WebAssembly text
WAT emission from the same MIR.
Part of the MIR lane. Every panel below is compiler output.
How to read it#
A stack machine, so the arithmetic reads inside out. Useful as a check on what actually crosses into a sandboxed runtime.
Typed tensors#
Builds two shaped matrices, multiplies them, and reduces the product to a scalar. Exercises shape-bearing types and a reduction.
Faber source
faber format --locale en — English reader surfacemain {
const list<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
const list<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
const tensor<f32, []> seed ← vacua
const tensor<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
const tensor<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
const tensor<f32, [2, 4]> product ← a.matmul(b)
const f32 mean ← product.media()
print mean
}faber format --locale la — canonical Faberincipit {
fixum lista<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
fixum lista<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
fixum tensor<f32, []> seed ← vacua
fixum tensor<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
fixum tensor<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
fixum tensor<f32, [2, 4]> product ← a.matmul(b)
fixum f32 mean ← product.media()
nota mean
}faber format --locale th-TH — Thaiเริ่ม {
คงที่ รายการ<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
คงที่ รายการ<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
คงที่ เทนเซอร์<f32, []> seed ← เซตว่าง
คงที่ เทนเซอร์<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
คงที่ เทนเซอร์<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
คงที่ เทนเซอร์<f32, [2, 4]> product ← a.matmul(b)
คงที่ f32 mean ← product.media()
บันทึก mean
}faber format --locale zh-Hans — Simplified Chinese入口 {
常量 列表<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
常量 列表<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
常量 张量<f32, []> seed ← 空集
常量 张量<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
常量 张量<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
常量 张量<f32, [2, 4]> product ← a.matmul(b)
常量 f32 mean ← product.media()
显示 mean
}faber format --locale zh-Hant — Traditional Chinese入口 {
定值 列表<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
定值 列表<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
定值 張量<f32, []> seed ← 空集
定值 張量<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
定值 張量<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
定值 張量<f32, [2, 4]> product ← a.matmul(b)
定值 f32 mean ← product.media()
註記 mean
}faber format --locale vi — Vietnamesebắt_đầu {
hằng danh_sách<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
hằng danh_sách<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
hằng ten_xo<f32, []> seed ← tập_rỗng
hằng ten_xo<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
hằng ten_xo<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
hằng ten_xo<f32, [2, 4]> product ← a.matmul(b)
hằng f32 mean ← product.media()
ghi_chú mean
}faber format --locale ar — Arabicبداية {
ثابت قائمة<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
ثابت قائمة<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
ثابت موتر<f32, []> seed ← فارغ
ثابت موتر<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
ثابت موتر<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
ثابت موتر<f32, [2, 4]> product ← a.matmul(b)
ثابت f32 mean ← product.media()
اعرض mean
}faber format --locale hi — Hindiआरंभ {
स्थिर सूची<f32> flat_a ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0]
स्थिर सूची<f32> flat_b ← [1.0, 2.0, 3.0, 4.0, 5.0, 6.0, 7.0, 8.0, 9.0, 10.0, 11.0, 12.0]
स्थिर टेंसर<f32, []> seed ← खाली
स्थिर टेंसर<f32, [2, 3]> a ← seed.strue(flat_a, [2, 3])
स्थिर टेंसर<f32, [3, 4]> b ← seed.strue(flat_b, [3, 4])
स्थिर टेंसर<f32, [2, 4]> product ← a.matmul(b)
स्थिर f32 mean ← product.media()
दिखाओ mean
}WebAssembly text — 10 lines in, 104 out (10.4×)
;; Generated by radix MIR WASM text probe - experimental artifact.
(module
(import "faber_rt_v1" "__faber_rt_v1_array_new" (func $__faber_rt_v1_array_new__construct (param i32) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_array_push" (func $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (param i32 i64) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_tensor_new" (func $__faber_rt_v1_tensor_new__construct (param i32) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_tensor_from_flat" (func $__faber_rt_v1_tensor_from_flat__tensor_from_flat_3_aggregate_aggregate_aggregate_to_aggregate (param i32 i32 i32) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_tensor_matmul" (func $__faber_rt_v1_tensor_matmul__tensor_matmul_2_aggregate_aggregate_to_aggregate (param i32 i32) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_tensor_mean" (func $__faber_rt_v1_tensor_mean__tensor_mean_1_aggregate_to_f64 (param i32) (result f64)))
(import "faber_rt_v1" "__faber_rt_v1_diagnostic_nota_f64" (func $__faber_rt_v1_diagnostic_nota_f64 (param f64)))
(func $incipit (export "incipit")
(local $l0 i32)
(local $l1 i32)
(local $l2 i32)
(local $l3 i32)
(local $l4 i32)
(local $l5 i32)
(local $l6 f64)
(local $t0 f64)
(local $t1 f64)
(local $t2 f64)
(local $t3 f64)
(local $t4 f64)
(local $t5 f64)
(local $t6 i32)
(local $t7 f64)
(local $t8 f64)
(local $t9 f64)
(local $t10 f64)
(local $t11 f64)
(local $t12 f64)
(local $t13 f64)
(local $t14 f64)
(local $t15 f64)
(local $t16 f64)
(local $t17 f64)
(local $t18 f64)
(local $t19 i32)
(local $t20 i32)
(local $t21 i32)
(local $t22 i32)
(local $t23 i32)
(local $t24 i32)
(local $t25 i32)
(local $t26 f64)
(local.set $t0 (f64.const 1.0))
(local.set $t1 (f64.const 2.0))
(local.set $t2 (f64.const 3.0))
(local.set $t3 (f64.const 4.0))
(local.set $t4 (f64.const 5.0))
(local.set $t5 (f64.const 6.0))
(local.set $t6 (call $__faber_rt_v1_array_new__construct (i32.const 6)))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t0))))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t1))))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t2))))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t3))))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t4))))
(local.set $t6 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t6) (i64.reinterpret_f64 (local.get $t5))))
(local.set $l0 (local.get $t6))
(local.set $t7 (f64.const 1.0))
(local.set $t8 (f64.const 2.0))
(local.set $t9 (f64.const 3.0))
(local.set $t10 (f64.const 4.0))
(local.set $t11 (f64.const 5.0))
(local.set $t12 (f64.const 6.0))
(local.set $t13 (f64.const 7.0))
(local.set $t14 (f64.const 8.0))
(local.set $t15 (f64.const 9.0))
(local.set $t16 (f64.const 10.0))
(local.set $t17 (f64.const 11.0))
(local.set $t18 (f64.const 12.0))
(local.set $t19 (call $__faber_rt_v1_array_new__construct (i32.const 6)))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t7))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t8))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t9))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t10))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t11))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t12))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t13))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t14))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t15))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t16))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t17))))
(local.set $t19 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t19) (i64.reinterpret_f64 (local.get $t18))))
(local.set $l1 (local.get $t19))
(local.set $t20 (call $__faber_rt_v1_tensor_new__construct (i32.const 6)))
(local.set $l2 (local.get $t20))
(local.set $t21 (call $__faber_rt_v1_array_new__construct (i32.const 4)))
(local.set $t21 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t21) (i64.const 2)))
(local.set $t21 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t21) (i64.const 3)))
(local.set $t22 (call $__faber_rt_v1_tensor_from_flat__tensor_from_flat_3_aggregate_aggregate_aggregate_to_aggregate (local.get $l2) (local.get $l0) (local.get $t21)))
(local.set $l3 (local.get $t22))
(local.set $t23 (call $__faber_rt_v1_array_new__construct (i32.const 4)))
(local.set $t23 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t23) (i64.const 3)))
(local.set $t23 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t23) (i64.const 4)))
(local.set $t24 (call $__faber_rt_v1_tensor_from_flat__tensor_from_flat_3_aggregate_aggregate_aggregate_to_aggregate (local.get $l2) (local.get $l1) (local.get $t23)))
(local.set $l4 (local.get $t24))
(local.set $t25 (call $__faber_rt_v1_tensor_matmul__tensor_matmul_2_aggregate_aggregate_to_aggregate (local.get $l3) (local.get $l4)))
(local.set $l5 (local.get $t25))
(local.set $t26 (call $__faber_rt_v1_tensor_mean__tensor_mean_1_aggregate_to_f64 (local.get $l5)))
(local.set $l6 (local.get $t26))
(call $__faber_rt_v1_diagnostic_nota_f64 (local.get $l6))
(return)
)
)The error channel#
A function that may fail, and a caller that catches. Shows how the ⇥ channel becomes each target's own error idiom.
WebAssembly text does not lower this scenario. That is a measured gap, not an omission — the emitter rejects it rather than producing something that would not run.
Collections and iteration#
A list folded to a total with itera ex. The plainest possible read on how loops lower.
Faber source
faber format --locale en — English reader surfacefn summa(list<int> numeri) → int {
var int total ← 0
for from numeri const n {
total ← total + n
}
return total
}
main {
const list<int> valores ← [1, 2, 3, 4, 5]
print summa(valores)
}faber format --locale la — canonical Faberfunctio summa(lista<numerus> numeri) → numerus {
varia numerus total ← 0
itera ex numeri fixum n {
total ← total + n
}
redde total
}
incipit {
fixum lista<numerus> valores ← [1, 2, 3, 4, 5]
nota summa(valores)
}faber format --locale th-TH — Thaiฟังก์ชัน summa(รายการ<จำนวน> numeri) → จำนวน {
แปร จำนวน total ← 0
วน ออก numeri คงที่ n {
total ← total + n
}
คืน total
}
เริ่ม {
คงที่ รายการ<จำนวน> valores ← [1, 2, 3, 4, 5]
บันทึก summa(valores)
}faber format --locale zh-Hans — Simplified Chinese函数 summa(列表<整数> numeri) → 整数 {
变量 整数 total ← 0
遍历 取自 numeri 常量 n {
total ← total + n
}
返回 total
}
入口 {
常量 列表<整数> valores ← [1, 2, 3, 4, 5]
显示 summa(valores)
}faber format --locale zh-Hant — Traditional Chinese函式 summa(列表<整數> numeri) → 整數 {
變值 整數 total ← 0
遍歷 取自 numeri 定值 n {
total ← total + n
}
傳回 total
}
入口 {
定值 列表<整數> valores ← [1, 2, 3, 4, 5]
註記 summa(valores)
}faber format --locale vi — Vietnamesehàm summa(danh_sách<số> numeri) → số {
biến số total ← 0
lặp từ numeri hằng n {
total ← total + n
}
trả total
}
bắt_đầu {
hằng danh_sách<số> valores ← [1, 2, 3, 4, 5]
ghi_chú summa(valores)
}faber format --locale ar — Arabicدالة summa(قائمة<عدد> numeri) → عدد {
متغير عدد total ← 0
كرر من numeri ثابت n {
total ← total + n
}
أعد total
}
بداية {
ثابت قائمة<عدد> valores ← [1, 2, 3, 4, 5]
اعرض summa(valores)
}faber format --locale hi — Hindiफलन summa(सूची<संख्या> numeri) → संख्या {
चर संख्या total ← 0
दोहराओ सेवन numeri स्थिर n {
total ← total + n
}
लौटाओ total
}
आरंभ {
स्थिर सूची<संख्या> valores ← [1, 2, 3, 4, 5]
दिखाओ summa(valores)
}WebAssembly text — 12 lines in, 87 out (7.2×)
;; Generated by radix MIR WASM text probe - experimental artifact.
(module
(import "faber_rt_v1" "__faber_rt_v1_array_new" (func $__faber_rt_v1_array_new__construct (param i32) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_array_push" (func $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (param i32 i64) (result i32)))
(import "faber_rt_v1" "__faber_rt_v1_array_get" (func $__faber_rt_v1_array_get__index_2_aggregate_i64_to_i64 (param i32 i64) (result i64)))
(import "faber_rt_v1" "__faber_rt_v1_array_length" (func $__faber_rt_v1_array_length__length_1_aggregate_to_i64 (param i32) (result i64)))
(import "faber_rt_v1" "__faber_rt_v1_diagnostic_nota_i64" (func $__faber_rt_v1_diagnostic_nota_i64 (param i64)))
(func $summa (export "summa") (param $l0 i32) (result i64)
(local $l1 i64)
(local $l2 i64)
(local $l3 i64)
(local $t0 i64)
(local $t1 i32)
(local $t2 i64)
(local $t3 i64)
(local $p0 i32)
(local.set $p0 (i32.const 0))
(loop $dispatch
(if (i32.eq (local.get $p0) (i32.const 0))
(then
(local.set $l1 (i64.const 0))
(local.set $l2 (i64.const 0))
(local.set $t0 (call $__faber_rt_v1_array_length__length_1_aggregate_to_i64 (local.get $l0)))
(local.set $p0 (i32.const 1))
(br $dispatch)
)
)
(if (i32.eq (local.get $p0) (i32.const 1))
(then
;; v2 = (i64.lt_s (local.get $l2) (local.get $t0))
(local.set $t1 (i64.lt_s (local.get $l2) (local.get $t0)))
(if (local.get $t1)
(then
(local.set $p0 (i32.const 2))
(br $dispatch)
)
(else
(local.set $p0 (i32.const 4))
(br $dispatch)
)
)
)
)
(if (i32.eq (local.get $p0) (i32.const 2))
(then
(local.set $l3 (call $__faber_rt_v1_array_get__index_2_aggregate_i64_to_i64 (local.get $l0) (local.get $l2)))
;; v4 = (i64.add (local.get $l1) (local.get $l3))
(local.set $t2 (i64.add (local.get $l1) (local.get $l3)))
(local.set $l1 (local.get $t2))
(local.set $p0 (i32.const 3))
(br $dispatch)
)
)
(if (i32.eq (local.get $p0) (i32.const 3))
(then
;; v6 = (i64.add (local.get $l2) (i64.const 1))
(local.set $t3 (i64.add (local.get $l2) (i64.const 1)))
(local.set $l2 (local.get $t3))
(local.set $p0 (i32.const 1))
(br $dispatch)
)
)
(if (i32.eq (local.get $p0) (i32.const 4))
(then
(return (local.get $l1))
)
)
(unreachable)
)
(unreachable)
)
(func $incipit (export "incipit")
(local $l0 i32)
(local $t0 i32)
(local $t1 i64)
(local.set $t0 (call $__faber_rt_v1_array_new__construct (i32.const 4)))
(local.set $t0 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t0) (i64.const 1)))
(local.set $t0 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t0) (i64.const 2)))
(local.set $t0 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t0) (i64.const 3)))
(local.set $t0 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t0) (i64.const 4)))
(local.set $t0 (call $__faber_rt_v1_array_push__construct_2_aggregate_i64_to_aggregate (local.get $t0) (i64.const 5)))
(local.set $l0 (local.get $t0))
(local.set $t1 (call $summa (local.get $l0)))
(call $__faber_rt_v1_diagnostic_nota_i64 (local.get $t1))
(return)
)
)---