Torch Mutable Modules
Use in-place and assignment operations on PyTorch module parameters with support for autograd.
Why does this exist?
PyTorch does not allow in-place operations on module parameters (usually desirable):
linear_layer = torch.nn.Linear(1, 1)
linear_layer.weight.data += 69
# ^^^^^^^^^^^^^^^^^^^^^^^^^^^^
# Valid, but will NOT store grad_fn=<AddBackward0>
linear_layer.weight += 420
# ^^^^^^^^^^^^^^^^^^^^^^^^
# RuntimeError: a leaf Variable that requires grad is being used in an in-place operation.
In some cases, however, it is useful to be able to modify module parameters in-place. For example, if we have a neural network (net_1) that predicts the parameter values to another neural network (net_2), we need to be able to modify the weights of net_2 in-place and backpropagate the gradients to net_1.
# create a parameter predictor network (net_1)
net_1 = torch.nn.Linear(1, 2)
# predict the weights and biases of net_2 using net_1
p_weight_and_bias = net_1(input_0).unsqueeze(2)
p_weight, p_bias = p_weight_and_bias[:, 0], p_weight_and_bias[:, 1]
# create a mutable network (net_2)
net_2 = to_mutable_module(torch.nn.Linear(1, 1))
# hot-swap the weights and biases of net_2 with the predicted values
net_2.weight = p_weight
net_2.bias = p_bias
# compute the output and backpropagate the gradients to net_1
output = net_2(input_1)
loss = criterion(output, label)
loss.backward()
optimizer.step()
This library provides a way to easily convert PyTorch modules into mutable modules with the to_mutable_module function.
Installation
You can install torch-mutable-modules from PyPI.
pip install torch-mutable-modules
To upgrade an existing installation of torch-mutable-modules, use the following command:
pip install --upgrade --no-cache-dir torch-mutable-modules
Importing
You can use wildcard imports or import specific functions directly:
# import all functions
from torch_mutable_modules import *
# ... or import the function manually
from torch_mutable_modules import to_mutable_module
Usage
To convert an existing PyTorch module into a mutable module, use the to_mutable_module function:
converted_module = to_mutable_module(
torch.nn.Linear(1, 1)
) # type of converted_module is still torch.nn.Linear
converted_module.weight *= 0
convreted_module.weight += 69
convreted_module.weight # tensor([[69.]], grad_fn=<AddBackward0>)
You can also declare your own PyTorch module classes as mutable, and all child modules will be recursively converted into mutable modules:
class MyModule(nn.Module):
def __init__(self):
super().__init__()
self.linear = nn.Linear(1, 1)
def forward(self, x):
return self.linear(x)
my_module = to_mutable_module(MyModule())
my_module.linear.weight *= 0
my_module.linear.weight += 69
my_module.linear.weight # tensor([[69.]], grad_fn=<AddBackward0>)
Usage with CUDA
To create a module on the GPU, simply pass a PyTorch module that is already on the GPU to the to_mutable_module function:
converted_module = to_mutable_module(
torch.nn.Linear(1, 1).cuda()
) # converted_module is now a mutable module on the GPU
Moving a module to the GPU with .to() and .cuda() after instanciation is NOT supported. Instead, hot-swap the module parameter tensors with their CUDA counterparts.
# both of these are valid
converted_module.weight = converted_module.weight.cuda()
converted_module.bias = converted_module.bias.to("cuda")
Detailed examples
Please check out example.py to see more detailed example usages of the to_mutable_module function.
Contributing
Please feel free to submit issues or pull requests!
21 Dec 14, 2022
1 Nov 16, 2021
536 Jan 6, 2023
187 Dec 24, 2022
2 Feb 11, 2022
1 Jan 19, 2022
41 Jan 7, 2023
625 Dec 30, 2022
52 Dec 22, 2022
2.3k Jan 08, 2023
927 Jan 05, 2023
3.8k Jan 01, 2023
112 Dec 25, 2022
48 Nov 27, 2022
81 Jan 04, 2023
72 May 07, 2022
2.9k Dec 16, 2022
0 Nov 25, 2021
17 Dec 12, 2022
1 Jan 02, 2022
89 Jan 09, 2023
2 Jan 14, 2022
30 Dec 22, 2022
61 Apr 15, 2022
65 Dec 21, 2022
3 Oct 31, 2022
817 Jan 02, 2023
8 Oct 03, 2022
684 Dec 26, 2022