nshutils 0.14.0

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nshutils

nshutils is a collection of utility functions and classes that I've found useful in my day-to-day work as an ML researcher. This library includes utilities for typechecking, logging, and saving/loading activations from neural networks.

Installation

To install nshutils, simply run:

pip install nshutils

Features

Typechecking

nshutils provides a simple way to typecheck your code using the jaxtyping library. Simply call typecheck_this_module() at the top of your module (i.e., in the root __init__.py file) to enable typechecking for the entire module:

from nshutils.typecheck import typecheck_this_module

typecheck_this_module()

You can also use the tassert function to assert that a value is of a certain type:

import nshutils.typecheck as tc

def my_function(x: tc.Float[torch.Tensor, "bsz seq len"]) -> tc.Float[torch.Tensor, "bsz seq len"]:
    tc.tassert(tc.Float[torch.Tensor, "bsz seq len"], x)
    ...

Logging

nshutils provides a simple way to configure logging for your project. Simply call one of the logging setup functions:

from nshutils.logging import init_python_logging

init_python_logging()

This will configure logging to use pretty formatting for PyTorch tensors and numpy arrays (inspired by and/or utilizing lovely-numpy and lovely-tensors), and will also enable rich logging if the rich library is installed.

Activation Saving/Loading

nshutils provides a simple way to save and load activations from neural networks. To save activations, use the ActSave object:

from nshutils import ActSave

def my_model_forward(x):
    ...
    # Save activations to "{save_dir}/encoder.activations/{idx}.npy"
    ActSave({"encoder.activations": x})

    # Equivalent to the above
    with ActSave.context("encoder"):
        ActSave(activations=x)
    ...

ActSave.enable(save_dir="path/to/activations")
x = torch.randn(...)
my_model_forward(x)
# Activations are saved to disk under the "path/to/activations" directory

This will save the x tensor to disk under the encoder prefix.

To load activations, use the ActLoad class:

from nshutils import ActLoad

act_load = ActLoad.from_latest_version("path/to/activations")
encoder_acts = act_load["encoder"]

for act in encoder_acts:
    print(act.shape)

This will load all of the activations saved under the encoder prefix.

Other Utilities

nshutils also provides a few other utility functions/classes:

• snoop: A simple way to debug your code using the pysnooper library, based on the torchsnooper library.
• apply_to_collection: Recursively apply a function to all elements of a collection that match a certain type, taken from the pytorch-lightning library.

Contributing

Contributions to nshutils are welcome! Please open an issue or submit a pull request on the GitHub repository.

License

nshutils is released under the MIT License. See the LICENSE file for more details.