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submission 663912

bhagawan-yantrion · python · License unknown
Kernel source · 539 lines ↓

Use it

Vendorable · source mirrored · license unknownView source →

No package. Vendor the mirrored source: 539 lines, June 9 Researcher Reciprocity License v1.0.

submission.py
curl "https://kernelindex.com/api/v1/implementations/kernelbot-amd-mxfp4-mm-663912?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
12.7µs
#393 of 1143
2026-03-29
Run →

Reported · How evidence levels are derived →

Source and license

sourceavailable
revision digestsha256:ee838da2505a45d8dc182b531bd443ec3cadcd8ce45bad52cada2e42926a7c62
license declaredunknown
license concludedunknown
authorsbhagawan-yantrion
imported2026-08-15

Techniques

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

fp4MXFP4-MM: Hybrid with direct ASM GEMM launch.
split-klog2_ks = 0 # explicit split-K is slower for all our shapes

Kernel source

submission.py539 lines
#!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
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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def _load():
    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

_lib = _load()
_out = {}
_qs = {}

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 _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 _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

    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]

    if _lib is not None and k <= 1024:
        return _fused_gemm(A, B_q, B_scale_sh, m, n, 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

    # 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)
scrolls · 539 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 658361.

  #!POPCORN leaderboard amd-mxfp4-mm
  #!POPCORN gpu MI355X
  """
- MXFP4-MM Hybrid: dispatch by K size.
-   K <= 1024: fused quant+GEMM HIP kernel (1 launch, ~9 us)
-   K > 1024:  HIP quant+shuffle (1 launch) + ASM gemm_a4w4 (1 launch, ~14-26 us)
+ 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
  import torch
  ⋯ 3 unchanged lines
  from aiter.utility.fp4_utils import e8m0_shuffle
  from task import input_t, output_t
   
- # ---- Embedded .so (both kernels) ----
  _SO_B64 = (
-     "f0VMRgIBAQAAAAAAAAAAAAMAPgABAAAAAAAAAAAAAABAAAAAAAAAADBkAAAAAAAAAAAAAEAAOAAJAEAAHgAcAAYAAAAEAAAAQAAA"
+     "f0VMRgIBAQAAAAAAAAAAAAMAPgABAAAAAAAAAAAAAABAAAAAAAAAAOBqAAAAAAAAAAAAAEAAOAAJAEAAHgAcAAYAAAAEAAAAQAAA"
      "AAAAAABAAAAAAAAAAEAAAAAAAAAA+AEAAAAAAAD4AQAAAAAAAAgAAAAAAAAAAQAAAAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AACkUAAAAAAAAKRQAAAAAAAAABAAAAAAAAABAAAABQAAALBQAAAAAAAAsGAAAAAAAACwYAAAAAAAAHAHAAAAAAAAcAcAAAAAAAAA"
-     "EAAAAAAAAAEAAAAGAAAAIFgAAAAAAAAgeAAAAAAAACB4AAAAAAAAKAIAAAAAAADgBwAAAAAAAAAQAAAAAAAAAQAAAAYAAABIWgAA"
-     "AAAAAEiKAAAAAAAASIoAAAAAAACQAAAAAAAAAOkAAAAAAAAAABAAAAAAAAACAAAABgAAAEhYAAAAAAAASHgAAAAAAABIeAAAAAAA"
-     "ANABAAAAAAAA0AEAAAAAAAAIAAAAAAAAAFLldGQEAAAAIFgAAAAAAAAgeAAAAAAAACB4AAAAAAAAKAIAAAAAAADgBwAAAAAAAAEA"
-     "AAAAAAAAUOV0ZAQAAACoTgAAAAAAAKhOAAAAAAAAqE4AAAAAAABUAAAAAAAAAFQAAAAAAAAABAAAAAAAAABR5XRkBgAAAAAAAAAA"
+     "AAAkUQAAAAAAACRRAAAAAAAAABAAAAAAAAABAAAABQAAADBRAAAAAAAAMGEAAAAAAAAwYQAAAAAAAAAMAAAAAAAAAAwAAAAAAAAA"
+     "EAAAAAAAAAEAAAAGAAAAMF0AAAAAAAAwfQAAAAAAADB9AAAAAAAAKAIAAAAAAADQAgAAAAAAAAAQAAAAAAAAAQAAAAYAAABYXwAA"
+     "AAAAAFiPAAAAAAAAWI8AAAAAAAC4AAAAAAAAADEBAAAAAAAAABAAAAAAAAACAAAABgAAAFhdAAAAAAAAWH0AAAAAAABYfQAAAAAA"
+     "ANABAAAAAAAA0AEAAAAAAAAIAAAAAAAAAFLldGQEAAAAMF0AAAAAAAAwfQAAAAAAADB9AAAAAAAAKAIAAAAAAADQAgAAAAAAAAEA"
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-     "AAAAAEB6AAAAAAAABgAAABAAAAAAAAAAAAAAAICKAAAAAAAABwAAAAIAAAAAAAAAAAAAAIiKAAAAAAAABwAAAAMAAAAAAAAAAAAA"
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-     "AAAAAAAABwAAAAcAAAAAAAAAAAAAALCKAAAAAAAABwAAAAgAAAAAAAAAAAAAALiKAAAAAAAABwAAAAkAAAAAAAAAAAAAAMCKAAAA"
-     "AAAABwAAAAoAAAAAAAAAAAAAAMiKAAAAAAAABwAAAAsAAAAAAAAAAAAAANCKAAAAAAAABwAAAAwAAAAAAAAAAAAAAGZ1c2VkX3F1"
-     "YW50X3NodWZmbGVfa2VybmVsAGZ1c2VkX214ZnA0X2dlbW1fa2VybmVsAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
+     "AAAAAAAAAAAAACgBAAASAAAAAAAAAAAAAAAAAAAAAAAAAGMBAAASAAAAAAAAAAAAAAAAAAAAAAAAAGoBAAASAAAAAAAAAAAAAAAA"
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      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
  ⋯ 7 unchanged lines
      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
      "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
-     "AAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
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      "AAAAAAAAaGlwdjQtYW1kZ2NuLWFtZC1hbWRoc2EtLWdmeDk1MAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA"
  ⋯ 208 unchanged lines
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      "AAAAAAAAAAAAAAAAAAAAAAAA8CQAAAAAAAAoAgAAAAAAAA8AAAASAAAACAAAAAAAAAAYAAAAAAAAAHgAAAADAAAAAAAAAAAAAAAA"
      "AAAAAAAAABgnAAAAAAAAigAAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAACCAAAAAwAAAAAAAAAAAAAAAAAAAAAAAACiJwAA"
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-     "AAAcAAAAkBEAADgAAAAAQQ4QhgJDDQZzDAcIAAAAHAAAADwAAACwEQAASgAAAABBDhCGAkMNBgJFDAcIAAAgAAAAXAAAAOARAAD0"
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+     "ADBmAAAAAAAAGQEAAAAAAAAAAAAAAAAAAAEAAAAAAAAAAAAAAAAAAAARAQAAAwAAAAAAAAAAAAAAAAAAAAAAAABJZwAAAAAAAJcD"
+     "AAAAAAAAAAAAAAAAAAABAAAAAAAAAAAAAAAAAAAA"
  )
   
- # ---- Load library ----
  def _load():
      try:
          so = base64.b64decode(_SO_B64)
  ⋯ 3 unchanged lines
          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]
-         # Fused quant+shuffle
-         lib.launch_fused_quant_shuffle.restype = ctypes.c_int
-         lib.launch_fused_quant_shuffle.argtypes = [
-             ctypes.c_void_p]*3 + [ctypes.c_int]*6 + [ctypes.c_void_p]
+         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
   
  _lib = _load()
+ _out = {}
+ _qs = {}
   
- # ---- Caches ----
- _out_cache = {}
- _qs_cache = {}
-  
  def _fused_gemm(A, B_q, B_sc, m, n, k):
-     """Single launch: quant A + GEMM → C. Best for small K."""
      B_q_u8 = B_q.view(torch.uint8)
      B_sc_u8 = B_sc.contiguous().view(torch.uint8)
-     ks_pad = ((k//32+7)//8)*8
+     ksp = ((k//32+7)//8)*8
      key = (m,n)
-     if key not in _out_cache:
-         _out_cache[key] = torch.empty((m,n), dtype=torch.bfloat16, device='cuda')
-     C = _out_cache[key]
+     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(ks_pad), ctypes.c_void_p(0))
+         ctypes.c_int(ksp), ctypes.c_void_p(0))
      return C
   
- def _hip_quant_shuffle(A, m, k):
-     """HIP quant+shuffle → (A_q, A_scale_sh). For large K path."""
+ 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 _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 = ((m+255)//256)*256
+     m_pad_q = ((m+255)//256)*256
      ks_pad = ((ks+7)//8)*8
-     key = (m_pad, k, ks_pad)
-     if key not in _qs_cache:
-         _qs_cache[key] = {
-             'Aq': torch.zeros(m_pad, k//2, dtype=torch.uint8, device='cuda'),
-             'As': torch.zeros(m_pad*ks_pad, dtype=torch.uint8, device='cuda'),
+     m_pad_gemm = ((m+31)//32)*32
+  
+     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_cache[key]
-     _lib.launch_fused_quant_shuffle(
-         ctypes.c_void_p(A.data_ptr()), ctypes.c_void_p(c['Aq'].data_ptr()),
+     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), ctypes.c_int(ks_pad),
-         ctypes.c_int(A.stride(0)), ctypes.c_int(c['Aq'].stride(0)),
-         ctypes.c_void_p(0))
-     return c['Aq'][:m].view(dtypes.fp4x2), c['As'].view(m_pad, ks_pad).view(dtypes.fp8_e8m0)
+         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 ----
+ # Fallback
  _a_cache = None
- def _fallback_quant(A):
+ def _fallback(data):
      global _a_cache
-     if _a_cache is not None and _a_cache[0] is A:
-         return _a_cache[1], _a_cache[2]
+     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)
-     r = (Aq.view(dtypes.fp4x2), Ash.view(dtypes.fp8_e8m0))
-     _a_cache = (A, r[0], r[1])
-     return r
+     Ash = e8m0_shuffle(As).view(dtypes.fp8_e8m0)
+     Aq = Aq.view(dtypes.fp4x2)
+     _a_cache = (A, Aq, Ash)
+     return Aq, Ash
   
- # ---- Entry ----
+ _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]
   
      if _lib is not None and k <= 1024:
-         # Small K: fused single-launch kernel
          return _fused_gemm(A, B_q, B_scale_sh, m, n, k)
   
-     if _lib is not None:
-         # Large K: HIP quant+shuffle + ASM GEMM (2 launches)
-         Aq, Ash = _hip_quant_shuffle(A, m, k)
-     else:
-         # Fallback: Python quant+shuffle
-         Aq, Ash = _fallback_quant(A)
+     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
   
+     # 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)
scrolls · 510 diff lines total

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