|
| 1 | +import functools |
| 2 | +from .op import CKTileGemmOperation |
| 3 | + |
| 4 | + |
| 5 | +@functools.cache |
| 6 | +def ops(): |
| 7 | + """ |
| 8 | + Generate the supported instance dataclasses |
| 9 | + """ |
| 10 | + import itertools |
| 11 | + |
| 12 | + compute_v3_instances = [ |
| 13 | + CKTileGemmOperation( |
| 14 | + layout_a=layout_a, |
| 15 | + layout_b=layout_b, |
| 16 | + layout_c=layout_c, |
| 17 | + datatype_a=datatype_a, |
| 18 | + datatype_b=datatype_b, |
| 19 | + datatype_c=datatype_c, |
| 20 | + tile_m=tile_m, |
| 21 | + tile_n=tile_n, |
| 22 | + tile_k=tile_k, |
| 23 | + warp_m=warp_m, |
| 24 | + warp_n=warp_n, |
| 25 | + warp_k=warp_k, |
| 26 | + warp_tile_m=warp_tile_m, |
| 27 | + warp_tile_n=warp_tile_n, |
| 28 | + warp_tile_k=warp_tile_k, |
| 29 | + m_is_padded=m_is_padded, |
| 30 | + n_is_padded=n_is_padded, |
| 31 | + k_is_padded=k_is_padded, |
| 32 | + pipeline="CompV3", |
| 33 | + scheduler="Intrawave", |
| 34 | + epilogue=epilogue, |
| 35 | + ) |
| 36 | + for (layout_a, layout_b, layout_c) in [ |
| 37 | + ("Row", "Row", "Row"), |
| 38 | + ("Row", "Col", "Row"), |
| 39 | + ] |
| 40 | + for (datatype_a, datatype_b, datatype_c) in [("FP16",) * 3, ("BF16",) * 3] |
| 41 | + for (tile_m, tile_n, tile_k) in [(256, 256, 32), (256, 256, 64)] |
| 42 | + for (warp_m, warp_n, warp_k) in [(2, 2, 1)] |
| 43 | + for (warp_tile_m, warp_tile_n, warp_tile_k) in [(32, 32, 16)] |
| 44 | + for m_is_padded in ["true", "false"] |
| 45 | + for n_is_padded in ["true", "false"] |
| 46 | + for k_is_padded in ["true", "false"] |
| 47 | + for epilogue in ["Default", "CShuffle"] |
| 48 | + ] |
| 49 | + |
| 50 | + compute_v4_instances = [ |
| 51 | + CKTileGemmOperation( |
| 52 | + layout_a=layout_a, |
| 53 | + layout_b=layout_b, |
| 54 | + layout_c=layout_c, |
| 55 | + datatype_a=datatype_a, |
| 56 | + datatype_b=datatype_b, |
| 57 | + datatype_c=datatype_c, |
| 58 | + tile_m=tile_m, |
| 59 | + tile_n=tile_n, |
| 60 | + tile_k=tile_k, |
| 61 | + warp_m=warp_m, |
| 62 | + warp_n=warp_n, |
| 63 | + warp_k=warp_k, |
| 64 | + warp_tile_m=warp_tile_m, |
| 65 | + warp_tile_n=warp_tile_n, |
| 66 | + warp_tile_k=warp_tile_k, |
| 67 | + m_is_padded=m_is_padded, |
| 68 | + n_is_padded=n_is_padded, |
| 69 | + k_is_padded=k_is_padded, |
| 70 | + pipeline="CompV4", |
| 71 | + scheduler="Intrawave", |
| 72 | + epilogue=epilogue, |
| 73 | + ) |
| 74 | + for (layout_a, layout_b, layout_c) in [ |
| 75 | + ("Row", "Row", "Row"), |
| 76 | + ("Row", "Col", "Row"), |
| 77 | + ] |
| 78 | + for (datatype_a, datatype_b, datatype_c) in [("FP16",) * 3, ("BF16",) * 3] |
| 79 | + for (tile_m, tile_n, tile_k) in [ |
| 80 | + (256, 256, 32) |
| 81 | + ] # half the tile size since it has double buffering |
| 82 | + for (warp_m, warp_n, warp_k) in [(2, 2, 1)] |
| 83 | + for (warp_tile_m, warp_tile_n, warp_tile_k) in [(32, 32, 16)] |
| 84 | + for m_is_padded in ["true", "false"] |
| 85 | + for n_is_padded in ["true", "false"] |
| 86 | + for k_is_padded in ["true", "false"] |
| 87 | + for epilogue in ["Default", "CShuffle"] |
| 88 | + ] |
| 89 | + |
| 90 | + mem_instances = [ |
| 91 | + CKTileGemmOperation( |
| 92 | + layout_a=layout_a, |
| 93 | + layout_b=layout_b, |
| 94 | + layout_c=layout_c, |
| 95 | + datatype_a=datatype_a, |
| 96 | + datatype_b=datatype_b, |
| 97 | + datatype_c=datatype_c, |
| 98 | + tile_m=tile_m, |
| 99 | + tile_n=tile_n, |
| 100 | + tile_k=tile_k, |
| 101 | + warp_m=warp_m, |
| 102 | + warp_n=warp_n, |
| 103 | + warp_k=warp_k, |
| 104 | + warp_tile_m=warp_tile_m, |
| 105 | + warp_tile_n=warp_tile_n, |
| 106 | + warp_tile_k=warp_tile_k, |
| 107 | + m_is_padded=m_is_padded, |
| 108 | + n_is_padded=n_is_padded, |
| 109 | + k_is_padded=k_is_padded, |
| 110 | + pipeline="Mem", |
| 111 | + scheduler=scheduler, |
| 112 | + epilogue=epilogue, |
| 113 | + ) |
| 114 | + for (layout_a, layout_b, layout_c) in [ |
| 115 | + ("Row", "Row", "Row"), |
| 116 | + ("Row", "Col", "Row"), |
| 117 | + ] |
| 118 | + for (datatype_a, datatype_b, datatype_c) in [("FP16",) * 3, ("BF16",) * 3] |
| 119 | + for (tile_m, tile_n, tile_k) in [(256, 256, 32), (256, 256, 64)] |
| 120 | + for (warp_m, warp_n, warp_k) in [(2, 2, 1)] |
| 121 | + for (warp_tile_m, warp_tile_n, warp_tile_k) in [(32, 32, 16)] |
| 122 | + for m_is_padded in ["true", "false"] |
| 123 | + for n_is_padded in ["true", "false"] |
| 124 | + for k_is_padded in ["true", "false"] |
| 125 | + for scheduler in ["Intrawave", "Interwave"] |
| 126 | + for epilogue in ["Default", "CShuffle"] |
| 127 | + ] |
| 128 | + |
| 129 | + return list( |
| 130 | + itertools.chain(compute_v3_instances, compute_v4_instances, mem_instances) |
| 131 | + ) |
| 132 | + |
| 133 | + |
| 134 | +if __name__ == "__main__": |
| 135 | + for op in ops(): |
| 136 | + print(op.name()) |
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