submission 46609

  """
- Baseline copy with tuned einsum paths (batched GEMM) and small memory/layout tweaks.
- Single-file, entry: custom_kernel(data). Keeps DisableCuDNNTF32 semantics.
+ H100 Ultra-Optimized TriMul - Targeting 1.3-2.0ms
+ Strategy: Maximum fusion + TF32 + optimal memory patterns + zero overhead
  """
- import os
  import torch
  import torch.nn.functional as F
  from task import input_t, output_t
  from utils import DisableCuDNNTF32
   
- torch.backends.cuda.matmul.allow_tf32 = True
- torch.backends.cudnn.allow_tf32 = False
-  
-  
- def _einsum_opt(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor:
-     """Compute einsum('bikh,bjkh->bijh') via batched GEMM on (B,H).
-     left/right: [B,N,N,H]
-     returns: [B,N,N,H]
-     """
-     B, N, _, H = left.shape
-     L = left.permute(0, 3, 1, 2).contiguous()   # [B,H,N,N]
-     R = right.permute(0, 3, 1, 2).contiguous()  # [B,H,N,N]
-     out_bh = torch.matmul(L.bfloat16(), R.transpose(-2, -1).bfloat16()).float()  # [B,H,N,N]
-     return out_bh.permute(0, 2, 3, 1).contiguous()
-  
-  
- # -------- Optional Triton einsum (batched NT GEMM) --------
- try:
-     import triton
-     import triton.language as tl
-  
-     @triton.jit
-     def _bmm_nt_kernel(
-         A, B, C,
-         BH, N, K,
-         stride_ab, stride_am, stride_ak,
-         stride_bb, stride_bn, stride_bk,
-         stride_cb, stride_cm, stride_cn,
-         BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
-     ):
-         pid_b = tl.program_id(0)
-         pid_m = tl.program_id(1)
-         pid_n = tl.program_id(2)
-  
-         if pid_b >= BH:
-             return
-  
-         m0 = pid_m * BLOCK_M
-         n0 = pid_n * BLOCK_N
-  
-         offs_m = m0 + tl.arange(0, BLOCK_M)
-         offs_n = n0 + tl.arange(0, BLOCK_N)
-  
-         acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
-  
-         for k0 in range(0, K, BLOCK_K):
-             offs_k = k0 + tl.arange(0, BLOCK_K)
-             a_ptrs = A + pid_b * stride_ab + offs_m[:, None] * stride_am + offs_k[None, :] * stride_ak
-             b_ptrs = B + pid_b * stride_bb + offs_n[:, None] * stride_bn + offs_k[None, :] * stride_bk
-             a = tl.load(a_ptrs, mask=(offs_m[:, None] < N) & (offs_k[None, :] < K), other=0).to(tl.bfloat16)
-             b = tl.load(b_ptrs, mask=(offs_n[:, None] < N) & (offs_k[None, :] < K), other=0).to(tl.bfloat16)
-             acc += tl.dot(a, tl.trans(b), out_dtype=tl.float32)
-  
-         c_ptrs = C + pid_b * stride_cb + offs_m[:, None] * stride_cm + offs_n[None, :] * stride_cn
-         tl.store(c_ptrs, acc, mask=(offs_m[:, None] < N) & (offs_n[None, :] < N))
-  
-     def _einsum_triton_batched(left: torch.Tensor, right: torch.Tensor) -> torch.Tensor:
-         # left/right: [B,N,N,H] -> operate as [B,H,N,N]
-         B, N, _, H = left.shape
-         L = left.permute(0, 3, 1, 2).contiguous()
-         R = right.permute(0, 3, 1, 2).contiguous()
-         BH = B * H
-         A = L.view(BH, N, N)
-         Bm = R.view(BH, N, N)
-         C = torch.empty((BH, N, N), device=left.device, dtype=torch.float32)
-  
-         BLOCK_M = 64
-         BLOCK_N = 64
-         BLOCK_K = 64
-         grid = (BH, (N + BLOCK_M - 1) // BLOCK_M, (N + BLOCK_N - 1) // BLOCK_N)
-         _bmm_nt_kernel[grid](
-             A, Bm, C,
-             BH, N, N,
-             A.stride(0), A.stride(1), A.stride(2),
-             Bm.stride(0), Bm.stride(1), Bm.stride(2),
-             C.stride(0), C.stride(1), C.stride(2),
-             BLOCK_M=BLOCK_M, BLOCK_N=BLOCK_N, BLOCK_K=BLOCK_K,
-             num_warps=4, num_stages=3,
-         )
-         out_bh = C.view(B, H, N, N)
-         return out_bh.permute(0, 2, 3, 1).contiguous()
- except Exception:
-     triton = None
-  
-  
-  
  def _custom_kernel_core(data: input_t) -> output_t:
      input_tensor, mask, weights, config = data
      B, N, _, D = input_tensor.shape
      H = config["hidden_dim"]
-     device = input_tensor.device
-  
      M = B * N * N
   
-     # Heuristic low-rank path as in baseline
-     use_lr = (N >= 512 and H >= 384)
+     # === ULTRA-OPTIMIZED PATH FOR H100 ===
+     # Strategy: Minimize memory traffic, maximize compute intensity
   
+     # 1. Input LayerNorm - FP32 required
      x = F.layer_norm(
          input_tensor, (D,),
          weight=weights["norm.weight"],
  ⋯ 1 unchanged lines
          eps=1e-5,
      )
   
-     W_key = "__W_concat__"
-     if W_key not in weights or weights[W_key].shape != (5 * H, D):
+     # 2. Concatenate and convert weights to FP16 once
+     W_key = "__W_h16__"
+     if W_key not in weights:
          weights[W_key] = torch.cat([
              weights['left_proj.weight'],
              weights['right_proj.weight'],
              weights['left_gate.weight'],
              weights['right_gate.weight'],
              weights['out_gate.weight'],
-         ], dim=0).contiguous().half()
-     W = weights[W_key]
+         ], dim=0).half()  # [5H, D] in FP16
   
-     x_T = x.view(M, D).t().half()
-     P = torch.matmul(W, x_T).view(5, H, M)
+     # 3. Single fused projection in FP16 (faster on H100)
+     x_T = x.view(M, D).t().half()  # [D, M] in FP16
+     P = torch.matmul(weights[W_key], x_T).view(5, H, M)  # [5, H, M] in FP16
   
-     # In-place style gating to reduce allocs; always apply mask (no data-dependent branch)
-     LEFT_T = P[0] * torch.sigmoid(P[2])
-     LEFT_T = LEFT_T * mask.view(1, M).to(P.dtype)
-     RIGHT_T = P[1]
-     RIGHT_T = RIGHT_T * torch.sigmoid(P[3])
-     OG_T = torch.sigmoid(P[4])
+     # 4. Gating in FP16 (fused)
+     LEFT_T = torch.sigmoid(P[2]) * P[0]  # [H, M] FP16
+     if mask.min() < 1.0:
+         LEFT_T *= mask.view(1, M).half()
+     RIGHT_T = torch.sigmoid(P[3]) * P[1]  # [H, M] FP16
+     OG_T = torch.sigmoid(P[4])  # [H, M] FP16
   
-     LEFT = LEFT_T.view(H, B, N, N).permute(1, 2, 3, 0)
-     RIGHT = RIGHT_T.view(H, B, N, N).permute(1, 2, 3, 0)
-     OG = OG_T.view(H, B, N, N).permute(1, 2, 3, 0)
+     # 5-6. ULTRA-OPTIMIZED PATH: Minimal reshapes, maximum contiguity
+     LEFT_bhnn = LEFT_T.view(H, B, N, N).permute(1, 0, 2, 3).contiguous()  # [B, H, N, N]
+     RIGHT_bhnn = RIGHT_T.view(H, B, N, N).permute(1, 0, 2, 3).contiguous()  # [B, H, N, N]
   
-     use_triton = (os.getenv('TRITON_EINSUM', '') == '1') and (triton is not None)
+     LEFT_flat = LEFT_bhnn.view(B * H, N, N)
+     RIGHT_flat = RIGHT_bhnn.view(B * H, N, N)
   
-     if use_lr:
-         RANK = min(64, H // 4)
-         LEFT_lr = LEFT[..., :RANK].contiguous()
-         RIGHT_lr = RIGHT[..., :RANK].contiguous()
-         EIN_lr = _einsum_triton_batched(LEFT_lr, RIGHT_lr) if use_triton else _einsum_opt(LEFT_lr, RIGHT_lr)
+     # Critical bmm - ALWAYS use FP16 for H100 Tensor Cores
+     EIN_flat = torch.bmm(LEFT_flat, RIGHT_flat.transpose(1, 2))
   
-         proj_key = "__proj_lr__"
-         if proj_key not in weights or weights[proj_key].shape != (H, RANK):
-             weights[proj_key] = torch.eye(H, device=device)[:, :RANK].contiguous()
-         EIN = torch.matmul(EIN_lr, weights[proj_key].t())
+     # Reshape output
+     EIN = EIN_flat.view(B, H, N, N).permute(0, 2, 3, 1).contiguous()
   
-         if H > RANK:
-             LEFT_res = LEFT[..., RANK:min(RANK*2, H)]
-             RIGHT_res = RIGHT[..., RANK:min(RANK*2, H)]
-             EIN_res = _einsum_opt(LEFT_res, RIGHT_res)
-             EIN[..., RANK:min(RANK*2, H)] += EIN_res
-     else:
-         EIN = _einsum_triton_batched(LEFT, RIGHT) if use_triton else _einsum_opt(LEFT, RIGHT)
+     # 7. Output gating
+     OG = OG_T.view(H, B, N, N).permute(1, 2, 3, 0)  # [B, N, N, H] FP16
   
+     # 8. Output LayerNorm + gate (convert to FP32 only here)
      G = F.layer_norm(
-         EIN, (H,),
+         EIN.float(), (H,),
          weight=weights['to_out_norm.weight'],
          bias=weights['to_out_norm.bias'],
          eps=1e-5
      ) * OG.float()
   
-     Wt_out_key = "__Wt_out__"
-     if Wt_out_key not in weights or weights[Wt_out_key].shape != (H, D):
-         weights[Wt_out_key] = weights['to_out.weight'].t().half()
+     # 9. Final projection in FP16
+     Wt_key = "__Wt_h16__"
+     if Wt_key not in weights:
+         weights[Wt_key] = weights['to_out.weight'].t().half()  # [H, D] FP16
   
-     OUT = torch.matmul(G.view(M, H).half(), weights[Wt_out_key]).float()
+     OUT = torch.matmul(G.half().view(M, H), weights[Wt_key]).float()  # [M, D]
+  
      return OUT.view(B, N, N, D)
   
   
- _COMPILE_FLAG = os.getenv('COMPILE', '') == '1'
- if _COMPILE_FLAG:
-     try:
-         _COMPILED_CORE = torch.compile(_custom_kernel_core, mode="reduce-overhead", fullgraph=True)
-     except Exception:
-         _COMPILED_CORE = _custom_kernel_core
- else:
-     _COMPILED_CORE = _custom_kernel_core
-  
-  
  def custom_kernel(data: input_t) -> output_t:
      with DisableCuDNNTF32():
-         torch.set_float32_matmul_precision('medium')
-         return _COMPILED_CORE(data)
+         # Respect DisableCuDNNTF32 - do NOT override cudnn.allow_tf32
+         # Only enable matmul TF32 which is separate from cuDNN TF32
+         torch.backends.cuda.matmul.allow_tf32 = True
+         torch.set_float32_matmul_precision('high')
+  
+         # Enable all precision reductions for maximum speed
+         if hasattr(torch.backends.cuda.matmul, 'allow_bf16_reduced_precision_reduction'):
+             torch.backends.cuda.matmul.allow_bf16_reduced_precision_reduction = True
+         if hasattr(torch.backends.cuda.matmul, 'allow_fp16_reduced_precision_reduction'):
+             torch.backends.cuda.matmul.allow_fp16_reduced_precision_reduction = True
+  
+         # H100: Enable Flash Attention and other CUDA optimizations
+         if hasattr(torch.backends.cuda, 'enable_flash_sdp'):
+             torch.backends.cuda.enable_flash_sdp(True)
+         if hasattr(torch.backends.cuda, 'enable_mem_efficient_sdp'):
+             torch.backends.cuda.enable_mem_efficient_sdp(True)
+         if hasattr(torch.backends.cuda, 'enable_math_sdp'):
+             torch.backends.cuda.enable_math_sdp(True)
+  
+         # Enable cuDNN benchmark for optimal kernel selection
+         torch.backends.cudnn.benchmark = True
+  
+         return _custom_kernel_core(data)
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