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Dynamic Batching with PyTorch

Project description


Blog post:

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.


We recommend using pip package manager:

pip install torchfold


    f = torchfold.Fold()

    def dfs(node):
        if is_leaf(node):
            return f.add('leaf', node)
            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]])

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