Einsum

Recently learned about einsum, a flexible operator that can represent a lot of matrix operations. Essentially, einsum conceptually works by writing input and output in terms of axes, which implicitly shows how the output should be calculated.

# setup example matrices
a = np.random()

# permute axis

MACs and FLOPs

• MAC: Multiply Accumulate (more commonly used for matrix operations)
• FLOPs: Floating point operation (includes any common but elementary calculations e.g. +,-,*,/ )

« operator in YAML

The & operator assigns reference key to a mapping, while * calls it, and « does a merge of mappings accordingly. Hence for

foo:
  a: b
  <<:
    c: d
  e: f

The output comes out as

foo:
  a: b
  c: d
  e: f

While if we are merging them via references, the merging mapping has less priority such that

foo: &foo
  a: b
  c: d
  e: f

fee: &fee
  a: q
  <<: *foo

far: &far
  z: x
  <<: *foo

Produces

foo:
  a: b
  c: d
  e: f

fee:
  a: q
  c: d
  e: f

far:
  a: b
  c: d
  e: f
  z: x

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