| /third_party/mindspore/tests/ut/python/parallel/ |
| D | test_conv2d.py | 27 strategy1=None, strategy2=None): argument
31 self.neg = P.Neg().shard(strategy2)
62 strategy2 = ((8, 1, 1, 1),)
63 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
70 strategy2 = ((8, 1, 1, 1),)
72 strategy1=strategy1, strategy2=strategy2)
80 strategy2 = ((8, 1, 1, 1),)
82 strategy1=strategy1, strategy2=strategy2)
89 strategy2 = ((8, 1, 1, 1),)
91 strategy1=strategy1, strategy2=strategy2)
[all …]
|
| D | test_maxpool_avgpool.py | 27 strategy1=None, strategy2=None): argument
32 … self.max_pool = P.MaxPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
42 strategy1=None, strategy2=None): argument
47 … self.avg_pool = P.AvgPool(kernel_size=pool_kernel_size, strides=pool_strides).shard(strategy2)
73 strategy2 = ((8, 1, 1, 1),)
75 strategy1=strategy1, strategy2=strategy2)
82 strategy2 = ((2, 1, 2, 2),)
84 strategy1=strategy1, strategy2=strategy2)
91 strategy2 = ((2, 1, 2, 2),)
93 strategy1=strategy1, strategy2=strategy2)
[all …]
|
| D | test_conv2d_transpose.py | 27 strategy1=None, strategy2=None): argument
31 self.neg = P.Neg().shard(strategy2)
42 strategy1=None, strategy2=None): argument
46 self.neg = P.Neg().shard(strategy2)
75 strategy2 = ((8, 1, 1, 1),)
76 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
83 strategy2 = ((8, 1, 1, 1),)
84 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
91 strategy2 = ((2, 1, 1, 4),)
93 strategy1=strategy1, strategy2=strategy2)
[all …]
|
| D | test_layer_norm_further.py | 28 strategy1=None, strategy2=None, strategy3=None): argument
34 self.begin_norm_axis, self.begin_params_axis).shard(strategy2)
52 strategy1=None, strategy2=None, strategy3=None): argument
58 self.begin_norm_axis, self.begin_params_axis).shard(strategy2)
76 strategy1=None, strategy2=None, strategy3=None): argument
82 self.begin_norm_axis, self.begin_params_axis).shard(strategy2)
100 strategy1=None, strategy2=None, strategy3=None): argument
106 self.begin_norm_axis, self.begin_params_axis).shard(strategy2)
122 strategy1=None, strategy2=None, strategy3=None): argument
128 self.begin_norm_axis, self.begin_params_axis).shard(strategy2)
[all …]
|
| D | test_gathernd_further.py | 48 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
53 self.gathernd = P.GatherNd().shard(strategy2)
64 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
69 self.gathernd = P.GatherNd().shard(strategy2)
79 def __init__(self, w1_shape, indices_shape, strategy1=None, strategy2=None, strategy3=None): argument
84 self.gathernd = P.GatherNd().shard(strategy2)
116 strategy2 = ((1, 1, 1), (8, 1, 1, 1))
118 net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
128 strategy2 = ((1, 1, 1), (8, 1, 1, 1))
130 net = Net(w1_shape, indices_shape, strategy1, strategy2, strategy3)
[all …]
|
| D | test_batchnorm.py | 26 strategy1=None, strategy2=None): argument
32 self.bn.bn_train.shard(strategy2)
57 strategy2 = ((8, 1, 1, 1), (1,), (1,), (1,), (1,))
58 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
65 strategy2 = ((2, 1, 2, 2), (1,), (1,), (1,), (1,))
66 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
73 strategy2 = ((1, 8, 1, 1), (8,), (8,), (8,), (8,))
74 … out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strategy2=strategy2)
79 def __init__(self, strategy1=None, strategy2=None): argument
83 self.relu = P.ReLU().shard(strategy2)
[all …]
|
| D | test_reduce_method_info.py | 85 def __init__(self, strategy1, strategy2, strategy3): argument
88 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
99 strategy2 = ((4, 1, 2),)
101 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
112 def __init__(self, strategy1, strategy2, strategy3): argument
115 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
126 strategy2 = ((2, 4, 1, 1),)
128 net = GradWrap(NetWithLoss(Net(strategy1, strategy2, strategy3)))
139 def __init__(self, strategy1, strategy2, strategy3): argument
142 self.reduce_sum = P.ReduceSum(keep_dims=False).shard(strategy2)
[all …]
|
| D | test_manual_embedding_lookup.py | 35 strategy2=None, argument
46 self.mul = P.Mul().shard(strategy2)
81 strategy2 = ((1, 2, 1), (1, 2, 1))
83 net = Net(strategy1, strategy2, strategy3)
90 strategy2 = ((1, 4, 1), (1, 4, 1))
92 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
99 strategy2 = ((1, 4, 8), (1, 4, 8))
101 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
108 strategy2 = ((1, 2, 1), (1, 2, 1))
110 …net = Net(strategy1, strategy2, strategy3, split_string="manual_split_with_offset", split_tuple=((…
[all …]
|
| D | test_manual_gatherv2.py | 28 strategy2=None, argument
38 self.mul = P.Mul().shard(strategy2)
74 strategy2 = ((1, 2, 1), (1, 2, 1))
76 net = Net(strategy1, strategy2, strategy3)
83 strategy2 = ((1, 4, 1), (1, 4, 1))
85 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
92 strategy2 = ((1, 4, 8), (1, 4, 8))
94 net = Net(strategy1, strategy2, strategy3, split_tuple=(10, 20, 30, 4), param_shape=(64, 8))
101 strategy2 = ((1, 2, 1), (1, 2, 1))
103 …net = Net(strategy1, strategy2, strategy3, split_string="manual_split_with_offset", split_tuple=((…
[all …]
|
| D | test_print.py | 26 strategy1=None, strategy2=None): argument
32 self.bn.bn_train.shard(strategy2)
60 strategy2 = ((8, 1, 1, 1), (1,), (1,), (1,), (1,))
61 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
69 strategy2 = ((2, 1, 2, 2), (1,), (1,), (1,), (1,))
70 … out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
78 strategy2 = ((1, 8, 1, 1), (8,), (8,), (8,), (8,))
79 … out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strategy2=strategy2)
84 def __init__(self, strategy1=None, strategy2=None): argument
88 self.relu = P.ReLU().shard(strategy2)
[all …]
|
| D | test_comparison_function_info.py | 58 def __init__(self, strategy1, strategy2): argument
61 self.equal = P.Equal().shard(strategy2)
70 strategy2 = ((4, 2), (4, 2))
71 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
81 def __init__(self, strategy1, strategy2): argument
84 self.notequal = P.NotEqual().shard(strategy2)
93 strategy2 = ((4, 2), (4, 2))
94 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
104 def __init__(self, strategy1, strategy2): argument
107 self.approximateEqual = P.ApproximateEqual(tolerance=0.5).shard(strategy2)
[all …]
|
| D | test_stridedslice.py | 26 …def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=T… argument
29 self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
48 def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None): argument
51 self.strided_slice = P.StridedSlice().shard(strategy2)
81 strategy2 = ((2, 2, 2),)
82 … net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True)
90 strategy2 = ((1, 2, 2),)
91 … net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2), strategy1, strategy2, is_parameter=True)
99 strategy2 = ((1, 2, 2),)
100 …net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1), strategy1, strategy2, is_parameter=True, …
[all …]
|
| D | test_onehot.py | 57 def __init__(self, strategy1, strategy2): argument
60 self.gelu = P.GeLU().shard(strategy2)
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…
86 strategy2 = ((2, 8),)
89 compile_graph(strategy1, strategy2, strategy3, strategy4)
95 strategy2 = ((2, 8),)
98 compile_graph(strategy1, strategy2, strategy3, strategy4)
104 strategy2 = ((2, 8),)
108 compile_graph(strategy1, strategy2, strategy3, strategy4)
[all …]
|
| D | test_arithmetic.py | 57 def __init__(self, strategy1, strategy2): argument
60 self.sub = P.Sub().shard(strategy2)
70 strategy2 = ((4, 2), (4, 2))
71 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
81 def __init__(self, strategy1, strategy2): argument
84 self.add = P.Add().shard(strategy2)
94 strategy2 = ((4, 2), (4, 2))
95 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
105 def __init__(self, strategy1, strategy2): argument
108 self.mul = P.Mul().shard(strategy2)
[all …]
|
| 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)))
82 strategy2 = (8,)
83 compile_graph(x, y, num_segments, strategy1, strategy2)
92 strategy2 = (1,)
93 compile_graph(x, y, num_segments, strategy1, strategy2)
102 strategy2 = (4,)
103 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
|
| 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)))
83 strategy2 = (8,)
84 compile_graph(x, y, num_segments, strategy1, strategy2)
93 strategy2 = (1,)
94 compile_graph(x, y, num_segments, strategy1, strategy2)
103 strategy2 = (4,)
104 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
|
| D | test_tile.py | 25 def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True): argument
28 self.tile = P.Tile().shard(strategy2)
44 def __init__(self, weight2, strategy1=None, strategy2=None): argument
47 self.tile = P.Tile().shard(strategy2)
56 def __init__(self, weight, strategy1=None, strategy2=None, is_parameter=True): argument
59 self.tile = P.Tile().shard(strategy2)
92 strategy2 = ((2, 2, 2),)
93 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
100 strategy2 = ((2, 2, 1),)
101 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
[all …]
|
| 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)))
83 strategy2 = (8,)
84 compile_graph(x, y, num_segments, strategy1, strategy2)
93 strategy2 = (1,)
94 compile_graph(x, y, num_segments, strategy1, strategy2)
103 strategy2 = (4,)
104 compile_graph(x, y, num_segments, strategy1, strategy2)
[all …]
|
| D | test_o2_level.py | 48 …def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=T… argument
51 self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
71 def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None): argument
74 self.strided_slice = P.StridedSlice().shard(strategy2)
107 strategy2 = ((2, 2, 2),)
109 strategy1, strategy2, is_parameter=True)
118 strategy2 = ((1, 2, 2),)
120 strategy1, strategy2, is_parameter=True)
129 strategy2 = ((1, 2, 2),)
131 strategy1, strategy2, is_parameter=True, mask=1)
[all …]
|
| D | test_element_wise_function.py | 58 def __init__(self, strategy1, strategy2): argument
61 self.pow = P.Pow().shard(strategy2)
72 strategy2 = ((4, 2), ())
73 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
84 def __init__(self, strategy1, strategy2): argument
87 self.exp = P.Exp().shard(strategy2)
98 strategy2 = ((4, 2),)
99 net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
110 def __init__(self, strategy1, strategy2): argument
113 self.log = P.Log().shard(strategy2)
[all …]
|
| D | test_slice.py | 26 def __init__(self, weight, w2, begin, end, strategy1=None, strategy2=None, is_parameter=True): argument
29 self.slice = P.Slice().shard(strategy2)
47 def __init__(self, weight2, begin, end, strategy1=None, strategy2=None): argument
50 self.slice = P.Slice().shard(strategy2)
79 strategy2 = ((2, 2, 2),)
80 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2, is_parameter=True)
87 strategy2 = ((1, 4, 2),)
88 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2)
95 strategy2 = ((1, 4, 2),)
96 net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), strategy1, strategy2, is_parameter=False)
[all …]
|
| D | test_resizebilinear.py | 29 strategy1=None, strategy2=None): argument
34 self.resize_bilinear = P.ResizeBilinear((16, 16)).shard(strategy2)
47 strategy1=None, strategy2=None): argument
52 self.resize_neighbor = P.ResizeNearestNeighbor((16, 16)).shard(strategy2)
93 strategy2 = ((8, 1, 1, 1),)
95 strategy1=strategy1, strategy2=strategy2)
102 strategy2 = ((4, 2, 1, 1),)
104 strategy1=strategy1, strategy2=strategy2)
111 strategy2 = ((2, 1, 1, 1),)
113 strategy1=strategy1, strategy2=strategy2)
[all …]
|
| D | test_concat.py | 24 def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True): argument
31 self.mul = P.Mul().shard(strategy2)
41 def __init__(self, weight, strategy1=None, strategy2=None, axis=0): argument
44 self.concat = P.Concat(axis=axis).shard(strategy2)
54 def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True): argument
61 self.mul = P.Mul().shard(strategy2)
94 strategy2 = ((1, 4, 2), (1, 4, 2))
95 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
102 strategy2 = ((1, 4, 2), (1, 4, 2))
103 net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
[all …]
|
| D | test_parameter_multi_users.py | 25 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
28 self.mul2 = P.Mul().shard(strategy2)
38 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
41 self.mul2 = P.Mul().shard(strategy2)
51 def __init__(self, mul_weight, strategy1=None, strategy2=None): argument
54 self.mul2 = P.MatMul().shard(strategy2)
80 strategy2 = ((16, 1), (16, 1))
81 net = Net(_w, strategy1, strategy2)
88 strategy2 = ((4, 4), (4, 4))
89 net = Net(_w, strategy1, strategy2)
[all …]
|
| D | test_gather_v2.py | 50 def __init__(self, axis=0, strategy1=None, strategy2=None, shape=None, target=""): argument
55 self.mul = P.Mul().shard(strategy2)
68 strategy2 = ((4, 2, 1), (4, 2, 1))
69 net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
81 strategy2 = ((4, 2, 1), (4, 2, 1))
82 net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
94 strategy2 = ((4, 2, 1), (4, 2, 1))
95 net = GradWrap(NetWithLoss(Net(0, strategy1, strategy2)))
107 strategy2 = ((4, 2, 1), (4, 2, 1))
108 net = GradWrap(NetWithLoss(Net(1, strategy1, strategy2)))
[all …]
|