forked from mindspore-Ecosystem/mindspore
132 lines
4.0 KiB
Python
132 lines
4.0 KiB
Python
# Copyright 2021 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 pytest
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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 _cell_graph_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 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):
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predict = self.network(x, y)
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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):
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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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_cell_graph_executor.compile(net, x, y)
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def test_cumsum_semi():
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"""
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Feature: CumSum operatorInfo in parallel.
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Description: MatMul->CumSum
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Expectation: Currently, CumSum does not support the axis dimension split. compile done without error.
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"""
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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().shard(((16, 1), (1, 1)))
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self.cumsum = P.CumSum().shard(((16, 1),))
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def construct(self, x, y):
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out = self.matmul1(x, y)
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out = self.cumsum(out, 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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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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with pytest.raises(RuntimeError):
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compile_net(net, x, y)
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def test_cumsum_semi2():
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"""
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Feature: CumSum operatorInfo in parallel.
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Description: MatMul->CumSum
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Expectation: Compile done without error.
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"""
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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().shard(((16, 1), (1, 1)))
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self.cumsum = P.CumSum().shard(((1, 16),))
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def construct(self, x, y):
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out = self.matmul1(x, y)
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out = self.cumsum(out, 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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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel")
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compile_net(net, x, y)
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def test_cumsum_auto():
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"""
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Feature: CumSum operatorInfo in parallel.
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Description: MatMul->CumSum
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Expectation: Compile done without error.
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"""
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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().shard(((16, 1), (1, 1)))
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self.cumsum = P.CumSum()
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def construct(self, x, y):
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out = self.matmul1(x, y)
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out = self.cumsum(out, -1)
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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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net = GradWrap(NetWithLoss(Net()))
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context.set_auto_parallel_context(parallel_mode="auto_parallel")
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compile_net(net, x, y)
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