| /third_party/mindspore/tests/ut/python/parallel/ |
| D | test_conv2d.py | 27 strategy1=None, strategy2=None): argument
30 … pad_mode=pad_mode, stride=stride, dilation=dilation, group=group).shard(strategy1)
61 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
63 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
69 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
72 strategy1=strategy1, strategy2=strategy2)
79 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
82 strategy1=strategy1, strategy2=strategy2)
88 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
91 strategy1=strategy1, strategy2=strategy2)
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| D | test_maxpool_avgpool.py | 27 strategy1=None, strategy2=None): argument
30 pad_mode=pad_mode, stride=stride).shard(strategy1)
42 strategy1=None, strategy2=None): argument
45 pad_mode=pad_mode, stride=stride).shard(strategy1)
72 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
75 strategy1=strategy1, strategy2=strategy2)
81 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
84 strategy1=strategy1, strategy2=strategy2)
90 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
93 strategy1=strategy1, strategy2=strategy2)
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| D | test_conv2d_transpose.py | 27 strategy1=None, strategy2=None): argument
30 pad_mode=pad_mode, stride=stride).shard(strategy1)
42 strategy1=None, strategy2=None): argument
45 pad_mode=pad_mode, stride=stride).shard(strategy1)
74 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
76 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
82 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
84 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
90 strategy1 = ((2, 1, 1, 4), (1, 1, 1, 1))
93 strategy1=strategy1, strategy2=strategy2)
[all …]
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| D | test_layer_norm_further.py | 28 strategy1=None, strategy2=None, strategy3=None): argument
32 self.mul = P.Mul().shard(strategy1)
52 strategy1=None, strategy2=None, strategy3=None): argument
56 self.mul = P.Mul().shard(strategy1)
76 strategy1=None, strategy2=None, strategy3=None): argument
80 self.mul = P.Mul().shard(strategy1)
100 strategy1=None, strategy2=None, strategy3=None): argument
104 self.mul = P.Mul().shard(strategy1)
122 strategy1=None, strategy2=None, strategy3=None): argument
126 self.mul = P.Mul().shard(strategy1)
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| D | test_gathernd_further.py | 48 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
50 self.mul = P.Mul().shard(strategy1)
64 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
66 self.mul = P.Mul().shard(strategy1)
79 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
81 self.mul = P.Mul().shard(strategy1)
115 strategy1 = ((8, 1, 1), (8, 1, 1))
118 net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
127 strategy1 = ((8, 1, 1), (8, 1, 1))
130 net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
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| D | test_element_wise_function.py | 58 def __init__(self, strategy1, strategy2): argument
60 self.matmul = P.MatMul().shard(strategy1)
62 self.matmul2 = P.MatMul().shard(strategy1)
71 strategy1 = ((2, 2), (2, 2))
73 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
84 def __init__(self, strategy1, strategy2): argument
86 self.matmul = P.MatMul().shard(strategy1)
88 self.matmul2 = P.MatMul().shard(strategy1)
97 strategy1 = ((2, 2), (2, 2))
99 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
[all …]
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| D | test_uniform_candidate_sampler.py | 28 strategy1=None): argument
32 if strategy1:
33 self.sampler.shard(strategy1)
53 strategy1=None): argument
60 if strategy1:
61 self.sampler.shard(strategy1)
87 strategy1 = ((4, 1),)
89 remove_accidential=False, strategy1=strategy1)
95 strategy1 = ((1, 4),)
97 remove_accidential=False, strategy1=strategy1)
[all …]
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| D | test_batchnorm.py | 26 strategy1=None, strategy2=None): argument
29 pad_mode=pad_mode, stride=stride).shard(strategy1)
56 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
58 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
64 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
66 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
72 strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
74 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strat…
79 def __init__(self, strategy1=None, strategy2=None): argument
82 self.bn.bn_train.shard(strategy1)
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| D | test_reduce_method_info.py | 85 def __init__(self, strategy1, strategy2, strategy3): argument
87 self.mul1 = P.Mul().shard(strategy1)
98 strategy1 = ((1, 1, 8), (1, 1, 8))
101 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
112 def __init__(self, strategy1, strategy2, strategy3): argument
114 self.mul1 = P.Mul().shard(strategy1)
125 strategy1 = ((1, 1, 4, 2), (1, 1, 4, 2))
128 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
139 def __init__(self, strategy1, strategy2, strategy3): argument
141 self.mul1 = P.Mul().shard(strategy1)
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| D | test_manual_embedding_lookup.py | 34 strategy1=None, argument
43 self.gatherv2 = P.EmbeddingLookup().shard(strategy1)
80 strategy1 = ((2, 1), (1, 2))
83 net = Net(strategy1, strategy2, strategy3)
89 strategy1 = ((4, 1), (1, 4))
92 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
98 strategy1 = ((4, 8), (1, 4))
101 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
107 strategy1 = ((2, 1), (1, 2))
110 …net = Net(strategy1, strategy2, strategy3, split_string="manual_split_with_offset", split_tuple=((…
[all …]
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| D | test_manual_gatherv2.py | 27 strategy1=None, argument
36 self.gatherv2 = P.Gather().shard(strategy1)
73 strategy1 = ((2, 1), (1, 2))
76 net = Net(strategy1, strategy2, strategy3)
82 strategy1 = ((4, 1), (1, 4))
85 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
91 strategy1 = ((4, 8), (1, 4))
94 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
100 strategy1 = ((2, 1), (1, 2))
103 …net = Net(strategy1, strategy2, strategy3, split_string="manual_split_with_offset", split_tuple=((…
[all …]
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| D | test_print.py | 26 strategy1=None, strategy2=None): argument
29 pad_mode=pad_mode, stride=stride).shard(strategy1)
59 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
61 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
68 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
70 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strat…
77 strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
79 …net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strat…
84 def __init__(self, strategy1=None, strategy2=None): argument
87 self.bn.bn_train.shard(strategy1)
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| D | test_comparison_function_info.py | 58 def __init__(self, strategy1, strategy2): argument
60 self.matmul = P.MatMul().shard(strategy1)
69 strategy1 = ((2, 2), (2, 2))
71 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
81 def __init__(self, strategy1, strategy2): argument
83 self.matmul = P.MatMul().shard(strategy1)
92 strategy1 = ((2, 2), (2, 2))
94 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
104 def __init__(self, strategy1, strategy2): argument
106 self.matmul = P.MatMul().shard(strategy1)
[all …]
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| D | test_stridedslice.py | 26 …def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=T… argument
28 self.mul = P.Mul().shard(strategy1)
48 def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None): argument
50 self.mul = P.Mul().shard(strategy1)
80 strategy1 = ((2, 2, 2), (2, 2, 2))
82 … net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
89 strategy1 = ((2, 2, 2), (2, 2, 2))
91 … net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
98 strategy1 = ((2, 2, 2), (2, 2, 2))
100 …net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, …
[all …]
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| D | test_onehot.py | 57 def __init__(self, strategy1, strategy2): argument
59 self.matmul = P.MatMul().shard(strategy1)
68 def compile_graph(strategy1, strategy2, strategy3, strategy4, auto=False, onthot_axis=-1): argument
69 …net = GradWrap(_VirtualDatasetCell(NetWithLoss(Net(strategy1, strategy2), strategy3, strategy4, ax…
85 strategy1 = ((2, 4), (4, 2))
89 compile_graph(strategy1, strategy2, strategy3, strategy4)
94 strategy1 = ((2, 4), (4, 2))
98 compile_graph(strategy1, strategy2, strategy3, strategy4)
103 strategy1 = ((2, 4), (4, 2))
108 compile_graph(strategy1, strategy2, strategy3, strategy4)
[all …]
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| D | test_unsortedsegmentsum.py | 35 def __init__(self, strategy1, strategy2, num_segments): argument
37 self.merge_op = P.UnsortedSegmentSum().shard((strategy1, strategy2))
65 def compile_graph(x, y, segments, strategy1, strategy2, auto=False): argument
70 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, segments)))
81 strategy1 = (8,)
83 compile_graph(x, y, num_segments, strategy1, strategy2)
91 strategy1 = (1,)
93 compile_graph(x, y, num_segments, strategy1, strategy2)
101 strategy1 = (4, 1)
103 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
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| D | test_resizebilinear.py | 29 strategy1=None, strategy2=None): argument
32 pad_mode=pad_mode, stride=stride).shard(strategy1)
47 strategy1=None, strategy2=None): argument
50 pad_mode=pad_mode, stride=stride).shard(strategy1)
64 strategy1=None): argument
67 pad_mode=pad_mode, stride=stride).shard(strategy1)
92 strategy1 = ((8, 1, 1, 1), (1, 1, 1, 1))
95 strategy1=strategy1, strategy2=strategy2)
101 strategy1 = ((2, 2, 1, 1), (2, 2, 1, 1))
104 strategy1=strategy1, strategy2=strategy2)
[all …]
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| D | test_arithmetic.py | 57 def __init__(self, strategy1, strategy2): argument
59 self.matmul = P.MatMul().shard(strategy1)
69 strategy1 = ((2, 2), (2, 2))
71 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
81 def __init__(self, strategy1, strategy2): argument
83 self.matmul = P.MatMul().shard(strategy1)
93 strategy1 = ((2, 2), (2, 2))
95 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
105 def __init__(self, strategy1, strategy2): argument
107 self.matmul = P.MatMul().shard(strategy1)
[all …]
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| D | test_unsortedsegmentmax.py | 35 def __init__(self, strategy1, strategy2, num_segments): argument
38 self.merge_op = P.UnsortedSegmentMax().shard((strategy1, strategy2))
66 def compile_graph(x, y, segments, strategy1, strategy2, auto=False): argument
67 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, segments)))
82 strategy1 = (8,)
84 compile_graph(x, y, num_segments, strategy1, strategy2)
92 strategy1 = (1,)
94 compile_graph(x, y, num_segments, strategy1, strategy2)
102 strategy1 = (4, 1)
104 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
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| D | test_unsortedsegmentmin.py | 35 def __init__(self, strategy1, strategy2, num_segments): argument
38 self.merge_op = P.UnsortedSegmentMin().shard((strategy1, strategy2))
66 def compile_graph(x, y, segments, strategy1, strategy2, auto=False): argument
67 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, segments)))
82 strategy1 = (8,)
84 compile_graph(x, y, num_segments, strategy1, strategy2)
92 strategy1 = (1,)
94 compile_graph(x, y, num_segments, strategy1, strategy2)
102 strategy1 = (4, 1)
104 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
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| D | test_tile.py | 25 def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True): argument
27 self.mul = P.Mul().shard(strategy1)
44 def __init__(self, weight2, strategy1=None, strategy2=None): argument
46 self.mul = P.Mul().shard(strategy1)
56 def __init__(self, weight, strategy1=None, strategy2=None, is_parameter=True): argument
58 self.mul = P.Mul().shard(strategy1)
91 strategy1 = ((2, 2, 2), (2, 2, 2))
93 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
99 strategy1 = ((2, 2, 2), (2, 2, 2))
101 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
[all …]
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| D | test_o2_level.py | 48 …def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=T… argument
50 self.mul = P.Mul().shard(strategy1)
71 def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None): argument
73 self.mul = P.Mul().shard(strategy1)
106 strategy1 = ((2, 2, 2), (2, 2, 2))
109 strategy1, strategy2, is_parameter=True)
117 strategy1 = ((2, 2, 2), (2, 2, 2))
120 strategy1, strategy2, is_parameter=True)
128 strategy1 = ((2, 2, 2), (2, 2, 2))
131 strategy1, strategy2, is_parameter=True, mask=1)
[all …]
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| D | test_slice.py | 26 def __init__(self, weight, w2, begin, end, strategy1=None, strategy2=None, is_parameter=True): argument
28 self.mul = P.Mul().shard(strategy1)
47 def __init__(self, weight2, begin, end, strategy1=None, strategy2=None): argument
49 self.mul = P.Mul().shard(strategy1)
78 strategy1 = ((2, 2, 2), (2, 2, 2))
80 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2, is_parameter=True)
86 strategy1 = ((1, 4, 1), (1, 4, 2))
88 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2)
94 strategy1 = ((1, 4, 1), (1, 4, 2))
96 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2, is_parameter=False)
[all …]
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| D | test_concat.py | 24 def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True): argument
26 self.concat = P.Concat(axis=0).shard(strategy1)
41 def __init__(self, weight, strategy1=None, strategy2=None, axis=0): argument
43 self.mul = P.Mul().shard(strategy1)
54 def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True): argument
56 self.concat = P.Concat(axis=0).shard(strategy1)
93 strategy1 = ((1, 4, 2), (1, 4, 2))
95 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
101 strategy1 = ((1, 2, 2), (1, 2, 2))
103 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
[all …]
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| D | test_parameter_multi_users.py | 25 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
27 self.mul = P.Mul().shard(strategy1)
38 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
40 self.mul = P.Mul().shard(strategy1)
51 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
53 self.mul = P.MatMul().shard(strategy1)
79 strategy1 = ((16, 1), (16, 1))
81 net = Net(_w, strategy1, strategy2)
87 strategy1 = ((16, 1), (16, 1))
89 net = Net(_w, strategy1, strategy2)
[all …]
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