code: def matmul_kernel(
      # Pointers to matrices
      a_ptr,
      b_ptr,
      c_ptr,
      # Matrix dimensions
      M,
      N,
      K,
      # The stride variables represent how much to increase the ptr by when moving by 1
      # element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
      # by to get the element one row down (A has M rows).
      stride_am,
      stride_ak,  #
      stride_bk,
      stride_bn,  #
      stride_cm,
      stride_cn,
      # Meta-parameters
      BLOCK_SIZE_M: tl.constexpr,
      BLOCK_SIZE_N: tl.constexpr,
      BLOCK_SIZE_K: tl.constexpr,  #
      GROUP_SIZE_M: tl.constexpr,  #
      ACTIVATION: tl.constexpr,  #
  ):
      """Kernel for computing the matmul C = A x B.
      A has shape (M, K), B has shape (K, N) and C has shape (M, N)
      """
      # -----------------------------------------------------------
      # Map program ids `pid` to the block of C it should compute.
      # This is done in a grouped ordering to promote L2 data reuse.
      # See above `L2 Cache Optimizations` section for details.
      pid = tl.program_id(axis=0)
      num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
      num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
      num_pid_in_group = GROUP_SIZE_M * num_pid_n
      group_id = pid // num_pid_in_group
      first_pid_m = group_id * GROUP_SIZE_M
      group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
      pid_m = first_pid_m + (pid % group_size_m)
      pid_n = (pid % num_pid_in_group) // group_size_m
  
      # ----------------------------------------------------------
      # Create pointers for the first blocks of A and B.
      # We will advance this pointer as we move in the K direction
      # and accumulate
      # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
      # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
      # See above `Pointer Arithmetic` section for details
      offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
      offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
      offs_k = tl.arange(0, BLOCK_SIZE_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
      )
  
      # -----------------------------------------------------------
      # Iterate to compute a block of the C matrix.
      # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
      # of fp32 values for higher accuracy.
      # `accumulator` will be converted back to fp16 after the loop.
      accumulator = tl.zeros(
          (BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32
      )
      for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
          # Load the next block of A and B, generate a mask by checking the K dimension.
          # If it is out of bounds, set it to 0.
          a = tl.load(
              a_ptrs,
              mask=offs_k[None, :] < K - k * BLOCK_SIZE_K,
              other=0.0,
          )
          b = tl.load(
              b_ptrs,
              mask=offs_k[:, None] < K - k * BLOCK_SIZE_K,
              other=0.0,
          )
          # We accumulate along the K dimension.
          accumulator += tl.dot(a, b)
          # Advance the ptrs to the next K block.
          a_ptrs += BLOCK_SIZE_K * stride_ak
          b_ptrs += BLOCK_SIZE_K * stride_bk
      # You can fuse arbitrary activation functions here
      # while the accumulator is still in FP32!
      if ACTIVATION == "leaky_relu":
          accumulator = leaky_relu(accumulator)
      c = accumulator.to(tl.float16)
  
      # -----------------------------------------------------------
      # Write back the block of the output matrix C with masks.
      offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
      offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_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)
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 1 for calling a global function with example usage: matmul_kernel(a_ptr, b_ptr, c_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, stride_cn, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, GROUP_SIZE_M, ACTIVATION) without return types