• Home
  • Line#
  • Scopes#
  • Navigate#
  • Raw
  • Download
1# Copyright 2020 Huawei Technologies Co., Ltd
2#
3# Licensed under the Apache License, Version 2.0 (the "License");
4# you may not use this file except in compliance with the License.
5# You may obtain a copy of the License at
6#
7# http://www.apache.org/licenses/LICENSE-2.0
8#
9# Unless required by applicable law or agreed to in writing, software
10# distributed under the License is distributed on an "AS IS" BASIS,
11# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
12# See the License for the specific language governing permissions and
13# limitations under the License.
14
15import numpy as np
16
17import mindspore as ms
18import mindspore.nn as nn
19from mindspore import Tensor
20from mindspore import context
21from mindspore.common.api import _cell_graph_executor
22from mindspore.ops import composite as C
23from mindspore.ops import operations as P
24from tests.ut.python.ops.test_math_ops import VirtualLoss
25
26
27grad_all = C.GradOperation(get_all=True)
28
29
30class NetWithLoss(nn.Cell):
31    def __init__(self, network):
32        super(NetWithLoss, self).__init__()
33        self.loss = VirtualLoss()
34        self.network = network
35
36    def construct(self, x, y, b):
37        predict = self.network(x, y, b)
38        return self.loss(predict)
39
40
41class GradWrap(nn.Cell):
42    def __init__(self, network):
43        super(GradWrap, self).__init__()
44        self.network = network
45
46    def construct(self, x, y, b):
47        return grad_all(self.network)(x, y, b)
48
49
50def compile_net(net, x, y, b):
51    net.set_auto_parallel()
52    net.set_train()
53    _cell_graph_executor.compile(net, x, y, b)
54
55
56# it has not redistribution
57def test_tensoradd_reshape_matmul():
58    class Net(nn.Cell):
59        def __init__(self, strategy1, strategy2):
60            super().__init__()
61            self.add = P.Add().shard(strategy1)
62            self.reshape = P.Reshape()
63            self.matmul = P.MatMul().shard(strategy2)
64
65        def construct(self, x, y, b):
66            out = self.add(x, y)
67            out = self.reshape(out, (256, 16))
68            out = self.matmul(out, b)
69            return out
70
71    context.set_auto_parallel_context(device_num=64, global_rank=0, gradients_mean=True)
72    strategy1 = ((8, 1, 1), (8, 1, 1))
73    strategy2 = ((8, 1), (1, 8))
74    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
75    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
76
77    x = Tensor(np.ones([32, 8, 16]), dtype=ms.float32)
78    y = Tensor(np.ones([32, 8, 16]), dtype=ms.float32)
79    b = Tensor(np.ones([16, 16]), dtype=ms.float32)
80
81    compile_net(net, x, y, b)
82
83
84def test_two_matmul():
85    class Net(nn.Cell):
86        def __init__(self, strategy1, strategy2):
87            super().__init__()
88            self.matmul1 = P.MatMul().shard(strategy1)
89            self.matmul2 = P.MatMul().shard(strategy2)
90
91        def construct(self, x, y, b):
92            out = self.matmul1(x, y)
93            out = self.matmul2(out, b)
94            return out
95
96    context.set_auto_parallel_context(device_num=64, global_rank=0, gradients_mean=True)
97    strategy1 = ((8, 8), (8, 1))
98    strategy2 = ((8, 1), (1, 1))
99    net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
100    context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
101
102    x = Tensor(np.ones([128, 32]), dtype=ms.float32)
103    y = Tensor(np.ones([32, 64]), dtype=ms.float32)
104    b = Tensor(np.ones([64, 64]), dtype=ms.float32)
105
106    compile_net(net, x, y, b)
107