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ggml-cpu: vectorize the fused RMS-norm × weight kernel #29

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ggml-cpu: vectorize the fused RMS-norm × weight kernel #29

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13 changes: 5 additions & 8 deletions ggml/src/ggml-cpu/ops.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -3825,11 +3825,10 @@ static void ggml_compute_forward_rms_norm_f32(
for (int64_t i01 = ith; i01 < ne01; i01 += nth) {
const float * x = (float *) ((char *) src0->data + i01*nb01 + i02*nb02 + i03*nb03);

ggml_float sum = 0.0;
// worth switching to explicit SIMD?
for (int64_t i00 = 0; i00 < ne00; i00++) {
sum += (ggml_float)(x[i00] * x[i00]);
}
// sum(x[i]*x[i]) == dot(x, x); use the vectorized dot-product kernel
float sumf = 0.0f;
ggml_vec_dot_f32(ne00, &sumf, 0, x, 0, x, 0, 1);
const ggml_float sum = (ggml_float) sumf;

const float mean = sum/ne00;
const float scale = 1.0f/sqrtf(mean + eps);
Expand All @@ -3845,9 +3844,7 @@ static void ggml_compute_forward_rms_norm_f32(
const int64_t i13 = i03 % ne13;
const float * w = (float *) ((char *) src1->data + i11*nb11 + i12*nb12 + i13*nb13);

for (int64_t i00 = 0; i00 < ne00; i00++) {
y[i00] = x[i00] * scale * w[i00];
}
ggml_vec_scale_mul_f32(ne00, y, x, w, scale);
} else {
memcpy(y, x, ne00 * sizeof(float));
ggml_vec_scale_f32(ne00, y, scale);
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77 changes: 77 additions & 0 deletions ggml/src/ggml-cpu/vec.h
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Expand Up @@ -766,6 +766,83 @@ inline static void ggml_vec_scale_f32(const int n, float * y, const float v) {
#endif
}

// y[i] = (x[i] * s) * w[i]
// The two multiplies are kept separate (no FMA) so each product is rounded
// exactly as in the equivalent scalar expression `x[i] * s * w[i]`, keeping
// the result bit-identical to the scalar reference.
inline static void ggml_vec_scale_mul_f32(const int n, float * GGML_RESTRICT y,
const float * GGML_RESTRICT x,
const float * GGML_RESTRICT w, const float s) {
#if defined(GGML_SIMD)
#if defined(__ARM_FEATURE_SVE)
const int sve_register_length = ggml_cpu_get_sve_cnt() * 8;
const int ggml_f32_epr = sve_register_length / 32;
const int ggml_f32_step = 2 * ggml_f32_epr;

GGML_F32_VEC vs = GGML_F32_VEC_SET1(s);
const int np = (n & ~(ggml_f32_step - 1));
for (int i = 0; i < np; i += ggml_f32_step) {
svfloat32_t ax1 = GGML_F32_VEC_LOAD(x + i);
svfloat32_t aw1 = GGML_F32_VEC_LOAD(w + i);
ax1 = GGML_F32_VEC_MUL(ax1, vs);
ax1 = GGML_F32_VEC_MUL(ax1, aw1);
GGML_F32_VEC_STORE(y + i, ax1);

svfloat32_t ax2 = GGML_F32_VEC_LOAD(x + i + 1*ggml_f32_epr);
svfloat32_t aw2 = GGML_F32_VEC_LOAD(w + i + 1*ggml_f32_epr);
ax2 = GGML_F32_VEC_MUL(ax2, vs);
ax2 = GGML_F32_VEC_MUL(ax2, aw2);
GGML_F32_VEC_STORE(y + i + 1*ggml_f32_epr, ax2);
}
for (int i = np; i < n; i += ggml_f32_epr) {
svbool_t pg = svwhilelt_b32(i, n);
svfloat32_t ax = svld1_f32(pg, x + i);
svfloat32_t aw = svld1_f32(pg, w + i);
ax = svmul_f32_m(pg, ax, vs);
ax = svmul_f32_m(pg, ax, aw);
svst1_f32(pg, y + i, ax);
}
#elif defined(__riscv_v_intrinsic)
for (int i = 0, avl; i < n; i += avl) {
avl = __riscv_vsetvl_e32m8(n - i);
vfloat32m8_t ax = __riscv_vle32_v_f32m8(&x[i], avl);
vfloat32m8_t aw = __riscv_vle32_v_f32m8(&w[i], avl);
vfloat32m8_t ny = __riscv_vfmul_vf_f32m8(ax, s, avl);
ny = __riscv_vfmul_vv_f32m8(ny, aw, avl);
__riscv_vse32_v_f32m8(&y[i], ny, avl);
}
#else
const int np = (n & ~(GGML_F32_STEP - 1));

GGML_F32_VEC vs = GGML_F32_VEC_SET1(s);

GGML_F32_VEC ax[GGML_F32_ARR];
GGML_F32_VEC aw[GGML_F32_ARR];

for (int i = 0; i < np; i += GGML_F32_STEP) {
for (int j = 0; j < GGML_F32_ARR; j++) {
ax[j] = GGML_F32_VEC_LOAD(x + i + j*GGML_F32_EPR);
aw[j] = GGML_F32_VEC_LOAD(w + i + j*GGML_F32_EPR);
ax[j] = GGML_F32_VEC_MUL(ax[j], vs);
ax[j] = GGML_F32_VEC_MUL(ax[j], aw[j]);

GGML_F32_VEC_STORE(y + i + j*GGML_F32_EPR, ax[j]);
}
}

// leftovers
for (int i = np; i < n; ++i) {
y[i] = x[i]*s*w[i];
}
#endif
#else
// scalar
for (int i = 0; i < n; ++i) {
y[i] = x[i]*s*w[i];
}
#endif
}

inline static void ggml_vec_scale_f16(const int n, ggml_fp16_t * y, const float v) {
#if defined(GGML_SIMD) && defined(__ARM_FEATURE_SVE)
const int sve_register_length = svcntb() * 8;
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