forked from mindspore-Ecosystem/mindspore
86 lines
2.9 KiB
Python
86 lines
2.9 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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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 mindspore.parallel._utils import _reset_op_id as reset_op_id
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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 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, y, b):
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predict = self.network(x, y, b)
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return self.loss(predict)
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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, b):
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return grad_all(self.network)(x, y, b)
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# core dump, step_auto_parallel should SetInputs for transpose axis
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def test_two_matmul_transpose():
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class Net(nn.Cell):
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def __init__(self):
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super().__init__()
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self.matmul1 = P.MatMul()
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self.matmul2 = P.MatMul()
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self.transpose1 = P.Transpose()
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self.transpose2 = P.Transpose()
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def construct(self, x, y, b):
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out = self.matmul1(x, y)
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out = self.matmul2(out, b)
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out = self.transpose1(out, (1, 0))
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out = self.transpose2(out, (1, 0))
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return out
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size = 16
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([128, 32]), dtype=ms.float32)
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y = Tensor(np.ones([32, 64]), dtype=ms.float32)
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b = Tensor(np.ones([64, 64]), dtype=ms.float32)
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net = NetWithLoss(Net())
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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net.set_auto_parallel()
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reset_op_id()
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_executor.compile(net, x, y, b, phase='train')
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strategies = _executor._get_strategy(net)
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expected_strategies = {'Default/network-Net/Transpose-op0': [[1, 16]],
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'Default/network-Net/Transpose-op1': [[16, 1]],
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'Default/network-Net/MatMul-op2': [[16, 1], [1, 1]],
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'Default/network-Net/MatMul-op3': [[16, 1], [1, 1]]}
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assert strategies == expected_strategies
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