ikawrakow/ik_llama.cpp
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q6_0_r4: NEON

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We get PP-512(LLaMA-3.1-8B) = 95 t/s on M2-Max.
In terms of ops, q6_0_r4 is identical to q5_0_r4
except for loading the high bits being
vld1q_u8_x2 instead of vld1q_u8. It is strange that
this can make a 5% difference in performance, especially
considering that this is amortized (re-used) over 8 columns
in the right matrix. Or am I running out of vector registers?
This commit is contained in:
Iwan Kawrakow
2024-12-03 14:32:03 +01:00
parent e345ee837d
commit 1d2cdf2f58
1 changed files with 60 additions and 0 deletions
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60
ggml/src/iqk/iqk_mul_mat.cpp
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@@ -7263,6 +7263,55 @@ void mul_mat_q5_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& inf
} }
} }
template <int nrc_y>
void mul_mat_q6_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0);
Q8<nrc_y, block_q8_0_x4> q8(info);
auto m4 = vdupq_n_u8(0x0f);
auto m6 = vdupq_n_u8(0x30);
auto m32 = vdupq_n_s8(-32);
int nb = n / QK6_0;
GGML_ASSERT(nb%4 == 0);
int8x16_t qx[8];
float32x4_t acc[nrc_y] = {};
for (int ix = 0; ix < nrc_x; ix += 4) {
const block_q6_0_r4 * iq6 = (const block_q6_0_r4 *)((const char *)vx + ix*bx);
for (int ib4 = 0; ib4 < nb/4; ++ib4) {
for (int k = 0; k < 4; ++k) {
auto scales = vcvt_f32_f16(vld1_f16((const float16_t *)iq6[4*ib4+k].d));
auto lbits = vld1q_u8_x4(iq6[4*ib4+k].qs);
auto hbits = vld1q_u8_x2(iq6[4*ib4+k].qh);
qx[0] = vaddq_s8(vandq_u8(lbits.val[0], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 4), m6), m32); // 0...3
qx[1] = vaddq_s8(vandq_u8(lbits.val[1], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 4), m6), m32); // 16..19
qx[2] = vaddq_s8(vandq_u8(lbits.val[2], m4) | vandq_u8(vshlq_n_u8(hbits.val[0], 2), m6), m32); // 4...7
qx[3] = vaddq_s8(vandq_u8(lbits.val[3], m4) | vandq_u8(vshlq_n_u8(hbits.val[1], 2), m6), m32); // 20..23
qx[4] = vaddq_s8(vshrq_n_u8(lbits.val[0], 4)| vandq_u8(hbits.val[0], m6), m32); // 8..11
qx[5] = vaddq_s8(vshrq_n_u8(lbits.val[1], 4)| vandq_u8(hbits.val[1], m6), m32); // 24..27
qx[6] = vaddq_s8(vshrq_n_u8(lbits.val[2], 4)| vandq_u8(vshrq_n_u8(hbits.val[0], 2), m6), m32); // 12..15
qx[7] = vaddq_s8(vshrq_n_u8(lbits.val[3], 4)| vandq_u8(vshrq_n_u8(hbits.val[1], 2), m6), m32); // 28..31
for (int iy = 0; iy < nrc_y; ++iy) {
auto y = vld1q_s8_x2(q8.y[iy][ib4].qs+32*k);
auto sumi = vdupq_n_s32(0);
sumi = vdotq_laneq_s32(sumi, qx[0], y.val[0], 0);
sumi = vdotq_laneq_s32(sumi, qx[1], y.val[1], 0);
sumi = vdotq_laneq_s32(sumi, qx[2], y.val[0], 1);
sumi = vdotq_laneq_s32(sumi, qx[3], y.val[1], 1);
sumi = vdotq_laneq_s32(sumi, qx[4], y.val[0], 2);
sumi = vdotq_laneq_s32(sumi, qx[5], y.val[1], 2);
sumi = vdotq_laneq_s32(sumi, qx[6], y.val[0], 3);
sumi = vdotq_laneq_s32(sumi, qx[7], y.val[1], 3);
auto d4d8 = vmulq_f32(scales, vdupq_n_f32(GGML_FP16_TO_FP32(q8.y[iy][ib4].d[k])));
acc[iy] = vfmaq_f32(acc[iy], d4d8, vcvtq_f32_s32(sumi));
}
}
}
for (int iy = 0; iy < nrc_y; ++iy) {
info.store(ix, iy, acc[iy]);
acc[iy] = vdupq_n_f32(0.f);
}
}
}
template <int nrc_y> template <int nrc_y>
void mul_mat_q8_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) { void mul_mat_q8_0_r4_q8_0(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(nrc_x%4 == 0); GGML_ASSERT(nrc_x%4 == 0);
@@ -7502,6 +7551,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
m.funcs[7] = mul_mat_q5_0_r4_q8_0<8>; m.funcs[7] = mul_mat_q5_0_r4_q8_0<8>;
expected_Btype = GGML_TYPE_Q8_0; expected_Btype = GGML_TYPE_Q8_0;
break; break;
case GGML_TYPE_Q6_0_R4:
m.funcs[0] = mul_mat_q6_0_r4_q8_0<1>;
m.funcs[1] = mul_mat_q6_0_r4_q8_0<2>;
m.funcs[2] = mul_mat_q6_0_r4_q8_0<3>;
m.funcs[3] = mul_mat_q6_0_r4_q8_0<4>;
m.funcs[4] = mul_mat_q6_0_r4_q8_0<5>;
m.funcs[5] = mul_mat_q6_0_r4_q8_0<6>;
m.funcs[6] = mul_mat_q6_0_r4_q8_0<7>;
m.funcs[7] = mul_mat_q6_0_r4_q8_0<8>;
expected_Btype = GGML_TYPE_Q8_0;
break;
case GGML_TYPE_Q8_0_R4: case GGML_TYPE_Q8_0_R4:
m.funcs[0] = mul_mat_q8_0_r4_q8_0<1>; m.funcs[0] = mul_mat_q8_0_r4_q8_0<1>;
m.funcs[1] = mul_mat_q8_0_r4_q8_0<2>; m.funcs[1] = mul_mat_q8_0_r4_q8_0<2>;