forked from huawei/mindspore2022
116 lines
3.8 KiB
Python
116 lines
3.8 KiB
Python
# Copyright 2020 Huawei Technologies Co., Ltd
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#
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# Licensed under the Apache License, Version 2.0 (the "License");
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# you may not use this file except in compliance with the License.
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# You may obtain a copy of the License at
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#
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# http://www.apache.org/licenses/LICENSE-2.0
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#
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# Unless required by applicable law or agreed to in writing, software
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# distributed under the License is distributed on an "AS IS" BASIS,
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# WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
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# See the License for the specific language governing permissions and
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# limitations under the License.
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import numpy as np
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import mindspore as ms
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import mindspore.nn as nn
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from mindspore import Tensor
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from mindspore import context
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from mindspore.common.api import _cell_graph_executor
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from mindspore.ops import composite as C
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from mindspore.ops import operations as P
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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grad_all = C.GradOperation(get_all=True)
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class NetWithLoss(nn.Cell):
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def __init__(self, network):
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super(NetWithLoss, self).__init__()
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self.loss = VirtualLoss()
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self.network = network
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def construct(self, x, y):
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predict = self.network(x, y)
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return self.loss(predict)
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class GradWrap(nn.Cell):
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def __init__(self, network):
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super(GradWrap, self).__init__()
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self.network = network
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def construct(self, x, y):
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return grad_all(self.network)(x, y)
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class Net(nn.Cell):
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def __init__(self, strategy1=None, strategy2=None):
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super().__init__()
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self.dropout = P.Dropout(keep_prob=0.6).shard(strategy1)
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self.matmul = P.MatMul().shard(strategy2)
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def construct(self, x, y):
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out = self.matmul(x, y)
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out, _ = self.dropout(out)
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return out
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def compile_graph(net, device_num, parallel_mode, x, y):
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context.set_auto_parallel_context(device_num=device_num, global_rank=0, parallel_mode=parallel_mode)
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net.set_auto_parallel()
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net.set_train()
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_cell_graph_executor.compile(net, x, y)
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def test_dropout_semi_auto():
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"""
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Feature: distribute operator dropout in auto parallel with gpu backend.
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Description: dropout net without strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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net = GradWrap(NetWithLoss(Net()))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 128]), dtype=ms.float32)
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compile_graph(net, 8, "semi_auto_parallel", x, y)
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def test_dropout_semi_auto2():
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"""
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Feature: distribute operator dropout in auto parallel with gpu backend.
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Description: dropout net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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strategy1 = ((8, 1),)
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strategy2 = ((4, 2), (2, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 128]), dtype=ms.float32)
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compile_graph(net, 8, "semi_auto_parallel", x, y)
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def test_dropout_semi_auto3():
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"""
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Feature: distribute operator dropout in auto parallel with gpu backend.
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Description: dropout net with strategy in semi auto parallel.
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Expectation: compile done without error.
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"""
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strategy1 = ((2, 4),)
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strategy2 = ((4, 2), (2, 1))
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net = GradWrap(NetWithLoss(Net(strategy1, strategy2)))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 128]), dtype=ms.float32)
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compile_graph(net, 8, "semi_auto_parallel", x, y)
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def test_dropout_auto():
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"""
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Feature: distribute operator dropout in auto parallel with gpu backend.
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Description: dropout net in auto parallel.
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Expectation: compile done without error.
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"""
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net = GradWrap(NetWithLoss(Net()))
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 128]), dtype=ms.float32)
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compile_graph(net, 8, "auto_parallel", x, y)
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