@@ -7,35 +7,85 @@ layout(local_size_x_id = 0, local_size_y = 1, local_size_z = 1) in;
77
88FLOAT_TYPE temp[NUM_COLS][NUM_ROWS];
99
10- void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i, const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
10+ void calc_superblock(const uint a_offset, const uint b_offset, const uint ib32, const uint i,
11+ const uint num_blocks_per_row, const uint first_row, const uint num_rows) {
12+ // Compute starting index in matrix B for this superblock
1113 const uint y_idx = i * QUANT_K + 32 * ib32;
12-
1314 uint ibi = a_offset / QUANT_K + first_row * num_blocks_per_row + i;
15+
16+ // Precompute indices for quantization lookup tables
17+ const uint qh_base = 2 * ib32;
18+ const uint qs_base = 4 * ib32;
19+ const uint sc_index = ib32 / 2;
20+ const uint sc_shift = 6 * (ib32 & 1);
21+
22+ // Loop over rows in the superblock
1423 [[unroll]] for (uint n = 0; n < num_rows; ++n) {
24+ // Load per-block scales and shift for quantization
1525 const uint16_t[4] scales = data_a[ibi].scales;
1626 const u16vec4 s = u16vec4(scales[0], scales[1], scales[2], scales[3]) >> 12;
1727 const float d = float(unpackHalf2x16(s.x | (s.y << 4) | (s.z << 8) | (s.w << 12)).x);
28+ const uint sc = data_a[ibi].scales[sc_index] >> sc_shift;
1829
19- const uint sc = data_a[ibi].scales[ib32 / 2] >> (6 * (ib32 & 1));
30+ // Temporary caches for decoding
31+ FLOAT_TYPE dl_cache[4];
32+ uint16_t gvf_cache[4];
33+ float delta_cache[4];
34+
35+ // Precompute the multiplier and lookup values for 4 sub-blocks
2036 [[unroll]] for (uint l = 0; l < 4; ++l) {
21- const uint qh = data_a[ibi].qh[2 * ib32 + l / 2] >> (4 * (l&1));
22- const uint qs = data_a[ibi].qs[4 * ib32 + l];
23- const float delta = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
24- const float dl = d * (2 * bitfieldExtract(sc, 3 * int(l / 2), 3) + 1);
25-
26- const int16_t grid = int16_t(iq1s_grid[qs | ((qh & 7) << 8)]);
27-
28- [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
29- vec4 b0 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 0]);
30- vec4 b4 = vec4(data_b_v4[(j*p.batch_stride_b + b_offset + y_idx) / 4 + 2*l + 1]);
31-
32- FLOAT_TYPE sum = FLOAT_TYPE(0.0);
33- [[unroll]] for (int k = 0; k < 4; ++k) {
34- sum = fma(FLOAT_TYPE(b0[k]), bitfieldExtract(grid, 2 * k, 2) + delta,
35- fma(FLOAT_TYPE(b4[k]), bitfieldExtract(grid, 8 + 2 * k, 2) + delta, sum));
36- }
37- temp[j][n] = fma(dl, sum, temp[j][n]);
37+ dl_cache[l] = FLOAT_TYPE(d * (2 * bitfieldExtract(sc, 3 * int(l / 2), 3) + 1));
38+ const uint qh = data_a[ibi].qh[qh_base + l / 2] >> (4 * (l & 1));
39+ const uint qs = data_a[ibi].qs[qs_base + l];
40+ gvf_cache[l] = iq1s_grid[qs | ((qh & 7) << 8)];
41+ delta_cache[l] = ((qh & 8) != 0) ? -IQ1M_DELTA : IQ1M_DELTA;
42+ }
43+
44+ // Loop over columns of the output
45+ [[unroll]] for (uint j = 0; j < NUM_COLS; ++j) {
46+ // Compute base index for matrix B
47+ const uint base_b_idx = (j * p.batch_stride_b + b_offset + y_idx) / 4;
48+ vec4 b_vals[8];
49+
50+ // Load 8 vec4 values from matrix B
51+ [[unroll]] for (int idx = 0; idx < 8; ++idx) {
52+ b_vals[idx] = vec4(data_b_v4[base_b_idx + idx]);
53+ }
54+
55+ FLOAT_TYPE col_sum = FLOAT_TYPE(0.0);
56+
57+ // Loop over sub-blocks
58+ [[unroll]] for (uint l = 0; l < 4; ++l) {
59+ const uint16_t grid = gvf_cache[l];
60+ const float dl = dl_cache[l];
61+
62+ // Decode 8 2-bit fbits from gvf_cache
63+ float f0 = float(bitfieldExtract(grid, 0, 2));
64+ float f1 = float(bitfieldExtract(grid, 2, 2));
65+ float f2 = float(bitfieldExtract(grid, 4, 2));
66+ float f3 = float(bitfieldExtract(grid, 6, 2));
67+ float f4 = float(bitfieldExtract(grid, 8, 2));
68+ float f5 = float(bitfieldExtract(grid, 10, 2));
69+ float f6 = float(bitfieldExtract(grid, 12, 2));
70+ float f7 = float(bitfieldExtract(grid, 14, 2));
71+
72+ // Pack into vec4 for vectorized FMA
73+ const vec4 fbits_v0 = vec4(f0, f1, f2, f3);
74+ const vec4 fbits_v1 = vec4(f4, f5, f6, f7);
75+ const vec4 delta_v = vec4(delta_cache[l]);
76+
77+ // Vectorized fused multiply-add
78+ vec4 sum_v = fma(b_vals[2*l + 0], fbits_v0 + delta_v, vec4(0.0));
79+ sum_v = fma(b_vals[2*l + 1], fbits_v1 + delta_v, sum_v);
80+
81+ // Horizontal add to get scalar sum
82+ FLOAT_TYPE sum = sum_v.x + sum_v.y + sum_v.z + sum_v.w;
83+
84+ // Accumulate to column sum
85+ col_sum = fma(dl, sum, col_sum);
3886 }
87+ // Write result to temporary buffer
88+ temp[j][n] += col_sum;
3989 }
4090 ibi += num_blocks_per_row;
4191 }
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