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Unverified Commit 14251504 authored by nv-dlasalle's avatar nv-dlasalle Committed by GitHub
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Fix test naming (#4023) 


Co-authored-by: default avatarMufei Li <mufeili1996@gmail.com>
parent 7c598aac
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Showing with 134 additions and 134 deletions
tests/compute/test_apply_edges_hetero.py
View file @ 14251504
...@@ -10,7 +10,7 @@ import networkx as nx ...@@ -10,7 +10,7 @@ import networkx as nx
import unittest, pytest import unittest, pytest
from dgl import DGLError from dgl import DGLError
import test_utils import test_utils
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
from scipy.sparse import rand from scipy.sparse import rand
rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean} rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
...@@ -39,7 +39,7 @@ def create_test_heterograph(idtype): ...@@ -39,7 +39,7 @@ def create_test_heterograph(idtype):
return g return g
@parametrize_dtype @parametrize_idtype
def test_unary_copy_u(idtype): def test_unary_copy_u(idtype):
def _test(mfunc): def _test(mfunc):
...@@ -92,7 +92,7 @@ def test_unary_copy_u(idtype): ...@@ -92,7 +92,7 @@ def test_unary_copy_u(idtype):
_test(fn.copy_u) _test(fn.copy_u)
@parametrize_dtype @parametrize_idtype
def test_unary_copy_e(idtype): def test_unary_copy_e(idtype):
def _test(mfunc): def _test(mfunc):
...@@ -148,7 +148,7 @@ def test_unary_copy_e(idtype): ...@@ -148,7 +148,7 @@ def test_unary_copy_e(idtype):
_test(fn.copy_e) _test(fn.copy_e)
@parametrize_dtype @parametrize_idtype
def test_binary_op(idtype): def test_binary_op(idtype):
def _test(lhs, rhs, binary_op): def _test(lhs, rhs, binary_op):
......
tests/compute/test_basics.py
View file @ 14251504
...@@ -6,7 +6,7 @@ import networkx as nx ...@@ -6,7 +6,7 @@ import networkx as nx
from dgl import DGLGraph from dgl import DGLGraph
from collections import defaultdict as ddict from collections import defaultdict as ddict
import unittest import unittest
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
D = 5 D = 5
reduce_msg_shapes = set() reduce_msg_shapes = set()
...@@ -89,7 +89,7 @@ def generate_graph(idtype, grad=False): ...@@ -89,7 +89,7 @@ def generate_graph(idtype, grad=False):
def test_compatible(): def test_compatible():
g = generate_graph_old() g = generate_graph_old()
@parametrize_dtype @parametrize_idtype
def test_batch_setter_getter(idtype): def test_batch_setter_getter(idtype):
def _pfc(x): def _pfc(x):
return list(F.zerocopy_to_numpy(x)[:,0]) return list(F.zerocopy_to_numpy(x)[:,0])
...@@ -159,7 +159,7 @@ def test_batch_setter_getter(idtype): ...@@ -159,7 +159,7 @@ def test_batch_setter_getter(idtype):
v = F.tensor([6, 9, 7], g.idtype) v = F.tensor([6, 9, 7], g.idtype)
assert _pfc(g.edges[u, v].data['l']) == [1.0, 1.0, 0.0] assert _pfc(g.edges[u, v].data['l']) == [1.0, 1.0, 0.0]
@parametrize_dtype @parametrize_idtype
def test_batch_setter_autograd(idtype): def test_batch_setter_autograd(idtype):
g = generate_graph(idtype, grad=True) g = generate_graph(idtype, grad=True)
h1 = g.ndata['h'] h1 = g.ndata['h']
...@@ -290,7 +290,7 @@ def _test_nx_conversion(): ...@@ -290,7 +290,7 @@ def _test_nx_conversion():
assert F.allclose(g.edata['h'], F.tensor([[1., 2.], [1., 2.], assert F.allclose(g.edata['h'], F.tensor([[1., 2.], [1., 2.],
[2., 3.], [2., 3.]])) [2., 3.], [2., 3.]]))
@parametrize_dtype @parametrize_idtype
def test_apply_nodes(idtype): def test_apply_nodes(idtype):
def _upd(nodes): def _upd(nodes):
return {'h' : nodes.data['h'] * 2} return {'h' : nodes.data['h'] * 2}
...@@ -302,7 +302,7 @@ def test_apply_nodes(idtype): ...@@ -302,7 +302,7 @@ def test_apply_nodes(idtype):
g.apply_nodes(lambda nodes : {'h' : nodes.data['h'] * 0.}, u) g.apply_nodes(lambda nodes : {'h' : nodes.data['h'] * 0.}, u)
assert F.allclose(F.gather_row(g.ndata['h'], u), F.zeros((4, D))) assert F.allclose(F.gather_row(g.ndata['h'], u), F.zeros((4, D)))
@parametrize_dtype @parametrize_idtype
def test_apply_edges(idtype): def test_apply_edges(idtype):
def _upd(edges): def _upd(edges):
return {'w' : edges.data['w'] * 2} return {'w' : edges.data['w'] * 2}
...@@ -316,7 +316,7 @@ def test_apply_edges(idtype): ...@@ -316,7 +316,7 @@ def test_apply_edges(idtype):
eid = F.tensor(g.edge_ids(u, v)) eid = F.tensor(g.edge_ids(u, v))
assert F.allclose(F.gather_row(g.edata['w'], eid), F.zeros((6, D))) assert F.allclose(F.gather_row(g.edata['w'], eid), F.zeros((6, D)))
@parametrize_dtype @parametrize_idtype
def test_update_routines(idtype): def test_update_routines(idtype):
g = generate_graph(idtype) g = generate_graph(idtype)
...@@ -353,7 +353,7 @@ def test_update_routines(idtype): ...@@ -353,7 +353,7 @@ def test_update_routines(idtype):
assert(reduce_msg_shapes == {(1, 8, D), (9, 1, D)}) assert(reduce_msg_shapes == {(1, 8, D), (9, 1, D)})
reduce_msg_shapes.clear() reduce_msg_shapes.clear()
@parametrize_dtype @parametrize_idtype
def test_update_all_0deg(idtype): def test_update_all_0deg(idtype):
# test#1 # test#1
g = dgl.graph(([1, 2, 3, 4], [0, 0, 0, 0]), idtype=idtype, device=F.ctx()) g = dgl.graph(([1, 2, 3, 4], [0, 0, 0, 0]), idtype=idtype, device=F.ctx())
...@@ -384,7 +384,7 @@ def test_update_all_0deg(idtype): ...@@ -384,7 +384,7 @@ def test_update_all_0deg(idtype):
# should fallback to apply # should fallback to apply
assert F.allclose(new_repr, 2*old_repr) assert F.allclose(new_repr, 2*old_repr)
@parametrize_dtype @parametrize_idtype
def test_pull_0deg(idtype): def test_pull_0deg(idtype):
g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx())
def _message(edges): def _message(edges):
...@@ -450,7 +450,7 @@ def test_dynamic_addition(): ...@@ -450,7 +450,7 @@ def test_dynamic_addition():
assert len(g.edata['h1']) == len(g.edata['h2']) assert len(g.edata['h1']) == len(g.edata['h2'])
@parametrize_dtype @parametrize_idtype
def test_repr(idtype): def test_repr(idtype):
g = dgl.graph(([0, 0, 1], [1, 2, 2]), num_nodes=10, idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 0, 1], [1, 2, 2]), num_nodes=10, idtype=idtype, device=F.ctx())
repr_string = g.__repr__() repr_string = g.__repr__()
...@@ -460,7 +460,7 @@ def test_repr(idtype): ...@@ -460,7 +460,7 @@ def test_repr(idtype):
repr_string = g.__repr__() repr_string = g.__repr__()
print(repr_string) print(repr_string)
@parametrize_dtype @parametrize_idtype
def test_local_var(idtype): def test_local_var(idtype):
g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx())
g.ndata['h'] = F.zeros((g.number_of_nodes(), 3)) g.ndata['h'] = F.zeros((g.number_of_nodes(), 3))
...@@ -518,7 +518,7 @@ def test_local_var(idtype): ...@@ -518,7 +518,7 @@ def test_local_var(idtype):
assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]])) assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
foo(g) foo(g)
@parametrize_dtype @parametrize_idtype
def test_local_scope(idtype): def test_local_scope(idtype):
g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 2, 3], [1, 2, 3, 4]), idtype=idtype, device=F.ctx())
g.ndata['h'] = F.zeros((g.number_of_nodes(), 3)) g.ndata['h'] = F.zeros((g.number_of_nodes(), 3))
...@@ -590,7 +590,7 @@ def test_local_scope(idtype): ...@@ -590,7 +590,7 @@ def test_local_scope(idtype):
assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]])) assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
foo(g) foo(g)
@parametrize_dtype @parametrize_idtype
def test_isolated_nodes(idtype): def test_isolated_nodes(idtype):
g = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=F.ctx())
assert g.number_of_nodes() == 5 assert g.number_of_nodes() == 5
...@@ -610,7 +610,7 @@ def test_isolated_nodes(idtype): ...@@ -610,7 +610,7 @@ def test_isolated_nodes(idtype):
assert g.number_of_nodes('user') == 5 assert g.number_of_nodes('user') == 5
assert g.number_of_nodes('game') == 7 assert g.number_of_nodes('game') == 7
@parametrize_dtype @parametrize_idtype
def test_send_multigraph(idtype): def test_send_multigraph(idtype):
g = dgl.graph(([0, 0, 0, 2], [1, 1, 1, 1]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 0, 0, 2], [1, 1, 1, 1]), idtype=idtype, device=F.ctx())
...@@ -636,7 +636,7 @@ def test_send_multigraph(idtype): ...@@ -636,7 +636,7 @@ def test_send_multigraph(idtype):
assert F.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3])) assert F.allclose(new_repr[1], answer(old_repr[0], old_repr[2], old_repr[3]))
assert F.allclose(new_repr[[0, 2]], F.zeros((2, 5))) assert F.allclose(new_repr[[0, 2]], F.zeros((2, 5)))
@parametrize_dtype @parametrize_idtype
def test_issue_1088(idtype): def test_issue_1088(idtype):
# This test ensures that message passing on a heterograph with one edge type # This test ensures that message passing on a heterograph with one edge type
# would not crash (GitHub issue #1088). # would not crash (GitHub issue #1088).
...@@ -645,7 +645,7 @@ def test_issue_1088(idtype): ...@@ -645,7 +645,7 @@ def test_issue_1088(idtype):
g.nodes['U'].data['x'] = F.randn((3, 3)) g.nodes['U'].data['x'] = F.randn((3, 3))
g.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'y')) g.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'y'))
@parametrize_dtype @parametrize_idtype
def test_degree_bucket_edge_ordering(idtype): def test_degree_bucket_edge_ordering(idtype):
import dgl.function as fn import dgl.function as fn
g = dgl.graph( g = dgl.graph(
...@@ -658,7 +658,7 @@ def test_degree_bucket_edge_ordering(idtype): ...@@ -658,7 +658,7 @@ def test_degree_bucket_edge_ordering(idtype):
return {'n': F.sum(nodes.mailbox['eid'], 1)} return {'n': F.sum(nodes.mailbox['eid'], 1)}
g.update_all(fn.copy_e('eid', 'eid'), reducer) g.update_all(fn.copy_e('eid', 'eid'), reducer)
@parametrize_dtype @parametrize_idtype
def test_issue_2484(idtype): def test_issue_2484(idtype):
import dgl.function as fn import dgl.function as fn
g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
......
tests/compute/test_batched_graph.py
View file @ 14251504
...@@ -2,7 +2,7 @@ import dgl ...@@ -2,7 +2,7 @@ import dgl
import numpy as np import numpy as np
import backend as F import backend as F
import unittest import unittest
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
def tree1(idtype): def tree1(idtype):
"""Generate a tree """Generate a tree
...@@ -42,7 +42,7 @@ def tree2(idtype): ...@@ -42,7 +42,7 @@ def tree2(idtype):
g.edata['h'] = F.randn((4, 10)) g.edata['h'] = F.randn((4, 10))
return g return g
@parametrize_dtype @parametrize_idtype
def test_batch_unbatch(idtype): def test_batch_unbatch(idtype):
t1 = tree1(idtype) t1 = tree1(idtype)
t2 = tree2(idtype) t2 = tree2(idtype)
...@@ -60,7 +60,7 @@ def test_batch_unbatch(idtype): ...@@ -60,7 +60,7 @@ def test_batch_unbatch(idtype):
assert F.allclose(t2.ndata['h'], tt2.ndata['h']) assert F.allclose(t2.ndata['h'], tt2.ndata['h'])
assert F.allclose(t2.edata['h'], tt2.edata['h']) assert F.allclose(t2.edata['h'], tt2.edata['h'])
@parametrize_dtype @parametrize_idtype
def test_batch_unbatch1(idtype): def test_batch_unbatch1(idtype):
t1 = tree1(idtype) t1 = tree1(idtype)
t2 = tree2(idtype) t2 = tree2(idtype)
...@@ -81,7 +81,7 @@ def test_batch_unbatch1(idtype): ...@@ -81,7 +81,7 @@ def test_batch_unbatch1(idtype):
assert F.allclose(t2.edata['h'], s3.edata['h']) assert F.allclose(t2.edata['h'], s3.edata['h'])
@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support inplace update") @unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support inplace update")
@parametrize_dtype @parametrize_idtype
def test_batch_unbatch_frame(idtype): def test_batch_unbatch_frame(idtype):
"""Test module of node/edge frames of batched/unbatched DGLGraphs. """Test module of node/edge frames of batched/unbatched DGLGraphs.
Also address the bug mentioned in https://github.com/dmlc/dgl/issues/1475. Also address the bug mentioned in https://github.com/dmlc/dgl/issues/1475.
...@@ -118,7 +118,7 @@ def test_batch_unbatch_frame(idtype): ...@@ -118,7 +118,7 @@ def test_batch_unbatch_frame(idtype):
assert F.allclose(_g2.ndata['h'], F.zeros((N2, D))) assert F.allclose(_g2.ndata['h'], F.zeros((N2, D)))
assert F.allclose(_g2.edata['h'], F.zeros((E2, D))) assert F.allclose(_g2.edata['h'], F.zeros((E2, D)))
@parametrize_dtype @parametrize_idtype
def test_batch_unbatch2(idtype): def test_batch_unbatch2(idtype):
# test setting/getting features after batch # test setting/getting features after batch
a = dgl.graph(([], [])).astype(idtype).to(F.ctx()) a = dgl.graph(([], [])).astype(idtype).to(F.ctx())
...@@ -133,7 +133,7 @@ def test_batch_unbatch2(idtype): ...@@ -133,7 +133,7 @@ def test_batch_unbatch2(idtype):
assert F.allclose(c.ndata['h'], F.ones((7, 1))) assert F.allclose(c.ndata['h'], F.ones((7, 1)))
assert F.allclose(c.edata['w'], F.ones((5, 1))) assert F.allclose(c.edata['w'], F.ones((5, 1)))
@parametrize_dtype @parametrize_idtype
def test_batch_send_and_recv(idtype): def test_batch_send_and_recv(idtype):
t1 = tree1(idtype) t1 = tree1(idtype)
t2 = tree2(idtype) t2 = tree2(idtype)
...@@ -150,7 +150,7 @@ def test_batch_send_and_recv(idtype): ...@@ -150,7 +150,7 @@ def test_batch_send_and_recv(idtype):
assert F.asnumpy(t1.ndata['h'][1]) == 7 assert F.asnumpy(t1.ndata['h'][1]) == 7
assert F.asnumpy(t2.ndata['h'][4]) == 2 assert F.asnumpy(t2.ndata['h'][4]) == 2
@parametrize_dtype @parametrize_idtype
def test_batch_propagate(idtype): def test_batch_propagate(idtype):
t1 = tree1(idtype) t1 = tree1(idtype)
t2 = tree2(idtype) t2 = tree2(idtype)
...@@ -178,7 +178,7 @@ def test_batch_propagate(idtype): ...@@ -178,7 +178,7 @@ def test_batch_propagate(idtype):
assert F.asnumpy(t1.ndata['h'][0]) == 9 assert F.asnumpy(t1.ndata['h'][0]) == 9
assert F.asnumpy(t2.ndata['h'][1]) == 5 assert F.asnumpy(t2.ndata['h'][1]) == 5
@parametrize_dtype @parametrize_idtype
def test_batched_edge_ordering(idtype): def test_batched_edge_ordering(idtype):
g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx()) g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
g1.add_nodes(6) g1.add_nodes(6)
...@@ -195,7 +195,7 @@ def test_batched_edge_ordering(idtype): ...@@ -195,7 +195,7 @@ def test_batched_edge_ordering(idtype):
r2 = g1.edata['h'][g1.edge_id(4, 5)] r2 = g1.edata['h'][g1.edge_id(4, 5)]
assert F.array_equal(r1, r2) assert F.array_equal(r1, r2)
@parametrize_dtype @parametrize_idtype
def test_batch_no_edge(idtype): def test_batch_no_edge(idtype):
g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx()) g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
g1.add_nodes(6) g1.add_nodes(6)
...@@ -207,7 +207,7 @@ def test_batch_no_edge(idtype): ...@@ -207,7 +207,7 @@ def test_batch_no_edge(idtype):
g3.add_nodes(1) # no edges g3.add_nodes(1) # no edges
g = dgl.batch([g1, g3, g2]) # should not throw an error g = dgl.batch([g1, g3, g2]) # should not throw an error
@parametrize_dtype @parametrize_idtype
def test_batch_keeps_empty_data(idtype): def test_batch_keeps_empty_data(idtype):
g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx()) g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
g1.ndata["nh"] = F.tensor([]) g1.ndata["nh"] = F.tensor([])
...@@ -248,7 +248,7 @@ def _get_subgraph_batch_info(keys, induced_indices_arr, batch_num_objs): ...@@ -248,7 +248,7 @@ def _get_subgraph_batch_info(keys, induced_indices_arr, batch_num_objs):
ret[key] = F.tensor(new_batch_num_objs, dtype=F.dtype(batch_num_objs)) ret[key] = F.tensor(new_batch_num_objs, dtype=F.dtype(batch_num_objs))
return ret return ret
@parametrize_dtype @parametrize_idtype
def test_set_batch_info(idtype): def test_set_batch_info(idtype):
ctx = F.ctx() ctx = F.ctx()
......
tests/compute/test_batched_heterograph.py
View file @ 14251504
...@@ -4,7 +4,7 @@ import unittest ...@@ -4,7 +4,7 @@ import unittest
import pytest import pytest
from dgl.base import ALL from dgl.base import ALL
from utils import parametrize_dtype from test_utils import parametrize_idtype
from test_utils import check_graph_equal, get_cases from test_utils import check_graph_equal, get_cases
...@@ -46,7 +46,7 @@ def check_equivalence_between_heterographs(g1, g2, node_attrs=None, edge_attrs=N ...@@ -46,7 +46,7 @@ def check_equivalence_between_heterographs(g1, g2, node_attrs=None, edge_attrs=N
@pytest.mark.parametrize('gs', get_cases(['two_hetero_batch'])) @pytest.mark.parametrize('gs', get_cases(['two_hetero_batch']))
@parametrize_dtype @parametrize_idtype
def test_topology(gs, idtype): def test_topology(gs, idtype):
"""Test batching two DGLHeteroGraphs where some nodes are isolated in some relations""" """Test batching two DGLHeteroGraphs where some nodes are isolated in some relations"""
g1, g2 = gs g1, g2 = gs
...@@ -110,7 +110,7 @@ def test_topology(gs, idtype): ...@@ -110,7 +110,7 @@ def test_topology(gs, idtype):
assert bg.batch_size == bg_local.batch_size assert bg.batch_size == bg_local.batch_size
@parametrize_dtype @parametrize_idtype
def test_batching_batched(idtype): def test_batching_batched(idtype):
"""Test batching a DGLHeteroGraph and a BatchedDGLHeteroGraph.""" """Test batching a DGLHeteroGraph and a BatchedDGLHeteroGraph."""
g1 = dgl.heterograph({ g1 = dgl.heterograph({
...@@ -168,7 +168,7 @@ def test_batching_batched(idtype): ...@@ -168,7 +168,7 @@ def test_batching_batched(idtype):
check_equivalence_between_heterographs(g3, g6) check_equivalence_between_heterographs(g3, g6)
@parametrize_dtype @parametrize_idtype
def test_features(idtype): def test_features(idtype):
"""Test the features of batched DGLHeteroGraphs""" """Test the features of batched DGLHeteroGraphs"""
g1 = dgl.heterograph({ g1 = dgl.heterograph({
...@@ -243,7 +243,7 @@ def test_features(idtype): ...@@ -243,7 +243,7 @@ def test_features(idtype):
@unittest.skipIf(F.backend_name == 'mxnet', reason="MXNet does not support split array with zero-length segment.") @unittest.skipIf(F.backend_name == 'mxnet', reason="MXNet does not support split array with zero-length segment.")
@parametrize_dtype @parametrize_idtype
def test_empty_relation(idtype): def test_empty_relation(idtype):
"""Test the features of batched DGLHeteroGraphs""" """Test the features of batched DGLHeteroGraphs"""
g1 = dgl.heterograph({ g1 = dgl.heterograph({
...@@ -308,7 +308,7 @@ def test_empty_relation(idtype): ...@@ -308,7 +308,7 @@ def test_empty_relation(idtype):
dgl.batch([g1, g2]) dgl.batch([g1, g2])
@parametrize_dtype @parametrize_idtype
def test_unbatch2(idtype): def test_unbatch2(idtype):
# batch 3 graphs but unbatch to 2 # batch 3 graphs but unbatch to 2
g1 = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx()) g1 = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
...@@ -333,7 +333,7 @@ def test_unbatch2(idtype): ...@@ -333,7 +333,7 @@ def test_unbatch2(idtype):
check_graph_equal(g3, gg3) check_graph_equal(g3, gg3)
@parametrize_dtype @parametrize_idtype
def test_slice_batch(idtype): def test_slice_batch(idtype):
g1 = dgl.heterograph({ g1 = dgl.heterograph({
('user', 'follows', 'user'): ([0, 1], [1, 2]), ('user', 'follows', 'user'): ([0, 1], [1, 2]),
...@@ -376,7 +376,7 @@ def test_slice_batch(idtype): ...@@ -376,7 +376,7 @@ def test_slice_batch(idtype):
assert F.allclose(g_i.edges[ety].data[feat], g_slice.edges[ety].data[feat]) assert F.allclose(g_i.edges[ety].data[feat], g_slice.edges[ety].data[feat])
@parametrize_dtype @parametrize_idtype
def test_batch_keeps_empty_data(idtype): def test_batch_keeps_empty_data(idtype):
g1 = dgl.heterograph({("a", "to", "a"): ([], [])} g1 = dgl.heterograph({("a", "to", "a"): ([], [])}
).astype(idtype).to(F.ctx()) ).astype(idtype).to(F.ctx())
......
tests/compute/test_csrmm.py
View file @ 14251504
...@@ -2,7 +2,7 @@ import numpy as np ...@@ -2,7 +2,7 @@ import numpy as np
import scipy.sparse as ssp import scipy.sparse as ssp
import pytest import pytest
import dgl import dgl
from utils import parametrize_dtype from test_utils import parametrize_idtype
import backend as F import backend as F
def _random_simple_graph(idtype, dtype, ctx, M, N, max_nnz, srctype, dsttype, etype): def _random_simple_graph(idtype, dtype, ctx, M, N, max_nnz, srctype, dsttype, etype):
...@@ -27,7 +27,7 @@ def _random_simple_graph(idtype, dtype, ctx, M, N, max_nnz, srctype, dsttype, et ...@@ -27,7 +27,7 @@ def _random_simple_graph(idtype, dtype, ctx, M, N, max_nnz, srctype, dsttype, et
A.edata['w'] = F.copy_to(F.tensor(val, dtype=dtype), ctx) A.edata['w'] = F.copy_to(F.tensor(val, dtype=dtype), ctx)
return a, A return a, A
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrmm(idtype, dtype): def test_csrmm(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB') a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
...@@ -39,7 +39,7 @@ def test_csrmm(idtype, dtype): ...@@ -39,7 +39,7 @@ def test_csrmm(idtype, dtype):
c = F.tensor((a * b).todense(), dtype=dtype) c = F.tensor((a * b).todense(), dtype=dtype)
assert F.allclose(C_adj, c) assert F.allclose(C_adj, c)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('num_vtypes', [1, 2]) @pytest.mark.parametrize('num_vtypes', [1, 2])
def test_csrmm_backward(idtype, dtype, num_vtypes): def test_csrmm_backward(idtype, dtype, num_vtypes):
...@@ -77,7 +77,7 @@ def test_csrmm_backward(idtype, dtype, num_vtypes): ...@@ -77,7 +77,7 @@ def test_csrmm_backward(idtype, dtype, num_vtypes):
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4) assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
assert np.allclose(b_dense_grad, B_spspmm_grad, rtol=1e-4, atol=1e-4) assert np.allclose(b_dense_grad, B_spspmm_grad, rtol=1e-4, atol=1e-4)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrsum(idtype, dtype): def test_csrsum(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB') a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
...@@ -89,7 +89,7 @@ def test_csrsum(idtype, dtype): ...@@ -89,7 +89,7 @@ def test_csrsum(idtype, dtype):
c = F.tensor((a + b).todense(), dtype=dtype) c = F.tensor((a + b).todense(), dtype=dtype)
assert F.allclose(C_adj, c) assert F.allclose(C_adj, c)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('nelems', [1, 2]) @pytest.mark.parametrize('nelems', [1, 2])
def test_csrsum_backward(idtype, dtype, nelems): def test_csrsum_backward(idtype, dtype, nelems):
...@@ -144,7 +144,7 @@ def test_csrsum_backward(idtype, dtype, nelems): ...@@ -144,7 +144,7 @@ def test_csrsum_backward(idtype, dtype, nelems):
A_spspmm_grad = F.asnumpy(F.grad(A.edata['w'])) A_spspmm_grad = F.asnumpy(F.grad(A.edata['w']))
assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4) assert np.allclose(a_dense_grad, A_spspmm_grad, rtol=1e-4, atol=1e-4)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
@pytest.mark.parametrize('A_nnz', [9000, 0]) @pytest.mark.parametrize('A_nnz', [9000, 0])
@pytest.mark.parametrize('B_nnz', [9000, 0]) @pytest.mark.parametrize('B_nnz', [9000, 0])
...@@ -158,7 +158,7 @@ def test_csrmask(idtype, dtype, A_nnz, B_nnz): ...@@ -158,7 +158,7 @@ def test_csrmask(idtype, dtype, A_nnz, B_nnz):
c = F.tensor(a.todense()[B_row, B_col], dtype) c = F.tensor(a.todense()[B_row, B_col], dtype)
assert F.allclose(C, c) assert F.allclose(C, c)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('dtype', [F.float32, F.float64]) @pytest.mark.parametrize('dtype', [F.float32, F.float64])
def test_csrmask_backward(idtype, dtype): def test_csrmask_backward(idtype, dtype):
a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB') a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')
......
tests/compute/test_dataloader.py
View file @ 14251504
...@@ -3,7 +3,7 @@ import backend as F ...@@ -3,7 +3,7 @@ import backend as F
import dgl import dgl
from dgl.dataloading import NeighborSampler, negative_sampler, \ from dgl.dataloading import NeighborSampler, negative_sampler, \
as_edge_prediction_sampler as_edge_prediction_sampler
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
def create_test_graph(idtype): def create_test_graph(idtype):
# test heterograph from the docstring, plus a user -- wishes -- game relation # test heterograph from the docstring, plus a user -- wishes -- game relation
...@@ -25,7 +25,7 @@ def create_test_graph(idtype): ...@@ -25,7 +25,7 @@ def create_test_graph(idtype):
return g return g
@parametrize_dtype @parametrize_idtype
def test_edge_prediction_sampler(idtype): def test_edge_prediction_sampler(idtype):
g = create_test_graph(idtype) g = create_test_graph(idtype)
sampler = NeighborSampler([10,10]) sampler = NeighborSampler([10,10])
......
tests/compute/test_edge_softmax_hetero.py
View file @ 14251504
...@@ -10,7 +10,7 @@ import networkx as nx ...@@ -10,7 +10,7 @@ import networkx as nx
import unittest, pytest import unittest, pytest
from dgl import DGLError from dgl import DGLError
import test_utils import test_utils
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
from scipy.sparse import rand from scipy.sparse import rand
rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean} rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
...@@ -42,7 +42,7 @@ def create_test_heterograph(idtype): ...@@ -42,7 +42,7 @@ def create_test_heterograph(idtype):
@pytest.mark.parametrize('g', get_cases(['clique'])) @pytest.mark.parametrize('g', get_cases(['clique']))
@pytest.mark.parametrize('norm_by', ['src', 'dst']) @pytest.mark.parametrize('norm_by', ['src', 'dst'])
# @pytest.mark.parametrize('shp', edge_softmax_shapes) # @pytest.mark.parametrize('shp', edge_softmax_shapes)
@parametrize_dtype @parametrize_idtype
def test_edge_softmax(g, norm_by, idtype): def test_edge_softmax(g, norm_by, idtype):
print("params", norm_by, idtype) print("params", norm_by, idtype)
......
tests/compute/test_filter.py
View file @ 14251504
...@@ -2,7 +2,7 @@ import dgl ...@@ -2,7 +2,7 @@ import dgl
import backend as F import backend as F
import numpy as np import numpy as np
import unittest import unittest
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
from dgl.utils import Filter from dgl.utils import Filter
def test_graph_filter(): def test_graph_filter():
...@@ -41,7 +41,7 @@ def test_graph_filter(): ...@@ -41,7 +41,7 @@ def test_graph_filter():
@unittest.skipIf(F._default_context_str == 'cpu', @unittest.skipIf(F._default_context_str == 'cpu',
reason="CPU not yet supported") reason="CPU not yet supported")
@parametrize_dtype @parametrize_idtype
def test_array_filter(idtype): def test_array_filter(idtype):
f = Filter(F.copy_to(F.tensor([0,1,9,4,6,5,7], dtype=idtype), F.ctx())) f = Filter(F.copy_to(F.tensor([0,1,9,4,6,5,7], dtype=idtype), F.ctx()))
x = F.copy_to(F.tensor([0,3,9,11], dtype=idtype), F.ctx()) x = F.copy_to(F.tensor([0,3,9,11], dtype=idtype), F.ctx())
......
tests/compute/test_frame.py
View file @ 14251504
...@@ -4,7 +4,7 @@ from dgl.frame import Column ...@@ -4,7 +4,7 @@ from dgl.frame import Column
import numpy as np import numpy as np
import backend as F import backend as F
import unittest import unittest
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
def test_column_subcolumn(): def test_column_subcolumn():
data = F.copy_to(F.tensor([[1., 1., 1., 1.], data = F.copy_to(F.tensor([[1., 1., 1., 1.],
......
tests/compute/test_gatherMM.py
View file @ 14251504
...@@ -5,7 +5,7 @@ import dgl.function as fn ...@@ -5,7 +5,7 @@ import dgl.function as fn
import time import time
import numpy as np import numpy as np
import unittest, pytest import unittest, pytest
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
iters = 5 iters = 5
n_edge_scale = 1 n_edge_scale = 1
......
tests/compute/test_heterograph.py
View file @ 14251504
...@@ -9,7 +9,7 @@ import networkx as nx ...@@ -9,7 +9,7 @@ import networkx as nx
import unittest, pytest import unittest, pytest
from dgl import DGLError from dgl import DGLError
import test_utils import test_utils
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
from utils import assert_is_identical_hetero from utils import assert_is_identical_hetero
from scipy.sparse import rand from scipy.sparse import rand
import multiprocessing as mp import multiprocessing as mp
...@@ -102,7 +102,7 @@ def create_test_heterograph5(idtype): ...@@ -102,7 +102,7 @@ def create_test_heterograph5(idtype):
def get_redfn(name): def get_redfn(name):
return getattr(F, name) return getattr(F, name)
@parametrize_dtype @parametrize_idtype
def test_create(idtype): def test_create(idtype):
device = F.ctx() device = F.ctx()
g0 = create_test_heterograph(idtype) g0 = create_test_heterograph(idtype)
...@@ -233,7 +233,7 @@ def test_create2(): ...@@ -233,7 +233,7 @@ def test_create2():
g = dgl.heterograph( g = dgl.heterograph(
{('A', 'AB', 'B'): ('csc', (indptr, indices, data))}, num_nodes_dict={'A': 20, 'B': 30}) {('A', 'AB', 'B'): ('csc', (indptr, indices, data))}, num_nodes_dict={'A': 20, 'B': 30})
@parametrize_dtype @parametrize_idtype
def test_query(idtype): def test_query(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
...@@ -393,7 +393,7 @@ def test_query(idtype): ...@@ -393,7 +393,7 @@ def test_query(idtype):
# test repr # test repr
print(g) print(g)
@parametrize_dtype @parametrize_idtype
def test_empty_query(idtype): def test_empty_query(idtype):
g = dgl.graph(([1, 2, 3], [0, 4, 5]), idtype=idtype, device=F.ctx()) g = dgl.graph(([1, 2, 3], [0, 4, 5]), idtype=idtype, device=F.ctx())
g.add_nodes(0) g.add_nodes(0)
...@@ -512,7 +512,7 @@ def _test_edge_ids(): ...@@ -512,7 +512,7 @@ def _test_edge_ids():
eid = g2.edge_ids(0, 1, etype='follows') eid = g2.edge_ids(0, 1, etype='follows')
assert eid == 0 assert eid == 0
@parametrize_dtype @parametrize_idtype
def test_adj(idtype): def test_adj(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
adj = F.sparse_to_numpy(g.adj(transpose=True, etype='follows')) adj = F.sparse_to_numpy(g.adj(transpose=True, etype='follows'))
...@@ -568,7 +568,7 @@ def test_adj(idtype): ...@@ -568,7 +568,7 @@ def test_adj(idtype):
[1., 0., 0.], [1., 0., 0.],
[0., 1., 0.]])) [0., 1., 0.]]))
@parametrize_dtype @parametrize_idtype
def test_inc(idtype): def test_inc(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
adj = F.sparse_to_numpy(g['follows'].inc('in')) adj = F.sparse_to_numpy(g['follows'].inc('in'))
...@@ -607,7 +607,7 @@ def test_inc(idtype): ...@@ -607,7 +607,7 @@ def test_inc(idtype):
[1., -1.], [1., -1.],
[0., 1.]])) [0., 1.]]))
@parametrize_dtype @parametrize_idtype
def test_view(idtype): def test_view(idtype):
# test single node type # test single node type
g = dgl.heterograph({ g = dgl.heterograph({
...@@ -689,7 +689,7 @@ def test_view(idtype): ...@@ -689,7 +689,7 @@ def test_view(idtype):
assert F.array_equal(g.dstnodes('user'), F.arange(0, 3, idtype)) assert F.array_equal(g.dstnodes('user'), F.arange(0, 3, idtype))
g.dstnodes['user'].data.pop('h') g.dstnodes['user'].data.pop('h')
@parametrize_dtype @parametrize_idtype
def test_view1(idtype): def test_view1(idtype):
# test relation view # test relation view
HG = create_test_heterograph(idtype) HG = create_test_heterograph(idtype)
...@@ -818,7 +818,7 @@ def test_view1(idtype): ...@@ -818,7 +818,7 @@ def test_view1(idtype):
assert F.array_equal(f3, f4) assert F.array_equal(f3, f4)
assert F.array_equal(g.edges(form='eid'), F.arange(0, 2, g.idtype)) assert F.array_equal(g.edges(form='eid'), F.arange(0, 2, g.idtype))
@parametrize_dtype @parametrize_idtype
def test_flatten(idtype): def test_flatten(idtype):
def check_mapping(g, fg): def check_mapping(g, fg):
if len(fg.ntypes) == 1: if len(fg.ntypes) == 1:
...@@ -924,7 +924,7 @@ def test_flatten(idtype): ...@@ -924,7 +924,7 @@ def test_flatten(idtype):
check_mapping(g, fg) check_mapping(g, fg)
@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test") @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
@parametrize_dtype @parametrize_idtype
def test_to_device(idtype): def test_to_device(idtype):
# TODO: rewrite this test case to accept different graphs so we # TODO: rewrite this test case to accept different graphs so we
# can test reverse graph and batched graph # can test reverse graph and batched graph
...@@ -966,7 +966,7 @@ def test_to_device(idtype): ...@@ -966,7 +966,7 @@ def test_to_device(idtype):
g1.edges['plays'].data['e'] = F.copy_to(F.ones((4, 4)), F.cpu()) g1.edges['plays'].data['e'] = F.copy_to(F.ones((4, 4)), F.cpu())
@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test") @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['block'])) @pytest.mark.parametrize('g', get_cases(['block']))
def test_to_device2(g, idtype): def test_to_device2(g, idtype):
g = g.astype(idtype) g = g.astype(idtype)
...@@ -981,7 +981,7 @@ def test_to_device2(g, idtype): ...@@ -981,7 +981,7 @@ def test_to_device2(g, idtype):
@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test") @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
@unittest.skipIf(dgl.backend.backend_name != "pytorch", reason="Pinning graph inplace only supported for PyTorch") @unittest.skipIf(dgl.backend.backend_name != "pytorch", reason="Pinning graph inplace only supported for PyTorch")
@parametrize_dtype @parametrize_idtype
def test_pin_memory_(idtype): def test_pin_memory_(idtype):
# TODO: rewrite this test case to accept different graphs so we # TODO: rewrite this test case to accept different graphs so we
# can test reverse graph and batched graph # can test reverse graph and batched graph
...@@ -1039,7 +1039,7 @@ def test_pin_memory_(idtype): ...@@ -1039,7 +1039,7 @@ def test_pin_memory_(idtype):
with pytest.raises(DGLError): with pytest.raises(DGLError):
g1.pin_memory_() g1.pin_memory_()
@parametrize_dtype @parametrize_idtype
def test_convert_bound(idtype): def test_convert_bound(idtype):
def _test_bipartite_bound(data, card): def _test_bipartite_bound(data, card):
with pytest.raises(DGLError): with pytest.raises(DGLError):
...@@ -1057,7 +1057,7 @@ def test_convert_bound(idtype): ...@@ -1057,7 +1057,7 @@ def test_convert_bound(idtype):
_test_graph_bound(([0, 1], [1, 3]), 3) _test_graph_bound(([0, 1], [1, 3]), 3)
@parametrize_dtype @parametrize_idtype
def test_convert(idtype): def test_convert(idtype):
hg = create_test_heterograph(idtype) hg = create_test_heterograph(idtype)
hs = [] hs = []
...@@ -1186,7 +1186,7 @@ def test_convert(idtype): ...@@ -1186,7 +1186,7 @@ def test_convert(idtype):
assert g.num_nodes() == 2 assert g.num_nodes() == 2
@unittest.skipIf(F._default_context_str == 'gpu', reason="Test on cpu is enough") @unittest.skipIf(F._default_context_str == 'gpu', reason="Test on cpu is enough")
@parametrize_dtype @parametrize_idtype
def test_to_homo_zero_nodes(idtype): def test_to_homo_zero_nodes(idtype):
# Fix gihub issue #2870 # Fix gihub issue #2870
g = dgl.heterograph({ g = dgl.heterograph({
...@@ -1198,7 +1198,7 @@ def test_to_homo_zero_nodes(idtype): ...@@ -1198,7 +1198,7 @@ def test_to_homo_zero_nodes(idtype):
gg = dgl.to_homogeneous(g, ['x']) gg = dgl.to_homogeneous(g, ['x'])
assert 'x' in gg.ndata assert 'x' in gg.ndata
@parametrize_dtype @parametrize_idtype
def test_to_homo2(idtype): def test_to_homo2(idtype):
# test the result homogeneous graph has nodes and edges sorted by their types # test the result homogeneous graph has nodes and edges sorted by their types
hg = create_test_heterograph(idtype) hg = create_test_heterograph(idtype)
...@@ -1228,7 +1228,7 @@ def test_to_homo2(idtype): ...@@ -1228,7 +1228,7 @@ def test_to_homo2(idtype):
for i, count in enumerate(etype_count): for i, count in enumerate(etype_count):
assert count == hg.num_edges(hg.canonical_etypes[i]) assert count == hg.num_edges(hg.canonical_etypes[i])
@parametrize_dtype @parametrize_idtype
def test_invertible_conversion(idtype): def test_invertible_conversion(idtype):
# Test whether to_homogeneous and to_heterogeneous are invertible # Test whether to_homogeneous and to_heterogeneous are invertible
hg = create_test_heterograph(idtype) hg = create_test_heterograph(idtype)
...@@ -1236,7 +1236,7 @@ def test_invertible_conversion(idtype): ...@@ -1236,7 +1236,7 @@ def test_invertible_conversion(idtype):
hg2 = dgl.to_heterogeneous(g, hg.ntypes, hg.etypes) hg2 = dgl.to_heterogeneous(g, hg.ntypes, hg.etypes)
assert_is_identical_hetero(hg, hg2, True) assert_is_identical_hetero(hg, hg2, True)
@parametrize_dtype @parametrize_idtype
def test_metagraph_reachable(idtype): def test_metagraph_reachable(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
x = F.randn((3, 5)) x = F.randn((3, 5))
...@@ -1255,7 +1255,7 @@ def test_metagraph_reachable(idtype): ...@@ -1255,7 +1255,7 @@ def test_metagraph_reachable(idtype):
assert F.asnumpy(new_g.has_edges_between([0, 1], [1, 2])).all() assert F.asnumpy(new_g.has_edges_between([0, 1], [1, 2])).all()
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet doesn't support bool tensor") @unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet doesn't support bool tensor")
@parametrize_dtype @parametrize_idtype
def test_subgraph_mask(idtype): def test_subgraph_mask(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
g_graph = g['follows'] g_graph = g['follows']
...@@ -1297,7 +1297,7 @@ def test_subgraph_mask(idtype): ...@@ -1297,7 +1297,7 @@ def test_subgraph_mask(idtype):
'wishes': F.tensor([False, True], dtype=F.bool)}) 'wishes': F.tensor([False, True], dtype=F.bool)})
_check_subgraph(g, sg2) _check_subgraph(g, sg2)
@parametrize_dtype @parametrize_idtype
def test_subgraph(idtype): def test_subgraph(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
g_graph = g['follows'] g_graph = g['follows']
...@@ -1446,7 +1446,7 @@ def test_subgraph(idtype): ...@@ -1446,7 +1446,7 @@ def test_subgraph(idtype):
sg5 = g.edge_type_subgraph(['follows', 'plays', 'wishes']) sg5 = g.edge_type_subgraph(['follows', 'plays', 'wishes'])
_check_typed_subgraph1(g, sg5) _check_typed_subgraph1(g, sg5)
@parametrize_dtype @parametrize_idtype
def test_apply(idtype): def test_apply(idtype):
def node_udf(nodes): def node_udf(nodes):
return {'h': nodes.data['h'] * 2} return {'h': nodes.data['h'] * 2}
...@@ -1484,7 +1484,7 @@ def test_apply(idtype): ...@@ -1484,7 +1484,7 @@ def test_apply(idtype):
with pytest.raises(DGLError): with pytest.raises(DGLError):
g.apply_edges(edge_udf) g.apply_edges(edge_udf)
@parametrize_dtype @parametrize_idtype
def test_level2(idtype): def test_level2(idtype):
#edges = { #edges = {
# 'follows': ([0, 1], [1, 2]), # 'follows': ([0, 1], [1, 2]),
...@@ -1608,7 +1608,7 @@ def test_level2(idtype): ...@@ -1608,7 +1608,7 @@ def test_level2(idtype):
g.nodes['game'].data.clear() g.nodes['game'].data.clear()
@parametrize_dtype @parametrize_idtype
@unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test") @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
def test_more_nnz(idtype): def test_more_nnz(idtype):
g = dgl.graph(([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 0, 0, 0, 0], [1, 1, 1, 1, 1]), idtype=idtype, device=F.ctx())
...@@ -1620,7 +1620,7 @@ def test_more_nnz(idtype): ...@@ -1620,7 +1620,7 @@ def test_more_nnz(idtype):
ans = F.copy_to(F.tensor(ans, dtype=F.dtype(y)), ctx=F.ctx()) ans = F.copy_to(F.tensor(ans, dtype=F.dtype(y)), ctx=F.ctx())
assert F.array_equal(y, ans) assert F.array_equal(y, ans)
@parametrize_dtype @parametrize_idtype
def test_updates(idtype): def test_updates(idtype):
def msg_func(edges): def msg_func(edges):
return {'m': edges.src['h']} return {'m': edges.src['h']}
...@@ -1662,7 +1662,7 @@ def test_updates(idtype): ...@@ -1662,7 +1662,7 @@ def test_updates(idtype):
del g.nodes['game'].data['y'] del g.nodes['game'].data['y']
@parametrize_dtype @parametrize_idtype
def test_backward(idtype): def test_backward(idtype):
g = create_test_heterograph(idtype) g = create_test_heterograph(idtype)
x = F.randn((3, 5)) x = F.randn((3, 5))
...@@ -1681,7 +1681,7 @@ def test_backward(idtype): ...@@ -1681,7 +1681,7 @@ def test_backward(idtype):
[2., 2., 2., 2., 2.]])) [2., 2., 2., 2., 2.]]))
@parametrize_dtype @parametrize_idtype
def test_empty_heterograph(idtype): def test_empty_heterograph(idtype):
def assert_empty(g): def assert_empty(g):
assert g.number_of_nodes('user') == 0 assert g.number_of_nodes('user') == 0
...@@ -1708,7 +1708,7 @@ def test_empty_heterograph(idtype): ...@@ -1708,7 +1708,7 @@ def test_empty_heterograph(idtype):
assert g.number_of_edges('develops') == 2 assert g.number_of_edges('develops') == 2
assert g.number_of_nodes('developer') == 2 assert g.number_of_nodes('developer') == 2
@parametrize_dtype @parametrize_idtype
def test_types_in_function(idtype): def test_types_in_function(idtype):
def mfunc1(edges): def mfunc1(edges):
assert edges.canonical_etype == ('user', 'follow', 'user') assert edges.canonical_etype == ('user', 'follow', 'user')
...@@ -1763,7 +1763,7 @@ def test_types_in_function(idtype): ...@@ -1763,7 +1763,7 @@ def test_types_in_function(idtype):
g.filter_nodes(filter_nodes2, ntype='game') g.filter_nodes(filter_nodes2, ntype='game')
g.filter_edges(filter_edges2) g.filter_edges(filter_edges2)
@parametrize_dtype @parametrize_idtype
def test_stack_reduce(idtype): def test_stack_reduce(idtype):
#edges = { #edges = {
# 'follows': ([0, 1], [1, 2]), # 'follows': ([0, 1], [1, 2]),
...@@ -1790,7 +1790,7 @@ def test_stack_reduce(idtype): ...@@ -1790,7 +1790,7 @@ def test_stack_reduce(idtype):
'stack') 'stack')
assert g.nodes['game'].data['y'].shape == (g.number_of_nodes('game'), 1, 200) assert g.nodes['game'].data['y'].shape == (g.number_of_nodes('game'), 1, 200)
@parametrize_dtype @parametrize_idtype
def test_isolated_ntype(idtype): def test_isolated_ntype(idtype):
g = dgl.heterograph({ g = dgl.heterograph({
('A', 'AB', 'B'): ([0, 1, 2], [1, 2, 3])}, ('A', 'AB', 'B'): ([0, 1, 2], [1, 2, 3])},
...@@ -1817,7 +1817,7 @@ def test_isolated_ntype(idtype): ...@@ -1817,7 +1817,7 @@ def test_isolated_ntype(idtype):
assert g.number_of_nodes('C') == 4 assert g.number_of_nodes('C') == 4
@parametrize_dtype @parametrize_idtype
def test_ismultigraph(idtype): def test_ismultigraph(idtype):
g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5])}, g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5])},
{'A': 6, 'B': 6}, idtype=idtype, device=F.ctx()) {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
...@@ -1850,7 +1850,7 @@ def test_ismultigraph(idtype): ...@@ -1850,7 +1850,7 @@ def test_ismultigraph(idtype):
{'A': 6, 'C': 6}, idtype=idtype, device=F.ctx()) {'A': 6, 'C': 6}, idtype=idtype, device=F.ctx())
assert g.is_multigraph == True assert g.is_multigraph == True
@parametrize_dtype @parametrize_idtype
def test_bipartite(idtype): def test_bipartite(idtype):
g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5])}, g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5])},
idtype=idtype, device=F.ctx()) idtype=idtype, device=F.ctx())
...@@ -1910,7 +1910,7 @@ def test_bipartite(idtype): ...@@ -1910,7 +1910,7 @@ def test_bipartite(idtype):
assert not g4.is_unibipartite assert not g4.is_unibipartite
@parametrize_dtype @parametrize_idtype
def test_dtype_cast(idtype): def test_dtype_cast(idtype):
g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
assert g.idtype == idtype assert g.idtype == idtype
...@@ -1983,7 +1983,7 @@ def test_float_cast(): ...@@ -1983,7 +1983,7 @@ def test_float_cast():
assert len(values) == len(pvalues) assert len(values) == len(pvalues)
assert F.allclose(values, F.tensor(pvalues), 0, 0) assert F.allclose(values, F.tensor(pvalues), 0, 0)
@parametrize_dtype @parametrize_idtype
def test_format(idtype): def test_format(idtype):
# single relation # single relation
g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
...@@ -2020,7 +2020,7 @@ def test_format(idtype): ...@@ -2020,7 +2020,7 @@ def test_format(idtype):
assert g.in_degrees(vid) == ind_arr[vid] assert g.in_degrees(vid) == ind_arr[vid]
assert F.array_equal(in_degrees, g.in_degrees()) assert F.array_equal(in_degrees, g.in_degrees())
@parametrize_dtype @parametrize_idtype
def test_edges_order(idtype): def test_edges_order(idtype):
# (0, 2), (1, 2), (0, 1), (0, 1), (2, 1) # (0, 2), (1, 2), (0, 1), (0, 1), (2, 1)
g = dgl.graph(( g = dgl.graph((
...@@ -2033,7 +2033,7 @@ def test_edges_order(idtype): ...@@ -2033,7 +2033,7 @@ def test_edges_order(idtype):
assert F.array_equal(src, F.tensor([0, 0, 0, 1, 2], dtype=idtype)) assert F.array_equal(src, F.tensor([0, 0, 0, 1, 2], dtype=idtype))
assert F.array_equal(dst, F.tensor([1, 1, 2, 2, 1], dtype=idtype)) assert F.array_equal(dst, F.tensor([1, 1, 2, 2, 1], dtype=idtype))
@parametrize_dtype @parametrize_idtype
def test_reverse(idtype): def test_reverse(idtype):
g = dgl.heterograph({ g = dgl.heterograph({
('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]), ('user', 'follows', 'user'): ([0, 1, 2, 4, 3 ,1, 3], [1, 2, 3, 2, 0, 0, 1]),
...@@ -2160,7 +2160,7 @@ def test_reverse(idtype): ...@@ -2160,7 +2160,7 @@ def test_reverse(idtype):
assert F.array_equal(g_s, rg_d) assert F.array_equal(g_s, rg_d)
assert F.array_equal(g_d, rg_s) assert F.array_equal(g_d, rg_s)
@parametrize_dtype @parametrize_idtype
def test_clone(idtype): def test_clone(idtype):
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
g.ndata['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([1, 1, 1], dtype=idtype), ctx=F.ctx())
...@@ -2220,7 +2220,7 @@ def test_clone(idtype): ...@@ -2220,7 +2220,7 @@ def test_clone(idtype):
assert g.edges['plays'].data['h'].shape[0] != new_g.edges['plays'].data['h'].shape[0] assert g.edges['plays'].data['h'].shape[0] != new_g.edges['plays'].data['h'].shape[0]
@parametrize_dtype @parametrize_idtype
def test_add_edges(idtype): def test_add_edges(idtype):
# homogeneous graph # homogeneous graph
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
...@@ -2390,7 +2390,7 @@ def test_add_edges(idtype): ...@@ -2390,7 +2390,7 @@ def test_add_edges(idtype):
assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 1, 1], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 1, 1], dtype=idtype))
assert F.array_equal(g.edges['develops'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype)) assert F.array_equal(g.edges['develops'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype))
@parametrize_dtype @parametrize_idtype
def test_add_nodes(idtype): def test_add_nodes(idtype):
# homogeneous Graphs # homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
...@@ -2427,7 +2427,7 @@ def test_add_nodes(idtype): ...@@ -2427,7 +2427,7 @@ def test_add_nodes(idtype):
assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 2], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2, 2, 2], dtype=idtype))
@unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet has error with (0,) shape tensor.") @unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet has error with (0,) shape tensor.")
@parametrize_dtype @parametrize_idtype
def test_remove_edges(idtype): def test_remove_edges(idtype):
# homogeneous Graphs # homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
...@@ -2520,7 +2520,7 @@ def test_remove_edges(idtype): ...@@ -2520,7 +2520,7 @@ def test_remove_edges(idtype):
assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2], dtype=idtype)) assert F.array_equal(g.nodes['game'].data['h'], F.tensor([2, 2], dtype=idtype))
assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype)) assert F.array_equal(g.nodes['developer'].data['h'], F.tensor([3, 3], dtype=idtype))
@parametrize_dtype @parametrize_idtype
def test_remove_nodes(idtype): def test_remove_nodes(idtype):
# homogeneous Graphs # homogeneous Graphs
g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
...@@ -2618,7 +2618,7 @@ def test_remove_nodes(idtype): ...@@ -2618,7 +2618,7 @@ def test_remove_nodes(idtype):
assert F.array_equal(u, F.tensor([1], dtype=idtype)) assert F.array_equal(u, F.tensor([1], dtype=idtype))
assert F.array_equal(v, F.tensor([0], dtype=idtype)) assert F.array_equal(v, F.tensor([0], dtype=idtype))
@parametrize_dtype @parametrize_idtype
def test_frame(idtype): def test_frame(idtype):
g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx()) g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
g.ndata['h'] = F.copy_to(F.tensor([0, 1, 2, 3], dtype=idtype), ctx=F.ctx()) g.ndata['h'] = F.copy_to(F.tensor([0, 1, 2, 3], dtype=idtype), ctx=F.ctx())
...@@ -2659,7 +2659,7 @@ def test_frame(idtype): ...@@ -2659,7 +2659,7 @@ def test_frame(idtype):
@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TensorFlow always create a new tensor") @unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TensorFlow always create a new tensor")
@unittest.skipIf(F._default_context_str == 'cpu', reason="cpu do not have context change problem") @unittest.skipIf(F._default_context_str == 'cpu', reason="cpu do not have context change problem")
@parametrize_dtype @parametrize_idtype
def test_frame_device(idtype): def test_frame_device(idtype):
g = dgl.graph(([0,1,2], [2,3,1])) g = dgl.graph(([0,1,2], [2,3,1]))
g.ndata['h'] = F.copy_to(F.tensor([1,1,1,2], dtype=idtype), ctx=F.cpu()) g.ndata['h'] = F.copy_to(F.tensor([1,1,1,2], dtype=idtype), ctx=F.cpu())
...@@ -2710,7 +2710,7 @@ def test_frame_device(idtype): ...@@ -2710,7 +2710,7 @@ def test_frame_device(idtype):
assert F.context(ng._node_frames[0]._columns['hh'].storage) == F.ctx() assert F.context(ng._node_frames[0]._columns['hh'].storage) == F.ctx()
assert F.context(ng._edge_frames[0]._columns['h'].storage) == F.cpu() assert F.context(ng._edge_frames[0]._columns['h'].storage) == F.cpu()
@parametrize_dtype @parametrize_idtype
def test_create_block(idtype): def test_create_block(idtype):
block = dgl.create_block(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx()) block = dgl.create_block(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
assert block.num_src_nodes() == 3 assert block.num_src_nodes() == 3
...@@ -2822,7 +2822,7 @@ def test_create_block(idtype): ...@@ -2822,7 +2822,7 @@ def test_create_block(idtype):
assert hg.edges['AB'].data['x'] is eabx assert hg.edges['AB'].data['x'] is eabx
assert hg.edges['BA'].data['x'] is ebax assert hg.edges['BA'].data['x'] is ebax
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('fmt', ['coo', 'csr', 'csc']) @pytest.mark.parametrize('fmt', ['coo', 'csr', 'csc'])
def test_adj_sparse(idtype, fmt): def test_adj_sparse(idtype, fmt):
if fmt == 'coo': if fmt == 'coo':
......
tests/compute/test_kernel.py
View file @ 14251504
...@@ -4,7 +4,7 @@ import networkx as nx ...@@ -4,7 +4,7 @@ import networkx as nx
import numpy as np import numpy as np
import backend as F import backend as F
from itertools import product from itertools import product
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
import pytest import pytest
def udf_copy_src(edges): def udf_copy_src(edges):
...@@ -362,7 +362,7 @@ def test_all_binary_builtins(): ...@@ -362,7 +362,7 @@ def test_all_binary_builtins():
_test(g, lhs, rhs, binary_op, reducer, partial, nid, _test(g, lhs, rhs, binary_op, reducer, partial, nid,
broadcast=broadcast) broadcast=broadcast)
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo-zero-degree'])) @pytest.mark.parametrize('g', get_cases(['homo-zero-degree']))
def test_mean_zero_degree(g, idtype): def test_mean_zero_degree(g, idtype):
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
......
tests/compute/test_merge.py
View file @ 14251504
import backend as F import backend as F
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
import dgl import dgl
@parametrize_dtype @parametrize_idtype
def test_heterograph_merge(idtype): def test_heterograph_merge(idtype):
g1 = dgl.heterograph({("a", "to", "b"): ([0,1], [1,0])}).astype(idtype).to(F.ctx()) g1 = dgl.heterograph({("a", "to", "b"): ([0,1], [1,0])}).astype(idtype).to(F.ctx())
g1_n_edges = g1.num_edges(etype="to") g1_n_edges = g1.num_edges(etype="to")
......
tests/compute/test_new_update_all_hetero.py
View file @ 14251504
...@@ -10,7 +10,7 @@ import networkx as nx ...@@ -10,7 +10,7 @@ import networkx as nx
import unittest, pytest import unittest, pytest
from dgl import DGLError from dgl import DGLError
import test_utils import test_utils
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
from scipy.sparse import rand from scipy.sparse import rand
rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean} rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
feat_size = 2 feat_size = 2
...@@ -69,7 +69,7 @@ def create_test_heterograph_large(idtype): ...@@ -69,7 +69,7 @@ def create_test_heterograph_large(idtype):
assert g.device == F.ctx() assert g.device == F.ctx()
return g return g
@parametrize_dtype @parametrize_idtype
def test_unary_copy_u(idtype): def test_unary_copy_u(idtype):
def _test(mfunc, rfunc): def _test(mfunc, rfunc):
g = create_test_heterograph_2(idtype) g = create_test_heterograph_2(idtype)
...@@ -134,7 +134,7 @@ def test_unary_copy_u(idtype): ...@@ -134,7 +134,7 @@ def test_unary_copy_u(idtype):
_test(fn.copy_u, fn.min) _test(fn.copy_u, fn.min)
# _test('copy_u', 'mean') # _test('copy_u', 'mean')
@parametrize_dtype @parametrize_idtype
def test_unary_copy_e(idtype): def test_unary_copy_e(idtype):
def _test(mfunc, rfunc): def _test(mfunc, rfunc):
...@@ -219,7 +219,7 @@ def test_unary_copy_e(idtype): ...@@ -219,7 +219,7 @@ def test_unary_copy_e(idtype):
_test(fn.copy_e, fn.min) _test(fn.copy_e, fn.min)
# _test('copy_e', 'mean') # _test('copy_e', 'mean')
@parametrize_dtype @parametrize_idtype
def test_binary_op(idtype): def test_binary_op(idtype):
def _test(lhs, rhs, binary_op, reducer): def _test(lhs, rhs, binary_op, reducer):
......
tests/compute/test_partition.py
View file @ 14251504
...@@ -2,12 +2,12 @@ from dgl.partition import NDArrayPartition ...@@ -2,12 +2,12 @@ from dgl.partition import NDArrayPartition
from dgl.distributed import graph_partition_book as gpb from dgl.distributed import graph_partition_book as gpb
import unittest import unittest
import backend as F import backend as F
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
@unittest.skipIf(F._default_context_str == 'cpu', reason="NDArrayPartition only works on GPU.") @unittest.skipIf(F._default_context_str == 'cpu', reason="NDArrayPartition only works on GPU.")
@parametrize_dtype @parametrize_idtype
def test_get_node_partition_from_book(idtype): def test_get_node_partition_from_book(idtype):
node_map = { node_map = {
"type_n": F.tensor([ "type_n": F.tensor([
......
tests/compute/test_pickle.py
View file @ 14251504
...@@ -10,7 +10,7 @@ import pickle ...@@ -10,7 +10,7 @@ import pickle
import io import io
import unittest, pytest import unittest, pytest
import test_utils import test_utils
from test_utils import parametrize_dtype, get_cases from test_utils import parametrize_idtype, get_cases
from utils import assert_is_identical, assert_is_identical_hetero from utils import assert_is_identical, assert_is_identical_hetero
def _assert_is_identical_nodeflow(nf1, nf2): def _assert_is_identical_nodeflow(nf1, nf2):
...@@ -92,7 +92,7 @@ def _global_message_func(nodes): ...@@ -92,7 +92,7 @@ def _global_message_func(nodes):
return {'x': nodes.data['x']} return {'x': nodes.data['x']}
@unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(exclude=['dglgraph', 'two_hetero_batch'])) @pytest.mark.parametrize('g', get_cases(exclude=['dglgraph', 'two_hetero_batch']))
def test_pickling_graph(g, idtype): def test_pickling_graph(g, idtype):
g = g.astype(idtype) g = g.astype(idtype)
...@@ -165,7 +165,7 @@ def test_pickling_subgraph(): ...@@ -165,7 +165,7 @@ def test_pickling_subgraph():
@unittest.skipIf(F._default_context_str != 'gpu', reason="Need GPU for pin") @unittest.skipIf(F._default_context_str != 'gpu', reason="Need GPU for pin")
@unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TensorFlow create graph on gpu when unpickle") @unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TensorFlow create graph on gpu when unpickle")
@parametrize_dtype @parametrize_idtype
def test_pickling_is_pinned(idtype): def test_pickling_is_pinned(idtype):
from copy import deepcopy from copy import deepcopy
g = dgl.rand_graph(10, 20, idtype=idtype, device=F.cpu()) g = dgl.rand_graph(10, 20, idtype=idtype, device=F.cpu())
......
tests/compute/test_propagate.py
View file @ 14251504
...@@ -3,7 +3,7 @@ import networkx as nx ...@@ -3,7 +3,7 @@ import networkx as nx
import backend as F import backend as F
import unittest import unittest
import utils as U import utils as U
from utils import parametrize_dtype from test_utils import parametrize_idtype
def create_graph(idtype): def create_graph(idtype):
g = dgl.from_networkx(nx.path_graph(5), idtype=idtype, device=F.ctx()) g = dgl.from_networkx(nx.path_graph(5), idtype=idtype, device=F.ctx())
...@@ -17,7 +17,7 @@ def rfunc(nodes): ...@@ -17,7 +17,7 @@ def rfunc(nodes):
return {'x' : nodes.data['x'] + msg} return {'x' : nodes.data['x'] + msg}
@unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
@parametrize_dtype @parametrize_idtype
def test_prop_nodes_bfs(idtype): def test_prop_nodes_bfs(idtype):
g = create_graph(idtype) g = create_graph(idtype)
g.ndata['x'] = F.ones((5, 2)) g.ndata['x'] = F.ones((5, 2))
...@@ -27,7 +27,7 @@ def test_prop_nodes_bfs(idtype): ...@@ -27,7 +27,7 @@ def test_prop_nodes_bfs(idtype):
F.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]])) F.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))
@unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
@parametrize_dtype @parametrize_idtype
def test_prop_edges_dfs(idtype): def test_prop_edges_dfs(idtype):
g = create_graph(idtype) g = create_graph(idtype)
g.ndata['x'] = F.ones((5, 2)) g.ndata['x'] = F.ones((5, 2))
...@@ -49,7 +49,7 @@ def test_prop_edges_dfs(idtype): ...@@ -49,7 +49,7 @@ def test_prop_edges_dfs(idtype):
F.tensor([[3., 3.], [5., 5.], [7., 7.], [9., 9.], [5., 5.]])) F.tensor([[3., 3.], [5., 5.], [7., 7.], [9., 9.], [5., 5.]]))
@unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented") @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
@parametrize_dtype @parametrize_idtype
def test_prop_nodes_topo(idtype): def test_prop_nodes_topo(idtype):
# bi-directional chain # bi-directional chain
g = create_graph(idtype) g = create_graph(idtype)
......
tests/compute/test_readout.py
View file @ 14251504
...@@ -5,9 +5,9 @@ import networkx as nx ...@@ -5,9 +5,9 @@ import networkx as nx
import unittest import unittest
import pytest import pytest
from test_utils.graph_cases import get_cases from test_utils.graph_cases import get_cases
from utils import parametrize_dtype from test_utils import parametrize_idtype
@parametrize_dtype @parametrize_idtype
def test_sum_case1(idtype): def test_sum_case1(idtype):
# NOTE: If you want to update this test case, remember to update the docstring # NOTE: If you want to update this test case, remember to update the docstring
# example too!!! # example too!!!
...@@ -21,7 +21,7 @@ def test_sum_case1(idtype): ...@@ -21,7 +21,7 @@ def test_sum_case1(idtype):
assert F.allclose(F.tensor([3., 6.]), dgl.sum_nodes(bg, 'h')) assert F.allclose(F.tensor([3., 6.]), dgl.sum_nodes(bg, 'h'))
assert F.allclose(F.tensor([.5, 1.7]), dgl.sum_nodes(bg, 'h', 'w')) assert F.allclose(F.tensor([.5, 1.7]), dgl.sum_nodes(bg, 'h', 'w'))
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph'])) @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
@pytest.mark.parametrize('reducer', ['sum', 'max', 'mean']) @pytest.mark.parametrize('reducer', ['sum', 'max', 'mean'])
def test_reduce_readout(g, idtype, reducer): def test_reduce_readout(g, idtype, reducer):
...@@ -67,7 +67,7 @@ def test_reduce_readout(g, idtype, reducer): ...@@ -67,7 +67,7 @@ def test_reduce_readout(g, idtype, reducer):
subx.append(sx) subx.append(sx)
assert F.allclose(x, F.cat(subx, dim=0)) assert F.allclose(x, F.cat(subx, dim=0))
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph'])) @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
@pytest.mark.parametrize('reducer', ['sum', 'max', 'mean']) @pytest.mark.parametrize('reducer', ['sum', 'max', 'mean'])
def test_weighted_reduce_readout(g, idtype, reducer): def test_weighted_reduce_readout(g, idtype, reducer):
...@@ -115,7 +115,7 @@ def test_weighted_reduce_readout(g, idtype, reducer): ...@@ -115,7 +115,7 @@ def test_weighted_reduce_readout(g, idtype, reducer):
subx.append(sx) subx.append(sx)
assert F.allclose(x, F.cat(subx, dim=0)) assert F.allclose(x, F.cat(subx, dim=0))
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph'])) @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
@pytest.mark.parametrize('descending', [True, False]) @pytest.mark.parametrize('descending', [True, False])
def test_topk(g, idtype, descending): def test_topk(g, idtype, descending):
...@@ -181,7 +181,7 @@ def test_topk(g, idtype, descending): ...@@ -181,7 +181,7 @@ def test_topk(g, idtype, descending):
assert F.allclose(val, F.cat(subval, dim=0)) assert F.allclose(val, F.cat(subval, dim=0))
assert F.allclose(indices, F.cat(subidx, dim=0)) assert F.allclose(indices, F.cat(subidx, dim=0))
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph'])) @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
def test_softmax(g, idtype): def test_softmax(g, idtype):
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -204,7 +204,7 @@ def test_softmax(g, idtype): ...@@ -204,7 +204,7 @@ def test_softmax(g, idtype):
subx.append(F.softmax(sg.edata['h'], dim=0)) subx.append(F.softmax(sg.edata['h'], dim=0))
assert F.allclose(x, F.cat(subx, dim=0)) assert F.allclose(x, F.cat(subx, dim=0))
@parametrize_dtype @parametrize_idtype
@pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph'])) @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
def test_broadcast(idtype, g): def test_broadcast(idtype, g):
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
......
tests/compute/test_removal.py
View file @ 14251504
import backend as F import backend as F
import numpy as np import numpy as np
import dgl import dgl
from test_utils import parametrize_dtype from test_utils import parametrize_idtype
@parametrize_dtype @parametrize_idtype
def test_node_removal(idtype): def test_node_removal(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -31,7 +31,7 @@ def test_node_removal(idtype): ...@@ -31,7 +31,7 @@ def test_node_removal(idtype):
assert dgl.NID in g.ndata assert dgl.NID in g.ndata
assert dgl.EID in g.edata assert dgl.EID in g.edata
@parametrize_dtype @parametrize_idtype
def test_multigraph_node_removal(idtype): def test_multigraph_node_removal(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -59,7 +59,7 @@ def test_multigraph_node_removal(idtype): ...@@ -59,7 +59,7 @@ def test_multigraph_node_removal(idtype):
assert g.number_of_nodes() == 3 assert g.number_of_nodes() == 3
assert g.number_of_edges() == 6 assert g.number_of_edges() == 6
@parametrize_dtype @parametrize_idtype
def test_multigraph_edge_removal(idtype): def test_multigraph_edge_removal(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -86,7 +86,7 @@ def test_multigraph_edge_removal(idtype): ...@@ -86,7 +86,7 @@ def test_multigraph_edge_removal(idtype):
assert g.number_of_nodes() == 5 assert g.number_of_nodes() == 5
assert g.number_of_edges() == 8 assert g.number_of_edges() == 8
@parametrize_dtype @parametrize_idtype
def test_edge_removal(idtype): def test_edge_removal(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -117,7 +117,7 @@ def test_edge_removal(idtype): ...@@ -117,7 +117,7 @@ def test_edge_removal(idtype):
assert F.array_equal(g.edata['id'], F.tensor([0, 1, 10, 11, 12, 20, 21, 22, 23, 24, 0])) assert F.array_equal(g.edata['id'], F.tensor([0, 1, 10, 11, 12, 20, 21, 22, 23, 24, 0]))
assert dgl.EID in g.edata assert dgl.EID in g.edata
@parametrize_dtype @parametrize_idtype
def test_node_and_edge_removal(idtype): def test_node_and_edge_removal(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -156,7 +156,7 @@ def test_node_and_edge_removal(idtype): ...@@ -156,7 +156,7 @@ def test_node_and_edge_removal(idtype):
assert g.number_of_nodes() == 10 assert g.number_of_nodes() == 10
assert g.number_of_edges() == 48 assert g.number_of_edges() == 48
@parametrize_dtype @parametrize_idtype
def test_node_frame(idtype): def test_node_frame(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -169,7 +169,7 @@ def test_node_frame(idtype): ...@@ -169,7 +169,7 @@ def test_node_frame(idtype):
g.remove_nodes(range(3, 7)) g.remove_nodes(range(3, 7))
assert F.allclose(g.ndata['h'], F.tensor(new_data)) assert F.allclose(g.ndata['h'], F.tensor(new_data))
@parametrize_dtype @parametrize_idtype
def test_edge_frame(idtype): def test_edge_frame(idtype):
g = dgl.DGLGraph() g = dgl.DGLGraph()
g = g.astype(idtype).to(F.ctx()) g = g.astype(idtype).to(F.ctx())
...@@ -183,7 +183,7 @@ def test_edge_frame(idtype): ...@@ -183,7 +183,7 @@ def test_edge_frame(idtype):
g.remove_edges(range(3, 7)) g.remove_edges(range(3, 7))
assert F.allclose(g.edata['h'], F.tensor(new_data)) assert F.allclose(g.edata['h'], F.tensor(new_data))
@parametrize_dtype @parametrize_idtype
def test_issue1287(idtype): def test_issue1287(idtype):
# reproduce https://github.com/dmlc/dgl/issues/1287. # reproduce https://github.com/dmlc/dgl/issues/1287.
# setting features after remove nodes # setting features after remove nodes
......
tests/compute/test_shared_mem.py
View file @ 14251504
...@@ -9,7 +9,7 @@ import dgl.function as fn ...@@ -9,7 +9,7 @@ import dgl.function as fn
import pickle import pickle
import io import io
import unittest import unittest
from utils import parametrize_dtype from test_utils import parametrize_idtype
import multiprocessing as mp import multiprocessing as mp
import os import os
...@@ -43,7 +43,7 @@ def _assert_is_identical_hetero(g, g2): ...@@ -43,7 +43,7 @@ def _assert_is_identical_hetero(g, g2):
assert F.array_equal(dst, dst2) assert F.array_equal(dst, dst2)
@unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now') @unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now')
@parametrize_dtype @parametrize_idtype
def test_single_process(idtype): def test_single_process(idtype):
hg = create_test_graph(idtype=idtype) hg = create_test_graph(idtype=idtype)
hg_share = hg.shared_memory("hg") hg_share = hg.shared_memory("hg")
...@@ -60,7 +60,7 @@ def sub_proc(hg_origin, name): ...@@ -60,7 +60,7 @@ def sub_proc(hg_origin, name):
_assert_is_identical_hetero(hg_origin, hg_save_again) _assert_is_identical_hetero(hg_origin, hg_save_again)
@unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now') @unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now')
@parametrize_dtype @parametrize_idtype
def test_multi_process(idtype): def test_multi_process(idtype):
hg = create_test_graph(idtype=idtype) hg = create_test_graph(idtype=idtype)
hg_share = hg.shared_memory("hg1") hg_share = hg.shared_memory("hg1")
......
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