# Copyright 2017 The TensorFlow 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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# ==============================================================================
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"""Tests for random-number generation ops in the XLA JIT compiler."""
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from __future__ import absolute_import
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from __future__ import division
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from __future__ import print_function
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import math
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import numpy as np
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from tensorflow.compiler.tests import xla_test
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from tensorflow.python.framework import dtypes
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from tensorflow.python.ops import array_ops
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from tensorflow.python.ops import math_ops
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from tensorflow.python.ops import random_ops
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from tensorflow.python.ops.distributions import special_math
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from tensorflow.python.platform import googletest
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class RandomOpsTest(xla_test.XLATestCase):
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"""Test cases for random-number generating operators."""
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def _random_types(self):
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return set(self.numeric_types) - set(
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self.complex_types) - {np.uint64, np.int64, np.uint8, np.int8}
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def _testRngIsNotConstant(self, rng, dtype):
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# Tests that 'rng' does not always return the same value.
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with self.cached_session() as sess:
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with self.test_scope():
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x = rng(dtype)
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# The random-number generator, if working correctly, should produce the
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# same output multiple times with low probability.
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y = self.evaluate(x)
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z = self.evaluate(x)
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w = self.evaluate(x)
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# We use exact equality here. If the random-number generator is producing
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# deterministic output, all three outputs will be bitwise identical.
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self.assertTrue((not np.array_equal(y, z)) or
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(not np.array_equal(z, w)) or (not np.array_equal(y, w)))
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def testRandomUniformIsNotConstant(self):
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def rng(dtype):
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dtype = dtypes.as_dtype(dtype)
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return random_ops.random_uniform(shape=[2], dtype=dtype, maxval=dtype.max)
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for dtype in self._random_types():
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self._testRngIsNotConstant(rng, dtype)
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def testRandomNormalIsNotConstant(self):
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def rng(dtype):
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return random_ops.random_normal(shape=[2], dtype=dtype)
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for dtype in self._random_types() & self.float_types:
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self._testRngIsNotConstant(rng, dtype)
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def testRandomUniformIsInRange(self):
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for dtype in self._random_types():
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# TODO (b/112272078): enable bfloat16 for CPU and GPU when the bug is
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# fixed.
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if (self.device in ["XLA_GPU", "XLA_CPU"
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]) and (dtype in [dtypes.bfloat16, dtypes.half]):
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continue
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with self.cached_session() as sess:
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with self.test_scope():
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x = random_ops.random_uniform(
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shape=[1000], dtype=dtype, minval=-2, maxval=33)
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y = self.evaluate(x)
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self.assertTrue((y >= -2).sum() == 1000)
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self.assertTrue((y < 33).sum() == 1000)
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def testTruncatedNormalIsNotConstant(self):
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def rng(dtype):
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return random_ops.truncated_normal(shape=[2], dtype=dtype)
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for dtype in self._random_types() & self.float_types:
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self._testRngIsNotConstant(rng, dtype)
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def testTruncatedNormalIsInRange(self):
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count = 10000000
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# TODO(b/34339814): make this test work with 16 bit float types.
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for dtype in self._random_types() & {dtypes.float32, dtypes.float64}:
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with self.cached_session() as sess:
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with self.test_scope():
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x = random_ops.truncated_normal(shape=[count], dtype=dtype)
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y = self.evaluate(x)
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def normal_cdf(x):
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return .5 * math.erfc(-x / math.sqrt(2))
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def normal_pdf(x):
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return math.exp(-(x**2) / 2.) / math.sqrt(2 * math.pi)
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def probit(x, sess=sess):
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return self.evaluate(special_math.ndtri(x))
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a = -2.
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b = 2.
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mu = 0.
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sigma = 1.
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alpha = (a - mu) / sigma
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beta = (b - mu) / sigma
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z = normal_cdf(beta) - normal_cdf(alpha)
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self.assertEqual((y >= a).sum(), count)
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self.assertEqual((y <= b).sum(), count)
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# For more information on these calculations, see:
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# Burkardt, John. "The Truncated Normal Distribution".
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# Department of Scientific Computing website. Florida State University.
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expected_mean = mu + (normal_pdf(alpha) - normal_pdf(beta)) / z * sigma
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actual_mean = np.mean(y)
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self.assertAllClose(actual_mean, expected_mean, atol=2e-3)
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expected_median = mu + probit(
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(normal_cdf(alpha) + normal_cdf(beta)) / 2.) * sigma
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actual_median = np.median(y)
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self.assertAllClose(actual_median, expected_median, atol=1e-2)
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expected_variance = sigma**2 * (1 + (
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(alpha * normal_pdf(alpha) - beta * normal_pdf(beta)) / z) - (
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(normal_pdf(alpha) - normal_pdf(beta)) / z)**2)
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actual_variance = np.var(y)
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self.assertAllClose(actual_variance, expected_variance, rtol=2*1e-3)
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def testShuffle1d(self):
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with self.cached_session() as sess:
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with self.test_scope():
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x = math_ops.range(1 << 16)
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shuffle = random_ops.random_shuffle(x)
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result = self.evaluate(shuffle)
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expected = range(1 << 16)
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# Compare sets to avoid randomness behavior changes but make sure still
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# have all the values.
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self.assertAllEqual(set(result), set(expected))
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def testShuffle2d(self):
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with self.cached_session() as sess:
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with self.test_scope():
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x = array_ops.diag(math_ops.range(20))
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shuffle = random_ops.random_shuffle(x)
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result = self.evaluate(shuffle)
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expected = np.diag(range(20)).flatten()
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# Compare sets to avoid randomness behavior changes but make sure still
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# have all the values.
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self.assertAllEqual(len(result.flatten()), len(expected))
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self.assertAllEqual(set(result.flatten()), set(expected))
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if __name__ == '__main__':
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googletest.main()
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