dmlc--dgl
nv-dlasalle
d2a22984c1
* * Workaround for graph data saving/loading compatibility problem in Column class. There may be more places in DGL with the same issue, due to using Python serialization, instead of a more cohesive, comprehensive strategy. This is just a local fix. * Add checking for non-empty states * Add unit test * Handle the case of columns without storage Co-authored-by: ndickson <ndickson@nvidia.com> Co-authored-by: Xin Yao <xiny@nvidia.com>
85 行
2.7 KiB
Python
85 行
2.7 KiB
Python
import dgl
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import dgl.ndarray as nd
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from dgl.frame import Column
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import numpy as np
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import backend as F
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import unittest
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import pickle
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from test_utils import parametrize_idtype
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def test_column_subcolumn():
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data = F.copy_to(F.tensor([[1., 1., 1., 1.],
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[0., 2., 9., 0.],
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[3., 2., 1., 0.],
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[1., 1., 1., 1.],
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[0., 2., 4., 0.]]), F.ctx())
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original = Column(data)
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# subcolumn from cpu context
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i1 = F.tensor([0, 2, 1, 3], dtype=F.int64)
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l1 = original.subcolumn(i1)
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assert len(l1) == i1.shape[0]
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assert F.array_equal(l1.data, F.gather_row(data, i1))
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# next subcolumn from target context
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i2 = F.copy_to(F.tensor([0, 2], dtype=F.int64), F.ctx())
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l2 = l1.subcolumn(i2)
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assert len(l2) == i2.shape[0]
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i1i2 = F.copy_to(F.gather_row(i1, F.copy_to(i2, F.context(i1))), F.ctx())
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assert F.array_equal(l2.data, F.gather_row(data,i1i2))
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# next subcolumn also from target context
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i3 = F.copy_to(F.tensor([1], dtype=F.int64), F.ctx())
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l3 = l2.subcolumn(i3)
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assert len(l3) == i3.shape[0]
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i1i2i3 = F.copy_to(F.gather_row(i1i2, F.copy_to(i3, F.context(i1i2))), F.ctx())
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assert F.array_equal(l3.data, F.gather_row(data, i1i2i3))
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def test_serialize_deserialize_plain():
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data = F.copy_to(F.tensor([[1., 1., 1., 1.],
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[0., 2., 9., 0.],
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[3., 2., 1., 0.],
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[1., 1., 1., 1.],
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[0., 2., 4., 0.]]), F.ctx())
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original = Column(data)
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serial = pickle.dumps(original)
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new = pickle.loads(serial)
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print("new = {}".format(new))
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assert F.array_equal(new.data, original.data)
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def test_serialize_deserialize_subcolumn():
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data = F.copy_to(F.tensor([[1., 1., 1., 1.],
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[0., 2., 9., 0.],
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[3., 2., 1., 0.],
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[1., 1., 1., 1.],
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[0., 2., 4., 0.]]), F.ctx())
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original = Column(data)
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# subcolumn from cpu context
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i1 = F.tensor([0, 2, 1, 3], dtype=F.int64)
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l1 = original.subcolumn(i1)
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serial = pickle.dumps(l1)
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new = pickle.loads(serial)
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assert F.array_equal(new.data, l1.data)
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def test_serialize_deserialize_dtype():
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data = F.copy_to(F.tensor([[1., 1., 1., 1.],
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[0., 2., 9., 0.],
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[3., 2., 1., 0.],
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[1., 1., 1., 1.],
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[0., 2., 4., 0.]]), F.ctx())
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original = Column(data)
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original = original.astype(F.int64)
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serial = pickle.dumps(original)
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new = pickle.loads(serial)
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assert new.dtype == F.int64
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