chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
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# Copyright (c) 2021 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 unittest
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import numpy as np
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import parameterize
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import scipy
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from distribution import config
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import paddle
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from paddle.distribution.multivariate_normal import MultivariateNormal
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paddle.enable_static()
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'covariance_matrix'),
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[
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(
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'one-batch',
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parameterize.xrand((2,), dtype='float32', min=1, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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),
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(
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'multi-batch',
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parameterize.xrand((2, 3), dtype='float64', min=-2, max=-1),
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np.array([[6.0, 2.5, 3.0], [2.5, 4.0, 5.0], [3.0, 5.0, 7.0]]),
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),
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],
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)
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class TestMVN(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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covariance_matrix = paddle.static.data(
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'covariance_matrix',
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self.covariance_matrix.shape,
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self.covariance_matrix.dtype,
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)
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dist = MultivariateNormal(
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loc=loc, covariance_matrix=covariance_matrix
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)
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mean = dist.mean
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var = dist.variance
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entropy = dist.entropy()
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mini_samples = dist.sample(shape=())
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large_samples = dist.sample(shape=(10000,))
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fetch_list = [mean, var, entropy, mini_samples, large_samples]
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feed = {'loc': self.loc, 'covariance_matrix': self.covariance_matrix}
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executor.run(startup_program)
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[
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self.mean,
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self.var,
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self.entropy,
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self.mini_samples,
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self.large_samples,
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] = executor.run(main_program, feed=feed, fetch_list=fetch_list)
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def test_mean(self):
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self.assertEqual(str(self.mean.dtype).split('.')[-1], self.loc.dtype)
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np.testing.assert_allclose(
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self.mean,
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self._np_mean(),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_variance(self):
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self.assertEqual(str(self.var.dtype).split('.')[-1], self.loc.dtype)
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np.testing.assert_allclose(
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self.var,
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self._np_variance(),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_entropy(self):
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self.assertEqual(str(self.entropy.dtype).split('.')[-1], self.loc.dtype)
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np.testing.assert_allclose(
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self.entropy,
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self._np_entropy(),
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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def test_sample(self):
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self.assertEqual(
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str(self.mini_samples.dtype).split('.')[-1], self.loc.dtype
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)
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sample_mean = self.large_samples.mean(axis=0)
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sample_variance = self.large_samples.var(axis=0)
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# `atol` and `rtol` refer to ``test_distribution_normal`` and ``test_distribution_lognormal``
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np.testing.assert_allclose(sample_mean, self.mean, atol=0, rtol=0.1)
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np.testing.assert_allclose(sample_variance, self.var, atol=0, rtol=0.1)
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def _np_variance(self):
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batch_shape = np.broadcast_shapes(
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self.covariance_matrix.shape[:-2], self.loc.shape[:-1]
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)
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event_shape = self.loc.shape[-1:]
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return np.broadcast_to(
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np.diag(self.covariance_matrix), batch_shape + event_shape
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)
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def _np_mean(self):
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return self.loc
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def _np_entropy(self):
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if len(self.loc.shape) <= 1:
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return scipy.stats.multivariate_normal.entropy(
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self.loc, self.covariance_matrix
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)
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else:
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return np.apply_along_axis(
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lambda i: scipy.stats.multivariate_normal.entropy(
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i, self.covariance_matrix
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),
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axis=1,
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arr=self.loc,
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)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'loc', 'covariance_matrix', 'value'),
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[
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(
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'value-same-shape',
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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parameterize.xrand((2,), dtype='float32', min=-5, max=5),
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),
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(
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'value-broadcast-shape',
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parameterize.xrand((2,), dtype='float64', min=-2, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]),
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parameterize.xrand((3, 2), dtype='float64', min=-5, max=5),
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),
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],
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)
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class TestMVNProbs(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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loc = paddle.static.data('loc', self.loc.shape, self.loc.dtype)
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covariance_matrix = paddle.static.data(
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'covariance_matrix',
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self.covariance_matrix.shape,
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self.covariance_matrix.dtype,
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)
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value = paddle.static.data(
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'value', self.value.shape, self.value.dtype
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)
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dist = MultivariateNormal(
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loc=loc, covariance_matrix=covariance_matrix
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)
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pmf = dist.prob(value)
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feed = {
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'loc': self.loc,
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'covariance_matrix': self.covariance_matrix,
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'value': self.value,
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}
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fetch_list = [pmf]
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executor.run(startup_program)
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[self.pmf] = executor.run(
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main_program, feed=feed, fetch_list=fetch_list
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)
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def test_prob(self):
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if len(self.value.shape) <= 1:
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scipy_pdf = scipy.stats.multivariate_normal.pdf(
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self.value, self.loc, self.covariance_matrix
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)
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else:
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scipy_pdf = np.apply_along_axis(
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lambda i: scipy.stats.multivariate_normal.pdf(
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i, self.loc, self.covariance_matrix
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),
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axis=1,
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arr=self.value,
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)
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np.testing.assert_allclose(
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self.pmf,
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scipy_pdf,
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rtol=config.RTOL.get(str(self.loc.dtype)),
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atol=config.ATOL.get(str(self.loc.dtype)),
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)
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@parameterize.place(config.DEVICES)
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@parameterize.parameterize_cls(
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(parameterize.TEST_CASE_NAME, 'mu_1', 'sig_1', 'mu_2', 'sig_2'),
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[
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(
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'one-batch',
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[2.0, 1.0], [1.0, 2.0]]).astype('float32'),
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parameterize.xrand((2,), dtype='float32', min=-2, max=2),
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np.array([[3.0, 2.0], [2.0, 3.0]]).astype('float32'),
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)
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],
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)
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class TestMVNKL(unittest.TestCase):
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def setUp(self):
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startup_program = paddle.static.Program()
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main_program = paddle.static.Program()
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executor = paddle.static.Executor(self.place)
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with paddle.static.program_guard(main_program, startup_program):
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mu_1 = paddle.static.data('mu_1', self.mu_1.shape)
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sig_1 = paddle.static.data('sig_1', self.sig_1.shape)
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mu_2 = paddle.static.data('mu_2', self.mu_2.shape)
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sig_2 = paddle.static.data('sig_2', self.sig_2.shape)
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dist1 = MultivariateNormal(loc=mu_1, covariance_matrix=sig_1)
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dist2 = MultivariateNormal(loc=mu_2, covariance_matrix=sig_2)
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kl_dist1_dist2 = dist1.kl_divergence(dist2)
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feed = {
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'mu_1': self.mu_1,
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'sig_1': self.sig_1,
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'mu_2': self.mu_2,
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'sig_2': self.sig_2,
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}
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fetch_list = [kl_dist1_dist2]
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executor.run(startup_program)
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[self.kl_dist1_dist2] = executor.run(
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main_program, feed=feed, fetch_list=fetch_list
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)
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def test_kl_divergence(self):
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kl0 = self.kl_dist1_dist2
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kl1 = self.kl_divergence()
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batch_shape = np.broadcast_shapes(
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self.sig_1.shape[:-2], self.mu_1.shape[:-1]
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)
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self.assertEqual(tuple(kl0.shape), batch_shape)
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self.assertEqual(tuple(kl1.shape), batch_shape)
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np.testing.assert_allclose(kl0, kl1, rtol=0.1, atol=0.1)
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def kl_divergence(self):
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t1 = np.array(np.linalg.cholesky(self.sig_1))
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t2 = np.array(np.linalg.cholesky(self.sig_2))
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half_log_det_1 = np.log(t1.diagonal(axis1=-2, axis2=-1)).sum(-1)
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half_log_det_2 = np.log(t2.diagonal(axis1=-2, axis2=-1)).sum(-1)
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new_perm = list(range(len(t1.shape)))
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new_perm[-1], new_perm[-2] = new_perm[-2], new_perm[-1]
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cov_mat_1 = np.matmul(t1, t1.transpose(new_perm))
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cov_mat_2 = np.matmul(t2, t2.transpose(new_perm))
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expectation = (
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np.linalg.solve(cov_mat_2, cov_mat_1)
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.diagonal(axis1=-2, axis2=-1)
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.sum(-1)
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)
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tmp = np.linalg.solve(t2, self.mu_1 - self.mu_2)
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expectation += np.matmul(tmp.T, tmp)
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return half_log_det_2 - half_log_det_1 + 0.5 * (expectation - 2.0)
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if __name__ == '__main__':
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unittest.main(argv=[''], verbosity=3, exit=False)
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