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04f1f94a21- comparisons of vectors give masks (boolxN): select(m, a, b), m.any/all/count, & | ^ !;
f32x8(list, i, fill) and v.store_part(list, i) for the end of a list (masked load/store)
- v.shuffle(constant lanes), v.lo(), v.hi(), concat(a, b), table.lookup(indices):
pshufb / vpshufb on x86 (SSSE3 and up), tbl on ARM64 (checked in the assembly, LLVM 18 and 21)
- acc.dot_add(a, b): i32 lanes gain 4 byte products each, exactly; vpdpbusd with AVX-VNNI or AVX-512 VNNI,
usdot/sdot/udot on ARM64, the sum written out elsewhere (pmaddwd); run here with all three paths agreeing
- @cpu("avx2,fma") fn: LLVM target-features for one function (+evex512 for AVX-512, which LLVM 18+ needs);
cpu_has("avx2"): CPUID + XGETBV on x86, hwcaps on Linux ARM64, sysctl on macOS; a wide vector can't be passed
between functions with different @cpu (its registers differ), and the compiler says so
- the compiler asks clang which features --cpu means (its predefined macros, cached), so dot_add picks the instruction
- Nix's clang wrapper dropped -march=native in nix-shell (reproduced): finch tells it NIX_ENFORCE_NO_NATIVE=0, shell.nix too,
and warns if --cpu native still made no difference
Co-Authored-By: Claude Opus 5.5 <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01Psc42moPLwP43Cmb9oZqSR
69 lines
1.8 KiB
Text
69 lines
1.8 KiB
Text
// The int8 dot product of quantized models: plain, with dot_add, and in @cpu functions picked by cpu_has
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fn plain([]u8 a, []i8 b) -> int {
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total := 0
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for i in 0..a.len {
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total += int(a[i]) * int(b[i])
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}
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return total
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}
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// 32 bytes a step, 8 sums kept in a vector until the end
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fn with_vectors([]u8 a, []i8 b) -> int {
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acc := i32x8(0)
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i := 0
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while i + 32 <= a.len {
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acc = acc.dot_add(u8x32(a, i), i8x32(b, i))
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i += 32
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}
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return int(acc.sum())
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}
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@cpu("avxvnni")
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fn with_vnni([]u8 a, []i8 b) -> int {
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acc := i32x8(0)
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i := 0
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while i + 32 <= a.len {
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acc = acc.dot_add(u8x32(a, i), i8x32(b, i))
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i += 32
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}
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return int(acc.sum())
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}
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@cpu("avx512f,avx512bw,avx512vl,avx512vnni,i8mm")
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fn with_avx512([]u8 a, []i8 b) -> int {
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acc := i32x16(0)
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i := 0
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while i + 64 <= a.len {
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acc = acc.dot_add(u8x64(a, i), i8x64(b, i))
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i += 64
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}
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return int(acc.sum())
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}
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fn best([]u8 a, []i8 b) -> int {
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if cpu_has("avx512vnni") && cpu_has("avx512vl") && cpu_has("avx512bw") {
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return with_avx512(a, b)
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}
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if cpu_has("avxvnni") {
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return with_vnni(a, b)
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}
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return with_vectors(a, b)
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}
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fn main() {
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[]u8 a
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[]i8 b
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for k in 0..1024 {
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a.push(u8((k * 37 + 11) % 256))
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b.push(i8((k * 53 + 7) % 256 - 128))
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}
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want := plain(a, b)
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print(want, with_vectors(a, b) == want, best(a, b) == want)
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// the extremes: 255 * -128 four times in a lane, no saturation
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acc := i32x4(1, 2, 3, 4).dot_add(u8x16(255), i8x16(-128))
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print(acc, i32x4(0).dot_add(i8x16(-128), i8x16(-128)), i32x4(0).dot_add(u8x16(255), u8x16(255)))
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print(i32x4(0).dot_add(i8x16(-1), u8x16(200)))
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print(cpu_has("avx2") || cpu_has("neon") || true)
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}
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