pieces are B's columns, A is shared 1053 def t8_cpar(t: PT, +cnt: Nat, +j0: U32, +n: Nat, +tk: Nat, +tku: U32, +rot: Nat, a: Array) -> List<&2, CB>: 1054 match t: 1055 case PLeaf{b}: 1056 [CB{j0, cnt, t8_lfin(t8_lrows(n, cnt, tk, tku, rot, 0, TL{a, b, Nil{}}))}] 1057 case PNode{l, r}: 1058 t8_with2(List<&2, CB>, Array.clone(T8, a), a1 => a2 => ( 1059 x y = t8_cpar(l, Nat.div(cnt, 2n), j0, n, tk, tku, rot, a1) t8_cpar(r, Nat.sub(cnt, Nat.div(cnt, 2n)), (j0 + U32.from_nat(Nat.div(cnt, 2n)) : U32), n, tk, tku, rot, a2) 1060 List.append(&2, CB, x, y))) 1061 1062 type RW is Type: 1063 RW{c: Array, xs: List<&2, F32>} 1064 1065 # w numbers of xs into c from idx; what is left of xs comes back 1066 def cb_row(+ac: Bool, w: Nat, +idx: U32, c: Array, xs: List<&2, F32>) -> RW: 1067 match w xs: 1068 case 1n+p Con{+h, t}: 1069 cb_row(ac, p, (idx + 1 : U32), cwr(ac, idx, h, c), t) 1070 case _ _: 1071 RW{c, xs} 1072 1073 def cb_rows(+ac: Bool, n: Nat, +i: U32, +j0: U32, +w: Nat, +mu: U32, r: RW) -> Array: 1074 match n r: 1075 case 0n RW{c, xs}: 1076 c 1077 case 1n+p RW{c, xs}: 1078 cb_rows(ac, p, (i + 1 : U32), j0, w, mu, cb_row(ac, w, ((i * mu : U32) + j0 : U32), c, xs)) 1079 1080 def cb_write(+ac: Bool, bs: List<&2, CB>, +n: Nat, +mu: U32, c: Array) -> Array: 1081 match bs: 1082 case Nil{}: 1083 c 1084 case Con{CB{+j0, +w, xs}, t}: 1085 cb_write(ac, t, n, mu, cb_rows(ac, n, 0, j0, w, mu, RW{c, xs})) 1086 1087 def t8_pr2(+ac: Bool, +n: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, c: Array, ra: PKT, rb: PK) -> G: 1088 match ra rb: 1089 case PKT{a, t} PK{b, tb}: 1090 G{a, b, write_la(ac, t8_rpar(t, n, m, tk, tku, rot, tb), 0, c)} 1091 1092 def t8_pc2(+ac: Bool, +n: Nat, +m: Nat, +tk: Nat, +tku: U32, +rot: Nat, c: Array, ra: PK, rb: PKT) -> G: 1093 match ra rb: 1094 case PK{a, ta} PKT{b, t}: 1095 G{a, b, cb_write(ac, t8_cpar(t, m, 0, n, tk, tku, rot, ta), n, U32.from_nat(m), c)} 1096 1097 # 2^d blocks along the larger of n (rows of C, pieces of A) and m (columns of C, pieces of B) 1098 def t8_par2(+ac: Bool, byrows: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +tk: Nat, +tku: U32, +rot: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1099 match byrows: 1100 case True{}: 1101 t8_pr2(ac, n, m, tk, tku, rot, c, pk_tree(d, n, 0, tk, tku, U32.from_nat(k), arow, sa, a), pk_pack(m, tk, k, bcol, sb, b)) 1102 case False{}: 1103 t8_pc2(ac, n, m, tk, tku, rot, c, pk_pack(n, tk, k, arow, sa, a), pk_tree(d, m, 0, tk, tku, U32.from_nat(k), bcol, sb, b)) 1104 1105 def gemm_t8b(+ac: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +tk: Nat, +tku: U32, +rot: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1106 match d: 1107 case 0n: 1108 t8_out_seq(ac, n, m, tk, tku, rot, c, t8_pk2(pk_pack(n, tk, k, arow, sa, a), pk_pack(m, tk, k, bcol, sb, b))) 1109 case 1n+q: 1110 t8_par2(ac, Nat.is_le(m, n), 1n+q, n, m, k, tk, tku, rot, arow, sa, sb, bcol, a, b, c) 1111 1112 # C (n x m) = A · B (or C + A · B when ac) in cells of 8, sequential (d = 0) or with 2^d blocks of rows in parallel; A 1113 # and B come back intact 1114 def gemm_t8_ac(+ac: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1115 gemm_t8b(ac, d, n, m, k, Nat.div(Nat.add(k, 7n), 8n), U32.from_nat(Nat.div(Nat.add(k, 7n), 8n)), Nat.mod(k, 4n), arow, sa, sb, bcol, a, b, c) 1116 1117 def gemm_t8(d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1118 gemm_t8_ac(False{}, d, n, m, k, arow, sa, sb, bcol, a, b, c) 1119 1120 # one row (matrix · vector): the scalar loop at every depth; two or more rows: cells of 8. Up to 0.1.11.0 one row in 1121 # parallel ran gemm_par_cols, which clones both operands at every node of the fork tree: a 50,257 x 768 weight matrix at 1122 # depth 4 ran out of the default heap (5.1 GB) and 8192 x 2048 took 124 ms against 10 ms sequentially. Packing the 1123 # matrix into cells of 8 instead costs more than the product, which reads each weight once (64 ms against 24 at 1124 # 50,257 x 768). Matrix · vector in parallel is Bands.matvec. Measured 2026-10-16 (NOTES.md). 1125 def gemm_any3(+ac: Bool, one: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1126 match one: 1127 case True{}: 1128 gemm_ac(ac, n, m, k, arow, sa, sb, bcol, a, b, c) 1129 case False{}: 1130 gemm_t8_ac(ac, d, n, m, k, arow, sa, sb, bcol, a, b, c) 1131 1132 def gemm_any2(+ac: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1133 gemm_any3(ac, Nat.is_le(n, 1n), d, n, m, k, arow, sa, sb, bcol, a, b, c) 1134 1135 def gemm_any(+ac: Bool, d: Nat, +n: Nat, +m: Nat, +k: Nat, +arow: U32, +sa: U32, +sb: U32, +bcol: U32, a: Array, b: Array, c: Array) -> G: 1136 gemm_any2(ac, d, n, m, k, arow, sa, sb, bcol, a, b, c) 1137 1138 def mmul_fin(-n: Nat, -k: Nat, -m: Nat, g: G) -> MMul: 1139 match g: 1140 case G{a, b, c}: 1141 MMul{Mat{a}, Mat{b}, Mat{c}} 1142 1143 def mmul_nt_fin(-n: Nat, -k: Nat, -m: Nat, g: G) -> MMulNT: 1144 match g: 1145 case G{a, b, c}: 1146 MMulNT{Mat{a}, Mat{b}, Mat{c}} 1147 1148 def mmul_tn_fin(-n: Nat, -k: Nat, -m: Nat, g: G) -> MMulTN: 1149 match g: 1150 case G{a, b, c}: 1151 MMulTN{Mat{a}, Mat{b}, Mat{c}} 1152 1153 # ---- the split depth of a product (0.1.11.0): par is an upper bound, and a product too small to gain splits 0 times ---- 1154 # 1155 # A product of n x k by k x m splits into 2^par blocks only when it does at least 2^23 multiply-adds and each cell of C 1156 # sums at least 64 of them; otherwise it runs sequentially. Measured on 16 threads (2026-10-16, 18 shapes): below 2^23 the 1157 # split costs more than it saves, and with a short inner dimension (a weight gradient over a few tokens) every split 1158 # was 2-3.6x slower, because each block hands its rows of C back as a list that is then written into C, a cost per cell 1159 # of C that k multiply-adds per cell do not pay for. The result does not depend on the depth: the same bits at every 1160 # depth (tests/t8.bend, tests/acc.bend, tests/split.bend). 1161 def split_pick(big: Bool, d: Nat) -> Nat: 1162 match big: 1163 case True{}: 1164 d 1165 case False{}: 1166 0n 1167 1168 def split_depth(d: Nat, +n: Nat, +m: Nat, +k: Nat) -> Nat: 1169 split_pick(Bool.and(Nat.is_le(64n, k), Nat.is_le(8388608n, Nat.mul(Nat.mul(n, m), k))), d) 1170 1171 def mm_a(+n: Nat, +k: Nat, +m: Nat, d: Nat, a: Array, b: Array) -> MMul: 1172 mmul_fin(n, k, m, gemm_any(False{}, d, n, m, k, U32.from_nat(k), 1, U32.from_nat(m), 1, a, b, Array.new(F32, cap_depth(Nat.mul(n, m)), 0.0))) 1173 1174 # C = A · B (n x k) · (k x m); par = log2 of the most parallel blocks (0 = sequential); split_depth picks 0 or par 1175 1176 def Mat.matmul(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat) -> MMul: 1177 match a b: 1178 case Mat{x} Mat{y}: 1179 mm_a(n, k, m, split_depth(par, n, m, k), x, y) 1180 1181 # C + A · B, into C (n x m); A and B come back with the result, as Mat.matmul returns them. Each cell of A · B is 1182 # computed as Mat.matmul computes it and added once to C's cell: the same bits as Mat.add(c, Mat.matmul(a, b)'s c), 1183 # without allocating the product or walking it a second time. 1184 1185 def Mat.matmul_acc(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat, c: Mat) -> MMul: 1186 match a b c: 1187 case Mat{x} Mat{y} Mat{z}: 1188 mmul_fin(n, k, m, gemm_any(True{}, split_depth(par, n, m, k), n, m, k, U32.from_nat(k), 1, U32.from_nat(m), 1, x, y, z)) 1189 1190 def mm_nt(+n: Nat, +k: Nat, +m: Nat, d: Nat, a: Array, b: Array) -> MMulNT: 1191 mmul_nt_fin(n, k, m, gemm_any(False{}, d, n, m, k, U32.from_nat(k), 1, 1, U32.from_nat(k), a, b, Array.new(F32, cap_depth(Nat.mul(n, m)), 0.0))) 1192 1193 # C = A · Bᵀ (n x k) · (m x k)ᵀ: for weights stored with one row per output 1194 1195 def Mat.matmul_nt(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat) -> MMulNT: 1196 match a b: 1197 case Mat{x} Mat{y}: 1198 mm_nt(n, k, m, split_depth(par, n, m, k), x, y) 1199 1200 # C + A · Bᵀ, into C (n x m) 1201 1202 def Mat.matmul_nt_acc(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat, c: Mat) -> MMulNT: 1203 match a b c: 1204 case Mat{x} Mat{y} Mat{z}: 1205 mmul_nt_fin(n, k, m, gemm_any(True{}, split_depth(par, n, m, k), n, m, k, U32.from_nat(k), 1, 1, U32.from_nat(k), x, y, z)) 1206 1207 def mm_tn(+n: Nat, +k: Nat, +m: Nat, d: Nat, a: Array, b: Array) -> MMulTN: 1208 mmul_tn_fin(n, k, m, gemm_any(False{}, d, n, m, k, 1, U32.from_nat(n), U32.from_nat(m), 1, a, b, Array.new(F32, cap_depth(Nat.mul(n, m)), 0.0))) 1209 1210 # C = Aᵀ · B (k x n)ᵀ · (k x m): weight gradient, X^T · dY 1211 1212 def Mat.matmul_tn(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat) -> MMulTN: 1213 match a b: 1214 case Mat{x} Mat{y}: 1215 mm_tn(n, k, m, split_depth(par, n, m, k), x, y) 1216 1217 # C + Aᵀ · B, into C (n x m): a weight gradient added into the gradient it accumulates, dW += Xᵀ · dY 1218 1219 def Mat.matmul_tn_acc(+n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat, c: Mat) -> MMulTN: 1220 match a b c: 1221 case Mat{x} Mat{y} Mat{z}: 1222 mmul_tn_fin(n, k, m, gemm_any(True{}, split_depth(par, n, m, k), n, m, k, 1, U32.from_nat(n), U32.from_nat(m), 1, x, y, z)) 1223 1224 # ---- a precise product for long dots (0.1.14.0) ---- 1225 # 1226 # Mat.matmul_precise is Mat.matmul with k summed in blocks of 1024 (the last one 1..1024): block b's columns of A and 1227 # rows of B are copied out, the first block's product written into C, each next one's added to it as matmul_acc adds, 1228 # so every cell is ((x0 + x1) + x2) .., x_b the block's dot with the usual four partial sums. A dot of 49,152 products 1229 # as four running sums of 12,288 lost the small terms (SmolLM2's head: its gradient 1.8x further from float64 than 1230 # PyTorch's float32, 0.03x in blocks; BendTorch NOTES, 2026-10-18). Opt-in: it copies the slices and runs one product 1231 # a block, and the default products keep their speed and bits. Up to 1024 steps it is Mat.matmul itself. 1232 1233 type SL is Type: 1234 SL{a: Array, s: Array} 1235 1236 # d[di..] = src[si..], k numbers; r holds src[si], already read 1237 def sl_row(k: Nat, +si: U32, +di: U32, d: Array, r: Array & F32) -> SL: 1238 match k r: 1239 case 0n Tuple{a, x}: SL{a, d} 1240 case 1n+p Tuple{a, +x}: sl_row(p, (si + 1 : U32), (di + 1 : U32), Array.set(F32, d, di, x), Array.get(F32, a, (si + 1 : U32))) 1241 1242 # rows i.. of a slice: w numbers a row from src[i*st + o], into d[i*w] 1243 def sl_rows(r: Nat, +i: U32, +w: Nat, +w32: U32, +st: U32, +o: U32, sl: SL) -> SL: 1244 match r sl: 1245 case 0n sl: sl 1246 case 1n+p SL{a, d}: sl_rows(p, (i + 1 : U32), w, w32, st, o, sl_row(w, ((i * st : U32) + o : U32), (i * w32 : U32), d, Array.get(F32, a, ((i * st : U32) + o : U32)))) 1247 1248 def blk_len(+bi: U32, +k: U32) -> Nat: 1249 U32.to_nat(Bool.pick(U32, U32.is_lt((k - (bi * 1024 : U32) : U32), 1024), (k - (bi * 1024 : U32) : U32), 1024)) 1250 1251 def mp_s4(a: Array, b: Array, g: G) -> G: 1252 match g: 1253 case G{x, y, c}: G{a, b, c} 1254 1255 def mp_s3(+ac: Bool, +par: Nat, +n: Nat, +m: Nat, +w: Nat, +w32: U32, +m32: U32, a: Array, as: Array, c: Array, sb: SL) -> G: 1256 match sb: 1257 case SL{b, bs}: mp_s4(a, b, gemm_any(ac, split_depth(par, n, m, w), n, m, w, w32, 1, m32, 1, as, bs, c)) 1258 1259 def mp_s2(+ac: Bool, +par: Nat, +n: Nat, +m: Nat, +w: Nat, +w32: U32, +m32: U32, +o: U32, c: Array, b: Array, sa: SL) -> G: 1260 match sa: 1261 case SL{a, as}: mp_s3(ac, par, n, m, w, w32, m32, a, as, c, sl_row(Nat.mul(w, m), (o * m32 : U32), 0, Array.new(F32, cap_depth(Nat.mul(w, m)), 0.0), Array.get(F32, b, (o * m32 : U32)))) 1262 1263 # block bi: A's columns o..o+w (A is n x k) and B's rows o..o+w (B is k x m), multiplied into C (added when ac) 1264 def mp_step(+ac: Bool, +par: Nat, +n: Nat, +m: Nat, +k32: U32, +bi: U32, a: Array, b: Array, c: Array) -> G: 1265 mp_s2(ac, par, n, m, blk_len(bi, k32), U32.from_nat(blk_len(bi, k32)), U32.from_nat(m), (bi * 1024 : U32), c, b, sl_rows(n, 0, blk_len(bi, k32), U32.from_nat(blk_len(bi, k32)), k32, (bi * 1024 : U32), SL{a, Array.new(F32, cap_depth(Nat.mul(n, blk_len(bi, k32))), 0.0)})) 1266 1267 def mp_go(nb: Nat, +bi: U32, +par: Nat, +n: Nat, +m: Nat, +k32: U32, g: G) -> G: 1268 match nb g: 1269 case 0n g: g 1270 case 1n++q G{a, b, c}: mp_go(q, (bi + 1 : U32), par, n, m, k32, mp_step(U32.is_lt(0, bi), par, n, m, k32, bi, a, b, c)) 1271 1272 def mm_precise2(long: Bool, +n: Nat, +k: Nat, +m: Nat, par: Nat, a: Mat, b: Mat) -> MMul: 1273 match long a b: 1274 case True{} Mat{x} Mat{y}: mmul_fin(n, k, m, mp_go(U32.to_nat(U32.div((U32.from_nat(k) + 1023 : U32), 1024)), 0, par, n, m, U32.from_nat(k), G{x, y, Array.new(F32, cap_depth(Nat.mul(n, m)), 0.0)})) 1275 case False{} a b: Mat.matmul(n, k, m, par, a, b) 1276 1277 # C = A · B (n x k) · (k x m), k summed in blocks of 1024 (above); opt-in 1278 def Mat.matmul_precise(+n: Nat, +k: Nat, +m: Nat, +par: Nat, a: Mat, b: Mat) -> MMul: 1279 mm_precise2(Nat.is_lt(1024n, k), n, k, m, par, a, b) 1280 1281 # ---- element-wise and training operations ---- 1282 1283 # adds the bias (one row, m numbers) to each row of C (n x m) 1284 1285 type MBias<-n: Nat, -m: Nat> is Type: 1286 MBias{c: Mat, b: Mat<1n, m>} 1287 1288 def add_row2(-n: Nat, -m: Nat, +nn: Nat, +mm: Nat, cd: Array, al: AL) -> MBias: 1289 match al: 1290 case AL{bd, bl}: 1291 MBias{Mat{bias_rows(nn, 0, U32.from_nat(mm), bl, cd)}, Mat{bd}} 1292 1293 def Mat.add_row(+n: Nat, +m: Nat, c: Mat, b: Mat<1n, m>) -> MBias: 1294 match c b: 1295 case Mat{cd} Mat{bd}: 1296 add_row2(n, m, n, m, cd, read_l(Nat.sub(m, 1n), 0, Nil{}, Array.get(F32, bd, 0))) 1297 1298 # pair of matrices of the same shape (the result of an operation that reads two operands) 1299 1300 type MPair<-r: Nat, -c: Nat> is Type: 1301 MPair{a: Mat, b: Mat} 1302 1303 def mpair_fin(-r: Nat, -c: Nat, p: P2) -> MPair: 1304 match p: 1305 case P2{a, b}: 1306 MPair{Mat{a}, Mat{b}} 1307 1308 def relu_fin(-r: Nat, -c: Nat, d: Array) -> Mat: 1309 Mat{d} 1310 1311 def Mat.relu(+r: Nat, +c: Nat, a: Mat) -> Mat: 1312 match a: 1313 case Mat{d}: 1314 relu_fin(r, c, relu_l(Nat.mul(r, c), 0, Array.get(F32, d, 0))) 1315 1316 # (dy * (h > 0), h): the gradient that passes through the relu 1317 1318 def Mat.relu_bwd(+r: Nat, +c: Nat, dy: Mat, h: Mat) -> MPair: 1319 match dy h: 1320 case Mat{d} Mat{x}: 1321 mpair_fin(r, c, mask_l(Nat.mul(r, c), 0, Array.get(F32, d, 0), Array.get(F32, x, 0))) 1322 1323 # (w - lr * dw, dw): one SGD step 1324 1325 def Mat.sgd(+r: Nat, +c: Nat, +lr: F32, w: Mat, dw: Mat) -> MPair: 1326 match w dw: 1327 case Mat{x} Mat{y}: 1328 mpair_fin(r, c, sgd_l(Nat.mul(r, c), 0, lr, Array.get(F32, x, 0), Array.get(F32, y, 0))) 1329 1330 # (a + b, b): element-wise sum (a - (-1)*b is exactly a + b) 1331 1332 def Mat.add(+r: Nat, +c: Nat, a: Mat, b: Mat) -> MPair: 1333 match a b: 1334 case Mat{x} Mat{y}: 1335 mpair_fin(r, c, sgd_l(Nat.mul(r, c), 0, (0.0 - 1.0 : F32), Array.get(F32, x, 0), Array.get(F32, y, 0))) 1336 1337 # sum of each column (n x m -> 1 x m), returning the matrix 1338 1339 type MColSum<-n: Nat, -m: Nat> is Type: 1340 MColSum{a: Mat, s: Mat<1n, m>} 1341 1342 def colsum_fin(-n: Nat, -m: Nat, g: G) -> MColSum: 1343 match g: 1344 case G{ones, a, s}: 1345 MColSum{Mat{a}, Mat{s}} 1346 1347 def Mat.col_sums(+n: Nat, +m: Nat, a: Mat) -> MColSum: 1348 match a: 1349 case Mat{d}: 1350 colsum_fin(n, m, gemm(1n, m, n, 0, 1, U32.from_nat(m), 1, Array.new(F32, cap_depth(n), 1.0), d, Array.new(F32, cap_depth(m), 0.0))) 1351 1352 # s + the sum of each column, into s (1 x m): a bias gradient added into its accumulator, with the bits of 1353 # Mat.add(s, Mat.col_sums(a)'s s) 1354 1355 def Mat.col_sums_acc(+n: Nat, +m: Nat, a: Mat, s: Mat<1n, m>) -> MColSum: 1356 match a s: 1357 case Mat{d} Mat{z}: 1358 colsum_fin(n, m, gemm_ac(True{}, 1n, m, n, 0, 1, U32.from_nat(m), 1, Array.new(F32, cap_depth(n), 1.0), d, z)) 1359 1360 # mean cross-entropy and its gradient on the logits: (softmax - one-hot) / n. 1361 # labels has one entry per row (n); the gradient replaces the logits. 1362 1363 type MCE<-n: Nat, -c: Nat> is Type: 1364 MCE{loss: F32, grad: Mat} 1365 1366 def ce_fin(-n: Nat, -c: Nat, +nf: F32, r: L2) -> MCE: 1367 match r: 1368 case L2{loss, d}: 1369 MCE{(loss / nf : F32), Mat{d}} 1370 1371 def Mat.softmax_ce(+n: Nat, +c: Nat, z: Mat, labels: List<&2, Nat>) -> MCE: 1372 match z: 1373 case Mat{d}: 1374 ce_fin(n, c, F32.from_nat(n), cea_rows(labels, Nat.sub(c, 1n), U32.from_nat(c), F32.from_nat(n), 0, L2{0.0, d})) 1375 1376 # argmax hits per row against labels 1377 1378 type MHits<-n: Nat, -c: Nat> is Type: 1379 MHits{z: Mat, hits: Nat} 1380 1381 def hits_fin(-n: Nat, -c: Nat, r: CR) -> MHits: 1382 match r: 1383 case CR{d, k}: 1384 MHits{Mat{d}, k} 1385 1386 def Mat.count_correct(+n: Nat, +c: Nat, z: Mat, labels: List<&2, Nat>) -> MHits: 1387 match z: 1388 case Mat{d}: 1389 hits_fin(n, c, count_rows(labels, Nat.sub(c, 1n), U32.from_nat(c), 0, CR{d, 0n})) 1390 1391 # a row as a list (returning the matrix) and the other way around 1392 1393 type MRow<-r: Nat, -c: Nat> is Type: 1394 MRow{m: Mat, row: List<&2, F32>} 1395 1396 def row_fin(-r: Nat, -c: Nat, al: AL) -> MRow: 1397 match al: 1398 case AL{d, l}: 1399 MRow{Mat{d}, l} 1400 1401 def Mat.read_row(+r: Nat, +c: Nat, m: Mat, +i: U32) -> MRow: 1402 match m: 1403 case Mat{d}: 1404 row_fin(r, c, read_l(Nat.sub(c, 1n), (i * U32.from_nat(c) : U32), Nil{}, Array.get(F32, d, (i * U32.from_nat(c) : U32)))) 1405 1406 def Mat.write_row(-r: Nat, +c: Nat, m: Mat, +i: U32, xs: List<&2, F32>) -> Mat: 1407 match m: 1408 case Mat{d}: 1409 Mat{write_l(xs, (i * U32.from_nat(c) : U32), d)} 1410 1411 # ---- variants that check the number of labels ---- 1412 1413 # does the list of labels have exactly n entries (one per row)? 1414 def labels_len_is(xs: List<&2, Nat>, n: Nat) -> Bool: 1415 match xs n: 1416 case Nil{} 0n: 1417 True{} 1418 case Con{h, t} 1n+p: 1419 labels_len_is(t, p) 1420 case _ _: 1421 False{} 1422 1423 def ce_checked(ok: Bool, +n: Nat, +c: Nat, z: Mat, labels: List<&2, Nat>) -> Maybe<&1, MCE>: 1424 match ok: 1425 case True{}: 1426 Some{Mat.softmax_ce(n, c, z, labels)} 1427 case False{}: 1428 None{} 1429 1430 # like Mat.softmax_ce, but returns None unless labels has exactly n entries 1431 def Mat.softmax_ce_checked(+n: Nat, +c: Nat, z: Mat, +labels: List<&2, Nat>) -> Maybe<&1, MCE>: 1432 ce_checked(labels_len_is(labels, n), n, c, z, labels) 1433 1434 def hits_checked(ok: Bool, +n: Nat, +c: Nat, z: Mat, labels: List<&2, Nat>) -> Maybe<&1, MHits>: 1435 match ok: 1436 case True{}: 1437 Some{Mat.count_correct(n, c, z, labels)} 1438 case False{}: 1439 None{} 1440 1441 # like Mat.count_correct, but returns None unless labels has exactly n entries 1442 def Mat.count_correct_checked(+n: Nat, +c: Nat, z: Mat, +labels: List<&2, Nat>) -> Maybe<&1, MHits>: 1443 hits_checked(labels_len_is(labels, n), n, c, z, labels) 1444 1445 # --------------------------------------------------------------------- 1446 # Bands: a matrix cut into bands of rows, for matrix · vector in parallel 1447 # without copying the weights. 1448 # 1449 # Mat.matmul_nt with n = 1 can only run in parallel by cloning the whole weight 1450 # matrix for every task, and in matrix · vector each weight is read once, so the 1451 # copies cost more than the products (NOTES.md, exp. 9). Bands keeps each band in 1452 # its own Array: a task takes its band and nothing is copied except the input vector. 1453 # 1454 # A node with r rows has children with half(r) and r - half(r) rows. The split is 1455 # part of the type, so the row count of every band follows from r, and the law 1456 # half_cover says that the two halves always cover the r rows exactly. 1457 # --------------------------------------------------------------------- 1458 1459 # n / 2 rounded down. A tail loop with an accumulator: a non-tail recursion here would put a 1460 # continuation on every node of the band tree and keep the bands from running in parallel. 1461 def half.go(n: Nat, acc: Nat) -> Nat: 1462 match n: 1463 case 0n: 1464 acc 1465 case 1n+p: 1466 match p: 1467 case 0n: 1468 acc 1469 case 1n+q: 1470 half.go(q, 1n+acc) 1471 1472 def half(n: Nat) -> Nat: 1473 half.go(n, 0n) 1474 1475 # a <= a 1476 def leb_refl(+a: Nat) -> {True{} == leb(a, a) : Bool}: 1477 match a: 1478 case 0n: 1479 {==} 1480 case 1n+p: 1481 leb_refl(p) 1482 1483 # 1 + (a + b) == a + (1 + b) 1484 def add_succ_r(+a: Nat, -b: Nat) -> {1n+Nat.add(a, b) == Nat.add(a, 1n+b) : Nat}: 1485 match a: 1486 case 0n: 1487 {==} 1488 case 1n+p: 1489 %add_succ_r(p, b) : {2n+Nat.add(p, b) == 1n+_ : Nat} 1490 {==} 1491 1492 # a + 0 == a 1493 def add_zero_r(+a: Nat) -> {Nat.add(a, 0n) == a : Nat}: 1494 match a: 1495 case 0n: 1496 {==} 1497 case 1n+p: 1498 %add_zero_r(p) : {1n+Nat.add(p, 0n) == 1n+_ : Nat} 1499 {==} 1500 1501 # half.go(n, acc) <= n + acc 1502 def half_go_le(+n: Nat, +acc: Nat) -> {True{} == leb(half.go(n, acc), Nat.add(n, acc)) : Bool}: 1503 match n: 1504 case 0n: 1505 leb_refl(acc) 1506 case 1n+p: 1507 match p: 1508 case 0n: 1509 leb_succ(acc, acc, leb_refl(acc)) 1510 case 1n+q: 1511 %Equal.sym(Nat, 1n+Nat.add(q, acc), Nat.add(q, 1n+acc), add_succ_r(q, acc)) : {True{} == leb(half.go(q, 1n+acc), 1n+_) : Bool} 1512 leb_succ(half.go(q, 1n+acc), Nat.add(q, 1n+acc), half_go_le(q, 1n+acc)) 1513 1514 # half(n) <= n 1515 def half_le(+n: Nat) -> {True{} == leb(half(n), n) : Bool}: 1516 %add_zero_r(n) : {True{} == leb(half(n), _) : Bool} 1517 half_go_le(n, 0n) 1518 1519 # a <= r -> r == a + (r - a) 1520 def add_sub(+a: Nat, +r: Nat, e: {True{} == leb(a, r) : Bool}) -> {r == Nat.add(a, Nat.sub(r, a)) : Nat}: 1521 match a r: 1522 case 0n 0n: 1523 {==} 1524 case 0n 1n+q: 1525 {==} 1526 case 1n+p 0n: 1527 %e : BD(_, Unit, {0n == Nat.add(1n+p, Nat.sub(0n, 1n+p)) : Nat}) 1528 Unit{} 1529 case 1n+p 1n+q: 1530 %add_sub(p, q, e) : {1n+q == 1n+_ : Nat} 1531 {==} 1532 1533 # LAW (half_cover): the two bands of a node, half(r) rows and r - half(r) rows, 1534 # add up to exactly r rows: no row is lost and none is counted twice. 1535 law half_cover: 1536 for +n: Nat 1537 {n == Nat.add(half(n), Nat.sub(n, half(n))) : Nat} 1538 1539 def half_cover(n): 1540 add_sub(half(n), n, half_le(n)) 1541 1542 type Bands<-r: Nat, -c: Nat> is Type: 1543 BLeaf{m: Mat} 1544 BNode{x: Bands, y: Bands} 1545 1546 # r x c of zeros in 2^d bands (d = 0: a single band, the same as a Mat) 1547 def Bands.zeros(+d: Nat, +r: Nat, +c: Nat) -> Bands: 1548 match d: 1549 case 0n: 1550 BLeaf{Mat.zeros(r, c)} 1551 case 1n+q: 1552 BNode{Bands.zeros(q, half(r), c), Bands.zeros(q, Nat.sub(r, half(r)), c)} 1553 1554 # the row counts of the two bands follow the row count of the node: if rr == r at run 1555 # time, the same holds for both halves (the bands carry r only in their type) 1556 def eq_half(-a: Nat, -b: Nat, e: {a == b : Nat}) -> {half(a) == half(b) : Nat}: 1557 %e : {half(a) == half(_) : Nat} 1558 {==} 1559 1560 def eq_rest(-a: Nat, -b: Nat, e: {a == b : Nat}) -> {Nat.sub(a, half(a)) == Nat.sub(b, half(b)) : Nat}: 1561 %e : {Nat.sub(a, half(a)) == Nat.sub(_, half(_)) : Nat} 1562 {==} 1563 1564 # does a block of n numbers starting at flat index i end before the second band of a 1565 # node with r rows? does it start in the second band? 1566 def bfill_l(+r: Nat, +c: Nat, +i: Nat, +n: Nat) -> Bool: 1567 Nat.is_le(Nat.add(i, n), Nat.mul(half(r), c)) 1568 1569 def bfill_r(+r: Nat, +c: Nat, +i: Nat) -> Bool: 1570 Nat.is_le(Nat.mul(half(r), c), i) 1571 1572 # writes the first n numbers of xs at index i (and nothing past them) 1573 def fill_n(n: Nat, xs: List<&2, F32>, +i: U32, a: Array) -> Array: 1574 match n xs: 1575 case 1n+p Con{h, t}: 1576 fill_n(p, t, (i + 1 : U32), Array.set(F32, a, i, h)) 1577 case _ _: 1578 a 1579 1580 def fill_band(-r: Nat, -c: Nat, n: Nat, xs: List<&2, F32>, +i: U32, m: Mat) -> Mat: 1581 match m: 1582 case Mat{d}: 1583 Mat{fill_n(n, xs, i, d)} 1584 1585 # writes the first n numbers of xs at flat index i; l and rt say which bands they touch 1586 # (bfill_l, bfill_r); rr is the row count r at run time. A block that crosses into the second 1587 # band is not copied: the first band writes only its part, the second gets the rest by List.drop. 1588 def bfill(+c: Nat, -r: Nat, b: Bands, l: Bool, rt: Bool, +rr: Nat, -e: {rr == r : Nat}, +i: Nat, +n: Nat, +xs: List<&2, F32>) -> Bands: 1589 match b l rt: 1590 case BLeaf{m} _ _: 1591 BLeaf{fill_band(r, c, n, xs, U32.from_nat(i), m)} 1592 case BNode{x, y} True{} _: 1593 BNode{bfill(c, half(r), x, bfill_l(half(rr), c, i, n), bfill_r(half(rr), c, i), half(rr), eq_half(rr, r, e), i, n, xs), y} 1594 case BNode{x, y} False{} True{}: 1595 BNode{x, bfill(c, Nat.sub(r, half(r)), y, bfill_l(Nat.sub(rr, half(rr)), c, Nat.sub(i, Nat.mul(half(rr), c)), n), bfill_r(Nat.sub(rr, half(rr)), c, Nat.sub(i, Nat.mul(half(rr), c))), Nat.sub(rr, half(rr)), eq_rest(rr, r, e), Nat.sub(i, Nat.mul(half(rr), c)), n, xs)} 1596 case BNode{x, y} False{} False{}: 1597 BNode{bfill(c, half(r), x, bfill_l(half(rr), c, i, Nat.sub(Nat.mul(half(rr), c), i)), bfill_r(half(rr), c, i), half(rr), eq_half(rr, r, e), i, Nat.sub(Nat.mul(half(rr), c), i), xs), bfill(c, Nat.sub(r, half(r)), y, bfill_l(Nat.sub(rr, half(rr)), c, 0n, Nat.sub(n, Nat.sub(Nat.mul(half(rr), c), i))), bfill_r(Nat.sub(rr, half(rr)), c, 0n), Nat.sub(rr, half(rr)), eq_rest(rr, r, e), 0n, Nat.sub(n, Nat.sub(Nat.mul(half(rr), c), i)), List.drop(&2, F32, xs, Nat.sub(Nat.mul(half(rr), c), i)))} 1598 1599 def bfill_top(ok: Bool, +r: Nat, +c: Nat, b: Bands, +i: Nat, +n: Nat, +xs: List<&2, F32>) -> Bands: 1600 match ok: 1601 case True{}: 1602 bfill(c, r, b, bfill_l(r, c, i, n), bfill_r(r, c, i), r, {==}, i, n, xs) 1603 case False{}: 1604 bfill(c, r, b, bfill_l(r, c, i, Nat.sub(Nat.mul(r, c), i)), bfill_r(r, c, i), r, {==}, i, Nat.sub(Nat.mul(r, c), i), xs) 1605 1606 # writes the n numbers of xs into b starting at flat index i (row-major, as Mat.fill_at); 1607 # numbers past r*c are dropped. For loading a large matrix block by block. 1608 def Bands.fill_at(+r: Nat, +c: Nat, +i: Nat, +n: Nat, +xs: List<&2, F32>, b: Bands) -> Bands: 1609 bfill_top(Nat.is_le(Nat.add(i, n), Nat.mul(r, c)), r, c, b, i, n, xs) 1610 1611 # ---- y = B · x for B with r rows of c numbers (a weight matrix stored out x in) ---- 1612 1613 # B (intact) and y = B · x, typed like Mat.matmul_nt: x is 1 x c, y is 1 x r 1614 type BV<-r: Nat, -c: Nat> is Type: 1615 BV{b: Bands, y: Mat<1n, r>} 1616 1617 # the bands and y as a list in row order (the result of Bands.matvec_l) 1618 type BL<-r: Nat, -c: Nat> is Type: 1619 BL{b: Bands, y: List<&2, F32>} 1620 1621 def bv_g2(-r: Nat, -c: Nat, w: Array, al: AL) -> BL: 1622 match al: 1623 case AL{d, l}: 1624 BL{BLeaf{Mat{w}}, l} 1625 1626 # the band's 1 + m rows came out in an Array (one number per row); read them back as a list 1627 def bv_g(-r: Nat, -c: Nat, +m: Nat, g: G) -> BL: 1628 match g: 1629 case G{a, b, y}: 1630 bv_g2(r, c, b, read_l(m, 0, Nil{}, Array.get(F32, y, 0))) 1631 1632 # the rows of one band, one dot product each, with the gemm of Mat.matmul_nt (n = 1), so the 1633 # results are identical. Writing the results into an Array and reading them back is faster than 1634 # building the list row by row (NOTES.md, exp. 14). 1635 def bv_gemm(-r: Nat, +c: Nat, rows: Nat, +rr: Nat, x: Array, w: Array) -> BL: 1636 match rows: 1637 case 0n: 1638 BL{BLeaf{Mat{w}}, Nil{}} 1639 case 1n+p: 1640 bv_g(r, c, p, gemm(1n, rr, c, U32.from_nat(c), 1, 1, U32.from_nat(c), x, w, Array.new(F32, cap_depth(rr), 0.0))) 1641 1642 def bv_mat(-r: Nat, +c: Nat, +rr: Nat, x: Array, m: Mat) -> BL: 1643 match m: 1644 case Mat{w}: 1645 bv_gemm(r, c, rr, rr, x, w) 1646 1647 def bv_join(-r: Nat, -c: Nat, p: BL, q: BL) -> BL: 1648 match p q: 1649 case BL{bx, yx} BL{by, yy}: 1650 BL{BNode{bx, by}, List.append(&2, F32, yx, yy)} 1651 1652 # the two bands of a node run in parallel, each with its own copy of x (c numbers); 1653 # xp is a pair of copies of x (a leaf uses one). xp comes after the erased e: up to Bend 2.0.36 an erased 1654 # parameter at the end of the list turned the parallel let into two sequential calls (NOTES.md, exp. 11; fixed in 1655 # 2.0.37 by bendlang/bend#1377). The order is kept, and harmless. 1656 def bmv(+c: Nat, -r: Nat, b: Bands, +rr: Nat, -e: {rr == r : Nat}, xp: Array & Array) -> BL: 1657 match b xp: 1658 case BLeaf{m} Tuple{x, x2}: 1659 bv_mat(r, c, rr, x, m) 1660 case BNode{bx, by} Tuple{x1, x2}: 1661 p q = bmv(c, half(r), bx, half(rr), eq_half(rr, r, e), Array.clone(F32, x1)) bmv(c, Nat.sub(r, half(r)), by, Nat.sub(rr, half(rr)), eq_rest(rr, r, e), Array.clone(F32, x2)) 1662 bv_join(r, c, p, q) 1663 1664 def bv_fin(+r: Nat, -c: Nat, bl: BL) -> BV: 1665 match bl: 1666 case BL{b, y}: 1667 BV{b, Mat.from_list(1n, r, y)} 1668 1669 # y = B · x for B (r x c, one row per output) and x (1 x c); returns B intact and y (1 x r). 1670 # The same product as Mat.matmul_nt(1n, c, r, ..), and the same numbers, bit for bit. 1671 def Bands.matvec(+r: Nat, +c: Nat, b: Bands, x: Mat<1n, c>) -> BV: 1672 match x: 1673 case Mat{xa}: 1674 bv_fin(r, c, bmv(c, r, b, r, {==}, Array.clone(F32, xa))) 1675 1676 # the same product with x and y as lists (x: c numbers, y: r numbers in row order), without the 1677 # Mat conversions of Bands.matvec: for callers that already hold the vector as a list 1678 def Bands.matvec_l(+r: Nat, +c: Nat, b: Bands, +x: List<&2, F32>) -> BL: 1679 bmv(c, r, b, r, {==}, Array.clone(F32, fill_list(x, 0, Array.new(F32, cap_depth(c), 0.0)))) 1680 1681 # ---- reading rows and the whole matrix ---- 1682 1683 type BRow<-r: Nat, -c: Nat> is Type: 1684 BRow{b: Bands, row: List<&2, F32>} 1685 1686 def brow_fin(-r: Nat, -c: Nat, al: AL) -> BRow: 1687 match al: 1688 case AL{d, l}: 1689 BRow{BLeaf{Mat{d}}, l} 1690 1691 # n numbers starting at flat index i of one band (a band may have no rows) 1692 def brow_leaf2(-r: Nat, -c: Nat, n: Nat, +i: U32, d: Array) -> BRow: 1693 match n: 1694 case 0n: 1695 BRow{BLeaf{Mat{d}}, Nil{}} 1696 case 1n+p: 1697 brow_fin(r, c, read_l(p, i, Nil{}, Array.get(F32, d, i))) 1698 1699 def brow_leaf(-r: Nat, -c: Nat, m: Mat, +i: U32, n: Nat) -> BRow: 1700 match m: 1701 case Mat{d}: 1702 brow_leaf2(r, c, n, i, d) 1703 1704 def brow_l(-r: Nat, -c: Nat, p: BRow, y: Bands) -> BRow: 1705 match p: 1706 case BRow{bx, row}: 1707 BRow{BNode{bx, y}, row} 1708 1709 def brow_r(-r: Nat, -c: Nat, x: Bands, q: BRow) -> BRow: 1710 match q: 1711 case BRow{by, row}: 1712 BRow{BNode{x, by}, row} 1713 1714 # row i; left says whether i is in the first band of the node (i < half(rr)) 1715 def brow(+c: Nat, -r: Nat, b: Bands, left: Bool, +rr: Nat, -e: {rr == r : Nat}, +i: Nat) -> BRow: 1716 match b left: 1717 case BLeaf{m} _: 1718 brow_leaf(r, c, m, U32.from_nat(Nat.mul(i, c)), c) 1719 case BNode{x, y} True{}: 1720 brow_l(r, c, brow(c, half(r), x, Nat.is_lt(i, half(half(rr))), half(rr), eq_half(rr, r, e), i), y) 1721 case BNode{x, y} False{}: 1722 brow_r(r, c, x, brow(c, Nat.sub(r, half(r)), y, Nat.is_lt(Nat.sub(i, half(rr)), half(Nat.sub(rr, half(rr)))), Nat.sub(rr, half(rr)), eq_rest(rr, r, e), Nat.sub(i, half(rr)))) 1723 1724 # row i (c numbers), for example an embedding lookup 1725 def Bands.read_row(+r: Nat, +c: Nat, b: Bands, +i: Nat) -> BRow: 1726 brow(c, r, b, Nat.is_lt(i, half(r)), r, {==}, i) 1727 1728 def bl_join(-r: Nat, -c: Nat, p: BRow, q: BRow) -> BRow: 1729 match p q: 1730 case BRow{bx, xs} BRow{by, ys}: 1731 BRow{BNode{bx, by}, List.append(&2, F32, xs, ys)} 1732 1733 def blist(+c: Nat, -r: Nat, b: Bands, +rr: Nat, -e: {rr == r : Nat}) -> BRow: 1734 match b: 1735 case BLeaf{m}: 1736 brow_leaf(r, c, m, 0, Nat.mul(rr, c)) 1737 case BNode{x, y}: 1738 bl_join(r, c, blist(c, half(r), x, half(rr), eq_half(rr, r, e)), blist(c, Nat.sub(r, half(r)), y, Nat.sub(rr, half(rr)), eq_rest(rr, r, e))) 1739 1740 # all r*c numbers in row order (in BRow.row) 1741 def Bands.to_list(+r: Nat, +c: Nat, b: Bands) -> BRow: 1742 blist(c, r, b, r, {==}) 1743 1744 # ---- the list path returns exactly r numbers ---- 1745 # 1746 # The numbers do not matter here, only how many there are: each lemma below counts the list a 1747 # function builds, following the function's own recursion. 1748 1749 def bl_len(-r: Nat, -c: Nat, bl: BL) -> Nat: 1750 match bl: 1751 case BL{b, y}: 1752 List.length(&2, F32, y) 1753 1754 # s == a + (1 + b) -> s == 1 + (a + b) 1755 def succ_out(-s: Nat, +a: Nat, +b: Nat, e: {s == Nat.add(a, 1n+b) : Nat}) -> {s == 1n+Nat.add(a, b) : Nat}: 1756 %Equal.sym(Nat, 1n+Nat.add(a, b), Nat.add(a, 1n+b), add_succ_r(a, b)) : {s == _ : Nat} 1757 e 1758 1759 # s == 1 + (p + (1 + l)) -> s == 2 + (p + l) 1760 def len_step(-s: Nat, +p: Nat, +l: Nat, e: {s == 1n+Nat.add(p, 1n+l) : Nat}) -> {s == 2n+Nat.add(p, l) : Nat}: 1761 %Equal.sym(Nat, 1n+Nat.add(p, l), Nat.add(p, 1n+l), add_succ_r(p, l)) : {s == 1n+_ : Nat} 1762 e 1763 1764 # s == n + 0 -> s == n 1765 def zero_out(-s: Nat, +n: Nat, e: {s == Nat.add(n, 0n) : Nat}) -> {s == n : Nat}: 1766 %add_zero_r(n) : {s == _ : Nat} 1767 e 1768 1769 # reversing onto acc adds the lengths 1770 def rev_len(+xs: List<&2, F32>, +acc: List<&2, F32>) -> {List.length(&2, F32, List.reverse.go(&2, F32, xs, acc)) == Nat.add(List.length(&2, F32, xs), List.length(&2, F32, acc)) : Nat}: 1771 match xs: 1772 case Nil{}: 1773 {==} 1774 case Con{h, t}: 1775 succ_out(List.length(&2, F32, List.reverse.go(&2, F32, t, h <> acc)), List.length(&2, F32, t), List.length(&2, F32, acc), rev_len(t, h <> acc)) 1776 1777 # appending adds the lengths 1778 def app_len(+xs: List<&2, F32>, +ys: List<&2, F32>) -> {List.length(&2, F32, List.append(&2, F32, xs, ys)) == Nat.add(List.length(&2, F32, xs), List.length(&2, F32, ys)) : Nat}: 1779 match xs: 1780 case Nil{}: 1781 {==} 1782 case Con{h, t}: 1783 %app_len(t, ys) : {1n+List.length(&2, F32, List.append(&2, F32, t, ys)) == 1n+_ : Nat} 1784 {==} 1785 1786 # the same count for the list of a whole Mat: to_list2 wraps read_l's answer, and ML.len counts it 1787 def read_ml_len(-r: Nat, -c: Nat, +mleft: Nat, +idx: U32, +acc: List<&2, F32>, rc: Array & F32) -> {ML.len(r, c, Mat.to_list2(r, c, read_l(mleft, idx, acc, rc))) == Nat.add(1n+mleft, List.length(&2, F32, acc)) : Nat}: 1788 match mleft rc: 1789 case 0n Tuple{d, +v}: 1790 zero_out(ML.len(r, c, Mat.to_list2(r, c, read_l(0n, idx, acc, Tuple{d, v}))), 1n+List.length(&2, F32, acc), rev_len(v <> acc, Nil{})) 1791 case 1n+p Tuple{d, +v}: 1792 len_step(ML.len(r, c, Mat.to_list2(r, c, read_l(p, (idx + 1 : U32), v <> acc, Array.get(F32, d, (idx + 1 : U32))))), p, List.length(&2, F32, acc), read_ml_len(r, c, p, (idx + 1 : U32), v <> acc, Array.get(F32, d, (idx + 1 : U32)))) 1793 1794 # a leaf's list: read_l reads mleft + 1 numbers onto acc and reverses them. By induction on 1795 # mleft: with 0 left it reads one number (rev_len counts the reversed list); with 1 + p left it 1796 # reads one and recurses, and len_step moves the 1 out of the sum. 1797 law leaf_len: 1798 for -r: Nat 1799 for -c: Nat 1800 for w: Array 1801 for +mleft: Nat 1802 for +idx: U32 1803 for +acc: List<&2, F32> 1804 for rc: Array & F32 1805 {bl_len(r, c, bv_g2(r, c, w, read_l(mleft, idx, acc, rc))) == Nat.add(1n+mleft, List.length(&2, F32, acc)) : Nat} 1806 1807 def leaf_len(r, c, w, mleft, idx, acc, rc): 1808 match mleft rc: 1809 case 0n Tuple{d, +v}: 1810 zero_out(List.length(&2, F32, List.reverse.go(&2, F32, v <> acc, Nil{})), 1n+List.length(&2, F32, acc), rev_len(v <> acc, Nil{})) 1811 case 1n+p Tuple{d, +v}: 1812 len_step(bl_len(r, c, bv_g2(r, c, w, read_l(p, (idx + 1 : U32), v <> acc, Array.get(F32, d, (idx + 1 : U32))))), p, List.length(&2, F32, acc), leaf_len(r, c, w, p, (idx + 1 : U32), v <> acc, Array.get(F32, d, (idx + 1 : U32)))) 1813 1814 # whatever the gemm wrote, reading 1 + p rows back gives 1 + p numbers (G has one constructor, so 1815 # matching g opens it, and leaf_len counts the list) 1816 def g_len(-r: Nat, -c: Nat, +p: Nat, g: G) -> {bl_len(r, c, bv_g(r, c, p, g)) == 1n+p : Nat}: 1817 match g: 1818 case G{a, b, y}: 1819 zero_out(bl_len(r, c, bv_g2(r, c, b, read_l(p, 0, Nil{}, Array.get(F32, y, 0)))), 1n+p, leaf_len(r, c, b, p, 0, Nil{}, Array.get(F32, y, 0))) 1820 1821 # LAW (band_len): the product of one band with `rows` rows gives exactly `rows` numbers. 1822 # 0 rows give the empty list; 1 + p rows go through the gemm, and g_len counts what is read back. 1823 law band_len: 1824 for -r: Nat 1825 for +c: Nat 1826 for +rows: Nat 1827 for x: Array 1828 for w: Array 1829 {bl_len(r, c, bv_gemm(r, c, rows, rows, x, w)) == rows : Nat} 1830 1831 def band_len(r, c, rows, x, w): 1832 match rows: 1833 case 0n: 1834 {==} 1835 case 1n+p: 1836 g_len(r, c, p, gemm(1n, 1n+p, c, U32.from_nat(c), 1, 1, U32.from_nat(c), x, w, Array.new(F32, cap_depth(1n+p), 0.0))) 1837 1838 # joining two halves of h1 and h2 numbers gives h1 + h2 numbers. (The statement for the whole tree, 1839 # "Bands.matvec_l gives r numbers", would apply this to the results of the two recursive calls and 1840 # to the induction hypotheses about the same calls; that uses each band's Array twice, which Bend's 1841 # affine rules refuse. It stays open, covered by tests: NOTES.md, v3.1.) 1842 def join_len(-r: Nat, -c: Nat, -h1: Nat, -h2: Nat, p: BL, q: BL, hp: {bl_len(half(r), c, p) == h1 : Nat}, hq: {bl_len(Nat.sub(r, half(r)), c, q) == h2 : Nat}) -> {bl_len(r, c, bv_join(r, c, p, q)) == Nat.add(h1, h2) : Nat}: 1843 match p q: 1844 case BL{bx, +yx} BL{by, +yy}: 1845 %hp : {List.length(&2, F32, List.append(&2, F32, yx, yy)) == Nat.add(_, h2) : Nat} 1846 %hq : {List.length(&2, F32, List.append(&2, F32, yx, yy)) == Nat.add(List.length(&2, F32, yx), _) : Nat} 1847 app_len(yx, yy) 1848 1849 # Mat.to_list_count reads exactly n numbers, and n = 0 reads none. 1850 def to_list_count_len(-r: Nat, -c: Nat, d: Array, +n: Nat) -> {ML.len(r, c, Mat.to_list_count(r, c, d, n)) == n : Nat}: 1851 match n: 1852 case 0n: 1853 {==} 1854 case 1n+p: 1855 zero_out(ML.len(r, c, Mat.to_list_count(r, c, d, 1n+p)), 1n+p, read_ml_len(r, c, p, 0, Nil{}, Array.get(F32, d, 0))) 1856 1857 # LAW (to_list_len): Mat.to_list reads exactly r*c numbers, no more and no fewer. 1858 law to_list_len: 1859 for +r: Nat 1860 for +c: Nat 1861 for m: Mat 1862 {ML.len(r, c, Mat.to_list(r, c, m)) == Nat.mul(r, c) : Nat} 1863 1864 def to_list_len(r, c, m): 1865 match m: 1866 case Mat{d}: 1867 to_list_count_len(r, c, d, Nat.mul(r, c))