class torch.nn.modules.lazy.LazyModuleMixin(*args, **kwargs) [source]
 
A mixin for modules that lazily initialize parameters, also known as “lazy modules.” Modules that lazily initialize parameters, or “lazy modules”, derive the shapes of their parameters from the first input(s) to their forward method. Until that first forward they contain torch.nn.UninitializedParameter`s that should not be accessed
or used, and afterward they contain regular :class:`torch.nn.Parameter`s.
Lazy modules are convenient since they don't require computing some
module arguments, like the `in_features argument of a typical torch.nn.Linear. After construction, networks with lazy modules should first be converted to the desired dtype and placed on the desired device. The lazy modules should then be initialized with one or more “dry runs”. These “dry runs” send inputs of the correct size, dtype, and device through the network and to each one of its lazy modules. After this the network can be used as usual. >>> class LazyMLP(torch.nn.Module):
...    def __init__(self):
...        super().__init__()
...        self.fc1 = torch.nn.LazyLinear(10)
...        self.relu1 = torch.nn.ReLU()
...        self.fc2 = torch.nn.LazyLinear(1)
...        self.relu2 = torch.nn.ReLU()
...
...    def forward(self, input):
...        x = self.relu1(self.fc1(input))
...        y = self.relu2(self.fc2(x))
...        return y
>>> # constructs a network with lazy modules
>>> lazy_mlp = LazyMLP()
>>> # transforms the network's device and dtype
>>> # NOTE: these transforms can and should be applied after construction and before any 'dry runs'
>>> lazy_mlp = mlp.cuda().double()
>>> lazy_mlp
LazyMLP(
  (fc1): LazyLinear(in_features=0, out_features=10, bias=True)
  (relu1): ReLU()
  (fc2): LazyLinear(in_features=0, out_features=1, bias=True)
  (relu2): ReLU()
)
>>> # performs a dry run to initialize the network's lazy modules
>>> lazy_mlp(torch.ones(10,10).cuda())
>>> # after initialization, LazyLinear modules become regular Linear modules
>>> lazy_mlp
LazyMLP(
  (fc1): Linear(in_features=10, out_features=10, bias=True)
  (relu1): ReLU()
  (fc2): Linear(in_features=10, out_features=1, bias=True)
  (relu2): ReLU()
)
>>> # attaches an optimizer, since parameters can now be used as usual
>>> optim = torch.optim.SGD(mlp.parameters(), lr=0.01)
 A final caveat when using lazy modules is that the order of initialization of a network’s parameters may change, since the lazy modules are always initialized after other modules. This can cause the parameters of a network using lazy modules to be initialized differently than the parameters of a network without lazy modules. For example, if the LazyMLP class defined above had a torch.nn.LazyLinear module first and then a regular torch.nn.Linear second, the second module would be initialized on construction and the first module would be initialized during the first dry run. Lazy modules can be serialized with a state dict like other modules. For example: >>> lazy_mlp = LazyMLP()
>>> # The state dict shows the uninitialized parameters
>>> lazy_mlp.state_dict()
OrderedDict([('fc1.weight', Uninitialized parameter),
             ('fc1.bias',
              tensor([-1.8832e+25,  4.5636e-41, -1.8832e+25,  4.5636e-41, -6.1598e-30,
                       4.5637e-41, -1.8788e+22,  4.5636e-41, -2.0042e-31,  4.5637e-41])),
             ('fc2.weight', Uninitialized parameter),
             ('fc2.bias', tensor([0.0019]))])
 Lazy modules can also load regular torch.nn.Parameter s, which replace their torch.nn.UninitializedParameter s: >>> full_mlp = LazyMLP()
>>> # Dry run to initialize another module
>>> full_mlp.forward(torch.ones(10, 1))
>>> # Load an initialized state into a lazy module
>>> lazy_mlp.load_state_dict(full_mlp.state_dict())
>>> # The state dict now holds valid values
>>> lazy_mlp.state_dict()
OrderedDict([('fc1.weight',
              tensor([[-0.3837],
                      [ 0.0907],
                      [ 0.6708],
                      [-0.5223],
                      [-0.9028],
                      [ 0.2851],
                      [-0.4537],
                      [ 0.6813],
                      [ 0.5766],
                      [-0.8678]])),
             ('fc1.bias',
              tensor([-1.8832e+25,  4.5636e-41, -1.8832e+25,  4.5636e-41, -6.1598e-30,
                       4.5637e-41, -1.8788e+22,  4.5636e-41, -2.0042e-31,  4.5637e-41])),
             ('fc2.weight',
              tensor([[ 0.1320,  0.2938,  0.0679,  0.2793,  0.1088, -0.1795, -0.2301,  0.2807,
                        0.2479,  0.1091]])),
             ('fc2.bias', tensor([0.0019]))])
 Note, however, that lazy modules cannot validate that the shape of parameters they load is correct.  
has_uninitialized_params() [source]
 
Check if a module has parameters that are not initialized 
  
initialize_parameters(*args, **kwargs) [source]
 
Initialize parameters according to the input batch properties. This adds an interface to isolate parameter initialization from the forward pass when doing parameter shape inference.