oneflow.nn.Module¶

Module class for building neural networks¶

class oneflow.nn.Module¶

Base class for all neural network modules.

This class is consistent with PyTorch. The documentation is referenced from: https://pytorch.org/docs/stable/generated/torch.nn.Module.html.

Your models should also subclass this class.

Modules can also contain other Modules, allowing to nest them in a tree structure. You can assign the submodules as regular attributes:

import oneflow.nn as nn
import oneflow.nn.functional as F

class Model(nn.Module):
    def __init__(self):
        super().__init__()
        self.conv1 = nn.Conv2d(1, 20, 5)
        self.conv2 = nn.Conv2d(20, 20, 5)

    def forward(self, x):
        x = F.relu(self.conv1(x))
        return F.relu(self.conv2(x))

Submodules assigned in this way will be registered, and will have their parameters converted too when you call to(), etc.

Note

As per the example above, an __init__() call to the parent class must be made before assignment on the child.

Variables: training (bool) – Boolean represents whether this module is in training or evaluation mode.

add_module(name, module)¶

Adds a child module to the current module.

The module can be accessed as an attribute using the given name.

Parameters

name (string) – name of the child module. The child module can be accessed from this module using the given name
module (Module) – child module to be added to the module.

apply(fn)¶

Applies fn recursively to every submodule (as returned by .children()) as well as self. Typical use includes initializing the parameters of a model.

Parameters: fn (Module -> None) – function to be applied to each submodule
Returns: self
Return type: Module

Example:

>>> import oneflow as flow
>>> import oneflow.nn as nn
>>> @flow.no_grad()
... def init_weights(m):
...     print(m)
...     if type(m) == nn.Linear:
...         m.weight.fill_(1.0)
...         print(m.weight)
>>> net = nn.Sequential(nn.Linear(2, 2), nn.Linear(2, 2))
>>> net.apply(init_weights)
Linear(in_features=2, out_features=2, bias=True)
tensor([[1., 1.],
        [1., 1.]], dtype=oneflow.float32, requires_grad=True)
Linear(in_features=2, out_features=2, bias=True)
tensor([[1., 1.],
        [1., 1.]], dtype=oneflow.float32, requires_grad=True)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)

buffers(recurse=True) → Iterator[Tensor]¶

Returns an iterator over module buffers.

Parameters: recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.
Yields: oneflow.Tensor – module buffer

Example:

>>> for buf in model.buffers(): 
...     print(type(buf), buf.size()) 
<class 'oneflow.Tensor'> oneflow.Size([10])

children() → Iterator[“Module”]¶

Returns an iterator over immediate children modules.

Yields: Module – a child module

Example:

>>> import oneflow.nn as nn
>>> l1 = nn.Linear(2, 2)
>>> l2 = nn.Linear(2, 2)
>>> net = nn.Sequential(l1, l2)
>>> for idx, m in enumerate(net.children()):
...     print(idx, '->', m)
0 -> Linear(in_features=2, out_features=2, bias=True)
1 -> Linear(in_features=2, out_features=2, bias=True)

cpu()¶

Moves all model parameters and buffers to the CPU.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

cuda(device=None)¶

Moves all model parameters and buffers to the GPU.

This also makes associated parameters and buffers different objects. So it should be called before constructing optimizer if the module will live on GPU while being optimized.

Note

This method modifies the module in-place.

Parameters: device (int, optional) – if specified, all parameters will be copied to that device
Returns: self
Return type: Module

double()¶

Casts all floating point parameters and buffers to double datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

eval()¶

Sets the module in evaluation mode.

This has any effect only on certain modules. See documentations of particular modules for details of their behaviors in training/evaluation mode, if they are affected, e.g. Dropout, BatchNorm1d, etc.

This is equivalent with self.train(False).

Returns: self
Return type: Module

float()¶

Casts all floating point parameters and buffers to float datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

half()¶

Casts all floating point parameters and buffers to half datatype.

Note

This method modifies the module in-place.

Returns: self
Return type: Module

load_state_dict(state_dict, strict=True)¶

Copies parameters and buffers from state_dict into this module and its descendants. If strict is True, then the keys of state_dict must exactly match the keys returned by this module’s state_dict() function.

Parameters

state_dict (dict) – a dict containing parameters and persistent buffers.
strict (bool, optional) – whether to strictly enforce that the keys in state_dict match the keys returned by this module’s state_dict() function. Default: True

Returns

missing_keys is a list of str containing the missing keys
unexpected_keys is a list of str containing the unexpected keys

Return type

NamedTuple with missing_keys and unexpected_keys fields

Note

If a parameter or buffer is registered as None and its corresponding key exists in state_dict, load_state_dict() will raise a RuntimeError.

modules() → Iterator[“Module”]¶

Returns an iterator over all modules in the network.

Yields: Module – a module in the network

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> import oneflow.nn as nn
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.modules()):
...     print(idx, '->', m)
0 -> Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
)
1 -> Linear(in_features=2, out_features=2, bias=True)

named_buffers(prefix='', recurse=True) → Iterator[Tuple[str, Tensor]]¶

Returns an iterator over module buffers, yielding both the name of the buffer as well as the buffer itself.

Parameters

prefix (str) – prefix to prepend to all buffer names.
recurse (bool) – if True, then yields buffers of this module and all submodules. Otherwise, yields only buffers that are direct members of this module.

Yields

(string, oneflow.Tensor) – Tuple containing the name and buffer

Example:

>>> for name, buf in self.named_buffers(): 
...    if name in ['running_var']: 
...        print(buf.size()) 

named_children() → Iterator[Tuple[str, “Module”]]¶

Returns an iterator over immediate children modules, yielding both the name of the module as well as the module itself.

Yields: (string, Module) – Tuple containing a name and child module

Example:

>>> for name, module in model.named_children(): 
...     if name in ['conv4', 'conv5']: 
...         print(module) 

named_modules(memo=None, prefix='')¶

Returns an iterator over all modules in the network, yielding both the name of the module as well as the module itself.

Parameters

memo – a memo to store the set of modules already added to the result
prefix – a prefix that will be added to the name of the module

Yields

(string, Module) – Tuple of name and module

Note

Duplicate modules are returned only once. In the following example, l will be returned only once.

Example:

>>> import oneflow.nn as nn
>>> l = nn.Linear(2, 2)
>>> net = nn.Sequential(l, l)
>>> for idx, m in enumerate(net.named_modules()):
...     print(idx, '->', m)
0 -> ('', Sequential(
  (0): Linear(in_features=2, out_features=2, bias=True)
  (1): Linear(in_features=2, out_features=2, bias=True)
))
1 -> ('0', Linear(in_features=2, out_features=2, bias=True))

named_parameters(prefix='', recurse=True) → Iterator[Tuple[str, Tensor]]¶

Returns an iterator over module parameters, yielding both the name of the parameter as well as the parameter itself.

Parameters

prefix (str) – prefix to prepend to all parameter names.
recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.

Yields

(string, Parameter) – Tuple containing the name and parameter

Example:

>>> for name, param in self.named_parameters(): 
...    if name in ['bias']: 
...        print(param.size()) 

parameters(recurse=True) → Iterator[Parameter]¶

Returns an iterator over module parameters.

This is typically passed to an optimizer.

Parameters: recurse (bool) – if True, then yields parameters of this module and all submodules. Otherwise, yields only parameters that are direct members of this module.
Yields: Parameter – module parameter

Example:

>>> for param in model.parameters(): 
...     print(type(param), param.size()) 
<class 'oneflow.Tensor'> oneflow.Size([10])

register_buffer(name, tensor, persistent=True)¶

Adds a buffer to the module.

This is typically used to register a buffer that should not to be considered a model parameter. For example, BatchNorm’s running_mean is not a parameter, but is part of the module’s state. Buffers, by default, are persistent and will be saved alongside parameters. This behavior can be changed by setting persistent to False. The only difference between a persistent buffer and a non-persistent buffer is that the latter will not be a part of this module’s state_dict.

Buffers can be accessed as attributes using given names.

Parameters

name (string) – name of the buffer. The buffer can be accessed from this module using the given name
tensor (Tensor or None) – buffer to be registered. If None, then operations that run on buffers, such as cuda, are ignored. If None, the buffer is not included in the module’s state_dict.
persistent (bool) – whether the buffer is part of this module’s state_dict.

Example:

>>> self.register_buffer('running_mean', oneflow.zeros(num_features)) 

register_forward_hook(hook)¶

Registers a forward hook on the module.

The hook will be called every time after forward() has computed an output. It should have the following signature:

hook(module, input, output) -> None or modified output

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the output. It can modify the input inplace but it will not have effect on forward since this is called after forward() is called.

register_forward_pre_hook(hook)¶

Registers a forward pre-hook on the module.

The hook will be called every time before forward() is invoked. It should have the following signature:

hook(module, input) -> None or modified input

The input contains only the positional arguments given to the module. Keyword arguments won’t be passed to the hooks and only to the forward. The hook can modify the input. User can either return a tuple or a single modified value in the hook. We will wrap the value into a tuple if a single value is returned(unless that value is already a tuple).

register_parameter(name, param)¶

Adds a parameter to the module.

The parameter can be accessed as an attribute using given name.

Parameters

name (string) – name of the parameter. The parameter can be accessed from this module using the given name
param (Parameter or None) – parameter to be added to the module. If None, then operations that run on parameters, such as cuda, are ignored. If None, the parameter is not included in the module’s state_dict.

state_dict(destination=None, prefix='', keep_vars=False) → Dict[str, Tensor]¶

Returns a dictionary containing a whole state of the module.

Both parameters and persistent buffers (e.g. running averages) are included. Keys are corresponding parameter and buffer names. Parameters and buffers set to None are not included.

Parameters

destination (dict, optional) – Deprecated. This dict is returned with the module state saved in it. It should also have an attribute _metadata: dict to save metadata of the module state. If it’s not provided, an OrderedDict is created and returned. Default: None
prefix (str, optional) – a prefix added to parameter and buffer names to compose the keys in dict. Default: ''
keep_vars (bool, optional) – by default the Tensor s returned in the state dict are detached from autograd. If it’s set to True, detaching is not performed. Default: False

Returns

a dictionary containing a whole state of the module

Return type

dict

Example:

>>> import oneflow.nn as nn
>>> l1 = nn.Linear(2, 2)
>>> l2 = nn.Linear(2, 2)
>>> net = nn.Sequential(l1, l2)
>>> net.state_dict().keys()
odict_keys(['0.weight', '0.bias', '1.weight', '1.bias'])

to(device=None)¶

Moves the parameters and buffers.

Its signature is similar to oneflow.Tensor.to(). The parameters and buffers will be moved device, if that is given.

See below for examples.

Note

This method modifies the module in-place.

Parameters: device (oneflow.device) – the desired device of the parameters and buffers in this module
Returns: self
Return type: Module

Examples:

>>> import oneflow as flow
>>> import oneflow.nn as nn
>>> linear = nn.Linear(2, 2)
>>> linear.weight.device
device(type='cpu', index=0)
>>> gpu1 = flow.device("cuda:1")
>>> linear.to(gpu1)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight.device
device(type='cuda', index=1)
>>> cpu = flow.device("cpu")
>>> linear.to(cpu)
Linear(in_features=2, out_features=2, bias=True)
>>> linear.weight.device
device(type='cpu', index=0)

to_consistent(*args, **kwargs)¶: This interface is no longer available, please use oneflow.nn.Module.to_global() instead.

to_global(placement=None, sbp=None)¶

Convert the parameters and buffers to global.

It performs the same oneflow.Tensor.to_global() conversion to each parameter and buffer in this module.

Note

This method modifies the module in-place.

Both placement and sbp are required if the parameters and buffers of this module are local, otherwise at least one of placement and sbp is required.

Parameters

placement (flow.placement, optional) – the desired placement of the parameters and buffers in this module. Default: None
sbp (flow.sbp.sbp or tuple of flow.sbp.sbp, optional) – the desired sbp of the parameters and buffers in this module. Default: None

For example:

>>> import oneflow as flow
>>> m = flow.nn.Conv2d(in_channels=3, out_channels=4, kernel_size=3)
>>> m.to_global(placement=flow.placement("cpu", ranks=[0]), sbp=[flow.sbp.split(0)])
>>> m.weight.is_global
True
>>> m.bias.is_global
True

train(mode=True)¶

Sets the module in training mode.

Parameters: mode (bool) – whether to set training mode (True) or evaluation mode (False). Default: True.
Returns: self
Return type: Module

zero_grad(set_to_none=False)¶

Sets gradients of all model parameters to zero. See similar function under oneflow.optim.Optimizer for more context.

Parameters: set_to_none (bool) – instead of setting to zero, set the grads to None. See oneflow.optim.Optimizer.zero_grad() for details.