main.bend
bendtorch-gpu@1.2.0.0 · 1,274 lines · 0 definitions
1# BendTorch's layers with their work on the GPU (bend-ml-tensor-gpu): the same layers, types and bits as
2# torch/main.bend's and torch/fast.bend's.
3#
4# import ../torch/main.bend as T
5# import ../torch/gpu.bend as F
6# F.LinearFirstF(100n, 784n, 128n, 4n) # this layer's products and optimizer step run as bangs
7#
8# Every layer here runs its work as bangs (one `!` call each), which run on the GPU in a binary built with CUDA and on
9# the CPU threads otherwise:
10# - LinearF, LinearFirstF and LinearNBF: their three products (x·Wᵀ, dY·W, dYᵀ·X added into dW) from tensor-gpu, and
11# their optimizer step.
12# - SiLU, Mul2, Add2, Residual, Mat.dup and Mat.sum2: their element-wise loops.
13# - RMSNorm, RoPE and Attention (main.bend's names; fast.bend's are main.bend's, on the CPU).
14# - MSE, CrossEntropy and Embedding (its rows, its gradient and its step), so that a training step's work, the loss
15# included, stays on the device; the host reads back a few numbers a row for the loss.
16# The names are fast.bend's, so a program switches by its import line alone (the Makefile builds the GPU demos so).
17#
18# Each bang is one of tensor-gpu's run1..run4 (outputs in place) or run1_new..run4_new (outputs made on the device):
19# a tree of leaves, every node sharing the arrays. The code of a leaf is below, in plain Bend, and computes each number
20# with main.bend's own expression or row code (rmsf_row, ssq, rmsb_dot, rope_one, dot0, psmc, g_set2, adam_m..), so the bits
21# are main.bend's, except where an exponential is taken on the device (SiLU, attention's softmax, cross-entropy): CUDA
22# takes the float overload of exp there and its last bit can differ from the host's (the known
23# limit of bendlang/bend#1305); 1 ulp is accepted there, my decision of 2026-10-18 (tests: demo/gpu_test.bend
24# and demo/gpu_layers_test.bend). Sizes, depths and capacities are
25# computed on the host and passed in. The arrays stay on the device from one bang to the next as long as the host only
26# passes their handles; a bang that follows the host's parallel lets in one evaluation runs on the CPU threads.
27# A sum whose order fixes the bits (a row's softmax, a row's squares) is a row bang: a leaf a row, a tail loop along
28# it. Everything else is a cell bang: a leaf takes cells t, t + l, .., each one number (a dot product, or one
29# expression of a row's sums), so a bang has as many independent pieces of work as the output has numbers.
30#
31# Outside the proofs, on purpose, like fast.bend: tensor-gpu shares arrays between the leaves of a bang through Base's
32# @unsafe Array.fork, so `bend` names the defs below that reach it, and no file with laws imports this one (`make laws`
33# checks both). This file writes no @unsafe of its own. All of it is Bend.
34
35import Base
36import bend-ml-tensor-array@0.1.14.0/main.bend as TA
37import bend-ml-tensor-gpu@0.1.2.1/main.bend as TG
38import bendtorch@1.2.0.0/main.bend as T
39
40# ---- Linear: y = x·Wᵀ + b ----
41
42def lin_fwd(+n: Nat, +i: Nat, +o: Nat, p: T.Lin<i, o>, x: TA.Mat<n, i>) -> T.Fwd<T.Lin<i, o>, TA.Mat<n, o>, TA.Mat<n, i>>:
43 match p:
44 case T.Lin{w, b}:
45 T.lin_f2(n, i, o, b, TG.matmul_nt(n, i, o, 0n, x, w))
46
47def lin_b2(+n: Nat, +i: Nat, +o: Nat, b: TA.Mat<1n, o>, x: TA.Mat<n, i>, g: T.Lin<i, o>, r: TA.MMul<n, o, i>) -> T.Bwd<T.Lin<i, o>, TA.Mat<n, i>>:
48 match g r:
49 case T.Lin{gw, gb} TA.MMul{dy, w, dx}:
50 T.lin_b3(n, i, o, w, b, dx, gb, TG.matmul_tn_acc(o, n, i, 0n, dy, x, gw))
51
52def lin_bwd(+n: Nat, +i: Nat, +o: Nat, p: T.Lin<i, o>, x: TA.Mat<n, i>, dy: TA.Mat<n, o>, g: T.Lin<i, o>) -> T.Bwd<T.Lin<i, o>, TA.Mat<n, i>>:
53 match p:
54 case T.Lin{w, b}:
55 lin_b2(n, i, o, b, x, g, TG.matmul_precise(n, o, i, 0n, dy, w))
56
57# =====================================================================================================
58# The leaves' shared pieces. A leaf gets its index t among l leaves, a record of scalars and four input arrays, and
59# returns its outputs; an input it does not use is tensor-gpu's none(Unit{}). Element-wise leaves take the cells t,
60# t + l, .. (TG.share), so neighbouring lanes read neighbouring cells; row and column leaves take one row or column
61# each and leave their outputs unchanged past the last one.
62# =====================================================================================================
63
64type EN is Data:
65 EN{n: U32}
66
67def nn(u: Unit) -> Array<F32>:
68 TG.none(u)
69
70# out[i] = f(x[i]) for the leaf's cells
71def ew1_loop(~f: @+a: F32 -> F32, k: Nat, +i: U32, +l: U32, o: Array<F32>, r: Array<F32> & F32) -> Array<F32>:
72 match k r:
73 case 0n Tuple{a, +x}: o
74 case 1n+q Tuple{a, +x}: ew1_loop(~f, q, (i + l : U32), l, Array.set(F32, o, i, f(x)), Array.get(F32, a, (i + l : U32)))
75
76def ew1(~f: @+a: F32 -> F32, +t: U32, +l: U32, +e: EN, x: Array<F32>, o: Array<F32>) -> Array<F32>:
77 match e:
78 case EN{+n}: ew1_loop(~f, TG.share(t, n, l), t, l, o, Array.get(F32, x, t))
79
80# out[i] = f(x[i], y[i]) for the leaf's cells
81def ew2_loop(~f: @+a: F32 -> @+b: F32 -> F32, k: Nat, +i: U32, +l: U32, o: Array<F32>, ra: Array<F32> & F32, rb: Array<F32> & F32) -> Array<F32>:
82 match k ra rb:
83 case 0n Tuple{a, +x} Tuple{b, +y}: o
84 case 1n+q Tuple{a, +x} Tuple{b, +y}: ew2_loop(~f, q, (i + l : U32), l, Array.set(F32, o, i, f(x, y)), Array.get(F32, a, (i + l : U32)), Array.get(F32, b, (i + l : U32)))
85
86def ew2(~f: @+a: F32 -> @+b: F32 -> F32, +t: U32, +l: U32, +e: EN, x: Array<F32>, y: Array<F32>, o: Array<F32>) -> Array<F32>:
87 match e:
88 case EN{+n}: ew2_loop(~f, TG.share(t, n, l), t, l, o, Array.get(F32, x, t), Array.get(F32, y, t))
89
90# o[i] = f(o[i], y[i]) for the leaf's cells, in place (o's old value is the first argument)
91def ewi_loop(~f: @+a: F32 -> @+b: F32 -> F32, k: Nat, +i: U32, +l: U32, ro: Array<F32> & F32, ry: Array<F32> & F32) -> Array<F32>:
92 match k ro ry:
93 case 0n Tuple{o, +x} Tuple{y, +v}: o
94 case 1n+q Tuple{o, +x} Tuple{y, +v}: ewi_loop(~f, q, (i + l : U32), l, Array.get(F32, Array.set(F32, o, i, f(x, v)), (i + l : U32)), Array.get(F32, y, (i + l : U32)))
95
96def ewi(~f: @+a: F32 -> @+b: F32 -> F32, +t: U32, +l: U32, +e: EN, o: Array<F32>, y: Array<F32>) -> Array<F32>:
97 match e:
98 case EN{+n}: ewi_loop(~f, TG.share(t, n, l), t, l, Array.get(F32, o, t), Array.get(F32, y, t))
99
100def f32_id(+x: F32) -> F32:
101 x
102
103def lf_copy(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
104 ew1(~f32_id, t, l, e, x1, o)
105
106def lf_silu(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
107 ew1(~T.silu_v, t, l, e, x1, o)
108
109# o = dy, written in place, x1 = x: main.bend's silu_d(dy, x)
110def lf_silud(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
111 ewi(~T.silu_d, t, l, e, o, x1)
112
113# o = a, written in place, x1 = b: a + b
114def lf_add(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
115 ewi(~T.f32_add, t, l, e, o, x1)
116
117def lf_mul(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
118 ew2(~T.f32_mul, t, l, e, x1, x2, o)
119
120# x1 = dy; the saved a and b, written in place: a becomes db = dy * a and b becomes da = dy * b, in one bang
121def mulb_loop(k: Nat, +i: U32, +l: U32, rd: Array<F32> & F32, ra: Array<F32> & F32, rb: Array<F32> & F32) -> TG.O2:
122 match k rd ra rb:
123 case 0n Tuple{d, +x} Tuple{a, +y} Tuple{b, +z}: TG.O2{a, b}
124 case 1n+q Tuple{d, +x} Tuple{a, +y} Tuple{b, +z}:
125 +u = (i + l : U32)
126 mulb_loop(q, u, l, Array.get(F32, d, u), Array.get(F32, Array.set(F32, a, i, (x * y : F32)), u), Array.get(F32, Array.set(F32, b, i, (x * z : F32)), u))
127
128def lf_mulb(+t: U32, +l: U32, +e: EN, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
129 match e o:
130 case EN{+n} TG.O2{a, b}: mulb_loop(TG.share(t, n, l), t, l, Array.get(F32, x1, t), Array.get(F32, a, t), Array.get(F32, b, t))
131
132def en(+cnt: Nat) -> EN:
133 EN{U32.from_nat(cnt)}
134
135def r1_a(r: TG.R1) -> Array<F32>:
136 match r:
137 case TG.R1{x1, x2, x3, x4, a}: a
138
139# ---- element-wise layers ----
140
141def dup_fin(-r: Nat, -c: Nat, q: TG.R1) -> T.Two<TA.Mat<r, c>, TA.Mat<r, c>>:
142 match q:
143 case TG.R1{x1, x2, x3, x4, a}: T.Two{TA.Mat{x1}, TA.Mat{a}}
144
145# two copies of a matrix, the second made on the device
146def Mat.dup(+r: Nat, +c: Nat, m: TA.Mat<r, c>) -> T.Two<TA.Mat<r, c>, TA.Mat<r, c>>:
147 match m:
148 case TA.Mat{a}: dup_fin(r, c, TG.run1_new(~EN, ~lf_copy, TG.depth(Nat.mul(r, c)), en(Nat.mul(r, c)), a, nn(Unit{}), nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(r, c))))
149
150# a + b, as main.bend's Mat.sum2 adds each cell, written over a (both are consumed)
151def Mat.sum2(+r: Nat, +c: Nat, a: TA.Mat<r, c>, b: TA.Mat<r, c>) -> TA.Mat<r, c>:
152 match a b:
153 case TA.Mat{x} TA.Mat{y}: TA.Mat{r1_a(TG.run1(~EN, ~lf_add, TG.depth(Nat.mul(r, c)), en(Nat.mul(r, c)), y, nn(Unit{}), nn(Unit{}), nn(Unit{}), x))}
154
155def silu_f2(-n: Nat, -c: Nat, q: TG.R1) -> T.Fwd<Unit, TA.Mat<n, c>, TA.Mat<n, c>>:
156 match q:
157 case TG.R1{x1, x2, x3, x4, a}: T.Fwd{Unit{}, TA.Mat{a}, TA.Mat{x1}}
158
159def silu_f(+n: Nat, +c: Nat, x: TA.Mat<n, c>) -> T.Fwd<Unit, TA.Mat<n, c>, TA.Mat<n, c>>:
160 match x:
161 case TA.Mat{a}: silu_f2(n, c, TG.run1_new(~EN, ~lf_silu, TG.depth(Nat.mul(n, c)), en(Nat.mul(n, c)), a, nn(Unit{}), nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, c))))
162
163def silu_b(+n: Nat, +c: Nat, s: TA.Mat<n, c>, dy: TA.Mat<n, c>) -> T.Bwd<Unit, TA.Mat<n, c>>:
164 match s dy:
165 case TA.Mat{x} TA.Mat{d}: T.Bwd{Unit{}, TA.Mat{r1_a(TG.run1(~EN, ~lf_silud, TG.depth(Nat.mul(n, c)), en(Nat.mul(n, c)), x, nn(Unit{}), nn(Unit{}), nn(Unit{}), d))}, Unit{}}
166
167# SiLU: y = silu(x), the input saved; dx = silu'(x) * dy
168def SiLU(+n: Nat, +c: Nat) -> T.Layer<Unit, TA.Mat<n, c>, TA.Mat<n, c>, TA.Mat<n, c>>:
169 T.Layer{p => x => silu_f(n, c, x), p => s => dy => g => silu_b(n, c, s, dy), op => p => g => st => T.no_step(Unit, op, p, g, st), u => Unit{}, p => T.PL{p, Nil{}}, p => xs => off => T.PA{p, xs, off}, p => xs => off => T.PA{p, xs, off}, u => 0}
170
171def mul2_f2(-n: Nat, -c: Nat, q: TG.R1) -> T.Fwd<Unit, TA.Mat<n, c>, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>>:
172 match q:
173 case TG.R1{x1, x2, x3, x4, y}: T.Fwd{Unit{}, TA.Mat{y}, T.Two{TA.Mat{x1}, TA.Mat{x2}}}
174
175def mul2_f(+n: Nat, +c: Nat, x: T.Two<TA.Mat<n, c>, TA.Mat<n, c>>) -> T.Fwd<Unit, TA.Mat<n, c>, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>>:
176 match x:
177 case T.Two{TA.Mat{a}, TA.Mat{b}}: mul2_f2(n, c, TG.run1_new(~EN, ~lf_mul, TG.depth(Nat.mul(n, c)), en(Nat.mul(n, c)), a, b, nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, c))))
178
179def mul2_b2(-n: Nat, -c: Nat, q: TG.R2) -> T.Bwd<Unit, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>>:
180 match q:
181 case TG.R2{x1, x2, x3, x4, TG.O2{db, da}}: T.Bwd{Unit{}, T.Two{TA.Mat{da}, TA.Mat{db}}, Unit{}}
182
183def mul2_b(+n: Nat, +c: Nat, s: T.Two<TA.Mat<n, c>, TA.Mat<n, c>>, dy: TA.Mat<n, c>) -> T.Bwd<Unit, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>>:
184 match s dy:
185 case T.Two{TA.Mat{a}, TA.Mat{b}} TA.Mat{d}: mul2_b2(n, c, TG.run2(~EN, ~lf_mulb, TG.depth(Nat.mul(n, c)), en(Nat.mul(n, c)), d, nn(Unit{}), nn(Unit{}), nn(Unit{}), a, b))
186
187# Mul2: y = a * b, the inputs saved; da = dy * b, db = dy * a
188def Mul2(+n: Nat, +c: Nat) -> T.Layer<Unit, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>, TA.Mat<n, c>, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>>:
189 T.Layer{p => x => mul2_f(n, c, x), p => s => dy => g => mul2_b(n, c, s, dy), op => p => g => st => T.no_step(Unit, op, p, g, st), u => Unit{}, p => T.PL{p, Nil{}}, p => xs => off => T.PA{p, xs, off}, p => xs => off => T.PA{p, xs, off}, u => 0}
190
191def add2_f(+n: Nat, +c: Nat, x: T.Two<TA.Mat<n, c>, TA.Mat<n, c>>) -> T.Fwd<Unit, TA.Mat<n, c>, Unit>:
192 match x:
193 case T.Two{a, b}: T.Fwd{Unit{}, Mat.sum2(n, c, a, b), Unit{}}
194
195def Add2(+n: Nat, +c: Nat) -> T.Layer<Unit, T.Two<TA.Mat<n, c>, TA.Mat<n, c>>, TA.Mat<n, c>, Unit>:
196 T.Layer{p => x => add2_f(n, c, x), p => s => dy => g => T.Bwd{p, Mat.dup(n, c, dy), g}, op => p => g => st => T.no_step(Unit, op, p, g, st), u => Unit{}, p => T.PL{p, Nil{}}, p => xs => off => T.PA{p, xs, off}, p => xs => off => T.PA{p, xs, off}, u => 0}
197
198# T.Residual with these copies and sums
199def Residual(~n: Nat, ~c: Nat, ~P: Type, ~S: Type, ~f: T.Layer<P, TA.Mat<n, c>, TA.Mat<n, c>, S>) -> T.Layer<T.Two<T.Two<Unit, P>, Unit>, TA.Mat<n, c>, TA.Mat<n, c>, T.Two<T.Two<Unit, S>, Unit>>:
200 T.Seq(~T.Two<Unit, P>, ~Unit, ~TA.Mat<n, c>, ~T.Two<TA.Mat<n, c>, TA.Mat<n, c>>, ~TA.Mat<n, c>, ~T.Two<Unit, S>, ~Unit, ~T.Fork(~Unit, ~P, ~TA.Mat<n, c>, ~TA.Mat<n, c>, ~TA.Mat<n, c>, ~Unit, ~S, ~(m => Mat.dup(n, c, m)), ~(a => b => Mat.sum2(n, c, a, b)), ~T.Id(~TA.Mat<n, c>), ~f), ~Add2(n, c))
201
202# ---- the optimizer step in place, each cell as main.bend's sgd_l, mom_l and adam_l compute it ----
203
204type ESGD is Data:
205 ESGD{n: U32, lr: F32}
206
207def sgd_loop(k: Nat, +i: U32, +l: U32, +lr: F32, rw: Array<F32> & F32, rg: Array<F32> & F32) -> TG.O2:
208 match k rw rg:
209 case 0n Tuple{w, +x} Tuple{g, +y}: TG.O2{w, g}
210 case 1n+q Tuple{w, +x} Tuple{g, +y}:
211 sgd_loop(q, (i + l : U32), l, lr, Array.get(F32, Array.set(F32, w, i, (x + (F32.neg(lr) * y : F32) : F32)), (i + l : U32)), Array.get(F32, Array.set(F32, g, i, 0.0), (i + l : U32)))
212
213def lf_sgd(+t: U32, +l: U32, +e: ESGD, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
214 match e o:
215 case ESGD{+n, +lr} TG.O2{w, g}: sgd_loop(TG.share(t, n, l), t, l, lr, Array.get(F32, w, t), Array.get(F32, g, t))
216
217type EMOM is Data:
218 EMOM{n: U32, lr: F32, mu: F32}
219
220def mom_loop(k: Nat, +i: U32, +l: U32, +lr: F32, +mu: F32, rw: Array<F32> & F32, rg: Array<F32> & F32, rm: Array<F32> & F32) -> TG.O3:
221 match k rw rg rm:
222 case 0n Tuple{w, +x} Tuple{g, +y} Tuple{m, +b}: TG.O3{w, g, m}
223 case 1n+q Tuple{w, +x} Tuple{g, +y} Tuple{m, +b}:
224 +nb = ((b * mu : F32) + y : F32)
225 +u = (i + l : U32)
226 mom_loop(q, u, l, lr, mu, Array.get(F32, Array.set(F32, w, i, (x + (F32.neg(lr) * nb : F32) : F32)), u), Array.get(F32, Array.set(F32, g, i, 0.0), u), Array.get(F32, Array.set(F32, m, i, nb), u))
227
228def lf_mom(+t: U32, +l: U32, +e: EMOM, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O3) -> TG.O3:
229 match e o:
230 case EMOM{+n, +lr, +mu} TG.O3{w, g, m}: mom_loop(TG.share(t, n, l), t, l, lr, mu, Array.get(F32, w, t), Array.get(F32, g, t), Array.get(F32, m, t))
231
232type EADAM is Data:
233 EADAM{n: U32, lr: F32, c1: F32, b2: F32, c2: F32, eps: F32, wd: F32, dec: Bool, step: F32, bc2: F32}
234
235def adam_loop(k: Nat, +i: U32, +l: U32, +lr: F32, +c1: F32, +b2: F32, +c2: F32, +eps: F32, +wd: F32, +dec: Bool, +step: F32, +bc2: F32, rw: Array<F32> & F32, rg: Array<F32> & F32, rm: Array<F32> & F32, rv: Array<F32> & F32) -> TG.O4:
236 match k rw rg rm rv:
237 case 0n Tuple{w, +x} Tuple{g, +y} Tuple{m, +a} Tuple{v, +b}: TG.O4{w, g, m, v}
238 case 1n+q Tuple{w, +x} Tuple{g, +y} Tuple{m, +a} Tuple{v, +b}:
239 +gg = T.adam_grad(dec, wd, x, y)
240 +nm = T.adam_m(c1, a, gg)
241 +nv = T.adam_v(b2, c2, b, gg)
242 +nw = T.adam_w(step, bc2, eps, T.adam_decay(dec, lr, wd, x), nm, nv)
243 +u = (i + l : U32)
244 adam_loop(q, u, l, lr, c1, b2, c2, eps, wd, dec, step, bc2, Array.get(F32, Array.set(F32, w, i, nw), u), Array.get(F32, Array.set(F32, g, i, 0.0), u), Array.get(F32, Array.set(F32, m, i, nm), u), Array.get(F32, Array.set(F32, v, i, nv), u))
245
246def lf_adam(+t: U32, +l: U32, +e: EADAM, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O4) -> TG.O4:
247 match e o:
248 case EADAM{+n, +lr, +c1, +b2, +c2, +eps, +wd, +dec, +step, +bc2} TG.O4{w, g, m, v}: adam_loop(TG.share(t, n, l), t, l, lr, c1, b2, c2, eps, wd, dec, step, bc2, Array.get(F32, w, t), Array.get(F32, g, t), Array.get(F32, m, t), Array.get(F32, v, t))
249
250def sgd_fin(-r: Nat, -c: Nat, q: TG.R2) -> T.MatStep<r, c>:
251 match q:
252 case TG.R2{x1, x2, x3, x4, TG.O2{w, g}}: T.MatStep{TA.Mat{w}, TA.Mat{g}, T.NoMom{}}
253
254def mom_fin(-r: Nat, -c: Nat, q: TG.R3) -> T.MatStep<r, c>:
255 match q:
256 case TG.R3{x1, x2, x3, x4, TG.O3{w, g, m}}: T.MatStep{TA.Mat{w}, TA.Mat{g}, T.OneMom{TA.Mat{m}}}
257
258def opt_fin(-r: Nat, -c: Nat, q: TG.R4) -> T.MatStep<r, c>:
259 match q:
260 case TG.R4{x1, x2, x3, x4, TG.O4{w, g, m, v}}: T.MatStep{TA.Mat{w}, TA.Mat{g}, T.TwoMom{TA.Mat{m}, TA.Mat{v}}}
261
262# one matrix's step, in place: SGD writes two arrays, momentum three, Adam four
263def opt_mat(+r: Nat, +c: Nat, +o: T.Opt, w: TA.Mat<r, c>, g: TA.Mat<r, c>, st: T.OptState<TA.Mat<r, c>>) -> T.MatStep<r, c>:
264 match o w g st:
265 case T.SGD{+lr} TA.Mat{wa} TA.Mat{ga} _:
266 sgd_fin(r, c, TG.run2(~ESGD, ~lf_sgd, TG.depth(Nat.mul(r, c)), ESGD{U32.from_nat(Nat.mul(r, c)), lr}, nn(Unit{}), nn(Unit{}), nn(Unit{}), nn(Unit{}), wa, ga))
267 case T.Momentum{+lr, +mu} TA.Mat{wa} TA.Mat{ga} T.OneMom{TA.Mat{ma}}:
268 mom_fin(r, c, TG.run3(~EMOM, ~lf_mom, TG.depth(Nat.mul(r, c)), EMOM{U32.from_nat(Nat.mul(r, c)), lr, mu}, nn(Unit{}), nn(Unit{}), nn(Unit{}), nn(Unit{}), wa, ga, ma))
269 case T.Adam{+lr, +b1, +b2, +eps, +wd, +dec, +step, +bc2, +c1, +c2} TA.Mat{wa} TA.Mat{ga} T.TwoMom{TA.Mat{ma}, TA.Mat{va}}:
270 opt_fin(r, c, TG.run4(~EADAM, ~lf_adam, TG.depth(Nat.mul(r, c)), EADAM{U32.from_nat(Nat.mul(r, c)), lr, c1, b2, c2, eps, wd, dec, step, bc2}, nn(Unit{}), nn(Unit{}), nn(Unit{}), nn(Unit{}), wa, ga, ma, va))
271 # packed BF16 moments (CAdamWBF16): main.bend's step, in one block
272 case o w g T.Packed{t}: T.Opt.mat(r, c, o, w, g, T.Packed{t})
273 case _ w g st: T.MatStep{w, g, st}
274
275def mat_step(+r: Nat, +c: Nat, +op: T.Opt, p: TA.Mat<r, c>, g: TA.Mat<r, c>, st: T.OptState<TA.Mat<r, c>>) -> T.Step<TA.Mat<r, c>>:
276 T.mat_step_fin(r, c, opt_mat(r, c, op, p, g, st))
277
278def lin_step2(+i: Nat, +o: Nat, +op: T.Opt, p: T.Lin<i, o>, g: T.Lin<i, o>, st: T.Two<T.OptState<TA.Mat<o, i>>, T.OptState<TA.Mat<1n, o>>>) -> T.Step<T.Lin<i, o>>:
279 match p g st:
280 case T.Lin{w, b} T.Lin{gw, gb} T.Two{sw, sb}:
281 T.lin_s2(i, o, opt_mat(o, i, op, w, gw, sw), opt_mat(1n, o, op, b, gb, sb))
282
283def lin_step(+i: Nat, +o: Nat, +op: T.Opt, p: T.Lin<i, o>, g: T.Lin<i, o>, st: T.OptState<T.Lin<i, o>>) -> T.Step<T.Lin<i, o>>:
284 lin_step2(i, o, op, p, g, T.OptState.split(TA.Mat<o, i>, TA.Mat<1n, o>, T.OptState.map(T.Lin<i, o>, T.Two<TA.Mat<o, i>, TA.Mat<1n, o>>, st, x => T.lin_pair(i, o, x), x => T.lin_pair(i, o, x))))
285
286# ---- the linear layers ----
287
288def LinearF(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<T.Lin<i, o>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
289 T.Layer{p => x => lin_fwd(n, i, o, p, x), p => s => dy => g => lin_bwd(n, i, o, p, s, dy, g), op => p => g => st => lin_step(i, o, op, p, g, st), u => T.lin_zeros(i, o), p => T.lin_dump(i, o, p), p => xs => off => T.lin_load(i, o, p, xs, off), p => xs => off => T.lin_store(i, o, p, xs, off), u => U32.from_nat(Nat.mul(o, 1n+i))}
290
291# LinearFirst: no dX (the first layer's input needs no gradient)
292def linf_bwd(+n: Nat, +i: Nat, +o: Nat, p: T.Lin<i, o>, x: TA.Mat<n, i>, dy: TA.Mat<n, o>, g: T.Lin<i, o>) -> T.Bwd<T.Lin<i, o>, TA.Mat<n, i>>:
293 match p g:
294 case T.Lin{w, b} T.Lin{gw, gb}:
295 T.linf_b3(n, i, o, w, b, gb, TG.matmul_tn_acc(o, n, i, 0n, dy, x, gw))
296
297def LinearFirstF(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<T.Lin<i, o>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
298 T.Layer{p => x => lin_fwd(n, i, o, p, x), p => s => dy => g => linf_bwd(n, i, o, p, s, dy, g), op => p => g => st => lin_step(i, o, op, p, g, st), u => T.lin_zeros(i, o), p => T.lin_dump(i, o, p), p => xs => off => T.lin_load(i, o, p, xs, off), p => xs => off => T.lin_store(i, o, p, xs, off), u => U32.from_nat(Nat.mul(o, 1n+i))}
299
300# LinearNB: y = x·Wᵀ, no bias (Llama's projections)
301def lnb_b2(+n: Nat, +i: Nat, +o: Nat, x: TA.Mat<n, i>, g: TA.Mat<o, i>, r: TA.MMul<n, o, i>) -> T.Bwd<TA.Mat<o, i>, TA.Mat<n, i>>:
302 match r:
303 case TA.MMul{dy, w, dx}:
304 T.lnb_b3(n, i, o, w, dx, TG.matmul_tn_acc(o, n, i, 0n, dy, x, g))
305
306def LinearNBF(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<TA.Mat<o, i>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
307 T.Layer{w => x => T.lnb_f2(n, i, o, TG.matmul_nt(n, i, o, 0n, x, w)), w => s => dy => g => lnb_b2(n, i, o, s, g, TG.matmul_precise(n, o, i, 0n, dy, w)), op => p => g => st => mat_step(o, i, op, p, g, st), u => TA.Mat.zeros(o, i), p => T.Mat.dump(o, i, p), p => xs => off => T.Mat.restore(o, i, p, xs, off), p => xs => off => T.Mat.store(o, i, p, xs, off), u => U32.from_nat(Nat.mul(o, i))}
308
309# ---- RMSNorm: a leaf a row with main.bend's own row code (rmsf_row) ----
310
311type ERF is Data:
312 ERF{n: U32, d: Nat, d32: U32, df: F32, eps: F32}
313
314def r3_y(s: T.R3) -> Array<F32>:
315 match s:
316 case T.R3{x, g, y}: y
317
318def rmsf_if(ok: Bool, +t: U32, +d: Nat, +d32: U32, +df: F32, +eps: F32, x: Array<F32>, g: Array<F32>, y: Array<F32>) -> Array<F32>:
319 match ok:
320 case True{}: r3_y(T.rmsf_row(d, (t * d32 : U32), df, eps, T.R3{x, g, y}))
321 case False{}: y
322
323# x1 = x, x2 = the gain g; y a row a leaf
324def lf_rmsf(+t: U32, +l: U32, +e: ERF, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, y: Array<F32>) -> Array<F32>:
325 match e:
326 case ERF{+n, +d, +d32, +df, +eps}: rmsf_if(U32.is_lt(t, n), t, d, d32, df, eps, x1, x2, y)
327
328def erf(+n: Nat, +d: Nat, +eps: F32) -> ERF:
329 ERF{U32.from_nat(n), d, U32.from_nat(d), F32.from_nat(d), eps}
330
331def rms_f2(-n: Nat, -d: Nat, q: TG.R1) -> T.Fwd<TA.Mat<1n, d>, TA.Mat<n, d>, TA.Mat<n, d>>:
332 match q:
333 case TG.R1{x1, x2, x3, x4, y}: T.Fwd{TA.Mat{x2}, TA.Mat{y}, TA.Mat{x1}}
334
335def rms_f(+n: Nat, +d: Nat, +eps: F32, p: TA.Mat<1n, d>, x: TA.Mat<n, d>) -> T.Fwd<TA.Mat<1n, d>, TA.Mat<n, d>, TA.Mat<n, d>>:
336 match p x:
337 case TA.Mat{g} TA.Mat{a}: rms_f2(n, d, TG.run1_new(~ERF, ~lf_rmsf, TA.cap_depth(n), erf(n, d, eps), a, g, nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, d))))
338
339# the backward pass in two bangs: a leaf a row writes dx and the row's 1/rms (main.bend's rmsb_row without dg), then a
340# leaf a column sums dg down the rows in order from +0.0 and adds it to the gradient it is given, as main.bend adds each
341# row's term into dg
342
343def gdx_w(k: Nat, +j: U32, +off: U32, +r: F32, +m: F32, dx: Array<F32>, rx: Array<F32> & F32, rd: Array<F32> & F32, rg: Array<F32> & F32) -> Array<F32>:
344 match k rx rd rg:
345 case 0n Tuple{x, +a} Tuple{dy, +b} Tuple{g, +c}:
346 +xh = (a * r : F32)
347 Array.set(F32, dx, (off + j : U32), (((b * c : F32) - (xh * m : F32) : F32) * r : F32))
348 case 1n+p Tuple{x, +a} Tuple{dy, +b} Tuple{g, +c}:
349 +xh = (a * r : F32)
350 gdx_w(p, (j + 1 : U32), off, r, m, Array.set(F32, dx, (off + j : U32), (((b * c : F32) - (xh * m : F32) : F32) * r : F32)), Array.get(F32, x, ((off + j : U32) + 1 : U32)), Array.get(F32, dy, ((off + j : U32) + 1 : U32)), Array.get(F32, g, (j + 1 : U32)))
351
352def gdx_3(+d: Nat, +off: U32, +df: F32, +r: F32, +t: U32, dx: Array<F32>, rr: Array<F32>, q: T.R3S) -> TG.O2:
353 match q:
354 case T.R3S{x, dy, g, +s}:
355 TG.O2{gdx_w(Nat.sub(d, 1n), 0, off, r, (s / df : F32), dx, Array.get(F32, x, off), Array.get(F32, dy, off), Array.get(F32, g, 0)), Array.set(F32, rr, t, r)}
356
357def gdx_2(+d: Nat, +off: U32, +df: F32, +eps: F32, +t: U32, dy: Array<F32>, g: Array<F32>, dx: Array<F32>, rr: Array<F32>, q: T.AS) -> TG.O2:
358 match q:
359 case T.AS{x, +s}:
360 +r = T.rms_inv(s, df, eps)
361 gdx_3(d, off, df, r, t, dx, rr, T.rmsb_dot(Nat.sub(d, 1n), 0, off, r, 0.0, Array.get(F32, x, off), Array.get(F32, dy, off), Array.get(F32, g, 0)))
362
363def gdx_if(ok: Bool, +t: U32, +d: Nat, +d32: U32, +df: F32, +eps: F32, x: Array<F32>, dy: Array<F32>, g: Array<F32>, dx: Array<F32>, rr: Array<F32>) -> TG.O2:
364 match ok:
365 case True{}: gdx_2(d, (t * d32 : U32), df, eps, t, dy, g, dx, rr, T.ssq(Nat.sub(d, 1n), (t * d32 : U32), 0.0, Array.get(F32, x, (t * d32 : U32))))
366 case False{}: TG.O2{dx, rr}
367
368# x1 = x, x2 = dy, x3 = g; outputs dx and 1/rms of each row
369def lf_gdx(+t: U32, +l: U32, +e: ERF, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
370 match e o:
371 case ERF{+n, +d, +d32, +df, +eps} TG.O2{dx, rr}: gdx_if(U32.is_lt(t, n), t, d, d32, df, eps, x1, x2, x3, dx, rr)
372
373# acc + dy[i]·(x[i]·r[row]) down a column, i from j by d
374def gdg_loop(k: Nat, +i: U32, +d: U32, +row: U32, acc: F32, rx: Array<F32> & F32, rd: Array<F32> & F32, rr: Array<F32> & F32) -> F32:
375 match k rx rd rr:
376 case 0n Tuple{x, +a} Tuple{dy, +b} Tuple{r, +c}: (acc + (b * (a * c : F32) : F32) : F32)
377 case 1n+p Tuple{x, +a} Tuple{dy, +b} Tuple{r, +c}:
378 gdg_loop(p, (i + d : U32), d, (row + 1 : U32), (acc + (b * (a * c : F32) : F32) : F32), Array.get(F32, x, (i + d : U32)), Array.get(F32, dy, (i + d : U32)), Array.get(F32, r, (row + 1 : U32)))
379
380def gdg_put(+j: U32, +s: F32, r: Array<F32> & F32) -> Array<F32>:
381 match r:
382 case Tuple{ga, +v}: Array.set(F32, ga, j, (v + s : F32))
383
384type EDG is Data:
385 EDG{d: U32, n: Nat}
386
387def gdg_if(ok: Bool, +j: U32, +n: Nat, +d: U32, x: Array<F32>, dy: Array<F32>, rr: Array<F32>, ga: Array<F32>) -> Array<F32>:
388 match ok:
389 case True{}: gdg_put(j, gdg_loop(Nat.sub(n, 1n), j, d, 0, 0.0, Array.get(F32, x, j), Array.get(F32, dy, j), Array.get(F32, rr, 0)), Array.get(F32, ga, j))
390 case False{}: ga
391
392# x1 = x, x2 = dy, x3 = 1/rms; the gradient of the gain, in place: a leaf a column
393def lf_gdg(+t: U32, +l: U32, +e: EDG, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, ga: Array<F32>) -> Array<F32>:
394 match e:
395 case EDG{+d, +n}: gdg_if(U32.is_lt(t, d), t, n, d, x1, x2, x3, ga)
396
397def rms_b3(-n: Nat, -d: Nat, g: Array<F32>, dx: Array<F32>, q: TG.R1) -> T.Bwd<TA.Mat<1n, d>, TA.Mat<n, d>>:
398 match q:
399 case TG.R1{x1, x2, x3, x4, ga}: T.Bwd{TA.Mat{g}, TA.Mat{dx}, TA.Mat{ga}}
400
401def rms_b2(+n: Nat, +d: Nat, ga: Array<F32>, q: TG.R2) -> T.Bwd<TA.Mat<1n, d>, TA.Mat<n, d>>:
402 match q:
403 case TG.R2{x, dy, g, x4, TG.O2{dx, rr}}: rms_b3(n, d, g, dx, TG.run1(~EDG, ~lf_gdg, TA.cap_depth(d), EDG{U32.from_nat(d), n}, x, dy, rr, x4, ga))
404
405def rms_b(+n: Nat, +d: Nat, +eps: F32, p: TA.Mat<1n, d>, x: TA.Mat<n, d>, dy: TA.Mat<n, d>, gacc: TA.Mat<1n, d>) -> T.Bwd<TA.Mat<1n, d>, TA.Mat<n, d>>:
406 match p x dy gacc:
407 case TA.Mat{g} TA.Mat{xa} TA.Mat{da} TA.Mat{ga}: rms_b2(n, d, ga, TG.run2_new(~ERF, ~lf_gdx, TA.cap_depth(n), erf(n, d, eps), xa, da, g, nn(Unit{}), TA.cap_depth(Nat.mul(n, d)), TA.cap_depth(n)))
408
409# RMSNorm over the last dimension, its gain trained (main.bend's RMSNorm)
410def RMSNorm(+n: Nat, +d: Nat, +eps: F32) -> T.Layer<TA.Mat<1n, d>, TA.Mat<n, d>, TA.Mat<n, d>, TA.Mat<n, d>>:
411 T.Layer{p => x => rms_f(n, d, eps, p, x), p => s => dy => g => rms_b(n, d, eps, p, s, dy, g), op => p => g => st => mat_step(1n, d, op, p, g, st), u => TA.Mat.zeros(1n, d), p => T.Mat.dump(1n, d, p), p => xs => off => T.Mat.restore(1n, d, p, xs, off), p => xs => off => T.Mat.store(1n, d, p, xs, off), u => U32.from_nat(d)}
412
413# ---- RoPE: the tables on the host (main.bend's rope_tab, so the host's cos and sin), each pair rotated in place by
414# main.bend's rope_one, the pairs dealt to the leaves round-robin ----
415
416type ERO is Data:
417 ERO{n: U32, fwd: Bool, hh: U32, half: U32, w: U32, t: U32}
418
419def rope_loop(k: Nat, +e: U32, +l: U32, +fwd: Bool, +hh: U32, +half: U32, +w: U32, +t: U32, st: T.RS3) -> T.RS3:
420 match k:
421 case 0n: st
422 case 1n+q: rope_loop(q, (e + l : U32), l, fwd, hh, half, w, t, T.rope_one(e, fwd, hh, half, w, t, st))
423
424def rs3_x(st: T.RS3) -> Array<F32>:
425 match st:
426 case T.RS3{x, c, s}: x
427
428# x1 = cos, x2 = sin; x rotated in place
429def lf_rope(+t: U32, +l: U32, +e: ERO, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, x: Array<F32>) -> Array<F32>:
430 match e:
431 case ERO{+n, +fwd, +hh, +half, +w, +tt}: rs3_x(rope_loop(TG.share(t, n, l), t, l, fwd, hh, half, w, tt, T.RS3{x, x1, x2}))
432
433def rope_go2(+n: Nat, +w: Nat, +fwd: Bool, +t: Nat, +h: Nat, +half: Nat, x: Array<F32>, tb: T.CS) -> TA.Mat<n, w>:
434 match tb:
435 case T.CS{cs, sn}: TA.Mat{r1_a(TG.run1(~ERO, ~lf_rope, TG.depth(Nat.mul(n, Nat.mul(h, half))), ERO{U32.from_nat(Nat.mul(n, Nat.mul(h, half))), fwd, U32.from_nat(Nat.mul(h, half)), U32.from_nat(half), U32.from_nat(w), U32.from_nat(t)}, cs, sn, nn(Unit{}), nn(Unit{}), x))}
436
437def rope_go(+n: Nat, +w: Nat, +fwd: Bool, +t: Nat, +h: Nat, +half: Nat, +base: F32, m: TA.Mat<n, w>) -> TA.Mat<n, w>:
438 match m:
439 case TA.Mat{x}: rope_go2(n, w, fwd, t, h, half, x, T.rope_tab(Nat.sub(Nat.mul(t, half), 1n), 0, U32.from_nat(half), base, T.CS{T.zeros_a(Nat.mul(t, half)), T.zeros_a(Nat.mul(t, half))}))
440
441# RoPE on n = b·t rows of h heads of 2·half (main.bend's RoPE)
442def RoPE(+n: Nat, +w: Nat, +t: Nat, +h: Nat, +half: Nat, +base: F32, -e: {w == Nat.mul(h, Nat.add(half, half)) : Nat}) -> T.Layer<Unit, TA.Mat<n, w>, TA.Mat<n, w>, Unit>:
443 T.Layer{p => x => T.Fwd{p, rope_go(n, w, True{}, t, h, half, base, x), Unit{}}, p => s => dy => g => T.Bwd{p, rope_go(n, w, False{}, t, h, half, base, dy), g}, op => p => g => st => T.no_step(Unit, op, p, g, st), u => Unit{}, p => T.PL{p, Nil{}}, p => xs => off => T.PA{p, xs, off}, p => xs => off => T.PA{p, xs, off}, u => 0}
444
445type S4 is Type:
446 S4{a: Array<F32>, b: Array<F32>, c: Array<F32>, d: Array<F32>}
447
448# ---- attention in cell bangs and row bangs. A leaf handles cells or rows t, t + l, .. of one bang. A cell is one number
449# of an output: a score, a dP, a number of o, dQ, dK or dV, each one dot product with main.bend's own code (dot0, or
450# tdot, dot0's sum down a column of the packed triangle below; tsum_h adds the group's heads as main.bend's pacc). A
451# row is one row of P (and of G), whose softmax and its backward pass are sums along the row in order: a leaf a row with
452# main.bend's row code (psmc, g_set2), a tail loop over the row's i + 1 positions. Every number is computed by
453# main.bend's expression in main.bend's order, so the bits are main.bend's (but CUDA's exp in the softmax).
454# forward: 0. cells: kᵀ 1. cells: P's scores (q·k scaled, j <= i) 2. rows: the softmax over the i + 1
455# 3. cells: o = P·V over the query's i + 1 positions
456# backward: 0. cells: kᵀ and vᵀ 1. cells: P's scores again and dP = dO·V into G 2. rows: P's softmax, then
457# G = (P * (dP - s)) * sc with s = dP·P over the row, as gr3 3. cells: dQ = G·K, dK = Σ Gᵀ·Q and
458# dV = Σ Pᵀ·dO over the group's heads, dK written over kᵀ and dV over V (neither read any more)
459# The old form (a leaf per row doing the scores, the softmax and the row's hd outputs, ~8,000 steps in a row at
460# SmolLM2's t = 128, and dK and dV a leaf per key position) took 56 ms a layer's forward and backward at SmolLM2-135M's
461# sizes on the RTX 4050; this one takes 17 ms, about half of it filling the new arrays (one lane, Array.new). ----
462
463# the sizes; n is how many cells (or rows) the bang has, nw and nk the cells of dQ and of dK, tr = t(t + 1)/2 the cells
464# of a head's P
465type EAC is Data:
466 EAC{n: U32, t: Nat, t32: U32, h: U32, g: U32, gn: Nat, hd: Nat, hd32: U32, w: U32, kvw: U32, sc: F32, nw: U32, nk: U32, tr: U32}
467
468# where row i of head bh starts in P and G, which keep only the lower triangle (j <= i), row after row: main.bend's P
469# and G are t·t with zeros above the diagonal, and a product by one of those zeros adds +0.0 or -0.0 to a sum that
470# started at +0.0, which leaves it as it is; so the sums below skip them and give the same bits (for finite numbers:
471# an infinity or a NaN in a masked key's row would reach main.bend's sums as 0·inf = NaN, and does not reach these)
472def toff(+bh: U32, +i: U32, +tr: U32) -> U32:
473 ((bh * tr : U32) + U32.div((i * (i + 1 : U32) : U32), 2) : U32)
474
475def eac_n(+c: EAC) -> U32:
476 match c:
477 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: n
478
479type S3 is Type:
480 S3{a: Array<F32>, b: Array<F32>, c: Array<F32>}
481
482def s3_c(s: S3) -> Array<F32>:
483 match s:
484 case S3{a, b, c}: c
485
486def sc_fin(y: Array<F32>, s: S3) -> TG.O2:
487 match s:
488 case S3{q, k, p}: TG.O2{p, y}
489
490# the cell e = row·w + col of o (a = P, b = V) or of dQ (a = G, b = K): row i of P (or G) for the query's head, its
491# i + 1 positions, · V's (or K's) column of the head's key/value head
492def pv_val(+e: U32, +c: EAC, a: Array<F32>, b: Array<F32>) -> T.D2:
493 match c:
494 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}:
495 +row = U32.div(e, w)
496 +col = U32.mod(e, w)
497 +bi = U32.div(row, t32)
498 +hi = U32.div(col, hd32)
499 +i = U32.mod(row, t32)
500 T.dot0(U32.to_nat((i + 1 : U32)), toff(((bi * h : U32) + hi : U32), i, tr), ((((bi * t32 : U32) * kvw : U32) + (U32.div(hi, g) * hd32 : U32) : U32) + U32.mod(col, hd32) : U32), 1, kvw, a, b)
501
502# acc + a[ia]·b[ib] + .. down column j of a head's packed triangle (row i + 1's cell is i + 1 after row i's) and b's
503# rows (sb apart), from row i on: dot0's sum, but with the step in a growing by one a row
504def tdot(k: Nat, +ia: U32, +ib: U32, +i: U32, +sb: U32, acc: F32, ra: Array<F32> & F32, rb: Array<F32> & F32) -> T.D2:
505 match k ra rb:
506 case 0n Tuple{a, x} Tuple{b, y}: T.D2{a, b, acc}
507 case 1n+p Tuple{a, +x} Tuple{b, +y}: tdot(p, (ia + (i + 1 : U32) : U32), (ib + sb : U32), (i + 1 : U32), sb, (acc + (x * y : F32) : F32), Array.get(F32, a, (ia + (i + 1 : U32) : U32)), Array.get(F32, b, (ib + sb : U32)))
508
509# acc + the group's heads' sums, in head order (main.bend's pacc): a's column j (tr apart a head) · b's column (hd
510# apart a head), rows j..t-1
511def tsum_h(k: Nat, +ia: U32, +ib: U32, +j: U32, +tn: Nat, +w: U32, +sa: U32, +sb: U32, acc: F32, r: T.D2) -> T.D2:
512 match k r:
513 case 0n T.D2{a, b, +s}: T.D2{a, b, (acc + s : F32)}
514 case 1n+q T.D2{a, b, +s}: tsum_h(q, (ia + sa : U32), (ib + sb : U32), j, tn, w, sa, sb, (acc + s : F32), tdot(tn, (ia + sa : U32), (ib + sb : U32), j, w, 0.0, Array.get(F32, a, (ia + sa : U32)), Array.get(F32, b, (ib + sb : U32))))
515
516# the cell e = row·kvw + col of dK (a = G, b = Q) or of dV (a = P, b = dO): key j = row % t, over the group's g query
517# heads: the head's column j of a (rows j..t-1) · the head's column of b
518def kv_val(+e: U32, +c: EAC, a: Array<F32>, b: Array<F32>) -> T.D2:
519 match c:
520 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}:
521 +row = U32.div(e, kvw)
522 +col = U32.mod(e, kvw)
523 +bi = U32.div(row, t32)
524 +jj = U32.div(col, hd32)
525 +j = U32.mod(row, t32)
526 +ia = (toff(((bi * h : U32) + (jj * g : U32) : U32), j, tr) + j : U32)
527 +ib = ((((bi * t32 : U32) + j : U32) * w : U32) + (((jj * g : U32) * hd32 : U32) + U32.mod(col, hd32) : U32) : U32)
528 tsum_h(Nat.sub(gn, 1n), ia, ib, j, U32.to_nat((t32 - j : U32)), w, tr, hd32, 0.0, tdot(U32.to_nat((t32 - j : U32)), ia, ib, j, w, 0.0, Array.get(F32, a, ia), Array.get(F32, b, ib)))
529
530# K and V transposed for the score cells (kᵀ: b·kv·hd·t, by sequence, key/value head, column c, position j), so
531# that neighbouring lanes, which take neighbouring keys j, read neighbouring cells. dot0 reads kᵀ with stride t: the same
532# numbers in the same order as reading k's row, so the same bits.
533def tk_src(+e: U32, +t32: U32, +kv: U32, +hd32: U32, +kvw: U32) -> U32:
534 +r = U32.div(e, t32)
535 +bj = U32.div(r, hd32)
536 +row = ((U32.div(bj, kv) * t32 : U32) + U32.mod(e, t32) : U32)
537 ((row * kvw : U32) + ((U32.mod(bj, kv) * hd32 : U32) + U32.mod(r, hd32) : U32) : U32)
538
539def tk_put(+e: U32, o: Array<F32>, r: Array<F32> & F32) -> TG.O2:
540 match r:
541 case Tuple{a, +x}: TG.O2{a, Array.set(F32, o, e, x)}
542
543def tk_loop(k: Nat, +e: U32, +l: U32, +t32: U32, +kv: U32, +hd32: U32, +kvw: U32, s: TG.O2) -> TG.O2:
544 match k s:
545 case 0n r: r
546 case 1n+q TG.O2{a, o}: tk_loop(q, (e + l : U32), l, t32, kv, hd32, kvw, tk_put(e, o, Array.get(F32, a, tk_src(e, t32, kv, hd32, kvw))))
547
548def o2_b(s: TG.O2) -> Array<F32>:
549 match s:
550 case TG.O2{a, b}: b
551
552# x1 = k; kᵀ written (made here)
553def lf_tk(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
554 match c:
555 case EAC{+n, +tt, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: o2_b(tk_loop(TG.share(t, n, l), t, l, t32, U32.div(h, g), hd32, kvw, TG.O2{x1, o}))
556
557# both: cells [0, nk) are kᵀ's from k, the next nk vᵀ's from v (S4{k, v, kᵀ, vᵀ})
558def tkv_k(v: Array<F32>, vt: Array<F32>, r: TG.O2) -> S4:
559 match r:
560 case TG.O2{k, kt}: S4{k, v, kt, vt}
561
562def tkv_v(k: Array<F32>, kt: Array<F32>, r: TG.O2) -> S4:
563 match r:
564 case TG.O2{v, vt}: S4{k, v, kt, vt}
565
566def tkv_sel(lo: Bool, +e: U32, +nk: U32, +t32: U32, +kv: U32, +hd32: U32, +kvw: U32, s: S4) -> S4:
567 match lo s:
568 case True{} S4{k, v, kt, vt}: tkv_k(v, vt, tk_put(e, kt, Array.get(F32, k, tk_src(e, t32, kv, hd32, kvw))))
569 case False{} S4{k, v, kt, vt}: tkv_v(k, kt, tk_put((e - nk : U32), vt, Array.get(F32, v, tk_src((e - nk : U32), t32, kv, hd32, kvw))))
570
571def tkv_loop(k: Nat, +e: U32, +l: U32, +nk: U32, +t32: U32, +kv: U32, +hd32: U32, +kvw: U32, s: S4) -> S4:
572 match k:
573 case 0n: s
574 case 1n+q: tkv_loop(q, (e + l : U32), l, nk, t32, kv, hd32, kvw, tkv_sel(U32.is_lt(e, nk), e, nk, t32, kv, hd32, kvw, s))
575
576def tkv_o(dq: Array<F32>, s: S4) -> TG.O3:
577 match s:
578 case S4{k, v, kt, vt}: TG.O3{kt, vt, dq}
579
580# x1 = k, x2 = v; outputs kᵀ and vᵀ (written), dQ (made here, written by backward 3)
581def lf_tkv(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O3) -> TG.O3:
582 match c o:
583 case EAC{+n, +tt, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr} TG.O3{kt, vt, dq}: tkv_o(dq, tkv_loop(TG.share(t, n, l), t, l, nk, t32, U32.div(h, g), hd32, kvw, S4{x1, x2, kt, vt}))
584
585# the score cells are P's lower triangle (j <= i), dealt in pairs of rows i and t - 1 - i (t + 1 cells a pair), so
586# every lane gets real work: cell e is pair p of head bh, place c; c <= p is (p, c), the rest (t - 1 - p, c - p - 1);
587# in the middle row of an odd t the second half repeats the first and is skipped (ok false)
588type IJ is Data:
589 IJ{ok: Bool, bh: U32, i: U32, j: U32}
590
591def ij_of(+e: U32, +t32: U32) -> IJ:
592 +np = U32.div((t32 + 1 : U32), 2)
593 +c = U32.mod(e, (t32 + 1 : U32))
594 +pr = U32.div(e, (t32 + 1 : U32))
595 +p = U32.mod(pr, np)
596 +lo = U32.is_le(c, p)
597 IJ{Bool.or(lo, U32.is_ne(((t32 - 1 : U32) - p : U32), p)), U32.div(pr, np), Bool.pick(U32, lo, p, ((t32 - 1 : U32) - p : U32)), Bool.pick(U32, lo, c, ((c - p : U32) - 1 : U32))}
598
599# forward 1: P's cell (i, j) = (q's row i · k's row j) * sc, as qrow's dot and pw's product (S3{q, kᵀ, p})
600def sc_put(+e: U32, +sc: F32, p: Array<F32>, r: T.D2) -> S3:
601 match r:
602 case T.D2{q, k, +s}: S3{q, k, Array.set(F32, p, e, (s * sc : F32))}
603
604def sc_if(ok: Bool, +e: U32, +qa: U32, +ka: U32, +hd: Nat, +t32: U32, +sc: F32, s: S3) -> S3:
605 match ok s:
606 case True{} S3{q, k, p}: sc_put(e, sc, p, T.dot0(hd, qa, ka, 1, t32, q, k))
607 case False{} S3{q, k, p}: S3{q, k, p}
608
609def sc_ij(+a: IJ, +t32: U32, +h: U32, +g: U32, +hd: Nat, +hd32: U32, +w: U32, +sc: F32, +tr: U32, s: S3) -> S3:
610 match a:
611 case IJ{+ok, +bh, +i, +j}: sc_if(ok, (toff(bh, i, tr) + j : U32), ((((U32.div(bh, h) * t32 : U32) + i : U32) * w : U32) + (U32.mod(bh, h) * hd32 : U32) : U32), (((U32.div(bh, g) * hd32 : U32) * t32 : U32) + j : U32), hd, t32, sc, s)
612
613def sc_cell(+e: U32, +c: EAC, s: S3) -> S3:
614 match c:
615 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: sc_ij(ij_of(e, t32), t32, h, g, hd, hd32, w, sc, tr, s)
616
617def sc_loop(k: Nat, +e: U32, +l: U32, +c: EAC, s: S3) -> S3:
618 match k:
619 case 0n: s
620 case 1n+q: sc_loop(q, (e + l : U32), l, c, sc_cell(e, c, s))
621
622# x1 = q, x2 = k; outputs P (written) and o (made here, written by forward 3)
623def lf_sc(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
624 match o:
625 case TG.O2{p, y}: sc_fin(y, sc_loop(TG.share(t, eac_n(c), l), t, l, c, S3{x1, x2, p}))
626
627# forward 2: P's row r (query i = r % t) by main.bend's softmax over its i + 1 positions
628def sm_loop(k: Nat, +r: U32, +l: U32, +t32: U32, +tr: U32, p: Array<F32>) -> Array<F32>:
629 match k:
630 case 0n: p
631 case 1n+q: sm_loop(q, (r + l : U32), l, t32, tr, T.pc_a(T.psmc(U32.to_nat((U32.mod(r, t32) + 1 : U32)), toff(U32.div(r, t32), U32.mod(r, t32), tr), p)))
632
633def lf_sm(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, p: Array<F32>) -> Array<F32>:
634 match c:
635 case EAC{+n, +tt, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: sm_loop(TG.share(t, n, l), t, l, t32, tr, p)
636
637# forward 3 (and backward 3's dQ): o[e] from pv_val (S3{a, b, o})
638def put_c(+e: U32, o: Array<F32>, r: T.D2) -> S3:
639 match r:
640 case T.D2{a, b, +s}: S3{a, b, Array.set(F32, o, e, s)}
641
642def pv_loop(k: Nat, +e: U32, +l: U32, +c: EAC, s: S3) -> S3:
643 match k s:
644 case 0n r: r
645 case 1n+q S3{a, b, o}: pv_loop(q, (e + l : U32), l, c, put_c(e, o, pv_val(e, c, a, b)))
646
647# x1 = P, x2 = v; o written in place
648def lf_pv(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
649 s3_c(pv_loop(TG.share(t, eac_n(c), l), t, l, c, S3{x1, x2, o}))
650
651# backward 1: P's score cell as forward 1, and G's cell dP = dO's row · v's row, as drow's dot (S6{q, k, v, d, p, g})
652type S6 is Type:
653 S6{q: Array<F32>, k: Array<F32>, v: Array<F32>, d: Array<F32>, p: Array<F32>, g: Array<F32>}
654
655def b1_g(+e: U32, q: Array<F32>, k: Array<F32>, p: Array<F32>, g: Array<F32>, r: T.D2) -> S6:
656 match r:
657 case T.D2{d, v, +s}: S6{q, k, v, d, p, Array.set(F32, g, e, s)}
658
659def b1_p(+e: U32, +sc: F32, +qa: U32, +ka: U32, +hd: Nat, +t32: U32, v: Array<F32>, d: Array<F32>, p: Array<F32>, g: Array<F32>, r: T.D2) -> S6:
660 match r:
661 case T.D2{q, k, +s}: b1_g(e, q, k, Array.set(F32, p, e, (s * sc : F32)), g, T.dot0(hd, qa, ka, 1, t32, d, v))
662
663def b1_if(ok: Bool, +e: U32, +qa: U32, +ka: U32, +hd: Nat, +t32: U32, +sc: F32, s: S6) -> S6:
664 match ok s:
665 case True{} S6{q, k, v, d, p, g}: b1_p(e, sc, qa, ka, hd, t32, v, d, p, g, T.dot0(hd, qa, ka, 1, t32, q, k))
666 case False{} S6{q, k, v, d, p, g}: S6{q, k, v, d, p, g}
667
668def b1_ij(+a: IJ, +t32: U32, +h: U32, +g: U32, +hd: Nat, +hd32: U32, +w: U32, +sc: F32, +tr: U32, s: S6) -> S6:
669 match a:
670 case IJ{+ok, +bh, +i, +j}: b1_if(ok, (toff(bh, i, tr) + j : U32), ((((U32.div(bh, h) * t32 : U32) + i : U32) * w : U32) + (U32.mod(bh, h) * hd32 : U32) : U32), (((U32.div(bh, g) * hd32 : U32) * t32 : U32) + j : U32), hd, t32, sc, s)
671
672def b1_cell(+e: U32, +c: EAC, s: S6) -> S6:
673 match c:
674 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: b1_ij(ij_of(e, t32), t32, h, g, hd, hd32, w, sc, tr, s)
675
676def b1_loop(k: Nat, +e: U32, +l: U32, +c: EAC, s: S6) -> S6:
677 match k:
678 case 0n: s
679 case 1n+q: b1_loop(q, (e + l : U32), l, c, b1_cell(e, c, s))
680
681def b1_o(s: S6) -> TG.O2:
682 match s:
683 case S6{q, k, v, d, p, g}: TG.O2{p, g}
684
685# x1 = q, x2 = kᵀ, x3 = vᵀ, x4 = dO; outputs P and G (made here, written)
686def lf_b1(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
687 match o:
688 case TG.O2{p, g}: b1_o(b1_loop(TG.share(t, eac_n(c), l), t, l, c, S6{x1, x2, x3, x4, p, g}))
689
690# backward 2: row r of P by the softmax, then G's row: s = dP·P over the i + 1 positions and G = (P * (dP - s)) * sc,
691# main.bend's gr3 and g_set2
692def gr_2(+n: Nat, +off: U32, +sc: F32, g: Array<F32>, r: T.PC) -> T.A2:
693 match r:
694 case T.PC{p, c}: T.g_set2(n, sc, off, T.dot0(n, off, off, 1, 1, g, p))
695
696def gr_loop(k: Nat, +r: U32, +l: U32, +t32: U32, +tr: U32, +sc: F32, s: T.A2) -> T.A2:
697 match k s:
698 case 0n r: r
699 case 1n+q T.A2{p, g}: gr_loop(q, (r + l : U32), l, t32, tr, sc, gr_2(U32.to_nat((U32.mod(r, t32) + 1 : U32)), toff(U32.div(r, t32), U32.mod(r, t32), tr), sc, g, T.psmc(U32.to_nat((U32.mod(r, t32) + 1 : U32)), toff(U32.div(r, t32), U32.mod(r, t32), tr), p)))
700
701def a2_o2(s: T.A2) -> TG.O2:
702 match s:
703 case T.A2{p, g}: TG.O2{p, g}
704
705def lf_gr(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
706 match c o:
707 case EAC{+n, +tt, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr} TG.O2{p, gb}: a2_o2(gr_loop(TG.share(t, n, l), t, l, t32, tr, sc, T.A2{p, gb}))
708
709# backward 3: cells [0, nw) are dQ's, [nw, nw + nk) dK's, the next nk dV's (S8{q, k, d, g, p, dq, dk, v})
710type S8 is Type:
711 S8{q: Array<F32>, k: Array<F32>, d: Array<F32>, g: Array<F32>, p: Array<F32>, dq: Array<F32>, dk: Array<F32>, v: Array<F32>}
712
713def b3_q(+e: U32, q: Array<F32>, d: Array<F32>, p: Array<F32>, dq: Array<F32>, dk: Array<F32>, v: Array<F32>, r: T.D2) -> S8:
714 match r:
715 case T.D2{g, k, +s}: S8{q, k, d, g, p, Array.set(F32, dq, e, s), dk, v}
716
717def b3_k(+e: U32, k: Array<F32>, d: Array<F32>, p: Array<F32>, dq: Array<F32>, dk: Array<F32>, v: Array<F32>, r: T.D2) -> S8:
718 match r:
719 case T.D2{g, q, +s}: S8{q, k, d, g, p, dq, Array.set(F32, dk, e, s), v}
720
721def b3_v(+e: U32, q: Array<F32>, k: Array<F32>, g: Array<F32>, dq: Array<F32>, dk: Array<F32>, v: Array<F32>, r: T.D2) -> S8:
722 match r:
723 case T.D2{p, d, +s}: S8{q, k, d, g, p, dq, dk, Array.set(F32, v, e, s)}
724
725def b3_sel(a: Bool, b: Bool, +e: U32, +nw: U32, +nk: U32, +c: EAC, s: S8) -> S8:
726 match a b s:
727 case True{} _ S8{q, k, d, g, p, dq, dk, v}: b3_q(e, q, d, p, dq, dk, v, pv_val(e, c, g, k))
728 case False{} True{} S8{q, k, d, g, p, dq, dk, v}: b3_k((e - nw : U32), k, d, p, dq, dk, v, kv_val((e - nw : U32), c, g, q))
729 case False{} False{} S8{q, k, d, g, p, dq, dk, v}: b3_v(((e - nw : U32) - nk : U32), q, k, g, dq, dk, v, kv_val(((e - nw : U32) - nk : U32), c, p, d))
730
731def b3_cell(+e: U32, +c: EAC, s: S8) -> S8:
732 match c:
733 case EAC{+n, +t, +t32, +h, +g, +gn, +hd, +hd32, +w, +kvw, +sc, +nw, +nk, +tr}: b3_sel(U32.is_lt(e, nw), U32.is_lt(e, (nw + nk : U32)), e, nw, nk, c, s)
734
735def b3_loop(k: Nat, +e: U32, +l: U32, +c: EAC, s: S8) -> S8:
736 match k:
737 case 0n: s
738 case 1n+q: b3_loop(q, (e + l : U32), l, c, b3_cell(e, c, s))
739
740def b3_o(s: S8) -> TG.O4:
741 match s:
742 case S8{q, k, d, g, p, dq, dk, v}: TG.O4{p, dq, dk, v}
743
744# x1 = q, x2 = k, x3 = dO, x4 = G; P read from the first output (unchanged), dQ, dK and dV (over V) written
745def lf_b3(+t: U32, +l: U32, +c: EAC, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O4) -> TG.O4:
746 match o:
747 case TG.O4{p, dq, dk, v}: b3_o(b3_loop(TG.share(t, eac_n(c), l), t, l, c, S8{x1, x2, x3, x4, p, dq, dk, v}))
748
749# the sizes of a bang of cnt cells (or rows)
750def eac(+cnt: Nat, +n: Nat, +w: Nat, +kvw: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32) -> EAC:
751 EAC{U32.from_nat(cnt), t, U32.from_nat(t), U32.from_nat(Nat.mul(kv, g)), U32.from_nat(g), g, hd, U32.from_nat(hd), U32.from_nat(w), U32.from_nat(kvw), sc, U32.from_nat(Nat.mul(n, w)), U32.from_nat(Nat.mul(n, kvw)), U32.div((U32.from_nat(t) * (U32.from_nat(t) + 1 : U32) : U32), 2)}
752
753# P's cells (b·h·t(t + 1)/2), the score cells dealt in pairs of rows (b·h·ceil(t/2)·(t + 1)) and P's rows (b·h·t)
754def pcells(+b: Nat, +t: Nat, +kv: Nat, +g: Nat) -> Nat:
755 Nat.mul(Nat.mul(b, Nat.mul(kv, g)), Nat.div(Nat.mul(t, 1n+t), 2n))
756
757def tcells(+b: Nat, +t: Nat, +kv: Nat, +g: Nat) -> Nat:
758 Nat.mul(Nat.mul(Nat.mul(b, Nat.mul(kv, g)), Nat.div(1n+t, 2n)), 1n+t)
759
760def prows(+b: Nat, +t: Nat, +kv: Nat, +g: Nat) -> Nat:
761 Nat.mul(Nat.mul(b, Nat.mul(kv, g)), t)
762
763def af3(-n: Nat, -w: Nat, -kvw: Nat, q: Array<F32>, k: Array<F32>, r: TG.R1) -> T.Fwd<Unit, TA.Mat<n, w>, T.AttnS>:
764 match r:
765 case TG.R1{p, v, x3, x4, o}: T.Fwd{Unit{}, TA.Mat{o}, T.AttnS{q, k, v}}
766
767def af2(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, q: Array<F32>, k: Array<F32>, v: Array<F32>, o: Array<F32>, r: TG.R1) -> T.Fwd<Unit, TA.Mat<n, w>, T.AttnS>:
768 match r:
769 case TG.R1{x1, x2, x3, x4, p}: af3(n, w, kvw, q, k, TG.run1(~EAC, ~lf_pv, TG.depth(Nat.mul(n, w)), eac(Nat.mul(n, w), n, w, kvw, t, kv, g, hd, sc), p, v, nn(Unit{}), nn(Unit{}), o))
770
771def af1(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, k: Array<F32>, v: Array<F32>, r: TG.R2) -> T.Fwd<Unit, TA.Mat<n, w>, T.AttnS>:
772 match r:
773 case TG.R2{q, kt, x3, x4, TG.O2{p, o}}: af2(n, w, kvw, b, t, kv, g, hd, sc, q, k, v, o, TG.run1(~EAC, ~lf_sm, TG.depth(prows(b, t, kv, g)), eac(prows(b, t, kv, g), n, w, kvw, t, kv, g, hd, sc), nn(Unit{}), nn(Unit{}), nn(Unit{}), nn(Unit{}), p))
774
775def af0(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, q: Array<F32>, v: Array<F32>, r: TG.R1) -> T.Fwd<Unit, TA.Mat<n, w>, T.AttnS>:
776 match r:
777 case TG.R1{k, x2, x3, x4, kt}: af1(n, w, kvw, b, t, kv, g, hd, sc, k, v, TG.run2_new(~EAC, ~lf_sc, TG.depth(tcells(b, t, kv, g)), eac(tcells(b, t, kv, g), n, w, kvw, t, kv, g, hd, sc), q, kt, nn(Unit{}), nn(Unit{}), TA.cap_depth(pcells(b, t, kv, g)), TA.cap_depth(Nat.mul(n, w))))
778
779def attn_f(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, x: T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>) -> T.Fwd<Unit, TA.Mat<n, w>, T.AttnS>:
780 match x:
781 case T.Two{TA.Mat{q}, T.Two{TA.Mat{k}, TA.Mat{v}}}: af0(n, w, kvw, b, t, kv, g, hd, sc, q, v, TG.run1_new(~EAC, ~lf_tk, TG.depth(Nat.mul(n, kvw)), eac(Nat.mul(n, kvw), n, w, kvw, t, kv, g, hd, sc), k, nn(Unit{}), nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, kvw))))
782
783def ab4(-n: Nat, -w: Nat, -kvw: Nat, r: TG.R4) -> T.Bwd<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>>:
784 match r:
785 case TG.R4{x1, x2, x3, x4, TG.O4{p, dq, dk, dv}}: T.Bwd{Unit{}, T.Two{TA.Mat{dq}, T.Two{TA.Mat{dk}, TA.Mat{dv}}}, Unit{}}
786
787# backward 3 writes dK over kᵀ and dV over v (neither read any more)
788def ab3(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, q: Array<F32>, k: Array<F32>, v: Array<F32>, d: Array<F32>, dq: Array<F32>, kt: Array<F32>, r: TG.R2) -> T.Bwd<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>>:
789 match r:
790 case TG.R2{x1, x2, x3, x4, TG.O2{p, gb}}: ab4(n, w, kvw, TG.run4(~EAC, ~lf_b3, TG.depth(Nat.add(Nat.mul(n, w), Nat.mul(2n, Nat.mul(n, kvw)))), eac(Nat.add(Nat.mul(n, w), Nat.mul(2n, Nat.mul(n, kvw))), n, w, kvw, t, kv, g, hd, sc), q, k, d, gb, p, dq, kt, v))
791
792def ab2(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, k: Array<F32>, v: Array<F32>, dq: Array<F32>, r: TG.R2) -> T.Bwd<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>>:
793 match r:
794 case TG.R2{q, kt, vt, d, TG.O2{p, gb}}: ab3(n, w, kvw, b, t, kv, g, hd, sc, q, k, v, d, dq, kt, TG.run2(~EAC, ~lf_gr, TG.depth(prows(b, t, kv, g)), eac(prows(b, t, kv, g), n, w, kvw, t, kv, g, hd, sc), nn(Unit{}), nn(Unit{}), nn(Unit{}), nn(Unit{}), p, gb))
795
796def ab1(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, q: Array<F32>, d: Array<F32>, r: TG.R3) -> T.Bwd<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>>:
797 match r:
798 case TG.R3{k, v, x3, x4, TG.O3{kt, vt, dq}}: ab2(n, w, kvw, b, t, kv, g, hd, sc, k, v, dq, TG.run2_new(~EAC, ~lf_b1, TG.depth(tcells(b, t, kv, g)), eac(tcells(b, t, kv, g), n, w, kvw, t, kv, g, hd, sc), q, kt, vt, d, TA.cap_depth(pcells(b, t, kv, g)), TA.cap_depth(pcells(b, t, kv, g))))
799
800def attn_b(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, s: T.AttnS, dy: TA.Mat<n, w>) -> T.Bwd<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>>:
801 match s dy:
802 case T.AttnS{q, k, v} TA.Mat{d}: ab1(n, w, kvw, b, t, kv, g, hd, sc, q, d, TG.run3_new(~EAC, ~lf_tkv, TG.depth(Nat.mul(2n, Nat.mul(n, kvw))), eac(Nat.mul(2n, Nat.mul(n, kvw)), n, w, kvw, t, kv, g, hd, sc), k, v, nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, kvw)), TA.cap_depth(Nat.mul(n, kvw)), TA.cap_depth(Nat.mul(n, w))))
803
804# causal attention with grouped heads over b sequences of t positions (main.bend's Attention)
805def Attention(+n: Nat, +w: Nat, +kvw: Nat, +b: Nat, +t: Nat, +kv: Nat, +g: Nat, +hd: Nat, +sc: F32, -e1: {n == Nat.mul(b, t) : Nat}, -e2: {w == Nat.mul(Nat.mul(kv, g), hd) : Nat}, -e3: {kvw == Nat.mul(kv, hd) : Nat}) -> T.Layer<Unit, T.Two<TA.Mat<n, w>, T.Two<TA.Mat<n, kvw>, TA.Mat<n, kvw>>>, TA.Mat<n, w>, T.AttnS>:
806 T.Layer{p => x => attn_f(n, w, kvw, b, t, kv, g, hd, sc, x), p => s => dy => g2 => attn_b(n, w, kvw, b, t, kv, g, hd, sc, s, dy), op => p => g2 => st => T.no_step(Unit, op, p, g2, st), u => Unit{}, p => T.PL{p, Nil{}}, p => xs => off => T.PA{p, xs, off}, p => xs => off => T.PA{p, xs, off}, u => 0}
807
808# ---- MSE on the device. main.bend's MSE writes each gradient cell 2 (y - t) / (n·c) over y and sums the squares with
809# tree_sum, whose passes add neighbouring pairs; its first p passes carry no odd cell when 2^p divides the count, so a
810# leaf that sums a block of 2^p squares as a balanced tree of pairs gets exactly the number those passes leave at the
811# block's place. The leaves do that on the device (y written in place), and the host finishes tree_sum over the
812# count / 2^p block sums: the same bits as main.bend's MSE, with only the block sums read back. ----
813
814type EMS is Data:
815 EMS{nb: U32, p: Nat, bs: U32, kk: F32}
816
817type MS is Type:
818 MS{y: Array<F32>, t: Array<F32>, s: F32}
819
820def ms_add(+s: F32, r: MS) -> MS:
821 match r:
822 case MS{y, t, +s2}: MS{y, t, (s + s2 : F32)}
823
824def ms_then(r: MS, k: @+s: F32 -> Array<F32> -> Array<F32> -> MS) -> MS:
825 match r:
826 case MS{y, t, +s}: k(s, y, t)
827
828def ms_cell(+i: U32, +kk: F32, ry: Array<F32> & F32, rt: Array<F32> & F32) -> MS:
829 match ry rt:
830 case Tuple{y, +a} Tuple{t, +b}: MS{Array.set(F32, y, i, ((a - b : F32) * kk : F32)), t, ((a - b : F32) * (a - b : F32) : F32)}
831
832# the squares of the block of 2^p cells from i summed as a balanced tree of pairs (h = 2^(p-1)), gradients written
833def ms_blk(p: Nat, +i: U32, +h: U32, +kk: F32, y: Array<F32>, t: Array<F32>) -> MS:
834 match p:
835 case 0n: ms_cell(i, kk, Array.get(F32, y, i), Array.get(F32, t, i))
836 case 1n++q: ms_then(ms_blk(q, i, U32.shr(h), kk, y, t), +s => y2 => t2 => ms_add(s, ms_blk(q, (i + h : U32), U32.shr(h), kk, y2, t2)))
837
838def ms_put(+t: U32, ps: Array<F32>, r: MS) -> TG.O2:
839 match r:
840 case MS{y, tt, +s}: TG.O2{y, Array.set(F32, ps, t, s)}
841
842def ms_if(ok: Bool, +t: U32, +p: Nat, +bs: U32, +kk: F32, tg: Array<F32>, y: Array<F32>, ps: Array<F32>) -> TG.O2:
843 match ok:
844 case True{}: ms_put(t, ps, ms_blk(p, (t * bs : U32), U32.shr(bs), kk, y, tg))
845 case False{}: TG.O2{y, ps}
846
847# x1 = the target; y written in place with the gradient, ps[t] = leaf t's block sum (one leaf a block)
848def lf_mse(+t: U32, +l: U32, +e: EMS, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
849 match e o:
850 case EMS{+nb, +p, +bs, +kk} TG.O2{y, ps}: ms_if(U32.is_lt(t, nb), t, p, bs, kk, x1, y, ps)
851
852# the largest p <= k with 2^p dividing m (m > 0)
853def ms_p(k: Nat, +m: U32) -> Nat:
854 match k:
855 case 0n: 0n
856 case 1n+q: Bool.pick(Nat, U32.is_eq(U32.mod(m, 2), 0), 1n+ms_p(q, U32.div(m, 2)), 0n)
857
858def mse_fin(-n: Nat, -c: Nat, +nb: U32, +nc: F32, q: TG.R2) -> T.LossOut<TA.Mat<n, c>>:
859 match q:
860 case TG.R2{x1, x2, x3, x4, TG.O2{y, ps}}: T.LossOut{(T.tree_sum(nb, ps) / nc : F32), TA.Mat{y}}
861
862def mse_go(+n: Nat, +c: Nat, +m: U32, +p: Nat, y: Array<F32>, t: Array<F32>) -> T.LossOut<TA.Mat<n, c>>:
863 mse_fin(n, c, U32.shrn(m, p), F32.from_nat(Nat.mul(n, c)), TG.run2(~EMS, ~lf_mse, TA.cap_depth(U32.to_nat(U32.shrn(m, p))), EMS{U32.shrn(m, p), p, U32.pow(2, p), (2.0 / F32.from_nat(Nat.mul(n, c)) : F32)}, t, nn(Unit{}), nn(Unit{}), nn(Unit{}), y, T.zeros_a(U32.to_nat(U32.shrn(m, p)))))
864
865# mean squared error over every number (main.bend's MSE, the same bits)
866def MSE(+n: Nat, +c: Nat, y: TA.Mat<n, c>, t: TA.Mat<n, c>) -> T.LossOut<TA.Mat<n, c>>:
867 match y t:
868 case TA.Mat{ya} TA.Mat{ta}: mse_go(n, c, U32.from_nat(Nat.mul(n, c)), ms_p(6n, U32.from_nat(Nat.mul(n, c))), ya, ta)
869
870# ---- ids and labels on the device: a list of n Nats as an array of n cells, each holding the id's U32 word as an F32's
871# bits (a bit copy, not a conversion, so every U32 id is exact); the count is how many of the list's first n there are ----
872
873def u2f(x: U32) -> F32:
874 match x:
875 case U32{w}: F32{w}
876
877def f2u(x: F32) -> U32:
878 match x:
879 case F32{w}: U32{w}
880
881type IA is Type:
882 IA{a: Array<F32>, k: Nat}
883
884def ia_go(left: Nat, ids: List<&2, Nat>, +i: U32, k: Nat, a: Array<F32>) -> IA:
885 match left ids:
886 case 1n+q Con{+id, t}: ia_go(q, t, (i + 1 : U32), 1n+k, Array.set(F32, a, i, u2f(U32.from_nat(id))))
887 case _ _: IA{a, k}
888
889def ids_arr(+n: Nat, ids: List<&2, Nat>) -> IA:
890 ia_go(n, ids, 0, 0n, T.zeros_a(n))
891
892# ---- Embedding: a leaf a column j of the table (d of them), each looping over the ids in order: the forward pass
893# copies e[id_i, j] into row i, the backward adds dy[i, j] into g[id_i, j] as main.bend's emb_add does (g + dy), one id
894# after the other, so every cell of the gradient gets its rows added in main.bend's order, with no two leaves writing
895# one cell. Neighbouring lanes read neighbouring cells. ----
896
897type EEM is Data:
898 EEM{k: Nat, d: U32}
899
900type ES3 is Type:
901 ES3{ids: Array<F32>, a: Array<F32>, o: Array<F32>}
902
903def ef_3(+i: U32, +j: U32, +d: U32, ids: Array<F32>, o: Array<F32>, r: Array<F32> & F32) -> ES3:
904 match r:
905 case Tuple{e, +x}: ES3{ids, e, Array.set(F32, o, ((i * d : U32) + j : U32), x)}
906
907def ef_2(+i: U32, +j: U32, +d: U32, e: Array<F32>, o: Array<F32>, r: Array<F32> & F32) -> ES3:
908 match r:
909 case Tuple{ids, +v}: ef_3(i, j, d, ids, o, Array.get(F32, e, ((f2u(v) * d : U32) + j : U32)))
910
911# o[i, j] = e[ids[i], j]
912def ef_cell(+i: U32, +j: U32, +d: U32, st: ES3) -> ES3:
913 match st:
914 case ES3{ids, e, o}: ef_2(i, j, d, e, o, Array.get(F32, ids, i))
915
916def ef_loop(k: Nat, +i: U32, +j: U32, +d: U32, st: ES3) -> ES3:
917 match k:
918 case 0n: st
919 case 1n+q: ef_loop(q, (i + 1 : U32), j, d, ef_cell(i, j, d, st))
920
921def es3_o(st: ES3) -> Array<F32>:
922 match st:
923 case ES3{ids, a, o}: o
924
925def ef_if(ok: Bool, +t: U32, +k: Nat, +d: U32, e: Array<F32>, ids: Array<F32>, o: Array<F32>) -> Array<F32>:
926 match ok:
927 case True{}: es3_o(ef_loop(k, 0, t, d, ES3{ids, e, o}))
928 case False{}: o
929
930# x1 = the table, x2 = the ids; o (made on the device, zeros) gets the rows
931def lf_embf(+t: U32, +l: U32, +e: EEM, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: Array<F32>) -> Array<F32>:
932 match e:
933 case EEM{+k, +d}: ef_if(U32.is_lt(t, d), t, k, d, x1, x2, o)
934
935def eb_4(+gi: U32, ids: Array<F32>, g: Array<F32>, +a: F32, r: Array<F32> & F32) -> ES3:
936 match r:
937 case Tuple{dy, +b}: ES3{ids, dy, Array.set(F32, g, gi, (a + b : F32))}
938
939def eb_3(+i: U32, +j: U32, +d: U32, +gi: U32, ids: Array<F32>, dy: Array<F32>, r: Array<F32> & F32) -> ES3:
940 match r:
941 case Tuple{g, +a}: eb_4(gi, ids, g, a, Array.get(F32, dy, ((i * d : U32) + j : U32)))
942
943def eb_2(+i: U32, +j: U32, +d: U32, dy: Array<F32>, g: Array<F32>, r: Array<F32> & F32) -> ES3:
944 match r:
945 case Tuple{ids, +v}: eb_3(i, j, d, ((f2u(v) * d : U32) + j : U32), ids, dy, Array.get(F32, g, ((f2u(v) * d : U32) + j : U32)))
946
947# g[ids[i], j] = g[ids[i], j] + dy[i, j]
948def eb_cell(+i: U32, +j: U32, +d: U32, st: ES3) -> ES3:
949 match st:
950 case ES3{ids, dy, g}: eb_2(i, j, d, dy, g, Array.get(F32, ids, i))
951
952def eb_loop(k: Nat, +i: U32, +j: U32, +d: U32, st: ES3) -> ES3:
953 match k:
954 case 0n: st
955 case 1n+q: eb_loop(q, (i + 1 : U32), j, d, eb_cell(i, j, d, st))
956
957def eb_if(ok: Bool, +t: U32, +k: Nat, +d: U32, dy: Array<F32>, ids: Array<F32>, g: Array<F32>) -> Array<F32>:
958 match ok:
959 case True{}: es3_o(eb_loop(k, 0, t, d, ES3{ids, dy, g}))
960 case False{}: g
961
962# x1 = dy, x2 = the ids; the gradient g, in place
963def lf_embb(+t: U32, +l: U32, +e: EEM, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, g: Array<F32>) -> Array<F32>:
964 match e:
965 case EEM{+k, +d}: eb_if(U32.is_lt(t, d), t, k, d, x1, x2, g)
966
967def emb_f3(-n: Nat, -v: Nat, -d: Nat, +ids: List<&2, Nat>, q: TG.R1) -> T.Fwd<TA.Mat<v, d>, TA.Mat<n, d>, List<&2, Nat>>:
968 match q:
969 case TG.R1{x1, x2, x3, x4, o}: T.Fwd{TA.Mat{x1}, TA.Mat{o}, ids}
970
971def emb_f2(+n: Nat, +v: Nat, +d: Nat, +ids: List<&2, Nat>, ea: Array<F32>, r: IA) -> T.Fwd<TA.Mat<v, d>, TA.Mat<n, d>, List<&2, Nat>>:
972 match r:
973 case IA{ia, +k}: emb_f3(n, v, d, ids, TG.run1_new(~EEM, ~lf_embf, TA.cap_depth(d), EEM{k, U32.from_nat(d)}, ea, ia, nn(Unit{}), nn(Unit{}), TA.cap_depth(Nat.mul(n, d))))
974
975def emb_f(+n: Nat, +v: Nat, +d: Nat, +ids: List<&2, Nat>, e: TA.Mat<v, d>) -> T.Fwd<TA.Mat<v, d>, TA.Mat<n, d>, List<&2, Nat>>:
976 match e:
977 case TA.Mat{ea}: emb_f2(n, v, d, ids, ea, ids_arr(n, ids))
978
979def emb_b3(-v: Nat, -d: Nat, e: TA.Mat<v, d>, q: TG.R1) -> T.Bwd<TA.Mat<v, d>, List<&2, Nat>>:
980 match q:
981 case TG.R1{x1, x2, x3, x4, g}: T.Bwd{e, Nil{}, TA.Mat{g}}
982
983def emb_b2(+v: Nat, +d: Nat, e: TA.Mat<v, d>, da: Array<F32>, ga: Array<F32>, r: IA) -> T.Bwd<TA.Mat<v, d>, List<&2, Nat>>:
984 match r:
985 case IA{ia, +k}: emb_b3(v, d, e, TG.run1(~EEM, ~lf_embb, TA.cap_depth(d), EEM{k, U32.from_nat(d)}, da, ia, nn(Unit{}), nn(Unit{}), ga))
986
987def emb_bwd(+n: Nat, +v: Nat, +d: Nat, e: TA.Mat<v, d>, ids: List<&2, Nat>, dy: TA.Mat<n, d>, g: TA.Mat<v, d>) -> T.Bwd<TA.Mat<v, d>, List<&2, Nat>>:
988 match dy g:
989 case TA.Mat{da} TA.Mat{ga}: emb_b2(v, d, e, da, ga, ids_arr(n, ids))
990
991# a table of v rows of d numbers, looked up by n ids (main.bend's Embedding, the same bits); its step on the device
992def Embedding(+n: Nat, +v: Nat, +d: Nat) -> T.Layer<TA.Mat<v, d>, List<&2, Nat>, TA.Mat<n, d>, List<&2, Nat>>:
993 T.Layer{p => x => emb_f(n, v, d, x, p), p => s => dy => g => emb_bwd(n, v, d, p, s, dy, g), op => p => g => st => mat_step(v, d, op, p, g, st), u => TA.Mat.zeros(v, d), p => T.Mat.dump(v, d, p), p => xs => off => T.Mat.restore(v, d, p, xs, off), p => xs => off => T.Mat.store(v, d, p, xs, off), u => U32.from_nat(Nat.mul(v, d))}
994
995# ---- cross-entropy on the device, the same numbers as main.bend's CrossEntropy (but CUDA's exp on the GPU), in four
996# bangs; only 3 numbers a row come back to the host, and the gradient stays on the device:
997# 1. a leaf a row: its max m by main.bend's pmax0 (the same order of comparisons) and x[y] - m;
998# 2. a leaf for each block of 2^p cells of a row (2^p dividing c, p <= 9): e = exp(x - m) written over each cell, as
999# pexp writes it, and the block's sum as a balanced tree of pairs. tree_sum's first p passes over a row add exactly
1000# these pairs (no odd cell is carried while 2^p divides the count), so a block's sum is the number they leave there;
1001# 3. a leaf a row: tree_sum's remaining passes over its c / 2^p block sums (tnode below), which is s, cesm's sum;
1002# 4. a leaf for 2^14 cells at a time: ((e / s) - [j == y]) / n, pdiv's and ce_g's two expressions.
1003# The host takes log(s) and adds the rows' terms in order from 0.0, as ce_rows does. Rows past the labels are left as
1004# they are, as in main.bend; labels past the n rows are not read (main.bend would write past the matrix). ----
1005
1006type TS is Type:
1007 TS{a: Array<F32>, s: F32}
1008
1009def ts_leaf(r: Array<F32> & F32) -> TS:
1010 match r:
1011 case Tuple{a, +x}: TS{a, x}
1012
1013def ts_plus(+s: F32, r: TS) -> TS:
1014 match r:
1015 case TS{a, +s2}: TS{a, (s + s2 : F32)}
1016
1017def ts_then(r: TS, k: @+s: F32 -> Array<F32> -> TS) -> TS:
1018 match r:
1019 case TS{a, +s}: k(s, a)
1020
1021# tree_sum of the len cells from lo, read in place: a node l passes above the cells sums its left child (the first h =
1022# 2^(l-1) cells) and its right child (the rest) when there is a rest (big = h < len), and is its left child otherwise
1023# (tree_sum's odd last cell carried up a pass)
1024def tnode(l: Nat, big: Bool, +lo: U32, +len: U32, +h: U32, a: Array<F32>) -> TS:
1025 match l big:
1026 case 0n _: ts_leaf(Array.get(F32, a, lo))
1027 case 1n++q True{}: ts_then(tnode(q, U32.is_lt(U32.shr(h), h), lo, h, U32.shr(h), a), +s => a2 => ts_plus(s, tnode(q, U32.is_lt(U32.shr(h), (len - h : U32)), (lo + h : U32), (len - h : U32), U32.shr(h), a2)))
1028 case 1n+q False{}: tnode(q, U32.is_lt(U32.shr(h), len), lo, len, U32.shr(h), a)
1029
1030# the passes tree_sum makes over m cells: the smallest l with 2^l >= m (f is fuel)
1031def ts_lv(f: Nat, +m: U32, +p: U32) -> Nat:
1032 match f:
1033 case 0n: 0n
1034 case 1n+q: Bool.pick(Nat, U32.is_lt(p, m), 1n+ts_lv(q, m, (p * 2 : U32)), 0n)
1035
1036# the host's part, row by row in order: loss - ((x[y] - m) - log(s)), or main.bend's clamped term for a label past the
1037# last class
1038type LS is Type:
1039 LS{a: Array<F32>, l: F32}
1040
1041def ce_t2(ok: Bool, +loss: F32, +xm: F32, r: Array<F32> & F32) -> LS:
1042 match ok r:
1043 case True{} Tuple{a, +s}: LS{a, (loss - (xm - F32.log(s) : F32) : F32)}
1044 case False{} Tuple{a, s}: LS{a, (loss - F32.log(F32.max(0.0, 0.000000000001)) : F32)}
1045
1046def ce_g4(+n: Nat, -c: Nat, z: Array<F32>, r: LS) -> T.LossOut<TA.Mat<n, c>>:
1047 match r:
1048 case LS{a, +l}: T.LossOut{(l / F32.from_nat(n) : F32), TA.Mat{z}}
1049
1050# 1. a leaf a row: st[3t] = x[y] - m, st[3t + 1] = m (x[y] read only when y is a class)
1051type EC1 is Data:
1052 EC1{k: U32, c1: Nat, c32: U32}
1053
1054def c1_3(+t: U32, +m: F32, st: Array<F32>, r: Array<F32> & F32) -> Array<F32>:
1055 match r:
1056 case Tuple{z, +xy}: Array.set(F32, Array.set(F32, st, (t * 3 : U32), (xy - m : F32)), ((t * 3 : U32) + 1 : U32), m)
1057
1058def c1_2(ok: Bool, +t: U32, +off: U32, +y: U32, st: Array<F32>, r: T.AS) -> Array<F32>:
1059 match ok r:
1060 case True{} T.AS{z, +m}: c1_3(t, m, st, Array.get(F32, z, (off + y : U32)))
1061 case False{} T.AS{z, +m}: c1_3(t, m, st, Tuple{z, 0.0})
1062
1063def c1_1(+t: U32, +off: U32, +c1: Nat, +c32: U32, z: Array<F32>, st: Array<F32>, r: Array<F32> & F32) -> Array<F32>:
1064 match r:
1065 case Tuple{la, +yv}: c1_2(U32.is_lt(f2u(yv), c32), t, off, f2u(yv), st, T.pmax0(c1, off, Array.get(F32, z, off)))
1066
1067def c1_if(ok: Bool, +t: U32, +c1: Nat, +c32: U32, lab: Array<F32>, z: Array<F32>, st: Array<F32>) -> Array<F32>:
1068 match ok:
1069 case True{}: c1_1(t, (t * c32 : U32), c1, c32, z, st, Array.get(F32, lab, t))
1070 case False{}: st
1071
1072def lf_c1(+t: U32, +l: U32, +e: EC1, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, st: Array<F32>) -> Array<F32>:
1073 match e:
1074 case EC1{+k, +c1, +c32}: c1_if(U32.is_lt(t, k), t, c1, c32, x1, x2, st)
1075
1076# 2. the 2^l cells from lo: e = exp(x - m) written over each, and their sum as a balanced tree of pairs (h = 2^(l-1))
1077def tsx_leaf(+lo: U32, +m: F32, r: Array<F32> & F32) -> TS:
1078 match r:
1079 case Tuple{a, +x}:
1080 +e = F32.exp((x - m : F32))
1081 TS{Array.set(F32, a, lo, e), e}
1082
1083def tsx(l: Nat, +lo: U32, +h: U32, +m: F32, a: Array<F32>) -> TS:
1084 match l:
1085 case 0n: tsx_leaf(lo, m, Array.get(F32, a, lo))
1086 case 1n++q: ts_then(tsx(q, lo, U32.shr(h), m, a), +s => a2 => ts_plus(s, tsx(q, (lo + h : U32), U32.shr(h), m, a2)))
1087
1088# the same over 8 cells in straight-line code (no closure a node): e0..e7 written, then ((e0 + e1) + (e2 + e3)) +
1089# ((e4 + e5) + (e6 + e7)), the balanced tree's three levels
1090def x8_7(+lo: U32, +m: F32, +e0: F32, +e1: F32, +e2: F32, +e3: F32, +e4: F32, +e5: F32, +e6: F32, r: Array<F32> & F32) -> TS:
1091 match r:
1092 case Tuple{a, +x}:
1093 +e7 = F32.exp((x - m : F32))
1094 TS{Array.set(F32, a, (lo + 7 : U32), e7), ((((e0 + e1 : F32) + (e2 + e3 : F32) : F32) + ((e4 + e5 : F32) + (e6 + e7 : F32) : F32)) : F32)}
1095
1096def x8_6(+lo: U32, +m: F32, +e0: F32, +e1: F32, +e2: F32, +e3: F32, +e4: F32, +e5: F32, r: Array<F32> & F32) -> TS:
1097 match r:
1098 case Tuple{a, +x}:
1099 +e = F32.exp((x - m : F32))
1100 x8_7(lo, m, e0, e1, e2, e3, e4, e5, e, Array.get(F32, Array.set(F32, a, (lo + 6 : U32), e), (lo + 7 : U32)))
1101
1102def x8_5(+lo: U32, +m: F32, +e0: F32, +e1: F32, +e2: F32, +e3: F32, +e4: F32, r: Array<F32> & F32) -> TS:
1103 match r:
1104 case Tuple{a, +x}:
1105 +e = F32.exp((x - m : F32))
1106 x8_6(lo, m, e0, e1, e2, e3, e4, e, Array.get(F32, Array.set(F32, a, (lo + 5 : U32), e), (lo + 6 : U32)))
1107
1108def x8_4(+lo: U32, +m: F32, +e0: F32, +e1: F32, +e2: F32, +e3: F32, r: Array<F32> & F32) -> TS:
1109 match r:
1110 case Tuple{a, +x}:
1111 +e = F32.exp((x - m : F32))
1112 x8_5(lo, m, e0, e1, e2, e3, e, Array.get(F32, Array.set(F32, a, (lo + 4 : U32), e), (lo + 5 : U32)))
1113
1114def x8_3(+lo: U32, +m: F32, +e0: F32, +e1: F32, +e2: F32, r: Array<F32> & F32) -> TS:
1115 match r:
1116 case Tuple{a, +x}:
1117 +e = F32.exp((x - m : F32))
1118 x8_4(lo, m, e0, e1, e2, e, Array.get(F32, Array.set(F32, a, (lo + 3 : U32), e), (lo + 4 : U32)))
1119
1120def x8_2(+lo: U32, +m: F32, +e0: F32, +e1: F32, r: Array<F32> & F32) -> TS:
1121 match r:
1122 case Tuple{a, +x}:
1123 +e = F32.exp((x - m : F32))
1124 x8_3(lo, m, e0, e1, e, Array.get(F32, Array.set(F32, a, (lo + 2 : U32), e), (lo + 3 : U32)))
1125
1126def x8_1(+lo: U32, +m: F32, +e0: F32, r: Array<F32> & F32) -> TS:
1127 match r:
1128 case Tuple{a, +x}:
1129 +e = F32.exp((x - m : F32))
1130 x8_2(lo, m, e0, e, Array.get(F32, Array.set(F32, a, (lo + 1 : U32), e), (lo + 2 : U32)))
1131
1132def x8_0(+lo: U32, +m: F32, r: Array<F32> & F32) -> TS:
1133 match r:
1134 case Tuple{a, +x}:
1135 +e = F32.exp((x - m : F32))
1136 x8_1(lo, m, e, Array.get(F32, Array.set(F32, a, lo, e), (lo + 1 : U32)))
1137
1138# tsx over 2^(l+3) cells, its last three levels in blocks of 8
1139def tsx8(l: Nat, +lo: U32, +h: U32, +m: F32, a: Array<F32>) -> TS:
1140 match l:
1141 case 0n: x8_0(lo, m, Array.get(F32, a, lo))
1142 case 1n++q: ts_then(tsx8(q, lo, U32.shr(h), m, a), +s => a2 => ts_plus(s, tsx8(q, (lo + h : U32), U32.shr(h), m, a2)))
1143
1144# a leaf takes the blocks b, b + l, .. of the nb = rows * per; block b is row b / per, cells (b % per) * 2^p on
1145type EC2 is Data:
1146 EC2{nb: U32, per: U32, p: Nat, bsz: U32, c32: U32}
1147
1148type C3S is Type:
1149 C3S{a: Array<F32>, b: Array<F32>, c: Array<F32>}
1150
1151def c2_put(+b: U32, st: Array<F32>, bs: Array<F32>, r: TS) -> C3S:
1152 match r:
1153 case TS{z, +s}: C3S{st, z, Array.set(F32, bs, b, s)}
1154
1155def c2_m(b8: Bool, +b: U32, +lo: U32, +p: Nat, +h: U32, z: Array<F32>, bs: Array<F32>, r: Array<F32> & F32) -> C3S:
1156 match b8 r:
1157 case True{} Tuple{st, +m}: c2_put(b, st, bs, tsx8(Nat.sub(p, 3n), lo, h, m, z))
1158 case False{} Tuple{st, +m}: c2_put(b, st, bs, tsx(p, lo, h, m, z))
1159
1160def c2_cell(+b: U32, +per: U32, +p: Nat, +bsz: U32, +c32: U32, s: C3S) -> C3S:
1161 match s:
1162 case C3S{st, z, bs}: c2_m(Nat.is_le(3n, p), b, ((U32.div(b, per) * c32 : U32) + (U32.mod(b, per) * bsz : U32) : U32), p, U32.shr(bsz), z, bs, Array.get(F32, st, ((U32.div(b, per) * 3 : U32) + 1 : U32)))
1163
1164def c2_loop(k: Nat, +b: U32, +l: U32, +per: U32, +p: Nat, +bsz: U32, +c32: U32, s: C3S) -> C3S:
1165 match k:
1166 case 0n: s
1167 case 1n+q: c2_loop(q, (b + l : U32), l, per, p, bsz, c32, c2_cell(b, per, p, bsz, c32, s))
1168
1169def c2_o(s: C3S) -> TG.O2:
1170 match s:
1171 case C3S{st, z, bs}: TG.O2{z, bs}
1172
1173def lf_c2(+t: U32, +l: U32, +e: EC2, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, o: TG.O2) -> TG.O2:
1174 match e o:
1175 case EC2{+nb, +per, +p, +bsz, +c32} TG.O2{z, bs}: c2_o(c2_loop(TG.share(t, nb, l), t, l, per, p, bsz, c32, C3S{x1, z, bs}))
1176
1177# 3. a leaf a row: st[3t + 2] = s, tree_sum's remaining passes over the row's per block sums
1178type EC3 is Data:
1179 EC3{k: U32, per: U32, tl: Nat, th: U32}
1180
1181def c3_put(+t: U32, st: Array<F32>, r: TS) -> Array<F32>:
1182 match r:
1183 case TS{bs, +s}: Array.set(F32, st, ((t * 3 : U32) + 2 : U32), s)
1184
1185def c3_if(ok: Bool, +t: U32, +per: U32, +tl: Nat, +th: U32, bs: Array<F32>, st: Array<F32>) -> Array<F32>:
1186 match ok:
1187 case True{}: c3_put(t, st, tnode(tl, U32.is_lt(th, per), (t * per : U32), per, th, bs))
1188 case False{}: st
1189
1190def lf_c3(+t: U32, +l: U32, +e: EC3, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, st: Array<F32>) -> Array<F32>:
1191 match e:
1192 case EC3{+k, +per, +tl, +th}: c3_if(U32.is_lt(t, k), t, per, tl, th, x1, st)
1193
1194# 4. element-wise over the first k rows: z[i] = ((e / s) - [j == y]) / nf, j = i % c, s and y of row i / c
1195type EC4 is Data:
1196 EC4{n: U32, c32: U32, nf: F32}
1197
1198def c4_4(+i: U32, +j: U32, +y: U32, +s: F32, +nf: F32, lab: Array<F32>, st: Array<F32>, r: Array<F32> & F32) -> C3S:
1199 match r:
1200 case Tuple{z, +x}: C3S{lab, st, Array.set(F32, z, i, (((x / s : F32) - T.hit(U32.is_eq(j, y)) : F32) / nf : F32))}
1201
1202def c4_3(+i: U32, +j: U32, +y: U32, +nf: F32, lab: Array<F32>, z: Array<F32>, r: Array<F32> & F32) -> C3S:
1203 match r:
1204 case Tuple{st, +s}: c4_4(i, j, y, s, nf, lab, st, Array.get(F32, z, i))
1205
1206def c4_2(+i: U32, +row: U32, +j: U32, +nf: F32, st: Array<F32>, z: Array<F32>, r: Array<F32> & F32) -> C3S:
1207 match r:
1208 case Tuple{lab, +yv}: c4_3(i, j, f2u(yv), nf, lab, z, Array.get(F32, st, ((row * 3 : U32) + 2 : U32)))
1209
1210def c4_cell(+i: U32, +c32: U32, +nf: F32, s: C3S) -> C3S:
1211 match s:
1212 case C3S{lab, st, z}: c4_2(i, U32.div(i, c32), (i - (U32.div(i, c32) * c32 : U32) : U32), nf, st, z, Array.get(F32, lab, U32.div(i, c32)))
1213
1214def c4_loop(k: Nat, +i: U32, +l: U32, +c32: U32, +nf: F32, s: C3S) -> C3S:
1215 match k:
1216 case 0n: s
1217 case 1n+q: c4_loop(q, (i + l : U32), l, c32, nf, c4_cell(i, c32, nf, s))
1218
1219def c3s_c(s: C3S) -> Array<F32>:
1220 match s:
1221 case C3S{a, b, c}: c
1222
1223def lf_c4(+t: U32, +l: U32, +e: EC4, x1: Array<F32>, x2: Array<F32>, x3: Array<F32>, x4: Array<F32>, z: Array<F32>) -> Array<F32>:
1224 match e:
1225 case EC4{+n, +c32, +nf}: c3s_c(c4_loop(TG.share(t, n, l), t, l, c32, nf, C3S{x1, x2, z}))
1226
1227# the host's part over st, row by row
1228def cx_t1(ok: Bool, +i: U32, +loss: F32, r: Array<F32> & F32) -> LS:
1229 match r:
1230 case Tuple{a, +xm}: ce_t2(ok, loss, xm, Array.get(F32, a, ((i * 3 : U32) + 2 : U32)))
1231
1232def cx_ls(left: Nat, ls: List<&2, Nat>, +i: U32, +c: Nat, r: LS) -> LS:
1233 match left ls r:
1234 case 1n+q Con{+y, t} LS{a, +loss}: cx_ls(q, t, (i + 1 : U32), c, cx_t1(Nat.is_le(1n+y, c), i, loss, Array.get(F32, a, (i * 3 : U32))))
1235 case _ _ r: r
1236
1237def cx_5(+n: Nat, +c: Nat, +labels: List<&2, Nat>, +k: Nat, q: TG.R1) -> T.LossOut<TA.Mat<n, c>>:
1238 match q:
1239 case TG.R1{x1, x2, x3, x4, z}: ce_g4(n, c, z, cx_ls(k, labels, 0, c, LS{x2, 0.0}))
1240
1241def cx_4(+n: Nat, +c: Nat, +labels: List<&2, Nat>, +k: Nat, la: Array<F32>, z: Array<F32>, q: TG.R1) -> T.LossOut<TA.Mat<n, c>>:
1242 match q:
1243 case TG.R1{x1, x2, x3, x4, st}: cx_5(n, c, labels, k, TG.run1(~EC4, ~lf_c4, TG.depth(Nat.mul(k, c)), EC4{U32.from_nat(Nat.mul(k, c)), U32.from_nat(c), F32.from_nat(n)}, la, st, nn(Unit{}), nn(Unit{}), z))
1244
1245def cx_3(+n: Nat, +c: Nat, +labels: List<&2, Nat>, +k: Nat, +per: U32, la: Array<F32>, q: TG.R2) -> T.LossOut<TA.Mat<n, c>>:
1246 match q:
1247 case TG.R2{st, x2, x3, x4, TG.O2{z, bs}}: cx_4(n, c, labels, k, la, z, TG.run1(~EC3, ~lf_c3, TA.cap_depth(k), EC3{U32.from_nat(k), per, ts_lv(32n, per, 1), U32.shr(U32.pow(2, ts_lv(32n, per, 1)))}, bs, nn(Unit{}), nn(Unit{}), nn(Unit{}), st))
1248
1249def cx_2(+n: Nat, +c: Nat, +labels: List<&2, Nat>, +k: Nat, +p: Nat, q: TG.R1) -> T.LossOut<TA.Mat<n, c>>:
1250 match q:
1251 case TG.R1{la, z, x3, x4, st}: cx_3(n, c, labels, k, U32.shrn(U32.from_nat(c), p), la, TG.run2(~EC2, ~lf_c2, TG.depth(Nat.mul(k, U32.to_nat(U32.shrn(U32.from_nat(c), p)))), EC2{(U32.from_nat(k) * U32.shrn(U32.from_nat(c), p) : U32), U32.shrn(U32.from_nat(c), p), p, U32.pow(2, p), U32.from_nat(c)}, st, nn(Unit{}), nn(Unit{}), nn(Unit{}), z, T.zeros_a(Nat.mul(k, U32.to_nat(U32.shrn(U32.from_nat(c), p))))))
1252
1253def cx_1(+n: Nat, +c: Nat, +labels: List<&2, Nat>, z: Array<F32>, r: IA) -> T.LossOut<TA.Mat<n, c>>:
1254 match r:
1255 case IA{la, +k}: cx_2(n, c, labels, k, ms_p(9n, U32.from_nat(c)), TG.run1(~EC1, ~lf_c1, TA.cap_depth(n), EC1{U32.from_nat(k), Nat.sub(c, 1n), U32.from_nat(c)}, la, z, nn(Unit{}), nn(Unit{}), T.zeros_a(Nat.mul(3n, n))))
1256
1257# cross-entropy over n rows of c classes against the labels (main.bend's CrossEntropy; its bits but CUDA's exp)
1258def CrossEntropy(+n: Nat, +c: Nat, z: TA.Mat<n, c>, +labels: List<&2, Nat>) -> T.LossOut<TA.Mat<n, c>>:
1259 match z:
1260 case TA.Mat{d}: cx_1(n, c, labels, d, ids_arr(n, labels))
1261
1262# ---- main.bend's names for the same layers (#13) ----
1263#
1264# A program written against torch/main.bend switches to this lane by its import line alone:
1265# `import ../torch/gpu.bend as F` and F.Linear / F.LinearFirst / F.LinearNB, no renames.
1266
1267def Linear(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<T.Lin<i, o>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
1268 LinearF(n, i, o, par)
1269
1270def LinearFirst(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<T.Lin<i, o>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
1271 LinearFirstF(n, i, o, par)
1272
1273def LinearNB(+n: Nat, +i: Nat, +o: Nat, +par: Nat) -> T.Layer<TA.Mat<o, i>, TA.Mat<n, i>, TA.Mat<n, o>, TA.Mat<n, i>>:
1274 LinearNBF(n, i, o, par)