message: add UT for conv and sum in different blocks
diff: @@ -150,6 +150,22 @@ class LinearRelu(nn.Module):
  def forward(self, x):
  return F.relu(self.linear(x), inplace=True)
  +class ConvSumInDiffBlock(nn.Module):
  + def __init__(self, dim, in_channels, out_channels, **kwargs):
  + super(ConvSumInDiffBlock, self).__init__()
  + seed = 2018
  + torch.manual_seed(seed)
  + self.conv = conv_module[dim](in_channels, out_channels, bias=False, **kwargs)
  +
  + def forward(self, x):
  + y = self.conv(x)
  + if y.size(1) != x.size(1):
  + y += F.pad(x,
  + (0, 0, 0, 0, 0, y.size(1) - x.size(1)), 'constant', 0.)
  + else:
  + y += x
  + return y
  +
  class Tester(TestCase):
  def _test_output(self, model, x, kind_in_graph=None, kind_not_in_graph=None):
  @@ -381,6 +397,17 @@ class Tester(TestCase):
  self.assertEqual(traced_fn(input, weight, bias), result)
  + def test_jit_conv_sum_in_diff_block(self):
  + self._test_output(
  + ConvSumInDiffBlock(2, 3, 32, kernel_size=1, stride=1, padding=0),
  + torch.rand(32, 3, 64, 64),
  + kind_not_in_graph="ipex::conv2d_sum")
  + self._test_output_bf16(
  + ConvSumInDiffBlock(2, 3, 32, kernel_size=1, stride=1, padding=0),
  + torch.rand(32, 3, 64, 64),
  + kind_not_in_graph="ipex::conv2d_sum")
  +
  +
  if __name__ == '__main__':
  torch.manual_seed(2020)
  core.enable_auto_dnnl()
  