torchcache 0.6.0

Cache PyTorch module outputs on the fly

torchcache

Effortlessly cache PyTorch module outputs or PyTorch-heavy functions on-the-fly with torchcache.

Particularly useful for caching and serving the outputs of computationally expensive large, pre-trained PyTorch modules, such as vision transformers. Note that gradients will not flow through the cached outputs.

• torchcache
  • Features
  • Installation
  • Citation
  • Basic usage
  • Assumptions
  • Contribution

Features

• Cache PyTorch module outputs or pure Python functions either in-memory or persistently to disk.
• Simple decorator-based interface for easy usage.
• Uses an MRU (most-recently-used) cache, which evicts the most recently used items first to manage memory/disk usage in a training setting. Learn more about MRU caches here.

Installation

pip install torchcache

Citation

If you use our work, please consider citing our paper:

@inproceedings{akbiyik2023routeformer,
    title={Leveraging Driver Field-of-View for Multimodal Ego-Trajectory Prediction},
    author={M. Eren Akbiyik, Nedko Savov, Danda Pani Paudel, Nikola Popovic, Christian Vater, Otmar Hilliges, Luc Van Gool, Xi Wang},
    booktitle={International Conference on Learning Representations},
    year={2025}
}

Basic usage

Quickly cache the output of your PyTorch module with a single decorator:

from torchcache import torchcache

@torchcache()
class MyModule(nn.Module):
    def __init__(self):
        super().__init__()
        self.linear = nn.Linear(10, 10)

    def forward(self, x):
        # This output will be cached
        return self.linear(x)

input_tensor = torch.ones(10, dtype=torch.float32)
# Output is cached during the first call...
output = model(input_tensor)
# ...and is retrieved from the cache for the next one
output_cached = model(input_tensor)

You can also cache the output of any function, not just PyTorch modules:

from torchcache import torchcache
@torchcache()
def my_function(x):
    # This output will be cached
    return x * 2

See documentation at torchcache.readthedocs.io for more examples.

Assumptions

To ensure seamless operation, torchcache assumes the following:

• Your module is a subclass of nn.Module.
• The module's forward method accepts any number of positional or keyword arguments with shapes (B, *), where B is the batch size and * represents any number of dimensions, or any other basic immutable Python types (int, str, float, boolean). All tensors should be on the same device and have the same dtype.
• The forward method returns a single tensor of shape (B, *).

Contribution

1. Ensure you have Python installed.
2. Install poetry.
3. Run poetry install to set up dependencies.
4. Run poetry run pre-commit install to install pre-commit hooks.
5. Create a branch, make your changes, and open a pull request.