nearai / Torchfold
Licence: apache-2.0
Tools for PyTorch
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TorchFold
Blog post: http://near.ai/articles/2017-09-06-PyTorch-Dynamic-Batching/
Analogous to TensorFlow Fold, implements dynamic batching with super simple interface.
Replace every direct call in your computation to nn module with f.add('function name', arguments)
.
It will construct an optimized version of computation and on f.apply
will dynamically batch and execute the computation on given nn module.
Installation
We recommend using pip package manager:
pip install torchfold
Example
f = torchfold.Fold()
def dfs(node):
if is_leaf(node):
return f.add('leaf', node)
else:
prev = f.add('init')
for child in children(node):
prev = f.add('child', prev, child)
return prev
class Model(nn.Module):
def __init__(self, ...):
...
def leaf(self, leaf):
...
def child(self, prev, child):
...
res = dfs(my_tree)
model = Model(...)
f.apply(model, [[res]])
To cite this repository in publications:
@misc{illia_polosukhin_2018_1299387,
author = {Illia Polosukhin and
Maksym Zavershynskyi},
title = {nearai/torchfold: v0.1.0},
month = jun,
year = 2018,
doi = {10.5281/zenodo.1299387},
url = {https://doi.org/10.5281/zenodo.1299387}
}
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