# Copyright 2019 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 stateful random-number generation ops."""
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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.client import device_lib
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from tensorflow.python.eager import def_function
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from tensorflow.python.framework import dtypes
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from tensorflow.python.framework import errors_impl
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from tensorflow.python.framework import ops
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from tensorflow.python.framework import test_util
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from tensorflow.python.ops import gen_stateful_random_ops
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from tensorflow.python.ops import stateful_random_ops as \
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random
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from tensorflow.python.ops import variables
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from tensorflow.python.platform import test
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def xla_device_name():
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devices = device_lib.list_local_devices()
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def find_type(device_type):
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for d in devices:
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if d.device_type == device_type:
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return d.name
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return None
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name = find_type("TPU") or find_type("XLA_GPU") or find_type("XLA_CPU")
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if name is None:
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raise ValueError(
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"Can't find any XLA device. Available devices:\n%s" % devices)
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return str(name)
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class StatefulRandomOpsTest(xla_test.XLATestCase):
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"""Test cases for stateful random-number generator operators."""
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@test_util.run_v2_only
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def testSimple(self):
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"""A simple test.
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"""
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=0, algorithm=random.RNG_ALG_THREEFRY)
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gen.normal(shape=(3,))
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gen.uniform(shape=(3,), minval=0, maxval=10, dtype=dtypes.uint32)
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gen.uniform_full_int(shape=(3,))
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@test_util.run_v2_only
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def testDefun(self):
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"""Test for defun.
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"""
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=0, algorithm=random.RNG_ALG_THREEFRY)
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@def_function.function
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def f():
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x = gen.normal(shape=(3,))
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y = gen.uniform(shape=(3,), minval=0, maxval=10, dtype=dtypes.uint32)
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z = gen.uniform_full_int(shape=(3,))
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return (x, y, z)
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f()
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@test_util.run_v2_only
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def testThreefry2x32(self):
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"""Tests ThreeFry2x32 conforms to known results.
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"""
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# Based on
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# https://github.com/google/jax/blob/8565a3486adf16beb388b2364c9cd930d7a0d92d/tests/random_test.py#L65-L85
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# which is in turn based on
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# https://github.com/DEShawResearch/Random123-Boost/blob/65e3d874b67aa7b3e02d5ad8306462f52d2079c0/libs/random/test/test_threefry.cpp#L30-L32
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def uint32s_to_uint64(a, b):
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return b << 32 | a
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def verify(counter1, counter2, key1, key2, expect1, expect2):
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counter = uint32s_to_uint64(counter1, counter2)
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key = uint32s_to_uint64(key1, key2)
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random.get_global_generator().reset([counter, key])
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got = random.get_global_generator().uniform_full_int(
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shape=(2,), dtype=dtypes.uint32)
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expect = [expect1, expect2]
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self.assertAllEqual(expect, got)
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random.get_global_generator().reset([counter, key])
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got = random.get_global_generator().uniform_full_int(
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shape=(), dtype=dtypes.uint64)
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self.assertAllEqual(uint32s_to_uint64(*expect), got)
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with ops.device(xla_device_name()):
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random.reset_global_generator(seed=0, algorithm=random.RNG_ALG_THREEFRY)
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verify(0x00000000, 0x00000000, 0x00000000, 0x00000000,
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0x6b200159, 0x99ba4efe)
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verify(0xffffffff, 0xffffffff, 0xffffffff, 0xffffffff,
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0x1cb996fc, 0xbb002be7)
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verify(0x243f6a88, 0x85a308d3, 0x13198a2e, 0x03707344,
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0xc4923a9c, 0x483df7a0)
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@test_util.run_v2_only
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def testNewState(self):
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"""Tests that the new state is correct.
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"""
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with ops.device(xla_device_name()):
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counter = 57
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key = 0x1234
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size = 46
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seed = [counter, key]
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gen = random.Generator(
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seed=seed, algorithm=random.RNG_ALG_THREEFRY)
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gen.uniform_full_int(shape=(size,), dtype=dtypes.uint32)
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self.assertAllEqual([counter+(size+1)//2, key], gen.state.read_value())
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gen.reset(seed=seed)
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gen.uniform_full_int(shape=(size,), dtype=dtypes.uint64)
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self.assertAllEqual([counter+size, key], gen.state.read_value())
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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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# 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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x = rng(dtype).numpy()
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y = rng(dtype).numpy()
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self.assertFalse(np.array_equal(x, y))
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@test_util.run_v2_only
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def testUniformIsNotConstant(self):
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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def rng(dtype):
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maxval = dtype.max
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# Workaround for b/125364959
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if dtype == dtypes.uint64:
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maxval = 10000000
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return gen.uniform(shape=[2], dtype=dtype, maxval=maxval)
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for dtype in {dtypes.int32, dtypes.uint32, dtypes.int64, dtypes.uint64}:
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self._testRngIsNotConstant(rng, dtype)
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@test_util.run_v2_only
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def testNormalIsNotConstant(self):
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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def rng(dtype):
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return gen.normal(shape=[2], dtype=dtype)
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for dtype in {dtypes.float32}:
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self._testRngIsNotConstant(rng, dtype)
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@test_util.run_v2_only
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def testUniformIntIsInRange(self):
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minval = 2
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maxval = 33
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size = 1000
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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for dtype in {dtypes.int32, dtypes.uint32, dtypes.int64, dtypes.uint64}:
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x = gen.uniform(
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shape=[size], dtype=dtype, minval=minval, maxval=maxval).numpy()
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self.assertTrue(np.all(x >= minval))
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self.assertTrue(np.all(x < maxval))
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@test_util.run_v2_only
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def testNormalIsFinite(self):
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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for dtype in {dtypes.float32}:
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x = gen.normal(shape=[10000], dtype=dtype).numpy()
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self.assertTrue(np.all(np.isfinite(x)))
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def _chi_squared(self, x, bins):
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"""Pearson's Chi-squared test."""
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x = np.ravel(x)
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n = len(x)
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histogram, _ = np.histogram(x, bins=bins, range=(0, 1))
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expected = n / float(bins)
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return np.sum(np.square(histogram - expected) / expected)
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@test_util.run_v2_only
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def testDistributionOfUniform(self):
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"""Use Pearson's Chi-squared test to test for uniformity."""
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with ops.device(xla_device_name()):
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n = 1000
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seed = 12
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for dtype in {dtypes.int32, dtypes.uint32, dtypes.int64, dtypes.uint64}:
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gen = random.Generator(seed=seed, algorithm=random.RNG_ALG_THREEFRY)
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maxval = 1
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if dtype.is_integer:
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maxval = 100
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x = gen.uniform(shape=[n], maxval=maxval, dtype=dtype).numpy()
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if maxval > 1:
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# Normalize y to range [0, 1).
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x = x.astype(float) / maxval
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# Tests that the values are distributed amongst 10 bins with equal
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# probability. 16.92 is the Chi^2 value for 9 degrees of freedom with
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# p=0.05. This test is probabilistic and would be flaky if the random
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# seed were not fixed.
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val = self._chi_squared(x, 10)
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self.assertLess(val, 16.92)
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def _normal_cdf(self, x):
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"""Cumulative distribution function for a standard normal distribution."""
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return 0.5 + 0.5 * np.vectorize(math.erf)(x / math.sqrt(2))
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def _anderson_darling(self, x):
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"""Anderson-Darling test for a standard normal distribution."""
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x = np.sort(np.ravel(x))
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n = len(x)
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i = np.linspace(1, n, n)
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z = np.sum((2 * i - 1) * np.log(self._normal_cdf(x)) +
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(2 * (n - i) + 1) * np.log(1 - self._normal_cdf(x)))
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return -n - z / n
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@test_util.run_v2_only
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def testDistributionOfNormal(self):
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"""Use Anderson-Darling test to test distribution appears normal."""
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with ops.device(xla_device_name()):
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n = 1000
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for dtype in {dtypes.float32}:
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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x = gen.normal(shape=[n], dtype=dtype).numpy()
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# The constant 2.492 is the 5% critical value for the Anderson-Darling
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# test where the mean and variance are known. This test is probabilistic
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# so to avoid flakiness the seed is fixed.
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self.assertLess(self._anderson_darling(x.astype(float)), 2.492)
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@test_util.run_v2_only
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def testErrors(self):
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"""Tests that proper errors are raised.
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"""
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shape = [2, 3]
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with ops.device(xla_device_name()):
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gen = random.Generator(seed=1234, algorithm=random.RNG_ALG_THREEFRY)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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r"algorithm must be of shape \[\], not"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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gen.state.handle, [0, 0], shape)
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with self.assertRaisesWithPredicateMatch(
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TypeError, "Requested dtype: int64"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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gen.state.handle, 1.1, shape)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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"Unsupported algorithm id"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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gen.state.handle, 123, shape)
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var = variables.Variable([0, 0], dtype=dtypes.uint32)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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"Type mismatch for read of variable .* Expected int64; got"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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var.handle, random.RNG_ALG_THREEFRY, shape)
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var = variables.Variable([[0]], dtype=dtypes.int64)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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"RNG state must have one and only one dimension, not"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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var.handle, random.RNG_ALG_THREEFRY, shape)
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var = variables.Variable([0], dtype=dtypes.int64)
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with self.assertRaisesWithPredicateMatch(
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errors_impl.InvalidArgumentError,
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"For the ThreeFry algorithm, the size of state must be at least"):
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gen_stateful_random_ops.stateful_standard_normal_v2(
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var.handle, random.RNG_ALG_THREEFRY, shape)
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if __name__ == "__main__":
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test.main()
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