submission 544928

  ⋯ 1 unchanged lines
  os.environ["CUBLAS_WORKSPACE_CONFIG"] = ":4096:8"
   
  import torch
- import triton
- import triton.language as tl
+ import torch.nn.functional as F
  from task import input_t, output_t
   
- @triton.autotune(
-     configs=[
-         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 64, 'GROUP_M': 8}, num_stages=3, num_warps=8),
-         triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 64, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 256, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=3, num_warps=8),
-         triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=3, num_warps=8),
-         triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'BLOCK_K': 32, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 64, 'BLOCK_N': 256, 'BLOCK_K': 64, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-         triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'BLOCK_K': 64, 'GROUP_M': 8}, num_stages=3, num_warps=8),
-         triton.Config({'BLOCK_M': 64, 'BLOCK_N': 64, 'BLOCK_K': 64, 'GROUP_M': 8}, num_stages=3, num_warps=8),
-         triton.Config({'BLOCK_M': 32, 'BLOCK_N': 256, 'BLOCK_K': 64, 'GROUP_M': 8}, num_stages=4, num_warps=4),
-     ],
-     key=['M', 'N', 'K'],
- )
- @triton.jit
- def matmul_kernel(
-     a_ptr, b_ptr, c_ptr,
-     M, N, K,
-     stride_am, stride_ak,
-     stride_bk, stride_bn,
-     stride_cm, stride_cn,
-     BLOCK_M: tl.constexpr, BLOCK_N: tl.constexpr, BLOCK_K: tl.constexpr,
-     GROUP_M: tl.constexpr,
- ):
-     pid = tl.program_id(0)
-     num_pid_m = tl.cdiv(M, BLOCK_M)
-     num_pid_n = tl.cdiv(N, BLOCK_N)
-     num_pid_in_group = GROUP_M * num_pid_n
-     group_id = pid // num_pid_in_group
-     first_pid_m = group_id * GROUP_M
-     group_size_m = min(num_pid_m - first_pid_m, GROUP_M)
-     pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
-     pid_n = (pid % num_pid_in_group) // group_size_m
+ # cuBLAS is already highly optimized for A100
+ # Try torch.mm which goes through cuBLAS internally but avoid overhead
+ # Also try torch.matmul with contiguous tensors
   
-     offs_am = (pid_m * BLOCK_M + tl.arange(0, BLOCK_M)) % M
-     offs_bn = (pid_n * BLOCK_N + tl.arange(0, BLOCK_N)) % N
-     offs_k = tl.arange(0, BLOCK_K)
-     a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
-     b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
-  
-     acc = tl.zeros((BLOCK_M, BLOCK_N), dtype=tl.float32)
-     for k in range(0, tl.cdiv(K, BLOCK_K)):
-         a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_K, other=0.0)
-         b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_K, other=0.0)
-         acc = tl.dot(a, b, acc)
-         a_ptrs += BLOCK_K * stride_ak
-         b_ptrs += BLOCK_K * stride_bk
-  
-     c = acc.to(tl.float16)
-     offs_cm = pid_m * BLOCK_M + tl.arange(0, BLOCK_M)
-     offs_cn = pid_n * BLOCK_N + tl.arange(0, BLOCK_N)
-     c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
-     c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
-     tl.store(c_ptrs, c, mask=c_mask)
-  
-  
  def custom_kernel(data: input_t) -> output_t:
      a, b, c = data
-     M, K = a.shape
-     K, N = b.shape
-     grid = lambda META: (triton.cdiv(M, META['BLOCK_M']) * triton.cdiv(N, META['BLOCK_N']),)
-     matmul_kernel[grid](
-         a, b, c,
-         M, N, K,
-         a.stride(0), a.stride(1),
-         b.stride(0), b.stride(1),
-         c.stride(0), c.stride(1),
-     )
+     # torch.mm writes to c in-place via addmm with beta=0
+     torch.mm(a, b, out=c)
      return c