Visualization

Various aspects of the contraction process can be visualized using the following functions.

%config InlineBackend.figure_formats = ['svg']
import cotengra as ctg

# generate an equation representing a 2D lattice
inputs, output, shapes, size_dict = ctg.utils.lattice_equation([5, 6])

Hypergraph visualization

The first visualization we can do is to visualize the hypergraph corresponding to the geometry of the tensor network or equation with HyperGraph.plot:

hg = ctg.get_hypergraph(inputs, output, size_dict)
hg.plot(draw_edge_labels=True)

(<Figure size 500x500 with 1 Axes>, <Axes: >)

The default coloring of the nodes shows a simple centrality measure.

Note

Hyper edges are shown as a zero size vertex connecting the tensors they appear on, like a COPY-tensor.

ctg.get_hypergraph(['ax', 'bx', 'cx']).plot(draw_edge_labels=True)

(<Figure size 500x500 with 1 Axes>, <Axes: >)

Optimizer trials visualization

If we run an hyper optimizer, we can visualize how the scores progress with trials using the HyperOptimizer.plot_trials method:

opt = ctg.HyperOptimizer(methods=['greedy', 'kahypar', 'labels'], progbar=True)

# run it and generate a tree
tree = opt.search(inputs, output, size_dict)

log2[SIZE]: 6.00 log10[FLOPs]: 3.46: 100%|██████████| 128/128 [00:02<00:00, 63.13it/s]

By default the y-axis is the objective score, but you can specify e.g. 'flops'’:

opt.plot_trials('flops')

(<Figure size 800x300 with 1 Axes>,
 <Axes: xlabel='TRIAL', ylabel='log10[FLOPS]'>)

We can also plot the distribution of contraction costs against contraction widths using the HyperOptimizer.plot_scatter method:

opt.plot_scatter(x='size', y='flops')

(<Figure size 500x500 with 1 Axes>,
 <Axes: xlabel='log2[SIZE]', ylabel='log10[FLOPS]'>)

Tree visualizations

The following visualization functions are available for inspecting a single, complete ContractionTree once generated. They mostly wrap plot_tree, where you can see most of the extra options.

Contractions

tree.plot_contractions gives you an overview of the memory and costs throughout the contraction:

tree.plot_contractions()

(<Figure size 800x300 with 2 Axes>,
 <Axes: xlabel='contraction', ylabel='$\\log_2[SIZE]$'>)

Here, peak is the memory required for all intermediates to be stored at once, write is the size of the new intermedite tensor, the max of which is the contraction width. cost is the scalar operations of each contraction.

Tent

The most general purpose visualization for the structure of a ContractionTree is the ContractionTree.plot_tent method. This plots the input network (in grey) at the bottom, and the contraction tree intermediates laid out above. The width and color of the tree edges denote the intermediate tensor widths, and the size and color of the tree nodes denote the FLOPs required to contract each intermediate tensor:

tree.plot_tent()

(<Figure size 500x500 with 3 Axes>, <Axes: >)

If you supply order=True then the intermediate nodes will be in the exact vertical order than they would be performed:

tree.plot_tent(order=True)

(<Figure size 500x500 with 3 Axes>, <Axes: >)

Note

If you have sliced indices, these will appear as dashed lines in the input graph.

Ring

Another option is the ContractionTree.plot_ring method which lays out the input network on a ring, with the contraction tree intermediates laid out towards the center. The more arcs cross between branches the more expensive that contraction. This can be useful for inspecting how many ‘spines’ a contraction has or how balanced it is:

tree.plot_ring()

(<Figure size 500x500 with 3 Axes>, <Axes: >)

Rubberband

For small and close to planar graphs, an alternative visualization is the ContractionTree.plot_rubberband method. method using quimb. Here, nodes of the input graph are hierarchically grouped into bands according to the contraction tree. The order of contraction is represented by the colors:

tree.plot_rubberband()

OMP: Info #276: omp_set_nested routine deprecated, please use omp_set_max_active_levels instead.

(<Figure size 500x500 with 1 Axes>, <Axes: >)

All of the above methods can be pretty extensively customized, including by supplying custom colormaps. They also return (fig, ax) for further customization or embedding in other plots.

inputs, output, shapes, size_dict = ctg.utils.lattice_equation([5, 5, 5])
opt = ctg.HyperOptimizer(progbar=True, reconf_opts={}, minimize='combo-256')
tree = opt.search(inputs, output, size_dict)

log2[SIZE]: 31.00 log10[FLOPs]: 11.54: 100%|██████████| 128/128 [00:21<00:00,  6.08it/s]

tree.plot_tent(
    raw_edge_alpha=0.0,
    edge_colormap='Greens',
    node_colormap='Greens',
    edge_scale=2,
    node_scale=0.5,
    colorbars=False,
    tree_root_height=-1.0,
    figsize=(10, 10),
)

(<Figure size 1000x1000 with 1 Axes>, <Axes: >)