forked from mindspore-Ecosystem/mindspore
202 lines
6.7 KiB
Python
202 lines
6.7 KiB
Python
# Copyright 2020-2022 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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""" test optimizer """
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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 Tensor
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from mindspore.common.parameter import Parameter
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from mindspore.nn.optim import Optimizer, SGD, Adam, AdamWeightDecay
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from mindspore import context
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class IterableObjc:
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def __iter__(self):
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cont = 0
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while cont < 3:
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cont += 1
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yield Parameter(Tensor(cont), name="cont" + str(cont))
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params = IterableObjc()
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class TestOptimizer():
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def test_init(self):
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Optimizer(0.5, params)
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with pytest.raises(ValueError):
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Optimizer(-0.5, params)
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def test_construct(self):
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opt_2 = Optimizer(0.5, params)
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with pytest.raises(NotImplementedError):
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opt_2.construct()
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class TestAdam():
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""" TestAdam definition """
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def test_init(self):
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Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
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use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
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def test_construct(self):
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with pytest.raises(RuntimeError):
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gradient = Tensor(np.zeros([1, 2, 3]))
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adam = Adam(params, learning_rate=1e-3, beta1=0.9, beta2=0.999, eps=1e-8, use_locking=False,
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use_nesterov=False, weight_decay=0.0, loss_scale=1.0)
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adam(gradient)
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class TestSGD():
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""" TestSGD definition """
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def test_init(self):
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with pytest.raises(ValueError):
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SGD(params, learning_rate=0.1, momentum=-0.1, dampening=0, weight_decay=0, nesterov=False)
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with pytest.raises(ValueError):
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SGD(params, learning_rate=0.12, momentum=-0.1, dampening=0, weight_decay=0, nesterov=False)
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SGD(params)
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class TestNullParam():
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""" TestNullParam definition """
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def test_optim_init(self):
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with pytest.raises(ValueError):
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Optimizer(0.1, None)
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def test_AdamWightDecay_init(self):
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with pytest.raises(ValueError):
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AdamWeightDecay(None)
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def test_Sgd_init(self):
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with pytest.raises(ValueError):
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SGD(None)
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class TestUnsupportParam():
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""" TestUnsupportParam definition """
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def test_optim_init(self):
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with pytest.raises(TypeError):
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Optimizer(0.1, (1, 2, 3))
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def test_AdamWightDecay_init(self):
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with pytest.raises(TypeError):
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AdamWeightDecay(9)
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def test_Sgd_init(self):
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with pytest.raises(TypeError):
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paramsTensor = Parameter(Tensor(np.zeros([1, 2, 3])), "x")
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SGD(paramsTensor)
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def test_not_flattened_params():
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"""
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Feature: Flatten weights.
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Description: Optimizer with not flattened parameters.
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Expectation: The Optimizer works as expected.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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p1 = Parameter(Tensor([1], ms.float32), name="p1")
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p2 = Parameter(Tensor([2], ms.float32), name="p2")
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p3 = Parameter(Tensor([3], ms.float32), name="p3")
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paras = [p1, p2, p3]
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opt = Optimizer(0.1, paras)
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assert not opt._use_flattened_params # pylint: disable=W0212
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assert len(opt.parameters) == 3
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assert len(opt.cache_enable) == 3
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assert id(opt.parameters) == id(opt._parameters) # pylint: disable=W0212
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def test_with_flattened_params():
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"""
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Feature: Flatten weights.
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Description: Optimizer with flattened parameters.
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Expectation: The Optimizer works as expected.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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p1 = Parameter(Tensor([1], ms.float32), name="p1")
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p2 = Parameter(Tensor([2], ms.float32), name="p2")
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p3 = Parameter(Tensor([3], ms.float32), name="p3")
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paras = [p1, p2, p3]
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Tensor._flatten_tensors(paras) # pylint: disable=W0212
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opt = Optimizer(0.1, paras)
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assert opt._use_flattened_params # pylint: disable=W0212
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assert len(opt.parameters) == 3
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assert len(opt._parameters) == 1 # pylint: disable=W0212
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assert len(opt.cache_enable) == 1
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flat_param = opt._parameters[0] # pylint: disable=W0212
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assert flat_param.dtype == ms.float32
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assert flat_param.shape == [3]
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assert flat_param._size == 3 # pylint: disable=W0212
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assert np.allclose(flat_param.asnumpy(), np.array([1, 2, 3]))
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p1.asnumpy()[0] = 6
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p2.asnumpy()[0] = 6
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p3.asnumpy()[0] = 6
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assert np.allclose(flat_param.asnumpy(), np.array([6, 6, 6]))
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def test_adam_with_flattened_params():
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"""
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Feature: Flatten weights.
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Description: Adam optimizer with flattened parameters.
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Expectation: It is ok to compile the optimizer.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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p1 = Parameter(Tensor([1], ms.float32), name="p1")
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p2 = Parameter(Tensor([2], ms.float32), name="p2")
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p3 = Parameter(Tensor([3], ms.float32), name="p3")
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paras = [p1, p2, p3]
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Tensor._flatten_tensors(paras) # pylint: disable=W0212
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adam = Adam(paras)
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g1 = Tensor([0.1], ms.float32)
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g2 = Tensor([0.2], ms.float32)
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g3 = Tensor([0.3], ms.float32)
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grads = (g1, g2, g3)
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adam(grads)
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def test_adam_with_flattened_params_fusion_size():
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"""
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Feature: Flatten weights.
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Description: Adam optimizer with flattened parameters and fusion size.
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Expectation: It is ok to compile the optimizer.
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"""
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context.set_context(mode=context.GRAPH_MODE)
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p1 = Parameter(Tensor([1], ms.float32), name="p1")
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p2 = Parameter(Tensor([2], ms.float32), name="p2")
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p3 = Parameter(Tensor([3], ms.float32), name="p3")
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p4 = Parameter(Tensor([4], ms.float32), name="p4")
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p5 = Parameter(Tensor([5], ms.float32), name="p5")
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paras = [p1, p2, p3, p4, p5]
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Tensor._flatten_tensors(paras, fusion_size=12) # pylint: disable=W0212
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adam = Adam(paras)
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assert adam._use_flattened_params # pylint: disable=W0212
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assert adam._grad_fusion_size == 12 # pylint: disable=W0212
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assert len(adam.parameters) == 5
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assert len(adam._parameters) == 2 # pylint: disable=W0212
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g1 = Tensor([0.1], ms.float32)
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g2 = Tensor([0.2], ms.float32)
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g3 = Tensor([0.3], ms.float32)
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g4 = Tensor([0.4], ms.float32)
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g5 = Tensor([0.5], ms.float32)
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grads = (g1, g2, g3, g4, g5)
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adam(grads)
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