forked from huawei/mindspore2022
169 lines
6.4 KiB
Python
169 lines
6.4 KiB
Python
# Copyright 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 flatten tensors"""
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import numpy as np
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import mindspore as ms
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import mindspore.common.initializer as init
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from mindspore.common import Tensor, Parameter
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from mindspore.nn import Cell
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def test_flatten_tensors_basic():
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"""
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Feature: Flatten tensors.
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Description: Basic function for flatten tensors.
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Expectation: Flatten tensor works as expected.
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"""
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t1 = Tensor(np.ones([2], np.float32))
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t2 = Tensor(np.ones([2, 2], np.float32))
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t3 = Tensor(np.ones([2, 2, 2], np.float32))
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# Before flatten.
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assert not Tensor._is_flattened([t1, t2, t3]) # pylint: disable=W0212
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assert not Tensor._get_flattened_tensors([t1, t2, t3]) # pylint: disable=W0212
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# Do flatten.
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chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
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# After flatten.
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assert len(chunks) == 1
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assert Tensor._is_flattened([t1, t2, t3]) # pylint: disable=W0212
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assert chunks[0].dtype == ms.float32
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assert chunks[0].shape == [14]
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assert np.allclose(chunks[0].asnumpy(), np.ones([14], np.float32))
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# Get flattened tensors.
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chunks2 = Tensor._get_flattened_tensors([t1, t2, t3]) # pylint: disable=W0212
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assert chunks == chunks2
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def test_flatten_tensors_order():
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"""
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Feature: Flatten tensors.
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Description: Test flatten tensors in order.
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Expectation: Flatten tensor works as expected.
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"""
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t1 = Tensor([1], ms.float32)
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t2 = Tensor([2], ms.float32)
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t3 = Tensor([3], ms.float32)
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chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
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chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
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def test_flatten_tensors_float16():
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"""
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Feature: Flatten tensors.
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Description: Test flatten tensors for float16.
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Expectation: Flatten tensor works as expected.
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"""
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t1 = Tensor([1], ms.float16)
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t2 = Tensor([2], ms.float16)
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t3 = Tensor([3], ms.float16)
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chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
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chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
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def test_flatten_tensors_scalar():
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"""
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Feature: Flatten tensors.
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Description: Test flatten tensors for scalar tensor.
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Expectation: Flatten tensor works as expected.
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"""
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t1 = Tensor(1)
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t2 = Tensor(2)
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t3 = Tensor(3)
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chunks = Tensor._flatten_tensors([t1, t2, t3]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3]))
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chunks = Tensor._flatten_tensors([t3, t1, t2]) # pylint: disable=W0212
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assert len(chunks) == 1
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assert np.allclose(chunks[0].asnumpy(), np.array([3, 1, 2]))
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def test_flatten_tensors_dtypes():
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"""
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Feature: Flatten tensors.
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Description: Flatten tensors group by data types.
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Expectation: Flatten tensor works as expected.
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"""
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t1 = Tensor(np.ones([2], np.float32))
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t2 = Tensor(np.ones([2, 2], np.float32))
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t3 = Tensor(np.ones([2, 2, 2], np.float32))
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t4 = Tensor(np.ones([3, 3], np.float64))
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t5 = Tensor(np.ones([3, 3, 3], np.float64))
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chunks = Tensor._flatten_tensors([t1, t2, t3, t4, t5]) # pylint: disable=W0212
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assert len(chunks) == 2
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assert chunks[0].dtype == ms.float32
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assert chunks[0].shape == [14]
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assert np.allclose(chunks[0].asnumpy(), np.ones([14], np.float32))
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assert chunks[1].dtype == ms.float64
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assert chunks[1].shape == [36]
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assert np.allclose(chunks[1].asnumpy(), np.ones([36], np.float64))
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# Different order.
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chunks1 = Tensor._flatten_tensors([t4, t1, t2, t5, t3]) # pylint: disable=W0212
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assert np.allclose(chunks[0].asnumpy(), chunks1[0].asnumpy())
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def test_cell_flatten_weights():
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"""
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Feature: Flatten tensors.
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Description: Flatten weights for Cell.
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Expectation: Flatten weights works as expected.
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"""
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class MyCell(Cell):
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def __init__(self):
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super(MyCell, self).__init__()
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self.para1 = Parameter(Tensor([1, 2], ms.float32))
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self.para2 = Parameter(Tensor([3, 4, 5], ms.float32))
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self.para3 = Parameter(Tensor([6], ms.float32))
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def construct(self, x):
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return x
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net = MyCell()
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assert not Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
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net.flatten_weights()
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assert Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
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chunks = Parameter._get_flattened_tensors(net.trainable_params()) # pylint: disable=W0212
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assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 3, 4, 5, 6]))
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def test_cell_flatten_weights_with_init():
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"""
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Feature: Flatten tensors.
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Description: Flatten weights for Cell with parameter initializer.
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Expectation: Flatten weights works as expected.
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"""
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class MyCell(Cell):
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def __init__(self):
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super(MyCell, self).__init__()
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self.para1 = Parameter(Tensor([1, 2], ms.float32))
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self.para2 = Parameter(init.initializer('ones', [3], ms.float32))
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self.para3 = Parameter(Tensor([6], ms.float32))
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def construct(self, x):
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return x
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net = MyCell()
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assert not Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
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net.flatten_weights()
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assert Parameter._is_flattened(net.trainable_params()) # pylint: disable=W0212
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chunks = Parameter._get_flattened_tensors(net.trainable_params()) # pylint: disable=W0212
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assert np.allclose(chunks[0].asnumpy(), np.array([1, 2, 1, 1, 1, 6]))
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