Fix test naming (#4023)

Co-authored-by: Mufei Li <mufeili1996@gmail.com>

tests/compute/test_apply_edges_hetero.py

@@ -10,7 +10,7 @@ import networkx as nx
 import unittest, pytest
 from dgl import DGLError
 import test_utils
-from test_utils import parametrize_dtype, get_cases
+from test_utils import parametrize_idtype, get_cases
 from scipy.sparse import rand
 
 rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
@@ -39,7 +39,7 @@ def create_test_heterograph(idtype):
     return g
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_unary_copy_u(idtype):
     def _test(mfunc):
 
@@ -92,7 +92,7 @@ def test_unary_copy_u(idtype):
     _test(fn.copy_u)
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_unary_copy_e(idtype):
     def _test(mfunc):
 
@@ -148,7 +148,7 @@ def test_unary_copy_e(idtype):
     _test(fn.copy_e)
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_binary_op(idtype):
     def _test(lhs, rhs, binary_op):
 

tests/compute/test_basics.py

@@ -6,7 +6,7 @@ import networkx as nx
 from dgl import DGLGraph
 from collections import defaultdict as ddict
 import unittest
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
 D = 5
 reduce_msg_shapes = set()
@@ -89,7 +89,7 @@ def generate_graph(idtype, grad=False):
 def test_compatible():
     g = generate_graph_old()
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_setter_getter(idtype):
     def _pfc(x):
         return list(F.zerocopy_to_numpy(x)[:,0])
@@ -159,7 +159,7 @@ def test_batch_setter_getter(idtype):
     v = F.tensor([6, 9, 7], g.idtype)
     assert _pfc(g.edges[u, v].data['l']) == [1.0, 1.0, 0.0]
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_setter_autograd(idtype):
     g = generate_graph(idtype, grad=True)
     h1 = g.ndata['h']
@@ -290,7 +290,7 @@ def _test_nx_conversion():
     assert F.allclose(g.edata['h'], F.tensor([[1., 2.], [1., 2.],
                                               [2., 3.], [2., 3.]]))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_apply_nodes(idtype):
     def _upd(nodes):
         return {'h' : nodes.data['h'] * 2}
@@ -302,7 +302,7 @@ def test_apply_nodes(idtype):
     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)))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_apply_edges(idtype):
     def _upd(edges):
         return {'w' : edges.data['w'] * 2}
@@ -316,7 +316,7 @@ def test_apply_edges(idtype):
     eid = F.tensor(g.edge_ids(u, v))
     assert F.allclose(F.gather_row(g.edata['w'], eid), F.zeros((6, D)))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_update_routines(idtype):
     g = generate_graph(idtype)
 
@@ -353,7 +353,7 @@ def test_update_routines(idtype):
     assert(reduce_msg_shapes == {(1, 8, D), (9, 1, D)})
     reduce_msg_shapes.clear()
 
-@parametrize_dtype
+@parametrize_idtype
 def test_update_all_0deg(idtype):
     # test#1
     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):
     # should fallback to apply
     assert F.allclose(new_repr, 2*old_repr)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_pull_0deg(idtype):
     g = dgl.graph(([0], [1]), idtype=idtype, device=F.ctx())
     def _message(edges):
@@ -450,7 +450,7 @@ def test_dynamic_addition():
     assert len(g.edata['h1']) == len(g.edata['h2'])
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_repr(idtype):
     g = dgl.graph(([0, 0, 1], [1, 2, 2]), num_nodes=10, idtype=idtype, device=F.ctx())
     repr_string = g.__repr__()
@@ -460,7 +460,7 @@ def test_repr(idtype):
     repr_string = g.__repr__()
     print(repr_string)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_local_var(idtype):
     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))
@@ -518,7 +518,7 @@ def test_local_var(idtype):
         assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
     foo(g)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_local_scope(idtype):
     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))
@@ -590,7 +590,7 @@ def test_local_scope(idtype):
             assert F.allclose(g.edata['w'], F.tensor([[1.], [0.]]))
     foo(g)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_isolated_nodes(idtype):
     g = dgl.graph(([0, 1], [1, 2]), num_nodes=5, idtype=idtype, device=F.ctx())
     assert g.number_of_nodes() == 5
@@ -610,7 +610,7 @@ def test_isolated_nodes(idtype):
     assert g.number_of_nodes('user') == 5
     assert g.number_of_nodes('game') == 7
 
-@parametrize_dtype
+@parametrize_idtype
 def test_send_multigraph(idtype):
     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):
     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)))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_issue_1088(idtype):
     # This test ensures that message passing on a heterograph with one edge type
     # would not crash (GitHub issue #1088).
@@ -645,7 +645,7 @@ def test_issue_1088(idtype):
     g.nodes['U'].data['x'] = F.randn((3, 3))
     g.update_all(fn.copy_u('x', 'm'), fn.sum('m', 'y'))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_degree_bucket_edge_ordering(idtype):
     import dgl.function as fn
     g = dgl.graph(
@@ -658,7 +658,7 @@ def test_degree_bucket_edge_ordering(idtype):
         return {'n': F.sum(nodes.mailbox['eid'], 1)}
     g.update_all(fn.copy_e('eid', 'eid'), reducer)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_issue_2484(idtype):
     import dgl.function as fn
     g = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())

tests/compute/test_batched_graph.py

@@ -2,7 +2,7 @@ import dgl
 import numpy as np
 import backend as F
 import unittest
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
 def tree1(idtype):
     """Generate a tree
@@ -42,7 +42,7 @@ def tree2(idtype):
     g.edata['h'] = F.randn((4, 10))
     return g
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_unbatch(idtype):
     t1 = tree1(idtype)
     t2 = tree2(idtype)
@@ -60,7 +60,7 @@ def test_batch_unbatch(idtype):
     assert F.allclose(t2.ndata['h'], tt2.ndata['h'])
     assert F.allclose(t2.edata['h'], tt2.edata['h'])
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_unbatch1(idtype):
     t1 = tree1(idtype)
     t2 = tree2(idtype)
@@ -81,7 +81,7 @@ def test_batch_unbatch1(idtype):
     assert F.allclose(t2.edata['h'], s3.edata['h'])
 
 @unittest.skipIf(dgl.backend.backend_name == "tensorflow", reason="TF doesn't support inplace update")
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_unbatch_frame(idtype):
     """Test module of node/edge frames of batched/unbatched DGLGraphs.
     Also address the bug mentioned in https://github.com/dmlc/dgl/issues/1475.
@@ -118,7 +118,7 @@ def test_batch_unbatch_frame(idtype):
     assert F.allclose(_g2.ndata['h'], F.zeros((N2, D)))
     assert F.allclose(_g2.edata['h'], F.zeros((E2, D)))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_unbatch2(idtype):
     # test setting/getting features after batch
     a = dgl.graph(([], [])).astype(idtype).to(F.ctx())
@@ -133,7 +133,7 @@ def test_batch_unbatch2(idtype):
     assert F.allclose(c.ndata['h'], F.ones((7, 1)))
     assert F.allclose(c.edata['w'], F.ones((5, 1)))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_send_and_recv(idtype):
     t1 = tree1(idtype)
     t2 = tree2(idtype)
@@ -150,7 +150,7 @@ def test_batch_send_and_recv(idtype):
     assert F.asnumpy(t1.ndata['h'][1]) == 7
     assert F.asnumpy(t2.ndata['h'][4]) == 2
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_propagate(idtype):
     t1 = tree1(idtype)
     t2 = tree2(idtype)
@@ -178,7 +178,7 @@ def test_batch_propagate(idtype):
     assert F.asnumpy(t1.ndata['h'][0]) == 9
     assert F.asnumpy(t2.ndata['h'][1]) == 5
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batched_edge_ordering(idtype):
     g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
     g1.add_nodes(6)
@@ -195,7 +195,7 @@ def test_batched_edge_ordering(idtype):
     r2 = g1.edata['h'][g1.edge_id(4, 5)]
     assert F.array_equal(r1, r2)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_no_edge(idtype):
     g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
     g1.add_nodes(6)
@@ -207,7 +207,7 @@ def test_batch_no_edge(idtype):
     g3.add_nodes(1)  # no edges
     g = dgl.batch([g1, g3, g2]) # should not throw an error
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batch_keeps_empty_data(idtype):
     g1 = dgl.graph(([], [])).astype(idtype).to(F.ctx())
     g1.ndata["nh"] = F.tensor([])
@@ -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))
     return ret
 
-@parametrize_dtype
+@parametrize_idtype
 def test_set_batch_info(idtype):
     ctx = F.ctx()
 

tests/compute/test_batched_heterograph.py

@@ -4,7 +4,7 @@ import unittest
 import pytest
 
 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
 
 
@@ -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']))
-@parametrize_dtype
+@parametrize_idtype
 def test_topology(gs, idtype):
     """Test batching two DGLHeteroGraphs where some nodes are isolated in some relations"""
     g1, g2 = gs
@@ -110,7 +110,7 @@ def test_topology(gs, idtype):
     assert bg.batch_size == bg_local.batch_size
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_batching_batched(idtype):
     """Test batching a DGLHeteroGraph and a BatchedDGLHeteroGraph."""
     g1 = dgl.heterograph({
@@ -168,7 +168,7 @@ def test_batching_batched(idtype):
     check_equivalence_between_heterographs(g3, g6)
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_features(idtype):
     """Test the features of batched DGLHeteroGraphs"""
     g1 = dgl.heterograph({
@@ -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.")
-@parametrize_dtype
+@parametrize_idtype
 def test_empty_relation(idtype):
     """Test the features of batched DGLHeteroGraphs"""
     g1 = dgl.heterograph({
@@ -308,7 +308,7 @@ def test_empty_relation(idtype):
     dgl.batch([g1, g2])
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_unbatch2(idtype):
     # batch 3 graphs but unbatch to 2
     g1 = dgl.graph(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
@@ -333,7 +333,7 @@ def test_unbatch2(idtype):
     check_graph_equal(g3, gg3)
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_slice_batch(idtype):
     g1 = dgl.heterograph({
         ('user', 'follows', 'user'): ([0, 1], [1, 2]),
@@ -376,7 +376,7 @@ def test_slice_batch(idtype):
                     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):
     g1 = dgl.heterograph({("a", "to", "a"): ([], [])}
                          ).astype(idtype).to(F.ctx())

tests/compute/test_csrmm.py

@@ -2,7 +2,7 @@ import numpy as np
 import scipy.sparse as ssp
 import pytest
 import dgl
-from utils import parametrize_dtype
+from test_utils import parametrize_idtype
 import backend as F
 
 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
     A.edata['w'] = F.copy_to(F.tensor(val, dtype=dtype), ctx)
     return a, A
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('dtype', [F.float32, F.float64])
 def test_csrmm(idtype, dtype):
     a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
@@ -39,7 +39,7 @@ def test_csrmm(idtype, dtype):
     c = F.tensor((a * b).todense(), dtype=dtype)
     assert F.allclose(C_adj, c)
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('dtype', [F.float32, F.float64])
 @pytest.mark.parametrize('num_vtypes', [1, 2])
 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(b_dense_grad, B_spspmm_grad, rtol=1e-4, atol=1e-4)
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('dtype', [F.float32, F.float64])
 def test_csrsum(idtype, dtype):
     a, A = _random_simple_graph(idtype, dtype, F.ctx(), 500, 600, 9000, 'A', 'B', 'AB')
@@ -89,7 +89,7 @@ def test_csrsum(idtype, dtype):
     c = F.tensor((a + b).todense(), dtype=dtype)
     assert F.allclose(C_adj, c)
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('dtype', [F.float32, F.float64])
 @pytest.mark.parametrize('nelems', [1, 2])
 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']))
             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('A_nnz', [9000, 0])
 @pytest.mark.parametrize('B_nnz', [9000, 0])
@@ -158,7 +158,7 @@ def test_csrmask(idtype, dtype, A_nnz, B_nnz):
     c = F.tensor(a.todense()[B_row, B_col], dtype)
     assert F.allclose(C, c)
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('dtype', [F.float32, F.float64])
 def test_csrmask_backward(idtype, dtype):
     a, A = _random_simple_graph(idtype, dtype, F.ctx(), 3, 4, 6, 'A', 'B', 'AB')

tests/compute/test_dataloader.py

@@ -3,7 +3,7 @@ import backend as F
 import dgl
 from dgl.dataloading import NeighborSampler, negative_sampler, \
     as_edge_prediction_sampler
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
 def create_test_graph(idtype):
     # test heterograph from the docstring, plus a user -- wishes -- game relation
@@ -25,7 +25,7 @@ def create_test_graph(idtype):
     return g
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_edge_prediction_sampler(idtype):
     g = create_test_graph(idtype)
     sampler = NeighborSampler([10,10])

tests/compute/test_edge_softmax_hetero.py

@@ -10,7 +10,7 @@ import networkx as nx
 import unittest, pytest
 from dgl import DGLError
 import test_utils
-from test_utils import parametrize_dtype, get_cases
+from test_utils import parametrize_idtype, get_cases
 from scipy.sparse import rand
 
 rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
@@ -42,7 +42,7 @@ def create_test_heterograph(idtype):
 @pytest.mark.parametrize('g', get_cases(['clique']))
 @pytest.mark.parametrize('norm_by', ['src', 'dst'])
 # @pytest.mark.parametrize('shp', edge_softmax_shapes)
-@parametrize_dtype
+@parametrize_idtype
 def test_edge_softmax(g, norm_by, idtype):
     print("params", norm_by, idtype)
 

tests/compute/test_filter.py

@@ -2,7 +2,7 @@ import dgl
 import backend as F
 import numpy as np
 import unittest
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 from dgl.utils import Filter
 
 def test_graph_filter():
@@ -41,7 +41,7 @@ def test_graph_filter():
 
 @unittest.skipIf(F._default_context_str == 'cpu',
                  reason="CPU not yet supported")
-@parametrize_dtype
+@parametrize_idtype
 def test_array_filter(idtype):
     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())

tests/compute/test_frame.py

@@ -4,7 +4,7 @@ from dgl.frame import Column
 import numpy as np
 import backend as F
 import unittest
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
 def test_column_subcolumn():
     data = F.copy_to(F.tensor([[1., 1., 1., 1.],

tests/compute/test_gatherMM.py

@@ -5,7 +5,7 @@ import dgl.function as fn
 import time
 import numpy as np
 import unittest, pytest
-from test_utils import parametrize_dtype, get_cases
+from test_utils import parametrize_idtype, get_cases
 
 iters = 5
 n_edge_scale = 1

tests/compute/test_heterograph.py

@@ -9,7 +9,7 @@ import networkx as nx
 import unittest, pytest
 from dgl import DGLError
 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 scipy.sparse import rand
 import multiprocessing as mp
@@ -102,7 +102,7 @@ def create_test_heterograph5(idtype):
 def get_redfn(name):
     return getattr(F, name)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_create(idtype):
     device = F.ctx()
     g0 = create_test_heterograph(idtype)
@@ -233,7 +233,7 @@ def test_create2():
     g = dgl.heterograph(
         {('A', 'AB', 'B'): ('csc', (indptr, indices, data))}, num_nodes_dict={'A': 20, 'B': 30})
 
-@parametrize_dtype
+@parametrize_idtype
 def test_query(idtype):
     g = create_test_heterograph(idtype)
 
@@ -393,7 +393,7 @@ def test_query(idtype):
     # test repr
     print(g)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_empty_query(idtype):
     g = dgl.graph(([1, 2, 3], [0, 4, 5]), idtype=idtype, device=F.ctx())
     g.add_nodes(0)
@@ -512,7 +512,7 @@ def _test_edge_ids():
     eid = g2.edge_ids(0, 1, etype='follows')
     assert eid == 0
 
-@parametrize_dtype
+@parametrize_idtype
 def test_adj(idtype):
     g = create_test_heterograph(idtype)
     adj = F.sparse_to_numpy(g.adj(transpose=True, etype='follows'))
@@ -568,7 +568,7 @@ def test_adj(idtype):
                       [1., 0., 0.],
                       [0., 1., 0.]]))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_inc(idtype):
     g = create_test_heterograph(idtype)
     adj = F.sparse_to_numpy(g['follows'].inc('in'))
@@ -607,7 +607,7 @@ def test_inc(idtype):
                       [1., -1.],
                       [0., 1.]]))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_view(idtype):
     # test single node type
     g = dgl.heterograph({
@@ -689,7 +689,7 @@ def test_view(idtype):
     assert F.array_equal(g.dstnodes('user'), F.arange(0, 3, idtype))
     g.dstnodes['user'].data.pop('h')
 
-@parametrize_dtype
+@parametrize_idtype
 def test_view1(idtype):
     # test relation view
     HG = create_test_heterograph(idtype)
@@ -818,7 +818,7 @@ def test_view1(idtype):
     assert F.array_equal(f3, f4)
     assert F.array_equal(g.edges(form='eid'), F.arange(0, 2, g.idtype))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_flatten(idtype):
     def check_mapping(g, fg):
         if len(fg.ntypes) == 1:
@@ -924,7 +924,7 @@ def test_flatten(idtype):
     check_mapping(g, fg)
 
 @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
-@parametrize_dtype
+@parametrize_idtype
 def test_to_device(idtype):
     # TODO: rewrite this test case to accept different graphs so we
     #  can test reverse graph and batched graph
@@ -966,7 +966,7 @@ def test_to_device(idtype):
             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")
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('g', get_cases(['block']))
 def test_to_device2(g, idtype):
     g = g.astype(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(dgl.backend.backend_name != "pytorch", reason="Pinning graph inplace only supported for PyTorch")
-@parametrize_dtype
+@parametrize_idtype
 def test_pin_memory_(idtype):
     # TODO: rewrite this test case to accept different graphs so we
     #  can test reverse graph and batched graph
@@ -1039,7 +1039,7 @@ def test_pin_memory_(idtype):
         with pytest.raises(DGLError):
             g1.pin_memory_()
 
-@parametrize_dtype
+@parametrize_idtype
 def test_convert_bound(idtype):
     def _test_bipartite_bound(data, card):
         with pytest.raises(DGLError):
@@ -1057,7 +1057,7 @@ def test_convert_bound(idtype):
     _test_graph_bound(([0, 1], [1, 3]), 3)
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_convert(idtype):
     hg = create_test_heterograph(idtype)
     hs = []
@@ -1186,7 +1186,7 @@ def test_convert(idtype):
     assert g.num_nodes() == 2
 
 @unittest.skipIf(F._default_context_str == 'gpu', reason="Test on cpu is enough")
-@parametrize_dtype
+@parametrize_idtype
 def test_to_homo_zero_nodes(idtype):
     # Fix gihub issue #2870
     g = dgl.heterograph({
@@ -1198,7 +1198,7 @@ def test_to_homo_zero_nodes(idtype):
     gg = dgl.to_homogeneous(g, ['x'])
     assert 'x' in gg.ndata
 
-@parametrize_dtype
+@parametrize_idtype
 def test_to_homo2(idtype):
     # test the result homogeneous graph has nodes and edges sorted by their types
     hg = create_test_heterograph(idtype)
@@ -1228,7 +1228,7 @@ def test_to_homo2(idtype):
     for i, count in enumerate(etype_count):
         assert count == hg.num_edges(hg.canonical_etypes[i])
 
-@parametrize_dtype
+@parametrize_idtype
 def test_invertible_conversion(idtype):
     # Test whether to_homogeneous and to_heterogeneous are invertible
     hg = create_test_heterograph(idtype)
@@ -1236,7 +1236,7 @@ def test_invertible_conversion(idtype):
     hg2 = dgl.to_heterogeneous(g, hg.ntypes, hg.etypes)
     assert_is_identical_hetero(hg, hg2, True)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_metagraph_reachable(idtype):
     g = create_test_heterograph(idtype)
     x = F.randn((3, 5))
@@ -1255,7 +1255,7 @@ def test_metagraph_reachable(idtype):
     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")
-@parametrize_dtype
+@parametrize_idtype
 def test_subgraph_mask(idtype):
     g = create_test_heterograph(idtype)
     g_graph = g['follows']
@@ -1297,7 +1297,7 @@ def test_subgraph_mask(idtype):
                                'wishes': F.tensor([False, True], dtype=F.bool)})
         _check_subgraph(g, sg2)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_subgraph(idtype):
     g = create_test_heterograph(idtype)
     g_graph = g['follows']
@@ -1446,7 +1446,7 @@ def test_subgraph(idtype):
     sg5 = g.edge_type_subgraph(['follows', 'plays', 'wishes'])
     _check_typed_subgraph1(g, sg5)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_apply(idtype):
     def node_udf(nodes):
         return {'h': nodes.data['h'] * 2}
@@ -1484,7 +1484,7 @@ def test_apply(idtype):
     with pytest.raises(DGLError):
         g.apply_edges(edge_udf)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_level2(idtype):
     #edges = {
     #    'follows': ([0, 1], [1, 2]),
@@ -1608,7 +1608,7 @@ def test_level2(idtype):
 
     g.nodes['game'].data.clear()
 
-@parametrize_dtype
+@parametrize_idtype
 @unittest.skipIf(F._default_context_str == 'cpu', reason="Need gpu for this test")
 def test_more_nnz(idtype):
     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):
     ans = F.copy_to(F.tensor(ans, dtype=F.dtype(y)), ctx=F.ctx())
     assert F.array_equal(y, ans)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_updates(idtype):
     def msg_func(edges):
         return {'m': edges.src['h']}
@@ -1662,7 +1662,7 @@ def test_updates(idtype):
         del g.nodes['game'].data['y']
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_backward(idtype):
     g = create_test_heterograph(idtype)
     x = F.randn((3, 5))
@@ -1681,7 +1681,7 @@ def test_backward(idtype):
                                               [2., 2., 2., 2., 2.]]))
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_empty_heterograph(idtype):
     def assert_empty(g):
         assert g.number_of_nodes('user') == 0
@@ -1708,7 +1708,7 @@ def test_empty_heterograph(idtype):
     assert g.number_of_edges('develops') == 2
     assert g.number_of_nodes('developer') == 2
 
-@parametrize_dtype
+@parametrize_idtype
 def test_types_in_function(idtype):
     def mfunc1(edges):
         assert edges.canonical_etype == ('user', 'follow', 'user')
@@ -1763,7 +1763,7 @@ def test_types_in_function(idtype):
     g.filter_nodes(filter_nodes2, ntype='game')
     g.filter_edges(filter_edges2)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_stack_reduce(idtype):
     #edges = {
     #    'follows': ([0, 1], [1, 2]),
@@ -1790,7 +1790,7 @@ def test_stack_reduce(idtype):
             'stack')
     assert g.nodes['game'].data['y'].shape == (g.number_of_nodes('game'), 1, 200)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_isolated_ntype(idtype):
     g = dgl.heterograph({
         ('A', 'AB', 'B'): ([0, 1, 2], [1, 2, 3])},
@@ -1817,7 +1817,7 @@ def test_isolated_ntype(idtype):
     assert g.number_of_nodes('C') == 4
 
 
-@parametrize_dtype
+@parametrize_idtype
 def test_ismultigraph(idtype):
     g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1, 2], [1, 2, 5, 5])},
                          {'A': 6, 'B': 6}, idtype=idtype, device=F.ctx())
@@ -1850,7 +1850,7 @@ def test_ismultigraph(idtype):
         {'A': 6, 'C': 6}, idtype=idtype, device=F.ctx())
     assert g.is_multigraph == True
 
-@parametrize_dtype
+@parametrize_idtype
 def test_bipartite(idtype):
     g1 = dgl.heterograph({('A', 'AB', 'B'): ([0, 0, 1], [1, 2, 5])},
                          idtype=idtype, device=F.ctx())
@@ -1910,7 +1910,7 @@ def test_bipartite(idtype):
 
     assert not g4.is_unibipartite
 
-@parametrize_dtype
+@parametrize_idtype
 def test_dtype_cast(idtype):
     g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
     assert g.idtype == idtype
@@ -1983,7 +1983,7 @@ def test_float_cast():
             assert len(values) == len(pvalues)
             assert F.allclose(values, F.tensor(pvalues), 0, 0)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_format(idtype):
     # single relation
     g = dgl.graph(([0, 1, 0, 2], [0, 1, 1, 0]), idtype=idtype, device=F.ctx())
@@ -2020,7 +2020,7 @@ def test_format(idtype):
         assert g.in_degrees(vid) == ind_arr[vid]
     assert F.array_equal(in_degrees, g.in_degrees())
 
-@parametrize_dtype
+@parametrize_idtype
 def test_edges_order(idtype):
     # (0, 2), (1, 2), (0, 1), (0, 1), (2, 1)
     g = dgl.graph((
@@ -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(dst, F.tensor([1, 1, 2, 2, 1], dtype=idtype))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_reverse(idtype):
     g = dgl.heterograph({
         ('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):
     assert F.array_equal(g_s, rg_d)
     assert F.array_equal(g_d, rg_s)
 
-@parametrize_dtype
+@parametrize_idtype
 def test_clone(idtype):
     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())
@@ -2220,7 +2220,7 @@ def test_clone(idtype):
     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):
     # homogeneous graph
     g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
@@ -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.edges['develops'].data['h'], F.tensor([0, 0, 2, 2], dtype=idtype))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_add_nodes(idtype):
     # homogeneous Graphs
     g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
@@ -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))
 
 @unittest.skipIf(dgl.backend.backend_name == "mxnet", reason="MXNet has error with (0,) shape tensor.")
-@parametrize_dtype
+@parametrize_idtype
 def test_remove_edges(idtype):
     # homogeneous Graphs
     g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
@@ -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['developer'].data['h'], F.tensor([3, 3], dtype=idtype))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_remove_nodes(idtype):
     # homogeneous Graphs
     g = dgl.graph(([0, 1], [1, 2]), idtype=idtype, device=F.ctx())
@@ -2618,7 +2618,7 @@ def test_remove_nodes(idtype):
     assert F.array_equal(u, F.tensor([1], dtype=idtype))
     assert F.array_equal(v, F.tensor([0], dtype=idtype))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_frame(idtype):
     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())
@@ -2659,7 +2659,7 @@ def test_frame(idtype):
 
 @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")
-@parametrize_dtype
+@parametrize_idtype
 def test_frame_device(idtype):
     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())
@@ -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._edge_frames[0]._columns['h'].storage) == F.cpu()
 
-@parametrize_dtype
+@parametrize_idtype
 def test_create_block(idtype):
     block = dgl.create_block(([0, 1, 2], [1, 2, 3]), idtype=idtype, device=F.ctx())
     assert block.num_src_nodes() == 3
@@ -2822,7 +2822,7 @@ def test_create_block(idtype):
     assert hg.edges['AB'].data['x'] is eabx
     assert hg.edges['BA'].data['x'] is ebax
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('fmt', ['coo', 'csr', 'csc'])
 def test_adj_sparse(idtype, fmt):
     if fmt == 'coo':

tests/compute/test_kernel.py

@@ -4,7 +4,7 @@ import networkx as nx
 import numpy as np
 import backend as F
 from itertools import product
-from test_utils import parametrize_dtype, get_cases
+from test_utils import parametrize_idtype, get_cases
 import pytest
 
 def udf_copy_src(edges):
@@ -362,7 +362,7 @@ def test_all_binary_builtins():
                         _test(g, lhs, rhs, binary_op, reducer, partial, nid,
                               broadcast=broadcast)
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('g', get_cases(['homo-zero-degree']))
 def test_mean_zero_degree(g, idtype):
     g = g.astype(idtype).to(F.ctx())

tests/compute/test_merge.py

 import backend as F
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 import dgl
 
-@parametrize_dtype
+@parametrize_idtype
 def test_heterograph_merge(idtype):
     g1 = dgl.heterograph({("a", "to", "b"): ([0,1], [1,0])}).astype(idtype).to(F.ctx())
     g1_n_edges = g1.num_edges(etype="to")

tests/compute/test_new_update_all_hetero.py

@@ -10,7 +10,7 @@ import networkx as nx
 import unittest, pytest
 from dgl import DGLError
 import test_utils
-from test_utils import parametrize_dtype, get_cases
+from test_utils import parametrize_idtype, get_cases
 from scipy.sparse import rand
 rfuncs = {'sum': fn.sum, 'max': fn.max, 'min': fn.min, 'mean': fn.mean}
 feat_size = 2
@@ -69,7 +69,7 @@ def create_test_heterograph_large(idtype):
     assert g.device == F.ctx()
     return g
 
-@parametrize_dtype
+@parametrize_idtype
 def test_unary_copy_u(idtype):
     def _test(mfunc, rfunc):
         g = create_test_heterograph_2(idtype)
@@ -134,7 +134,7 @@ def test_unary_copy_u(idtype):
     _test(fn.copy_u, fn.min)
     # _test('copy_u', 'mean')
 
-@parametrize_dtype
+@parametrize_idtype
 def test_unary_copy_e(idtype):
     def _test(mfunc, rfunc):
 
@@ -219,7 +219,7 @@ def test_unary_copy_e(idtype):
     _test(fn.copy_e, fn.min)
     # _test('copy_e', 'mean')
 
-@parametrize_dtype
+@parametrize_idtype
 def test_binary_op(idtype):
     def _test(lhs, rhs, binary_op, reducer):
 

tests/compute/test_partition.py

@@ -2,12 +2,12 @@ from dgl.partition import NDArrayPartition
 from dgl.distributed import graph_partition_book as gpb
 import unittest
 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.")
-@parametrize_dtype
+@parametrize_idtype
 def test_get_node_partition_from_book(idtype):
     node_map = {
         "type_n": F.tensor([

tests/compute/test_pickle.py

@@ -10,7 +10,7 @@ import pickle
 import io
 import unittest, pytest
 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
 
 def _assert_is_identical_nodeflow(nf1, nf2):
@@ -92,7 +92,7 @@ def _global_message_func(nodes):
     return {'x': nodes.data['x']}
 
 @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']))
 def test_pickling_graph(g, idtype):
     g = g.astype(idtype)
@@ -165,7 +165,7 @@ def test_pickling_subgraph():
 
 @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")
-@parametrize_dtype
+@parametrize_idtype
 def test_pickling_is_pinned(idtype):
     from copy import deepcopy
     g = dgl.rand_graph(10, 20, idtype=idtype, device=F.cpu())

tests/compute/test_propagate.py

@@ -3,7 +3,7 @@ import networkx as nx
 import backend as F
 import unittest
 import utils as U
-from utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
 def create_graph(idtype):
     g = dgl.from_networkx(nx.path_graph(5), idtype=idtype, device=F.ctx())
@@ -17,7 +17,7 @@ def rfunc(nodes):
     return {'x' : nodes.data['x'] + msg}
 
 @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
-@parametrize_dtype
+@parametrize_idtype
 def test_prop_nodes_bfs(idtype):
     g = create_graph(idtype)
     g.ndata['x'] = F.ones((5, 2))
@@ -27,7 +27,7 @@ def test_prop_nodes_bfs(idtype):
             F.tensor([[2., 2.], [4., 4.], [6., 6.], [8., 8.], [9., 9.]]))
 
 @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
-@parametrize_dtype
+@parametrize_idtype
 def test_prop_edges_dfs(idtype):
     g = create_graph(idtype)
     g.ndata['x'] = F.ones((5, 2))
@@ -49,7 +49,7 @@ def test_prop_edges_dfs(idtype):
             F.tensor([[3., 3.], [5., 5.], [7., 7.], [9., 9.], [5., 5.]]))
 
 @unittest.skipIf(F._default_context_str == 'gpu', reason="GPU not implemented")
-@parametrize_dtype
+@parametrize_idtype
 def test_prop_nodes_topo(idtype):
     # bi-directional chain
     g = create_graph(idtype)

tests/compute/test_readout.py

@@ -5,9 +5,9 @@ import networkx as nx
 import unittest
 import pytest
 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):
     # NOTE: If you want to update this test case, remember to update the docstring
     #  example too!!!
@@ -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([.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('reducer', ['sum', 'max', 'mean'])
 def test_reduce_readout(g, idtype, reducer):
@@ -67,7 +67,7 @@ def test_reduce_readout(g, idtype, reducer):
         subx.append(sx)
     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('reducer', ['sum', 'max', 'mean'])
 def test_weighted_reduce_readout(g, idtype, reducer):
@@ -115,7 +115,7 @@ def test_weighted_reduce_readout(g, idtype, reducer):
         subx.append(sx)
     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('descending', [True, False])
 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(indices, F.cat(subidx, dim=0))
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
 def test_softmax(g, idtype):
     g = g.astype(idtype).to(F.ctx())
@@ -204,7 +204,7 @@ def test_softmax(g, idtype):
         subx.append(F.softmax(sg.edata['h'], dim=0))
     assert F.allclose(x, F.cat(subx, dim=0))
 
-@parametrize_dtype
+@parametrize_idtype
 @pytest.mark.parametrize('g', get_cases(['homo'], exclude=['dglgraph']))
 def test_broadcast(idtype, g):
     g = g.astype(idtype).to(F.ctx())

tests/compute/test_removal.py

 import backend as F
 import numpy as np
 import dgl
-from test_utils import parametrize_dtype
+from test_utils import parametrize_idtype
 
-@parametrize_dtype
+@parametrize_idtype
 def test_node_removal(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -31,7 +31,7 @@ def test_node_removal(idtype):
     assert dgl.NID in g.ndata
     assert dgl.EID in g.edata
 
-@parametrize_dtype
+@parametrize_idtype
 def test_multigraph_node_removal(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -59,7 +59,7 @@ def test_multigraph_node_removal(idtype):
     assert g.number_of_nodes() == 3
     assert g.number_of_edges() == 6
 
-@parametrize_dtype
+@parametrize_idtype
 def test_multigraph_edge_removal(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -86,7 +86,7 @@ def test_multigraph_edge_removal(idtype):
     assert g.number_of_nodes() == 5
     assert g.number_of_edges() == 8
 
-@parametrize_dtype
+@parametrize_idtype
 def test_edge_removal(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -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 dgl.EID in g.edata
 
-@parametrize_dtype
+@parametrize_idtype
 def test_node_and_edge_removal(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -156,7 +156,7 @@ def test_node_and_edge_removal(idtype):
     assert g.number_of_nodes() == 10
     assert g.number_of_edges() == 48
 
-@parametrize_dtype
+@parametrize_idtype
 def test_node_frame(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -169,7 +169,7 @@ def test_node_frame(idtype):
     g.remove_nodes(range(3, 7))
     assert F.allclose(g.ndata['h'], F.tensor(new_data))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_edge_frame(idtype):
     g = dgl.DGLGraph()
     g = g.astype(idtype).to(F.ctx())
@@ -183,7 +183,7 @@ def test_edge_frame(idtype):
     g.remove_edges(range(3, 7))
     assert F.allclose(g.edata['h'], F.tensor(new_data))
 
-@parametrize_dtype
+@parametrize_idtype
 def test_issue1287(idtype):
     # reproduce https://github.com/dmlc/dgl/issues/1287.
     # setting features after remove nodes

tests/compute/test_shared_mem.py

@@ -9,7 +9,7 @@ import dgl.function as fn
 import pickle
 import io
 import unittest
-from utils import parametrize_dtype
+from test_utils import parametrize_idtype
 import multiprocessing as mp
 import os
 
@@ -43,7 +43,7 @@ def _assert_is_identical_hetero(g, g2):
         assert F.array_equal(dst, dst2)
 
 @unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now')
-@parametrize_dtype
+@parametrize_idtype
 def test_single_process(idtype):
     hg = create_test_graph(idtype=idtype)
     hg_share = hg.shared_memory("hg")
@@ -60,7 +60,7 @@ def sub_proc(hg_origin, name):
     _assert_is_identical_hetero(hg_origin, hg_save_again)
 
 @unittest.skipIf(dgl.backend.backend_name == 'tensorflow', reason='Not support tensorflow for now')
-@parametrize_dtype
+@parametrize_idtype
 def test_multi_process(idtype):
     hg = create_test_graph(idtype=idtype)
     hg_share = hg.shared_memory("hg1")