submission 528303
josusanmartin · python · License unknown
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No package. Vendor the mirrored source: 303 lines, June 9 Researcher Reciprocity License v1.0.
best.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-amd-moe-mxfp4-528303?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:f5988cf90fbe897b1fcc2e74b51e4ba183004e5a6736c1802932150d690c980f
license declaredunknown
license concludedunknown
authorsjosusanmartin
imported2026-08-15
Kernel source
best.py303 lines
import os
import sys
from pathlib import Path
import pandas as pd
import torch
from aiter import ActivationType, QuantType
from aiter.fused_moe import fused_moe
from task import input_t, output_t
DEBUG_FMOE = os.getenv("JOSU_DEBUG_FMOE") == "1"
FORCE_REBUILD_FMOE = DEBUG_FMOE or os.getenv("JOSU_REBUILD_FMOE") == "1"
CFG_VERSION = "v9"
TARGET_SHAPES = (
(16, 7168, 256, 257, 9),
(128, 7168, 256, 257, 9),
(512, 7168, 256, 257, 9),
(16, 7168, 512, 33, 9),
(128, 7168, 512, 33, 9),
(512, 7168, 512, 33, 9),
(512, 7168, 2048, 33, 9),
)
def _shape_mask(frame: pd.DataFrame, shape: tuple[int, int, int, int, int]) -> pd.Series:
token, model_dim, inter_dim, expert, topk = shape
return (
(frame["token"] == token)
& (frame["model_dim"] == model_dim)
& (frame["inter_dim"] == inter_dim)
& (frame["expert"] == expert)
& (frame["topk"] == topk)
)
def _debug_rows(label: str, frame: pd.DataFrame) -> None:
if not DEBUG_FMOE:
return
cols = [
"token",
"model_dim",
"inter_dim",
"expert",
"topk",
"block_m",
"ksplit",
"run_1stage",
"us",
"kernelName1",
"kernelName2",
"_tag",
]
keep_cols = [col for col in cols if col in frame.columns]
print(f"[best.py] {label}", file=sys.stderr)
for shape in TARGET_SHAPES:
rows = frame.loc[_shape_mask(frame, shape), keep_cols].sort_values("us").head(6)
print(f"[best.py] shape={shape} rows={len(rows)}", file=sys.stderr)
if not rows.empty:
print(rows.to_string(index=False), file=sys.stderr)
def _bootstrap_cfg() -> str:
out_path = Path(f"/tmp/josu_cfg_hybrid_under150_fmoe_{CFG_VERSION}.csv")
if out_path.exists() and out_path.stat().st_size > 0 and not FORCE_REBUILD_FMOE:
return str(out_path)
source_paths = [
Path("/home/runner/aiter/aiter/configs/tuned_fmoe.csv"),
Path(
"/home/runner/aiter/aiter/configs/model_configs/"
"a8w8_blockscale_tuned_fmoe_qwen3_235b.csv"
),
Path("/home/runner/aiter/aiter/configs/model_configs/dsv3_fp4_tuned_fmoe.csv"),
]
frames = [pd.read_csv(path) for path in source_paths if path.exists()]
merged = pd.concat(frames, ignore_index=True)
untuned_path = Path("/home/runner/aiter/aiter/configs/untuned_fmoe.csv")
keys = pd.read_csv(untuned_path, nrows=0).columns.tolist()
if "cu_num" not in keys:
keys.append("cu_num")
defaults = {column: "" for column in merged.columns}
defaults.update(
{
"cu_num": 256,
"act_type": "ActivationType.Silu",
"dtype": "torch.bfloat16",
"q_dtype_a": "torch.float4_e2m1fn_x2",
"q_dtype_w": "torch.float4_e2m1fn_x2",
"q_type": "QuantType.per_1x32",
"use_g1u1": 1,
"doweight_stage1": 0,
"block_m": 32,
"ksplit": 0,
"us1": 0.0,
"kernelName1": "",
"err1": "0.0%",
"us2": 0.0,
"kernelName2": "",
"err2": "0.0%",
"us": 0.0,
"run_1stage": 0,
"tflops": 0.0,
"bw": 0.0,
"_tag": "",
}
)
online_tuned_rows = [
{
"token": 16,
"model_dim": 7168,
"inter_dim": 256,
"expert": 257,
"topk": 9,
"block_m": 32,
"ksplit": 2,
"us": 0.0,
"us1": 0.0,
"kernelName1": "",
"err1": "0.0%",
"us2": 0.0,
"kernelName2": "",
"err2": "0.0%",
"run_1stage": 0,
"tflops": 0.0,
"bw": 0.0,
},
{
"token": 128,
"model_dim": 7168,
"inter_dim": 256,
"expert": 257,
"topk": 9,
"block_m": 32,
"ksplit": 2,
"us": 0.0,
"us1": 0.0,
"kernelName1": "",
"err1": "0.0%",
"us2": 0.0,
"kernelName2": "",
"err2": "0.0%",
"run_1stage": 0,
"tflops": 0.0,
"bw": 0.0,
},
{
"token": 512,
"model_dim": 7168,
"inter_dim": 512,
"expert": 33,
"topk": 9,
"block_m": 32,
"ksplit": 0,
"us": 129.6056,
"us1": 0.0,
"kernelName1": "moe_ck2stages_gemm1_256x32x128x128_1x4_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16",
"err1": "0.0%",
"us2": 0.0,
"kernelName2": "moe_ck2stages_gemm2_64x32x32x128_1x1_MulABScaleExpertWeightShuffled_v1_Nswizzle0_Quant3_MulRoutedWeight1_FP4X2_FP4X2_B16",
"err2": "2.9%",
"run_1stage": 0,
"tflops": 782.9,
"bw": 2888.31,
},
{
"token": 512,
"model_dim": 7168,
"inter_dim": 2048,
"expert": 33,
"topk": 9,
"block_m": 128,
"ksplit": 0,
"us": 275.9788,
"us1": 0.0,
"kernelName1": "moe_ck2stages_gemm1_256x128x128x128_1x4_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16",
"err1": "0.0%",
"us2": 0.0,
"kernelName2": "moe_ck2stages_gemm2_256x128x128x128_1x4_MulABScaleExpertWeightShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight1_FP4X2_FP4X2_B16",
"err2": "7.1%",
"run_1stage": 0,
"tflops": 1470.67,
"bw": 5305.97,
},
]
k2_overrides = []
default_2stage_rows = [
(16, 7168, 512, 33, 9),
(128, 7168, 512, 33, 9),
]
rows = []
for row in online_tuned_rows:
seeded = defaults.copy()
seeded.update(row)
seeded["us"] = -1.0
rows.append(seeded)
for token, model_dim, inter_dim, expert, topk in k2_overrides:
mask = (
(merged["cu_num"] == 256)
& (merged["token"] == token)
& (merged["model_dim"] == model_dim)
& (merged["inter_dim"] == inter_dim)
& (merged["expert"] == expert)
& (merged["topk"] == topk)
& (merged["act_type"] == "ActivationType.Silu")
& (merged["dtype"] == "torch.bfloat16")
& (merged["q_dtype_a"] == "torch.float4_e2m1fn_x2")
& (merged["q_dtype_w"] == "torch.float4_e2m1fn_x2")
& (merged["q_type"] == "QuantType.per_1x32")
& (merged["use_g1u1"] == 1)
& (merged["doweight_stage1"] == 0)
)
if not mask.any():
continue
row = merged.loc[mask].sort_values("us").iloc[0].to_dict()
kernel_name_1 = row.get("kernelName1")
kernel_name_2 = row.get("kernelName2")
if pd.isna(kernel_name_1) or pd.isna(kernel_name_2) or not kernel_name_1 or not kernel_name_2:
continue
row["ksplit"] = 2
row["us"] = -1.0
rows.append(row)
for token, model_dim, inter_dim, expert, topk in default_2stage_rows:
row = defaults.copy()
row.update(
{
"token": token,
"model_dim": model_dim,
"inter_dim": inter_dim,
"expert": expert,
"topk": topk,
"block_m": 32,
"ksplit": 2,
"us": -1.0,
"run_1stage": 0,
}
)
rows.append(row)
merged = pd.concat([merged, pd.DataFrame(rows)], ignore_index=True)
_debug_rows("pre-dedup", merged)
merged = (
merged.sort_values("us")
.drop_duplicates(subset=keys, keep="first")
.reset_index(drop=True)
)
_debug_rows("post-dedup", merged)
out_path.parent.mkdir(parents=True, exist_ok=True)
merged.to_csv(out_path, index=False)
return str(out_path)
os.environ["AITER_CONFIG_FMOE"] = _bootstrap_cfg()
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"]
return fused_moe(
hidden_states,
gate_up_weight_shuffled,
down_weight_shuffled,
topk_weights,
topk_ids,
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,
)
scrolls · 303 lines total
Source code from GPU Mode and the KernelBot dataset · June 9 Researcher Reciprocity License v1.0
Changes from previous submission
Against this author's previous submission submission 527260.
import os+ import sysfrom pathlib import Pathimport pandas as pd⋯ 3 unchanged linesfrom task import input_t, output_t+ DEBUG_FMOE = os.getenv("JOSU_DEBUG_FMOE") == "1"+ FORCE_REBUILD_FMOE = DEBUG_FMOE or os.getenv("JOSU_REBUILD_FMOE") == "1"+ CFG_VERSION = "v9"+ TARGET_SHAPES = (+ (16, 7168, 256, 257, 9),+ (128, 7168, 256, 257, 9),+ (512, 7168, 256, 257, 9),+ (16, 7168, 512, 33, 9),+ (128, 7168, 512, 33, 9),+ (512, 7168, 512, 33, 9),+ (512, 7168, 2048, 33, 9),+ )+++ def _shape_mask(frame: pd.DataFrame, shape: tuple[int, int, int, int, int]) -> pd.Series:+ token, model_dim, inter_dim, expert, topk = shape+ return (+ (frame["token"] == token)+ & (frame["model_dim"] == model_dim)+ & (frame["inter_dim"] == inter_dim)+ & (frame["expert"] == expert)+ & (frame["topk"] == topk)+ )+++ def _debug_rows(label: str, frame: pd.DataFrame) -> None:+ if not DEBUG_FMOE:+ return++ cols = [+ "token",+ "model_dim",+ "inter_dim",+ "expert",+ "topk",+ "block_m",+ "ksplit",+ "run_1stage",+ "us",+ "kernelName1",+ "kernelName2",+ "_tag",+ ]+ keep_cols = [col for col in cols if col in frame.columns]+ print(f"[best.py] {label}", file=sys.stderr)+ for shape in TARGET_SHAPES:+ rows = frame.loc[_shape_mask(frame, shape), keep_cols].sort_values("us").head(6)+ print(f"[best.py] shape={shape} rows={len(rows)}", file=sys.stderr)+ if not rows.empty:+ print(rows.to_string(index=False), file=sys.stderr)++def _bootstrap_cfg() -> str:- out_path = Path("/tmp/josu_cfg_hybrid_under150_fmoe.csv")- if out_path.exists() and out_path.stat().st_size > 0:+ out_path = Path(f"/tmp/josu_cfg_hybrid_under150_fmoe_{CFG_VERSION}.csv")+ if out_path.exists() and out_path.stat().st_size > 0 and not FORCE_REBUILD_FMOE:return str(out_path)source_paths = [⋯ 43 unchanged lines{"token": 16,"model_dim": 7168,- "inter_dim": 512,- "expert": 33,+ "inter_dim": 256,+ "expert": 257,"topk": 9,"block_m": 32,- "ksplit": 0,- "us": 47.3336,+ "ksplit": 2,+ "us": 0.0,"us1": 0.0,- "kernelName1": "moe_ck2stages_gemm1_64x32x32x128_1x1_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16",+ "kernelName1": "","err1": "0.0%","us2": 0.0,- "kernelName2": "moe_ck2stages_gemm2_64x32x32x128_1x1_MulABScaleExpertWeightShuffled_v1_Nswizzle0_Quant3_MulRoutedWeight1_FP4X2_FP4X2_B16",- "err2": "2.9%",+ "kernelName2": "",+ "err2": "0.0%","run_1stage": 0,- "tflops": 66.99,- "bw": 7683.25,+ "tflops": 0.0,+ "bw": 0.0,},{"token": 128,"model_dim": 7168,- "inter_dim": 512,- "expert": 33,+ "inter_dim": 256,+ "expert": 257,"topk": 9,"block_m": 32,- "ksplit": 0,- "us": 58.5681,+ "ksplit": 2,+ "us": 0.0,"us1": 0.0,- "kernelName1": "moe_ck2stages_gemm1_64x32x32x128_1x1_MulABScaleShuffled_v3_Nswizzle0_Quant3_MulRoutedWeight0_silu_FP4X2_FP4X2_B16",+ "kernelName1": "","err1": "0.0%","us2": 0.0,- "kernelName2": "moe_ck2stages_gemm2_64x32x32x128_1x1_MulABScaleExpertWeightShuffled_v1_Nswizzle0_Quant3_MulRoutedWeight1_FP4X2_FP4X2_B16",- "err2": "2.9%",+ "kernelName2": "",+ "err2": "0.0%","run_1stage": 0,- "tflops": 433.12,- "bw": 6250.57,+ "tflops": 0.0,+ "bw": 0.0,},{"token": 512,⋯ 35 unchanged lines},]- k2_overrides = [- (16, 7168, 256, 257, 9),- (128, 7168, 256, 257, 9),+ k2_overrides = []+ default_2stage_rows = [(16, 7168, 512, 33, 9),(128, 7168, 512, 33, 9),]⋯ 2 unchanged linesfor row in online_tuned_rows:seeded = defaults.copy()seeded.update(row)+ seeded["us"] = -1.0rows.append(seeded)for token, model_dim, inter_dim, expert, topk in k2_overrides:⋯ 12 unchanged lines& (merged["use_g1u1"] == 1)& (merged["doweight_stage1"] == 0))- if mask.any():- row = merged.loc[mask].sort_values("us").iloc[0].to_dict()- else:- row = defaults.copy()- row.update(- {- "token": token,- "model_dim": model_dim,- "inter_dim": inter_dim,- "expert": expert,- "topk": topk,- }- )+ if not mask.any():+ continue+ row = merged.loc[mask].sort_values("us").iloc[0].to_dict()+ kernel_name_1 = row.get("kernelName1")+ kernel_name_2 = row.get("kernelName2")+ if pd.isna(kernel_name_1) or pd.isna(kernel_name_2) or not kernel_name_1 or not kernel_name_2:+ continue+row["ksplit"] = 2row["us"] = -1.0rows.append(row)+ for token, model_dim, inter_dim, expert, topk in default_2stage_rows:+ row = defaults.copy()+ row.update(+ {+ "token": token,+ "model_dim": model_dim,+ "inter_dim": inter_dim,+ "expert": expert,+ "topk": topk,+ "block_m": 32,+ "ksplit": 2,+ "us": -1.0,+ "run_1stage": 0,+ }+ )+ rows.append(row)+merged = pd.concat([merged, pd.DataFrame(rows)], ignore_index=True)- dedup_keys = keys + (["_tag"] if "_tag" in merged.columns else [])+ _debug_rows("pre-dedup", merged)merged = (merged.sort_values("us")- .drop_duplicates(subset=dedup_keys, keep="first")+ .drop_duplicates(subset=keys, keep="first").reset_index(drop=True))+ _debug_rows("post-dedup", merged)out_path.parent.mkdir(parents=True, exist_ok=True)merged.to_csv(out_path, index=False)
scrolls · 208 diff lines total
Best evidence level for this revision: reported
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