When working with extremely deep neural networks, memory becomes an immediate concern. It will be filled with saved tensors that are needed for gradient computation. PyTorch provides a solution, checkpoint_sequential, that allows us to segment every few layers as a checkpoint. Such that, the forward pass is run in no_grad mode. Meanwhile, the inputs of every segment is saved in memory. Every other unreferenced tensor gets deallocated. In the backward pass, the forward pass of every segment, starting from the last to the first, will be run again using its saved inputs to compute its gradients. Refer to this for more details.
This module extends the functionality of torch.utils.checkpoint.checkpoint to work with invertible functions. So, everything now can be released from memory and recomputed later using the inverse function in the backward pass. This is useful for extremely wide networks where more compute is traded with memory. However, there are few considerations to keep in mind when working with invertible checkpoints and non-materialized tensors. Please, read the limitations section below for specifics.
InvTorch has minimal dependencies. It only requires Python >=3.6 and PyTorch >=1.10.0.
pip install invtorchWe are interested in invtorch.nn.Module which inherits from torch.nn.Module. Subclass it to implement your own invertible code.
import torch
from torch import nn
import invtorch.nn as inn
class InvertibleLinear(inn.Module):
def __init__(self, in_features, out_features):
super().__init__()
self.weight = nn.Parameter(torch.randn(out_features, in_features))
self.bias = nn.Parameter(torch.randn(out_features))
def function(self, inputs):
return inputs @ self.weight.T + self.bias
def inverse(self, outputs):
return (outputs - self.bias) @ self.weight.T.pinverse()
if __name__ == '__main__':
x = torch.randn(10, 3)
model = InvertibleLinear(3, 5)
print('Is invertible:', model.check(x))
y = model(x)
print('Input was freed:', x.storage().size() == 0)
y.backward(torch.randn_like(y))
print('Input was restored:', x.storage().size() != 0)You can immediately notice few differences to the regular PyTorch module here. There is no longer a need to define forward(). Instead, it is replaced with function(). Additionally, it is necessary to define its inverse function as inverse(). Both methods should input and output only positional arguments as a tuple or a single torch.Tensor. Furthermore, forward() accepts an optional keyword argument keep which is an iterable of input tensors that shouldn't be deallocated.
The first call to function() is always run in dry_mode (check it with invtorch.in_dry_mode()). This is a novel mode that has gradient graph construction enabled but doesn't allow for backward propagation. The second call is during the backward pass which is when the gradients will actually be computed (check it with invtorch.in_backward()).
You can verify your implementation of inverse() by calling check(). In some cases, switching to double precision is advised as invertible functions can run into some numerical instability when using single precision. For some functions, a view of an input tensor is passed in the outputs. In such case, this will be automatically detected and the input tensor will not be released from memory.
Sometimes, inverse() requires auxiliary outputs from function() that are not necessarily needed as an output for forward(). To specify how many outputs do we need to keep, we can modify the num_outputs property (None keeps everything). Extra outputs will be hidden using process() which is automatically called by forward(). Another property num_inputs is also defined which helps composing inverse calls like in invtorch.nn.Sequential.inverse().
invtorch.nn.Module can be implemented to be reversible, i.e. forward() will call inverse() instead of function(). Not all invertible modules need to support reversibility. If you want to support it on your own module, then you need to override the reversible property to return True. The module can be reversed by calling reverse() and checked with the reversed property. To avoid confusion, Module has call_function() and call_inverse() which will call the correct function based on the value of reversed.
invtorch.nn.Module has two flags which control the mode of operation; checkpoint and invertible. If checkpoint was set to False, or when working in no_grad mode, it acts exactly as a normal PyTorch module. Otherwise, the model is either invertible or a checkpoint depending on whether invertible is set to True or False, respectively. Those, flags can be changed at any time during operation without any repercussions. A third flag seed is by default False and if set to True, it ensures that the forward runs in the same random number generator's state of the devices of the input tensors.
PyTorch's checkpoint cannot track the requires_grad attribute for its output tensors since it is running in no_grad mode. Instead, InvTorch's checkpoint doesn't have this issue because it runs in dry_mode. In addition, it supports invertible functions.
There are few caveats to consider though. Invertible functions are hard to define without requiring more memory. Moreover, they are prone to numerical instabilities (e.g., multiplying by numbers close to zero). Even if we can get away with these fundamental problems, there are technical details to consider. There is no way of guarding against accessing the data in the input tensors after calling function() and before the backward pass. It is up to the user to ensure this. Otherwise, it is possible to run into illegal memory access errors. Think of residual connections as an example. In x + f(x), assuming f is an invertible checkpoint, x will be freed from memory before the sum is computed. On the other hand, we can maybe use x.clone() + f(x) (not f(x) + x.clone()!) but now we have a copy of x in memory. It is recommended to encapsulate this inside f itself or use a simple checkpoint instead. Other alternatives exists and you should study your case carefully before deciding to use this. For instance, check out torch.autograd.graph.saved_tensors_hooks() and torch.autograd.graph.save_on_cpu().