[CK_TILE] MX GEMM, non-preshuffled and RCR layout#3709
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[CK_TILE] MX GEMM, non-preshuffled and RCR layout#3709
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ThomasNing
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Feb 11, 2026
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ThomasNing
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Thank you for the PR @samremes Could I know when will be the estimation we could have the mxfp4 and mxfp8 working?
| (std::is_same<T, pk_fp6x16_t>::value && (N == 1)), | ||
| (N == 1 || N == 2 || N == 4 || N == 8 || N == 16 || N == 32) || | ||
| (std::is_same<T, pk_fp4_t>::value && | ||
| (N == 1 || N == 2 || N == 4 || N == 8 || N == 16 || N == 32))), |
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The largest granularity is b128, so I don't think we need the N = 32.
| merge_sequences(sequence<1, 1>{}, b_warp_y_lengths)); | ||
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| // get B scale for this N-K tile using get_y_sliced_thread_data | ||
| auto scale_b_slice = scale_b_tensor.get_y_sliced_thread_data( |
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For the packed data of scale, it is better that we could use the load_tile_offset + get_thread_buffer() method
| // warp GEMM with MX scaling | ||
| // Cast e8m0_t to int32_t, use OpSel=0 (least significant byte) | ||
| constexpr index_t kOpSel = 0; // Always use OpSel=0 | ||
| WarpGemm{}.template operator()<kOpSel, kOpSel>( |
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We could load 32 bits together and select based on the iterations.
| }); | ||
| } | ||
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| // C += A * B with MX scaling |
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We could migrate that to the gemm_mx block folder.
| // C Distributed tensor: register | ||
| // MX scaling support with OpSel | ||
| template <typename Problem> | ||
| struct BaseMXGemmPipelineAgBgCrCompAsync |
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If we need, we could first limit it without async feature :)
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