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85 lines
3.0 KiB
85 lines
3.0 KiB
# 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 import dtype as mstype
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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.parallel._utils import _reset_op_id as reset_op_id
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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, z, w):
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predict = self.network(x, y, z, w)
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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, z, w):
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return C.grad_all(self.network)(x, y, z, w)
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# model_parallel test
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def test_double_star_graph():
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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.matmul3 = P.MatMul()
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self.cast1 = P.Cast()
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self.cast2 = P.Cast()
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def construct(self, x, y, z, w):
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m1_result = self.matmul1(x, y)
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m2_result = self.matmul2(z, w)
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m3_result = self.matmul3(self.cast1(m2_result, mstype.float16), self.cast2(m1_result, mstype.float16))
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return m3_result
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size = 8
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context.set_auto_parallel_context(device_num=size, global_rank=0)
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x = Tensor(np.ones([32, 8]), dtype=ms.float32)
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y = Tensor(np.ones([8, 16]), dtype=ms.float32)
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z = Tensor(np.ones([8, 16]), dtype=ms.float32)
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w = Tensor(np.ones([16, 32]), 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, z, w, phase='train')
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strategies = _executor._get_strategy(net)
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expected_strategies = {'Default/network-Net/Cast-op1': [[8, 1]],
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'Default/network-Net/Cast-op3': [[1, 8]],
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'Default/network-Net/MatMul-op2': [[8, 1], [1, 1]],
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'Default/network-Net/MatMul-op4': [[1, 1], [1, 8]],
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'Default/network-Net/MatMul-op0': [[1, 8], [8, 1]]}
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assert strategies == expected_strategies
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