!6108 add model with loss test case
Merge pull request !6108 from hanyang/master
This commit is contained in:
Gitee
commit
02c6852699
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# Copyright 2020 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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# ============================================================================
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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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from mindspore.nn.loss import SoftmaxCrossEntropyWithLogits
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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class Net(Cell):
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def __init__(self, mul_weight, strategy1=None, strategy2=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.neg = P.Neg().shard(strategy2)
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self.mul_weight = Parameter(mul_weight, "w1")
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def construct(self, x):
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out = self.mul(x, self.mul_weight)
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out = self.neg(out)
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return out
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_x = Tensor(np.ones([32, 128]), dtype=ms.float32)
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_b = Tensor(np.ones([32]), dtype=ms.int32)
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_w1 = Tensor(np.ones([512, 128]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(save_graphs=True)
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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dataset = Dataset(_x, _b)
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loss = SoftmaxCrossEntropyWithLogits(sparse=True, reduction='mean')
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, loss, optimizer=opt)
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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context.reset_auto_parallel_context()
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def test_neg_data_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((16, 1), (16, 1))
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strategy2 = ((16, 1),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_model_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((1, 16), (1, 16))
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strategy2 = ((1, 16),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_hybrid_parallel():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_auto_parallel():
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context.set_auto_parallel_context(parallel_mode="auto_parallel", device_num=16, global_rank=0)
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net = Net(_w1)
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compile_net(net)
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def test_neg_repeat_calc():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 4), (4, 4))
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strategy2 = ((2, 2),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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def test_neg_repeat_calc2():
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context.set_auto_parallel_context(parallel_mode="semi_auto_parallel", device_num=16, global_rank=0)
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strategy1 = ((4, 2), (4, 2))
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strategy2 = ((4, 4),)
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net = Net(_w1, strategy1, strategy2)
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compile_net(net)
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@ -0,0 +1,191 @@
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# Copyright 2020 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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# ============================================================================
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import numpy as np
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import mindspore as ms
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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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class Net(Cell):
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def __init__(self, weight, weight2, strategy1=None, strategy2=None, is_parameter=True):
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super().__init__()
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self.concat = P.Concat(axis=0).shard(strategy1)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul = P.Mul().shard(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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def construct(self, x, b):
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out = self.concat((self.weight, self.weight2))
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out = self.mul(x, out)
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return out
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class Net2(Cell):
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def __init__(self, weight, strategy1=None, strategy2=None, axis=0):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.concat = P.Concat(axis=axis).shard(strategy2)
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self.weight = Parameter(weight, "w")
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def construct(self, x, b):
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out = self.mul(x, x)
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out = self.concat((out, self.weight))
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return out
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class Net3(Cell):
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def __init__(self, weight, weight2, weight3, strategy1=None, strategy2=None, is_parameter=True):
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super().__init__()
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self.concat = P.Concat(axis=0).shard(strategy1)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul = P.Mul().shard(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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self.weight3 = Parameter(weight3, "w3")
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def construct(self, x, b):
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out = self.concat((self.weight, self.weight2, self.weight3))
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out = self.mul(x, out)
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return out
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_x = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
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_b = Tensor(np.ones([16, 64, 32, 32]), dtype=ms.int32)
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_w1 = Tensor(np.ones([96, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([32, 64, 32]), dtype=ms.float32)
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_w3 = Tensor(np.ones([128, 16, 32]), dtype=ms.float32)
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w1 = Tensor(np.ones([48, 64, 32]), dtype=ms.float32)
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w2 = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
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w3 = Tensor(np.ones([64, 64, 32]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(save_graphs=True)
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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dataset = Dataset(_x, _b)
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, optimizer=opt, amp_level="O2")
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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context.reset_auto_parallel_context()
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def test_concat_parameter():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 4, 2), (1, 4, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
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compile_net(net)
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def test_concat_parameter_no_full_split():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2, 2), (1, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=True)
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compile_net(net)
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def test_concat_tensor_and_parameter():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((1, 2, 2), (1, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net(_w1, _w2, strategy1, strategy2, is_parameter=False)
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compile_net(net)
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def test_concat_output():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 4, 2), (1, 4, 2))
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net = Net2(_w1, strategy1, strategy2)
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compile_net(net)
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def test_concat_output_no_full_split():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 2, 2), (1, 2, 2))
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net = Net2(_w1, strategy1, strategy2)
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compile_net(net)
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def test_concat_no_strategy():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = None
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net = Net2(_w3, strategy1, strategy2, axis=1)
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compile_net(net)
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def test_concat_auto_parallel():
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context.set_auto_parallel_context(
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parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net2(_w2)
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compile_net(net)
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def test_concat_auto_parallel2():
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context.set_auto_parallel_context(
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parallel_mode="auto_parallel", device_num=8, global_rank=0)
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strategy1 = None
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strategy2 = None
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net = Net2(_w3, strategy1, strategy2, axis=1)
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compile_net(net)
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def test_concat_auto_parallel_3_tensor():
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context.set_auto_parallel_context(
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parallel_mode="auto_parallel", device_num=8, global_rank=0)
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net = Net3(w1, w2, w3)
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compile_net(net)
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@ -0,0 +1,208 @@
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# Copyright 2020 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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# ============================================================================
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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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from mindspore import context, Tensor, Parameter
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from mindspore.nn import Cell, Momentum
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from mindspore.ops import operations as P
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from mindspore.train import Model
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from tests.dataset_mock import MindData
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class Dataset(MindData):
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def __init__(self, predict, label, length=3):
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super(Dataset, self).__init__(size=length)
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self.predict = predict
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self.label = label
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self.index = 0
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self.length = length
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def __iter__(self):
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return self
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def __next__(self):
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if self.index >= self.length:
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raise StopIteration
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self.index += 1
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return self.predict, self.label
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def reset(self):
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self.index = 0
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class Net(Cell):
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def __init__(self, weight, w2, begin, end, strides, strategy1=None, strategy2=None, is_parameter=True, mask=0):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.strided_slice = P.StridedSlice(begin_mask=mask).shard(strategy2)
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if is_parameter:
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self.weight = Parameter(weight, "w1")
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else:
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self.weight = weight
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self.mul2 = P.Mul()
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self.weight2 = Parameter(w2, "w2")
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.strided_slice(
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self.weight, self.begin, self.end, self.strides)
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out = self.mul(x, out)
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out = self.mul2(out, self.weight2)
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return out
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class Net2(Cell):
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def __init__(self, weight2, begin, end, strides, strategy1=None, strategy2=None):
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super().__init__()
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self.mul = P.Mul().shard(strategy1)
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self.strided_slice = P.StridedSlice().shard(strategy2)
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self.weight2 = Parameter(weight2, "w2")
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self.begin = begin
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self.end = end
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self.strides = strides
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def construct(self, x, b):
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out = self.mul(x, self.weight2)
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out = self.strided_slice(out, self.begin, self.end, self.strides)
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return out
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_x = Tensor(np.ones([16, 64, 1]), dtype=ms.float32)
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_b = Tensor(np.ones([16, 64, 32]), dtype=ms.float32)
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_w1 = Tensor(np.ones([256, 64, 32]), dtype=ms.float32)
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_w2 = Tensor(np.ones([128, 64, 1]), dtype=ms.float32)
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def compile_net(net):
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context.set_context(save_graphs=True)
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learning_rate = 0.1
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momentum = 0.9
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epoch_size = 2
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dataset = Dataset(_x, _b)
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opt = Momentum(net.trainable_params(), learning_rate, momentum)
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model = Model(net, optimizer=opt, amp_level="O2")
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model.train(epoch_size, dataset, dataset_sink_mode=False)
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context.reset_auto_parallel_context()
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def test_stridedslice_no_fully_fetch_split_error():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((2, 2, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
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strategy1, strategy2, is_parameter=True)
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with pytest.raises(RuntimeError):
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compile_net(net)
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def test_stridedslice_strides_no_1_split_error():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 2, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 2),
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strategy1, strategy2, is_parameter=True)
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with pytest.raises(RuntimeError):
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compile_net(net)
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def test_stridedslice_mask_no_0_split_error():
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context.set_auto_parallel_context(
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
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strategy1 = ((2, 2, 2), (2, 2, 2))
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strategy2 = ((1, 2, 2),)
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net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
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strategy1, strategy2, is_parameter=True, mask=1)
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with pytest.raises(RuntimeError):
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||||
compile_net(net)
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def test_stridedslice_begin_size_smaller():
|
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context.set_auto_parallel_context(
|
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parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
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strategy1 = ((1, 4, 1), (1, 4, 2))
|
||||
strategy2 = ((1, 4, 2),)
|
||||
net = Net(_w1, _w2, (0, 0), (128, 64), (1, 1),
|
||||
strategy1, strategy2, is_parameter=True)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_parameter():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 4, 1), (1, 4, 2))
|
||||
strategy2 = ((1, 4, 2),)
|
||||
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
|
||||
strategy1, strategy2, is_parameter=True)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_tensor():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 4, 1), (1, 4, 2))
|
||||
strategy2 = ((1, 4, 2),)
|
||||
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
|
||||
strategy1, strategy2, is_parameter=False)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_parameter_no_full_split():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 4, 1), (1, 4, 2))
|
||||
strategy2 = ((1, 2, 2),)
|
||||
net = Net(_w1, _w2, (0, 0, 0), (128, 64, 32), (1, 1, 1),
|
||||
strategy1, strategy2, is_parameter=True)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_output():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 8, 1), (1, 8, 1))
|
||||
strategy2 = ((1, 8, 1),)
|
||||
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_output_no_full_split():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 8, 1), (1, 8, 1))
|
||||
strategy2 = ((1, 4, 1),)
|
||||
net = Net2(_w2, (0, 0, 0), (64, 64, 1), (1, 1, 1), strategy1, strategy2)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_no_strategy():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="semi_auto_parallel", device_num=8, global_rank=0)
|
||||
strategy1 = ((1, 8, 1), (1, 8, 1))
|
||||
strategy2 = None
|
||||
net = Net2(_w2, (0, 0, 0), (128, 64, 1), (1, 1, 1), strategy1, strategy2)
|
||||
compile_net(net)
|
||||
|
||||
|
||||
def test_stridedslice_auto_parallel():
|
||||
context.set_auto_parallel_context(
|
||||
parallel_mode="auto_parallel", device_num=8, global_rank=0)
|
||||
net = Net2(_w2, (0, 0, 0), (32, 64, 1), (1, 1, 1))
|
||||
compile_net(net)
|
||||
Loading…
Reference in New Issue