ChannelDependency does not handle concat properly

Describe the issue: During computing Channel Dependencies reshape_break_channel_dependency does following code to ensure that the number of input channels equals the number of output channels:

in_shape = op_node.auxiliary['in_shape']
out_shape = op_node.auxiliary['out_shape']
in_channel = in_shape[1]
out_channel = out_shape[1]
return in_channel != out_channel

This is correct for most reshape operations as long as they accept only one argument. In case of concatenation the in_shape is a list of the concatenated shapes and thus in_channel is being assigned a full shape (e.g. [1, 20, 32, 32]) instead of a single integer (e.g. 20).

This effectively prevents creation of channel dependencies caused by concatenations (although it's rather rare to concatenate feature maps in non-channel dimension).

Environment:

• NNI version: 2.5
• Training service local:
• Client OS: linux
• Server OS (for remote mode only):
• Python version: 3.8.6
• PyTorch/TensorFlow version: 1.10.0
• Is conda/virtualenv/venv used?: yes
• Is running in Docker?: no

Configuration: N/A

Log message: N/A

How to reproduce it?: Model code to quickly replicate the problem:

class NaiveModel(torch.nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = torch.nn.Conv2d(1, 20, 5, 1)
        self.conv2 = torch.nn.Conv2d(1, 20, 5, 1)
        self.fc1 = torch.nn.Linear(6 * 6 * 40, 500)
        self.fc2 = torch.nn.Linear(500, 10)
        self.relu1 = torch.nn.ReLU6()
        self.relu2 = torch.nn.ReLU6()
        self.relu3 = torch.nn.ReLU6()
        self.max_pool1 = torch.nn.MaxPool2d(4, 4)
        self.max_pool2 = torch.nn.MaxPool2d(4, 4)

    def forward(self, x):
        x1 = self.relu1(self.conv1(x))
        x1 = self.max_pool1(x1)
        x2 = self.relu2(self.conv2(x))
        x2 = self.max_pool2(x2)
        x = torch.cat([x1, x2], 2)
        x = x.view(-1, x.size()[1:].numel())
        x = self.relu3(self.fc1(x))
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

model = NaiveModel()
from nni.algorithms.compression.pytorch.pruning import L1FilterPruner

dummy_input = torch.ones([1, 1, 28, 28]).to(device)

config_list = [{"sparsity": 0.5, "op_types": ["Conv2d"]}]
pruner = L1FilterPruner(
    model, config_list, dependency_aware=True, dummy_input=dummy_input
)

# just step into the code to see that the dependencies are not parsed correctly