forked from mindspore-Ecosystem/mindspore
71 lines
2.6 KiB
Python
71 lines
2.6 KiB
Python
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# 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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class NetWithLoss(nn.Cell):
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def __init__(self, network, strategy3):
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super(NetWithLoss, self).__init__()
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self.loss = P.SoftmaxCrossEntropyWithLogits().set_strategy(strategy3)
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self.network = network
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def construct(self, x, y, bias, label):
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predict = self.network(x, y, bias)
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return self.loss(predict, label)[0]
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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, label):
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return C.grad_all(self.network)(x, y, bias, label)
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def test_linear():
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class Net(nn.Cell):
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def __init__(self, strategy0, strategy1, strategy2):
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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.add = P.TensorAdd().set_strategy(strategy1)
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self.gelu = P.Gelu().set_strategy(strategy2)
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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.add(out, bias)
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out = self.gelu(out)
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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 = ((2, 4), (2, 4))
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strategy1 = ((2, 4), (4, ))
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strategy2 = ((2, 8), )
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strategy3 = ((16, 1), (16, 1))
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net = GradWrap(NetWithLoss(Net(strategy0, strategy1, strategy2), strategy3))
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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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label = Tensor(np.ones([64, 64]), dtype=ms.float32)
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_executor.compile(net, x, y, bias, label)
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