content: import triton
  import torch
  import triton.language as tl
  from torch._inductor.runtime import triton_helpers
  from torch._inductor.runtime.triton_heuristics import grid
  
  @triton.jit
  def pointwise_add_relu_fusion_512(in_out_ptr0, in_ptr0, XBLOCK : tl.constexpr):
      xnumel = 65536
      xoffset = tl.program_id(0) * XBLOCK
      xindex = xoffset + tl.arange(0, XBLOCK)[:]
      xmask = xindex < xnumel
      # dense @ weights
      x2 = xindex
      # bias
      x0 = xindex % 512
      tmp0 = tl.load(in_out_ptr0 + (x2), xmask)
      tmp1 = tl.load(in_ptr0 + (x0), xmask, eviction_policy='evict_last')
      # bias + dense @ weights
      tmp2 = tmp0 + tmp1
      tmp3 = triton_helpers.maximum(0, tmp2)
      tl.store(in_out_ptr0 + (x2), tmp3, None)
  
  
  if __name__ == '__main__':
      torch.cuda.set_device(0)  # no-op to ensure context
      X = torch.ones(size=(128, 512), device='cuda')
      print(X[:3, :3])
      Y = torch.ones(size=(512,), device='cuda')
      print(Y[:3])
      eager_result = torch.maximum(X + Y, torch.tensor(0., device='cuda'))
      print(eager_result[:3, :3])
      pointwise_add_relu_fusion_512[grid(65536)](X, Y, 512)
      print(X)
      torch.testing.assert_close(X, eager_result, rtol=1e-4, atol=1e-4)
  