ikawrakow/ik_llama.cpp
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iqk_mul_mat(ARM_NEON): adding bf16 support

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It looks like ArmV8 ISA has support for bf16, but my M2 Max
does not have it, so resorting to bf16 -> f32 conversion and
computations in f32. This is 2x slower than f16, but 8x better
compared to what I get if I try to run a bf16 model on the M2
(NEON and Metal).
This commit is contained in:
Iwan Kawrakow
2024-09-05 13:04:03 +02:00
parent 20f3e6fd2d
commit e6d3b6b277
1 changed files with 144 additions and 1 deletions
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145
ggml/src/iqk/iqk_mul_mat.cpp
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@@ -5774,6 +5774,7 @@ struct QF16Base {
}
};
template <int nrc> struct QF16 final : public QF16Base {
using Base = QF16Base;
constexpr static int nrc_y = nrc;
QF16(const DataInfo& info) {
for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const __fp16 *)info.src1_row(iy);
@@ -5787,6 +5788,103 @@ template <int nrc> struct QF16 final : public QF16Base {
const __fp16 * y[nrc_y];
};
struct QBF16Base {
constexpr static int k_step = 4;
using Data = float32x4_t;
using Acc = float32x4_t;
static inline Data load(const uint16_t * x) { return vreinterpretq_f32_u32(vshlq_n_u32(vmovl_u16(vld1_u16(x)), 16)); }
static inline Data load4(const uint16_t * x) { return load(x); }
static inline Acc acc(Acc prev, const Data& y, const Data& x) {
return vfmaq_f32(prev, y, x);
}
static inline Acc acc_first(const Data& y, const Data& x) {
return vmulq_f32(y, x);
}
static inline float hsum(Acc acc) { return vaddvq_f32(acc); }
};
template <int nrc> struct QBF16 final : public QBF16Base {
using Base = QBF16Base;
constexpr static int nrc_y = nrc;
QBF16(const DataInfo& info) {
for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const uint16_t *)info.src1_row(iy);
}
QBF16(const char * cx, size_t bx) {
for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const uint16_t *)(cx + iy*bx);
}
IQK_ALWAYS_INLINE Data load1(int iy, int i) const { return load(y[iy] + k_step*i); }
IQK_ALWAYS_INLINE Data load_tail(int iy, int i) const { return load(y[iy] + 4*i); }
const uint16_t * y[nrc_y];
};
struct QF32Base {
constexpr static int k_step = 4;
using Data = float32x4_t;
using Acc = float32x4_t;
static inline Data load(const float * x) { return vld1q_f32(x); }
static inline Data load4(const float * x) { return load(x); }
static inline Acc acc(Acc prev, const Data& y, const Data& x) { return vfmaq_f32(prev, y, x); }
static inline Acc acc_first(const Data& y, const Data& x) { return vmulq_f32(y, x); }
static inline float hsum(Acc acc) { return vaddvq_f32(acc); }
};
template <int nrc> struct QF32 final : public QF32Base {
using Base = QF32Base;
constexpr static int nrc_y = nrc;
QF32(const DataInfo& info) {
for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const float *)info.src1_row(iy);
}
QF32(const char * cx, size_t bx) {
for (int iy = 0; iy < nrc_y; ++iy) y[iy] = (const float *)(cx + iy*bx);
}
IQK_ALWAYS_INLINE Data load1(int iy, int i) const { return load(y[iy] + k_step*i); }
IQK_ALWAYS_INLINE Data load_tail(int iy, int i) const { return load(y[iy] + 4*i); }
const float * y[nrc_y];
};
template <typename Qy, typename Qx, bool is_multiple_of_k_step>
IQK_NOINLINE void mul_mat_Qx_Qy_NxN(int n, const char * cx, size_t bx, int ix0, const DataInfo& info) {
GGML_ASSERT(Qx::Base::k_step == Qy::Base::k_step);
int nb = n/Qx::Base::k_step;
Qy y(info);
Qx x(cx + ix0*bx, bx);
typename Qx::Base::Data xv[Qx::nrc_y];
typename Qx::Base::Acc acc[Qx::nrc_y*Qy::nrc_y];
auto yv = y.load1(0, 0);
for (int ix = 0; ix < Qx::nrc_y; ++ix) {
xv[ix] = x.load1(ix, 0);
acc[ix] = Qx::Base::acc_first(yv, xv[ix]);
}
for (int iy = 1; iy < Qy::nrc_y; ++iy) {
yv = y.load1(iy, 0);
for (int ix = 0; ix < Qx::nrc_y; ++ix) acc[Qx::nrc_y*iy + ix] = Qx::Base::acc_first(yv, xv[ix]);
}
for (int i = 1; i < nb; ++i) {
yv = y.load1(0, i);
for (int ix = 0; ix < Qx::nrc_y; ++ix) {
xv[ix] = x.load1(ix, i);
acc[ix] = Qx::Base::acc(acc[ix], yv, xv[ix]);
}
for (int iy = 1; iy < Qy::nrc_y; ++iy) {
yv = y.load1(iy, i);
for (int ix = 0; ix < Qx::nrc_y; ++ix) acc[Qx::nrc_y*iy + ix] = Qx::Base::acc(acc[Qx::nrc_y*iy + ix], yv, xv[ix]);
}
}
if constexpr (Qx::Base::k_step > 4 && !is_multiple_of_k_step) {
int nb4 = n/4;
for (int i = (Qx::Base::k_step/4)*nb; i < nb4; ++i) {
yv = y.load_tail(0, i);
for (int ix = 0; ix < Qx::nrc_y; ++ix) {
xv[ix] = x.load_tail(ix, i);
acc[ix] = Qx::Base::acc(acc[ix], yv, xv[ix]);
}
for (int iy = 1; iy < Qy::nrc_y; ++iy) {
yv = y.load_tail(iy, i);
for (int ix = 0; ix < Qx::nrc_y; ++ix) acc[Qx::nrc_y*iy + ix] = Qx::Base::acc(acc[Qx::nrc_y*iy + ix], yv, xv[ix]);
}
}
}
for (int iy = 0; iy < Qy::nrc_y; ++iy) for (int ix = 0; ix < Qx::nrc_y; ++ix) info.store(ix0+ix, iy, Qx::Base::hsum(acc[Qx::nrc_y*iy+ix]));
}
template <int nrc_y, int nrc_x, bool is_multiple_of_k_step>
IQK_NOINLINE void mul_mat_f16_f16_NxN(int n, const char * cx, size_t bx, int ix0, const DataInfo& info) {
assert(n%QF16Base::k_step == 0);
@@ -5832,6 +5930,40 @@ IQK_NOINLINE void mul_mat_f16_f16_NxN(int n, const char * cx, size_t bx, int ix0
for (int iy = 0; iy < nrc_y; ++iy) for (int ix = 0; ix < nrc_x; ++ix) info.store(ix0+ix, iy, QF16Base::hsum(acc[nrc_x*iy+ix]));
}
template <typename Qy, template<int> typename Qx>
void mul_mat_Qx_Qy_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(n%4 == 0);
constexpr int k_nx = 5;
const char * cx = (const char *)vx;
if (n%Qx<k_nx>::Base::k_step == 0) {
for (int ix = 0; ix < nrc_x/k_nx; ++ix) {
mul_mat_Qx_Qy_NxN<Qy, Qx<k_nx>, true>(n, cx, bx, ix*k_nx, info);
}
int last_x = k_nx*(nrc_x/k_nx);
if (last_x == nrc_x) return;
int nx = nrc_x - last_x;
switch (nx) {
case 1: mul_mat_Qx_Qy_NxN<Qy, Qx<1>, true>(n, cx, bx, last_x, info); break;
case 2: mul_mat_Qx_Qy_NxN<Qy, Qx<2>, true>(n, cx, bx, last_x, info); break;
case 3: mul_mat_Qx_Qy_NxN<Qy, Qx<3>, true>(n, cx, bx, last_x, info); break;
case 4: mul_mat_Qx_Qy_NxN<Qy, Qx<4>, true>(n, cx, bx, last_x, info); break;
}
} else {
for (int ix = 0; ix < nrc_x/k_nx; ++ix) {
mul_mat_Qx_Qy_NxN<Qy, Qx<k_nx>, false>(n, cx, bx, ix*k_nx, info);
}
int last_x = k_nx*(nrc_x/k_nx);
if (last_x == nrc_x) return;
int nx = nrc_x - last_x;
switch (nx) {
case 1: mul_mat_Qx_Qy_NxN<Qy, Qx<1>, false>(n, cx, bx, last_x, info); break;
case 2: mul_mat_Qx_Qy_NxN<Qy, Qx<2>, false>(n, cx, bx, last_x, info); break;
case 3: mul_mat_Qx_Qy_NxN<Qy, Qx<3>, false>(n, cx, bx, last_x, info); break;
case 4: mul_mat_Qx_Qy_NxN<Qy, Qx<4>, false>(n, cx, bx, last_x, info); break;
}
}
}
template <int nrc_y>
void mul_mat_f16_f16_T(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
GGML_ASSERT(n%4 == 0);
@@ -5913,7 +6045,7 @@ IQK_NOINLINE void mul_mat_f16_f16_Nx1(int n, const char * cx, size_t bx, int ix0
}
}
// At least on my M2-Max the version below, which dows the multiplication row-by-row, is faster.
// At least on my M2-Max the version below, which does the multiplication row-by-row, is faster.
// But let's keep this version commented out for now.
//void mul_mat_f16_f16_1(int n, const void * vx, size_t bx, const DataInfo& info, int nrc_x) {
// GGML_ASSERT(n%4 == 0);
@@ -6231,6 +6363,17 @@ bool MulMat::prepare(int typeA, int typeB, int ne00, MulMat& m, int /*Ny*/) {
return true;
}
if (typeA == GGML_TYPE_BF16 && typeB == GGML_TYPE_F32) {
if (ne00%4) return false;
for (auto& f : m.funcs) f = nullptr;
m.funcs[0] = mul_mat_Qx_Qy_T<QF32<1>, QBF16>;
m.funcs[1] = mul_mat_Qx_Qy_T<QF32<2>, QBF16>;
m.funcs[2] = mul_mat_Qx_Qy_T<QF32<3>, QBF16>;
m.funcs[3] = mul_mat_Qx_Qy_T<QF32<4>, QBF16>;
m.funcs[4] = mul_mat_Qx_Qy_T<QF32<5>, QBF16>;
return true;
}
auto expected_Btype = GGML_TYPE_Q8_K;
switch (typeA) {