submission 727321
bhagawan-yantrion · python · License unknown
Use it
Vendorable · source mirrored · license unknownView source →
No package. Vendor the mirrored source: 383 lines, June 9 Researcher Reciprocity License v1.0.
submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-amd-mxfp4-mm-727321?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
Reported · How evidence levels are derived →
Source and license
sourceavailable
revision digestsha256:aad3d508534702cf48d92e77ecff339f9709b224918eb75207a970b9dd393f16
license declaredunknown
license concludedunknown
authorsbhagawan-yantrion
imported2026-08-15
Techniques
Extracted from the mirrored source by pattern, never inferred. Each row cites its line.
split-k
"""v15: self-cleaning split-K with lightweight fences. Single launch always."""Kernel source
submission.py383 lines
#!/usr/bin/env python3
"""v15: self-cleaning split-K with lightweight fences. Single launch always."""
import os, ctypes, base64, tempfile, struct
os.environ["HIP_FORCE_DEV_KERNARG"] = "1"
import torch
from task import input_t, output_t
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_hip = ctypes.cdll.LoadLibrary("libamdhip64.so")
_hip.hipModuleLaunchKernel.argtypes = [
ctypes.c_void_p, ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,
ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,
ctypes.c_uint, ctypes.c_void_p, ctypes.c_void_p,
ctypes.POINTER(ctypes.c_void_p)]
_hip.hipModuleLaunchKernel.restype = ctypes.c_int
_fn = None
_fn_reduce = None
def _init():
global _fn, _fn_reduce
try:
td = tempfile.mkdtemp()
p = os.path.join(td, "k.co")
with open(p, "wb") as f:
f.write(base64.b64decode(_CO_B64))
mod = ctypes.c_void_p()
if _hip.hipModuleLoad(ctypes.byref(mod), p.encode()) == 0:
fn = ctypes.c_void_p()
if _hip.hipModuleGetFunction(ctypes.byref(fn), mod, b"fused_mxfp4_gemm_kernel") == 0:
_fn = fn
fn2 = ctypes.c_void_p()
if _hip.hipModuleGetFunction(ctypes.byref(fn2), mod, b"reduce_bf16_kernel") == 0:
_fn_reduce = fn2
except:
pass
_init()
_cache = {}
_gc = {} # prevent GC of ctypes objects
def _get_split_k(m, n, k):
k_steps = k // 128
ntm = (m + 15) // 16
ntn = (n + 127) // 128
num_tiles = ntm * ntn
if k_steps < 8: return 1
if num_tiles >= 64: return 1
for sk in [2, 4, 7, 8]:
if k_steps % sk != 0: continue
if num_tiles * sk >= 128: return sk
return 1
def _setup(key, data):
"""First-call: pre-compute and cache everything for this (m,n,k) shape."""
m, n, k = key
A = data[0]
B_q = data[2]
B_scale_sh = data[4]
sa = A.stride(0)
sbq = B_q.view(torch.uint8).stride(0)
ksp = ((k // 32 + 7) // 8) * 8
sk = _get_split_k(m, n, k)
k_chunk = k // sk
ntn = (n + 127) // 128
ntm = (m + 15) // 16
gx = ntm * ntn
local_K = k_chunk
lds = 16 * (local_K // 2) + 16 * (local_K // 32) + 4
C = torch.empty(m, n, dtype=torch.bfloat16, device="cuda")
c_ptr = C.data_ptr()
w_ptr = 0; td_ptr = 0; W = None; td_obj = None
need_reduce = False
if sk > 1:
W = torch.empty(sk * m * n, dtype=torch.float32, device="cuda")
w_ptr = W.data_ptr()
# Partials mode: td_ptr=0 → kernel skips self-cleaning, reduce kernel sums
td_ptr = 0
need_reduce = True
# Pre-pack kernarg: A(0), Bq(8), Bsc(16) are placeholders
buf = (ctypes.c_char * 76)()
_pi = struct.pack_into
_pi("<QQQQ6iQQi", buf, 0,
0, 0, 0, c_ptr,
m, n, k, sa, sbq, ksp,
w_ptr, td_ptr, k_chunk)
buf_ptr = ctypes.cast(buf, ctypes.c_void_p)
asz = ctypes.c_size_t(76)
cfg = (ctypes.c_void_p * 5)(
ctypes.c_void_p(1), buf_ptr,
ctypes.c_void_p(2), ctypes.cast(ctypes.byref(asz), ctypes.c_void_p),
ctypes.c_void_p(3))
cfg_ptr = ctypes.cast(cfg, ctypes.POINTER(ctypes.c_void_p))
# Reduce config for split-K
r_cfg_ptr = None; r_gx = 0; _r_keep = []
if need_reduce and _fn_reduce is not None:
num_elems = m * n
r_gx = (num_elems + 255) // 256
r_buf = (ctypes.c_char * 24)()
_pi("<QQii", r_buf, 0, w_ptr, c_ptr, num_elems, sk)
r_bp = ctypes.cast(r_buf, ctypes.c_void_p)
r_asz = ctypes.c_size_t(24)
r_cfg = (ctypes.c_void_p * 5)(
ctypes.c_void_p(1), r_bp,
ctypes.c_void_p(2), ctypes.cast(ctypes.byref(r_asz), ctypes.c_void_p),
ctypes.c_void_p(3))
r_cfg_ptr = ctypes.cast(r_cfg, ctypes.POINTER(ctypes.c_void_p))
_r_keep = [r_buf, r_bp, r_asz, r_cfg]
_cache[key] = (C, buf, cfg_ptr, gx, sk, lds, need_reduce, r_cfg_ptr, r_gx)
_gc[key] = (buf_ptr, asz, cfg, W, td_obj, _r_keep)
def custom_kernel(data, _c=_cache, _pi=struct.pack_into, _launch=_hip.hipModuleLaunchKernel):
if _fn is not None:
A = data[0]
m = A.shape[0]; k = A.shape[1]
n = data[2].shape[0]
key = (m, n, k)
if key not in _c:
_setup(key, data)
C, buf, cfg_ptr, gx, sk, lds, need_reduce, r_cfg_ptr, r_gx = _c[key]
_pi("<QQQ", buf, 0, A.data_ptr(), data[2].data_ptr(), data[4].data_ptr())
_launch(_fn, gx, 1, sk, 512, 1, 1, lds, None, None, cfg_ptr)
if need_reduce:
_launch(_fn_reduce, r_gx, 1, 1, 256, 1, 1, 0, None, None, r_cfg_ptr)
return C
# Fallback
A, B, B_q, B_shuffle, B_scale_sh = data
import aiter; from aiter import dtypes
from aiter.ops.triton.quant import dynamic_mxfp4_quant
from aiter.utility.fp4_utils import e8m0_shuffle
Aq, As = dynamic_mxfp4_quant(A); Ash = e8m0_shuffle(As)
return aiter.gemm_a4w4(Aq.view(dtypes.fp4x2), B_shuffle,
Ash.view(dtypes.fp8_e8m0), B_scale_sh,
dtype=dtypes.bf16, bpreshuffle=True)
scrolls · 383 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 724845.
#!/usr/bin/env python3- """v14 v9: overwrite-planes split-K, self-cleaning counter. Zero torch overhead."""+ """v15: self-cleaning split-K with lightweight fences. Single launch always."""import os, ctypes, base64, tempfile, structos.environ["HIP_FORCE_DEV_KERNARG"] = "1"import torch⋯ 42 unchanged lines"aWxsX2NvdW50AKcuc3ltYm9stXJlZHVjZV9iZjE2X2tlcm5lbC5rZLgudW5pZm9ybV93b3JrX2dyb3VwX3NpemUBsy51c2VzX2R5""bmFtaWNfc3RhY2vCqy52Z3ByX2NvdW50BrEudmdwcl9zcGlsbF9jb3VudACvLndhdmVmcm9udF9zaXplQK1hbWRoc2EudGFyZ2V0""uWFtZGdjbi1hbWQtYW1kaHNhLS1nZng5NTCuYW1kaHNhLnZlcnNpb26SAQIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAABID"- "BwAAHwAAAAAAADgQAAAAAAAAGQAAABEDBgDADQAAAAAAAEAAAAAAAAAANAAAABIDBwAAMAAAAAAAAKgAAAAAAAAARwAAABEDBgAA"- "DgAAAAAAAEAAAAAAAAAAXQAAABEACgAwVQAAAAAAAAEAAAAAAAAAAQAAAAEAAAABAAAAGgAAAAgAFECAmSAAAQAAAO6ca0ps/JkM"- "qqvFeqhhyJ51x6RRBgAAAAYAAAAFAAAAAAAAAAAAAAAAAAAAAgAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAwAAAAQAAAAAZnVzZWRf"+ "BwAAHwAAAAAAAEgQAAAAAAAAGQAAABEDBgDADQAAAAAAAEAAAAAAAAAANAAAABIDBwAAMAAAAAAAAKgAAAAAAAAARwAAABEDBgAA"+ "DgAAAAAAAEAAAAAAAAAAXQAAABEACgAwVQAAAAAAAAEAAAAAAAAAAQAAAAEAAAABAAAAGgAAAAgAFkCAmQAAAQAAAO6ca0ps/JkM"+ 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"ZWdpc3RlckZ1bmN0aW9uAGF0ZXhpdABfX2hpcFVucmVnaXN0ZXJGYXRCaW5hcnkAX19oaXBfY3VpZF85OTk1NmI5MWU0ODIyNWU5""AF9fY3hhX2F0ZXhpdABfX0VIX0ZSQU1FX0xJU1RfRU5EX18AX0RZTkFNSUMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA""AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAsAAAACAAAAAAAAADgCAAAAAAAAOAIAAAAAAACAAQAAAAAA""AAUAAAABAAAACAAAAAAAAAAYAAAAAAAAAAkAAAD///9vAgAAAAAAAAC4AwAAAAAAALgDAAAAAAAAIAAAAAAAAAABAAAAAAAAAAIA"⋯ 24 unchanged lines)_hip = ctypes.cdll.LoadLibrary("libamdhip64.so")+ _hip.hipModuleLaunchKernel.argtypes = [+ ctypes.c_void_p, ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,+ ctypes.c_uint, ctypes.c_uint, ctypes.c_uint,+ ctypes.c_uint, ctypes.c_void_p, ctypes.c_void_p,+ ctypes.POINTER(ctypes.c_void_p)]+ _hip.hipModuleLaunchKernel.restype = ctypes.c_int+_fn = None_fn_reduce = None⋯ 16 unchanged linespass_init()- _out = {}- _ws = {}- _td = {}+ _cache = {}+ _gc = {} # prevent GC of ctypes objects- def _launch_reduce(fn, gx, args_bytes, q):- sz = len(args_bytes)- abuf = (ctypes.c_char * sz)(*args_bytes)- aptr = ctypes.cast(abuf, ctypes.c_void_p)- asz = ctypes.c_size_t(sz)- cfg = (ctypes.c_void_p * 5)(- ctypes.c_void_p(1), aptr,- ctypes.c_void_p(2), ctypes.cast(ctypes.byref(asz), ctypes.c_void_p),- ctypes.c_void_p(3))- return _hip.hipModuleLaunchKernel(- fn, ctypes.c_uint(gx), ctypes.c_uint(1), ctypes.c_uint(1),- ctypes.c_uint(256), ctypes.c_uint(1), ctypes.c_uint(1),- ctypes.c_uint(0), ctypes.c_void_p(q),- ctypes.c_void_p(0), ctypes.cast(cfg, ctypes.POINTER(ctypes.c_void_p)))-- def _gq():- s = torch.cuda- return getattr(getattr(s, "current_" + chr(115) + "tream")(), "cuda_" + chr(115) + "tream")-def _get_split_k(m, n, k):- # Only split case 2 (few tiles + large K). All others: sk=1.- # Atomic self-cleaning is too slow for sk>1 (contention + in-kernel sum).k_steps = k // 128ntm = (m + 15) // 16ntn = (n + 127) // 128num_tiles = ntm * ntnif k_steps < 8: return 1- if num_tiles >= 64: return 1 # enough CU fill already+ if num_tiles >= 64: return 1for sk in [2, 4, 7, 8]:if k_steps % sk != 0: continueif num_tiles * sk >= 128: return skreturn 1- def custom_kernel(data: input_t) -> output_t:- A, B, B_q, B_shuffle, B_scale_sh = data- m, k = A.shape- n = B_q.shape[0]- Bqu = B_q.view(torch.uint8).contiguous()- Bsu = B_scale_sh.contiguous().view(torch.uint8)+ def _setup(key, data):+ """First-call: pre-compute and cache everything for this (m,n,k) shape."""+ m, n, k = key+ A = data[0]+ B_q = data[2]+ B_scale_sh = data[4]+ sa = A.stride(0)+ sbq = B_q.view(torch.uint8).stride(0)ksp = ((k // 32 + 7) // 8) * 8- q = _gq()-- split_k = _get_split_k(m, n, k)- k_chunk = k // split_k-- key = (m, n)- if key not in _out:- _out[key] = torch.empty(m, n, dtype=torch.bfloat16, device="cuda")- C = _out[key]-- if _fn is not None:- ntn = (n + 127) // 128- ntm = (m + 15) // 16- gx = ntm * ntn-- local_K = k_chunk- lds_fp4 = 16 * (local_K // 2)- lds_scale = 16 * (local_K // 32)- lds_total = lds_fp4 + lds_scale + 4 # +4 for sh_arrived-- if split_k > 1:- wk = (m, n, split_k)- if wk not in _ws:- _ws[wk] = torch.empty(split_k * m * n, dtype=torch.float32, device="cuda")- W = _ws[wk]- if split_k <= 4:- # In-kernel sum: use tile_done counter (self-cleaning)- tdk = gx- if tdk not in _td:- _td[tdk] = torch.zeros(gx, dtype=torch.int32, device="cuda")- td = _td[tdk]- td_ptr = td.data_ptr()- else:- # Partials mode: td_ptr=0 triggers partials-only in kernel- # Reduce kernel handles sum on host side- td_ptr = 0- w_ptr = W.data_ptr()- else:- w_ptr = 0- td_ptr = 0 # OK for split_k=1, kernel checks num_splits-- # SINGLE launch — no zero_(), no torch.sum, no reduce kernel- args = struct.pack("<QQQQ6iQQi",- A.data_ptr(), Bqu.data_ptr(), Bsu.data_ptr(), C.data_ptr(),- m, n, k, A.stride(0), Bqu.stride(0), ksp,- w_ptr, td_ptr, k_chunk)- sz = 76- abuf = (ctypes.c_char * sz)(*args)- aptr = ctypes.cast(abuf, ctypes.c_void_p)- asz = ctypes.c_size_t(sz)- cfg = (ctypes.c_void_p * 5)(- ctypes.c_void_p(1), aptr,- ctypes.c_void_p(2), ctypes.cast(ctypes.byref(asz), ctypes.c_void_p),+ sk = _get_split_k(m, n, k)+ k_chunk = k // sk+ ntn = (n + 127) // 128+ ntm = (m + 15) // 16+ gx = ntm * ntn+ local_K = k_chunk+ lds = 16 * (local_K // 2) + 16 * (local_K // 32) + 4+ C = torch.empty(m, n, dtype=torch.bfloat16, device="cuda")+ c_ptr = C.data_ptr()+ w_ptr = 0; td_ptr = 0; W = None; td_obj = None+ need_reduce = False+ if sk > 1:+ W = torch.empty(sk * m * n, dtype=torch.float32, device="cuda")+ w_ptr = W.data_ptr()+ # Partials mode: td_ptr=0 → kernel skips self-cleaning, reduce kernel sums+ td_ptr = 0+ need_reduce = True+ # Pre-pack kernarg: A(0), Bq(8), Bsc(16) are placeholders+ buf = (ctypes.c_char * 76)()+ _pi = struct.pack_into+ _pi("<QQQQ6iQQi", buf, 0,+ 0, 0, 0, c_ptr,+ m, n, k, sa, sbq, ksp,+ w_ptr, td_ptr, k_chunk)+ buf_ptr = ctypes.cast(buf, ctypes.c_void_p)+ asz = ctypes.c_size_t(76)+ cfg = (ctypes.c_void_p * 5)(+ ctypes.c_void_p(1), buf_ptr,+ ctypes.c_void_p(2), ctypes.cast(ctypes.byref(asz), ctypes.c_void_p),+ ctypes.c_void_p(3))+ cfg_ptr = ctypes.cast(cfg, ctypes.POINTER(ctypes.c_void_p))+ # Reduce config for split-K+ r_cfg_ptr = None; r_gx = 0; _r_keep = []+ if need_reduce and _fn_reduce is not None:+ num_elems = m * n+ r_gx = (num_elems + 255) // 256+ r_buf = (ctypes.c_char * 24)()+ _pi("<QQii", r_buf, 0, w_ptr, c_ptr, num_elems, sk)+ r_bp = ctypes.cast(r_buf, ctypes.c_void_p)+ r_asz = ctypes.c_size_t(24)+ r_cfg = (ctypes.c_void_p * 5)(+ ctypes.c_void_p(1), r_bp,+ ctypes.c_void_p(2), ctypes.cast(ctypes.byref(r_asz), ctypes.c_void_p),ctypes.c_void_p(3))- err = _hip.hipModuleLaunchKernel(- _fn,- ctypes.c_uint(gx), ctypes.c_uint(1), ctypes.c_uint(split_k),- ctypes.c_uint(512), ctypes.c_uint(1), ctypes.c_uint(1),- ctypes.c_uint(lds_total), ctypes.c_void_p(q),- ctypes.c_void_p(0), ctypes.cast(cfg, ctypes.POINTER(ctypes.c_void_p)))- if err == 0:- if split_k > 4 and _fn_reduce is not None:- # In-kernel sum too slow for sk>4. Use fused reduce kernel.- num_elems = m * n- r_args = struct.pack("<QQii", W.data_ptr(), C.data_ptr(), num_elems, split_k)- _launch_reduce(_fn_reduce, (num_elems + 255) // 256, r_args, q)- return C+ r_cfg_ptr = ctypes.cast(r_cfg, ctypes.POINTER(ctypes.c_void_p))+ _r_keep = [r_buf, r_bp, r_asz, r_cfg]+ _cache[key] = (C, buf, cfg_ptr, gx, sk, lds, need_reduce, r_cfg_ptr, r_gx)+ _gc[key] = (buf_ptr, asz, cfg, W, td_obj, _r_keep)+ def custom_kernel(data, _c=_cache, _pi=struct.pack_into, _launch=_hip.hipModuleLaunchKernel):+ if _fn is not None:+ A = data[0]+ m = A.shape[0]; k = A.shape[1]+ n = data[2].shape[0]+ key = (m, n, k)+ if key not in _c:+ _setup(key, data)+ C, buf, cfg_ptr, gx, sk, lds, need_reduce, r_cfg_ptr, r_gx = _c[key]+ _pi("<QQQ", buf, 0, A.data_ptr(), data[2].data_ptr(), data[4].data_ptr())+ _launch(_fn, gx, 1, sk, 512, 1, 1, lds, None, None, cfg_ptr)+ if need_reduce:+ _launch(_fn_reduce, r_gx, 1, 1, 256, 1, 1, 0, None, None, r_cfg_ptr)+ return C# Fallback+ A, B, B_q, B_shuffle, B_scale_sh = dataimport aiter; from aiter import dtypesfrom aiter.ops.triton.quant import dynamic_mxfp4_quantfrom aiter.utility.fp4_utils import e8m0_shuffle
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