code: def residual_add_bias(
      hidden_state: torch.Tensor,
      residual: torch.Tensor,
      attn_output: torch.Tensor,
      attn_bias: torch.Tensor,
      final_bias: torch.Tensor,
      mp_size: int,
      mlp_after_attn: bool,
      add_attn_bias: bool,
      pre_attn_norm: bool,
  ):
      # check that all tensors are on the same device
      assert (
          get_accelerator().on_accelerator(hidden_state)
          and get_accelerator().on_accelerator(residual)
          and get_accelerator().on_accelerator(attn_output)
          and get_accelerator().on_accelerator(attn_bias)
          and get_accelerator().on_accelerator(final_bias)
      )
  
      # check that all tensors have the same dtype
      assert (
          hidden_state.dtype
          == residual.dtype
          == attn_output.dtype
          == attn_bias.dtype
          == final_bias.dtype
      )
  
      # check that all tensors have the right shape
      assert hidden_state.shape == residual.shape == attn_output.shape
      assert attn_bias.shape == final_bias.shape
      assert attn_bias.shape[0] == hidden_state.shape[2]
  
      output = torch.empty_like(hidden_state)
  
      hidden_state_size = output.numel()
      bias_size = attn_bias.numel()
  
      grid = lambda meta: (
          triton.cdiv(hidden_state_size, meta["BLOCK_SIZE"]),
      )
  
      residual_add_bias_kernel[grid](
          hidden_state,
          residual,
          attn_output,
          hidden_state_size,
          attn_bias,
          final_bias,
          bias_size,
          output,
          mp_size,
          mlp_after_attn,
          pre_attn_norm,
          add_attn_bias,
          BLOCK_SIZE=1024,
      )
  
      return output
  
imports: import torch
  import triton
  import triton.language
  from deepspeed.accelerator import get_accelerator
label_desc: Use image node_id 2 for calling a global function with example usage: residual_add_bias(hidden_state, residual, attn_output, attn_bias, final_bias, mp_size, mlp_after_attn, add_attn_bias, pre_attn_norm) and returns: output