forked from huawei/mindspore2022
119 lines
4.0 KiB
Python
119 lines
4.0 KiB
Python
# Copyright 2022 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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# ============================================================================
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import numpy as np
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import pytest
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import mindspore as ms
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import mindspore.context as context
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from mindspore import Tensor, Parameter
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import mindspore.nn as nn
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from mindspore.ops import operations as P
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from parallel.utils.utils import compile_net
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input_x_tensor_ = Tensor(np.ones([8, 8, 8]), ms.float32)
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input_x_parameter_ = Parameter(Tensor(np.ones([8, 8, 8]), ms.float32), "input_x")
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SIZE_SPLIT = [3, 3, 2]
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NUM_SPLIT = 3
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class Net(nn.Cell):
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def __init__(self, size_split, split_dim, num_split, strategy1=None, strategy2=None):
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super(Net, self).__init__()
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self.splitv = P.SplitV(size_split, split_dim, num_split).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight = Parameter(np.array([1.0]), name="mul_weight")
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def construct(self, x):
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out = self.splitv(x)
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out = self.mul(out[0], self.weight)
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return out
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class NetWithParameter(nn.Cell):
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def __init__(self, size_split, split_dim, num_split, strategy1=None, strategy2=None):
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super(NetWithParameter, self).__init__()
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self.splitv = P.SplitV(size_split, split_dim, num_split).shard(strategy1)
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self.mul = P.Mul().shard(strategy2)
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self.weight = input_x_parameter_
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def construct(self, x):
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out = self.splitv(self.weight)
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out = self.mul(x, out[0])
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return out
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def test_splitv_auto_parallel():
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"""
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Feature: test SplitV auto parallel
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Description: auto parallel
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net(SIZE_SPLIT, 0, NUM_SPLIT)
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compile_net(net, input_x_tensor_)
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def test_splitv_auto_parallel_with_parameter():
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"""
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Feature: test SplitV auto parallel with parameter input
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Description: auto parallel with parameter input
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = NetWithParameter(SIZE_SPLIT, 2, NUM_SPLIT)
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x = Tensor(np.ones([8, 8, 3]), ms.float32)
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compile_net(net, x)
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def test_splitv_data_parallel():
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"""
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Feature: test SplitV data parallel
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Description: data parallel
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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net = Net(SIZE_SPLIT, 1, NUM_SPLIT)
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compile_net(net, input_x_tensor_)
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def test_splitv_model_parallel():
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"""
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Feature: test SplitV model parallel
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Description: model parallel
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Expectation: compile success
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2, 2),)
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strategy2 = ((1, 2, 2), (1,))
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net = Net(SIZE_SPLIT, 0, NUM_SPLIT, strategy1, strategy2)
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compile_net(net, input_x_tensor_)
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def test_splitv_strategy_error():
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"""
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Feature: test SplitV parallel with invalid strategy
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Description: config invalid strategy
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Expectation: raise RuntimeError
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"""
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2),)
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strategy2 = ((8, 1, 1),)
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net = Net(SIZE_SPLIT, 0, NUM_SPLIT, strategy1, strategy2)
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with pytest.raises(RuntimeError):
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compile_net(net, input_x_tensor_)
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context.reset_auto_parallel_context()
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