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submission 647504

inje1571 · python · License unknown

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Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 43 lines, June 9 Researcher Reciprocity License v1.0.

submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-amd-mxfp4-mm-647504?include=source"
interfacepython
Compatibility
measured onAMD Instinct MI355X
declared hardwareAMD Instinct MI355X
architecturesgfx950
dtypesbf16, mxfp4

Benchmark evidence

1 measurement across 1 GPU, fastest first.

Operation / workload
Hardware
Latency
Rank
Observed
AMD MXFP4 GEMMsuite of 6 cases
AMD Instinct MI355X
24.2µs
#992 of 1143
2026-03-27

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:abb0e67be3cfec602e6ea59c48533121c19104d429c585883b3407ff5d0496cc
license declaredunknown
license concludedunknown
authorsinje1571
imported2026-08-26

Techniques

Extracted from the mirrored source by pattern, never inferred. Each row cites its line.

fp4FP4 quant + FP4 GEMM reference: bf16 A, MXFP4 B -> MXFP4 per-1x32 quant A -> gemm_a4w4 -> bf16 C.

Kernel source

submission.py43 lines
#!POPCORN leaderboard amd-mxfp4-mm
#!POPCORN gpu MI355X

"""
FP4 quant + FP4 GEMM reference: bf16 A, MXFP4 B -> MXFP4 per-1x32 quant A -> gemm_a4w4 -> bf16 C.
Quant logic follows aiter op_tests/test_gemm_a4w4.py (get_triton_quant(QuantType.per_1x32)).
"""
from task import input_t, output_t


def custom_kernel(data: input_t) -> output_t:
    """
    Reference: MXFP4 per-1x32 quant on A; B_shuffle, B_scale_sh from generate_input.
    gemm_a4w4 with bpreshuffle=True.
    """
    import aiter
    from aiter import QuantType, dtypes
    from aiter.ops.triton.quant import dynamic_mxfp4_quant 
    from aiter.utility.fp4_utils import e8m0_shuffle

    def _quant_mxfp4(x, shuffle=True):
        x_fp4, bs_e8m0 = dynamic_mxfp4_quant(x)
        if shuffle:
            bs_e8m0 = e8m0_shuffle(bs_e8m0)
        return x_fp4.view(dtypes.fp4x2), bs_e8m0.view(dtypes.fp8_e8m0)
    
    A, B, B_q, B_shuffle, B_scale_sh = data
    A = A.contiguous()
    B = B.contiguous()
    m, k = A.shape
    n, _ = B.shape

    A_q, A_scale_sh = _quant_mxfp4(A, shuffle=True)
    out_gemm = aiter.gemm_a4w4(
        A_q,
        B_shuffle,
        A_scale_sh,
        B_scale_sh,
        dtype=dtypes.bf16,
        bpreshuffle=True,
    )
    return out_gemm
scrolls · 43 lines total

Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0

Best evidence level for this revision: reported

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