dlvm is an actively used intermediate representation language created in 2017. Modern Compiler Infrastructure for Deep Learning Systems

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  • dlvm first appeared in 2017
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Example code from the web:

// Dimension-erased functions are flexible because input shapes are dynamic.
// They may be slower and less optimized than their shape-specialized counterparts.

// f(x, w, b) = dot(x, w) + pad(b, at: 0)
func @f: (<_ x _ x f32>, <_ x _ x f32>, <_ x f32>) -> <_ x _ x f32> {
'entry(%x: <_ x _ x f32>, %w: <_ x _ x f32>, %b: <_ x f32>):
    %0.0 = dot %x: <_ x _ x f32>, %w: <_ x _ x f32>
    %0.1 = padShape %b: <_ x f32> at 0
    %0.2 = add %0.0: <_ x _ x f32>, %0.1: <1 x _ x f32>
    return %0.2: <_ x _ x f32>
}

Last updated February 18th, 2020

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