torch-max-mem

This package provides decorators for memory utilization maximization with PyTorch and CUDA by starting with a maximum parameter size and applying successive halving until no more out-of-memory exception occurs.

💪 Getting Started

Assume you have a function for batched computation of nearest neighbors using brute-force distance calculation.

import torch

def knn(x, y, batch_size, k: int = 3):
    return torch.cat(
        [
            torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=1, largest=False).indices
            for start in range(0, x.shape[0], batch_size)
        ],
        dim=0,
    )

With torch_max_mem you can decorate this function to reduce the batch size until no more out-of-memory error occurs.

import torch
from torch_max_mem import maximize_memory_utilization

@maximize_memory_utilization()
def knn(x, y, batch_size, k: int = 3):
    return torch.cat(
        [
            torch.cdist(x[start : start + batch_size], y).topk(k=k, dim=1, largest=False).indices
            for start in range(0, x.shape[0], batch_size)
        ],
        dim=0,
    )

In the code, you can now always pass the largest sensible batch size, e.g.,

x = torch.rand(100, 100, device="cuda")
y = torch.rand(200, 100, device="cuda")
knn(x, y, batch_size=x.shape[0])

🚀 Installation

The most recent release can be installed from PyPI with:

$ pip install torch_max_mem

The most recent code and data can be installed directly from GitHub with:

$ pip install git+https://github.com/mberr/torch-max-mem.git

To install in development mode, use the following:

$ git clone git+https://github.com/mberr/torch-max-mem.git
$ cd torch-max-mem
$ pip install -e .

👐 Contributing

Contributions, whether filing an issue, making a pull request, or forking, are appreciated. See CONTRIBUTING.md for more information on getting involved.

👋 Attribution

Parts of the logic have been developed with Laurent Vermue for PyKEEN.

⚖️ License

The code in this package is licensed under the MIT License.

🍪 Cookiecutter

This package was created with @audreyfeldroy's cookiecutter package using @cthoyt's cookiecutter-snekpack template.

🛠️ For Developers