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ggml: aarch64: Implement SVE F32 kernels for Mamba Sequential Scan Algorithm #13882

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May 29, 2025
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ggml: aarch64: Implement SVE F32 kernels for Mamba Sequential Scan Algorithm #13882

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F32-Mamba-Seq_Scan-SVE
vineelabhinav May 29, 2025
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Fix formatting
vineelabhinav May 29, 2025
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ggml : missing space
ggerganov May 29, 2025
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104 changes: 74 additions & 30 deletions ggml/src/ggml-cpu/ops.cpp
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Original file line number Diff line number Diff line change
Expand Up @@ -7633,39 +7633,83 @@ static void ggml_compute_forward_ssm_scan_f32(
const int ir1 = MIN(ir0 + dr, nr);
const int ir = ir1 - ir0;

for (int i3 = 0; i3 < n_s; ++i3) {
for (int i2 = 0; i2 < n_t; ++i2) {
const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s}

// use the output as the source for the next token-wise iterations
if (i2 > 0) { s0 = s; }

// d_inner
for (int i1 = 0; i1 < ir; ++i1) {
// ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
float x_dt = x[i1] * dt_soft_plus;
float sumf = 0.0f;
// d_state
for (int i0 = 0; i0 < nc; ++i0) {
int i = i0 + i1*nc;
// state = prev_state * dA + dB * x
float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
// y = rowwise_dotprod(state, C)
sumf += state * C[i0];
s[i] = state;
#ifdef __ARM_FEATURE_SVE
for (int i3 = 0; i3 < n_s; ++i3) {
for (int i2 = 0; i2 < n_t; ++i2) {
const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s}

// use the output as the source for the next token-wise iterations
if (i2 > 0) { s0 = s; }

// d_inner
for (int i1 = 0; i1 < ir; ++i1) {
float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
float x_dt = x[i1] * dt_soft_plus;
svfloat32_t vx_dt = GGML_F32_VEC_SET1(x_dt);
svfloat32_t vdt_soft_plus = GGML_F32_VEC_SET1(dt_soft_plus);
svfloat32_t r1_vector = GGML_F32_VEC_ZERO;

for (int64_t k = 0; k < nc; k += svcntw()) {
svfloat32_t vA = GGML_F32_VEC_LOAD(&A[i1*nc + k]);
svfloat32_t vB = GGML_F32_VEC_LOAD(&B[k]);
svfloat32_t vC = GGML_F32_VEC_LOAD(&C[k]);
svfloat32_t vs0 = GGML_F32_VEC_LOAD(&s0[i1*nc + k]);

svfloat32_t t1 = GGML_F32_VEC_MUL(vdt_soft_plus, vA);
t1 = exp_ps_sve(svptrue_b32(), t1);
svfloat32_t t2 = GGML_F32_VEC_MUL(vx_dt, vB);

vs0 = GGML_F32_VEC_FMA(vs0, t1, t2);
r1_vector = GGML_F32_VEC_ADD(GGML_F32_VEC_MUL(vs0, vC), r1_vector);

GGML_F32_VEC_STORE(&s[i1*nc + k], vs0);
}
y[i1] = GGML_F32xt_REDUCE_ONE(r1_vector);
}
y[i1] = sumf;
}
}
}
#else
for (int i3 = 0; i3 < n_s; ++i3) {
for (int i2 = 0; i2 < n_t; ++i2) {
const float * s0 = (const float *) ((const char *) src0->data + ir0*(src0->nb[1]) + i3*(src0->nb[2])); // {d_state, d_inner, n_s}
const float * x = (const float *) ((const char *) src1->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
const float * dt = (const float *) ((const char *) src2->data + ir0*(src2->nb[0]) + i2*(src2->nb[1]) + i3*(src2->nb[2])); // {d_inner, n_t, n_s}
const float * A = (const float *) ((const char *) src3->data + ir0*(src3->nb[1])); // {d_state, d_inner}
const float * B = (const float *) ((const char *) src4->data + i2*(src4->nb[1]) + i3*(src4->nb[2])); // {d_state, n_t, n_s}
const float * C = (const float *) ((const char *) src5->data + i2*(src5->nb[1]) + i3*(src5->nb[2])); // {d_state, n_t, n_s}
float * y = ( float *) (( char *) dst->data + ir0*(src1->nb[0]) + i2*(src1->nb[1]) + i3*(src1->nb[2])); // {d_inner, n_t, n_s}
float * s = ( float *) (( char *) dst->data + ir0*(src0->nb[1]) + i3*(src0->nb[2]) + src1->nb[3]); // {d_state, d_inner, n_s}

// use the output as the source for the next token-wise iterations
if (i2 > 0) { s0 = s; }

// d_inner
for (int i1 = 0; i1 < ir; ++i1) {
// ref: https://github.com/state-spaces/mamba/blob/34076d664838588a3c97727b263478ab9f621a07/mamba_ssm/ops/triton/selective_state_update.py#L78
float dt_soft_plus = dt[i1] <= 20.0f ? log1pf(expf(dt[i1])) : dt[i1];
float x_dt = x[i1] * dt_soft_plus;
float sumf = 0.0f;
// d_state
for (int i0 = 0; i0 < nc; ++i0) {
int i = i0 + i1*nc;
// state = prev_state * dA + dB * x
float state = (s0[i] * expf(dt_soft_plus * A[i])) + (B[i0] * x_dt);
// y = rowwise_dotprod(state, C)
sumf += state * C[i0];
s[i] = state;
}
y[i1] = sumf;
}
}
}
#endif
}

void ggml_compute_forward_ssm_scan(
Expand Down
36 changes: 36 additions & 0 deletions ggml/src/ggml-cpu/vec.h
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Original file line number Diff line number Diff line change
Expand Up @@ -647,6 +647,42 @@ inline static ggml_fp16_t ggml_silu_f16(ggml_fp16_t x) {
#error "ref: https://github.com/ggml-org/llama.cpp/pull/7154#issuecomment-2143844461"
#endif

/* Below function was borrowed from the GitHub repository:
https://github.com/openvinotoolkit/openvino/blob/master/src/plugins/intel_cpu/src/nodes/kernels/scaled_attn/common.hpp */
#if defined(__ARM_FEATURE_SVE) && defined(__aarch64__)
inline static svfloat32_t exp_ps_sve(svbool_t pg, svfloat32_t src) {
// Constants
const svfloat32_t log2_e = svdup_n_f32(1.4426950409f);
const svfloat32_t ln2 = svdup_n_f32(0.6931473921f);
const svfloat32_t half_ln2_sq = svdup_n_f32(0.2413862043f);
const svuint32_t not_mask17 = svdup_n_u32(~((1u << 17) - 1));
const svfloat32_t one = svdup_n_f32(1.0f);
const svfloat32_t inactive1 = svdup_n_f32(0.0f);
const svint32_t inactive2 = svdup_n_s32(0);

// Algorithm starts here
svfloat32_t t0 = svmul_f32_m(pg, src, log2_e); // y = x * log2(e)
svfloat32_t t1 = svrintm_f32_m(inactive1, pg, t0); // rount to int (float)
svint32_t t2 = svcvt_s32_f32_m(inactive2, pg, t1); // n

t1 = svsub_f32_m(pg, t0, t1); // a = y - floor(y)
t1 = svadd_f32_m(pg, t1, one); // b = a + 1

svuint32_t t3 = svlsr_n_u32_m(pg, svreinterpret_u32_f32(t1), 17); // v = b >> 17 (u32)
svfloat32_t t4 = svexpa_f32(t3); // c = fexpa(v)
t4 = svscale_f32_m(pg, t4, t2); // fexpa(v) * 2^(n)

// and_(t2.d, t1.d, not_mask17.d)
svfloat32_t t5 = svreinterpret_f32_u32(svand_u32_m(pg, svreinterpret_u32_f32(t1), not_mask17));
t5 = svsub_f32_m(pg, t1, t5); // z
t0 = svmla_f32_m(pg, ln2, t5, half_ln2_sq); // ln2 + half_ln2_sq * z
t0 = svmla_f32_m(pg, one, t5, t0); // 1 + (ln2 * z) + (half_ln2_sq * z * z)
t0 = svmul_f32_m(pg, t0, t4); // Final result

return t0;
}
#endif

#if defined(__ARM_NEON) && defined(__aarch64__)

// adapted from arm limited optimized routine
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