chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2018 PaddlePaddle Authors. All Rights Reserved.
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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 copy
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import unittest
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import numpy as np
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import paddle
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from paddle.base.dygraph import guard
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from paddle.base.executor import Executor
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from paddle.base.framework import Variable, default_main_program
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paddle.enable_static()
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main_program = default_main_program()
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class ParameterChecks(unittest.TestCase):
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def test_parameter(self):
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with paddle.pir_utils.OldIrGuard():
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shape = [784, 100]
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val = 1.0625
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b = main_program.global_block()
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param = b.create_parameter(
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name='fc.w',
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shape=shape,
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dtype='float32',
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initializer=paddle.nn.initializer.Constant(val),
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)
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self.assertIsNotNone(param)
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self.assertEqual('fc.w', param.name)
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self.assertEqual((784, 100), param.shape)
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self.assertEqual(paddle.float32, param.dtype)
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self.assertEqual(0, param.block.idx)
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exe = Executor(paddle.CPUPlace())
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p = exe.run(main_program, fetch_list=[param])[0]
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np.testing.assert_array_equal(p, np.ones(shape) * val)
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zero_dim_param = b.create_parameter(
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name='x', shape=[], dtype='float32'
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)
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self.assertEqual(zero_dim_param.shape, ())
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def test_parambase(self):
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with guard():
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linear = paddle.nn.Linear(10, 10)
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param = linear.weight
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memo = {}
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param_copy = copy.deepcopy(param, memo)
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self.assertEqual(param_copy.shape, param.shape)
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self.assertEqual(param_copy.type, param.type)
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self.assertEqual(param_copy.dtype, param.dtype)
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self.assertEqual(str(param_copy.place), str(param.place))
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np.testing.assert_array_equal(param_copy.numpy(), param.numpy())
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self.assertEqual(param_copy.optimize_attr, param.optimize_attr)
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self.assertEqual(param_copy.regularizer, param.regularizer)
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self.assertEqual(
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param_copy.do_model_average, param.do_model_average
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)
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self.assertEqual(param_copy.need_clip, param.need_clip)
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self.assertEqual(param_copy.is_distributed, param.is_distributed)
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pram_copy2 = copy.deepcopy(param, memo)
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self.assertEqual(id(param_copy), id(pram_copy2))
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def test_create_0_size_param(self):
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with guard():
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shape = [0, 4]
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for dtype in [
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paddle.float32,
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paddle.float64,
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]:
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zero_size_param = paddle.create_parameter(
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shape,
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dtype,
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)
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self.assertEqual(zero_size_param.shape, shape)
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self.assertEqual(zero_size_param.data_ptr(), 0)
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self.assertEqual(zero_size_param.strides, [4, 1])
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def func_exception(self):
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b = main_program.global_block()
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with self.assertRaises(ValueError):
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b.create_parameter(
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name='test', shape=None, dtype='float32', initializer=None
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)
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with self.assertRaises(ValueError):
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b.create_parameter(
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name='test', shape=[1], dtype=None, initializer=None
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)
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with self.assertRaises(ValueError):
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b.create_parameter(
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name='test', shape=[], dtype='float32', initializer=None
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)
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with self.assertRaises(ValueError):
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b.create_parameter(
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name='test', shape=[-1], dtype='float32', initializer=None
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)
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def test_parambase_to_vector(self):
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with guard():
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initializer = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(3.0)
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)
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linear1 = paddle.nn.Linear(10, 15, initializer)
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vec = paddle.nn.utils.parameters_to_vector(linear1.parameters())
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self.assertEqual(linear1.weight.shape, [10, 15])
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self.assertEqual(linear1.bias.shape, [15])
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self.assertTrue(isinstance(vec, Variable))
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self.assertTrue(vec.shape, [165])
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linear2 = paddle.nn.Linear(10, 15)
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paddle.nn.utils.vector_to_parameters(vec, linear2.parameters())
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self.assertEqual(linear2.weight.shape, [10, 15])
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self.assertEqual(linear2.bias.shape, [15])
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np.testing.assert_array_equal(
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linear1.weight.numpy(), linear2.weight.numpy()
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)
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np.testing.assert_array_equal(
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linear1.bias.numpy(), linear2.bias.numpy()
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)
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self.assertTrue(linear2.weight.is_leaf, True)
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self.assertTrue(linear2.bias.is_leaf, True)
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def test_parambase_to_vector_zero(self):
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with guard():
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initializer = paddle.ParamAttr(
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initializer=paddle.nn.initializer.Constant(3.0)
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)
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linear1 = paddle.nn.Linear(0, 15, initializer)
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vec = paddle.nn.utils.parameters_to_vector(linear1.parameters())
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self.assertEqual(linear1.weight.shape, [0, 15])
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self.assertEqual(linear1.bias.shape, [15])
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self.assertTrue(isinstance(vec, Variable))
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self.assertEqual(vec.shape, [15])
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class TestVectorToParam(unittest.TestCase):
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def test_vector_to_param_zerosize(self):
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# test the case that the parameters contains zero size tensor
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with guard():
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vec = paddle.randn([18], dtype='float32')
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param1 = paddle.empty([5], dtype='float32')
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param2 = paddle.empty([5], dtype='float32')
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param3 = paddle.empty([8], dtype='float32')
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param4 = paddle.empty([0], dtype='float32')
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params = [param1, param2, param3, param4]
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paddle.nn.utils.vector_to_parameters(vec, params)
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# concat the parameters and get the original vector
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vec_ = paddle.concat(params, axis=0)
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np.testing.assert_array_equal(vec_.numpy(), vec.numpy())
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def test_vector_to_param1(self):
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# test the case that the sum of parameter's elements less than vector elements
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with guard():
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vec = paddle.randn([18], dtype='float32')
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param1 = paddle.empty([5], dtype='float32')
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param2 = paddle.empty([5], dtype='float32')
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param3 = paddle.empty([7], dtype='float32')
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params = [param1, param2, param3]
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paddle.nn.utils.vector_to_parameters(vec, params)
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# concat the parameters and get the original vector
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vec_ = paddle.concat(params, axis=0)
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np.testing.assert_array_equal(vec_.numpy(), vec[:17].numpy())
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def test_vector_to_param2(self):
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# test the case that the sum of parameter's elements grater than vector elements
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def _test_vector_to_param():
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with guard():
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vec = paddle.randn([18], dtype='float32')
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param1 = paddle.empty([5], dtype='float32')
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param2 = paddle.empty([5], dtype='float32')
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param3 = paddle.empty([9], dtype='float32')
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params = [param1, param2, param3]
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paddle.nn.utils.vector_to_parameters(vec, params)
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self.assertRaises(ValueError, _test_vector_to_param)
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if __name__ == '__main__':
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unittest.main()
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