dgl.DGLGraph.formats

DGLGraph.formats(formats=None)

Get a cloned graph with the specified allowed sparse format(s) or query for the usage status of sparse formats.

The API copies both the graph structure and the features.

If the input graph has multiple edge types, they will have the same sparse format.

When formats is not None, if the intersection between formats and the current graph’s created sparse format(s) is not empty, the returned cloned graph only retains all sparse format(s) in the intersection. If the intersection is empty, a sparse format will be selected to be created following the order of 'coo' -> 'csr' -> 'csc'.

Parameters

formats (str or list of str or None) –

• If formats is None, return the usage status of sparse formats

• Otherwise, it can be 'coo'/'csr'/'csc' or a sublist of them, specifying the sparse formats to use.

Returns

• If formats is None, the result will be a dict recording the usage status of sparse formats.

• Otherwise, a DGLGraph will be returned, which is a clone of the original graph with the specified allowed sparse format(s) formats.

Return type

dict or DGLGraph

Examples

The following example uses PyTorch backend.

>>> import dgl
>>> import torch

Homographs or Heterographs with A Single Edge Type

>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g.ndata['h'] = torch.ones(4, 1)
>>> # Check status of format usage.
>>> g.formats()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> # Get a clone of the graph with 'csr' format.
>>> csr_g = g.formats('csr')
>>> # Only allowed formats will be displayed in the status query.
>>> csr_g.formats()
{'created': ['csr'], 'not created': []}
>>> # Features are copied as well.
>>> csr_g.ndata['h']
tensor([[1.],
        [1.],
        [1.],
        [1.]])

Heterographs with Multiple Edge Types

>>> g = dgl.heterograph({
...     ('user', 'plays', 'game'): (torch.tensor([0, 1, 1, 2]),
...                                 torch.tensor([0, 0, 1, 1])),
...     ('developer', 'develops', 'game'): (torch.tensor([0, 1]),
...                                         torch.tensor([0, 1]))
...     })
>>> g.formats()
{'created': ['coo'], 'not created': ['csr', 'csc']}
>>> # Get a clone of the graph with 'csr' format.
>>> csr_g = g.formats('csr')
>>> # Only allowed formats will be displayed in the status query.
>>> csr_g.formats()
{'created': ['csr'], 'not created': []}

When formats intersects with created formats

>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g = g.formats(['coo', 'csr'])
>>> g.create_formats_()
>>> g.formats()
{'created': ['coo', 'csr'], 'not created': []}
>>> # Get a clone of the graph allowed formats 'csr' and 'csc'.
>>> csr_csc_g = g.formats(['csr', 'csc'])
>>> # Only the intersection 'csr' will be retained.
>>> csr_csc_g.formats()
{'created': ['csr'], 'not created': ['csc']}

When formats doesn’t intersect with created formats

>>> g = dgl.graph(([0, 0, 1], [2, 3, 2]))
>>> g = g.formats('coo')
>>> g.formats()
{'created': ['coo'], 'not created': []}
>>> # Get a clone of the graph allowed formats 'csr' and 'csc'.
>>> csr_csc_g = g.formats(['csr', 'csc'])
>>> # Since the intersection is empty, 'csr' will be created as it is
>>> # first in the order of 'coo' -> 'csr' -> 'csc'.
>>> csr_csc_g.formats()
{'created': ['csr'], 'not created': ['csc']}