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Another speed gain for Q4_0 and Q4_1 on Metal (#2375)
* Another speed gain for Q4_0 and Q4_1 on Metal * Have N_DST, etc., be template parameters --------- Co-authored-by: Iwan Kawrakow <iwan.kawrakow@gmail.com>
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1 changed files with 59 additions and 56 deletions
115
ggml-metal.metal
115
ggml-metal.metal
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@ -387,87 +387,90 @@ kernel void kernel_rms_norm(
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}
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}
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}
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}
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// function for calculate inner product between a q4_0 block and 32 floats (yl), sumy is SUM(yl[i])
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// function for calculate inner product between half a q4_0 block and 16 floats (yl), sumy is SUM(yl[i])
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float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl) {
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// il indicates where the q4 quants begin (0 or QK4_0/4)
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// we assume that the yl's have been multiplied with the appropriate scale factor
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// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
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inline float block_q_n_dot_y(device const block_q4_0 * qb_curr, float sumy, thread float * yl, int il) {
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float d = qb_curr->d;
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float d = qb_curr->d;
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float4 acc = 0.f;
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float2 acc = 0.f;
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device uint16_t * qs = ((device uint16_t *)qb_curr + 1);
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device const uint16_t * qs = ((device const uint16_t *)qb_curr + 1 + il/2);
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for (int i = 0; i < 16; i+=2) {
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for (int i = 0; i < 8; i+=2) {
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acc[0] += yl[i] * (qs[i / 2] & 0x000F);
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acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
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acc[1] += yl[i + 16] * (qs[i / 2] & 0x00F0);
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+ yl[i + 1] * (qs[i / 2] & 0x0F00);
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acc[2] += yl[i + 1] * (qs[i / 2] & 0x0F00);
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acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0)
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acc[3] += yl[i + 17] * (qs[i / 2] & 0xF000);
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+ yl[i + 9] * (qs[i / 2] & 0xF000);
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}
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}
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return d * (sumy * -8.f + acc[0] + acc[1]/16.f + acc[2]/256.f + acc[3]/4096.f);
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return d * (sumy * -8.f + acc[0] + acc[1]);
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}
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}
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// function for calculate inner product between a q4_1 block and 32 floats (yl), sumy is SUM(yl[i])
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// function for calculate inner product between half a q4_1 block and 16 floats (yl), sumy is SUM(yl[i])
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float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl) {
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// il indicates where the q4 quants begin (0 or QK4_0/4)
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// we assume that the yl's have been multiplied with the appropriate scale factor
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// that corresponds to the missing bit shifts (1, 1/16, 1/256, 1/4096)
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inline float block_q_n_dot_y(device const block_q4_1 * qb_curr, float sumy, thread float * yl, int il) {
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float d = qb_curr->d;
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float d = qb_curr->d;
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float m = qb_curr->m;
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float m = qb_curr->m;
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float4 acc = 0.f;
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device const uint16_t * qs = ((device const uint16_t *)qb_curr + 2 + il/2);
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device uint16_t * qs = ((device uint16_t *)qb_curr + 2);
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float2 acc = 0.f;
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for (int i = 0; i < 16; i+=2) {
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for (int i = 0; i < 8; i+=2) {
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acc[0] += yl[i] * (qs[i / 2] & 0x000F);
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acc[0] += yl[i + 0] * (qs[i / 2] & 0x000F)
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acc[1] += yl[i + 16] * (qs[i / 2] & 0x00F0);
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+ yl[i + 1] * (qs[i / 2] & 0x0F00);
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acc[2] += yl[i + 1] * (qs[i / 2] & 0x0F00);
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acc[1] += yl[i + 8] * (qs[i / 2] & 0x00F0)
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acc[3] += yl[i + 17] * (qs[i / 2] & 0xF000);
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+ yl[i + 9] * (qs[i / 2] & 0xF000);
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}
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}
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return d * (acc[0] + acc[1]/16.f + acc[2]/256.f + acc[3]/4096.f) + sumy * m;
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return d * (acc[0] + acc[1]) + sumy * m;
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}
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}
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// putting them in the kernel cause a significant performance penalty
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// putting them in the kernel cause a significant performance penalty
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#define N_DST 4 // each SIMD group works on 4 rows
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#define N_DST 4 // each SIMD group works on 4 rows
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#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
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#define N_SIMDGROUP 2 // number of SIMD groups in a thread group
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#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
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#define N_SIMDWIDTH 32 // assuming SIMD group size is 32
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template<typename block_q_type>
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//Note: This is a template, but strictly speaking it only applies to
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// quantizations where the block size is 32. It also does not
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// giard against the number of rows not being divisible by
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// N_DST, so this is another explicit assumption of the implementation.
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template<typename block_q_type, int nr, int nsg, int nw>
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void mul_vec_q_n_f32(device const void * src0, device const float * src1, device float * dst,
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void mul_vec_q_n_f32(device const void * src0, device const float * src1, device float * dst,
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int64_t ne00, int64_t ne10, int64_t ne0, int64_t ne01,
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int64_t ne00, int64_t ne10, int64_t ne0, int64_t ne01,
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uint2 tgpig, uint tiisg, uint sgitg) {
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uint2 tgpig, uint tiisg, uint sgitg) {
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const int nb = ne00/QK4_0;
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const int nb = ne00/QK4_0;
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const int r0 = tgpig.x;
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const int r0 = tgpig.x;
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const int r1 = tgpig.y;
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const int r1 = tgpig.y;
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device const block_q_type * x = (device const block_q_type *) src0 + (r0 * N_SIMDGROUP + sgitg) * N_DST * nb;
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const int first_row = (r0 * nsg + sgitg) * nr;
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device const block_q_type * x = (device const block_q_type *) src0 + first_row * nb;
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device const float * y = (device const float *) src1 + r1*ne10;
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device const float * y = (device const float *) src1 + r1*ne10;
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float4 y_curr[8]; // src1 vector cache
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float yl[16]; // src1 vector cache
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float sumf[N_DST]={0.f}, all_sum;
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float sumf[nr]={0.f};
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thread float * yl=(thread float *)y_curr;
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// each thread in a SIMD group deals with 1 block.
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const int ix = tiisg/2;
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for (int column = 0; column < nb / N_SIMDWIDTH; column++) {
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const int il = 8*(tiisg%2);
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device const float * yb = y + ix * QK4_0 + il;
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// each thread in a SIMD group deals with half a block.
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for (int ib = ix; ib < nb; ib += nw/2) {
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float sumy = 0;
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float sumy = 0;
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for (int i = 0; i < QK4_0 / 4; i++) {
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for (int i = 0; i < 8; i += 2) {
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y_curr[i] = *((device float4 *)(y + N_SIMDWIDTH * (tiisg + column * QK4_0)) + i);
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sumy += yb[i] + yb[i+1];
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sumy += y_curr[i][0] + y_curr[i][1] + y_curr[i][2] + y_curr[i][3];
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yl[i+0] = yb[i+ 0];
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yl[i+1] = yb[i+ 1]/256.f;
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sumy += yb[i+16] + yb[i+17];
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yl[i+8] = yb[i+16]/16.f;
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yl[i+9] = yb[i+17]/4096.f;
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}
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}
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for (int row = 0; row < N_DST; row++) {
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for (int row = 0; row < nr; row++) {
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sumf[row] += block_q_n_dot_y(x+(tiisg + row * nb + column * N_SIMDWIDTH), sumy, yl);
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sumf[row] += block_q_n_dot_y(x+ib+row*nb, sumy, yl, il);
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}
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}
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yb += QK4_0 * 16;
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}
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}
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// from now loads two rows every time and 16 blocks per row
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for (int row = 0; row < nr; ++row) {
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int ir = tiisg / (N_SIMDWIDTH / 2);
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const float tot = simd_sum(sumf[row]);
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int ib = tiisg % (N_SIMDWIDTH / 2);
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if (tiisg == 0 && first_row + row < ne01) {
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for (int ind = 0; ind < (nb % N_SIMDWIDTH + N_SIMDWIDTH / 2 - 1)/(N_SIMDWIDTH / 2); ind++) {
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dst[r1*ne0 + first_row + row] = tot;
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int nb_start = (nb / N_SIMDWIDTH) * N_SIMDWIDTH + ind * (N_SIMDWIDTH / 2); //where the left blocks start
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float sumy = 0;
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for (int i = 0; i < QK4_0 / 4; i++) {
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y_curr[i] = *((device float4 *)(y + (nb_start + ib) * QK4_0) + i);
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sumy += y_curr[i][0] + y_curr[i][1] + y_curr[i][2] + y_curr[i][3];
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}
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for (int row = 0; row < N_DST; row+=2) {
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if (nb_start + ib < nb) {
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sumf[row + ir] += block_q_n_dot_y(x + (nb_start + ib + (row + ir) * nb), sumy, yl);
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}
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}
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}
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for (int row = 0; row < N_DST; ++row) {
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all_sum = simd_sum(sumf[row]);
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if (tiisg == 0 && ((r0 * N_SIMDGROUP + sgitg) * N_DST + row) < ne01) {
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dst[r1*ne0 + (r0 * N_SIMDGROUP + sgitg) * N_DST + row] = all_sum;
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}
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}
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}
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}
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}
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}
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@ -483,7 +486,7 @@ kernel void kernel_mul_mat_q4_0_f32(
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uint2 tgpig[[threadgroup_position_in_grid]],
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uint2 tgpig[[threadgroup_position_in_grid]],
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uint tiisg[[thread_index_in_simdgroup]],
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uint tiisg[[thread_index_in_simdgroup]],
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uint sgitg[[simdgroup_index_in_threadgroup]]) {
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uint sgitg[[simdgroup_index_in_threadgroup]]) {
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mul_vec_q_n_f32<block_q4_0>(src0,src1,dst,ne00,ne10,ne0,ne01,tgpig,tiisg,sgitg);
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mul_vec_q_n_f32<block_q4_0, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne10,ne0,ne01,tgpig,tiisg,sgitg);
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}
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}
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kernel void kernel_mul_mat_q4_1_f32(
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kernel void kernel_mul_mat_q4_1_f32(
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@ -497,7 +500,7 @@ kernel void kernel_mul_mat_q4_1_f32(
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uint2 tgpig[[threadgroup_position_in_grid]],
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uint2 tgpig[[threadgroup_position_in_grid]],
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uint tiisg[[thread_index_in_simdgroup]],
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uint tiisg[[thread_index_in_simdgroup]],
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uint sgitg[[simdgroup_index_in_threadgroup]]) {
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uint sgitg[[simdgroup_index_in_threadgroup]]) {
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mul_vec_q_n_f32<block_q4_1>(src0,src1,dst,ne00,ne10,ne0,ne01,tgpig,tiisg,sgitg);
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mul_vec_q_n_f32<block_q4_1, N_DST, N_SIMDGROUP, N_SIMDWIDTH>(src0,src1,dst,ne00,ne10,ne0,ne01,tgpig,tiisg,sgitg);
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}
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}
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kernel void kernel_mul_mat_f16_f32(
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kernel void kernel_mul_mat_f16_f32(
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