How to obtain sequence of submodules from a pytorch module?

Question

For a pytorch module, I suppose I could use .named_children, .named_modules, etc. to obtain a list of the submodules. However, I suppose the list is not given in order, right? An example:

In [19]: import transformers

In [20]: model = transformers.DistilBertForSequenceClassification.from_pretrained('distilb
    ...: ert-base-cased')

In [21]: [name for name, _ in model.named_children()]
Out[21]: ['distilbert', 'pre_classifier', 'classifier', 'dropout']

The order of .named_children() in the above model is given as distilbert, pre_classifier, classifier, and dropout. However, if you examine the code, it is evident that dropout happens before classifier. So how do I get the order of these submodules?

Answer 1

In Pytorch, the results of print(model) or .named_children(), etc are listed based on the order they are declared in __init__ of the model's class e.g.

Case 1

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc1 = nn.Linear(320, 50)
        self.fc2 = nn.Linear(50, 10)
        self.conv2_drop = nn.Dropout2d()

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.6)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

model = Model()
print(model)
[name for name, _ in model.named_children()]
# output
['conv1', 'conv2', 'fc1', 'fc2', 'conv2_drop']

Case 2

Changed order of fc1 and fc2 layers in constructor.

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
        self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
        self.fc2 = nn.Linear(50, 10)
        self.fc1 = nn.Linear(320, 50)
        self.conv2_drop = nn.Dropout2d()

    def forward(self, x):
        x = F.relu(F.max_pool2d(self.conv1(x), 2))
        x = F.relu(F.max_pool2d(self.conv2_drop(self.conv2(x)), 2))
        x = x.view(-1, 320)
        x = F.relu(self.fc1(x))
        x = F.dropout(x, p=0.6)
        x = self.fc2(x)
        return F.log_softmax(x, dim=1)

model = Model()
print(model)
[name for name, _ in model.named_children()]
# output
['conv1', 'conv2', 'fc2', 'fc1', 'conv2_drop']

---

That's why classifier is printed before dropout as it's declared so in constructor:

class DistilBertForSequenceClassification(DistilBertPreTrainedModel):
        ...
        self.distilbert = DistilBertModel(config)
        self.pre_classifier = nn.Linear(config.dim, config.dim)
        self.classifier = nn.Linear(config.dim, config.num_labels)
        self.dropout = nn.Dropout(config.seq_classif_dropout)

---

Nevertheless, you can play with model's submodules using .modules(), etc. but they'll be listed only in the order they are declared in __init__. If you only want to print structure based on forward method, you may try using pytorch-summary.