forked from huawei/mindspore2022
131 lines
4.8 KiB
Python
131 lines
4.8 KiB
Python
# Copyright 2021 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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from mindspore import context, Tensor, Parameter
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from mindspore.common.api import _cell_graph_executor
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from mindspore.nn import Cell, TrainOneStepCell, Momentum, BatchNorm2d, BatchNorm1d
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from mindspore.ops import operations as P
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from tests.security_utils import security_off_wrap
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class Net(Cell):
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def __init__(self, conv2d_weight, out_channel, kernel_size, pad_mode, stride,
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strategy1=None, strategy2=None):
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super().__init__()
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self.conv2d = P.Conv2D(out_channel=out_channel, kernel_size=kernel_size,
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pad_mode=pad_mode, stride=stride).shard(strategy1)
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self.conv2d_weight = Parameter(conv2d_weight, "w1")
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self.bn = BatchNorm2d(8)
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self.bn.bn_train.shard(strategy2)
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self.print = P.Print()
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def construct(self, x, b):
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out = self.conv2d(x, self.conv2d_weight)
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self.print("output is", out)
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out = self.bn(out)
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return out
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_x = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
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_w1 = Tensor(np.ones([8, 16, 2, 2]), dtype=ms.float32)
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_b = Tensor(np.ones([32, 16, 8, 8]), dtype=ms.float32)
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def compile_net(net):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x, _b)
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context.reset_auto_parallel_context()
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@security_off_wrap
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def test_batchnorm_data_parallel():
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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 = ((8, 1, 1, 1), (1, 1, 1, 1))
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strategy2 = ((8, 1, 1, 1), (1,), (1,), (1,), (1,))
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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@security_off_wrap
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def test_batchnorm_model_parallel1():
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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, 1, 1), (2, 2, 1, 1))
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strategy2 = ((2, 1, 2, 2), (1,), (1,), (1,), (1,))
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=1, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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@security_off_wrap
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def test_batchnorm_model_parallel2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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strategy1 = ((2, 2, 2, 2), (2, 2, 1, 1))
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strategy2 = ((1, 8, 1, 1), (8,), (8,), (8,), (8,))
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net = Net(_w1, out_channel=8, kernel_size=2, pad_mode="same", stride=2, strategy1=strategy1, strategy2=strategy2)
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compile_net(net)
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class Net2(Cell):
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def __init__(self, strategy1=None, strategy2=None):
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super().__init__()
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self.bn = BatchNorm1d(8)
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self.bn.bn_train.shard(strategy1)
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self.relu = P.ReLU().shard(strategy2)
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def construct(self, x, b):
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out = self.bn(x)
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out = self.relu(out)
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return out
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_x1 = Tensor(np.ones([32, 8]), dtype=ms.float32)
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_b1 = Tensor(np.ones([32, 8]), dtype=ms.float32)
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def compile_net2(net):
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optimizer = Momentum(net.trainable_params(), learning_rate=0.1, momentum=0.9)
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train_net = TrainOneStepCell(net, optimizer)
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train_net.set_auto_parallel()
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train_net.set_train()
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_cell_graph_executor.compile(train_net, _x1, _b1)
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context.reset_auto_parallel_context()
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def test_batchnorm1d_data_parallel():
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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 = ((8, 1), (1,), (1,), (1,), (1,))
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strategy2 = ((8, 1),)
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net = Net2(strategy1=strategy1, strategy2=strategy2)
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compile_net2(net)
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def test_batchnorm1d_model_parallel1():
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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, 8), (8,), (8,), (8,), (8,))
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strategy2 = ((1, 8),)
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net = Net2(strategy1=strategy1, strategy2=strategy2)
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compile_net2(net)
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def test_batchnorm1d_model_parallel2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=32, global_rank=0)
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strategy1 = ((2, 4), (4,), (4,), (4,), (4,))
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strategy2 = ((2, 4),)
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net = Net2(strategy1=strategy1, strategy2=strategy2)
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compile_net2(net)
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