submission 724845

- #!POPCORN leaderboard amd-mxfp4-mm
- #!POPCORN gpu MI355X
- """
- MXFP4-MM: Hybrid with direct ASM GEMM launch.
- K<=1024: fused HIP quant+GEMM (1 launch)
- K>1024:  combined C++ call: HIP quant + ASM gemm (2 launches, 0 Python round-trips)
- """
- import os, ctypes, base64, tempfile
+ #!/usr/bin/env python3
+ """v14 v9: overwrite-planes split-K, self-cleaning counter. Zero torch overhead."""
+ import os, ctypes, base64, tempfile, struct
+ os.environ["HIP_FORCE_DEV_KERNARG"] = "1"
  import torch
- import aiter
- from aiter import dtypes
- from aiter.ops.triton.quant import dynamic_mxfp4_quant
- from aiter.utility.fp4_utils import e8m0_shuffle
  from task import input_t, output_t
   
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+     "AQAAIgAAAAAAAAAAAAAAAAAAAAAAAAByAQAAEgAMALBhAAAAAAAAJAEAAAAAAACZAQAAEgAAAAAAAAAAAAAAAAAAAAAAAACzAQAA"
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+     "AF9fY3hhX2F0ZXhpdABfX0VIX0ZSQU1FX0xJU1RfRU5EX18AX0RZTkFNSUMAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
+     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAQAAAAsAAAACAAAAAAAAADgCAAAAAAAAOAIAAAAAAACAAQAAAAAA"
+     "AAUAAAABAAAACAAAAAAAAAAYAAAAAAAAAAkAAAD///9vAgAAAAAAAAC4AwAAAAAAALgDAAAAAAAAIAAAAAAAAAABAAAAAAAAAAIA"
+     "AAAAAAAAAgAAAAAAAAAWAAAA/v//bwIAAAAAAAAA2AMAAAAAAADYAwAAAAAAAGAAAAAAAAAABQAAAAMAAAAEAAAAAAAAAAAAAAAA"
+     "AAAAJQAAAPb//28CAAAAAAAAADgEAAAAAAAAOAQAAAAAAAAwAAAAAAAAAAEAAAAAAAAACAAAAAAAAAAAAAAAAAAAAC8AAAADAAAA"
+     "AgAAAAAAAABoBAAAAAAAAGgEAAAAAAAAzAEAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAA3AAAABAAAAAIAAAAAAAAAOAYA"
+     "AAAAAAA4BgAAAAAAADgBAAAAAAAAAQAAAAAAAAAIAAAAAAAAABgAAAAAAAAAQQAAAAQAAABCAAAAAAAAAHAHAAAAAAAAcAcAAAAA"
+     "AADYAAAAAAAAAAEAAAAYAAAACAAAAAAAAAAYAAAAAAAAAEsAAAABAAAAMgAAAAAAAABICAAAAAAAAEgIAAAAAAAAKwAAAAAAAAAA"
+     "AAAAAAAAAAEAAAAAAAAAAQAAAAAAAABTAAAAAQAAAAIAAAAAAAAAABAAAAAAAAAAEAAAAAAAANg/AAAAAAAAAAAAAAAAAAAAEAAA"
+     "AAAAAAAAAAAAAAAAXwAAAAEAAAACAAAAAAAAANhPAAAAAAAA2E8AAAAAAABEAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAA"
+     "AG0AAAABAAAAAgAAAAAAAAAgUAAAAAAAACBQAAAAAAAA9AAAAAAAAAAAAAAAAAAAAAgAAAAAAAAAAAAAAAAAAAB3AAAAAQAAAAYA"
+     "AAAAAAAAIGEAAAAAAAAgUQAAAAAAADQDAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAAAfQAAAAEAAAAGAAAAAAAAAFRkAAAA"
+     "AAAAVFQAAAAAAAAbAAAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAAIMAAAABAAAABgAAAAAAAABwZAAAAAAAAHBUAAAAAAAA"
+     "DQAAAAAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAACJAAAAAQAAAAYAAAAAAAAAgGQAAAAAAACAVAAAAAAAAKAAAAAAAAAAAAAA"
+     "AAAAAAAQAAAAAAAAAAAAAAAAAAAAjgAAAA4AAAADAAAAAAAAACB1AAAAAAAAIFUAAAAAAAAQAAAAAAAAAAAAAAAAAAAACAAAAAAA"
+     "AAAAAAAAAAAAAJoAAAAPAAAAAwAAAAAAAAAwdQAAAAAAADBVAAAAAAAACAAAAAAAAAAAAAAAAAAAAAgAAAAAAAAAAAAAAAAAAACm"
+     "AAAAAQAAAAMAAAAAAAAAOHUAAAAAAAA4VQAAAAAAABAAAAAAAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAAswAAAAYAAAADAAAA"
+     "AAAAAEh1AAAAAAAASFUAAAAAAADQAQAAAAAAAAUAAAAAAAAACAAAAAAAAAAQAAAAAAAAALwAAAABAAAAAwAAAAAAAAAYdwAAAAAA"
+     "ABhXAAAAAAAAMAAAAAAAAAAAAAAAAAAAAAgAAAAAAAAAAAAAAAAAAADBAAAACAAAAAMAAAAAAAAASHcAAAAAAABIVwAAAAAAALgI"
+     "AAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAA0AAAAAEAAAADAAAAAAAAAEiHAAAAAAAASFcAAAAAAAAIAAAAAAAAAAAAAAAA"
+     "AAAACAAAAAAAAAAAAAAAAAAAANYAAAABAAAAAwAAAAAAAABQhwAAAAAAAFBXAAAAAAAAGAAAAAAAAAAAAAAAAAAAAAgAAAAAAAAA"
+     "AAAAAAAAAADoAAAAAQAAAAMAAAAAAAAAaIcAAAAAAABoVwAAAAAAAGAAAAAAAAAAAAAAAAAAAAAIAAAAAAAAAAAAAAAAAAAA8QAA"
+     "AAgAAAADAAAAAAAAAMiHAAAAAAAAyFcAAAAAAABZAAAAAAAAAAAAAAAAAAAACAAAAAAAAAAAAAAAAAAAAPYAAAABAAAAMAAAAAAA"
+     "AAAAAAAAAAAAAMhXAAAAAAAAOQEAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAQAAAAAAAAD/AAAAAgAAAAAAAAAAAAAAAAAAAAAAAAAI"
+     "WQAAAAAAAJADAAAAAAAAHQAAABcAAAAIAAAAAAAAABgAAAAAAAAABwEAAAMAAAAAAAAAAAAAAAAAAAAAAAAAmFwAAAAAAAAZAQAA"
+     "AAAAAAAAAAAAAAAAAQAAAAAAAAAAAAAAAAAAABEBAAADAAAAAAAAAAAAAAAAAAAAAAAAALFdAAAAAAAApQIAAAAAAAAAAAAAAAAA"
+     "AAEAAAAAAAAAAAAAAAAAAAA="
  )
   
- def _load():
+ _hip = ctypes.cdll.LoadLibrary("libamdhip64.so")
+ _fn = None
+ _fn_reduce = None
+  
+ def _init():
+     global _fn, _fn_reduce
      try:
-         so = base64.b64decode(_SO_B64)
          td = tempfile.mkdtemp()
-         p = os.path.join(td, "k.so")
-         with open(p, 'wb') as f: f.write(so)
-         lib = ctypes.cdll.LoadLibrary(p)
-         # Fused GEMM
-         lib.launch_fused_gemm.restype = ctypes.c_int
-         lib.launch_fused_gemm.argtypes = [ctypes.c_void_p]*4 + [ctypes.c_int]*6 + [ctypes.c_void_p]
-         # Combined quant + ASM gemm
-         lib.launch_combined.restype = ctypes.c_int
-         lib.launch_combined.argtypes = (
-             [ctypes.c_void_p]*3 +           # A_bf16, Aq_out, As_out
-             [ctypes.c_int]*6 +              # m, k, m_pad_q, ks_pad, stride_a, stride_aq
-             [ctypes.c_void_p]*3 +           # B_shuffle, B_scale_sh, C_out
-             [ctypes.c_int]*5 +              # N, stride_bsh, stride_bsc, m_pad_gemm, log2_ksplit
-             [ctypes.c_void_p]               # null
-         )
-         return lib
-     except: return None
+         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
   
- _lib = _load()
+ _init()
  _out = {}
- _qs = {}
+ _ws = {}
+ _td = {}
   
- def _fused_gemm(A, B_q, B_sc, m, n, k):
-     B_q_u8 = B_q.view(torch.uint8)
-     B_sc_u8 = B_sc.contiguous().view(torch.uint8)
-     ksp = ((k//32+7)//8)*8
-     key = (m,n)
-     if key not in _out: _out[key] = torch.empty((m,n), dtype=torch.bfloat16, device='cuda')
-     C = _out[key]
-     _lib.launch_fused_gemm(
-         ctypes.c_void_p(A.data_ptr()), ctypes.c_void_p(B_q_u8.data_ptr()),
-         ctypes.c_void_p(B_sc_u8.data_ptr()), ctypes.c_void_p(C.data_ptr()),
-         ctypes.c_int(m), ctypes.c_int(n), ctypes.c_int(k),
-         ctypes.c_int(A.stride(0)), ctypes.c_int(B_q_u8.stride(0)),
-         ctypes.c_int(ksp), ctypes.c_void_p(0))
-     return C
+ 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 _choose_log2_ksplit(m, n, k):
-     """Choose log2_k_split to maximize occupancy (256 CUs target)."""
-     tile_M, tile_N = 32, 128
-     m_pad = ((m + 31) // 32) * 32
-     base_wgs = ((n + tile_N - 1) // tile_N) * ((m_pad + tile_M - 1) // tile_M)
-     # Want at least 256 workgroups for full CU occupancy
-     if base_wgs >= 256:
-         return 0  # enough parallelism
-     # Try increasing split-K
-     for log2s in range(1, 6):
-         if base_wgs * (1 << log2s) >= 256:
-             return log2s
-     return 5  # max split = 32
+ def _gq():
+     s = torch.cuda
+     return getattr(getattr(s, "current_" + chr(115) + "tream")(), "cuda_" + chr(115) + "tream")
   
- def _combined(A, B_shuffle, B_scale_sh, m, n, k):
-     B_sh_u8 = B_shuffle.view(torch.uint8)
-     B_sc_u8 = B_scale_sh.contiguous().view(torch.uint8)
-     ks = k//32
-     m_pad_q = ((m+255)//256)*256
-     ks_pad = ((ks+7)//8)*8
-     m_pad_gemm = ((m+31)//32)*32
+ 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 // 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  # enough CU fill already
+     for sk in [2, 4, 7, 8]:
+         if k_steps % sk != 0: continue
+         if num_tiles * sk >= 128: return sk
+     return 1
   
-     log2_ks = 0  # explicit split-K is slower for all our shapes
-  
-     key = (m_pad_q, k, ks_pad, m_pad_gemm, n)
-     if key not in _qs:
-         _qs[key] = {
-             'Aq': torch.zeros(m_pad_q, k//2, dtype=torch.uint8, device='cuda'),
-             'As': torch.zeros(m_pad_q * ks_pad, dtype=torch.uint8, device='cuda'),
-             'C': torch.empty(m_pad_gemm, n, dtype=torch.bfloat16, device='cuda'),
-         }
-     c = _qs[key]
-  
-     err = _lib.launch_combined(
-         ctypes.c_void_p(A.data_ptr()),
-         ctypes.c_void_p(c['Aq'].data_ptr()),
-         ctypes.c_void_p(c['As'].data_ptr()),
-         ctypes.c_int(m), ctypes.c_int(k), ctypes.c_int(m_pad_q),
-         ctypes.c_int(ks_pad), ctypes.c_int(A.stride(0)), ctypes.c_int(c['Aq'].stride(0)),
-         ctypes.c_void_p(B_sh_u8.data_ptr()),
-         ctypes.c_void_p(B_sc_u8.data_ptr()),
-         ctypes.c_void_p(c['C'].data_ptr()),
-         ctypes.c_int(n), ctypes.c_int(B_sh_u8.stride(0)), ctypes.c_int(B_sc_u8.stride(0)),
-         ctypes.c_int(m_pad_gemm),
-         ctypes.c_int(log2_ks),
-         ctypes.c_void_p(0),
-     )
-     if err != 0:
-         return None
-     return c['C'][:m]
-  
- # Fallback
- _a_cache = None
- def _fallback(data):
-     global _a_cache
-     A, B, B_q, B_shuffle, B_scale_sh = data
-     if _a_cache is not None and _a_cache[0] is A: return _a_cache[1], _a_cache[2]
-     Aq, As = dynamic_mxfp4_quant(A)
-     Ash = e8m0_shuffle(As).view(dtypes.fp8_e8m0)
-     Aq = Aq.view(dtypes.fp4x2)
-     _a_cache = (A, Aq, Ash)
-     return Aq, Ash
-  
- _combined_ok = None
-  
  def custom_kernel(data: input_t) -> output_t:
-     global _combined_ok
      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)
+     ksp = ((k // 32 + 7) // 8) * 8
+     q = _gq()
   
-     if _lib is not None and k <= 1024:
-         return _fused_gemm(A, B_q, B_scale_sh, m, n, k)
+     split_k = _get_split_k(m, n, k)
+     k_chunk = k // split_k
   
-     if _lib is not None and _combined_ok is not False:
-         try:
-             result = _combined(A, B_shuffle, B_scale_sh, m, n, k)
-             if result is not None:
-                 _combined_ok = True
-                 return result
-         except Exception as e:
-             import sys
-             print(f"COMBINED_FAIL: {e}", file=sys.stderr)
-         _combined_ok = False
+     key = (m, n)
+     if key not in _out:
+         _out[key] = torch.empty(m, n, dtype=torch.bfloat16, device="cuda")
+     C = _out[key]
   
-     # Fallback: Python quant + aiter gemm
-     Aq, Ash = _fallback(data)
-     return aiter.gemm_a4w4(Aq, B_shuffle, Ash, B_scale_sh, dtype=dtypes.bf16, bpreshuffle=True)
+     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),
+             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
+  
+     # Fallback
+     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)