submission 565944
wuxin · python · License unknown
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No package. Vendor the mirrored source: 78 lines, June 9 Researcher Reciprocity License v1.0.
v205.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-amd-moe-mxfp4-565944?include=source"interfacepython
Compatibility
measured onAMD Instinct MI355X
declared hardwareAMD Instinct MI355X
architecturesgfx950
dtypesbf16, fp32, fp8_e8m0, int32, mxfp4
Benchmark evidence
1 measurement across 1 GPU, fastest first.
Operation / workload
Hardware
Latency
Rank
Observed
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:8015a8418a0c0e37e3b9ad96781e0c255febfdab687386a95edaddb236d372a9
license declaredunknown
license concludedunknown
authorswuxin
imported2026-08-26
Kernel source
v205.py78 lines
#!POPCORN leaderboard amd-moe-mxfp4
#!POPCORN gpu MI355X
import inspect
from task import input_t, output_t
from aiter import ActivationType, QuantType
from aiter.fused_moe import fused_moe
# Known kernels from your logs (N=256 cases)
K1_SMALLM = "moe_ck2stages_gemm1_64x32x32x128_1x1_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16"
K1_LARGEM = "moe_ck2stages_gemm1_256x32x128x128_1x4_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16"
K2_COMMON = "moe_ck2stages_gemm2_64x32x32x128_1x1_MulABScaleExpertWeightShuffled_v1_Nswizzle0_Quant3_MulRoutedWeight1_FP4X2_FP4X2_B16"
# Cache signature once (avoid per-call overhead)
try:
_SIG = inspect.signature(fused_moe)
FUSED_PARAMS = set(_SIG.parameters.keys())
except Exception:
FUSED_PARAMS = set()
def custom_kernel(data: input_t) -> output_t:
(
hidden_states,
gate_up_weight,
down_weight,
gate_up_weight_scale,
down_weight_scale,
gate_up_weight_shuffled,
down_weight_shuffled,
gate_up_weight_scale_shuffled,
down_weight_scale_shuffled,
topk_weights,
topk_ids,
config,
) = data
hidden_pad = config["d_hidden_pad"] - config["d_hidden"]
intermediate_pad = config["d_expert_pad"] - config["d_expert"]
K = int(gate_up_weight_shuffled.shape[-1]) # 7168
N = int(config["d_expert_pad"]) # 256/512/2048
TOPK = int(topk_ids.shape[-1]) # 9
M = int(hidden_states.shape[0]) # 16/128/512
kwargs = dict(
expert_mask=None,
activation=ActivationType.Silu,
quant_type=QuantType.per_1x32,
doweight_stage1=False,
w1_scale=gate_up_weight_scale_shuffled,
w2_scale=down_weight_scale_shuffled,
a1_scale=None,
a2_scale=None,
hidden_pad=hidden_pad,
intermediate_pad=intermediate_pad,
)
# Keep your only proven-positive lever if supported (H1-style)
if (K == 7168 and TOPK == 9 and N in (512, 2048) and M >= 256) and ("non_temporal_load" in FUSED_PARAMS):
kwargs["non_temporal_load"] = True
# Try kernel forcing ONLY for default family (N=512/2048)
if (K == 7168 and TOPK == 9 and N in (512, 2048)):
k1 = K1_SMALLM if M <= 32 else K1_LARGEM
if "kernelName1" in FUSED_PARAMS:
kwargs["kernelName1"] = k1
if "kernelName2" in FUSED_PARAMS:
kwargs["kernelName2"] = K2_COMMON
# IMPORTANT: positional for first 5 args
return fused_moe(
hidden_states,
gate_up_weight_shuffled,
down_weight_shuffled,
topk_weights,
topk_ids,
**kwargs,
)scrolls · 78 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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