code: def gelu_partial_layer_fused_forward(
      # Pointers to matrices
      x_ptr,
      W_ptr,
      A_ptr,
      z1_ptr,
      z2_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_xm is how much to increase x_ptr
      # by to get the element one row down (A has M rows)
      stride_xm,
      stride_xk,
      stride_Wk,
      stride_Wn,
      stride_Am,
      stride_An,
      # Meta-parameters
      BLOCK_SIZE_M: tl.constexpr,
      BLOCK_SIZE_N: tl.constexpr,
      BLOCK_SIZE_K: tl.constexpr,
      GROUP_SIZE_M: tl.constexpr,
  ):
      """
      1) Compute Z = matmul(x, W) into two halves, Z1 and Z2
      2) Compute element-wise multiply A = Z1 * gelu(Z2), this is the activation
      3) Return A
      x has shape (M, K), W has shape (K, N) and A has shape (M, N//2)
      """
      # L2 Cache Optimizations
      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 x and W.
      offs_xm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
      offs_Wn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
      offs_k = tl.arange(0, BLOCK_SIZE_K)
      x_ptrs = x_ptr + (
          offs_xm[:, None] * stride_xm + offs_k[None, :] * stride_xk
      )
      W_left_ptrs = W_ptr + (
          offs_k[:, None] * stride_Wk + offs_Wn[None, :] * stride_Wn
      )
      # offset the start by half of the number of columns
      W_right_ptrs = (
          W_ptr
          + (N // 2)
          + (offs_k[:, None] * stride_Wk + offs_Wn[None, :] * stride_Wn)
      )
  
      z1 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
      z2 = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
      # Matrix multiply A and B, accumulating the first half of columns into x1 and
      # the second half of columns into x2
      for _ in range(0, K, BLOCK_SIZE_K):  # type: ignore
          x = tl.load(x_ptrs)
  
          W_left = tl.load(W_left_ptrs)
          z1 += tl.dot(x, W_left)
  
          W_right = tl.load(W_right_ptrs)
          z2 += tl.dot(x, W_right)
  
          # Advance the ptrs to the next K block
          x_ptrs += BLOCK_SIZE_K * stride_xk
          W_left_ptrs += BLOCK_SIZE_K * stride_Wk
          W_right_ptrs += BLOCK_SIZE_K * stride_Wk
  
      # Store z1 in z1_ptr
      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)
      offsets = (
          stride_Am * offs_cm[:, None] + stride_An * offs_cn[None, :]
      )
      outs_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N // 2)
      tl.store(z1_ptr + offsets, z1, mask=outs_mask)
      # Store z2 in z2_ptr
      tl.store(z2_ptr + offsets, z2, mask=outs_mask)
  
      # Element-wise multiply of z1 and gelu(z2)
      z2 = gelu_fast(z2)
      c = z1 * z2
      c = c.to(tl.float16)
  
      # Write back the block of the output matrix C
      tl.store(A_ptr + offsets, c, mask=outs_mask)
  
imports: from typing import Tuple
  import torch
  import triton
  import triton.language
  import numpy
label_desc: Use image node_id 1 for calling a global function with example usage: gelu_partial_layer_fused_forward(x_ptr, W_ptr, A_ptr, z1_ptr, z2_ptr, M, N, K, stride_xm, stride_xk, stride_Wk, stride_Wn, stride_Am, stride_An, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K, GROUP_SIZE_M) without return types