Xiaoda Zhang
4 years ago
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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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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.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 mindspore import context, Tensor, Parameter
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from mindspore.parallel import set_algo_parameters
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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):
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predict = self.network(x)
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return self.loss(predict)
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class GradWarp(nn.Cell):
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def __init__(self, network):
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super(GradWarp, self).__init__()
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self.network = network
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def construct(self, x):
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return grad_all(self.network)(x)
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class Net(nn.Cell):
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def __init__(self, strategy_dict=None):
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super(Net, self).__init__()
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self.mul1 = P.Mul()
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self.mul2 = P.Mul()
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self.mul3 = P.Mul()
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self.mul4 = P.Mul()
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self.relu1 = P.ReLU()
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self.relu2 = P.ReLU()
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self.ba1 = P.BiasAdd()
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self.add = P.TensorAdd()
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self.weight = Parameter(Tensor(np.ones([128, 1000]), dtype=ms.float32), name="weight")
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self.bias = Parameter(Tensor(np.ones([1000]), dtype=ms.float32), name="bias")
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if strategy_dict is not None:
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self.mul1.shard(strategy_dict["mul1"])
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self.mul2.shard(strategy_dict["mul2"])
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self.relu1.shard(strategy_dict["relu1"])
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self.relu2.shard(strategy_dict["relu2"])
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self.ba1.shard(strategy_dict["bias_add"])
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self.add.shard(strategy_dict["add"])
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def construct(self, inputs):
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x = self.mul1(inputs, self.weight)
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y = self.relu1(x)
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y = self.mul2(y, self.weight)
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z = self.mul3(x, self.weight)
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z = self.ba1(z, self.bias)
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x = self.add(y, z)
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x = self.mul4(x, self.weight)
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x = self.relu2(x)
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return x
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def test_star_strategy_consistency1():
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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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set_algo_parameters(fully_use_devices=False)
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x = Tensor(np.ones([128, 1000]), dtype=ms.float32)
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strategy_dict = {"mul1": ((2, 4), (2, 4)), "mul2": None, "relu1": ((4, 1),), "bias_add": ((8, 1), (1,)),
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"relu2": ((2, 2),), "add": ((1, 8), (1, 8))}
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net = NetWithLoss(Net(strategy_dict))
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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, phase='train')
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def test_star_strategy_consistency2():
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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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set_algo_parameters(fully_use_devices=False)
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x = Tensor(np.ones([128, 1000]), dtype=ms.float32)
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strategy_dict = {"mul1": None, "mul2": ((1, 4), (1, 4)), "relu1": ((2, 1),), "bias_add": ((4, 2), (2,)),
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"relu2": ((2, 2),), "add": ((8, 1), (8, 1))}
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net = NetWithLoss(Net(strategy_dict))
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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, phase='train')
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def test_star_strategy_consistency3():
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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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set_algo_parameters(fully_use_devices=False)
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x = Tensor(np.ones([128, 1000]), dtype=ms.float32)
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strategy_dict = {"mul1": None, "mul2": None, "relu1": ((8, 1),), "bias_add": ((1, 4), (4,)),
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"relu2": ((4, 1),), "add": ((2, 2), (2, 2))}
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net = NetWithLoss(Net(strategy_dict))
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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, phase='train')
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def test_star_strategy_consistency4():
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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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set_algo_parameters(fully_use_devices=False)
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x = Tensor(np.ones([128, 1000]), dtype=ms.float32)
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strategy_dict = {"mul1": ((1, 8), (1, 8)), "mul2": ((1, 4), (1, 4)), "relu1": None, "bias_add": None,
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"relu2": None, "add": None}
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net = NetWithLoss(Net(strategy_dict))
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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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with pytest.raises(RuntimeError):
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_executor.compile(net, x, phase='train')
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