code: def masked_softmax_kernel(
      output_ptr,
      input_ptr,
      stride,
      mask_ptr,
      mask_stride,
      n_cols,
      BLOCK_SIZE: tl.constexpr,
  ):
      row_idx = tl.program_id(0)
      row_start_ptr = input_ptr + row_idx * stride
      col_offsets = tl.arange(0, BLOCK_SIZE)
      input_ptrs = row_start_ptr + col_offsets
      mask_ptrs = (
          mask_ptr + col_offsets + row_idx * mask_stride
      )  # mask_stride is 0 for 1d mask
      row = tl.load(
          input_ptrs, mask=col_offsets < n_cols, other=-float("inf")
      ).to(tl.float32)
      mask = tl.load(mask_ptrs, mask=col_offsets < n_cols, other=0).to(
          tl.float32
      )
      row_minus_max = row - tl.max(row, axis=0)
      row_minus_max = row_minus_max + mask
      numerator = tl.exp(row_minus_max)
      denominator = tl.sum(numerator, axis=0)
      softmax_output = numerator / denominator
      output_row_start_ptr = output_ptr + row_idx * stride
      output_ptrs = output_row_start_ptr + col_offsets
      tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols)
  
imports: import torch
  import triton
  import triton.language
label_desc: Use image node_id 2 for calling a global function with example usage: masked_softmax_kernel(output_ptr, input_ptr, stride, mask_ptr, mask_stride, n_cols, BLOCK_SIZE) without return types