ggml: vectorize ggml_vec_dot_q4_1_q8_1 with WASM SIMD128 (llama/22209)

* ggml: vectorize ggml_vec_dot_q4_1_q8_1 with WASM SIMD128

Optimize the inner loop of ggml_vec_dot_q4_1_q8_1_generic using
WASM SIMD128 intrinsics, gated behind #ifdef __wasm_simd128__ so
non-wasm builds are completely unaffected.

Approach:
- single wasm_v128_load covers all 32 packed 4-bit weights
- nibbles unpacked via AND/SHR into two u8x16 registers
- widened to i16 before multiply (WASM SIMD has no i8*i8 instruction)
- 4x wasm_i32x4_dot_i16x8 calls accumulate all 32 element pairs
- horizontal reduce via 4x wasm_i32x4_extract_lane

Benchmark (node v25, emcc -O3 -msimd128, 64 blocks x QK8_1=32,
200k iterations):

| impl   | ns/call | speedup |
|--------|---------|---------|
| scalar |   880.7 |   1.00x |
| simd   |   257.8 |   3.42x |

Correctness verified against scalar reference across 10 random seeds
with exact output match.

* ggml: move q4_1_q8_1 WASM SIMD implementation to wasm backend

Relocate the SIMD128 implementation of ggml_vec_dot_q4_1_q8_1 to ggml/src/ggml-cpu/arch/wasm/quants.c to follow architecture-specific layout. Restore the generic implementation in ggml/src/ggml-cpu/quants.c.
Move for loop in the else block.

* ggml: use generic q4_1_q8_1 fallback in wasm backend
This commit is contained in:
Kartik Sirohi 2026-06-04 18:42:38 +05:30 committed by Georgi Gerganov
parent 9d6e561f69
commit 991b5a8b4a
1 changed files with 72 additions and 0 deletions

View File

@ -355,6 +355,78 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const voi
*s = sumf;
}
void ggml_vec_dot_q4_1_q8_1(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
const int qk = QK8_1;
const int nb = n / qk;
assert(n % qk == 0);
assert(nrc == 1);
UNUSED(nrc);
UNUSED(bx);
UNUSED(by);
UNUSED(bs);
const block_q4_1 * GGML_RESTRICT x = vx;
const block_q8_1 * GGML_RESTRICT y = vy;
float sumf = 0;
#if defined __wasm_simd128__
v128_t sumv = wasm_f32x4_splat(0.0f);
float summs = 0.0f;
for (int ib = 0; ib < nb; ++ib) {
const block_q4_1 * GGML_RESTRICT x0 = &x[ib];
const block_q8_1 * GGML_RESTRICT y0 = &y[ib];
summs += GGML_CPU_FP16_TO_FP32(x0->m) * GGML_CPU_FP16_TO_FP32(y0->s);
const v128_t raw = wasm_v128_load(x0->qs);
const v128_t v0s = wasm_v128_and(raw, wasm_i8x16_splat(0x0F));
const v128_t v1s = wasm_u8x16_shr(raw, 4);
const v128_t ys_lo = wasm_v128_load(y0->qs);
const v128_t ys_hi = wasm_v128_load(y0->qs + 16);
const v128_t v0s_l = wasm_u16x8_extend_low_u8x16(v0s);
const v128_t v0s_h = wasm_u16x8_extend_high_u8x16(v0s);
const v128_t ylo_l = wasm_i16x8_extend_low_i8x16(ys_lo);
const v128_t ylo_h = wasm_i16x8_extend_high_i8x16(ys_lo);
const v128_t v1s_l = wasm_u16x8_extend_low_u8x16(v1s);
const v128_t v1s_h = wasm_u16x8_extend_high_u8x16(v1s);
const v128_t yhi_l = wasm_i16x8_extend_low_i8x16(ys_hi);
const v128_t yhi_h = wasm_i16x8_extend_high_i8x16(ys_hi);
const v128_t acc = wasm_i32x4_add(
wasm_i32x4_add(
wasm_i32x4_dot_i16x8(v0s_l, ylo_l),
wasm_i32x4_dot_i16x8(v0s_h, ylo_h)),
wasm_i32x4_add(
wasm_i32x4_dot_i16x8(v1s_l, yhi_l),
wasm_i32x4_dot_i16x8(v1s_h, yhi_h)));
sumv = wasm_f32x4_add(sumv,
wasm_f32x4_mul(
wasm_f32x4_convert_i32x4(acc),
wasm_f32x4_splat(GGML_CPU_FP16_TO_FP32(x0->d) * GGML_CPU_FP16_TO_FP32(y0->d))));
}
sumf = wasm_f32x4_extract_lane(sumv, 0) + wasm_f32x4_extract_lane(sumv, 1) +
wasm_f32x4_extract_lane(sumv, 2) + wasm_f32x4_extract_lane(sumv, 3) + summs;
*s = sumf;
#else
UNUSED(nb);
UNUSED(x);
UNUSED(y);
UNUSED(sumf);
ggml_vec_dot_q4_1_q8_1_generic(
n, s, bs, vx, bx, vy, by, nrc);
#endif
}
void ggml_vec_dot_q5_0_q8_0(int n, float * GGML_RESTRICT s, size_t bs, const void * GGML_RESTRICT vx, size_t bx, const void * GGML_RESTRICT vy, size_t by, int nrc) {
const int qk = QK8_0;
const int nb = n / qk;