AutoHOOT is a Python-based automatic differentiation framework targeting at high-order optimization for large scale tensor computations.
AutoHOOT contains a new explicit Jacobian / Hessian expression generation kernel whose outputs keep the input tensors’ granularity and are easy to optimize. It also contains a new computational graph optimizer that combines both the traditional optimization techniques for compilers and techniques based on specific tensor algebra. The optimization module generates expressions as good as manually written codes in other frameworks for the numerical algorithms of tensor computations.
The library is compatible with other AD libraries, including TensorFlow and Jax, and numerical libraries, including NumPy and Cyclops Tensor Framework.
The example usage of the libaray is shown in the examples and tests folders.
Consider install in editable mode as this package is in developement:
pip install -e path/to/the/project/directoryRun all tests with
# sudo pip install pytest
python -m pytest tests/*.py
# You can specify the python file as well.# Run with specific backends
pytest tests/autodiff_test.py --backendopt numpy jaxThis repo involves a lot of tensor contraction operations. We use the following notations in our documentation:
For the matrix multiplication C = A @ B, the following expressions are equal:
C = einsum("ij,jk->ik", A, B)
C["ik"] = A["ij"] * B["jk"]
C[0,2] = A[0,1] * B[1,2]
Suppose our expression is y = x1 @ x2 + x1, we first define our variables x1 and x2 symbolically,
import autodiff as ad
x1 = ad.Variable(name = "x1", shape=[3, 3])
x2 = ad.Variable(name = "x2", shape=[3, 3])Then, you can define the symoblic expression for y,
y = ad.einsum("ab,bc->ac", x1, x2) + x1With this computation graph, we can evaluate the value of y given any values of x1 and x2: simply walk the graph in a topological order, and for each node, use its associated operator to compute an output value given input values. The evaluation is done in Executor.run method.
executor = ad.Executor([y])
y_val = executor.run(feed_dict = {x1 : x1_val, x2 : x2_val})If we want to evaluate the gradients of y with respect to x1 and x2, as we would often do for loss function wrt parameters in usual machine learning training steps, we need to construct the gradient nodes, grad_x1 and grad_x2.
grad_x1, grad_x2 = ad.gradients(y, [x1, x2])Once we construct the gradients node, and have references to them, we can evaluate the gradients using Executor as before,
executor = ad.Executor([y, grad_x1, grad_x2])
y_val, grad_x1_val, grad_x2_val = executor.run(feed_dict = {x1 : x1_val, x2 : x2_val})grad_x1_val, grad_x2_val now contain the values of dy/dx1 and dy/dx2.
This repo also supports Hessian-vector products through reverse-mode autodiff. As to a expression y = x^T @ H @ x, we first define the expression
x = ad.Variable(name="x", shape=[3, 1])
H = ad.Variable(name="H", shape=[3, 3])
v = ad.Variable(name="v", shape=[3, 1])
y = ad.sum(ad.einsum("ab,bc->ac", ad.einsum("ab,bc->ac", ad.transpose(x), H), x))Then define the expression for the gradient and Hessian-vector product:
grad_x, = ad.gradients(y, [x])
Hv, = ad.hvp(output_node=y, node_list=[x], vector_list=[v])Then we can evaluate y, grad_x and Hv all at once:
y_val, grad_x_val, Hv_val = executor.run(feed_dict={
x: x_val, H: H_val, v: v_val
})This repo also supports source code generation for the constructed computational graphs. See the test file for example usage.
The library is available to everyone. If you would like to acknowledge the usage of the library, please cite the following paper:
Linjian Ma, Jiayu Ye, and Edgar Solomonik. AutoHOOT: Automatic High-Order Optimization for Tensors. International Conference on Parallel Architectures and Compilation Techniques (PACT), October 2020.
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