einx 0.4.2

Universal Notation for Tensor Operations in Python

einx - Universal Notation for Tensor Operations

einx is a notation and Python library that provides a universal interface to formulate tensor operations in frameworks such as Numpy, PyTorch, Jax, Tensorflow, and MLX.

• Quickstart
• What does einx look like?
• How does the notation work?
• How are einx operations implemented?
• Which operations are supported?

Quickstart

Installation:

pip install einx

Example code:

import einx
import numpy as np

x = np.ones((10, 20, 30)) # Create some tensor (numpy/torch/jax/tensorflow/mlx/...)

y = einx.sum("a [b] c", x) # Call an einx operation

print(y.shape)

Documentation and tutorials: https://einx.readthedocs.io

What does einx look like?

z = einx.id("a (b c) -> (b a) c", x, b=2)             # Permute and (un)flatten axes
z = einx.sum("a [b]", x)                              # Sum-reduction along second axis
z = einx.flip("... (g [c])", x, c=2)                  # Flip pairs of values along the last axis
z = einx.mean("b [...] c", x)                         # Spatial mean-pooling
z = einx.multiply("a..., b... -> (a b)...", x, y)     # Kronecker product
z = einx.sum("b (s [ds])... c", x, ds=(2, 2))         # Sum-pooling with 2x2 kernel
z = einx.add("a, b -> a b", x, y)                     # Outer sum
z = einx.dot("a [b], [b] c -> a c", x, y)             # Matrix multiplication
z = einx.get_at("b [h w] c, b i [2] -> b i c", x, y)  # Gather values at coordinates
z = einx.id("b (q + k) -> b q, b k", x, q=2)          # Split
z = einx.id("b c, -> b (c + 1)", x, 42)               # Append number to each channel

See the documentation for more examples.

How does the notation work?

An einx operation consists of (1) an elementary operation and (2) an einx expression that describes how the elementary operation is vectorized. For example, the code

z = einx.{OP}("[c d] a, b -> a [e] b", x, y)

vectorizes the elementary operation {OP} according to the expression "[c d] a, b -> a [e] b".

The meaning of the string expression is defined by analogy with loop notation as follows. The full operation einx.{OP} will yield the same output as if the elementary operation {OP} were invoked in an analogous loop expression:

for a in range(...):
    for b in range(...):
        z[a, :, b] = {OP}(x[:, :, a], y[b])

See the tutorial for how an einx expression is mapped to the analogous loop expression.

How are einx operations implemented?

The analogy with loop notation is used only to define what the output of an operation will be. Internally, einx operations are compiled to Python code snippets that invoke operations from the respective tensor framework, rather than using for loops.

The compiled code snippet can be inspected by passing graph=True to the einx operation. For example:

>>> x = np.zeros((2, 3))
>>> y = np.zeros((3, 4))
>>> code = einx.add("a b, b c -> c b a", x, y, graph=True)
>>> print(code)

import numpy as np
def op(a, b):
    a = np.transpose(a, (1, 0))
    a = np.reshape(a, (1, 3, 2))
    b = np.transpose(b, (1, 0))
    b = np.reshape(b, (4, 3, 1))
    c = np.add(a, b)
    return c

Different backends may be used to compile an operation to different implementations, for example following Numpy-like notation, vmap-based notation, or einsum notation.

Which operations are supported?

Operations in the API: einx supports a large set of tensor operations in the namespace einx.*, including reduction, scalar, indexing, some shape-preserving operations, identity map and dot-product. See the documentation for a complete list.

Operations not in the API: einx additionally allows adapting custom Python functions to einx notation using einx adapters. For example:

# Define a custom elementary operation
def myoperation(x, y):
    x = 2 * x
    z = x + torch.sum(y)
    return z

# Adapt the operation to einx notation
einmyoperation = einx.torch.adapt_with_vmap(myoperation)

# Invoke as einx operation
z = einmyoperation("a [c], b [c] -> a b [c]", x, y)

This will yield the same output as if myoperation were invoked in loop notation:

for a in range(...):
    for b in range(...):
        z[a, b, :] = myoperation(x[a, :], y[b, :])

The interface of einmyoperation matches that of other einx operations. For example, the compiled code snippet can be inspected using graph=True:

>>> code = einmyoperation("a [c], b [c] -> a b [c]", x, y, graph=True)
>>> print(code)
# Constant const1: <function myoperation at 0x49e9aa3cd8a0>
import torch
def op(a, b):
    def c(d, e):
        f = const1(d, e)
        assert isinstance(f, torch.Tensor), "Expected 1st return value of the adapted function to be a tensor"
        assert (tuple(f.shape) == (3,)), "Expected 1st return value of the adapted function to be a tensor with shape (3,)"
        return f
    c = torch.vmap(c, in_dims=(None, 0), out_dims=0)
    c = torch.vmap(c, in_dims=(0, None), out_dims=0)
    g = c(a, b)
    return g

See the documentation for a list of supported adapters.