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ggml : loop tiling optimizations for scalar path (ggml/898)
Apply a loop tiling technique to the generic path, which provides performance upside for ISAs with enough registers to take advantage of it. Also helps the compiler optimize this path.
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@ -4190,15 +4190,18 @@ void ggml_vec_dot_q4_0_q8_0(int n, float * restrict s, size_t bs, const void * r
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sumf = hsum_float_4x4(acc_0, acc_1, acc_2, acc_3);
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#endif
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for (; ib < nb; ++ib) {
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int sumi = 0;
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int sumi0 = 0;
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int sumi1 = 0;
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for (int j = 0; j < qk/2; ++j) {
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const int v0 = (x[ib].qs[j] & 0x0F) - 8;
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const int v1 = (x[ib].qs[j] >> 4) - 8;
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sumi += (v0 * y[ib].qs[j]) + (v1 * y[ib].qs[j + qk/2]);
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sumi0 += (v0 * y[ib].qs[j]);
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sumi1 += (v1 * y[ib].qs[j + qk/2]);
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}
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int sumi = sumi0 + sumi1;
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sumf += sumi*GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d);
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}
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@ -4474,15 +4477,18 @@ void ggml_vec_dot_q4_1_q8_1(int n, float * restrict s, size_t bs, const void * r
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sumf = hsum_float_8(acc) + summs;
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#endif
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for (; ib < nb; ++ib) {
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int sumi = 0;
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int sumi0 = 0
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int sumi1 = 0;
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for (int j = 0; j < qk/2; ++j) {
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const int v0 = (x[ib].qs[j] & 0x0F);
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const int v1 = (x[ib].qs[j] >> 4);
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sumi += (v0 * y[ib].qs[j]) + (v1 * y[ib].qs[j + qk/2]);
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sumi0 += (v0 * y[ib].qs[j]);
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sumi1 += (v1 * y[ib].qs[j + qk/2]);
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}
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int sumi = sumi0 + sumi1;
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sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
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}
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@ -4823,18 +4829,21 @@ void ggml_vec_dot_q5_0_q8_0(int n, float * restrict s, size_t bs, const void * r
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uint32_t qh;
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memcpy(&qh, x[ib].qh, sizeof(qh));
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int sumi = 0;
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int sumi0 = 0;
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int sumi1 = 0;
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for (int j = 0; j < qk/2; ++j) {
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const uint8_t xh_0 = ((qh & (1u << (j + 0 ))) >> (j + 0 )) << 4;
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const uint8_t xh_1 = ((qh & (1u << (j + 16))) >> (j + 12));
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const int32_t x0 = ((x[ib].qs[j] & 0x0F) | xh_0) - 16;
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const int32_t x1 = ((x[ib].qs[j] >> 4) | xh_1) - 16;
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const int32_t x0 = (int8_t)(((x[ib].qs[j] & 0x0F) | xh_0) - 16);
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const int32_t x1 = (int8_t)(((x[ib].qs[j] >> 4) | xh_1) - 16);
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sumi += (x0 * y[ib].qs[j]) + (x1 * y[ib].qs[j + qk/2]);
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sumi0 += (x0 * y[ib].qs[j]);
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sumi1 += (x1 * y[ib].qs[j + qk/2]);
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}
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int sumi = sumi0 + sumi1;
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sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d)) * sumi;
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}
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@ -5194,7 +5203,8 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
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uint32_t qh;
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memcpy(&qh, x[ib].qh, sizeof(qh));
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int sumi = 0;
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int sumi0 = 0;
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int sumi1 = 0;
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for (int j = 0; j < qk/2; ++j) {
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const uint8_t xh_0 = ((qh >> (j + 0)) << 4) & 0x10;
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@ -5203,9 +5213,11 @@ void ggml_vec_dot_q5_1_q8_1(int n, float * restrict s, size_t bs, const void * r
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const int32_t x0 = (x[ib].qs[j] & 0xF) | xh_0;
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const int32_t x1 = (x[ib].qs[j] >> 4) | xh_1;
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sumi += (x0 * y[ib].qs[j]) + (x1 * y[ib].qs[j + qk/2]);
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sumi0 += (x0 * y[ib].qs[j]);
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sumi1 += (x1 * y[ib].qs[j + qk/2]);
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}
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int sumi = sumi0 + sumi1;
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sumf += (GGML_FP16_TO_FP32(x[ib].d)*GGML_FP16_TO_FP32(y[ib].d))*sumi + GGML_FP16_TO_FP32(x[ib].m)*GGML_FP16_TO_FP32(y[ib].s);
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}
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