forked from mindspore-Ecosystem/mindspore
59 lines
2.1 KiB
Python
59 lines
2.1 KiB
Python
# Copyright 2019 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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from mindspore import context
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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 mindspore import Tensor
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from tests.ut.python.ops.test_math_ops import VirtualLoss
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import mindspore as ms
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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 functional as F
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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, bias):
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return C.grad_all(self.network)(x, y, bias)
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def test_sum_as_loss():
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class Net(nn.Cell):
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def __init__(self, strategy0, strategy1):
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super().__init__()
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self.fc_nobias = P.MatMul(transpose_b=True).set_strategy(strategy0)
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self.reduce_sum = P.ReduceSum(keep_dims=False).set_strategy(strategy1)
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self.mul = P.Mul().set_strategy(strategy=((), ()))
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def construct(self, x, y, bias):
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out = self.fc_nobias(x, y)
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out = self.reduce_sum(out, (0,1))
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out = self.mul(out, F.scalar_to_array(2.0))
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return out
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context.set_auto_parallel_context(device_num=16, global_rank=0)
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strategy0 = ((4, 1), (4, 1))
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strategy1 = ((4, 1), )
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net = GradWrap(Net(strategy0, strategy1))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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x = Tensor(np.ones([64, 32]), dtype=ms.float32)
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y = Tensor(np.ones([64, 32]), dtype=ms.float32)
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bias = Tensor(np.ones([64]), dtype=ms.float32)
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_executor.compile(net, x, y, bias)
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