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90 lines
2.6 KiB
90 lines
2.6 KiB
# 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 _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 AddRelu(nn.Cell):
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def __init__(self, strategy0=None, strategy1=None):
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super(AddRelu, self).__init__()
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self.add = P.Add().shard(strategy=strategy0)
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self.relu = P.ReLU().shard(strategy=strategy1)
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def construct(self, x, z):
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out = self.add(x, z)
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return self.relu(out)
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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, z):
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predict = self.network(x, z)
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return self.loss(predict)
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class Grad(nn.Cell):
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def __init__(self, network):
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super(Grad, 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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def compile_net(net, x, y):
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net.set_auto_parallel()
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net.set_train()
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_executor.compile(net, x, y)
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def test_add_relu_stride_slice():
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context.set_auto_parallel_context(device_num=8, global_rank=7)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy0 = ((1, 1), (1, 1))
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strategy1 = ((8, 1),)
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net = Grad(NetWithLoss(AddRelu(strategy0, strategy1)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32]), dtype=ms.float32)
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compile_net(net, x, y)
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def test_add_relu_all_gather():
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context.set_auto_parallel_context(device_num=8, global_rank=7)
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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strategy0 = ((8, 1), (8, 1))
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strategy1 = ((1, 1),)
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net = Grad(NetWithLoss(AddRelu(strategy0, strategy1)))
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([128, 32]), dtype=ms.float32)
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compile_net(net, x, y)
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