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1 | 1 | #version 450  | 
 | 2 | +#extension GL_EXT_shader_explicit_arithmetic_types : require  | 
2 | 3 | 
 
  | 
3 | 4 | #include "mul_mat_vec_base.comp"  | 
4 | 5 | 
 
  | 
@@ -32,38 +33,67 @@ void main() {  | 
32 | 33 |     const uint s_offset = 8*v_im;  | 
33 | 34 |     const uint y_offset = 128*v_im + l0;  | 
34 | 35 | 
 
  | 
35 |  | -    tmp[16 * ix + tid] = FLOAT_TYPE(0.0); // partial sum for thread in warp  | 
 | 36 | +    FLOAT_TYPE temp = FLOAT_TYPE(0.0); // partial sum for thread in warp  | 
36 | 37 | 
 
  | 
37 | 38 |     [[unroll]] for (uint i = ix; i < num_blocks_per_row; i += K_QUANTS_PER_ITERATION) {  | 
38 | 39 |         const uint y_idx = i * QUANT_K + y_offset;  | 
39 | 40 | 
 
  | 
40 |  | -        const FLOAT_TYPE dall = FLOAT_TYPE(data_a[ib0 + i].d.x);  | 
41 |  | -        const FLOAT_TYPE dmin = FLOAT_TYPE(data_a[ib0 + i].d.y);  | 
 | 41 | +        f16vec2 d = data_a[ib0 + i].d;  | 
 | 42 | +        const FLOAT_TYPE dall = d.x;  | 
 | 43 | +        const FLOAT_TYPE dmin = d.y;  | 
 | 44 | + | 
 | 45 | +        B_TYPE_VEC2 b0 = data_b_v2[(b_offset + y_idx) / 2 + 0];  | 
 | 46 | +        B_TYPE_VEC2 b16 = data_b_v2[(b_offset + y_idx) / 2 + 8];  | 
 | 47 | +        B_TYPE_VEC2 b32 = data_b_v2[(b_offset + y_idx) / 2 + 16];  | 
 | 48 | +        B_TYPE_VEC2 b48 = data_b_v2[(b_offset + y_idx) / 2 + 24];  | 
 | 49 | +        B_TYPE_VEC2 b64 = data_b_v2[(b_offset + y_idx) / 2 + 32];  | 
 | 50 | +        B_TYPE_VEC2 b80 = data_b_v2[(b_offset + y_idx) / 2 + 40];  | 
 | 51 | +        B_TYPE_VEC2 b96 = data_b_v2[(b_offset + y_idx) / 2 + 48];  | 
 | 52 | +        B_TYPE_VEC2 b112 = data_b_v2[(b_offset + y_idx) / 2 + 56];  | 
 | 53 | + | 
 | 54 | +        uint32_t s0_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 0];  | 
 | 55 | +        uint32_t s4_u32 = data_a_packed32[ib0 + i].scales[s_offset / 4 + 1];  | 
 | 56 | + | 
 | 57 | +        uint32_t s0_lo4_u32 = s0_u32 & 0x0F0F0F0F;  | 
 | 58 | +        uint32_t s0_hi4_u32 = (s0_u32 >> 4) & 0x0F0F0F0F;  | 
 | 59 | +        uint32_t s4_lo4_u32 = s4_u32 & 0x0F0F0F0F;  | 
 | 60 | +        uint32_t s4_hi4_u32 = (s4_u32 >> 4) & 0x0F0F0F0F;  | 
 | 61 | + | 
 | 62 | +        uvec4 s0_lo4 = uvec4(unpack8(s0_lo4_u32));  | 
 | 63 | +        uvec4 s4_lo4 = uvec4(unpack8(s4_lo4_u32));  | 
 | 64 | +        uvec4 s0_hi4 = uvec4(unpack8(s0_hi4_u32));  | 
 | 65 | +        uvec4 s4_hi4 = uvec4(unpack8(s4_hi4_u32));  | 
 | 66 | + | 
 | 67 | +        uint16_t qs0_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 0];  | 
 | 68 | +        uint16_t qs16_u16 = data_a_packed16[ib0 + i].qs[q_offset / 2 + 8];  | 
 | 69 | +        uvec2 qs0 =  uvec2(unpack8(qs0_u16));  | 
 | 70 | +        uvec2 qs16 = uvec2(unpack8(qs16_u16));  | 
42 | 71 | 
 
  | 
43 | 72 |         FLOAT_TYPE sum1 = FLOAT_TYPE(0.0);  | 
44 | 73 |         FLOAT_TYPE sum2 = FLOAT_TYPE(0.0);  | 
45 |  | -        for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {  | 
46 |  | -            sum1 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 0] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 0) & 3),  | 
47 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 1] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 0) & 3),  | 
48 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 2] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 2) & 3),  | 
49 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 3] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 2) & 3),  | 
50 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 4] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 4) & 3),  | 
51 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 5] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 4) & 3),  | 
52 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 6] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l + 0] >> 6) & 3),  | 
53 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE(data_a[ib0 + i].scales[s_offset + 7] & 0xF) * FLOAT_TYPE((data_a[ib0 + i].qs[q_offset + l +16] >> 6) & 3), sum1))))))));  | 
54 |  | -            sum2 = fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +  0]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 0] >> 4) & 0xF),  | 
55 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 16]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 1] >> 4) & 0xF),  | 
56 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 32]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 2] >> 4) & 0xF),  | 
57 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 48]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 3] >> 4) & 0xF),  | 
58 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 64]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 4] >> 4) & 0xF),  | 
59 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 80]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 5] >> 4) & 0xF),  | 
60 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l + 96]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 6] >> 4) & 0xF),  | 
61 |  | -                   fma(FLOAT_TYPE(data_b[b_offset + y_idx + l +112]), FLOAT_TYPE((data_a[ib0 + i].scales[s_offset + 7] >> 4) & 0xF), sum2))))))));  | 
 | 74 | +        [[unroll]] for (int l = 0; l < K_QUANTS_PER_ITERATION; ++l) {  | 
 | 75 | +            sum1 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 0) & 3),  | 
 | 76 | +                   fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_lo4[1]) * FLOAT_TYPE((qs16[l] >> 0) & 3),  | 
 | 77 | +                   fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 2) & 3),  | 
 | 78 | +                   fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_lo4[3]) * FLOAT_TYPE((qs16[l] >> 2) & 3),  | 
 | 79 | +                   fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_lo4[0]) * FLOAT_TYPE((qs0[l]  >> 4) & 3),  | 
 | 80 | +                   fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_lo4[1]) * FLOAT_TYPE((qs16[l] >> 4) & 3),  | 
 | 81 | +                   fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_lo4[2]) * FLOAT_TYPE((qs0[l]  >> 6) & 3),  | 
 | 82 | +                   fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_lo4[3]) * FLOAT_TYPE((qs16[l] >> 6) & 3), sum1))))))));  | 
 | 83 | +            sum2 = fma(FLOAT_TYPE(b0[l]),   FLOAT_TYPE(s0_hi4[0]),  | 
 | 84 | +                   fma(FLOAT_TYPE(b16[l]),  FLOAT_TYPE(s0_hi4[1]),  | 
 | 85 | +                   fma(FLOAT_TYPE(b32[l]),  FLOAT_TYPE(s0_hi4[2]),  | 
 | 86 | +                   fma(FLOAT_TYPE(b48[l]),  FLOAT_TYPE(s0_hi4[3]),  | 
 | 87 | +                   fma(FLOAT_TYPE(b64[l]),  FLOAT_TYPE(s4_hi4[0]),  | 
 | 88 | +                   fma(FLOAT_TYPE(b80[l]),  FLOAT_TYPE(s4_hi4[1]),  | 
 | 89 | +                   fma(FLOAT_TYPE(b96[l]),  FLOAT_TYPE(s4_hi4[2]),  | 
 | 90 | +                   fma(FLOAT_TYPE(b112[l]), FLOAT_TYPE(s4_hi4[3]), sum2))))))));  | 
62 | 91 |         }  | 
63 |  | -        const uint tmp_idx = 16 * ix + tid;  | 
64 |  | -        tmp[tmp_idx] = fma(dall, sum1, fma(-dmin, sum2, tmp[tmp_idx]));  | 
 | 92 | +        temp = fma(dall, sum1, fma(-dmin, sum2, temp));  | 
65 | 93 |     }  | 
66 | 94 | 
 
  | 
 | 95 | +    tmp[gl_LocalInvocationID.x] = temp;  | 
 | 96 | + | 
67 | 97 |     // sum up partial sums and write back result  | 
68 | 98 |     barrier();  | 
69 | 99 |     [[unroll]] for (uint s = 16; s > 0; s >>= 1) {  | 
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